Intelligent anti-rust packaging and storage method for locomotive import and export accessories

Abstract

The present application relates to the technical field of intelligent warehouse management, and more particularly to a locomotive import and export accessory intelligent anti-rust packaging and warehousing method, comprising: configuring initial parameters and arranging a monitoring unit, establishing a digital protection model and an anti-rust factor concentration distribution field map; real-time collection of environmental parameters and accessory surface temperature, determination of condensation risk state based on model comparison; when the risk meets the standard, driving the airflow adjusting device according to the concentration field map to optimize the distribution of anti-rust factors; recording the fluctuation trajectory and intervention frequency, combining field map evolution to calculate the efficiency attenuation, generating a health degree score and writing it into the electronic tag with the box; the warehouse system automatically configures the storage location based on the score, and outputs the delivery instruction from low to high according to the score. The present application realizes high-precision risk discrimination based on thermal inertia characteristic compensation, and solves the technical problem of hidden condensation of large locomotive accessories due to thermal hysteresis effect when the external environment temperature and humidity fluctuate sharply.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehouse management technology, and in particular to an intelligent rust-proof packaging and warehousing method for locomotive import and export parts. Background Technology

[0002] Currently, rust protection for imported and exported locomotive parts mainly relies on vapor phase corrosion inhibitor (VCI) technology. This involves placing vapor phase corrosion inhibitors and desiccants within sealed packaging, utilizing the spontaneous adsorption of rust-inhibiting agents on the metal surface to form a passivation protective film. During long-distance transportation and storage across climate zones, temperature and humidity sensors placed inside the packaging are typically used to collect environmental parameters in real time. By setting preset relative humidity thresholds or simple temperature and humidity change curves, the risk of condensation or rust prevention failure in the parts can be indirectly assessed.

[0003] Current technologies rely solely on ambient temperature and humidity as the primary basis for assessing condensation risk, neglecting the significant thermal inertia characteristics of large locomotive components. Because the rate of change in component surface temperature lags far behind the drastic fluctuations in ambient temperature, the component surface temperature is easily lower than the real-time dew point temperature of the ambient air, thus inducing hidden condensation on the component surface and causing localized corrosion at precision-machined areas. Summary of the Invention

[0004] To overcome the above deficiencies, this invention provides an intelligent anti-rust packaging and storage method for locomotive import and export parts, which aims to improve the problem that the surface temperature of the parts is easily lower than the real-time dew point temperature of the ambient air, thereby inducing hidden condensation on the surface of the parts and causing localized corrosion at precision machining positions.

[0005] This invention provides the following technical solution: a method for intelligent rust-proof packaging and storage of locomotive import and export parts, comprising:

[0006] S1. Configure the initial protection parameters of locomotive parts and set up environmental monitoring units to establish a digital protection model of locomotive parts in the encapsulated state and form a field map of rust-preventing factor concentration distribution.

[0007] S2. Real-time collection of environmental parameters inside the packaging and surface temperature of locomotive parts, and real-time comparison of environmental parameters and surface temperature based on digital protection model to determine the condensation risk status of locomotive parts surface;

[0008] S3. When the condensation risk state reaches the preset action threshold, drive the airflow adjustment device inside the packaging to adjust the airflow distribution inside the packaging according to the rust inhibitor concentration distribution field map, so as to change the rust inhibitor concentration distribution.

[0009] S4. Continuously record the fluctuation trajectory of environmental parameters and the intervention frequency of airflow regulation device, and input them into the digital protection model. Combine the evolution data of the rust-preventing factor concentration distribution field map to calculate the performance decay, generate a health score reflecting the remaining life of rust prevention, and write the score into the electronic tag attached to the box in real time.

[0010] S5. The warehouse management system obtains the health score from the electronic tag, automatically configures the warehouse location based on the numerical status of the health score of the locomotive parts, and outputs the corresponding outbound control command in the order of health score from low to high.

[0011] Preferably, in S1, the initial protection parameters for configuring the locomotive accessories include:

[0012] Obtain the material thermophysical parameters, mass, and total surface area of ​​locomotive parts, and identify the corresponding precision machining spatial coordinates based on the geometry of the locomotive parts;

[0013] Based on the thermophysical parameters of the material and the preset transportation cycle of locomotive parts, the critical protective concentration required for the anti-rust agent to maintain the passivation film on the metal surface in the closed cavity was determined.

[0014] The acquired material thermophysical parameters, precision machining position space coordinates, and critical protection concentration are used as initial baseline variables and loaded into the digital protection model to complete the configuration of the initial protection parameters for locomotive parts.

[0015] Preferably, in S1, the environmental monitoring unit includes:

[0016] Identify high-level condensation-prone areas of locomotive parts and low-level deposition areas within the packaging cavity;

[0017] The first monitoring group was set up in high-level, condensation-prone areas to collect the micro-environmental temperature and local rust-preventing agent concentration in close contact with the surface of locomotive parts;

[0018] A second monitoring group was set up in the low-level deposition area to collect the deposition concentration of anti-rust agents at the bottom of the packaging cavity and the cavity temperature and humidity parameters.

[0019] Establish a spatial topological association between the first and second monitoring groups, and feed the collected parameters back to the digital protection model to form a rust-preventing factor concentration distribution field map.

[0020] Preferably, in S1, establishing the digital protection model of the locomotive parts in their encapsulated state includes:

[0021] By integrating the thermal inertia characteristics of locomotive parts with the dynamic volatilization characteristics of rust-preventive agents, a nonlinear coupling relationship between the formation rate of the passivation film on the surface of locomotive parts and the ambient temperature and humidity is defined.

[0022] By combining the molecular diffusion rate of the rust-preventing agent, a spatial distribution mapping relationship of the concentration of the rust-preventing agent in the inner cavity of the packaging is established;

[0023] The extreme condensation boundary conditions on the surface of locomotive parts are set, and the extreme condensation boundary conditions are logically correlated with the monitoring value of the concentration of rust inhibitor in the inner cavity of the packaging to generate a dynamic monitoring benchmark for the digital protection model.

[0024] Preferably, in S2, the real-time comparison of environmental parameters with surface temperature based on the digital protection model includes:

[0025] Using a digital protection model, the real-time dew point temperature of the packaging cavity environment is calculated in real time based on the collected environmental parameters, including air temperature and relative humidity.

[0026] The difference between the surface temperature of the locomotive parts and the real-time dew point temperature is calculated to obtain the current dew deviation value.

[0027] The dew deviation value is input into the digital protection model and matched with the dynamic monitoring benchmark to identify whether the surface of locomotive parts meets the conditions for moisture condensation nucleation, and outputs the corresponding condensation risk status.

