Energy consumption collaborative optimization system and method for controlled atmosphere refrigerator car

Abstract

The invention relates to the technical field of refrigerator car program control, in particular to an energy consumption collaborative optimization system and method for an air-conditioned refrigerator car, and the system comprises a sensing detection module which is used for laying monitoring points in a compartment in a layered and zoned manner, synchronously collecting environment data, and constructing a space distribution field and a time change sequence of the space distribution field; the consistency verification module performs physical consistency verification on the distribution field based on a gas density change rule, and outputs an effective gradient field after removing abnormal data; the collaborative judgment module analyzes the spatial coupling degree of the temperature gradient and the oxygen concentration gradient in the effective gradient field, determines a collaborative imbalance index and divides risk grades; and the cooperative control module generates a continuous control instruction according to the risk level to perform linkage adjustment on the refrigeration and air adjustment execution piece, and verifies the gradient difference between the center and the edge area through overturning. According to the invention, through multi-gradient coupling analysis and closed-loop verification, refined cooperative control of the refrigeration and air conditioning system is realized, and the overall energy consumption is significantly reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of refrigerated truck program control, and in particular to an energy consumption collaborative optimization system and method for air-regulated refrigerated trucks. BACKGROUND

[0002] As a key equipment for fresh agricultural product transportation, air-regulated refrigerated trucks need to maintain a low-temperature environment while precisely regulating the oxygen concentration in the truck cabin to inhibit the respiration of goods and extend the preservation period. Traditional temperature control of refrigerated trucks mostly uses single-point feedback regulation, that is, the compressor is started or stopped or the fan speed is controlled according to the temperature deviation of a monitoring point in the truck cabin. This method ignores the spatial distribution difference of the temperature in the truck cabin, which easily leads to local overheating or cold accumulation, causing unnecessary energy consumption. With the application of air regulation technology, some refrigerated trucks begin to independently control the oxygen concentration, but the refrigeration system and the air regulation system are usually independent of each other, lacking collaborative linkage. When the refrigeration system runs, the airflow disturbance generated will destroy the established oxygen concentration distribution, causing the air regulation system to repeatedly adjust the valve opening, resulting in the superposition and waste of energy consumption.

[0003] Therefore, how to realize fine collaborative control of the refrigeration and air regulation systems and reduce the overall energy consumption of the system on the premise of ensuring uniform distribution of the temperature and oxygen concentration in the truck cabin has become a technical problem to be solved in the field. SUMMARY

[0004] To this end, the present application provides an energy consumption collaborative optimization system and method for air-regulated refrigerated trucks to overcome the problem that the refrigeration system and the air regulation system are usually independently controlled and lack collaborative perception of the spatial distribution state in the prior art, which cannot comprehensively and real-time analyze the temperature and oxygen concentration and dynamically adjust the control strategy according to the matching relationship, resulting in mismatch between energy input and regulation effect and unnecessary energy waste.

[0005] To achieve the above-mentioned purpose, on the one hand, the present application provides an energy consumption collaborative optimization system for air-regulated refrigerated trucks, comprising: a sensing and detecting module configured to collect environmental data at a plurality of monitoring points in the air-regulated refrigerated truck, the environmental data including temperature, humidity, and oxygen concentration, and obtain running state data of an actuator, construct a spatial distribution field of temperature, humidity, and oxygen concentration based on the environmental data, and form a change sequence of gradient fields in the time dimension; a consistency verification module configured to perform physical consistency verification on each of the spatial distribution fields to determine abnormal collection information and output an effective gradient field; a collaborative determination module configured to analyze the coupling degree of the temperature gradient and the oxygen concentration gradient in the effective gradient field, determine a collaborative imbalance index according to the coupling degree and the change of the temperature gradient field in the corresponding time period, and determine a collaborative risk level according to the collaborative imbalance index; a cooperative control module configured to generate continuous control instructions according to the cooperative risk level, and to coordinate and adjust the refrigeration execution device and the gas regulation execution device to regulate the running state of the vehicle compartment; The cooperative control module controls the sensing and detecting module to reconstruct the gradient field of each space after performing the coordinated adjustment, and to perform flip verification on the gradient difference between the central region and the edge region of the vehicle compartment, and in response to the flip verification result being unstable, the continuous control instructions are corrected and the cooperative control is re-executed.

