A container global environment intelligent regulation system based on multi-sensor fusion

Abstract

The application discloses a kind of container global environment intelligent regulation and control system based on multi-sensor fusion applied to the relevant field of intelligent regulation and control system, the multi-physical field data of temperature field, humidity field and gas concentration field in container are collected by sensing module, and coupling relationship is established, to generate differentiated air supply control instruction with temperature uniformity, humidity uniformity, gas concentration deviation as joint optimization target, terminal air supply module executes differentiated air supply through guide vane in main air duct and intelligent valve on air supply pipeline, solve the problem that temperature, humidity and gas concentration are independently controlled in prior art, resulting in low regulation and control efficiency, lack of multi-physical field collaborative optimization, and air supply strategy extensive leads to local environment out of control;In addition, selectively adding dew point calculation, the call of respiratory heat production model, can achieve accurate control of temperature, humidity and gas, condensation avoidance, respiratory heat production early prediction and equipment health management effect, and effectively ensure efficient regulation and control to the environment in container.

Description

Technical Field

[0001] This invention relates to the field of intelligent control systems, and in particular to an intelligent control system for the entire environment of a container based on multi-sensor fusion. Background Technology

[0002] As an important transportation medium, the internal environment of containers directly affects the quality of goods and transportation safety. Existing container environmental control systems have made some progress in temperature and humidity monitoring and control.

[0003] Existing technologies include methods that utilize multiple sensors inside refrigerated containers to collect environmental data on temperature and humidity. This data is then compared with standard environmental parameters required for cargo storage, and the trends in temperature and humidity changes are used to determine whether the current environmental conditions meet the storage requirements. For example, Chinese patent application CN119740936A discloses a refrigerated container monitoring system and its monitoring method. Additionally, IoT technology is applied to container transportation. The container's internal environmental control system includes a microcontroller module, an environmental temperature and humidity measurement module, an environmental temperature control module, and an environmental humidity control module, enabling independent measurement and separate control of temperature and humidity within the container. For example, Chinese patent application CN113009945A discloses an IoT-enabled railway transport container.

[0004] However, the aforementioned existing technologies all control temperature and humidity as independent parameters, resulting in low regulation efficiency and relatively overlapping regulation effects. In addition, the air supply is mostly global uniform air supply or coarse adjustment based on single temperature feedback, which makes it difficult to achieve precise differentiated air supply for different areas with different heat loads, and it is difficult to meet the needs of high-value-added goods such as fresh fruits and vegetables and high-end pharmaceuticals for coordinated control of multiple environmental parameters. Summary of the Invention

[0005] The core of this invention lies in collecting multi-physics field data of temperature, humidity, and gas concentration fields inside a container through a sensing module, and establishing a coupled model. Differentiated air supply control commands are generated with temperature uniformity, humidity uniformity, and gas concentration deviation as joint optimization objectives. The terminal air supply module executes differentiated air supply through the guide plate in the main air duct and the intelligent valve on the air supply pipe. This solves the problems of low control efficiency, lack of multi-physics field collaborative optimization, and local environmental loss of control caused by the independent control of temperature, humidity, and gas concentration in the existing technology.

[0006] To solve the above problems, the present invention adopts the following technical solution.

[0007] A container-wide intelligent environmental control system based on multi-sensor fusion, characterized in that it includes a sensing module, a control module, and a terminal air supply module; The sensing module is installed inside the container, and the sensing module includes at least a temperature sensor and a humidity sensor for collecting temperature and humidity data inside the container. Both the temperature sensor and the humidity sensor are connected to the control module via signals. The control module is used to establish a first coupling relationship between the temperature field and the humidity field based on the temperature and humidity data collected by the sensing module, and the control module generates a first regulation command based on the coupling relationship. The terminal air supply module is installed inside the container and is also connected to the control module. The terminal air supply module delivers differentiated air to different areas inside the container according to the first control command issued by the control module.

[0008] Furthermore, the terminal air supply module includes a main air duct installed inside the container, multiple guide vanes installed inside the main air duct, and multiple air supply pipes installed inside the container. The multiple air supply pipes are fixed to and connected to the main air duct. The air supply pipes are provided with multiple air outlets corresponding to different areas inside the container, and each of the multiple air outlets is equipped with an intelligent valve.

