Recommendation method for thermal insulation materials of all parts of refrigeration house suitable for different process requirements

Abstract

The invention relates to the field of refrigeration house construction and heat preservation system design, and discloses a recommendation method for heat preservation materials of all parts of a refrigeration house suitable for different process requirements, and the recommendation method comprises the following steps: S1, collecting demand parameters of the refrigeration house, including temperature, humidity, ground load, types of stored articles, area of the refrigeration house and number of layers of the refrigeration house, and converting the demand quantity of the refrigeration house into a parameter matrix; s2, according to the parameter matrix, the refrigeration house process requirements are graded into different grades, and according to the process grades, the selection standard of the thermal insulation material of each part is determined; and S3, constructing a material attribute matrix. Accurate selection of cold storage thermal insulation materials is achieved through demand quantification and a multi-objective optimization model, thermal conductivity, moisture resistance, compressive strength and cost are comprehensively considered, and the process requirements of different cold storage are effectively met; and through intelligent temperature and humidity monitoring and material aging management, the long-term stability of the thermal insulation material and the overall energy-saving effect of the refrigeration house are improved.

Description

Technical Field

[0001] The invention relates to the field of cold storage construction and insulation system design, and in particular to a method for recommending insulation materials for various parts of a cold storage that are adapted to different process requirements. Background Art

[0002] With the rapid development of cold chain logistics, food, medicine and other industries, the demand for cold storage construction is increasing. As a special low-temperature storage facility, cold storage needs to maintain a low-temperature environment through an efficient insulation system to ensure the quality of stored items. In the prior art, the selection of cold storage insulation materials is usually limited to several common materials, such as polyurethane, polystyrene boards and vacuum insulation panels. These materials have their own advantages and disadvantages, but it is difficult to fully meet the needs of different types of cold storage. Existing material selection methods are often based only on a single performance indicator of the material, while ignoring the specific process parameters of the cold storage such as temperature, humidity, and load, resulting in a lack of pertinence in material selection, which affects the insulation effect and service life of the cold storage.

[0003] In addition, the insulation effect of cold storage not only depends on the choice of materials, but is also closely related to the thickness of the insulation layer and the construction process. At present, the design of the thickness of the insulation layer is mainly based on experience or simple calculations, lacking accurate heat conduction calculations and energy consumption simulations, which can easily lead to unreasonable thickness design, affecting the insulation effect or increasing costs. The cold bridge problem is common in cold storage, but the existing technology lacks systematic cold bridge detection and sealing design, resulting in large heat loss and increased energy consumption in cold storage. In addition, the temperature and humidity control of traditional cold storage mostly relies on a constant temperature and humidity system, which is difficult to dynamically adjust according to actual changes, which not only increases energy consumption, but also easily leads to material aging and affects insulation performance. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for recommending insulation materials for various parts of a cold storage that can meet different process requirements, thereby solving the problems of lack of specificity in the selection of insulation materials for existing cold storages, inaccurate design of the thickness of the insulation layer, insufficient cold bridge treatment, and backward temperature and humidity control methods leading to a decrease in material performance.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for recommending insulation materials for various parts of cold storage that meet different process requirements, comprising the following steps:

[0006] S1. Collect the demand parameters of cold storage, including temperature, humidity, floor load, types of stored items, cold storage area and number of cold storage floors, and quantify the cold storage demand into a parameter matrix;

[0007] S2. Classify the cold storage process requirements into different levels according to the parameter matrix, and determine the selection criteria of insulation materials for each part according to the process level;

[0008] S3. Record the thermal conductivity, cost, density, moisture resistance, environmental protection factor, compressive strength and upper temperature limit of the candidate materials by constructing a material property matrix;

[0009] S4. Use a multi-objective optimization model to input the demand parameter matrix and material property matrix for calculation, and recommend the optimal insulation material combination suitable for different cold storage locations based on the goals of lowest thermal conductivity, lowest cost, highest compressive strength and best moisture resistance;

