Vehicle environment adaptability quantitative evaluation method and platform in damp and hot marine environment

Abstract

A vehicle environment adaptability quantitative evaluation method and platform in a humid and hot marine environment, and the method comprises the following steps: collecting original data of a specified environment, preprocessing the original data, and generating environment data; performing environmental stress normalization and material degradation prediction, constructing an environmental stress normalization model and a material degradation model, and outputting a normalized environmental stress index ESI and a material degradation index; loading an FAHP-Entropy fuzzy hierarchy comprehensive evaluation model, calculating a combination weight by adopting an FAHP-Entropy combination weighting method according to the normalized environmental stress index ESI, the material degradation index and the mechanical fault influence factor, and obtaining an adaptability score of the service vehicle in the specified environment; carrying out adaptability quantitative evaluation and maintenance decision output according to the adaptability score; the method is applied to a vehicle environmental adaptability quantitative evaluation platform, the evaluation method is programmed, and environmental adaptability design verification, service state monitoring and maintenance decision support of the vehicle are realized based on multi-source data fusion and dynamic weight optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle environmental adaptability assessment, and in particular to a method and platform for quantitatively assessing vehicle environmental adaptability in a humid and hot marine environment. Background Art

[0002] Ground vehicles in hot and humid marine environments are subject to high temperatures, high humidity, high salt spray, and strong solar radiation. These environmental stresses can lead to material corrosion and aging, electronic component malfunction, and, in severe cases, structural damage and loss of functionality. The increasing complexity of vehicle system design places higher demands on environmental adaptability.

[0003] The current vehicle environmental adaptability design and evaluation methods have the following problems:

[0004] (1) The product relies on the designer's personal experience and lacks historical data and effective and feasible quantitative evaluation methods;

[0005] (2) They often rely on a single environmental parameter (such as salt spray concentration) or laboratory steady-state tests, without considering the synergistic erosion effects of high temperature, humidity, salt spray, and ultraviolet rays, resulting in prediction and assessment errors exceeding 40%;

[0006] (3) The ground equipment environmental adaptability assessment method developed by some researchers only uses the hierarchical analysis method to assign indicator weights, and does not combine objective weighting methods such as material mechanism and entropy weight method, resulting in large assessment errors.

[0007] For example, patent document CN113793053B specifically discloses a method for creating and evaluating a ground equipment environmental adaptability assessment system, pertaining to the technical field of ground equipment environmental adaptability assessment. This assessment method involves steps 1: determining the operating environment of the ground equipment and selecting an expert knowledge base within the assessment system that matches the ground equipment's operating environment. Step 2: adjusting a general assessment model based on the actual composition of the ground equipment. Step 3: selecting elements of the bottom-level input indicators of a hierarchical structure from the expert knowledge base as design factors. Using the quantitative evaluation results of each element as input, the assessment system automatically calculates the environmental adaptability of each indicator at each level of the general assessment model using a fuzzy algorithm. Step 4: identifying weaknesses in the ground equipment's environmental adaptability design.

[0008] For example, the patent document with publication number CN106290126A specifically discloses a method for evaluating the environmental adaptability of rubber materials for rail vehicle shock absorbers, which includes the following steps: analyzing the temperature of the area where the rubber materials for rail vehicle shock absorbers are used to determine the test temperature; setting a typical temperature based on the determined test temperature, and setting high and low temperature humidity alternating test conditions based on the typical temperature and the humidity of the area where the rubber materials are used; testing the performance parameters of the rubber materials for rail vehicle shock absorbers at the set typical temperature and before and after the high and low temperature humidity alternating test; and judging the environmental adaptability of the rubber materials based on the measured parameters. If only the temperature and humidity are considered, the predicted evaluation error will be large.