[0006] As a preferred technical solution of the energy consumption cooperative optimization system for the gas-controlled refrigeration vehicle, the sensing and detecting module comprises: An environmental data acquisition unit comprising a detection device group arranged at a plurality of monitoring points in the gas-controlled refrigeration vehicle, each monitoring point being arranged in layers and zones according to the space region of the vehicle compartment, and synchronously acquiring the temperature, humidity and oxygen concentration of each monitoring point; An equipment state acquisition unit in communication connection with the refrigeration execution device and the gas regulation execution device of the gas-controlled refrigeration vehicle, and acquiring the running state data of each execution device in real time; A gradient field construction unit which, based on the spatial position coordinates of each monitoring point and the corresponding acquired environmental data, respectively constructs the corresponding spatial distribution field in the vehicle compartment, and updates each spatial distribution field at a preset time interval to form a sequence of changes in the spatial gradient field.

[0007] As a preferred technical solution of the energy consumption cooperative optimization system for the gas-controlled refrigeration vehicle, the acquisition data of each monitoring point in the sensing and detecting module carries a corresponding acquisition time stamp and a monitoring point position identifier, the gradient field construction unit normalizes and integrates the environmental data of each monitoring point at the same time dimension based on the acquisition time stamp, and then combines the monitoring point position identifier to complete the construction of the spatial distribution field, and each time node in the sequence of changes in the gradient field is associated with the equipment running state data of the corresponding period.

[0008] As a preferred technical solution of the energy consumption cooperative optimization system for the gas-controlled refrigeration vehicle, the consistency verification module performs physical consistency verification on the spatial distribution field of each time node in the sequence of changes in the gradient field based on the deterministic gas density variation law of the gas density varying with the temperature, humidity and oxygen concentration, takes the corresponding constraint between the density gradient field and each spatial distribution field as a verification condition, and calculates the constraint deviation of each monitoring point at the corresponding time node; In response to the constraint deviation exceeding the consistency threshold, the environmental data of the corresponding monitoring point at the corresponding acquisition time stamp is determined as abnormal acquisition information, and the environmental data corresponding to the abnormal acquisition information is subjected to constraint consistent replacement processing based on adjacent monitoring points to output an effective gradient field.

[0009] As a preferred technical scheme of the energy consumption collaborative optimization system for the air-cooled refrigeration vehicle, the collaborative determination module analyzes a spatial coupling degree of the temperature gradient and the oxygen concentration gradient in the effective gradient field, and determines a collaborative imbalance index according to a matching relationship between the spatial coupling degree and a temperature gradient field change rate in a corresponding time period.

[0010] As a preferred technical scheme of the energy consumption collaborative optimization system for the air-cooled refrigeration vehicle, the collaborative determination module determines a corresponding collaborative risk level according to the collaborative imbalance index. Wherein, in response to the collaborative imbalance index being lower than a low risk threshold, a low risk level is determined, in response to the collaborative imbalance index being between the low risk threshold and a high risk threshold, a medium risk level is determined, and in response to the collaborative imbalance index being higher than the high risk threshold, a high risk level is determined.

[0011] As a preferred technical scheme of the energy consumption collaborative optimization system for the air-cooled refrigeration vehicle, the collaborative control module determines a corresponding adjustment amplitude and adjustment rate according to the collaborative risk level, and generates a continuous control instruction based on the adjustment amplitude and adjustment rate, including: In response to the low risk level, the collaborative control module maintains the current continuous control instruction. In response to the medium risk level, the collaborative control module generates a continuous control instruction containing a medium level frequency adjustment amount or a medium level opening degree adjustment amount, the medium level frequency adjustment amount being used to adjust the operating frequency of the refrigeration execution member, and the medium level opening degree adjustment amount being used to adjust the valve opening degree of the gas regulation execution member. In response to the high risk level, the collaborative control module generates a continuous control instruction containing a high level frequency adjustment amount and / or a high level opening degree adjustment amount. Wherein, the high level frequency adjustment amount is greater than the medium level frequency adjustment amount, and the high level opening degree adjustment amount is greater than the medium level opening degree adjustment amount.