[0028] Preferably, in S3, adjusting the airflow distribution within the packaging based on the rust inhibitor concentration distribution field map includes:

[0029] The concentration differences in each region of the rust-preventive factor concentration distribution field map were analyzed, and the real-time concentration values ​​at the spatial coordinates of the precision machining position of the locomotive parts were extracted.

[0030] The real-time concentration value is compared with the critical protection concentration to determine the spatial displacement target and airflow intensity of the rust inhibitor molecules in the packaging cavity.

[0031] By controlling the operating parameters of the airflow regulating device, the rust-preventing agent in the low-level deposition area of ​​the packaging cavity is driven to circulate to the precision machining position spatial coordinates until the real-time concentration value at the precision machining position spatial coordinates, as shown in the rust-preventing agent concentration distribution field map, returns to the preset safe range.

[0032] Preferably, in S4, the continuous recording of the fluctuation trajectory of environmental parameters and the intervention frequency of the airflow regulation device, and inputting them into the digital protection model, includes:

[0033] The environmental monitoring unit is used to acquire real-time temperature and humidity fluctuation data inside the packaging cavity, and the rate of change and extreme values ​​of temperature and humidity fluctuation data are extracted based on time series.

[0034] The operating step length and number of operations of the airflow regulating device are collected simultaneously to obtain the cumulative intervention intensity of the airflow regulating device on the microenvironment inside the packaging cavity;

[0035] The rate of change, extreme values, and cumulative intervention intensity are fed back as dynamic input variables to the digital protection model.

[0036] Preferably, in S4, the performance degradation calculation based on the evolution data of the rust inhibitor concentration distribution field map includes:

[0037] By comparing the spatial distribution differences of the rust inhibitor concentration distribution field map at different observation times, the amount of rust inhibitor diffusion loss and local deposition consumption in each region can be obtained.

[0038] By combining the reaction kinetics model in the digital protection model, and based on the diffusion loss, local deposition consumption, and dynamic input variables, the passivation protection capability loss of the rust inhibitor on the surface of locomotive parts is calculated.

[0039] The remaining rust protection life of locomotive parts is predicted based on the passivation protection capability loss value, and a health score is generated based on the ratio of the remaining rust protection life to the initial protection life.

[0040] Preferably, in S5, the automatic configuration of storage locations based on the numerical status of locomotive parts health scores includes:

[0041] Identify the risk level range to which the health score in the electronic tag belongs;

[0042] Locomotive parts are mapped to corresponding warehouse environment functional areas. Among them, locomotive parts with a health score lower than the preset safety threshold are automatically assigned to controlled warehouse locations with active temperature and humidity control functions.

[0043] The inventory status information in the warehouse management system is updated synchronously to complete the assignment of locomotive parts to the warehouse location.

[0044] Preferably, in S5, the step of outputting the corresponding outbound control instructions according to the health score in ascending order includes:

[0045] Extract multiple health scores of locomotive parts of the same model from the warehouse management system and establish an ascending order queue based on the health scores;

[0046] Based on the remaining rust protection life in the digital protection model, calculate the critical failure warning time for each locomotive component in the ascending order queue;

[0047] Automated handling control instructions for locomotive parts are generated sequentially according to the ascending order of the queue, so as to give priority to locomotive parts with lower health scores when they are released from the warehouse.

[0048] The present invention has the following beneficial effects:

[0049] 1. In this invention, the surface temperature of locomotive parts and the dynamic dew point temperature are compared in real time by a digital protection model, which realizes high-precision risk identification based on thermal inertia characteristic compensation, and solves the technical problem of hidden condensation in large locomotive parts due to thermal hysteresis effect when the external environment temperature and humidity fluctuate drastically.

[0050] 2. In this invention, the directional circulation adjustment of the flow field inside the packaging cavity is driven by the concentration distribution field map of the rust inhibitor, which realizes the precise migration of the rust inhibitor from the low-level deposition area to the spatial coordinates of the precision machining position. This solves the problem of high-level depletion, low-level enrichment, and local protection failure of the rust inhibitor caused by the gravity deposition of molecular weight in the packaging cavity.

[0051] 3. In this invention, environmental fluctuation data and intervention frequency are converted into health scores as the boxes circulate through a reaction kinetic model, realizing intelligent warehousing zoning and first-to-out scheduling based on the remaining protection lifespan. This solves the risk of rework and scrapping of locomotive parts in import and export circulation and warehousing due to the invisible and uncontrollable protection status. Attached Figure Description

[0052] Figure 1 This is a flowchart of a method for intelligent rust-proof packaging and storage of locomotive import and export parts proposed in this invention. Detailed Implementation

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1:

[0055] In the first embodiment of the present invention, the present invention provides a method for intelligent rust-proof packaging and storage of locomotive import and export parts, such as... Figure 1 As shown, it includes the following steps:

[0056] S1. Configure the initial protection parameters of locomotive parts and set up environmental monitoring units to establish a digital protection model of locomotive parts in the encapsulated state and form a field map of rust-preventing factor concentration distribution.

[0057] Furthermore, in S1, the initial protection parameters for configuring locomotive components include:

[0058] Obtain the material thermophysical parameters, mass, and total surface area of ​​locomotive parts, and identify the corresponding precision machining spatial coordinates based on the geometry of the locomotive parts;

[0059] Based on the thermophysical parameters of the material and the preset transportation cycle of locomotive parts, the critical protective concentration required for the anti-rust agent to maintain the passivation film on the metal surface in the closed cavity was determined.

[0060] The acquired material thermophysical parameters, precision machining position space coordinates, and critical protection concentration are used as initial baseline variables and loaded into the digital protection model to complete the configuration of the initial protection parameters for locomotive parts.

[0061] Furthermore, in S1, the environmental monitoring unit includes:

[0062] Identify high-level condensation-prone areas of locomotive parts and low-level deposition areas within the packaging cavity;

[0063] The first monitoring group was set up in high-level, condensation-prone areas to collect the micro-environmental temperature and local rust-preventing agent concentration in close contact with the surface of locomotive parts;

[0064] A second monitoring group was set up in the low-level deposition area to collect the deposition concentration of anti-rust agents at the bottom of the packaging cavity and the cavity temperature and humidity parameters.

[0065] Establish a spatial topological association between the first and second monitoring groups, and feed the collected parameters back to the digital protection model to form a rust-preventing factor concentration distribution field map.

[0066] Furthermore, in S1, establishing a digital protection model for locomotive components in their encapsulated state includes:

[0067] By integrating the thermal inertia characteristics of locomotive parts with the dynamic volatilization characteristics of rust-preventive agents, a nonlinear coupling relationship between the formation rate of the passivation film on the surface of locomotive parts and the ambient temperature and humidity is defined.