[0009] Furthermore, the control module calculates the dew point temperature of each area inside the container in real time based on the first coupling relationship, and compares the dew point temperature with the wall temperature of the corresponding area. When the dew point temperature of any area is higher than or equal to the wall temperature of that area, the first control command includes at least one of prioritizing the activation of the dehumidification device, increasing the air supply temperature, and increasing the air supply volume of that area.

[0010] Furthermore, the sensing module also includes an oxygen sensor, a carbon dioxide sensor, and an ethylene sensor. The oxygen sensor, carbon dioxide sensor, and ethylene sensor are connected to the control module via signals. The control module establishes a second coupling relationship between the temperature field, humidity field, and gas concentration field based on the collected data. The control module generates a second control command based on the second coupling relationship among the three.

[0011] Furthermore, based on the type of cargo inside the container, the control module calls the breathing heat generation model corresponding to the cargo. Based on real-time collected temperature, humidity and gas concentration data, the control module predicts the heat load distribution trend of each area through the breathing heat generation model, and generates an advanced air supply command according to the heat load distribution trend, so that the terminal air supply module increases the air supply volume of the corresponding area at the current moment.

[0012] Furthermore, the respiratory heat production rate R of the cargo in the respiratory heat production model is calculated according to the following formula: R = R0 × f(T) × g(C) O2 )×h(C CO2 ); Where R0 is the baseline respiratory heat production rate, f(T) is the temperature correction function, and g(C) is the temperature correction function. O2 ) is the oxygen concentration correction function, h(C) CO2 ) is the carbon dioxide concentration correction function.

[0013] Furthermore, the sensing module also includes an operating parameter acquisition unit, which is signal-connected to the terminal air supply module and acquires the operating parameters of the terminal air supply module. The operating parameters include at least one of the following: compressor exhaust pressure, compressor return pressure, compressor operating current, fan speed, and fan operating current. The control module includes an attenuation identification unit and a compensation calculation unit. The attenuation identification unit is used to identify the performance attenuation curve of the terminal air supply module based on the time series data of the operating parameters. The compensation calculation unit is used to dynamically adjust the control output gain based on the performance attenuation curve.

[0014] A method for intelligent control of the entire environment of a container based on multi-sensor fusion includes the following steps: S1. Collect environmental parameters inside the container, including at least the temperature and humidity fields; S2. Establish the first coupling relationship between the temperature field and the humidity field; S3. Generate a first control command based on the coupling relationship; S4. According to the first control command, control the terminal air supply module to supply air to different areas inside the container in a differentiated manner.

[0015] Compared with the prior art, the advantages of this invention are: (1) This solution establishes the first coupling relationship between the temperature field and humidity field inside the container through the distributed arrangement of temperature and humidity sensors, and generates differentiated air supply control commands accordingly. It can achieve coordinated and precise control of the temperature field and humidity field inside the container, effectively avoiding energy waste and control lag caused by traditional independent temperature and humidity control. It is suitable for the basic temperature and humidity control needs of conventional cold chain goods (frozen meat, aquatic products).

[0016] (2) By establishing a second coupling relationship between the temperature field, humidity field and gas concentration field, and generating control instructions with the joint optimization objectives of minimizing the amplitude of temperature non-uniformity, maximizing the uniformity of humidity distribution and minimizing the deviation of gas concentration, the coordinated optimization control of the three fields of temperature, humidity and gas is realized, and the control efficiency and control effect are steadily improved.

[0017] (3) At the same time, the operating status of the terminal air supply module is monitored in real time through the operating parameter acquisition unit. Combined with the attenuation identification unit and the compensation calculation unit, the performance attenuation of the terminal air supply module is automatically compensated, which significantly improves the control stability and reliability of the system throughout the entire life cycle of the equipment. It is suitable for the transportation of high-end pharmaceuticals, vaccines, electronic components and other multi-parameter sensitive goods.