[0009] Therefore, in order to solve the problems of the above-mentioned existing technologies, it is necessary to design a vehicle environmental adaptability quantitative assessment technical solution that takes into account multiple environmental factors in the use environment and adopts objective empowerment means to reduce prediction and evaluation errors, so as to realize design verification of vehicle environmental adaptability, service status monitoring and maintenance decision support. Summary of the Invention

[0010] To solve the above technical problems, the present application provides a method for quantitatively evaluating the environmental adaptability of a vehicle in a humid and hot marine environment, comprising the following steps:

[0011] Collect the original data of the specified environment, which is obtained by the temperature and humidity sensor, salt spray deposition meter and ultraviolet sensor installed on the service vehicle. The original data includes temperature T, humidity RH, salt spray Cl and ultraviolet I UV , preprocessing the raw data to generate environmental data;

[0012] Performing environmental stress quantification and material degradation prediction, including: constructing an environmental stress quantification model and a material degradation model based on the environmental data and metal material parameters, and outputting a normalized environmental stress index (ESI) and a material degradation index;

[0013] Loading the FAHP-Entropy fuzzy hierarchical comprehensive evaluation model, and obtaining the adaptability score of the in-service vehicle in the specified environment based on the normalized environmental stress index (ESI), material degradation index, and mechanical failure influencing factors; wherein the FAHP-Entropy fuzzy hierarchical comprehensive evaluation model uses the FAHP-Entropy combined weighting method to calculate the combined weights and generate an adaptability score of 0-100 points;

[0014] Based on the adaptability score, a quantitative adaptability assessment and maintenance decision output are performed, including: based on the adaptability score results and score thresholds, identifying components with weak environmental adaptability design in the vehicle, triggering graded warnings and associating graded maintenance strategies, and taking corresponding environmental adaptability protection measures in a targeted manner.

[0015] Furthermore, the temperature and humidity sensors, salt spray deposition meter, and ultraviolet sensor are arranged on the cockpit, power compartment, front and rear axle beams, electrical control assembly, and inner door glass of the service vehicle.

[0016] Furthermore, the calculation formula of the normalized environmental stress index ESI is:

[0017]

[0018] Among them, w T The dynamic weight of temperature updated every 24 hours; w RH The dynamic weight of humidity updated every 24 hours; w Cl T is the dynamic weight of salt spray updated every 24 hours. max =50℃、RH max =95%, [Cl - ] threshold =7mg / cm 2 / day.

[0019] Furthermore, the material degradation model includes a metal corrosion rate model and a coating adhesion loss model; the metal corrosion rate model inputs the normalized environmental stress index ESI and metal material parameters, and outputs the metal material corrosion rate based on the modified Arrhenius-Salt equation; the coating adhesion loss model outputs the coating adhesion loss rate based on a multi-factor coupling equation.

[0020] Furthermore, the following operations are performed before calculating the combined weight using the AHP-Entropy combined weighting method:

[0021] Establishing a primary evaluation index and a secondary evaluation index corresponding to the primary evaluation index based on the normalized environmental stress index ESI, the material degradation index, and the mechanical failure influencing factor;

[0022] The importance of the first-level indicators is compared pairwise, and the evaluation is quantified into numerical intervals using triangular fuzzy number scale to construct a fuzzy judgment matrix. Calculate the maximum eigenvalue and the corresponding eigenvector, which is used to evaluate the importance of each factor. Indicates the importance of indicator i relative to indicator j;

[0023] According to the consistency CR verification formula, the fuzzy judgment matrix Perform consistency verification, Among them, CI is the consistency index of the judgment matrix, which is It is calculated that n is the matrix order, which is the same as the number of first-level indicators; RI is the average random consistency index of the judgment matrix, RI = 0.89.