[0068] By combining the molecular diffusion rate of the rust-preventing agent, a spatial distribution mapping relationship of the concentration of the rust-preventing agent in the inner cavity of the packaging is established;

[0069] The extreme condensation boundary conditions on the surface of locomotive parts are set, and the extreme condensation boundary conditions are logically correlated with the monitoring value of the concentration of rust inhibitor in the inner cavity of the packaging to generate a dynamic monitoring benchmark for the digital protection model.

[0070] Specifically, in the process of intelligent rust-proof packaging and warehousing of locomotive import and export parts, digital modeling in the initialization phase is the cornerstone of the entire solution's logical operation. First, the system needs to obtain the material's thermophysical parameters through data interfaces or manual input. These parameters cover the material's specific heat capacity, thermal conductivity, and the metal's density. Because locomotive parts typically have enormous mass and complex geometry, their thermal inertia is very significant; that is, changes in the part's surface temperature often lag significantly behind changes in the external ambient temperature. This lag is the main cause of condensation corrosion during long-distance import and export transportation. Therefore, accurate input of mass and total surface area is crucial when configuring initial parameters, as they jointly determine the part's thermal response rate when subjected to environmental temperature and humidity shocks.

[0071] Meanwhile, by reading the geometric shape information of locomotive components, the digital protection model can automatically identify the spatial coordinates of precision machining positions with extremely high rust prevention requirements, such as wheelset journals and motor rotor mating surfaces. These specific coordinate points will be set as the core target for subsequent environmental monitoring and airflow regulation. Based on these material parameters and the component's preset sea or rail transport cycle, the system will further determine the critical protective concentration required for the rust-preventive agent to maintain the passivation film on the metal surface within the enclosed cavity. This concentration determination incorporates a chemical reaction equilibrium mechanism to ensure that the molecular adsorption layer on the metal surface can always resist the intrusion of corrosive media throughout the entire transportation lifecycle. After these static variables are loaded into the digital protection model, the initial configuration of the component's digital twin is completed.

[0072] In terms of physical space deployment, the arrangement logic of the environmental monitoring unit follows the principles of fluid mechanics and gravity deposition. Locomotive parts inside the packaging create complex airflow dead zones, and since rust-preventive molecules are generally heavier than air, they tend to accumulate at the bottom of the packaging, leading to a protective vacuum at the top precision machining area. Therefore, the system first needs to identify high-level condensation-prone areas and low-level deposition areas. At high-level condensation-prone areas, typically the top of the parts or near the lid, the first monitoring group is deployed to specifically collect the micro-environmental temperature and local rust-preventive concentration on the surface of the precision machining area. At low-level deposition areas, i.e., the bottom of the packaging cavity, a second monitoring group is deployed to monitor the rust-preventive substance reserves in this area and the overall temperature and humidity of the cavity. By establishing spatial topological relationships between these monitoring groups, the system can perceive the physical parameter field inside the packaging in real time and feed the data back to the digital protection model. The model uses spatial interpolation algorithms to transform discrete sensor data into a continuous rust-preventive substance concentration distribution field map, intuitively displaying the spatial distribution of protective energy inside the packaging.

[0073] The establishment of a digital protection model is the core of this method's intelligent prediction. This model is not only a database but also a computational engine capable of simulating physicochemical changes. It integrates the thermal inertia characteristics of components with the dynamic volatilization characteristics of rust-preventing agents, aiming to define the nonlinear coupling relationship between the formation rate of the passivation film on the metal surface and ambient temperature and humidity. To accurately describe the evolution of rust-preventing agents within the complex geometric space of the packaging cavity, the model introduces a spatial diffusion and consumption correlation algorithm. The logical basis of this algorithm can be expressed by the following three-dimensional spatial diffusion equation:

[0074] ;

[0075] In this formula, Represents any coordinate point inside the packaging cavity. The local concentration of rust-preventive agents at any given time; This represents the effective diffusion coefficient of the rust-preventing agent in a specific air medium. These represent the three-dimensional spatial coordinate components of the packaging cavity; This represents the adsorption and consumption constant of rust-preventive agents on different surface areas of locomotive parts; This represents the area of ​​the corresponding surface region. This formula describes how the rust-preventing agent dissipates from its source into space and is consumed upon contact with the metal surface.

[0076] Through the calculations of this model, the system can establish a spatial distribution mapping relationship of rust-preventive factor concentration within the packaging cavity. More importantly, the model can set the extreme condensation boundary conditions for the surface of locomotive parts. This condition, combined with the heat conduction model of the parts, can predict when the precision-machined areas on the surface of the parts will drop below the real-time dew point temperature when the external ambient temperature drops sharply. The digital model logically correlates this extreme condensation boundary condition with the measured rust-preventive factor concentration, dynamically generating a monitoring benchmark. This benchmark is not a single numerical value, but a curve that changes with time and environmental stress. When the monitored physical parameters approach this boundary condition, the system can identify the potential tendency for moisture condensation and nucleation, thereby outputting a condensation risk status command. This model establishes a technical path from perception to decision-making, transforming rust prevention work from passive physical shielding to data-driven proactive intervention.

[0077] S2. Real-time collection of environmental parameters inside the packaging and surface temperature of locomotive parts, and real-time comparison of environmental parameters and surface temperature based on digital protection model to determine the condensation risk status of locomotive parts surface;

[0078] Furthermore, in S2, the real-time comparison of environmental parameters with surface temperature based on the digital protection model includes:

[0079] Using a digital protection model, the real-time dew point temperature of the packaging cavity environment is calculated in real time based on the collected environmental parameters, including air temperature and relative humidity.

[0080] The difference between the surface temperature of the locomotive parts and the real-time dew point temperature is calculated to obtain the current dew deviation value.

[0081] The dew deviation value is input into the digital protection model and matched with the dynamic monitoring benchmark to identify whether the surface of locomotive parts meets the conditions for moisture condensation nucleation, and outputs the corresponding condensation risk status.

[0082] Specifically, in the process of implementing intelligent rust-proof packaging and warehousing management for locomotive import and export parts, real-time sensing of fluctuations in the microenvironment inside the packaging is crucial to ensuring the effective triggering of the protection logic. This stage of operation begins with the synchronous acquisition of multi-dimensional environmental data. The system uses a sensor matrix pre-positioned in different quadrants of the packaging cavity to extract the air temperature and relative humidity inside the packaging at high frequency. Simultaneously, surface temperature probes fixed to the condensation-prone areas and precision-machined surfaces of the locomotive parts acquire the surface temperature of the locomotive parts in real time. Since locomotive parts are typically composed of large castings or complex metal components, possessing a large thermal mass, their surface temperature exhibits a significant thermal hysteresis effect when the external ambient temperature fluctuates drastically.