[0018] (4) By calculating the dew point temperature of each area in real time and comparing it with the wall temperature, dehumidification, heating and increasing air volume are actively implemented to avoid condensation before it forms, thus preventing the generation of condensate from the source, effectively preventing goods from getting damp and moldy, and significantly improving the transportation safety in high humidity environments (cross-sea transportation, tropical routes).

[0019] (5) By identifying the type of goods and calling the respiratory heat generation model, the respiratory heat generation rate is calculated in real time by combining the temperature correction function, oxygen concentration correction function and carbon dioxide concentration correction function. The heat load change trend of each area is predicted in advance and the air supply volume of the corresponding area is increased in advance. This realizes the leap from "passive response" to "active prediction", effectively eliminates the temperature fluctuation peak, and maximizes the shelf life of fresh fruits and vegetables, flowers and other goods. Attached Figure Description

[0020] Figure 1 This is a principle block diagram of the first embodiment of the present invention; Figure 2 This is a schematic diagram showing the arrangement of the terminal air supply module inside the container according to the present invention; Figure 3 This is a principle block diagram of the second embodiment of the present invention; Figure 4 This is a principle block diagram of the third embodiment of the present invention; Figure 5 This is a schematic diagram of the fourth embodiment of the present invention. Detailed Implementation

[0021] The technical solutions will now be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention.

[0022] First implementation method: like Figure 1 A container-wide intelligent environmental control system based on multi-sensor fusion includes a sensing module, a control module, and a terminal air supply module. The sensing module is installed inside the container and includes at least a temperature sensor and a humidity sensor for collecting temperature and humidity data inside the container. Both the temperature sensor and the humidity sensor are connected to the control module. The temperature sensor and the humidity sensor are arranged in a distributed array inside the container, with 4-16 temperature sensors and humidity sensors installed on the top wall, side walls and bottom wall of the container to achieve comprehensive coverage of the three-dimensional temperature and humidity field inside the container.

[0023] The control module is located outside the container or integrated into a control box at the end of the container. The control module includes a processor, memory, and a communication interface. It establishes a first coupling relationship between the temperature and humidity fields based on temperature and humidity data collected by the sensing module. The control module then generates a first control command based on this coupling relationship. This first coupling relationship represents the mutual influence between the temperature and humidity distributions in different areas within the container, specifically including the influence of temperature changes on relative humidity and the influence of humidity changes on the thermal conductivity coefficient.

[0024] The terminal air supply module is installed inside the container and is also connected to the control module. Based on the first control command issued by the control module, the terminal air supply module provides differentiated airflow to different areas within the container. Differential airflow means that for areas where the temperature deviates significantly from the target value, the terminal air supply module increases the airflow to that area; for areas where the temperature is close to the target value, the terminal air supply module decreases or maintains the airflow to that area. Through this differentiated airflow control, the temperature field within the container is made uniform, and the humidity field is stabilized.

[0025] like Figure 2The terminal air supply module includes a main air duct installed inside the container, multiple guide vanes installed within the main air duct, and multiple air supply ducts installed within the container. The main air duct extends along the length of the container and is located at the top center or above the side wall. The starting end of the main air duct connects to the air outlet of the container's refrigeration / heating unit. Multiple air supply ducts are fixed to and connected to the main air duct. Specifically, the air supply ducts are installed along the width or length of the container, and the inlet end of each air supply duct connects to a different air outlet position of the main air duct. Multiple air outlets corresponding to different areas within the container are installed on the air supply ducts, and each air outlet is equipped with a smart valve. The smart valves are electrically adjustable valves, and each smart valve is connected to a control module. The control module controls the air volume of each air outlet by adjusting the opening degree of each smart valve, thereby achieving differentiated air supply for different areas. The deflector is rotatably installed in the main air duct via an electric rotating shaft, which is also connected to the control module. The rotatable deflector is used to change the flow direction of the airflow in the main air duct, guiding the airflow to different air supply ducts or directly to specific areas inside the container.

