Method and system for evaluating health state of rescue helicopter

Abstract

The invention provides a health state assessment method and system for a rescue helicopter, and relates to the technical field of aviation engineering, and the method comprises the steps: 1, building an integrated data aggregation platform, synchronously collecting flight operation parameters, maintenance files, abnormal event records and surrounding environment monitoring indexes, and carrying out the maintenance of the maintenance files; comprehensively covering key dimensions of a rotor device, a power transmission unit, a propelling device and sea climate conditions to form a comprehensive original data set; and step 2, based on the comprehensive original data set, applying a self-adaptive parameter adjustment mechanism to perform comprehensive calculation on an accumulated operation time length index, a periodic component residual efficiency value, a core health score, an abnormal repeated occurrence probability, typical problem association strength, an emergency severity degree and a marine climate erosion factor, and generating a comprehensive state index. The method accurately evaluates the health state of the rescue helicopter, reduces the flight risk, and improves the rescue decision and execution efficiency.

Description

Technical Field

[0001] The present invention relates to the field of aviation engineering technology, and in particular to a health status assessment method and system for a rescue helicopter. Background Art

[0002] Most traditional technologies do not fully consider the impact of the special marine environment of high salt spray and high humidity on helicopters, and it is difficult to accurately assess the damage of corrosive environmental factors to the health of helicopters. For example, when the rescue flight team performs maritime rescue missions, the S-76C helicopter is in the marine environment for a long time. Because traditional assessment technologies do not incorporate marine environmental corrosion factors, they are unable to accurately quantify problems such as fuselage skin corrosion and electronic circuit board corrosion, resulting in multiple unplanned shutdowns due to corrosion, affecting the efficiency of rescue mission execution.

[0003] In addition, traditional technologies are relatively simple in terms of data fusion, and it is difficult to comprehensively process multi-dimensional data such as flight data, maintenance records, fault information, etc., and lack a dynamic weight distribution mechanism, making it impossible to perform accurate assessments based on the actual operating conditions of the helicopter, resulting in inaccurate assessments of the helicopter's health status and insufficient or excessive maintenance. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a health status assessment method and system for rescue helicopters, which can accurately diagnose the health status of helicopters, provide a reliable basis for timely maintenance and safe rescue, and improve the reliability and success rate of rescue operations.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows: In a first aspect, a method for assessing the health status of a rescue helicopter is provided, the method comprising: Step 1: Establish an integrated data aggregation platform to simultaneously collect flight operation parameters, maintenance records, abnormal event records, and surrounding environmental monitoring indicators, comprehensively covering the key dimensions of the rotor assembly, power transmission unit, propulsion unit, and marine climate conditions to form a comprehensive original data set; Step 2: Based on the comprehensive original data set, an adaptive parameter adjustment mechanism is used to comprehensively calculate the cumulative operating time index, the remaining efficiency value of periodic components, the core health score, the probability of abnormal recurrence, the correlation strength of typical problems, the severity of emergencies, and the marine climate erosion factor to generate a comprehensive status index; Step 3: Based on the comprehensive status indicators, a standardized assessment is conducted on the number of oxidation points on the helicopter surface, the salt corrosion exposure cycle, and the material stress microcrack characteristics, establishing a dynamic quantitative correspondence between environmental corrosion characteristics and equipment operating conditions; Step 4: Leveraging the quantitative relationship between environmental corrosion and equipment operating conditions, combined with comprehensive status indicators from multiple fleets, a distributed collaborative learning architecture is used to fuse diverse data from different fleets to construct a three-dimensional digital image of the helicopters. This allows for the deduction of component performance trends in environments with strong salt corrosion and high moisture content. Step 5: Based on the performance change trend of the components and the comprehensive status indicators, the status classification benchmark value is adjusted in real time to obtain a complete assessment report with visual warning signs, maintenance urgency and operation constraint recommendations.

[0006] Furthermore, based on the comprehensive original data set, an adaptive parameter adjustment mechanism is used to comprehensively calculate the cumulative operating time index, the remaining efficiency value of periodic components, the core health score, the probability of abnormal recurrence, the correlation strength of typical problems, the severity of emergencies and marine climate erosion factors to generate comprehensive status indicators, including: The vibration spectrum and torque fluctuation characteristics of the power transmission unit in the flight operation parameters are analyzed in the time and frequency domain. At the same time, the component degradation feature set is constructed by combining the component replacement cycle and maintenance record time series data in the maintenance archive. Based on the component degradation feature set, combined with the temporal and spatial distribution characteristics of the fault mode in the abnormal event records, and using the association rule mining algorithm to calculate the typical problem association strength index, the association relationship between faults is realized to construct the fault propagation path network; Based on the fault correlation framework, we collect marine climate erosion factors such as real-time salt spray concentration and relative humidity, and combine them with corrosion damage data to build a factor library that characterizes environmental stress intensity. At the same time, we perform spatial weighted fusion of climate erosion effects in different regions to form a comprehensive assessment of environmental erosion impacts. Based on the component degradation feature set, fault propagation path network and environmental erosion impact assessment value, the core health score and the severity of the emergency are aligned in time and space, and a comprehensive status indicator with time series characteristics is generated through a multidimensional computing framework, including mechanical health, environmental erosion and operational stability.

[0007] Furthermore, based on the fault correlation framework, we collected marine climate erosion factors such as real-time salt spray concentration and relative humidity, and combined them with corrosion damage data to construct a factor library representing environmental stress intensity. At the same time, we performed spatial weighted fusion on the climate erosion effects in different regions to form a comprehensive assessment value of environmental erosion impact, including: According to the spatial distribution of key fault nodes in the fault propagation path network, the coordinate set of the prominent erosion risk area is obtained; Based on the coordinate set of the prominent erosion risk area, the salt spray concentration, relative humidity data and corrosion damage data of the corresponding area are collected in real time; The salt spray concentration, relative humidity data and corrosion damage data are input into the dynamic coupling algorithm to generate the environmental corrosion rate coefficient and material degradation threshold, and build a partitioned environmental stress intensity factor library; Based on the coordinate set of the prominent erosion risk area, the rotor exposure area, the power cabin sealing area and the fuselage surface area are divided, and the spatial weighting coefficient is calculated based on the corrosion resistance level of the material in each area and the exposure time. The partitioned environmental stress intensity factor library and the spatial weighted coefficient are calculated to obtain a comprehensive assessment value of the environmental erosion impact.

[0008] Furthermore, based on comprehensive status indicators, a standardized assessment was conducted on the number of oxidation points on the helicopter surface, salt corrosion exposure cycles, and material stress microcrack characteristics, establishing a dynamic quantitative correspondence between environmental corrosion characteristics and equipment operating conditions, including: The sea salinity, air humidity, flight trajectory altitude, and crew maintenance record data were separated from the comprehensive status indicators. Combined with the corrosion case library, a corrosion environment feature vector including timestamps, geographic coordinates, and operating parameters was constructed. The flight parameters were spatially and temporally correlated with the environmental parameters to obtain a multidimensional environmental stress factor matrix. Based on a multidimensional environmental stress factor matrix, a 3D point cloud was used to extract features from helicopter surface structure data. The distribution of oxidation points and salt corrosion exposure cycles were mapped to the fuselage structure coordinate system. Furthermore, cluster analysis was used to generate distribution characteristics of corrosion-sensitive areas, combining the temporal changes in the material stress microcrack characteristics. Based on the corrosion distribution characteristics, the relationship between corrosion rate and mechanical fatigue is analyzed, and the material strength attenuation trend under different salt corrosion levels is fitted. Combined with the emergency severity data, a corrosion sensitivity classification table for key components is generated, including the remaining life prediction value and the degradation rate confidence interval; Based on the corrosion sensitivity classification data of key components, the corrosion damage information of multiple units is integrated, and a dynamic correlation between environmental erosion intensity and equipment health status is constructed to form a real-time updated corrosion assessment system.

[0009] Furthermore, based on the corrosion distribution characteristics, the relationship between corrosion rate and mechanical fatigue is analyzed, and the material strength attenuation trend under different salt corrosion levels is fitted. Combined with the emergency severity data, a corrosion sensitivity grading table for key components is generated, including the remaining life prediction value and degradation rate confidence interval, including: Extract the corrosion rate quantification value and mechanical fatigue strength index of key components based on the prominent corrosion risk area data in the corrosion distribution characteristics; Using the corrosion rate quantification value and the mechanical fatigue strength index, the dynamic correlation coefficient between the two is calculated through the correlation analysis algorithm, and a quantitative relationship is established to characterize the interaction between environmental stress and mechanical performance degradation; Based on the quantitative relationship and combined with the corrosion case library under different salt corrosion levels, the attenuation law reflecting the change of key component material strength with operation time is fitted; Using emergency severity data, dynamically calibrate the material strength attenuation law, and calculate the remaining life prediction value and degradation rate confidence interval of key components based on the dynamically calibrated material strength attenuation law; Based on the remaining life prediction value and degradation rate confidence interval, the corrosion sensitivity of key components is graded and a grading table is generated.