[0083] After acquiring these raw parameters, the digital protection model uses them as dynamic input variables. The model's internal processing logic first focuses on critical analysis of the gas-liquid phase transition within the packaging microenvironment. This process is not a simple threshold judgment, but rather uses the collected air temperature and relative humidity to calculate in real-time the physical boundary for moisture to transition from a gaseous to a liquid state under the current specific environment—the real-time dew point temperature—through the thermodynamic equation of state. This calculation process ensures that the system can identify the critical temperature scale at which water vapor undergoes physical adsorption and liquefaction on the surface of locomotive components.

[0084] To quantify the thermodynamic distance between the ambient moisture state and the metallic physical structure of locomotive components, the digital protection model then calculates the difference between the surface temperature of the locomotive components and the real-time dew point temperature. The calculated value is defined as the current dew point deviation value. The logic behind this deviation value follows the laws of energy transfer, reflecting the safety margin of the locomotive component's surface heat state relative to the ambient moisture saturation state. The operation of the dew point deviation value reflects the real-time nature of dynamic early warning: when the deviation value is positive and large, it indicates sufficient heat on the component surface, making it difficult for moisture to accumulate; when the external ambient temperature rises, causing the air dew point temperature to rise rapidly, while the locomotive components maintain a lower temperature due to thermal inertia, the deviation value will rapidly decrease.

[0085] To determine whether the physical conditions for water condensation nucleation are met, the system inputs the calculated dew tendency deviation value into the digital protection model and performs feature matching with a pre-constructed dynamic monitoring benchmark. The dynamic monitoring benchmark is a set of composite curves that dynamically drift with environmental stress, containing nucleation barrier data for the metal surface under different temperature and humidity conditions. The feature matching logic employs a multi-dimensional lookup table and linear interpolation algorithm. Specifically, the digital protection model corrects the nucleation critical value based on the currently collected pressure parameters and rust inhibitor concentration distribution map. Water condensation nucleation conditions depend not only on the dew tendency deviation value but also on the metal surface roughness and passivation film coverage. By comparing the dew tendency deviation value with the corrected nucleation critical value, the digital protection model determines whether the aggregation state of water molecules on the metal surface has reached the microscopic threshold for forming a liquid water film.

[0086] When performing the above logical comparison, the formula for calculating the real-time dew point temperature is as follows:

[0087] ;

[0088] In the above formula, Represents the real-time dew point temperature; This represents the current temperature of the air inside the packaging. This represents the relative humidity inside the packaging currently being collected; and These are empirical constants for different temperature ranges, used to fit the water vapor pressure curve; The function is an intermediate variable, and its expanded form is:

[0089] ;

[0090] Ultimately, the digital protection model identifies whether the surface of locomotive components meets the conditions for moisture condensation nucleation based on feature matching results, and outputs the corresponding condensation risk status. This status is output in the form of a digital signal, typically including three levels: safe, alert, and quasi-condensation. When the conditions for moisture condensation nucleation are detected, the condensation risk status directly serves as the trigger command for the airflow regulation device in subsequent steps. In this way, the system realizes a complete operational process from sensing environmental data to converting physical indicators, and then to logically determining the risk level. This judgment logic based on thermodynamic deviation effectively solves the problem of hidden condensation on the surface of large locomotive components due to thermal inertia, providing accurate decision-making basis for long-distance import and export transportation.

[0091] S3. When the condensation risk reaches the preset action threshold, the airflow adjustment device inside the packaging is activated to adjust the airflow distribution inside the packaging according to the rust inhibitor concentration distribution field map, so as to change the rust inhibitor concentration distribution.

[0092] Furthermore, in S3, adjusting the airflow distribution within the packaging based on the rust inhibitor concentration distribution field map includes:

[0093] The concentration differences in each region of the rust-preventive factor concentration distribution field map were analyzed, and the real-time concentration values ​​at the spatial coordinates of the precision machining position of the locomotive parts were extracted.

[0094] The real-time concentration value is compared with the critical protection concentration to determine the spatial displacement target and airflow intensity of the rust inhibitor molecules in the packaging cavity.

[0095] By controlling the operating parameters of the airflow regulating device, the rust-preventing agent in the low-level deposition area of ​​the packaging cavity is driven to circulate to the precision machining position spatial coordinates until the real-time concentration value at the precision machining position spatial coordinates, as shown in the rust-preventing agent concentration distribution field map, returns to the preset safe range.

[0096] Specifically, in the rust prevention protection process for locomotive import and export parts, the operational logic of the proactive intervention phase is based on the real-time assessment of condensation risk. When the digital protection model identifies that the condensation risk on the surface of the locomotive parts reaches a preset action threshold, it means that the thermodynamic balance between the metal surface and ambient moisture is about to be broken, and the probability of water nucleation increases significantly. At this time, the system immediately activates the airflow regulation device, which redistributes the rust-preventing factors in space by changing the flow field structure inside the packaging, thereby cutting off the path of rust formation in both physical and chemical dimensions.

[0097] The specific operational logic first manifests as a global analysis of the protective energy status within the packaging cavity. The system invokes the real-time updated rust-preventive factor concentration distribution field map from the digital protection model. This field map, as a three-dimensional numerical matrix, records the concentration value at each spatial coordinate point within the packaging at the current moment. By analyzing the concentration differences between different heights and quadrants in this field map, the system can accurately identify areas rich in rust-preventive factors and areas lacking them. Since the molecular weight of rust-preventive factors is typically greater than the average molecular weight of air, under the influence of gravity, effective protective components tend to accumulate in lower depositional areas within the packaging cavity, while the higher precision machining areas of locomotive parts are prone to low concentrations due to limited diffusion. The system extracts the real-time concentration values ​​of these core target areas from the field map by querying the previously configured spatial coordinates of the precision machining areas, using this as the baseline data for subsequent actions.