[0026] A method for intelligent control of the entire environment of a container based on multi-sensor fusion includes the following steps: S1. Collect environmental parameters inside the container, including at least temperature and humidity fields. The temperature field is obtained by interpolating and fitting the temperature data of each measuring point collected by multiple temperature sensors arranged in a distributed manner. The humidity field is obtained by interpolating and fitting the humidity data of each measuring point collected by multiple humidity sensors arranged in a distributed manner. S2. Establish the first coupling relationship between the temperature field and the humidity field; the first coupling relationship is represented by the temperature-humidity joint distribution matrix, which contains the temperature and humidity values ​​of each spatial location inside the container and their corresponding relationships. S3. Generate the first control command based on the coupling relationship; the first control command includes the opening adjustment amount of each smart valve, the rotation angle of the guide plate, and the adjustment value of the operating parameters of the refrigeration / heating unit; S4. According to the first control command, control the terminal air supply module to supply air to different areas inside the container in a differentiated manner. The terminal air supply module adjusts the rotation of the guide vane, the opening degree of the smart valve, and the operating parameters of the refrigeration / heating unit according to the issued command.

[0027] This implementation method can achieve basic precise temperature and humidity control, meet the needs of conventional cold chain, and is suitable for conventional cold chain goods (frozen meat, aquatic products).

[0028] Second implementation method: like Figure 3Based on the first implementation, the sensing module further includes an oxygen sensor, a carbon dioxide sensor, and an ethylene sensor. These sensors are signal-connected to the control module. Specifically, there are 2 to 8 oxygen sensors, carbon dioxide sensors, and ethylene sensors, arranged at the same or adjacent locations as the temperature and humidity sensors to facilitate the establishment of multi-physics field correlation data at the same spatial location. The control module establishes a second coupling relationship between the temperature field, humidity field, and gas concentration field based on the collected data, and generates a second control command based on this second coupling relationship. The second coupling relationship is a three-dimensional joint distribution model of temperature, humidity, and gas concentration, where the gas concentration field includes at least one of the oxygen concentration distribution, carbon dioxide concentration distribution, and ethylene concentration distribution. The second coupling relationship represents the influence of temperature on the cargo respiration rate, the influence of respiration rate on oxygen consumption and carbon dioxide / ethylene release, and the influence of temperature and humidity on the gas diffusion rate. Specifically, as the temperature increases, the cargo respiration enhances, the oxygen consumption rate accelerates, and the carbon dioxide and ethylene release rates accelerate; while humidity changes affect the gas exchange efficiency on the cargo surface.

[0029] Furthermore, the sensing module also includes an operating parameter acquisition unit. The parameter acquisition unit is connected to the terminal air supply module and acquires the operating parameters of the terminal air supply module. The operating parameters include at least one of the following: compressor exhaust pressure, compressor return pressure, compressor operating current, fan speed, and fan operating current. Specifically, the operating parameter acquisition unit continuously acquires the above operating parameters at a sampling frequency of not less than 1Hz and transmits the acquired data to the control module in real time.

[0030] The control module includes an attenuation identification unit and a compensation calculation unit. The attenuation identification unit identifies the performance attenuation curve of the terminal air supply module based on the time-series data of the operating parameters. The compensation calculation unit dynamically adjusts the control output gain based on the performance attenuation curve. Specifically, the attenuation identification unit compares the real-time collected operating parameters with the stored initial operating parameters, calculates the attenuation of each operating parameter, and weights and fuses the attenuation of multiple operating parameters to obtain the comprehensive performance attenuation curve of the air supply actuator. The horizontal axis of the performance attenuation curve represents the operating time, and the vertical axis represents the comprehensive performance retention rate. The compensation calculation unit calculates the output gain compensation coefficient of each terminal air supply module based on the performance attenuation curve. The output gain compensation coefficient increases non-linearly with the decrease in performance retention rate to compensate for the decrease in control accuracy caused by the performance attenuation of the terminal air supply module.

[0031] By setting up attenuation identification unit and compensation calculation unit, when the performance of the terminal air supply module (such as compressor, fan, motor) degrades due to long-term operation, the control module can automatically identify the degree of attenuation and adjust the control parameters, so that the regulation accuracy of the terminal air supply module remains relatively stable throughout the entire equipment life cycle.