[0010] Furthermore, by leveraging the quantitative relationship between environmental erosion and equipment operating conditions, combined with comprehensive status indicators from multiple fleets, and integrating diverse data from different fleets through a distributed collaborative learning architecture, a three-dimensional digital image of the helicopter is constructed to deduce component performance trends in environments with strong salt corrosion and high moisture content, including: Establish a multi-fleet data fusion platform based on digital twins to collect real-time salt spray concentration, humidity fluctuation values, and equipment operating load parameters of rescue helicopters in different regions. Use a federated learning mechanism to conduct collaborative encryption training on multi-source heterogeneous data to generate a fused global environment-operating condition feature matrix. The global environment-operating condition characteristic matrix is ​​input into the environmental degradation simulation processing unit, and the material corrosion resistance coefficient and structural stress distribution data of the components in the three-dimensional digital image are synchronously called. Based on the salt corrosion exposure cycle and humidity fluctuation range, the surface corrosion depth growth process and internal crack extension trajectory of the components are simulated to obtain a degradation data set including the mechanical property attenuation value; The degradation dataset is decoupled from regional features through the transfer learning processing unit, and the corrosion rate correlation function of the machine cluster in the high salt corrosion area is extracted. It is adapted to the three-dimensional digital image of the machine cluster in the low salt corrosion area to generate the performance change trend of components in the low salt corrosion area.

[0011] Furthermore, based on component performance trends and comprehensive status indicators, the status classification benchmark values ​​are adjusted in real time to obtain a complete assessment report with visual warning signs, maintenance urgency, and operational constraint recommendations, including: The component performance change trend and the real-time collected flight mission urgency parameters are input into the dynamic classification processing unit. The predicted value of the component's remaining life and the environmental corrosion rate are weighted and calculated through a deep reinforcement learning mechanism to dynamically generate a health status classification threshold parameter set. The health status classification threshold parameter set is input into the status identification processing unit, and the spatial positioning data of the parts in the three-dimensional digital image is called at the same time to perform the classification determination operation and generate an instruction data set bound to the three-dimensional space coordinates; The instruction data set bound to three-dimensional spatial coordinates is input into the decision generation processing unit, and combined with the external input spare parts supply cycle, alternate airport location information and remaining flight time window to obtain a structured evaluation report, including visual identification mapping relationship, maintenance time series table and airspace restriction parameters.

[0012] Furthermore, the component performance change trend and the real-time collected flight mission urgency parameters are input into the dynamic classification processing unit. Through the deep reinforcement learning mechanism, the predicted value of the component's remaining life and the environmental corrosion rate are weighted and calculated to dynamically generate a health status classification threshold parameter set, including: Based on the component failure time points in the fault records and the emergency maintenance frequency in the maintenance logs, the initial correlation weight matrix between the environmental corrosion rate and the remaining life prediction value is extracted; The initial correlation weight matrix and the real-time flight mission urgency parameter are input into the dynamic calibration mechanism, and the weight matrix is ​​processed by the mission urgency coefficient to obtain the real-time weight coefficient set; The real-time weight coefficient set, the predicted value of the remaining life of the components and the environmental erosion rate are input into the deep reinforcement learning mechanism to construct a multi-dimensional state feature vector in the state space and generate operation instructions in the action space; The action space operation instructions are input into the pre-trained reward function mechanism, and a health status classification threshold parameter set including the first warning threshold and the second grounding threshold is generated based on the component failure risk cost and sudden maintenance loss.

[0013] In a second aspect, a health status assessment system for a rescue helicopter includes: The data aggregation module is used to synchronously collect flight operation parameters, maintenance files, abnormal event records and surrounding environment monitoring indicators to form a comprehensive original data set; The status assessment module is used to generate comprehensive status indicators in multiple dimensions, including cumulative operating time indicators, component remaining performance values, and core health scores, based on the comprehensive original data set; The environmental corrosion module is used to perform standardized assessments of the number of oxidation points on the helicopter surface, salt corrosion exposure cycles, and material stress microcrack characteristics based on comprehensive status indicators, establishing a quantitative relationship between environmental corrosion and equipment operating conditions; The digital image module is used to build a three-dimensional digital image of helicopters to deduce performance trends by utilizing the quantitative relationship between environmental erosion and equipment operating conditions, combined with multiple fleet status indicators; The evaluation report module is used to adjust the grading benchmark values ​​in real time based on the performance change trends of components and comprehensive status indicators, and generate a complete evaluation report including warning signs, maintenance suggestions and operating constraints.

[0014] According to a third aspect, a computing device includes: one or more processors; The storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0015] In a fourth aspect, a computer-readable storage medium stores a program, which implements the method when executed by a processor.

[0016] The above solution of the present invention includes at least the following beneficial effects: Through this method, the health status information of the rescue helicopter can be obtained quickly and accurately. In emergency rescue scenarios, every second is crucial. Quickly understanding the operating status of the key components of the helicopter allows rescue commanders to immediately determine whether the helicopter is suitable for the mission, avoiding delays in rescue due to blindly dispatching faulty helicopters, improving the efficiency of rescue decision-making, and gaining precious time to save lives; based on a comprehensive health status assessment, it is possible to predict in advance the problems that may arise in the helicopter during the mission, so as to reasonably plan the rescue route and task arrangements, monitor the various performance indicators and component status of the helicopter in real time, and predict the occurrence of potential faults through data analysis. This enables maintenance personnel to take targeted preventive measures before the fault occurs, perform early repairs or replace components, avoid the expansion of faults, reduce the number of unplanned repairs, reduce maintenance costs, and extend the service life of the helicopter.

[0017] Through comprehensive assessment and data analysis of the health status of rescue helicopters, we can understand the failure frequency and maintenance requirements of different components. This helps the maintenance department to rationally plan spare parts inventory, ensure an adequate supply of key spare parts, and avoid inventory backlogs and waste of resources. In addition, it can also reasonably arrange the work tasks of maintenance personnel according to the health status of the helicopter, and improve the efficiency of human resource utilization. Rescue helicopters face complex and changing environments and various potential dangers when performing missions. Their health status is directly related to flight safety. Timely detection of safety hazards in helicopters, such as engine failure and structural damage, enables the crew to take corresponding measures before takeoff to avoid flying with faults, thereby reducing the probability of flight accidents and ensuring the safety of rescue personnel and passengers. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The figure is a flow chart of a method for assessing the health status of a rescue helicopter provided by an embodiment of the present invention.

[0019] Figure 2 Schematic diagram of a health status assessment system for a rescue helicopter provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a method for assessing the health status of a rescue helicopter, the method comprising the following steps: Step 1: Establish an integrated data aggregation platform to simultaneously collect flight operation parameters, maintenance records, abnormal event records, and surrounding environmental monitoring indicators, comprehensively covering the key dimensions of the rotor assembly, power transmission unit, propulsion unit, and marine climate conditions to form a comprehensive original data set; Step 2: Based on the comprehensive original data set, an adaptive parameter adjustment mechanism is used to comprehensively calculate the cumulative operating time index, the remaining performance value of periodic components, the core health score, the probability of abnormal recurrence, the correlation strength of typical problems, the severity of emergencies, and the marine climate erosion factor to generate a comprehensive status index; Step 3: Based on the comprehensive status indicators, a standardized assessment is conducted on the number of oxidation points on the helicopter surface, the salt corrosion exposure cycle, and the material stress microcrack characteristics, establishing a dynamic quantitative correspondence between environmental corrosion characteristics and equipment operating conditions; Step 4: Leveraging the quantitative relationship between environmental corrosion and equipment operating conditions, combined with comprehensive status indicators from multiple fleets, a distributed collaborative learning architecture is used to fuse diverse data from different fleets to construct a three-dimensional digital image of the helicopters. This allows for the deduction of component performance trends in environments with strong salt corrosion and high moisture content. Step 5: Based on the performance change trend of the components and the comprehensive status indicators, the status classification benchmark value is adjusted in real time to obtain a complete assessment report with visual warning signs, maintenance urgency and operation constraint recommendations.

[0022] In an embodiment of the present invention, an integrated data aggregation platform is established to achieve synchronous collection of flight operation parameters, maintenance files, abnormal event records and surrounding environment monitoring indicators, comprehensively covering the key dimensions of the rotor device and power transmission unit, forming a comprehensive original data set, which can eliminate data silos, ensure data integrity and consistency, and help to fully grasp the helicopter's operating status and environmental influencing factors; based on the comprehensive original data set, an adaptive parameter adjustment mechanism is used to comprehensively calculate multiple types of indicators to generate comprehensive status indicators, which can fully consider the various influencing factors in the helicopter operation process, realize dynamic and accurate evaluation of the helicopter's health status, timely discover potential problems, and improve the scientificity and reliability of the evaluation; a standardized assessment of the number of oxidation points on the helicopter surface is performed, and a dynamic quantitative correspondence between environmental erosion characteristics and equipment operating conditions is established, which can clarify the specific impact degree and law of marine environmental factors on helicopter equipment, provide a basis for the targeted formulation of protection and maintenance measures, and effectively deal with the damage to helicopters caused by harsh environments such as high corrosion at sea.

[0023] By utilizing quantitative relationships and combining multi-fleet data, a three-dimensional digital image is constructed through a distributed collaborative learning architecture to deduce the performance change trends of components in strong salt corrosion and high moisture environments. This can predict the performance degradation of components in advance, provide support for the formulation of reasonable maintenance cycles and replacement strategies, and improve the foresight and effectiveness of maintenance. Based on the performance change trends of components and comprehensive status indicators, the status classification benchmark values ​​are adjusted in real time to generate a complete assessment report containing visual warning signs, maintenance urgency, and operation constraint recommendations. This can provide operation and maintenance personnel with an intuitive and clear decision-making basis, realize dynamic management of helicopter status, optimize maintenance decisions, and ensure safe and efficient operation of helicopters.