[0024] Furthermore, the FAHP-Entropy combination weighting method for calculating the combination weight includes the following:

[0025] Calculate the FAHP fuzzy weight. When the fuzzy judgment matrix passes the CR consistency verification, the extended geometric mean method is used to calculate the fuzzy weight of each indicator. Then, the exact value of the fuzzy weight w is obtained by defuzzification j FAHP ;

[0026] The entropy weight method is used to determine the weight of the indicator according to the discrete degree of environmental data. Calculate the exact value w of the normalized secondary indicator data j Entropy and entropy value H j ;

[0027] Based on the exact value of the fuzzy weight wj FAHP and the exact value w of the normalized secondary indicator data j Entropy , calculate the FAHP-Entropy combination weight, the calculation formula is W j =yW FAHP +zW Entropy , where y and z are weight coefficients, and y+z=1, W j Represents the combined weight value of the first-level j indicators.

[0028] Furthermore, the first-level evaluation indicators include climate stress j1, material corrosion j2, mechanical properties j3, and electrical safety j4; the mechanical properties j3 and electrical safety j4 are factors affecting mechanical failure; the second-level evaluation indicators of the climate stress j1 are salt spray deposition rate k1, ultraviolet radiation intensity k2, temperature daily cycle amplitude k3, and humidity saturation time k4; the second-level evaluation indicators of the material corrosion j2 are metal corrosion rate k5, coating adhesion loss rate k6, and seal aging index k7; the second-level evaluation indicators of the mechanical properties j3 are structural deformation k8, lubricant moisture content k9, and vibration acceleration RMS value k 10 The secondary evaluation index of electrical safety j4 is the insulation resistance drop rate k 11 、PCB ion migration k 12 .

[0029] Furthermore, the calculation formula for the normalized exact value of the secondary indicator data is: The entropy value H j The calculation formula is

[0030] Furthermore, the calculation formula used for the adaptability score is: Among them, ujk is the standardized value (0-1) of the kth secondary indicator under the jth primary indicator, w jk is the weight of the kth secondary indicator under the jth primary indicator.

[0031] Another object of the present invention is to provide a quantitative assessment platform for vehicle environmental adaptability in a hot and humid marine environment, which uses the quantitative assessment method for vehicle environmental adaptability in a hot and humid marine environment to implement functions, including the following:

[0032] The data collection and processing layer is used to collect the original data of the specified environment and the information of the protection knowledge base, and provide input data to the model calculation layer;

[0033] The model calculation layer is used to calculate the environmental stress quantification model, material degradation model and FAHP-Entropy fuzzy hierarchical comprehensive evaluation model, and output the calculation results to the decision output layer for adaptive score generation;

[0034] The decision output layer is used to carry out the maintenance decision output, weak component identification and adaptability scoring. The maintenance decision output results are displayed through the user interaction layer, and dynamic optimization and feedback are simultaneously performed to the environmental stress quantification model;

[0035] The user interaction layer is used to display warning information, and the warning information is used to guide the environmental adaptability design of equipment.

[0036] The beneficial effects of the present invention are:

[0037] (1) Through the collection and preprocessing of raw data in a specified environment, it can automatically, comprehensively, and accurately quantify and evaluate the impact of key factors such as climate stress and material corrosion on in-service vehicles and monitor their status, providing guidance and design efficiency for the environmental adaptability design of vehicles, and helping to improve the environmental adaptability of vehicles;

[0038] (2) By integrating real-time environmental monitoring data, material degradation mechanism models and vehicle function failure thresholds, a multi-dimensional dynamic evaluation system is established, and an environmental stress quantification model and a material degradation model are constructed to ensure that the data input of the fuzzy hierarchical evaluation model is more complete, accurate and objective, and solve the problem of insufficient objectivity in the assignment of values ​​in the predictive evaluation, so as to better achieve accurate scoring of vehicle adaptability and avoid empiricism.

[0039] (3) A combined weighting strategy of fuzzy analytic hierarchy process and entropy weight method is adopted to overcome the limitations of single subjective or objective weight and greatly reduce the prediction error rate of environmental adaptability score.