[0032] A method for intelligent control of the entire environment of a container based on multi-sensor fusion includes the following steps: A1. Collect the gas concentration field inside the container. The gas concentration field is obtained by interpolation and fitting after collecting gas concentration data at each measuring point through multiple distributed oxygen sensors, carbon dioxide sensors and ethylene sensors. A2. Establish a second coupling relationship between the temperature field, humidity field and gas concentration field; the second coupling relationship is represented by a multi-physics joint matrix. Each row of the multi-physics joint matrix corresponds to a spatial location, and each column corresponds to a physical field parameter (temperature, humidity, oxygen concentration, carbon dioxide concentration, ethylene concentration). The value of each element in the matrix is ​​the specific value of the corresponding physical field at that spatial location. A3. A second control command is generated by taking at least two of the following as joint optimization objectives: minimizing the amplitude of temperature unevenness, maximizing the uniformity of humidity distribution, and minimizing the gas concentration deviation: The joint optimization objective is solved by using a weighted summation method to transform multiple objectives into a single objective. The weight coefficients of each objective are dynamically set according to the type of goods and transportation requirements. For example, for pharmaceutical transportation, the weight coefficient for minimizing the amplitude of temperature unevenness is set to 0.6, the weight coefficient for maximizing the uniformity of humidity distribution is set to 0.3, and the weight coefficient for minimizing the gas concentration deviation is set to 0.1; for fruit and vegetable transportation, the weight coefficient for minimizing the gas concentration deviation is increased to 0.4. By solving the above optimization problem, the optimal opening combination of each intelligent valve, the optimal angle combination of the guide vanes, and the optimal operating parameters of the refrigeration / heating unit are obtained.

[0033] A4. According to the second control command, the terminal air supply module is controlled to supply air to different areas inside the container in a differentiated manner. The terminal air supply module adjusts the rotation of the guide vane, the opening degree of the smart valve, and the operating parameters of the refrigeration / heating unit according to the issued command.

[0034] This implementation method optimizes the temperature, humidity, and gas fields together, ensuring uniform temperature, suitable humidity, and stable gas composition, making it suitable for the transportation of high-end pharmaceuticals, vaccines, and electronic components.

[0035] The third implementation method: like Figure 4The control module calculates the dew point temperature of each area inside the container in real time based on the first coupling relationship, and compares the dew point temperature with the wall temperature of the corresponding area. When the dew point temperature of any area is higher than or equal to the wall temperature of that area, the first control command includes at least one of the following: prioritizing the activation of the dehumidification device, increasing the supply air temperature, and increasing the supply air volume of that area. Specifically, dew point temperature T d Calculate using the following formula: T d =(b×α(T,RH)) / (a-α(T,RH)) Where α(T,RH)=(a×T) / (b+T)+ln(RH / 100), T is temperature, RH is relative humidity, and a and b are Magnus constants (a=17.27, b=237.7℃). When the dew point temperature T in any region... d The wall temperature T in this area is higher than or equal to that of the area. w When this occurs, it indicates a risk of condensation in the area. At this point, the control module generates a condensation avoidance control command: (1) Prioritize the activation of dehumidification devices: The control module activates the dehumidification devices (such as solid adsorption dehumidifiers or condensation dehumidifiers) inside the container to reduce the relative humidity in the area and lower the dew point temperature; (2) Increase the supply air temperature: The control module increases the supply air temperature setpoint of the refrigeration / heating unit, thereby increasing the temperature in the area and increasing the difference between the wall temperature and the dew point temperature. (3) Increase the air supply in the area: The control module increases the opening of the smart valve in the corresponding area, accelerates the air flow in the area, promotes the evaporation of condensate on the wall, and at the same time destroys the condensation boundary layer through airflow disturbance.

[0036] The three control commands mentioned above are executed in order of priority: first, the dehumidifier is activated; if the dew point temperature is still higher than the wall temperature after dehumidification, the supply air temperature is increased; if the risk of condensation is still not eliminated by the first two actions, the supply air volume in that area is further increased. The three control commands can be executed individually or in combination, depending on the urgency of the condensation risk.