[0010] S5. Perform thermal calculation and dynamic energy consumption simulation on the recommended insulation material combination to determine the material thickness that meets the insulation requirements;

[0011] S6. Evaluate the long-term performance of thermal insulation materials based on the material aging model and adjust the humidity conditions in combination with the humidity control model to determine the long-term stability of the materials;

[0012] S7. Determine the economic feasibility of the recommended material combination through cost-benefit analysis and payback period calculation;

[0013] S8. Integrate temperature and humidity sensors with the intelligent monitoring system and optimize temperature and humidity control through machine learning algorithms;

[0014] S9. Use modular construction methods to install insulation materials, combined with cold bridge detection and regular maintenance plans.

[0015] Preferably, in step S1, a standardized parameter matrix is ​​constructed by quantifying the demand parameters of the cold storage, including temperature, humidity, ground load, temperature resistance coefficient of stored items, cold storage area and cold storage layer coefficient.

[0016] Preferably, in step S2, the parameter matrix is ​​used to divide the cold storage into three process levels: high temperature, low temperature and ultra-low temperature cold storage, which correspond to material selection standards with different temperature, humidity and load requirements.

[0017] Preferably, in step S4, the multi-objective optimization model is based on the thermal conductivity, cost, compressive strength and moisture resistance objectives by adjusting the weight coefficient of each optimization objective.

[0018] Preferably, in step S5, the thermal calculation adopts a non-steady-state heat transfer model to analyze the temperature change of the insulation material and determine the material thickness of different cold storage parts.

[0019] Preferably, in step S5, the heat loss of the cold bridge of the cold storage is calculated, the additional heat transfer in the cold bridge area of ​​the cold storage is evaluated, and a sealing solution is designed at the cold bridge position.

[0020] Preferably, in step S6, the material aging model evaluates the change of thermal conductivity over time based on the initial thermal conductivity and aging coefficient of the material.

[0021] Preferably, in step S6, the humidity of the cold storage is monitored in real time through a humidity control model, and the humidity in the cold storage is maintained within a set range through a humidity regulating device to reduce the effect of humidity on the insulation material.

[0022] Preferably, in step S7, the cost-benefit analysis determines the investment payback period of the cold storage insulation system by calculating the sum of material and construction costs as the initial construction cost and combining it with the energy cost savings.

[0023] Preferably, in step S8, the temperature and humidity sensor collects temperature and humidity data of various parts of the cold storage, predicts the temperature and humidity change trend through a machine learning algorithm, and automatically adjusts the output of the refrigeration and dehumidification equipment based on the prediction results to optimize the temperature and humidity control of the cold storage.

[0024] The present invention provides a method for recommending insulation materials for various parts of cold storage that meets different process requirements. It has the following beneficial effects:

[0025] 1. The present invention constructs a cold storage demand parameter matrix to quantify and standardize demand parameters such as temperature, humidity, ground load, storage item type, cold storage area and number of floors, so that the process requirements of the cold storage can be clearly and uniformly expressed. This quantification and standardization method provides a basis for subsequent material selection and calculation, improves the versatility of the recommendation method, and enables the requirements of different types of cold storage to be quickly processed under the same system, which is convenient for adapting to a variety of cold storage application scenarios.

[0026] 2. The present invention achieves a balance between multiple objectives such as thermal conductivity, cost, moisture resistance and compressive strength through a multi-objective optimization model, and meets the process requirements of different cold storages by adjusting the weight coefficient. The model comprehensively considers factors such as thermal insulation performance, economy and structural safety when selecting materials, ensuring that the final selected material combination reaches the optimal state in multiple aspects, significantly improving the comprehensive performance and efficiency of the cold storage system.