[0040] Improve the accuracy of assessment systems;

[0041] (4) The vehicle environmental adaptability quantitative evaluation platform is used to program the process of the vehicle environmental adaptability evaluation method, thereby improving the evaluation efficiency. The vehicle adaptability score and early warning maintenance strategy can be intuitively understood, and the environmental adaptability of each subsystem in the equipment environmental adaptability design can be quantitatively evaluated, providing a reference for the environmental adaptability design of the vehicle during the design stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of the method for quantitatively evaluating vehicle environmental adaptability provided by the present invention;

[0043] Figure 2 This is a hierarchical model structure diagram of the vehicle environmental adaptability quantitative evaluation method provided by the present invention;

[0044] Figure 3 This is a flow chart of calculating the combination weights using the FAHP-Entropy combination weighting method provided by the present invention;

[0045] Figure 4 It is a logic diagram of the association between adaptability score and maintenance decision of the vehicle environmental adaptability quantitative evaluation method provided by the present invention;

[0046] Figure 5 This is an architectural diagram of the vehicle environmental adaptability quantitative evaluation platform provided by the present invention. DETAILED DESCRIPTION

[0047] The technical solution of the present invention is further described below, but the scope of protection claimed is not limited to the description.

[0048] The embodiment of the present invention provides a method for quantitatively evaluating the environmental adaptability of vehicles in a hot and humid marine environment. The method selects a typical in-service vehicle system as a research object to quantitatively evaluate the environmental adaptability of vehicles under various environmental stresses such as natural exposure to hot and humid marine environments and splashing seawater during driving. The process of the method is as follows: Figure 1 As shown, the specific steps include:

[0049] Step S100: Collect the original data of the specified environment, the original data is obtained by the temperature and humidity sensor, salt spray deposition meter, and ultraviolet sensor set on the service vehicle, the original data includes temperature T, humidity RH, salt spray Cl and ultraviolet I UV , preprocessing the raw data to generate environmental data.

[0050] The temperature and humidity sensor, salt spray deposition meter, and ultraviolet sensor are arranged on the cockpit, power compartment, front and rear axle beams, electrical control assembly, and inner door glass of the service vehicle, and the detection probes of the temperature and humidity sensor, salt spray deposition meter, and ultraviolet sensor are fixed at the test location.

[0051] The raw data is transmitted to the on-board industrial computer in real time via the CAN bus protocol with a sampling frequency of 1 Hz. The preprocessing is to filter outliers using the 3σ principle and align the data according to the principle that the timestamp error is less than 1 ms.

[0052] Step S200: quantifying environmental stress and predicting material degradation, including: constructing an environmental stress quantification model and a material degradation model based on the environmental data and metal material parameters, outputting a normalized environmental stress index (ESI) and a material degradation index, and using them as inputs to a FAHP-Entropy fuzzy hierarchical comprehensive evaluation model to form an "environment-material" response relationship.

[0053] The environmental stress quantification model is to convert the pre-processed real-time environmental data: temperature T, humidity RH, salt spray Cl, ultraviolet I UV Converted into the normalized environmental stress index ESI, the calculation formula of the normalized environmental stress index ESI is:

[0054]

[0055] Among them, w T The dynamic weight of temperature updated every 24 hours; w RH The dynamic weight of humidity updated every 24 hours; w Cl T is the dynamic weight of salt spray updated every 24 hours. max =50℃、RH max =95%, [Cl - ] threshold =7mg / cm 2 / day.

[0056] The normalized environmental stress index (ESI) characterizes the degree of environmental impact on in-service vehicles. The material degradation index predicts the performance degradation rate of key vehicle materials under the stress of a humid and hot marine environment, such as metals, coatings, and seals.

[0057] The material degradation model includes a metal corrosion rate model and a coating adhesion loss model or remaining life; the metal corrosion rate model inputs the normalized environmental stress index ESI and metal material parameters, and outputs the metal material corrosion rate based on the modified Arrhenius-Salt equation; the coating adhesion loss model outputs the coating adhesion loss rate based on a multi-factor coupling equation.