[0012] As a preferred technical scheme of the energy consumption collaborative optimization system for the air-cooled refrigeration vehicle, after the linkage adjustment is performed, the collaborative control module compares the temperature gradient intensity of the center area of the vehicle compartment with the temperature gradient intensity of the edge area of the vehicle compartment in the reconstructed temperature gradient field, and compares the concentration gradient intensity of the center area of the vehicle compartment with the concentration gradient intensity of the edge area of the vehicle compartment in the reconstructed oxygen concentration gradient field, to determine whether the linkage adjustment reduces the gradient intensity of the center area of the vehicle compartment to below the gradient intensity of the edge area of the vehicle compartment through flip verification.

[0013] As a preferred technical scheme of the energy consumption collaborative optimization system for the air-cooled refrigeration vehicle, in response to the flip verification result being unstable, the collaborative control module determines to correct the continuous control instruction and re-performs the collaborative control. The cooperative stability condition is that, during the rollover verification, the temperature gradient intensity in the central region of the carriage does not decrease below the temperature gradient intensity in the edge region of the carriage, and / or, the oxygen concentration gradient intensity in the central region of the carriage does not decrease below the oxygen concentration gradient intensity in the edge region of the carriage.

[0014] On the other hand, the present invention also provides a method, including: collecting environmental data at several monitoring points inside a controlled atmosphere refrigerated vehicle, constructing a spatial distribution field of temperature, humidity and oxygen concentration based on the environmental data, and forming a change sequence of the gradient field in the time dimension; Physical consistency verification is performed on each of the aforementioned spatial distribution fields to identify abnormal acquisition information and output a valid gradient field. The degree of coupling between the temperature gradient and the oxygen concentration gradient in the effective gradient field is analyzed. Based on the degree of coupling and the change of the temperature gradient field in the corresponding time period, a cooperative imbalance index is determined, and a cooperative risk level is determined based on the cooperative imbalance index. Continuous control commands are generated based on the collaborative risk level, and the refrigeration actuator and gas regulation actuator are adjusted in conjunction to make the carriage operation state gradually change according to the continuous control commands. After the linkage adjustment is executed, the spatial gradient fields are reconstructed, and the gradient difference between the central region and the edge region of the carriage is flipped for verification. If the flip verification result is unstable, the continuous control command is corrected and the cooperative control is re-executed.

[0015] Compared with existing technologies, the advantages of this invention lie in the following: By constructing a spatial distribution field of temperature, humidity, and oxygen concentration within the controlled atmosphere refrigerated vehicle and forming a temporal sequence of changes, the original data is physically verified based on the gas density variation law to eliminate abnormal data collection, ensuring the physical reliability of the data used for decision-making. By analyzing the spatial coupling degree between the temperature gradient and oxygen concentration gradient in the effective gradient field and matching it with the rate of change of the temperature gradient field, a collaborative imbalance index characterizing the coordinated state of refrigeration regulation and controlled atmosphere response is constructed, thereby classifying risk levels and achieving graded control. After the coordinated adjustment of the refrigeration actuator and the gas regulation actuator, the gradient field is reconstructed through a flip verification mechanism, and the gradient intensity difference between the central region and the edge region is compared. If the stability condition is not met, the control command is corrected and the collaborative control is re-executed, forming a complete closed-loop optimization link. This achieves refined collaborative control of the refrigeration and controlled atmosphere systems, avoiding ineffective regulation and energy waste caused by single-point detection deviations, sensor malfunctions, or control mismatches. It enables energy consumption input to accurately target environmental control needs, significantly reducing the overall energy consumption of the system while ensuring uniform and stable temperature and oxygen concentration within the vehicle compartment. Attached Figure Description

[0016] Figure 1This is a schematic diagram of the energy consumption collaborative optimization system for a controlled atmosphere refrigerated truck according to an embodiment of the present invention; Figure 2 This is a logic diagram for the flip verification in an embodiment of the present invention; Figure 3 This is a flowchart of the energy consumption collaborative optimization method for controlled atmosphere refrigerated trucks according to an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0018] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0019] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0020] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Please see Figure 1 and Figure 2 As shown, the present invention provides an energy consumption collaborative optimization system for modified atmosphere refrigerated trucks, comprising: a sensing and detection module, a consistency verification module, a collaborative determination module, and a collaborative control module.