[0098] After acquiring the real-time concentration value, the system quantitatively compares it with the critical protection concentration determined when establishing the digital protection model. This comparison process determines the intervention intensity of the airflow regulation device. If the real-time concentration value is significantly lower than the critical protection concentration, it means that the integrity of the passivation film on the surface of the component is seriously threatened. Based on the concentration difference and the geometric volume of the packaging cavity, the system determines the airflow delivery intensity through a rust-preventive factor spatial displacement algorithm. The operating logic of this algorithm is to calculate the airflow required to deliver a specific mass of rust-preventive factor from the low-level enrichment area to the high-level depletion area. The specific delivery intensity can be calculated with reference to the following momentum and mass transfer coupling formula:

[0099] ;

[0100] In the above formula, This represents the output air volume of the airflow control device, i.e., the airflow delivery intensity; The flow channel characteristic correction coefficient of the system is mainly determined by the blockage rate of locomotive parts and the roughness of the inner cavity of the packaging. This represents the preset safe concentration target value; Represents the real-time concentration value at the spatial coordinates of the precision machining position; This represents the density of the gas mixture inside the packaging; This represents the spatial displacement distance between the center point of the low-lying sedimentation area and the spatial coordinate point of the precision machining location. Using this formula, the system can accurately determine the minimum kinetic energy required to transport protective resources for low-lying subsidence to critical points.

[0101] After determining the airflow intensity and displacement target, the system further outputs control commands to the airflow regulating device. By adjusting the device's operating parameters, such as fan speed, piezoelectric ceramic drive frequency, or piston stroke, a directional forced circulation flow field is established within the packaging cavity. This process enables the directional migration of rust-preventive agents from the low-level deposition zone to the precision machining location. At the physical level, the high-speed airflow increases the probability of collisions between rust-preventive agent molecules and the metal surface, promoting rapid repair of the passivation film. Simultaneously, the weak pressure fluctuations generated by the flow field can disturb the air boundary layer adhering to the metal surface, preventing water molecules from accumulating and forming a continuous water film.

[0102] During application, this intervention exhibits dynamic closed-loop characteristics. As the airflow regulation device continues to operate, the digital protection model synchronously updates the rust-preventive factor concentration distribution field map. The system monitors the concentration changes at the spatial coordinates of the precision machining location in the field map in real time. Once the monitoring data shows that the real-time concentration value at that location has recovered to the preset safe range, sufficient to completely cover the active sites on the metal surface under the current temperature and humidity stress, the digital protection model will output a logic command to stop intervention, causing the airflow regulation device to return to standby mode. This adaptive adjustment mechanism based on field map feedback ensures the efficient utilization of rust-preventive resources within the packaging cavity, while also providing proactive defense against extreme environmental impacts during the transportation of locomotive parts across climate zones.

[0103] S4. Continuously record the fluctuation trajectory of environmental parameters and the intervention frequency of airflow regulation device, and input them into the digital protection model. Combine the evolution data of the rust-preventing factor concentration distribution field map to calculate the performance decay, generate a health score reflecting the remaining life of rust prevention, and write the score into the electronic tag attached to the box in real time.

[0104] Furthermore, in S4, the fluctuation trajectory of environmental parameters and the intervention frequency of airflow regulation devices are continuously recorded and input into the digital protection model, including:

[0105] The environmental monitoring unit is used to acquire real-time temperature and humidity fluctuation data inside the packaging cavity, and the rate of change and extreme values ​​of temperature and humidity fluctuation data are extracted based on time series.

[0106] The operating step length and number of operations of the airflow regulating device are collected simultaneously to obtain the cumulative intervention intensity of the airflow regulating device on the microenvironment inside the packaging cavity;

[0107] The rate of change, extreme values, and cumulative intervention intensity are fed back as dynamic input variables to the digital protection model.

[0108] Furthermore, in S4, the performance degradation calculation, which incorporates the evolution data of the rust inhibitor concentration distribution field map, includes:

[0109] By comparing the spatial distribution differences of the rust inhibitor concentration distribution field map at different observation times, the amount of rust inhibitor diffusion loss and local deposition consumption in each region can be obtained.

[0110] By combining the reaction kinetics model in the digital protection model, and based on the diffusion loss, local deposition consumption, and dynamic input variables, the passivation protection capability loss of the rust inhibitor on the surface of locomotive parts is calculated.

[0111] The remaining rust protection life of locomotive parts is predicted based on the passivation protection capability loss value, and a health score is generated based on the ratio of the remaining rust protection life to the initial protection life.

[0112] Specifically, in the long-distance transportation and cross-border logistics of locomotive import and export parts, real-time assessment of protective effectiveness and lifespan prediction are core aspects of ensuring part safety. Continuously recording the fluctuation trajectory of environmental parameters is the first step in achieving accurate assessment. The environmental monitoring unit collects temperature and humidity fluctuation data within the packaging cavity at a preset frequency, forming a continuous data stream distributed over time. Using time series analysis methods, the system can extract key features from the raw data. The rate of change reflects the severity of environmental changes, especially when crossing climate zones, where sudden drops in temperature or surges in humidity are often triggers for protective failure. Extreme values ​​represent the extreme environmental stresses experienced by the parts during transportation. By extracting these features, the digital protection model can quantify the stability of the passivation film on the part surface under different stress levels.

[0113] Simultaneously, the system collects the operating step length and number of operations of the airflow regulation device. The operating step length represents the duration or intensity of a single action, while the number of operations records the frequency of the action. These two together constitute the cumulative intervention intensity. During airflow circulation, although the anti-rust agents are redistributed, mechanical airflow disturbances can also accelerate the escape of molecules or the adsorption of molecules into non-target areas at the microscopic level. Therefore, the intervention frequency is not only a record of the protective measures but also an important parameter of effectiveness consumption. The rate of change, extreme values, and cumulative intervention intensity are fed back to the digital protection model in real time as dynamic input variables, providing data support for subsequent attenuation calculations.

[0114] The calculation of performance degradation is based on the evolution data of the rust-preventive factor concentration distribution field map. A digital protection model compares the spatial distribution differences at different observation times and quantifies the migration of rust-preventive factors in different areas within the packaging using a three-dimensional spatial integration algorithm. Diffusion loss mainly refers to the effective components that escape into the external environment through packaging sealing defects, while local deposition consumption focuses on describing the molecular weight consumed during film formation on the metal surface. Through comparison... Time and By analyzing the concentration gradient distribution in the time-domain map, the system can clearly identify the dynamic flow of protective resources.

[0115] Combining the reaction kinetics model within the digital protection model, the system calculates the passivation protection capacity loss of the anti-rust factor on the surface of locomotive parts based on diffusion loss, local deposition consumption, and dynamic input variables. Passivation protection capacity depends not only on the molecular coverage of the part surface but also on the stability of chemical bond binding energy after film formation. The calculation of the passivation protection capacity loss value here follows a variant of the Arrhenius empirical formula to correlate the effect of temperature stress on the chemical reaction rate. The calculation logic is as follows:

[0116] ;

[0117] In the above formula, This represents the loss of passivation protection capability of the anti-rust agent on the surface of locomotive parts; The frequency factor represents the chemical activity of the rust-preventing agent and the surface roughness of the parts. This represents the activation energy required for the passivation film to desorb or fail. It is the ideal gas constant; Real-time surface temperature of locomotive components as it changes over time; This refers to the real-time humidity inside the packaging cavity; This is a preset reference humidity constant; The influence of humidity is a weighted index; This represents the cumulative intervention intensity coefficient, calculated from the running step size and number of runs. This calculation process reveals the cumulative effect of environmental fluctuation stress, chemical reaction rate, and active adjustment actions on the wear and tear of the protective layer.