[0058] The modified Arrhenius-Salt equation is:

[0059]

[0060] Among them, through outdoor exposure test, it can be obtained that A=2.8×10 5 、E a =47.5kJ / mol, α=0.61, β=1.15, RH c =70% (critical humidity).

[0061] The multi-factor coupling equation is:

[0062]

[0063] Among them, k 机械 is the mechanical stress coefficient, k 机械 =3.2×10 -5 ;k 老化 is the light aging coefficient, k 老化 =3.2×10 -5 .

[0064] When the calculated normalized environmental stress index (ESI) exceeds the pre-set ESI threshold, a system warning will be triggered. This warning mainly provides an immediate risk warning of environmental mutations, which may accelerate material degradation and require protective measures. The threshold is 0.75.

[0065] Step S300: Load the FAHP-Entropy fuzzy hierarchical comprehensive evaluation model, and obtain the adaptability score of the service vehicle in the specified environment according to the normalized environmental stress index ESI, material degradation index and mechanical failure influencing factors; wherein, the FAHP-Entropy fuzzy hierarchical comprehensive evaluation model adopts the FAHP-Entropy combined weighting method to calculate the combined weight and generate an adaptability score of 0-100 points; the calculation process of the combined weight is as follows Figure 3 As shown, the normalized environmental stress index ESI and the material degradation index are used as input evaluation index data.

[0066] Before calculating the combination weight using the FAHP-Entropy combination weighting method, perform the following operations:

[0067] A hierarchical model is constructed, with the impact of the environmental adaptability of service vehicles and equipment in a humid and hot marine environment set as the target layer. A first-level evaluation index is established based on the normalized environmental stress index ESI, material degradation index, and mechanical failure influencing factors, as well as a second-level evaluation index corresponding to the first-level evaluation index. The structure of the hierarchical model is as follows: Figure 2 shown.

[0068] The first-level evaluation indicators include climate stress j1, material corrosion j2, mechanical properties j3, and electrical safety j4; the mechanical properties j3 and electrical safety j4 are factors affecting mechanical failure; the climate stress j1 is the normalized environmental stress index ESI, and the material corrosion j2 is a material degradation indicator.

[0069] The secondary evaluation indicators of the climate stress j1 are salt spray deposition rate k1, ultraviolet radiation intensity k2, temperature daily cycle amplitude k3, and humidity saturation time k4;

[0070] The secondary evaluation indicators of material corrosion j2 are metal corrosion rate k5, coating adhesion loss rate k6, and seal aging index k7;

[0071] The secondary evaluation indexes of mechanical performance j3 are structural deformation k8, lubricant water content k9, vibration acceleration RMS value k 10 ;

[0072] The secondary evaluation index of electrical safety j4 is the insulation resistance drop rate k 11 、PCB ion migration k 12 .

[0073] The importance of the first-level indicators is compared pairwise, and the evaluation is quantified into numerical intervals using triangular fuzzy number scale to construct a fuzzy judgment matrix. Calculate the maximum eigenvalue and the corresponding eigenvector. The eigenvector is used to evaluate the importance of each factor, that is, the weight distribution, where Indicates the importance of indicator i relative to indicator j, The values ​​of are shown in Table 1:

[0074] Table 1 Value Table

[0075] Triangular fuzzy number (l,m,u) meaning (1,1,1) The two indicators are equally important (1,2,3)、(1 / 3,1 / 2,1) One metric is slightly more important / not important relative to another (2,3,4)、(1 / 4,1 / 3,1 / 2) One metric is significantly more important / less important than another (3,4,5)、(1 / 5,1 / 4,1 / 3) One metric is strongly important / unimportant relative to another (4,5,6)、(1 / 6,1 / 5,1 / 4) One indicator is extremely important / unimportant relative to another indicator

[0076] The fuzzy judgment matrix The expression expands to:

[0077]

[0078] According to the consistency CR verification formula, the fuzzy judgment matrix Perform consistency verification. The CR verification formula is:

[0079]

[0080] Among them, CI is the consistency index of the judgment matrix, which is Calculated, n is the matrix order, and is the same as the number of first-level indicators; RI is the fuzzy judgment matrix The average random consistency index, RI = 0.89.