[0024] In a preferred embodiment of the present invention, in step 1, an integrated data aggregation platform is established to simultaneously collect flight operation parameters, maintenance files, abnormal event records, and surrounding environmental monitoring indicators, comprehensively covering the key dimensions of the rotor assembly, power transmission unit, propulsion unit, and marine climate conditions, to form a comprehensive raw data set, which may include: In an embodiment of the present invention, sensors (such as vibration sensors, temperature sensors, pressure sensors, etc.) are deployed at key positions of the core components of the rescue helicopter (rotor device, power transmission unit, and propulsion device) to collect physical parameters (such as speed, temperature, vibration frequency, oil pressure, etc.) in real time; marine environmental monitoring equipment (such as weather stations, wave sensors, and humidity sensors) are simultaneously planned and deployed at helicopter take-off and landing bases and around the sea areas where missions are often performed to collect climate data such as wind speed, wind direction, temperature, humidity, and wave height; a data acquisition terminal is installed inside the helicopter fuselage and connected to the sensor through a wired or wireless communication module (such as CAN bus, WiFi, 4G / 5G) to receive flight operation parameters in real time; a server cluster (including database servers and file storage servers) is deployed on the ground to receive and store data transmitted from the helicopter and environmental monitoring equipment; and a local area network is established to connect the terminal devices of maintenance workshops, command centers, and other departments to the network to facilitate uploading of maintenance files and abnormal event record data.

[0025] After the helicopter takes off, the data acquisition terminal reads the sensor data of each component in real time at a preset frequency (e.g., 100ms / time), for example: Rotor device: The angular velocity sensor is used to collect the rotor speed, and the vibration sensor is used to monitor the blade swing amplitude and abnormal vibration signals; Power transmission unit: The temperature sensor records the transmission oil temperature, and the pressure sensor monitors the pressure changes in the hydraulic system; Propulsion device (engine): collects engine oil temperature, oil pressure, fuel flow and other parameters; The acquisition terminal denoises and filters the raw data (such as removing instantaneous interference signals) and adds a timestamp (accurate to the millisecond level) to form a standardized real-time data stream, which is transmitted to the ground server in real time through the airborne communication module (such as 5G).

[0026] After each helicopter overhaul, maintenance personnel manually enter the maintenance records (such as overhaul time, component replacement status, maintenance process, model and batch number of replaced components) through the platform terminal, or export data from the maintenance management system (such as the enterprise resource planning ERP system) and import it into the platform in batches; the platform automatically associates the maintenance records with the corresponding helicopter number and flight hours to form a maintenance archive. During the flight, if the helicopter's onboard fault diagnosis system (such as the engine warning system) triggers an abnormal alarm, the data acquisition terminal will automatically capture the alarm signal, record the time of the abnormality, component location, alarm code and current flight parameters (such as altitude, speed), and transmit it to the ground platform in real time. Ground command center personnel can manually supplement the abnormal event description (such as visually observed oil leakage, abnormal noise, etc.) through the platform interface to form a complete abnormal event log.

[0027] Weather stations deployed around the sea collect parameters such as wind speed, direction, atmospheric temperature, humidity, and air pressure at a fixed frequency (e.g., once per minute). Wave sensors monitor wave height and direction using radar or ultrasonic technology. This data is sent to a ground server via wireless transmission (e.g., LoRa, NB-IoT). The platform automatically associates the geographic location (e.g., longitude and latitude) with the time of data collection to form a time series of environmental data. The ground server synchronously receives three types of data through different interfaces (e.g., API, FTP): Flight operation parameters: Create real-time data tables by helicopter number and component type and store them in a time series database (such as InfluxDB) to facilitate fast query of dynamic data; Maintenance files and abnormal events: Stored in a relational database (such as MySQL) in a structured data format (such as CSV, Excel), indexed by key fields (such as helicopter number, time); Environmental data: stored by geographic location and time dimensions to facilitate flight data correlation analysis.

[0028] The platform uses the helicopter's unique identifier (such as tail number) + timestamp as the primary key to automatically link flight parameters, environmental data for the corresponding time period (such as real-time wind speed in the flight area), and the helicopter's historical maintenance records (such as the time of the most recent engine maintenance). When a helicopter experiences an abnormal engine T5 temperature during flight, the platform automatically retrieves the flight parameters (speed, load), environmental data (external temperature, wind speed), and engine maintenance records from the past six months at that moment, forming a multi-dimensional data set. The platform then packages the integrated data by time period (such as daily or weekly) to form a comprehensive raw data set indexed by helicopter number, including: Flight parameter sequence (including time and component parameters); Maintenance record list (including time and maintenance content); Abnormal event log (including time and abnormal type); Environmental data series (including time and climate parameters).

[0029] Data management personnel ensure that there are no missing values ​​or logical contradictions in the original data set (such as temperature parameters are within a reasonable physical range) through manual sampling and automatic system verification (such as checking data integrity and timestamp continuity).

[0030] Specific implementation of key dimension coverage: Rotor device: Vibration sensors and speed sensors cover parameters such as blade status, bearing wear, and dynamic balance; Power transmission unit: covers the operating status of the gearbox, drive shaft, and hydraulic system through temperature, pressure, and oil sensors; Propulsion device: Covering power output efficiency and potential fault hazards through full engine parameter collection (lubricating oil temperature and pressure); Sea climate conditions: Weather stations and wave sensors cover environmental factors that have a direct impact on flight safety, such as wind speed, wave height, and temperature.

[0031] Through full-process operations, the integrated convergence of multi-source data is ultimately achieved, forming a comprehensive original data set covering the core components and operating environment of the helicopter.

[0032] In a preferred embodiment of the present invention, the above step 2, based on the comprehensive original data set, uses the adaptive parameter adjustment mechanism to comprehensively calculate the cumulative operating time index, the periodic component remaining efficiency value, the core health score, the probability of abnormal recurrence, the typical problem correlation strength, the severity of the emergency, and the marine climate erosion factor to generate a comprehensive status index, which may include: Step 220 , performing time-frequency domain analysis on the vibration spectrum and torque fluctuation characteristics of the power transmission unit in the flight operation parameters, and combining the component replacement cycle and maintenance record time series data in the maintenance file to construct a component degradation feature set; Step 221 , based on the component degradation feature set and in combination with the temporal and spatial distribution characteristics of the fault modes in the abnormal event records, an association rule mining algorithm is used to calculate the typical problem association strength index, thereby realizing the association relationship between faults and constructing a fault propagation path network; Step 222: Based on the fault association framework, real-time marine climate erosion factors of salt spray concentration and relative humidity are collected, and combined with corrosion damage data to construct a factor library representing environmental stress intensity. At the same time, spatially weighted fusion is performed on the climate erosion effects of different regions to form a comprehensive assessment value of the environmental erosion impact. Specifically, the following steps are performed: based on the spatial distribution of key fault nodes in the fault propagation path network, a coordinate set of prominent erosion risk areas is obtained; based on the coordinate set of prominent erosion risk areas, salt spray concentration, relative humidity data, and corrosion damage data of the corresponding areas are collected in real time; the salt spray concentration, relative humidity data, and corrosion damage data are input into a dynamic coupling algorithm to generate environmental corrosion rate coefficients and material degradation thresholds, and a partitioned environmental stress intensity factor library is constructed; based on the coordinate set of prominent erosion risk areas, the rotor exposure area, the power cabin sealing area, and the fuselage surface area are divided, and the spatial weighted coefficients are calculated based on the corrosion resistance level and exposure time of the materials in each area; the partitioned environmental stress intensity factor library is calculated with the spatial weighted coefficients to obtain a comprehensive assessment value of the environmental erosion impact; In step 223, based on the component degradation feature set, the fault propagation path network, and the environmental erosion impact assessment value, the core health score and the severity of the emergency are aligned in time and space, and a comprehensive status indicator with time series characteristics is generated through a multidimensional computing framework, including mechanical health, environmental erosion, and operational stability.

[0033] In an embodiment of the present invention, the vibration spectrum and power transmission unit torque fluctuation data in the flight operation parameters are normalized and divided into N data segments of equal length in chronological order. Each data segment is regarded as a sparrow, and an initial population of N sparrows is formed. The position vector of each sparrow is composed of the frequency components and amplitude of the vibration spectrum within the data segment, as well as the mean and variance characteristic parameters of the torque fluctuation. According to the sparrow search algorithm rules, the top 20% of sparrows with higher fitness (such as those that can reflect more data change characteristics) are determined as discoverers. The discoverer sparrows explore the data feature space with a larger step size. For example, in the vibration spectrum data, they first focus on the high-frequency area to look for abnormal peaks that may indicate component wear. In the torque fluctuation data, they explore the time period when the fluctuation amplitude suddenly increases. The discoverer explores the high-energy, high-variance feature area by continuously updating its own position (adjusting the feature parameter weights). After each exploration, the fitness of the new position is calculated (such as using the mean square error to measure the degree of fit of the new feature combination with the original data), and the position with higher fitness is retained.