[0118] After calculating the wear and tear, the digital protection model predicts the remaining rust protection life of locomotive components based on the passivation protection capability wear and tear. The prediction of remaining life not only considers the wear and tear already incurred but also incorporates pre-stored climate prediction data for subsequent transportation routes within the model. By comparing the predicted remaining rust protection life with the initial protection life, a health score reflecting the protection status is generated. The logic for generating the health score employs a linear weighted algorithm, converting the time margin into a percentage or graded evaluation index.

[0119] The generated health score is wirelessly transmitted and written in real-time to the accompanying electronic tag. The electronic tag, acting as the physical carrier, updates and displays the score throughout the entire transportation process. This operation allows on-site personnel to directly read the current score via handheld terminals or access control systems when parts arrive at their destination or enter transit points, enabling them to assess the protective status of the internal parts without unpacking. This provides a basis for subsequent warehousing strategy development, ensuring the integrity of the data chain from dynamic transportation to static warehousing. Through this lifespan prediction technology based on data trajectory and dynamic models, digital and transparent management of the rust prevention quality of locomotive parts is achieved.

[0120] S5. The warehouse management system obtains the health score from the electronic tag, automatically configures the warehouse location based on the numerical status of the health score of the locomotive parts, and outputs the corresponding outbound control command in the order of health score from low to high.

[0121] Furthermore, in S5, the automatic allocation of storage locations based on the numerical status of locomotive parts health scores includes:

[0122] Identify the risk level range to which the health score in the electronic tag belongs;

[0123] Locomotive parts are mapped to corresponding warehouse environment functional areas. Among them, locomotive parts with a health score lower than the preset safety threshold are automatically assigned to controlled warehouse locations with active temperature and humidity control functions.

[0124] The inventory status information in the warehouse management system is updated synchronously to complete the assignment of locomotive parts to the warehouse location.

[0125] Furthermore, in S5, the corresponding outbound control instructions are output in ascending order of health score, including:

[0126] Extract multiple health scores of locomotive parts of the same model from the warehouse management system and establish an ascending order queue based on the health scores;

[0127] Based on the remaining rust protection life in the digital protection model, calculate the critical failure warning time for each locomotive component in the ascending order queue;

[0128] Automated handling control instructions for locomotive parts are generated sequentially according to the ascending order of the queue, so as to give priority to locomotive parts with lower health scores when they are released from the warehouse.

[0129] Specifically, after locomotive parts have completed long-distance transportation and arrived at the warehousing node, the warehouse management system automatically obtains the health score stored in the electronic tags accompanying the boxes via radio frequency identification (RFID) or near-field communication (NFC) technology. This score is not only the technical endpoint of the logistics process but also the logical starting point for automated warehouse scheduling. The warehouse management system has a pre-set logic discrimination module that extracts the health score from the electronic tags in real time and maps it to a preset risk level range. The risk level range is typically determined based on the sensitivity of locomotive parts to environmental humidity and the residual effectiveness of rust-preventing agents on the metal surface. By identifying the range of the health score, the system can accurately classify the quality status of the parts.

[0130] Based on this numerical status identification, the warehouse management system executes an automated storage location allocation procedure. For locomotive parts with high health scores and within the safe range, the system assigns them to regular storage areas. For locomotive parts with health scores below a preset safety threshold, the system identifies that the concentration of rust-preventive agents inside these parts is approaching a critical state or that the passivation film is at risk of microscopic degradation. Therefore, it automatically issues a scheduling instruction to allocate them to controlled storage locations with active temperature and humidity regulation functions. These controlled storage locations are equipped with industrial-grade dehumidification systems, constant temperature control units, and local rust-preventive agent replenishment devices, which can actively intervene to prevent the rapid decline of remaining lifespan. After location assignment is completed, the system synchronously updates its internal inventory status database, associating the locomotive part's unique identification code, health score, and specific physical storage location coordinates to ensure that the protective background of each part during its storage is traceable.

[0131] The operational logic of the outbound control phase embodies a dynamic scheduling approach based on efficiency priority. Upon receiving an outbound request, the warehouse management system first retrieves the health scores of all in-stock parts of the same locomotive model from the database. To prevent parts with low protective effectiveness from becoming completely ineffective due to long-term stockpiling, the system establishes an ascending-order queue based on health scores. In this queue, parts with the lowest health scores, i.e., the highest protective risk, are assigned the highest priority retrieval weight.

[0132] To further refine the timing of outbound shipments, the system invokes a digital protection model to calculate the remaining lifespan of each component in the queue, thereby determining the critical failure warning time. The calculation logic for the critical failure warning time combines real-time environmental parameters of the current storage location with the historical degradation trajectory of the component; the calculation formula is expressed as follows:

[0133] ;

[0134] In the above formula, It represents the critical failure warning time, measured in hours; This represents the current health score extracted from the electronic tag and recorded by the system, expressed as a normalized numerical value; This represents the initial nominal protection life of the locomotive parts under standard storage conditions; The environmental degradation factor is obtained by fitting the historical average value of the reservoir location sensor. and These represent the deviations of the current storage environment temperature and relative humidity from the preset standard protective environment, respectively. This formula reflects the non-linear influence of health score and environmental stress on the failure time of components, ensuring that the system can quantify the remaining rescue operation time for each component before it completely rusts.

[0135] During application, the warehouse management system generates automated handling control instructions for locomotive parts sequentially based on the ascending order of the queues and the calculated critical failure warning time. These control instructions include key parameters such as storage location coordinates, grabbing paths, and target platform numbers, and are directly sent to automated execution terminals such as automated guided vehicles (AGVs) or stacker cranes. According to the instruction logic, the execution terminals prioritize retrieving locomotive parts with lower health scores and shorter remaining lifespans from their storage locations and transporting them to the outbound area. This operating mode transforms the traditional first-in, first-out (FIFO) model into a first-out (FIFO) model based on the physical protection status of the parts, effectively preventing rework or scrapping of locomotive parts due to protection failure during warehousing. Through this closed-loop digital management, the health data of locomotive parts is deeply coupled with physical actions across the three dimensions of warehousing, inventory, and outbound, supporting the reliable application of intelligent logistics systems in the field of precision equipment.