[0081] If CR < 0.1, the fuzzy judgment matrix There is consistency, indicating that the weight distribution is reasonable; if CR ≥ 0.1, the system prompts the fuzzy judgment matrix Does not meet the consistency requirements, or automatically adjust the fuzzy judgment matrix elements to meet the consistency requirements.

[0082] The FAHP-Entropy combination weighting method for calculating the combination weight includes the following:

[0083] Calculate the FAHP fuzzy weights. When the fuzzy judgment matrix passes the CR consistency verification, the final fuzzy judgment matrix obtained after integration is The extended geometric mean method is used to calculate the fuzzy weight of each indicator. Then, the exact value of the fuzzy weight w is obtained by defuzzification j FAHP ;

[0084] The exact value of the fuzzy weight w j FAHP The calculation formula is:

[0085]

[0086] In order to overcome the limitations of FAHP’s subjective weighting, the entropy weight method is used to determine the weight of the indicators according to the degree of dispersion of environmental data. Normalize the secondary indicator data of real-time monitoring and calculate the exact value w j Entropy and entropy H j ;

[0087] The exact value w of the normalized secondary indicator data j Entropy The calculation formula is:

[0088]

[0089] The entropy value H j The calculation formula is:

[0090]

[0091] Based on the exact value of the fuzzy weight w j FAHP and the exact value w of the normalized secondary indicator data j Entropy, calculate the FAHP-Entropy combined weight, that is, the combined weight of the four primary indicators of climate stress j1, material corrosion j2, mechanical properties j3, and electrical safety j4. The calculation formula is:

[0092] W j =yW FAHP +zW Entropy (9)

[0093] Where y and z are weight coefficients, and y+z=1, W j It represents the combined weight value of the first-level j indicators. In the present invention, it is set as: y=0.6, z=0.4.

[0094] Step S400: Calculate the adaptability score, and perform quantitative adaptability assessment and maintenance decision output based on the adaptability score, including: identifying the weak components of the vehicle's environmental adaptability design based on the adaptability score result, score threshold and early warning maintenance suggestions, triggering graded early warnings and associating graded maintenance strategies, taking corresponding environmental adaptability protection measures in a targeted manner, and outputting maintenance decisions. The logic of the association between the adaptability score and the maintenance decision is as follows: Figure 4 The scoring thresholds and early warning maintenance suggestions are shown in Table 2:

[0095] Table 2 Scoring thresholds and warning maintenance recommendations

[0096]

[0097] The calculation formula used for the adaptability score is:

[0098]

[0099] Among them, u jk is the standardized value (0-1) of the kth secondary indicator under the jth primary indicator, w jk is the weight of the kth secondary indicator under the jth primary indicator.

[0100] In this embodiment, a quantitative evaluation platform for vehicle environmental adaptability in a humid and hot marine environment is also provided, and the quantitative evaluation method for vehicle environmental adaptability in a humid and hot marine environment is applied to realize the function. The architecture diagram of the quantitative evaluation platform for vehicle environmental adaptability is shown as follows: Figure 5 As shown, including the following:

[0101] A data acquisition and processing layer, configured to collect raw data of the specified environment and information from a protection knowledge base, and provide input data to the model calculation layer, wherein the information from the protection knowledge base includes substrate information, surface treatment information, and coating information;

[0102] The model calculation layer is used to calculate the environmental stress quantification model, material degradation model and FAHP-Entropy fuzzy hierarchical comprehensive evaluation model, and output the calculation results to the decision output layer for adaptive score generation;

[0103] The decision output layer is used to carry out the maintenance decision output, weak component identification and adaptability scoring. The maintenance decision output results are displayed through the user interaction layer, and dynamic optimization and feedback are simultaneously performed to the environmental stress quantification model;

[0104] The user interaction layer is used to display warning information, which is used to guide the environmental adaptability design of equipment. The warning information includes: normalized environmental stress index ESI, adaptability score, warning level, maintenance work order and historical data.