[0022] Specifically, the sensing and detection module includes: an environmental data acquisition unit, an equipment status acquisition unit, and a gradient field construction unit.

[0023] Understandably, by deploying monitoring points in a layered and zoned manner to cover different spatial locations within the carriage, the collection of temperature, humidity, and oxygen concentrations can accurately reflect the spatial distribution characteristics, avoiding representative bias caused by single-point detection. At the same time, by acquiring real-time operational status data of refrigeration and gas regulation actuators, real-time data from the equipment side can be provided for energy consumption synergistic optimization.

[0024] The environmental data acquisition unit consists of detection equipment deployed at several monitoring points inside the controlled atmosphere refrigerated truck. The monitoring points are arranged in layers and zones according to the space of the truck compartment. In implementation, the refrigerated truck is divided into three layers (upper, middle, and lower) in terms of height and three zones (front, middle, and rear) in terms of horizontal plane. At the same time, three points are set on the left, middle, and right sides in each zone along the width direction, thus forming a grid layout of 3 layers × 3 zones × 3 points, with a total of 27 monitoring points. There are no detection devices in the central area.

[0025] Taking a standard refrigerated van as an example, its internal dimensions are approximately 8 meters long, 2.4 meters wide, and 2.2 meters high. To fully cover the van space, it can be divided along the height direction into an upper layer (0.3 meters from the top of the van), a middle layer (1.1 meters high), and a lower layer (0.3 meters from the bottom of the van). Within each layer, it can be divided along the length direction into a front area (within 1.5 meters from the van door), a middle area (within 4 meters in the middle of the van), and a rear area (within 1.5 meters from the rear of the van).

[0026] In implementation, each monitoring point integrates a temperature sensor, a humidity sensor, and an oxygen concentration sensor, and is connected to the central controller via a CAN bus or RS485 bus to achieve synchronous data acquisition from all monitoring points. The data acquisition frequency can be set according to actual needs, generally set to once every 10 seconds to capture rapid changes in the environment inside the carriage; under stable operation, it can be 30 seconds or 1 minute to balance data accuracy and system load. In specific implementation, the temperature sensor can be a PT100 platinum resistance thermometer or a DS18B20 digital temperature sensor, the humidity sensor can be a SHT30 polymer humidity-sensitive capacitive sensor, and the oxygen concentration sensor can be an electrochemical oxygen sensor. The choice of sensor type is not specifically limited, as long as it meets the detection requirements, which will not be elaborated further.

[0027] The equipment status acquisition unit connects to each actuator of the controlled atmosphere refrigerated truck via a communication interface to acquire its operational status data in real time. Specifically, this unit communicates with the refrigeration unit controller to read the compressor start / stop status, compressor power (kW), evaporator fan speed (rpm), and condenser fan speed (rpm); simultaneously, it connects with the controlled atmosphere control system to read the oxygen supply valve opening degree (%), carbon dioxide emission valve opening degree (%), gas circulation fan speed (rpm), and the opening status of the gas circulation damper. To ensure that environmental data and equipment status data are aligned in time, the refresh frequency of the equipment status data is consistent with the environmental data acquisition frequency.

[0028] The gradient field construction unit constructs a spatial distribution field inside the carriage based on the spatial coordinates of each monitoring point and the synchronously collected environmental data. A three-dimensional spatial coordinate system is established with the lower front corner of the carriage as the origin, and each monitoring point is assigned a unique coordinate value. The measured data of discrete points are transformed into a continuous distribution field using a spatial interpolation algorithm. The value of unknown points is obtained by weighting the values ​​of nearby known points according to the reciprocal of the distance.