[0027] 3. The present invention evaluates the performance change trend of the material in long-term use through the material aging model and the humidity control model, and maintains the stability of the humidity inside the cold storage through the humidity adjustment device. This method ensures the long-term stability of the insulation material in high humidity, low temperature and other environments, prolongs the service life of the material, avoids the problem of reduced insulation performance due to material aging, and effectively reduces maintenance costs.

[0028] 4. The present invention integrates temperature and humidity sensors with intelligent monitoring systems, uses machine learning algorithms to predict temperature and humidity changes inside the cold storage, and automatically adjusts the output of refrigeration and dehumidification equipment to ensure accurate control of the cold storage environment. The intelligent dynamic optimization control method of temperature and humidity avoids the impact of environmental fluctuations on the performance of thermal insulation materials, and further improves the energy saving effect and operating efficiency of the cold storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the overall steps of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Example:

[0032] Regarding the quantification and standardization steps of cold storage demand parameters, the first step is to collect and quantify the cold storage demand parameters, aiming to transform the various requirements of the cold storage into a quantifiable standardized parameter matrix, and provide a unified quantitative basis for the subsequent insulation material recommendation. The demand parameters of the cold storage include temperature, humidity, ground load, temperature resistance of stored items, cold storage area and number of cold storage layers. These parameters can fully describe the insulation requirements of the cold storage. By quantifying and standardizing the various demand parameters, a parameter matrix P is formed so that it can be used uniformly in material selection, optimization model calculation and thermal analysis, improving the overall consistency and operability of the method.

[0033] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for recommending insulation materials for various parts of a cold storage that meets different process requirements, including the following steps:

[0034] S1. Collect the demand parameters of cold storage, including temperature, humidity, floor load, types of stored items, cold storage area and number of cold storage floors, and quantify the cold storage demand into a parameter matrix;

[0035] First, by collecting various demand parameters of the cold storage, including temperature T, humidity H, ground load q, temperature resistance coefficient W of stored items, cold storage area S and number of cold storage floors M, these parameters will be used for demand standardization in subsequent insulation material selection and optimization model calculation. The specific meaning and quantification method of each parameter are as follows:

[0036] Temperature T: Temperature is one of the core requirements of cold storage. By setting the temperature parameter T, the temperature control requirements of cold storage for insulation materials can be quantified. High temperature cold storage (0℃-5℃), low temperature cold storage (below -18℃) and ultra-low temperature cold storage (below -30℃) will be assigned different temperature values ​​for classification in the parameter matrix.

[0037] 2. Humidity H: Humidity is an important factor affecting the insulation effect of cold storage. By quantifying the humidity parameter H, a specific numerical basis is provided for the humidity control of different cold storages. For example, fruit and vegetable cold storage usually requires higher humidity (80%-90%), while pharmaceutical cold storage requires lower humidity (30%-40%). Therefore, these humidity values ​​are directly assigned to H in the parameter matrix so that they can be used to match the moisture resistance requirements of materials in subsequent optimization calculations.

[0038] 3. Ground load q: Ground load is one of the important parameters in cold storage design. The quantitative ground load value q is used to represent the load-bearing requirement of the cold storage floor. When calculating the ground load, it is assumed that the weight of the cold storage cargo is W. 货物 (Unit: kN), the calculation formula of ground load q is:

[0039]

[0040] Where A is the ground area where the goods are piled. By quantifying the ground load q value, the compressive strength requirements of the ground material can be determined so that it can be screened when selecting materials.

[0041] 4. Temperature resistance coefficient W of stored items: Different stored items have different tolerance to temperature, so the temperature resistance coefficient W is defined to represent the temperature tolerance requirements of stored items. The temperature resistance coefficient is assigned according to the type of item. For example, the temperature resistance coefficient of medical items is set to 0.9, food is set to 0.8, and fruits and vegetables are set to 0.7. This coefficient is used as W in the parameter matrix to guide the classification of insulation requirements during material selection.

[0042] 5. Cold storage area S: The cold storage area S is used to indicate the size of the cold storage, usually in square meters, and is assigned to the parameter matrix for subsequent calculation of material usage and cost. Area S is a basic quantitative parameter in the recommendation of cold storage insulation materials.