[0034] The remaining 80% of the follower sparrows learn based on the location information of the discoverer. The followers will evaluate the fitness of the discoverer and approach the discoverer with higher fitness first. In the process of approaching, they will fine-tune and mine the features with smaller steps, such as mining periodic features in the low-frequency components of the vibration spectrum, or extracting patterns from the trend changes of torque fluctuations. The followers continuously update their own positions to mine more potential features, extract component replacement cycles and maintenance record time series data from the maintenance files, and correspond the operating parameter features with the maintenance data based on the maintenance time node. Let each sparrow "remember" the changes in component features before and after maintenance, such as the disappearance of abnormal peaks in the vibration spectrum or the stabilization of torque fluctuations after maintenance. These feature change points are included in the component degradation feature set, and finally a complete and dynamic feature set that reflects the degradation of component performance is constructed.

[0035] The fault occurrence time in the abnormal event record is divided into different time scales such as hours and days, and the fault location is divided according to the helicopter component area to form different fault mode space-time units. Each space-time unit is regarded as a sparrow individual, and all individuals constitute a sparrow population. The individual position is composed of the time identifier, location identifier and fault type identifier of the space-time unit; using the global search capability of the sparrow search algorithm, the sparrows in the population explore the potential connections between fault modes in the space-time dimension. The discoverer sparrow first searches in a large-scale space-time range, such as searching for different fault modes that appear in the same component area within a week; the follower sparrow, based on the finder search results, conducts in-depth exploration in a small-scale space-time range, such as exploring the occurrence pattern of different fault modes of the same component within two consecutive hours. By continuously adjusting the individual position (trying different space-time combinations), the clustering phenomenon and sequence pattern of fault modes in space and time are found.

[0036] An association rule mining algorithm is adopted, and the fitness function of the sparrow search algorithm is introduced. The frequency of occurrence of different fault mode combinations in the space-time unit is used as the support, and the closeness of the causal relationship between the fault modes is used as the confidence. The two are weighted to form the fitness evaluation standard. The discoverer sparrow continuously tries new fault mode combinations, calculates their fitness, and retains the combinations with high fitness; the follower sparrow makes local adjustments to the combination based on the information of the discoverer to further improve the fitness. After multiple rounds of adjustments, the fault combination with high typical problem correlation intensity is determined, and the correlation strength index is calculated; according to the calculated typical problem correlation strength index, the fault modes whose correlation strength exceeds the set threshold are connected with lines, and a complete fault propagation path network is constructed with the fault mode as the node and the correlation strength as the edge weight, which intuitively displays the correlation relationship and propagation path between faults.

[0037] Real-time collection of marine climate erosion factor data such as salt spray concentration and relative humidity, each data sample is regarded as a sparrow. When the data fluctuates abnormally (such as a sudden and substantial increase in salt spray concentration), the "anti-predation" behavior in the sparrow search algorithm is simulated, and the abnormal data sample (sparrow) will quickly approach the surrounding normal data samples. By adjusting its own value (position) multiple times, the abnormal data is corrected. At the same time, according to the data and the normal fluctuation range, the missing data is supplemented to ensure the data is complete and accurate. The search capability of the sparrow search algorithm is used to find the key factors that can reflect the intensity of environmental stress in the processed data. The discoverer sparrow first explores the areas in the data with drastic changes and great impact on components, such as the time period with high salt spray concentration and high humidity at the same time; the follower sparrow, based on the discoverer, digs out the areas related to component corrosion. The relevant potential factors, such as humidity change rate, are continuously adjusted by factor combinations (sparrow positions), and the combination's ability to represent environmental stress intensity (fitness) is calculated. Factor combinations with high fitness are retained, and an environmental stress intensity factor library is constructed. Different regions are regarded as sparrow individuals, and their climate erosion data are used as individual positions. According to the geographical location of each region (such as the distance from the helicopter landing point), area size and other factors, the group collaboration characteristics of the sparrow search algorithm are used to assign corresponding weights. The discoverer sparrow is responsible for finding regional data that can represent the overall environmental characteristics. The follower sparrow performs weighted fusion of data from different regions based on the discoverer information and weight. For example, the area close to the landing point and with a large area has a higher weight, and its data accounts for a larger proportion in the fusion, ultimately forming a comprehensive assessment value that accurately reflects the impact of environmental erosion.

[0038] The core health score is aligned with the severity of the emergency according to the timestamp and the helicopter flight position. The dynamic adjustment mechanism of the sparrow search algorithm is used to evaluate the accuracy of the data alignment in real time as time passes and the helicopter flight position changes. If deviations are found in time or space, such as the core health score time record lags behind the actual event time, the data weight is adjusted (similar to the sparrow adjusting its position) to make the data consistent in time and space dimensions to ensure data comparability. Based on the component degradation feature set, the fault propagation path network and the environmental erosion impact assessment value, a multi-dimensional computing framework is constructed, and data of different dimensions are regarded as different sparrow populations. Each population The individuals in the dimensionality represent the specific data features under this dimension. During each adjustment process, each population interacts with the information of other populations based on its own data characteristics. For example, individuals in the component degradation feature set population (such as vibration spectrum characteristics) will refer to the relevant fault mode information in the fault propagation path network population to adjust their own weight (position) in the comprehensive calculation; individuals in the environmental erosion impact assessment value population will optimize their contribution to the overall health status based on the data of other populations. Through multiple adjustments, the weights and combinations of data in each dimension are continuously adjusted to ultimately generate comprehensive status indicators such as mechanical health, environmental erosion, and operational stability with time series characteristics.

[0039] The Sparrow Search algorithm's unique finder-follower mechanism and global search capabilities enable in-depth exploration of high-value areas and potential connections in the data when constructing component degradation feature sets and mining fault correlations, improving assessment accuracy compared to traditional methods. When calculating the correlation strength of typical problems and constructing a fault propagation path network, the Sparrow Search algorithm rapidly identifies high-fitness fault mode combinations. The constructed fault propagation path network more closely matches actual fault occurrence patterns, providing a scientific and accurate basis for fault prevention and maintenance decision-making, and reducing the risk of rescue mission interruptions caused by faults. When processing marine climate erosion factor data, the Sparrow Search algorithm simulates "anti-predation" behavior to correct abnormal data. Combined with group collaboration for spatially weighted fusion, the resulting environmental stress intensity factor library and comprehensive assessment value more realistically reflect the impact of the environment on helicopter components, effectively addressing the challenges posed by complex marine environments to helicopter health assessment. When generating comprehensive status indicators, the Sparrow Search algorithm's dynamic adjustment allows real-time adaptive adjustment of the indicator calculation process based on changes in helicopter operating data, ensuring that indicators such as mechanical health, environmental erosion, and operational stability accurately and timely reflect the helicopter's true health status, providing reliable, real-time decision support for scientific rescue mission scheduling and helicopter maintenance.

[0040] In a preferred embodiment of the present invention, the above step 3, based on the comprehensive status index, performs a standardized assessment of the number of oxidation points on the helicopter surface, the salt corrosion exposure cycle, and the material stress microcrack characteristic quantity, and establishes a dynamic quantitative correspondence between the environmental corrosion characteristics and the equipment operating conditions, which may include: Step 330 , separating sea salinity, air humidity, flight trajectory altitude, and crew maintenance record data from the comprehensive status indicators, and combining them with the corrosion case library to construct a corrosion environment feature vector including timestamps, geographic coordinates, and operating condition parameters. The flight parameters are spatially and temporally correlated with the environmental parameters to obtain a multidimensional environmental stress factor matrix. Step 331 , based on the multi-dimensional environmental stress factor matrix, feature extraction is performed on the helicopter surface structure data using a three-dimensional point cloud. The oxidation point distribution and salt corrosion exposure period are mapped to the fuselage structure coordinate system. In combination with the temporal variation of the material stress microcrack characteristic quantity, the distribution characteristics of the corrosion sensitive area are generated through cluster analysis. Step 332: Analyze the correlation between corrosion rate and mechanical fatigue based on the corrosion distribution characteristics, fit the material strength attenuation trend under different salt corrosion levels, and generate a corrosion sensitivity grading table for key components including remaining life prediction values ​​and degradation rate confidence intervals in combination with the emergency severity data. Specifically, the table includes: extracting the corrosion rate quantification value and mechanical fatigue strength index of the key components based on the prominent corrosion risk area data in the corrosion distribution characteristics; using the corrosion rate quantification value and the mechanical fatigue strength index, calculate the dynamic correlation coefficient between the two through a correlation analysis algorithm, and establish a quantitative relationship that characterizes the interaction between environmental stress and mechanical performance degradation; based on the quantitative relationship and in combination with a corrosion case library under different salt corrosion levels, fit a decay law that reflects the change of material strength of key components with operating time; using the emergency severity data, dynamically calibrate the material strength attenuation law, and calculate the remaining life prediction value and degradation rate confidence interval of the key components based on the dynamically calibrated material strength attenuation law; based on the remaining life prediction value and degradation rate confidence interval, grade the corrosion sensitivity of the key components and generate a grading table; Step 333 , based on the corrosion sensitivity classification data of key components, integrate the corrosion damage information of multiple units, build a dynamic correlation between environmental erosion intensity and equipment health status, and form a real-time updated corrosion assessment system.