[0105] The vehicle environmental suitability quantitative assessment platform programmatically processes the vehicle environmental adaptability assessment process. By designing an interactive user interface for environmental adaptability assessment, the platform uses in-service vehicle, environmental data, and operating conditions as input, and the environmental adaptability of each vehicle component as output, enabling quantitative assessment and iterative design of the environmental adaptability of in-service vehicles. The user interaction layer displays warning information, providing an intuitive understanding of vehicle adaptability scores and early warning maintenance strategies. This quantitatively assesses the environmental adaptability of each subsystem in the equipment environmental adaptability design, providing a reference for environmental adaptability design during the vehicle design phase.

[0106] The above disclosure is only a specific embodiment of the present invention, but the present invention is not limited thereto. Any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present invention.

Claims

1. A quantitative evaluation method for vehicle environmental adaptability in a humid and hot marine environment, characterized in that: The following steps are involved: Collect the original data of the specified environment, which is obtained by the temperature and humidity sensor, salt spray deposition meter and ultraviolet sensor installed on the service vehicle. The original data includes temperature T, humidity RH, salt spray Cl and ultraviolet I UV , preprocessing the raw data to generate environmental data; Performing environmental stress quantification and material degradation prediction, including: constructing an environmental stress quantification model and a material degradation model based on the environmental data and metal material parameters, and outputting a normalized environmental stress index (ESI) and a material degradation index; Loading the FAHP-Entropy fuzzy hierarchical comprehensive evaluation model, and obtaining the adaptability score of the in-service vehicle in the specified environment based on the normalized environmental stress index (ESI), material degradation index, and mechanical failure influencing factors; wherein the FAHP-Entropy fuzzy hierarchical comprehensive evaluation model uses the FAHP-Entropy combined weighting method to calculate the combined weights and generate an adaptability score of 0-100 points; Based on the adaptability score, a quantitative adaptability assessment and maintenance decision output are performed, including: based on the adaptability score results and score thresholds, identifying components with weak environmental adaptability design in the vehicle, triggering graded warnings and associating graded maintenance strategies, and taking corresponding environmental adaptability protection measures in a targeted manner.

2. The method for quantitatively evaluating vehicle environmental adaptability in a humid and hot marine environment according to claim 1, wherein: The temperature and humidity sensors, salt spray deposition meter, and ultraviolet sensor are arranged on the cockpit, power compartment, front and rear axle beams, electrical control assembly, and inner door glass of the service vehicle.

3. The method for quantitatively evaluating vehicle environmental adaptability in a humid and hot marine environment according to claim 2, wherein: The calculation formula of the normalized environmental stress index ESI is: Among them, w T The dynamic weight of temperature updated every 24 hours; w RH The dynamic weight of humidity updated every 24 hours; w Cl T is the dynamic weight of salt spray updated every 24 hours. max =50℃、RH max =95%, [Cl - ] threshold =7mg / cm 2 / day.

4. The method for quantitatively evaluating vehicle environmental adaptability in a humid and hot marine environment according to claim 3, wherein: The material degradation model includes a metal corrosion rate model and a coating adhesion loss model; the metal corrosion rate model inputs the normalized environmental stress index ESI and metal material parameters, and outputs the metal material corrosion rate based on the modified Arrhenius-Salt equation; the coating adhesion loss model outputs the coating adhesion loss rate based on a multi-factor coupling equation.