[0043] 6. Cold storage layer coefficient M: The number of layers of the cold storage affects the selection of insulation materials. The requirements of single-layer cold storage and multi-layer cold storage are different. Define the cold storage layer coefficient M as 0 for single-layer cold storage and 1 for multi-layer cold storage, which represents the influence of the cold storage structure type on the material selection, so that the layer characteristics of the cold storage can be identified in the parameter matrix.

[0044] S2. Classify the cold storage process requirements into different levels according to the parameter matrix, and determine the selection criteria of insulation materials for each part according to the process level;

[0045] Through the above collected and quantified demand parameters, a standardized demand parameter matrix is ​​constructed

[0046] P=[T,H,q,W,S,M]

[0047] Each parameter corresponds to a quantified single variable in the matrix P, and the quantified results of all parameters together form a complete standardized representation of the demand. By converting the cold storage demand into a standardized matrix form, the matrix can be directly called in the subsequent material selection and optimization model, improving the overall efficiency and operational consistency of the insulation material recommendation method.

[0048] S3. Record the thermal conductivity, cost, density, moisture resistance, environmental protection factor, compressive strength and upper temperature limit of the candidate materials by constructing a material property matrix;

[0049] In order to select the best insulation material combination that meets the cold storage process requirements, the present invention constructs a material property matrix M for recording key properties of candidate insulation materials, and designs a multi-objective optimization model for optimization calculation.

[0050] The material property matrix M contains the following key properties, which are used to characterize the performance of different insulation materials:

[0051] Thermal conductivity λ: Thermal conductivity is a key indicator to measure the thermal insulation performance of materials. The lower the thermal conductivity, the better the thermal insulation performance of the material. In order to meet the thermal insulation requirements of the cold storage, the thermal conductivity λ of the material must meet the upper limit of the thermal conductivity set in the cold storage process requirement level. The thermal conductivity measured by the material experiment is assigned to the attribute matrix M.

[0052] 2. Cost C: Cost is an important parameter that affects the economic efficiency of cold storage construction. Material cost C represents the price of materials per unit area. In order to control the overall cost, the unit cost of materials is determined by market prices or enterprise quotations, and its value is filled into the attribute matrix M for weight allocation in the optimization process.

[0053] 3. Density ρ: Density is a physical property of a material that affects its load-bearing capacity and structural stability. Materials with lower density are suitable for ceilings and high-rise areas, while materials with higher density are suitable for ground-bearing areas. The density of a material is determined through physical experiments and assigned as the density parameter ρ in the property matrix.

[0054] 4. Moisture resistance coefficient R f :The moisture resistance coefficient is a parameter to measure the stability of materials in high humidity environments. When the humidity in the cold storage environment is high, moisture resistance is particularly important. The moisture resistance coefficient R of the material f Measured by humidity resistance test, it is used to characterize the moisture resistance of materials. The higher the moisture resistance, the higher the R f The larger the value, the more stable the material performance is in a humid environment.

[0055] 5. Environmental protection coefficient E: Environmental protection coefficient E is mainly used to evaluate the environmental protection of materials, including whether the material is polluting, degradable or meets environmental protection standards. The environmental protection coefficient is usually indicated in the material research and development or evaluation report and assigned to the E parameter in the attribute matrix for comprehensive evaluation in the multi-objective optimization process.

[0056] 6. Compressive strength P: Compressive strength is a key indicator for material selection for cold storage floors and load-bearing areas. Compressive strength P indicates the structural stability of a material when it is subjected to loads, usually measured experimentally in kN / m2. It is assigned to the material property matrix to ensure that the material selection priority under load requirements is considered during optimization calculations.