[0037] This implementation method, based on the second implementation method, actively identifies and avoids the risk of condensation inside the container, effectively preventing goods from getting damp and moldy, and is suitable for cross-sea transportation in high-humidity environments and tropical routes.

[0038] Fourth implementation method: like Figure 5Based on the type of cargo inside the container, the control module calls the breathing heat generation model corresponding to the cargo. Based on the real-time collected temperature, humidity and gas concentration data, the control module predicts the heat load distribution trend of each area through the breathing heat generation model, and generates an advanced air supply command according to the heat load distribution trend, so that the terminal air supply module increases the air supply volume of the corresponding area at the current moment.

[0039] Specifically, the control module's memory pre-stores a library of respiratory heat generation models for various types of goods. This library contains data on the respiratory heat generation rates of different types of goods under varying temperatures and gas concentrations. Once the sensing module identifies the type of goods, it determines this type by the ratio of oxygen consumption rate to carbon dioxide / ethylene release rate. Different goods exhibit characteristic gas release ratios. Based on these characteristics, the control module retrieves the corresponding respiratory heat generation model from the library.

[0040] The control module inputs real-time temperature, humidity, and gas concentration data into the respiration heat generation model. The model outputs the trend of heat load distribution over a future time period, ranging from 15 to 120 minutes. For example, when the model predicts that a certain area will generate additional heat load due to cargo respiration in the next 30 minutes, the control module generates an advance air supply command at the current moment to increase the air supply volume in that area in advance, ensuring that the area has sufficient cooling capacity stored before the peak heat load arrives.

[0041] The advance time for the advance air supply command is ΔT = k × τ, where τ is the system response time constant, and k is the advance coefficient, with a value ranging from 1.2 to 2.0. By setting the advance coefficient, the air supply actuator is ensured to complete its action before the peak heat load arrives, thereby eliminating temperature fluctuation peaks.

[0042] In the respiratory heat production model, the respiratory heat production rate R of the cargo is calculated using the following formula: R = R0 × f(T) × g(C) O2 )×h(C CO2 ); R0 is the baseline respiratory heat production rate, expressed in W / kg. It represents the respiratory heat production rate of a cargo under standard conditions (e.g., 0°C), standard oxygen concentration (21%), and standard carbon dioxide concentration (0.03%). The value of R0 varies depending on the type of cargo. For example, the R0 for apples is 10–30 W / kg, for strawberries it is 50–80 W / kg, and for fresh flowers it is 20–60 W / kg.

[0043] f(T) is the temperature correction function. Q 10The temperature coefficient ranges from 2.0 to 3.5, where T0 is the standard temperature and T is the actual temperature. The temperature correction function represents the increase in the respiration heat production rate (Q) for every 10°C increase in temperature. 10 times.

[0044] g(C O2 ) is the oxygen concentration correction function, g(C O2 )=C O2 / C O2 ´, where C O2 C represents the actual oxygen concentration. O2 ´ represents the standard oxygen concentration (21%). As oxygen concentration decreases, the rate of heat production during respiration decreases; when the oxygen concentration is below 5%, respiration is significantly weakened.

[0045] h(C CO2 h(C) is the carbon dioxide concentration correction function. CO2 )=1 / (1+(C CO2 / K i )), where C CO2 K represents the actual carbon dioxide concentration. i This is the inhibition constant, ranging from 5% to 15%. The rate of respiratory heat production is inhibited when carbon dioxide concentration increases, with a significant inhibitory effect when the carbon dioxide concentration exceeds 10%.

[0046] The product of the above four parameters is the real-time breathing heat generation rate R under the current operating conditions. The control module multiplies the cargo mass of each area by the breathing heat generation rate R to obtain the real-time breathing heat generation power of each area, and predicts the future increase and distribution changes of heat load accordingly. This enables comprehensive intelligent control of the container interior: precise temperature and humidity control, condensation avoidance, advanced prediction of breathing heat generation, and equipment health management. This can maximize the shelf life and is suitable for high-end fresh fruits and vegetables (such as cherries and blueberries), flowers, etc.