5. The method for quantitatively evaluating vehicle environmental adaptability in a humid and hot marine environment according to claim 4, characterized in that: Before calculating the combined weights using the AHP-Entropy combined weighting method, the following operations are performed: Establishing a primary evaluation index and a secondary evaluation index corresponding to the primary evaluation index based on the normalized environmental stress index ESI, the material degradation index, and the mechanical failure influencing factor; The importance of the first-level indicators is compared pairwise, and the evaluation is quantified into numerical intervals using triangular fuzzy number scale to construct a fuzzy judgment matrix. Calculate the maximum eigenvalue and the corresponding eigenvector, which is used to evaluate the importance of each factor. Indicates the importance of indicator i relative to indicator j; According to the consistency CR verification formula, the fuzzy judgment matrix Perform consistency verification, Among them, CI is the consistency index of the judgment matrix, which is It is calculated that n is the matrix order, which is the same as the number of first-level indicators; RI is the average random consistency index of the judgment matrix, RI = 0.

89.

6. The method for quantitatively evaluating vehicle environmental adaptability in a humid and hot marine environment according to claim 5, characterized in that: The FAHP-Entropy combination weighting method for calculating the combination weight includes the following: Calculate the FAHP fuzzy weight. When the fuzzy judgment matrix passes the CR consistency verification, the extended geometric mean method is used to calculate the fuzzy weight of each indicator. Then, the exact value of the fuzzy weight w is obtained by defuzzification j FAHP ; The entropy weight method is used to determine the weight of the indicator according to the discrete degree of environmental data. Calculate the exact value w of the normalized secondary indicator data j Entropy and entropy H j ; Based on the exact value of the fuzzy weight w j FAHP and the normalized exact value wj of the secondary indicator data Entropy , calculate the FAHP-Entropy combination weight, the calculation formula is W j =yW FAHP +zW Entropy , where y and z are weight coefficients, and y+z=1, W j Represents the combined weight value of the first-level j indicators.

7. The method for quantitatively evaluating vehicle environmental adaptability in a humid and hot marine environment according to claim 6, wherein: The first-level evaluation indicators include climate stress j1, material corrosion j2, mechanical properties j3, and electrical safety j4; The mechanical performance j3 and electrical safety j4 are factors affecting mechanical failure; The secondary evaluation indicators of the climate stress j1 are salt spray deposition rate k1, ultraviolet radiation intensity k2, temperature daily cycle amplitude k3, and humidity saturation time k4; The secondary evaluation indicators of material corrosion j2 are metal corrosion rate k5, coating adhesion loss rate k6, and seal aging index k7; The secondary evaluation indexes of mechanical performance j3 are structural deformation k8, lubricant water content k9, vibration acceleration RMS value k 10 ; The secondary evaluation index of electrical safety j4 is the insulation resistance drop rate k 11 、PCB ion migration k 12 .

8. The method for quantitatively evaluating vehicle environmental adaptability in a humid and hot marine environment according to claim 7, wherein: The calculation formula for the exact value of the normalized secondary indicator data is: The entropy value H j The calculation formula is 9. The method for quantitatively evaluating vehicle environmental adaptability in a humid and hot marine environment according to claim 8, wherein: The calculation formula used for the adaptability score is: Among them, u jk is the standardized value (0-1) of the kth secondary indicator under the jth primary indicator, w jk is the weight of the kth secondary indicator under the jth primary indicator.

10. A quantitative assessment platform for vehicle environmental adaptability in a humid and hot marine environment, characterized by: The function of the quantitative assessment method of vehicle environmental adaptability in a humid and hot marine environment described in claims 1 to 9 is realized by applying the following contents: The data collection and processing layer is used to collect the original data of the specified environment and the information of the protection knowledge base, and provide input data to the model calculation layer; The model calculation layer is used to calculate the environmental stress quantification model, material degradation model and FAHP-Entropy fuzzy hierarchical comprehensive evaluation model, and output the calculation results to the decision output layer for adaptive score generation; The decision output layer is used to carry out the maintenance decision output, weak component identification and adaptability scoring. The maintenance decision output results are displayed through the user interaction layer, and dynamic optimization and feedback are simultaneously performed to the environmental stress quantification model; The user interaction layer is used to display warning information, and the warning information is used to guide the environmental adaptability design of equipment.

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