[0057] 7. Temperature upper limit T max : Upper temperature limit T max Indicates the highest temperature that a material can withstand. It is used to ensure the safety and stability of the material under extreme temperature changes and prevent the material from failing under high temperature conditions. The upper temperature limit of the material is T max Determined through thermodynamic experiments and assigned to corresponding positions in the property matrix.

[0058] The above collected and quantified properties constitute the material property matrix:

[0059] M=[λ,C,ρ,R f ,E,P,T max ]

[0060] S4. Use a multi-objective optimization model to input the demand parameter matrix and material property matrix for calculation, and recommend the optimal insulation material combination suitable for different cold storage locations based on the goals of lowest thermal conductivity, lowest cost, highest compressive strength and best moisture resistance;

[0061] The optimal material selection is carried out through a multi-objective optimization model. The optimization objectives include thermal conductivity, cost, compressive strength and moisture resistance. The objective function of the optimization model is:

[0062]

[0063] Among them, Z is the optimized target value; w1, w2, w3, and w4 are the weight coefficients of thermal conductivity, cost, moisture resistance, and compressive strength, respectively, which are used to balance the priority of each target under different process requirements.

[0064] According to the different process levels and demand parameters of the cold storage, the weight coefficient w of each objective function is reasonably allocated i For example, for ultra-low temperature cold storage, the weight of thermal conductivity should be reduced first so that w i The value is larger, and for high humidity cold storage, the moisture resistance coefficient R fBy adjusting the weight coefficient, the model can adapt to different cold storage needs and achieve personalized optimization combination selection.

[0065] The required parameter moment P and the material property matrix M are input into the multi-objective optimization model to calculate the objective function value Z of each material combination. By calculating the weighted sum of thermal conductivity, cost, moisture resistance and compressive strength, the comprehensive optimization value of different material combinations is obtained to evaluate the applicability of each material combination.

[0066] By comparing the objective function values ​​Z of all candidate material combinations, the material combination with the smallest Z value is selected as the optimal insulation material combination suitable for the current cold storage needs. This combination will meet the comprehensive requirements of the cold storage in terms of insulation, economy, pressure resistance and moisture resistance.

[0067] S5. Perform thermal calculation and dynamic energy consumption simulation on the recommended insulation material combination to determine the material thickness that meets the insulation requirements;

[0068] To ensure that the selected material combination can meet the insulation requirements of the cold storage, thermal calculations and dynamic energy consumption simulations are performed to determine the optimal thickness of the material. The specific steps include:

[0069] Unsteady-state heat transfer calculations:

[0070] For the walls, floors and ceilings of the cold storage, due to the temperature difference between the inside and outside of the cold storage, it is necessary to use the non-steady-state heat transfer equation to calculate the temperature change of the insulation material:

[0071]

[0072] Where T(x,t) represents the temperature at position x and time t.

[0073] is the thermal diffusion coefficient of the material, ρ is the density of the material, C p is the specific heat capacity of the material.

[0074] By solving this equation, the temperature distribution of materials with different thicknesses is calculated, thereby determining the optimal thickness of the material in each part.

[0075] Calculation of heat loss due to cold bridge:

[0076] Cold bridges may occur in the design of cold storage, that is, additional heat transfer occurs due to insufficient local insulation. The formula for calculating the heat loss of cold bridges is:

[0077] Q 冷桥 =ψ·L·ΔT

[0078] Among them, ψ is the linear heat transfer coefficient of the cold bridge, L is the length of the cold bridge, and ΔT is the temperature difference between the inside and outside of the cold storage.

[0079] By calculating the heat loss of the cold bridge, the corresponding sealing solution can be designed at the cold bridge location to avoid unnecessary energy consumption.

[0080] Use energy consumption simulation software (such as EnergyPlus) to simulate the annual energy consumption of the cold storage under different seasons and day and night temperature differences. Input the material thickness, thermal conductivity and heat transfer coefficient of each part of the cold storage to obtain the annual energy consumption data E 年 , thereby evaluating the energy-saving effect of the material combination.