[0136] Example 2:

[0137] During the export sea transport of precision journals for a certain type of high-power electric locomotive traction motor, the cargo ship needs to cross multiple climate zones, experiencing a sudden change in environment from high-latitude, low-temperature sea areas to high-temperature, high-humidity sea areas near the equator. Because the traction motor journal is a large, solid steel component with significant thermal inertia, the temperature rise rate of the journal surface is severely lagging when the air temperature inside the packaging rises rapidly with the external environment. At this time, although the relative humidity sensing value inside the packaging has not yet reached the alarm threshold of traditional rust prevention solutions, the dew point temperature of the air inside the packaging is substantially higher than the surface temperature of the precision-machined surface of the journal, causing microscopic moisture condensation on the journal surface that is difficult to detect with the naked eye, thus leading to localized pitting corrosion at the precision-machined areas. To solve the above problems, this invention provides an intelligent rust-proof packaging and storage method for locomotive import and export parts, the structure of which is as follows: Figure 1 As shown. The specific implementation process of this method is as follows:

[0138] The initialization phase begins by acquiring the physical properties of the traction motor journal through the system data interface, including the thermophysical parameters of its steel material, the overall mass of the journal, and its surface area. Using the geometric features generated by 3D modeling software, the digital protection model identifies the spatial coordinates of the precision-machined area—the journal's mating surface. Based on the adsorption energy barrier of the journal material for rust-preventive agents and the preset transportation cycle of the route, the system determines the critical protective concentration of the rust-preventive agents to maintain the integrity of the passivation film on the metal surface within the sealed packaging cavity. These fundamental variables are loaded into the digital protection model as static parameters, completing the initial configuration of the protection parameters.

[0139] Before the cargo ship set sail, the environmental monitoring unit was physically deployed according to the identified geometric features. The first monitoring group was fixed at the high, condensation-prone area on top of the journal, with temperature probes installed close to the precision-machined surface to collect surface temperature and simultaneously monitor the local concentration of rust-preventing agents. The second monitoring group was deployed in the low-lying deposition area at the bottom of the packaging box to monitor the amount of rust-preventing agents and the ambient temperature and humidity. As the cargo ship sailed towards lower latitude waters, the ambient temperature rose sharply, and the air temperature inside the packaging cavity increased accordingly. However, due to its enormous mass, the traction motor journal exhibited significant thermal hysteresis, and its surface temperature rose slowly.

[0140] During operation, the digital protection model retrieves real-time data on the journal surface temperature from the first monitoring group and the cavity temperature and humidity from the second monitoring group. The model first calculates the real-time dew point temperature based on air state parameters. When the journal surface temperature remains at a lower temperature scale due to thermal inertia, while the air dew point temperature rises rapidly with the external environment, the difference between the two, i.e., the dew point deviation, begins to shrink. At this point, the system uses feature matching logic to detect that the dew point deviation has entered a preset action threshold range. This judgment logic can predict the tendency of water to nucleate at microscopic active sites on the metal surface before macroscopic condensation occurs.

[0141] Upon identifying a condensation risk, the digital protection model immediately invokes a real-time generated map of the rust-preventive agent concentration distribution. The map shows that, due to gravity deposition, the rust-preventive agents primarily accumulate in the deposition area at the bottom of the packaging box, while the concentration at the higher, precision-machined journal surface is relatively low. To enhance condensation protection in this area, the system determines the operating parameters of the airflow regulating device using an airflow delivery intensity calculation formula, driving a directional change in the internal flow field of the packaging. The rust-preventive agents enriched at the bottom are forced to circulate to the journal surface, accelerating the self-repair of the passivation film by increasing the local partial pressure of the rust-preventive agents, and using the flowing airflow to disturb the trapped air layer on the metal surface, preventing water molecule aggregation.

[0142] Throughout the voyage, the environmental monitoring unit continuously recorded the fluctuations in environmental parameters. When the cargo ship crossed extreme hot and humid regions such as the equator, the system extracted data on the rate of drastic changes and extreme values ​​of temperature and humidity, and recorded the frequency of intervention by the airflow control system to address the risk of condensation. This dynamic data was continuously fed into the digital protection model. The model, combined with the evolution data of the rust inhibitor concentration distribution field map over time, assessed the consumption of rust inhibitors due to active intervention and the diffusion loss due to packaging sealing performance.

[0143] To quantify the loss of protective effectiveness, a performance evaluation algorithm is introduced into the digital protection model. This algorithm combines pre-defined kinetic parameters to calculate the cumulative loss of the passivation protection capability of the journal surface due to rust-preventing factors. By comparing the initial protection life, the system predicts the remaining rust-preventing life of the journal and generates a health score out of 100. Before the goods arrive at the destination port's storage center, this health score is written in real time to the accompanying electronic tag, realizing the binding and transfer of the accessory's protective status with the physical goods.

[0144] When the traction motor journal enters the warehousing management stage, the warehousing system identifies, through scanning electronic tags, that the journal has experienced frequent environmental shocks during sea transport, causing its health score to drop below a preset safety threshold. Based on this score, the system automatically issues instructions during the inbound allocation process, assigning the journal to a controlled storage location with industrial dehumidification and temperature control functions, rather than a regular storage yard. Simultaneously, in the outbound scheduling logic, the system automatically prioritizes the journal, generating automated handling control instructions based on its early position in the ascending queue. This dynamic management based on health scores ensures that components damaged by environmental stress during sea transport receive tiered protection and are put into use as soon as possible, preventing secondary corrosion caused by prolonged storage.

[0145] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent rust-proof packaging and storage of locomotive import / export parts, characterized in that, include: S1. Configure the initial protection parameters of locomotive parts and set up environmental monitoring units to establish a digital protection model of locomotive parts in the encapsulated state and form a field map of rust-preventing factor concentration distribution. S2. Real-time collection of environmental parameters inside the packaging and surface temperature of locomotive parts, and real-time comparison of environmental parameters and surface temperature based on digital protection model to determine the condensation risk status of locomotive parts surface; S3. When the condensation risk state reaches the preset action threshold, drive the airflow adjustment device inside the packaging to adjust the airflow distribution inside the packaging according to the rust inhibitor concentration distribution field map, so as to change the rust inhibitor concentration distribution. S4. Continuously record the fluctuation trajectory of environmental parameters and the intervention frequency of airflow regulation device, and input them into the digital protection model. Combine the evolution data of the rust-preventing factor concentration distribution field map to calculate the performance decay, generate a health score reflecting the remaining life of rust prevention, and write the score into the electronic tag attached to the box in real time. S5. The warehouse management system obtains the health score from the electronic tag, automatically configures the warehouse location based on the numerical status of the health score of the locomotive parts, and outputs the corresponding outbound control command in the order of health score from low to high.