[0041] In an embodiment of the present invention, data tags and indexing technology are used to accurately determine environmental parameters such as sea salinity, air humidity, and flight trajectory altitude from massive data of comprehensive status indicators. For unit maintenance record data, not only component maintenance time and maintenance content are extracted, but also information that may affect the equipment status, such as the maintenance personnel's operating techniques and the maintenance tools used, is refined. The determined data are arranged in chronological order to ensure that the parameter information of each time node is complete and continuous; the corrosion case library stores a large number of corrosion cases of different models of helicopters in various environments. The environmental parameters of each time node are compared with the data in the case library, and a similarity calculation method is used to find multiple cases from the case library that are closest to the current helicopter environmental parameters. In addition to matching environmental parameters, factors such as the helicopter's service life and flight frequency are also considered to ensure a high degree of relevance of the reference cases.

[0042] A high-precision timestamp is added to the data of each time node, accurate to the millisecond level, to accurately record the data collection moment. At the same time, the precise geographic coordinates (latitude and longitude, altitude) of the helicopter at that moment are obtained through the satellite positioning system. The timestamp, geographic coordinates, sea salinity, air humidity, flight path altitude, crew maintenance records and other data are deeply integrated to form a corrosion environment feature vector containing multi-dimensional information. For example, when integrating the crew maintenance records, the maintenance information of different components is associated with the environmental parameters of the corresponding time nodes, so that the feature vector can fully reflect the environment and equipment maintenance of the helicopter at a certain moment. state; perform spatiotemporal alignment of flight parameters (such as rotor speed, engine temperature, power transmission unit torque, etc.) and environmental parameters. In the time dimension, use the timestamp as the benchmark to make a one-to-one correspondence between the flight parameters and environmental parameters at the same time point; in the spatial dimension, based on the flight trajectory of the helicopter and the location of the environmental monitoring point, use geographic information system (GIS) technology to ensure that the spatial positions of the two are accurately corresponded, and arrange the characteristic vectors of all time nodes in chronological order to form a multidimensional environmental stress factor matrix. During the matrix generation process, the data is standardized to make different parameters comparable.

[0043] A high-precision laser scanner was used to scan the helicopter surface in all directions, acquiring high-density 3D point cloud data to capture the subtle structural features of the fuselage surface. During the acquisition process, scanning parameters such as resolution and angle were adjusted according to different parts of the helicopter to ensure data integrity and accuracy. After acquisition, the raw point cloud data was denoised using a filtering algorithm to remove outliers and noise caused by equipment errors, environmental interference, and other factors. Furthermore, the data was smoothed to make the point cloud data more uniform. The oxidation point distribution data and salt corrosion exposure cycle data were mapped to the fuselage structural coordinate system constructed from the 3D point cloud using a coordinate transformation algorithm based on their actual locations on the helicopter surface. For the material stress microcrack characteristic data, their time-varying variations were extracted and deeply correlated with the corresponding locations in the point cloud data. For example, for an oxidation point on a specific part of the fuselage, its precise coordinates were located in the point cloud data, and the salt corrosion exposure cycle and stress microcrack characteristic data for that location at different time points were recorded. This information was integrated by establishing a data association table to form a dataset containing both spatial location and time series information.

[0044] An advanced clustering algorithm was used to analyze the integrated data. Appropriate clustering parameters, such as the number of cluster centers and the cluster distance metric, were first determined. Through multiple trials and adjustments, the clustering results were optimized to accurately reflect the differences in corrosion characteristics across different regions. Points with similar numbers of oxidation sites, salt exposure cycles, and stress microcracking characteristics were grouped together. Each cluster represented a corrosion-sensitive area. For each cluster, statistics such as the center position and characteristic mean were calculated to quantitatively describe and analyze the corrosion-sensitive areas. The clustering results were visualized to provide an intuitive representation of the distribution of corrosion-sensitive areas on the helicopter surface. For each corrosion-sensitive area, a large amount of corrosion data and mechanical performance test data were collected to analyze the intrinsic relationship between corrosion rate and mechanical fatigue. For example, fatigue tests were performed on components under simulated corrosion conditions, and performance changes during the corrosion process, such as strength, toughness, and vibration frequency, were recorded. Data analysis revealed the influence of corrosion rate on mechanical fatigue life and determined the quantitative relationship between the two. The reliability and applicability of the research results were verified using actual flight data.

[0045] Based on the corrosion conditions of components under different salt corrosion levels and material strength test data, trend fitting methods such as least squares method and polynomial fitting are used to fit the attenuation trend of material strength as the salt corrosion level changes. During the fitting process, the data is preprocessed to remove abnormal data points to ensure the accuracy of the fitting data. At the same time, the fitting trend is corrected and adjusted considering the influence of various factors on material strength, such as temperature, humidity, and stress. By adjusting the fitting parameters multiple times, the fitting trend can accurately reflect the attenuation trend of material strength under different salt corrosion levels. The severity data of emergency events are retrieved, including information such as the time of occurrence of the fault, the type of fault, and the degree of impact of the fault on the operation of the helicopter. The differences in the impact of emergency events on key helicopter components under different corrosion sensitivities are analyzed. For example, for components in high corrosion sensitivity areas, the same type of emergency event may be more likely to cause component failure. Taking these influencing factors into consideration, the material strength attenuation trend is comprehensively corrected. By combining the emergency event data with the corrosion data and adjusting the trend parameters, the actual situation can be more accurately reflected.

[0046] Based on the corrected material strength attenuation trend, combined with the current corrosion status and operating conditions of the components, a life prediction algorithm is used to calculate the remaining life prediction value of key components. During the calculation process, various uncertain factors are taken into account, such as environmental changes, equipment usage frequency, maintenance quality, etc. These uncertain factors are quantitatively analyzed through Monte Carlo simulation and other methods, and the confidence interval of the degradation rate is calculated. In specific operations, a large number of random simulation scenarios are set to simulate the operation of components under different conditions, and the distribution of the remaining life is statistically analyzed to determine the confidence interval to indicate the reliability of the prediction results; according to the remaining life prediction value and the degradation rate confidence interval, the corrosion sensitivity of key components is divided into multiple levels, such as low, lower, medium, higher, and high; detailed grading standards are formulated to clarify the remaining life range, degradation rate interval, and corrosion characteristic information corresponding to each level, and this information is organized into a table to form a corrosion sensitivity grading table for key components.

[0047] By establishing a unified data collection platform, corrosion damage data from multiple helicopters is collected, including corrosion conditions, maintenance records, and operational data from different units under the same or different environments. Data integrity and accuracy are ensured during data collection, with rigorous review and verification. The collected data is then aggregated and organized, categorized and stored according to helicopter model, age, and flight region, forming a comprehensive and organized multi-unit corrosion damage database. A data indexing and query system is also established to facilitate rapid data retrieval and analysis. The data in this database are then analyzed in depth, using data mining and statistical analysis methods to examine the relationship between environmental corrosion intensity (such as the combined effects of factors like salinity, humidity, and temperature) and equipment health (such as component performance, remaining life, and failure rate). By comparing the degree of corrosion and changes in equipment health under different environmental conditions, the inherent connections and patterns between the two are identified. For example, the relationship between the corrosion rate and failure rate of key helicopter components in high-salinity and high-humidity environments is analyzed to determine the extent and mechanism of the impact of environmental factors on equipment health.

[0048] From the multi-unit corrosion damage information database, we extract the corresponding data of different environmental conditions (salinity, humidity, temperature, etc.) and equipment health indicators (component wear degree, remaining life estimation, fault frequency), and use data mapping to match the environmental parameter combination with the equipment health status one by one. For example, we organize and classify the cases of accelerated component corrosion that occur repeatedly in high salinity and high humidity environments. By analyzing these matching data, we summarize the laws between environmental factors and equipment health changes. For example, it is found that for every 1‰ increase in salinity, the corrosion rate of a key component increases by an average of 5%. Based on these laws, we establish a set of dynamic association rules. Whenever new environmental data is input, we can quickly judge the changing trend of the equipment health status according to these rules. We connect the dynamic association rules with the real-time data acquisition system. During the operation of the helicopter, the sensors continuously collect sea salinity, air humidity, and flight time. Environmental data such as length, as well as equipment status data such as component vibration frequency and temperature, are transmitted to the ground monitoring platform in real time through a dedicated communication link. After receiving the data, the monitoring platform parses and processes the data according to the preset association rules. For example, when it is detected that the salinity in a certain area exceeds the warning value and lasts for 2 hours, and the temperature of the corresponding component fluctuates abnormally, the corrosion risk level and health status score of the component are immediately calculated according to the association rules. The evaluation results are presented through a visual interface, and the corrosion risk level of each key component is displayed in the form of a dashboard. At the same time, the built-in early warning logic will trigger an audible and visual alarm immediately when the corrosion risk level of the component reaches "high risk" or the health status score is lower than the safety threshold, and a prompt message containing the specific risk location and risk type will be sent to the maintenance personnel's terminal device, so that the maintenance personnel can respond quickly and take targeted maintenance measures.