[0029] Taking temperature as an example, based on the measured temperature values ​​Ti and their coordinates (xi,yi,zi) of 27 monitoring points, the theoretical temperature value at the center of the compartment is calculated through interpolation, forming a temperature distribution field. Similarly, a humidity distribution field and an oxygen concentration distribution field are constructed. The gradient field is updated at a preset time interval, which is determined based on the system's dynamic response characteristics and control accuracy requirements. Under stable operation of the refrigerated truck, data is collected at intervals of 10 seconds, 30 seconds, 1 minute, 2 minutes, and 5 minutes to construct the gradient field. The degree of matching between the gradient field change rate and the control command response time at each interval is observed. The experiment shows that an interval of 30-60 seconds can capture the temperature change caused by opening the door to receive goods without consuming controller resources due to excessive calculations. An interval exceeding 2 minutes may miss rapid changes.

[0030] Therefore, the system defaults to setting the gradient field update interval to 30 seconds, but in practice, it can be adjusted within the range of 30 seconds to 2 minutes depending on the type of goods and the stage of operation. The gradient field obtained from each update is stored in chronological order, forming a spatial gradient field change sequence, which provides a temporal dimension analysis basis for the subsequent collaborative determination module.

[0031] In this invention, the sensing and detection module can continuously output the spatiotemporal gradient field and its change sequence that reflect the real environmental state inside the carriage, providing a reliable data basis for subsequent physical consistency verification and collaborative imbalance judgment, so that energy consumption collaborative control can be accurately applied to the spatial area that really needs to be adjusted, avoiding energy waste caused by global adjustment, and ultimately achieving close coupling between environmental perception and energy consumption control.

[0032] In implementation, the temperature, humidity, and oxygen concentration data collected by each monitoring point in the sensing module are first assigned a collection timestamp and a monitoring point location identifier. The collection timestamp is uniformly assigned by the central controller when receiving data, ensuring that the time base of all monitoring points is consistent; the monitoring point location identifier is a pre-set number, with the lower front left point identified as L1, the lower front middle point identified as L2, and so on, forming a coding system that corresponds one-to-one with spatial coordinates.

[0033] The gradient field construction unit normalizes and integrates data based on the acquisition timestamps. Since the data is acquired synchronously at each monitoring point, there may be millisecond-level time differences due to bus transmission delays. The gradient field construction unit aligns the data using a preset time alignment window. Data whose timestamp deviation exceeds this window due to communication failures is marked as invalid and awaits re-acquisition in the next cycle, or it is compensated using the nearest time point, i.e., the most recent valid sample is used in the calculation. The width of this time alignment window is determined based on the statistical characteristics of system communication delays. Under full-load operation of a refrigerated truck, the arrival time of data from each monitoring point to the central controller was continuously recorded under 100 synchronous acquisition commands. The maximum transmission delay was approximately 0.6 seconds. Therefore, setting the time window width to 1 second ensures that over 95% of normal data is included in the same time dimension, while excluding timeout data caused by communication anomalies.

[0034] In implementation, the gradient field construction unit can perform standardization or interval normalization on data from various monitoring points within the same time dimension. It normalizes temperature deviations centered on the mean of that time dimension and scales humidity and oxygen concentration proportionally to the available range or historical stable intervals. The reference parameters required for normalization are obtained by collecting time series data from several stable operating conditions and statistically analyzing the stable fluctuation ranges of each parameter as a normalization reference. This allows the gradient field calculation to focus more on spatial relative differences rather than absolute sensor bias.

[0035] The gradient field construction unit maps the sampled data to a three-dimensional spatial coordinate system based on the environmental dataset of all monitoring points in the same time dimension, combined with the location identifiers of each monitoring point. It then spatially organizes the discrete point data in the same time dimension to form a spatial data structure suitable for gradient calculation. The gradient field construction unit employs a hierarchical and partitioned approach for gradient calculation. Within each layer, the gradient is calculated based on the data from each monitoring point. By comparing the numerical differences and spatial distances between adjacent monitoring points, the rate of change of temperature, humidity, and oxygen concentration in the horizontal direction within that layer is calculated. After completing the gradient calculations within each layer, inter-layer fusion is performed. The gradient information from the upper, middle, and lower layers is integrated using a weighted average to obtain a preliminary three-dimensional gradient field distribution across the entire carriage, effectively distinguishing between vertical natural stratification and horizontal environmental changes.