[0081] S6. Evaluate the long-term performance of thermal insulation materials based on the material aging model, and adjust the humidity conditions in combination with the humidity control model to ensure the long-term stability of the materials;

[0082] In order to ensure the performance stability of the material in long-term use, the present invention establishes a material aging model and a humidity control model.

[0083] Material aging models:

[0084] Assuming that the thermal conductivity of the material decays exponentially with time, the material aging model is:

[0085] λ(t)=λ0·e kt

[0086] Among them, λ0 is the initial thermal conductivity of the material, and k is the aging coefficient of the material.

[0087] The value of k is determined through experiments, and the attenuation trend of the thermal conductivity is calculated according to the material's usage time t to ensure the thermal insulation performance of the material in long-term use in cold storage.

[0088] Humidity Control Model:

[0089] Humidity will affect the thermal conductivity of the material. The present invention adjusts the humidity inside the cold storage through a humidity control model. The formula for the influence of humidity on thermal conductivity is:

[0090] λ(H)=λ 干 +a·H

[0091] Among them, λ 干 is the thermal conductivity of the material under dry conditions, a is the humidity influence coefficient, and H is the humidity value.

[0092] By installing humidity sensors and humidity regulators, the humidity inside the cold storage is kept within the set range to ensure the stability of the materials.

[0093] S7. Determine the economic feasibility of the recommended material combination through cost-benefit analysis and payback period calculation;

[0094] Conduct a cost-benefit analysis on the recommended material combination to determine the economic feasibility of the optimal material combination. The specific steps include:

[0095] Material and construction cost calculation:

[0096] The sum of material cost and construction cost is the initial construction cost of the cold storage C 初始 , calculate the total cost based on the amount of materials required for each part of the cold storage.

[0097] Energy consumption cost and payback period calculation:

[0098] Use annual energy consumption data E 年 Calculate the annual energy cost C using the electricity price p 能耗 =E 年 ·p. The payback period is calculated as:

[0099]

[0100] Among them, C 年节省 This is the annual energy saving after selecting high-quality materials.

[0101] S8. Integrate the temperature and humidity sensors with the intelligent monitoring system, optimize the temperature and humidity control through machine learning algorithms, and achieve precise control of the temperature and humidity inside the cold storage;

[0102] Dynamic optimization of temperature and humidity in cold storage is achieved through temperature and humidity sensors and intelligent control systems, including:

[0103] Sensor data collection:

[0104] Temperature and humidity sensors are placed at key locations in the cold storage to collect and form a time series data matrix:

[0105] D(t)=[T1(t),T2(t),…,T n (t),H1(t),H2(t),…,H m (t)

[0106] Machine learning prediction and optimization control:

[0107] The LSTM neural network is used to predict the temperature and humidity change trends of the cold storage, and the control strategy is optimized based on the prediction results to achieve dynamic control of temperature and humidity.

[0108] S9. Use modular construction methods to install insulation materials, and combine cold bridge detection and regular maintenance plans to ensure the insulation effect and long-term stability of the cold storage.

[0109] Modular prefabricated panel installation:

[0110] Prefabricate the insulation panels in the factory and assemble them directly on site to reduce errors. Ensure that the installation error is within 0.5cm.

[0111] Cold bridge detection and regular maintenance:

[0112] Cold bridges are inspected and repaired quarterly, material performance is inspected and necessary replacements are made annually to ensure long-term and stable operation of the cold storage.

[0113] Regular testing and material updates:

[0114] The thermal conductivity and moisture resistance of the material shall be tested annually. If the material performance deteriorates by 80% of the design index, the material shall be replaced.