[0049] Through in-depth data screening, precise matching with the corrosion case library and multi-dimensional data integration, the constructed multi-dimensional environmental stress factor matrix comprehensively and accurately considers various environmental factors and equipment maintenance factors, which can more accurately predict the environmental corrosion effect on helicopters and provide a reliable basis for helicopter protection and maintenance; using high-precision three-dimensional point cloud data acquisition and advanced cluster analysis technology, the generated corrosion sensitive area distribution characteristics can clearly and intuitively present the corrosion risk differences in different parts of the helicopter surface, maintenance personnel can quickly and accurately locate high-risk areas, reducing maintenance costs; through in-depth research on the relationship between corrosion rate and mechanical fatigue, fine-tuning the material strength attenuation trend, and combining emergency event data for comprehensive correction, the calculated remaining life prediction value and degradation rate confidence interval of key components are highly scientific and reliable, providing a basis for component replacement and maintenance plan formulation. It provides a solid basis for determination, effectively avoids the risk of rescue mission failure due to accidental component failure, and improves the success rate and safety of rescue missions; the dynamic correlation between environmental erosion and equipment health status and the real-time updated corrosion assessment system can quickly and accurately adjust the corrosion assessment results according to the real-time changes in the environment and equipment operation data, realize efficient dynamic monitoring of the helicopter corrosion status, timely discover potential corrosion problems and take measures, and extend the service life of the helicopter; integrates the corrosion damage information of multiple units, realizes comprehensive data sharing and in-depth analysis, summarizes the corrosion laws and lessons learned from the overall situation, and provides rich and comprehensive data support for the design improvement and maintenance strategy optimization of helicopters, promotes the management level of the entire rescue helicopter fleet, improves the overall reliability and safety of the fleet, and also provides a useful reference for the development of the industry.

[0050] In a preferred embodiment of the present invention, step 4 above utilizes the quantitative relationship between environmental corrosion and equipment operating conditions, combines comprehensive status indicators of multiple fleets, integrates diverse data from different fleets through a distributed collaborative learning architecture, and constructs a three-dimensional digital image of the helicopter to deduce component performance change trends in a strong salt corrosion and high moisture environment. This may include: Step 440: Establish a multi-fleet data fusion platform based on digital twins to collect real-time salt spray concentration, humidity fluctuation values, and equipment operating load parameters of rescue helicopters in different regions. Use a federated learning mechanism to perform collaborative encryption training on multi-source heterogeneous data to generate a fused global environment-operating condition feature matrix. Step 441: Input the global environment-operating condition characteristic matrix into the environmental degradation simulation processing unit, synchronously call the material corrosion resistance coefficient and structural stress distribution data of the component in the three-dimensional digital image, and simulate the surface corrosion depth growth process and internal crack extension trajectory of the component based on the salt corrosion exposure cycle and humidity fluctuation range to obtain a degradation data set including the mechanical property attenuation value; In step 442, the degradation dataset is decoupled from regional features through a transfer learning processing unit, the corrosion rate correlation function of the machine cluster in the high salt corrosion area is extracted, and the three-dimensional digital image of the machine cluster in the low salt corrosion area is adapted to generate the performance change trend of the components in the low salt corrosion area.

[0051] In an embodiment of the present invention, a data fusion platform is deployed on a central server, and data acquisition terminals are equipped for rescue helicopters in different regions. The acquisition terminals are connected to various sensors on the helicopters (such as salt spray concentration sensors, humidity sensors, and load sensors) to obtain in real time the salt spray concentration, humidity fluctuation values, and equipment operating load parameters (such as engine torque, rotor speed, etc.) of the helicopters during operation. At the same time, a corresponding digital twin is created for each helicopter. The digital twin pre-stores the basic parameters of the helicopter (such as model, production time, component material information) and operating data, so that it can map the status of the physical helicopter in real time. The data collected by helicopters in different regions have multi-source heterogeneous characteristics (such as different data formats and sampling frequencies). After uploading these data to the data fusion platform, the federated learning mechanism is activated. The data from each region does not need to leave the local area and is only trained locally (such as feature extraction of salt spray concentration data and smoothing of humidity data). Local parameters are generated and transmitted to the central server through a secure encrypted channel. The central server aggregates these parameters (such as taking the average and weighted sum) to obtain the global parameters, which are then sent to nodes in each region. Each node uses the global parameters to update the local area and continue with the next round of training. After multiple rounds of adjustment, each node is continuously optimized, and finally a fused global environment-operating condition feature matrix is ​​generated on the central server. This matrix integrates the environmental and operating condition feature data of different regions and different helicopters.

[0052] The global environment-operating condition characteristic matrix is ​​input into the environmental degradation simulation processing unit. Simultaneously, the material corrosion resistance coefficient (e.g., the corrosion resistance of aluminum alloys in salt spray environments) and structural stress distribution data (e.g., the magnitude and direction of stress experienced by various parts of the component under different flight attitudes) are retrieved from the three-dimensional digital image database for each component. Based on the salt corrosion exposure cycle and humidity fluctuation range data in the global environment-operating condition characteristic matrix, the component degradation process is simulated. For the helicopter fuselage skin, the initial corrosion depth is set to zero. Based on the salt spray concentration and exposure time, the corrosion depth is gradually increased at a certain corrosion rate (determined by the material corrosion resistance coefficient and environmental parameters). For internal cracks, the crack extension trajectory in the stress concentration area is simulated based on the structural stress distribution data and humidity changes (humidity affects material toughness). During the simulation, the component's mechanical property degradation values ​​(e.g., percentage strength reduction, degree of toughness reduction) are calculated in real time. Data such as corrosion depth, crack extension, and mechanical property degradation are recorded in chronological order, ultimately forming a degradation dataset containing multi-dimensional information.

[0053] The degradation dataset is input into the transfer learning processing unit for data analysis. Key features of the data from the high-salinity corrosion region (e.g., corrosion patterns unique to high-salinity corrosion regions and combinations of environmental parameters during the rapid corrosion phase) are first identified. These features are then separated from the overall data through comparative analysis to form a corrosion rate correlation function for the high-salinity corrosion region (e.g., a functional expression describing the relationship between salinity and corrosion rate). This corrosion rate correlation function is then adapted to the three-dimensional digital image of the low-salinity corrosion region (e.g., environmental parameters of the low-salinity corrosion region (e.g., average salinity, humidity range) and equipment operating data, and then adjusted (e.g., by reducing the corrosion rate coefficient in the function). This adjusted correlation function, combined with the material and structural data of components in the three-dimensional digital image of the low-salinity corrosion region, is used to simulate component performance changes in a low-salinity corrosion environment. Future performance trends (e.g., expected strength reduction and the likelihood of cracks) are calculated, providing a decision-making basis for the maintenance and use of helicopters in low-salinity corrosion regions.

[0054] A data fusion platform built on digital twins and federated learning enables efficient fusion of heterogeneous data from multiple sources across different regions, avoiding privacy risks during data transmission and ensuring data security across all fleets. By leveraging material and structural data from three-dimensional digital images and combining them with environmental parameters to simulate component degradation, the platform can more realistically reflect component corrosion and performance degradation in environments with strong salt corrosion and high moisture content. This helps maintenance personnel proactively understand component health changes, formulate timely maintenance plans, and reduce the probability of sudden failures. Transfer learning is used to process data from fleets in different regions, adapting corrosion patterns in high-salt corrosion areas to low-salt corrosion areas. This breaks geographical restrictions and enables cross-regional component performance trend analysis. This allows low-salt corrosion areas to learn from the experience of high-salt corrosion areas and take proactive protective measures, reducing the risk of equipment damage caused by environmental factors and extending the overall service life of helicopters. By constructing three-dimensional digital images of helicopters and analyzing component performance trends, a more scientific maintenance strategy can be developed for the entire fleet. Maintenance cycles and content can be rationally scheduled based on the environmental characteristics of different regions, avoiding over- or under-maintenance and improving the overall reliability and rescue mission execution capabilities of the fleet.

[0055] In a preferred embodiment of the present invention, step 5 adjusts the status classification benchmark value in real time based on the component performance change trend and comprehensive status indicators to obtain a complete assessment report with visual warning signs, maintenance urgency, and operational constraint recommendations, which may include: Step 550 inputs the component performance change trend and the real-time collected flight mission urgency parameter into a dynamic grading processing unit, performs weighted calculation on the component remaining life prediction value and the environmental corrosion rate through a deep reinforcement learning mechanism, and dynamically generates a health status grading threshold parameter set. Specifically, the steps include: extracting an initial correlation weight matrix between the environmental corrosion rate and the remaining life prediction value based on the component failure time point in the fault record and the emergency maintenance frequency in the maintenance log; inputting the initial correlation weight matrix and the real-time flight mission urgency parameter into a dynamic calibration mechanism, processing the weight matrix using the mission urgency coefficient to obtain a real-time weight coefficient set; inputting the real-time weight coefficient set, the component remaining life prediction value, and the environmental corrosion rate into a deep reinforcement learning mechanism, constructing a multi-dimensional state feature vector in the state space, and generating an action space operation instruction; inputting the action space operation instruction into a pre-trained reward function mechanism, and generating a health status grading threshold parameter set including a first warning threshold and a second grounding threshold based on the component failure risk cost and the sudden maintenance loss; Step 551: Input the health status classification threshold parameter set into the status identification processing unit, and simultaneously call the spatial positioning data of the component in the three-dimensional digital image to perform a classification determination operation to generate an instruction data set bound to the three-dimensional spatial coordinates; In step 552, the instruction data set bound to the three-dimensional spatial coordinates is input into the decision generation processing unit, and combined with the external input spare parts supply cycle, alternate airport location information and remaining flight time window, a structured evaluation report is obtained, including visual identification mapping relationship, maintenance time sequence table and airspace restriction parameters.