[0047] The above description is merely a preferred embodiment of the present invention; it encompasses all the protection scope of the present invention. Any equivalent substitutions or modifications made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solutions and improved concepts of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A container-based intelligent environmental control system based on multi-sensor fusion, characterized in that: It includes a sensing module, a control module, and a terminal air supply module; The sensing module is installed inside the container, and the sensing module includes at least a temperature sensor and a humidity sensor for collecting temperature and humidity data inside the container. Both the temperature sensor and the humidity sensor are connected to the control module via signals. The control module is used to establish a first coupling relationship between the temperature field and the humidity field based on the temperature and humidity data collected by the sensing module, and the control module generates a first regulation command based on the coupling relationship. The terminal air supply module is installed inside the container and is also connected to the control module. The terminal air supply module delivers differentiated air to different areas inside the container according to the first control command issued by the control module.

2. The intelligent control system for the entire environment of a container based on multi-sensor fusion as described in claim 1, characterized in that: The terminal air supply module includes a main air duct installed inside the container, multiple guide vanes installed inside the main air duct, and multiple air supply pipes installed inside the container. The multiple air supply pipes are fixed to and connected to the main air duct. The air supply pipes are provided with multiple air outlets corresponding to different areas inside the container, and each of the multiple air outlets is equipped with an intelligent valve.

3. The intelligent control system for the entire environment of a container based on multi-sensor fusion according to claim 1, characterized in that: The control module calculates the dew point temperature of each area inside the container in real time based on the first coupling relationship, and compares the dew point temperature with the wall temperature of the corresponding area. When the dew point temperature of any area is higher than or equal to the wall temperature of that area, the first control command includes at least one of prioritizing the activation of the dehumidification device, increasing the air supply temperature, and increasing the air supply volume of that area.

4. The intelligent control system for the entire environment of a container based on multi-sensor fusion according to claim 1, characterized in that: The sensing module also includes an oxygen sensor, a carbon dioxide sensor, and an ethylene sensor. The oxygen sensor, carbon dioxide sensor, and ethylene sensor are signal-connected to the control module. The control module establishes a second coupling relationship between the temperature field, humidity field, and gas concentration field based on the collected data. The control module generates a second control command based on the second coupling relationship between the three.

5. The intelligent control system for the entire environment of a container based on multi-sensor fusion according to claim 4, characterized in that: Based on the type of cargo inside the container, the control module calls the breathing heat generation model corresponding to the cargo. Based on real-time collected temperature, humidity and gas concentration data, the control module predicts the heat load distribution trend of each area through the breathing heat generation model, and generates an advanced air supply command according to the heat load distribution trend, so that the terminal air supply module increases the air supply volume of the corresponding area at the current moment.

6. The intelligent control system for the entire environment of a container based on multi-sensor fusion according to claim 5, characterized in that: In the respiratory heat production model, the respiratory heat production rate R of the cargo is calculated according to the following formula: R = R0 × f(T) × g(C) O2 )×h(C CO2 ); Where R0 is the baseline respiratory heat production rate, f(T) is the temperature correction function, and g(C) is the temperature correction function. O2 ) is the oxygen concentration correction function, h(C) CO2 ) is the carbon dioxide concentration correction function.

7. The intelligent control system for the entire environment of a container based on multi-sensor fusion according to claim 1, characterized in that: The sensing module also includes an operating parameter acquisition unit, which is signal-connected to the terminal air supply module and acquires the operating parameters of the terminal air supply module. The operating parameters include at least one of the following: compressor discharge pressure, compressor return pressure, compressor operating current, fan speed, and fan operating current. The control module includes an attenuation identification unit and a compensation calculation unit. The attenuation identification unit is used to identify the performance attenuation curve of the terminal air supply module based on the time series data of the operating parameters. The compensation calculation unit is used to dynamically adjust the control output gain based on the performance attenuation curve.

8. A method for intelligent control of the entire environment of a container based on multi-sensor fusion, characterized in that: Includes the following steps: S1. Collect environmental parameters inside the container, including at least the temperature and humidity fields; S2. Establish the first coupling relationship between the temperature field and the humidity field; S3. Generate a first control command based on the coupling relationship; S4. According to the first control command, control the terminal air supply module to supply air to different areas inside the container in a differentiated manner.

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