[0115] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for recommending insulation materials for various parts of cold storage that meet different process requirements, characterized in that: The following steps are involved: S1. Collect the demand parameters of cold storage, including temperature, humidity, floor load, types of stored items, cold storage area and number of cold storage floors, and quantify the cold storage demand into a parameter matrix; S2. Classify the cold storage process requirements into different levels according to the parameter matrix, and determine the selection criteria of insulation materials for each part according to the process level; S3. Record the thermal conductivity, cost, density, moisture resistance, environmental protection factor, compressive strength and upper temperature limit of the candidate materials by constructing a material property matrix; S4. Use a multi-objective optimization model to input the demand parameter matrix and material property matrix for calculation, and recommend the optimal insulation material combination suitable for different cold storage locations based on the goals of lowest thermal conductivity, lowest cost, highest compressive strength and best moisture resistance; S5. Perform thermal calculation and dynamic energy consumption simulation on the recommended insulation material combination to determine the material thickness that meets the insulation requirements; S6. Evaluate the long-term performance of thermal insulation materials based on the material aging model and adjust the humidity conditions in combination with the humidity control model to determine the long-term stability of the materials; S7. Determine the economic feasibility of the recommended material combination through cost-benefit analysis and payback period calculation; S8. Integrate temperature and humidity sensors with the intelligent monitoring system and optimize temperature and humidity control through machine learning algorithms; S9. Use modular construction methods to install insulation materials, combined with cold bridge detection and regular maintenance plans.

2. A method for recommending insulation materials for various parts of a cold storage that meets different process requirements according to claim 1, characterized in that: In step S1, a standardized parameter matrix is ​​constructed by quantifying the demand parameters of the cold storage, including temperature, humidity, ground load, temperature resistance coefficient of stored items, cold storage area and cold storage layer coefficient.

3. A method for recommending insulation materials for various parts of a cold storage that meets different process requirements according to claim 1, characterized in that: In step S2, the cold storage is divided into three process levels: high temperature, low temperature and ultra-low temperature cold storage through the parameter matrix, which correspond to material selection standards with different temperature, humidity and load requirements.

4. A method for recommending insulation materials for various parts of a cold storage adapted to different process requirements according to claim 1, characterized in that: In step S4, the multi-objective optimization model is based on the thermal conductivity, cost, compressive strength and moisture resistance objectives by adjusting the weight coefficient of each optimization objective.

5. According to claim 1, a method for recommending insulation materials for various parts of a cold storage that meets different process requirements is characterized in that: In step S5, the thermal calculation adopts a non-steady-state heat transfer model to analyze the temperature change of the insulation material and determine the material thickness of different cold storage parts.

6. A method for recommending thermal insulation materials for various parts of a cold storage adapted to different process requirements according to claim 1, characterized in that: In step S5, the heat loss of the cold bridge of the cold storage is calculated, the additional heat transfer in the cold bridge area of ​​the cold storage is evaluated, and a sealing solution is designed at the cold bridge location.

7. A method for recommending insulation materials for various parts of a cold storage adapted to different process requirements according to claim 1, characterized in that: In step S6, the material aging model evaluates the change of thermal conductivity over time based on the initial thermal conductivity and aging coefficient of the material.

8. The method for recommending insulation materials for various parts of a cold storage adapted to different process requirements according to claim 1 is characterized in that: In step S6, the humidity of the cold storage is monitored in real time through the humidity control model, and the humidity in the cold storage is maintained within a set range through a humidity regulating device to reduce the impact of humidity on the insulation material.

9. A method for recommending thermal insulation materials for various parts of a cold storage adapted to different process requirements according to claim 1, characterized in that: In step S7, the cost-benefit analysis determines the investment payback period of the cold storage insulation system by calculating the sum of material and construction costs as the initial construction cost and combining it with the energy cost savings.

10. A method for recommending thermal insulation materials for various parts of a cold storage adapted to different process requirements according to claim 1, characterized in that: In step S8, the temperature and humidity sensors collect temperature and humidity data from various parts of the cold storage, predict the temperature and humidity change trends through a machine learning algorithm, and automatically adjust the output of the refrigeration and dehumidification equipment based on the prediction results to optimize the temperature and humidity control of the cold storage.

Images