[0056] In an embodiment of the present invention, core parameters, such as the predicted remaining life of key components, material strength attenuation ratio, and surface corrosion depth growth rate, are extracted from the analyzed component performance trend data. These data are acquired through a combination of real-time sensor monitoring and data inference. For example, the remaining life of an engine turbine blade is predicted by referring to both current real-time sensor data such as vibration and temperature, as well as past failure cases under similar operating conditions. Flight mission-related parameters are collected in real time, including mission type (e.g., medical rescue, disaster investigation), mission priority (set by the command center, 1-5 levels), remaining fuel, and target area weather severity (converted to a quantitative level using meteorological satellite data). These parameters are integrated into a comprehensive mission urgency index. For example, if the rescue mission priority is level 1 and there is a strong storm in the target area, the urgency index increases significantly. All collected data is normalized, converting parameters of different dimensions (e.g., remaining life in hours, corrosion depth in millimeters) to a unified range of 0-1. At the same time, outliers, such as extreme data generated by transient sensor failures, are removed.

[0057] With the core goal of balancing flight safety and mission efficiency, high rewards are given for completing high-priority tasks while ensuring safety. Penalties are imposed for safety incidents or mission failures caused by improperly set grading thresholds. Parameters such as the predicted remaining life of components, environmental corrosion rates, and mission urgency index are combined into the system's "state." "Actions" involve adjusting the health status grading thresholds (the boundaries of the five levels: normal, caution, alert, severe, and dangerous). For example, the remaining life threshold for the "alert" level could be adjusted from 50 hours to 40 hours. By simulating a large number of flight scenarios (combining flight data with preset extreme conditions), different grading threshold settings are tried. After each simulation, a reward is calculated based on the actual results (such as safe arrival and timely mission completion). Based on reward feedback, the weighting strategy for each parameter is continuously adjusted. For example, under high mission urgency, the remaining life threshold for the "dangerous" level could be appropriately lowered to trigger early warnings. After repeated learning, the optimal health status grading threshold parameter set for different scenarios is obtained.

[0058] The health status classification threshold parameter set generated in step 550 is transmitted to the status identification processing unit as the basis for judging the health level of the components. The precise spatial positioning data of each component is obtained from the three-dimensional digital image database of the helicopter, including its three-dimensional coordinates (x, y, z) in the fuselage coordinate system, the structural area to which it belongs (such as the engine compartment, rotor system), and the connection relationship information with the surrounding components. For example, the coordinate information of the main rotor drive shaft and the connection point position with the engine and rotor blades will be accurately retrieved; the actual performance parameters of each component (such as the current remaining life, real-time corrosion rate) are compared with the classification threshold one by one. For example, if the remaining life of a hydraulic pump is less than the threshold of the "dangerous" level and the corrosion rate exceeds the threshold of the "dangerous" level, the component will be classified into the following categories: If the component does not meet the standards, its health level is determined to be "dangerous". For each component with a determined health level, its three-dimensional spatial coordinates are bound to the corresponding instruction code (such as "dangerous" corresponds to code 005) to generate a data set containing coordinates (x, y, z) and instruction codes. For example, (1.2, 3.5, -0.8, 005) indicates that the component at the specific coordinate position is in a "dangerous" state. The bound instruction data is classified according to the structural area of ​​the helicopter (fuselage, power system, avionics system, etc.). The components in the same area are then sorted according to their importance (such as critical flight control components are prioritized). The classified and sorted data are organized into a unified format, such as JSON or XML format, to form a structured instruction data set.

[0059] Spare parts supply information for each component is obtained from logistics management, including inventory quantity, replenishment time, and supplier location. The spare parts availability time for each component is calculated. For example, if a certain type of bearing has sufficient inventory but replenishment takes three days, its spare parts availability time is the current time + 3 days. Data such as the location coordinates, runway conditions (length, load capacity), and maintenance capabilities (whether specific component repair qualifications are available) of alternate airports are collected to calculate the flight distance and estimated flight time from the helicopter's current location to each alternate airport. The flight time is corrected based on real-time weather conditions. The remaining flight time window is calculated based on the helicopter's current fuel level, flight speed, fuel consumption rate, and mission requirements. For example, if the remaining fuel is only enough for two hours of flight and the mission requires arriving at the target area within 1.5 hours, the remaining flight time window is 0-1.5 hours. Corresponding visual warning symbols are assigned to each health level, and a mapping table between health levels and visual symbols is established to clarify how to display them in the 3D visualization interface.

[0060] A maintenance plan is formulated based on the health level of components, the available time of spare parts and the remaining flight time window. For "dangerous" level components, they are first arranged for immediate maintenance at the nearest alternate airport; "serious" level components, if the remaining flight time permits, can be repaired as soon as possible after completing the mission; other level components are included in the regular maintenance plan to form a detailed maintenance time series list; airspace restrictions are set in combination with the health status of components and the location of the alternate airport. For example, if a key navigation component is in a "dangerous" state, the helicopter is restricted to flying within 50 kilometers around the alternate airport, and the flight altitude does not exceed 3,000 meters; if problems occur with multiple components at the same time, the activity range is further narrowed and the flight speed is reduced. The visual identification mapping relationship, maintenance time series list, airspace restriction parameters and other contents are integrated into the evaluation report, and sections such as the report title, summary, table of contents, and text are set.

[0061] Through deep reinforcement learning, the classification threshold is dynamically adjusted, so that the health status assessment can adapt to the changes in tasks and environment in real time, reducing the risk of sudden failures caused by delayed assessment. The instruction data set bound to the three-dimensional space coordinates and the structured maintenance time series list allow maintenance personnel to quickly locate problem components and clarify maintenance priorities. The operational constraint recommendations formulated in combination with spare parts supply, alternate airports and remaining flight time provide a scientific basis for airspace management and flight decision-making, ensuring the safety of pilots and rescue missions; based on the assessment report, the maintenance plan is reasonably arranged to avoid excessive maintenance and waste of spare parts, and improve resource utilization efficiency; the structured assessment report presents information in a unified format and visual way, promoting efficient communication between pilots, maintenance teams and command centers, and ensuring the smooth execution of rescue missions.

[0062] like Figure 2 As shown, an embodiment of the present invention further provides a health status assessment system for a rescue helicopter, comprising: The data aggregation module is used to synchronously collect flight operation parameters, maintenance files, abnormal event records and surrounding environment monitoring indicators to form a comprehensive original data set; The status assessment module is used to generate comprehensive status indicators in multiple dimensions, including cumulative operating time indicators, component remaining performance values, and core health scores, based on the comprehensive original data set; The environmental corrosion module is used to perform standardized assessments of the number of oxidation points on the helicopter surface, salt corrosion exposure cycles, and material stress microcrack characteristics based on comprehensive status indicators, establishing a quantitative relationship between environmental corrosion and equipment operating conditions; The digital image module is used to build a three-dimensional digital image of helicopters to deduce performance trends by utilizing the quantitative relationship between environmental erosion and equipment operating conditions, combined with multiple fleet status indicators; The evaluation report module is used to adjust the grading benchmark values ​​in real time based on the performance change trends of components and comprehensive status indicators, and generate a complete evaluation report including warning signs, maintenance suggestions and operating constraints.

[0063] It should be noted that this system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0064] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the above-described method. All implementations in the above-described method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0065] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the above-described method. All implementations in the above-described method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0066] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for assessing the health status of a rescue helicopter, characterized in that: The method comprises: Step 1: Establish an integrated data aggregation platform to simultaneously collect flight operation parameters, maintenance records, abnormal event records, and surrounding environmental monitoring indicators, comprehensively covering the key dimensions of the rotor assembly, power transmission unit, propulsion unit, and marine climate conditions to form a comprehensive original data set; Step 2: Based on the comprehensive original data set, an adaptive parameter adjustment mechanism is used to comprehensively calculate the cumulative operating time index, the remaining performance value of periodic components, the core health score, the probability of abnormal recurrence, the correlation strength of typical problems, the severity of emergencies, and the marine climate erosion factor to generate a comprehensive status index; Step 3: Based on the comprehensive status indicators, a standardized assessment is conducted on the number of oxidation points on the helicopter surface, the salt corrosion exposure cycle, and the material stress microcrack characteristics, establishing a dynamic quantitative correspondence between environmental corrosion characteristics and equipment operating conditions; Step 4: Leveraging the quantitative relationship between environmental corrosion and equipment operating conditions, combined with comprehensive status indicators from multiple fleets, a distributed collaborative learning architecture is used to fuse diverse data from different fleets to construct a three-dimensional digital image of the helicopters. This allows for the deduction of component performance trends in environments with strong salt corrosion and high moisture content. Step 5: Based on the performance change trend of the components and the comprehensive status indicators, the status classification benchmark value is adjusted in real time to obtain a complete assessment report with visual warning signs, maintenance urgency and operation constraint recommendations.

2. The method for assessing the health status of a rescue helicopter according to claim 1, wherein: Based on the comprehensive original data set, the adaptive parameter adjustment mechanism is used to comprehensively calculate the cumulative operating time index, the remaining performance value of periodic components, the core health score, the probability of abnormal recurrence, the correlation strength of typical problems, the severity of emergencies and the marine climate erosion factors to generate comprehensive status indicators, including: The vibration spectrum and torque fluctuation characteristics of the power transmission unit in the flight operation parameters are analyzed in the time and frequency domain. At the same time, the component degradation feature set is constructed by combining the component replacement cycle and maintenance record time series data in the maintenance archive. Based on the component degradation feature set, combined with the temporal and spatial distribution characteristics of the fault mode in the abnormal event records, and using the association rule mining algorithm to calculate the typical problem association strength index, the association relationship between faults is realized to construct the fault propagation path network; Based on the fault correlation framework, we collect marine climate erosion factors such as real-time salt spray concentration and relative humidity, and combine them with corrosion damage data to build a factor library that characterizes environmental stress intensity. At the same time, we perform spatial weighted fusion of climate erosion effects in different regions to form a comprehensive assessment of environmental erosion impacts. Based on the component degradation feature set, fault propagation path network and environmental erosion impact assessment value, the core health score and the severity of the emergency are aligned in time and space, and a comprehensive status indicator with time series characteristics is generated through a multidimensional computing framework, including mechanical health, environmental erosion and operational stability.