2. The intelligent rust-proof packaging and storage method for locomotive import and export parts according to claim 1, characterized in that, In S1, the initial protection parameters for configuring the locomotive components include: Obtain the material thermophysical parameters, mass, and total surface area of ​​locomotive parts, and identify the corresponding precision machining spatial coordinates based on the geometry of the locomotive parts; Based on the thermophysical parameters of the material and the preset transportation cycle of locomotive parts, the critical protective concentration required for the anti-rust agent to maintain the passivation film on the metal surface in the closed cavity was determined. The acquired material thermophysical parameters, precision machining position space coordinates, and critical protection concentration are used as initial baseline variables and loaded into the digital protection model to complete the configuration of the initial protection parameters for locomotive parts.

3. The intelligent rust-proof packaging and storage method for locomotive import and export parts according to claim 1, characterized in that, In S1, the environmental monitoring unit includes: Identify high-level condensation-prone areas of locomotive parts and low-level deposition areas within the packaging cavity; The first monitoring group was set up in high-level, condensation-prone areas to collect the micro-environmental temperature and local rust-preventing agent concentration in close contact with the surface of locomotive parts; A second monitoring group was set up in the low-level deposition area to collect the deposition concentration of anti-rust agents at the bottom of the packaging cavity and the cavity temperature and humidity parameters. Establish a spatial topological association between the first and second monitoring groups, and feed the collected parameters back to the digital protection model to form a rust-preventing factor concentration distribution field map.

4. The intelligent rust-proof packaging and storage method for locomotive import and export parts according to claim 1, characterized in that, In S1, establishing a digital protection model for locomotive components in their encapsulated state includes: By integrating the thermal inertia characteristics of locomotive parts with the dynamic volatilization characteristics of rust-preventive agents, a nonlinear coupling relationship between the formation rate of the passivation film on the surface of locomotive parts and the ambient temperature and humidity is defined. By combining the molecular diffusion rate of the rust-preventing agent, a spatial distribution mapping relationship of the concentration of the rust-preventing agent in the inner cavity of the packaging is established; The extreme condensation boundary conditions on the surface of locomotive parts are set, and the extreme condensation boundary conditions are logically correlated with the monitoring value of the concentration of rust inhibitor in the inner cavity of the packaging to generate a dynamic monitoring benchmark for the digital protection model.

5. The intelligent rust-proof packaging and storage method for locomotive import and export parts according to claim 1, characterized in that, In S2, the real-time comparison of environmental parameters with surface temperature based on the digital protection model includes: Using a digital protection model, the real-time dew point temperature of the packaging cavity environment is calculated in real time based on the collected environmental parameters, including air temperature and relative humidity. The difference between the surface temperature of the locomotive parts and the real-time dew point temperature is calculated to obtain the current dew deviation value. The dew deviation value is input into the digital protection model and matched with the dynamic monitoring benchmark to identify whether the surface of locomotive parts meets the conditions for moisture condensation nucleation, and outputs the corresponding condensation risk status.

6. The intelligent rust-proof packaging and storage method for locomotive import and export parts according to claim 1, characterized in that, In S3, adjusting the airflow distribution within the packaging based on the rust inhibitor concentration distribution field map includes: The concentration differences in each region of the rust-preventive factor concentration distribution field map were analyzed, and the real-time concentration values ​​at the spatial coordinates of the precision machining position of the locomotive parts were extracted. The real-time concentration value is compared with the critical protection concentration to determine the spatial displacement target and airflow intensity of the rust inhibitor molecules in the packaging cavity. By controlling the operating parameters of the airflow regulating device, the rust-preventing agent in the low-level deposition area of ​​the packaging cavity is driven to circulate to the precision machining position spatial coordinates until the real-time concentration value at the precision machining position spatial coordinates, as shown in the rust-preventing agent concentration distribution field map, returns to the preset safe range.

7. The intelligent rust-proof packaging and storage method for locomotive import and export parts according to claim 1, characterized in that, In S4, continuously recording the fluctuation trajectory of environmental parameters and the intervention frequency of the airflow regulation device, and inputting them into the digital protection model includes: The environmental monitoring unit is used to acquire real-time temperature and humidity fluctuation data inside the packaging cavity, and the rate of change and extreme values ​​of temperature and humidity fluctuation data are extracted based on time series. The operating step length and number of operations of the airflow regulating device are collected simultaneously to obtain the cumulative intervention intensity of the airflow regulating device on the microenvironment inside the packaging cavity; The rate of change, extreme values, and cumulative intervention intensity are fed back as dynamic input variables to the digital protection model.

8. The intelligent rust-proof packaging and storage method for locomotive import and export parts according to claim 1, characterized in that, In S4, the performance degradation calculation based on the evolution data of the rust inhibitor concentration distribution field map includes: By comparing the spatial distribution differences of the rust inhibitor concentration distribution field map at different observation times, the amount of rust inhibitor diffusion loss and local deposition consumption in each region can be obtained. By combining the reaction kinetics model in the digital protection model, and based on the diffusion loss, local deposition consumption, and dynamic input variables, the passivation protection capability loss of the rust inhibitor on the surface of locomotive parts is calculated. The remaining rust protection life of locomotive parts is predicted based on the passivation protection capability loss value, and a health score is generated based on the ratio of the remaining rust protection life to the initial protection life.

9. A method for intelligent rust-proof packaging and storage of locomotive import and export parts according to claim 1, characterized in that, In S5, the automatic configuration of storage locations based on the numerical status of locomotive parts health scores includes: Identify the risk level range to which the health score in the electronic tag belongs; Locomotive parts are mapped to corresponding warehouse environment functional areas. Among them, locomotive parts with a health score lower than the preset safety threshold are automatically assigned to controlled warehouse locations with active temperature and humidity control functions. The inventory status information in the warehouse management system is updated synchronously to complete the assignment of locomotive parts to the warehouse location.

10. A method for intelligent rust-proof packaging and storage of locomotive import and export parts according to claim 1, characterized in that, In S5, the step of outputting the corresponding outbound control instructions according to the health score in ascending order includes: Extract multiple health scores of locomotive parts of the same model from the warehouse management system and establish an ascending order queue based on the health scores; Based on the remaining rust protection life in the digital protection model, calculate the critical failure warning time for each locomotive component in the ascending order queue; Automated handling control instructions for locomotive parts are generated sequentially according to the ascending order of the queue, so as to give priority to locomotive parts with lower health scores when they are released from the warehouse.

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