3. The method for assessing the health status of a rescue helicopter according to claim 2, wherein: Based on the fault correlation framework, we collect marine climate erosion factors such as real-time salt spray concentration and relative humidity, and combine them with corrosion damage data to build a factor library that characterizes environmental stress intensity. At the same time, we perform spatial weighted fusion of climate erosion effects in different regions to form a comprehensive assessment of environmental erosion impacts, including: According to the spatial distribution of key fault nodes in the fault propagation path network, the coordinate set of the prominent erosion risk area is obtained; Based on the coordinate set of the prominent erosion risk area, the salt spray concentration, relative humidity data and corrosion damage data of the corresponding area are collected in real time; The salt spray concentration, relative humidity data and corrosion damage data are input into the dynamic coupling algorithm to generate the environmental corrosion rate coefficient and material degradation threshold, and build a partitioned environmental stress intensity factor library; Based on the coordinate set of the prominent erosion risk area, the rotor exposure area, the power cabin sealing area and the fuselage surface area are divided, and the spatial weighting coefficient is calculated based on the corrosion resistance level of the material in each area and the exposure time. The partitioned environmental stress intensity factor library and the spatial weighted coefficient are calculated to obtain a comprehensive assessment value of the environmental erosion impact.

4. The method for assessing the health status of a rescue helicopter according to claim 3, wherein: Based on comprehensive status indicators, a standardized assessment is conducted on the number of oxidation points on the helicopter surface, salt corrosion exposure cycles, and material stress microcrack characteristics. A dynamic quantitative correspondence between environmental corrosion characteristics and equipment operating conditions is established, including: The sea salinity, air humidity, flight trajectory altitude, and crew maintenance record data were separated from the comprehensive status indicators. Combined with the corrosion case library, a corrosion environment feature vector including timestamps, geographic coordinates, and operating parameters was constructed. The flight parameters were spatially and temporally correlated with the environmental parameters to obtain a multidimensional environmental stress factor matrix. Based on a multidimensional environmental stress factor matrix, a 3D point cloud was used to extract features from helicopter surface structure data. The distribution of oxidation points and salt corrosion exposure cycles were mapped to the fuselage structure coordinate system. Furthermore, cluster analysis was used to generate distribution characteristics of corrosion-sensitive areas, combining the temporal changes in the material stress microcrack characteristics. Based on the corrosion distribution characteristics, the relationship between corrosion rate and mechanical fatigue is analyzed, and the material strength attenuation trend under different salt corrosion levels is fitted. Combined with the emergency severity data, a corrosion sensitivity classification table for key components is generated, including the remaining life prediction value and the degradation rate confidence interval; Based on the corrosion sensitivity classification data of key components, the corrosion damage information of multiple units is integrated, and a dynamic correlation between environmental erosion intensity and equipment health status is constructed to form a real-time updated corrosion assessment system.

5. The method for assessing the health status of a rescue helicopter according to claim 4, wherein: Based on the corrosion distribution characteristics, the relationship between corrosion rate and mechanical fatigue is analyzed, and the material strength attenuation trend under different salt corrosion levels is fitted. Combined with the emergency severity data, a corrosion sensitivity grading table for key components is generated, including the remaining life prediction value and degradation rate confidence interval, including: Extract the corrosion rate quantification value and mechanical fatigue strength index of key components based on the prominent corrosion risk area data in the corrosion distribution characteristics; Using the corrosion rate quantification value and the mechanical fatigue strength index, the dynamic correlation coefficient between the two is calculated through the correlation analysis algorithm, and a quantitative relationship is established to characterize the interaction between environmental stress and mechanical performance degradation; Based on the quantitative relationship and combined with the corrosion case library under different salt corrosion levels, the attenuation law reflecting the change of key component material strength with operation time is fitted; Using emergency severity data, dynamically calibrate the material strength attenuation law, and calculate the remaining life prediction value and degradation rate confidence interval of key components based on the dynamically calibrated material strength attenuation law; Based on the remaining life prediction value and degradation rate confidence interval, the corrosion sensitivity of key components is graded and a grading table is generated.

6. The method for assessing the health status of a rescue helicopter according to claim 5, characterized in that: By leveraging the quantitative relationship between environmental erosion and equipment operating conditions, combined with comprehensive status indicators from multiple fleets, and integrating diverse data from different fleets through a distributed collaborative learning architecture, a three-dimensional digital image of the helicopter is constructed to deduce component performance trends in environments with strong salt corrosion and high moisture content, including: Establish a multi-fleet data fusion platform based on digital twins to collect real-time salt spray concentration, humidity fluctuation values, and equipment operating load parameters of rescue helicopters in different regions. Use a federated learning mechanism to conduct collaborative encryption training on multi-source heterogeneous data to generate a fused global environment-operating condition feature matrix. The global environment-operating condition characteristic matrix is ​​input into the environmental degradation simulation processing unit, and the material corrosion resistance coefficient and structural stress distribution data of the components in the three-dimensional digital image are synchronously called. Based on the salt corrosion exposure cycle and humidity fluctuation range, the surface corrosion depth growth process and internal crack extension trajectory of the components are simulated to obtain a degradation data set including the mechanical property attenuation value; The degradation dataset is decoupled from regional features through the transfer learning processing unit, and the corrosion rate correlation function of the machine cluster in the high salt corrosion area is extracted. It is adapted to the three-dimensional digital image of the machine cluster in the low salt corrosion area to generate the performance change trend of components in the low salt corrosion area.

7. The method for assessing the health status of a rescue helicopter according to claim 6, wherein: Based on component performance trends and comprehensive status indicators, the status classification benchmark is adjusted in real time to provide a complete assessment report with visual warning signs, maintenance urgency, and operational constraint recommendations, including: The component performance change trend and the real-time collected flight mission urgency parameters are input into the dynamic classification processing unit. The predicted value of the component's remaining life and the environmental corrosion rate are weighted and calculated through a deep reinforcement learning mechanism to dynamically generate a health status classification threshold parameter set. The health status classification threshold parameter set is input into the status identification processing unit, and the spatial positioning data of the parts in the three-dimensional digital image is called at the same time to perform the classification determination operation and generate an instruction data set bound to the three-dimensional space coordinates; The instruction data set bound to three-dimensional spatial coordinates is input into the decision generation processing unit, and combined with the external input spare parts supply cycle, alternate airport location information and remaining flight time window to obtain a structured evaluation report, including visual identification mapping relationship, maintenance time series table and airspace restriction parameters.

8. The method for assessing the health status of a rescue helicopter according to claim 7, wherein: The component performance change trend and the real-time collected flight mission urgency parameters are input into the dynamic classification processing unit. The predicted remaining life of the component and the environmental corrosion rate are weighted and calculated through the deep reinforcement learning mechanism to dynamically generate the health status classification threshold parameter set, including: Based on the component failure time points in the fault records and the emergency maintenance frequency in the maintenance logs, the initial correlation weight matrix between the environmental corrosion rate and the remaining life prediction value is extracted; The initial correlation weight matrix and the real-time flight mission urgency parameter are input into the dynamic calibration mechanism, and the weight matrix is ​​processed by the mission urgency coefficient to obtain the real-time weight coefficient set; The real-time weight coefficient set, the predicted value of the remaining life of the components and the environmental erosion rate are input into the deep reinforcement learning mechanism to construct a multi-dimensional state feature vector in the state space and generate operation instructions in the action space; The action space operation instructions are input into the pre-trained reward function mechanism, and a health status classification threshold parameter set including the first warning threshold and the second grounding threshold is generated based on the component failure risk cost and sudden maintenance loss.

9. A health status assessment system for a rescue helicopter, the system implementing the method according to any one of claims 1 to 8, characterized in that: include: The data aggregation module is used to synchronously collect flight operation parameters, maintenance files, abnormal event records and surrounding environment monitoring indicators to form a comprehensive original data set; The status assessment module is used to generate comprehensive status indicators in multiple dimensions, including cumulative operating time indicators, component remaining performance values, and core health scores, based on the comprehensive original data set; The environmental corrosion module is used to perform standardized assessments of the number of oxidation points on the helicopter surface, salt corrosion exposure cycles, and material stress microcrack characteristics based on comprehensive status indicators, establishing a quantitative relationship between environmental corrosion and equipment operating conditions; The digital image module is used to build a three-dimensional digital image of helicopters to deduce performance trends by utilizing the quantitative relationship between environmental erosion and equipment operating conditions, combined with multiple fleet status indicators; The evaluation report module is used to adjust the grading benchmark values ​​in real time based on the performance change trends of components and comprehensive status indicators, and generate a complete evaluation report including warning signs, maintenance suggestions and operating constraints.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 8 when executed by a processor.

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