[0036] The upper layer near the top of the carriage and the lower layer near the bottom are more affected by external heat exchange and airflow boundaries, and their gradient information better reflects the heat exchange intensity of the edge areas. The middle layer, which typically represents the main cargo storage area, has gradient information that better reflects the uniformity of the internal environment. Therefore, during the fusion process, a higher weight can be assigned to the middle layer to highlight the environmental characteristics of the core area, while relatively lower weights can be assigned to the upper and lower layers to balance the boundary effects. Generally, the middle layer weight is 0.4 or 0.5, and the upper and lower layers have the same weight.

[0037] The gradient field construction unit further transforms discrete point data into a continuous distribution field. Taking the temperature gradient field as an example, for any point within the carriage, its temperature value is determined by spatial interpolation using the temperature values ​​of surrounding known monitoring points and their spatial relationships. During interpolation, the spatial Euclidean distance between the point and each known monitoring point is first calculated, and weights are determined based on the reciprocal of the distance. Monitoring points closer to the target point are assigned higher weights, ensuring that the estimated value of the target point is primarily influenced by its neighbors. The weighted sum of the temperature values ​​of all known monitoring points, divided by the total weight, yields the estimated temperature value of the target point. This process traverses all grid points within the carriage, generating a three-dimensional temperature distribution field covering the entire carriage. Similarly, the same interpolation calculation is performed on humidity and oxygen concentration to obtain the corresponding humidity and oxygen concentration distribution fields.

[0038] At each grid point, the partial derivatives of that point in each coordinate direction are calculated using the central difference method to obtain the gradient vector of that point, including the gradient intensity and gradient direction. This ultimately forms a complete spatial distribution field for temperature, humidity, and oxygen concentration. Each gradient field is stored in the form of a three-dimensional array, where each element corresponds to the parameter value and its gradient vector at a grid point, providing a basis for analyzing the spatial distribution characteristics of the subsequent collaborative decision-making module.

[0039] In implementation, taking the middle plane as an example, there are three monitoring points A(1,0.5), B(4,1.2), and C(7,2.0), with corresponding temperatures of 2.0℃, 2.2℃, and 2.5℃, respectively. The temperature value of the point P(4.5,1.5) needs to be calculated using inverse distance weighted interpolation, with the reciprocal of the square of the distance as the weight. First, the spatial Euclidean distances from point P to each monitoring point are calculated, yielding dA approximately 3.64 meters, dB approximately 0.583 meters, and dC approximately 2.55 meters. The weights are calculated based on the reciprocal of the square of the distance, with a sum of weights of approximately 3.17. The weighted sum of the temperatures at each point is then divided by the total weight, resulting in a temperature of approximately 2.21℃ at point P. To calculate the temperature gradient at point P, a differential grid is established around point P with a step size of 0.5 meters. The temperatures of the four adjacent grid points (east, west, north, and south) are calculated using the same interpolation method. According to the central difference method, the partial derivative in the x-direction is approximately 0.05℃ / m, the partial derivative in the y-direction is approximately 0.018℃ / m, the gradient intensity is approximately 0.053℃ / m, and the gradient direction makes an angle of approximately 19.8° with the x-axis. By repeating the above interpolation and difference calculations across all grid points, the temperature gradient field distribution of the entire middle plane can be obtained.

[0040] The gradient field construction unit repeats the above process at update intervals to generate a series of spatial distribution fields arranged in chronological order, forming a spatial gradient field change sequence. Specifically, the operating status data output by the device status acquisition unit also carries a timestamp; when generating the spatial distribution field for each time node, the gradient field construction unit binds the device status fragment corresponding to that node to the gradient field object according to the timestamp, forming an integrated record.

[0041] The corresponding time period refers to a time range extending forward and backward from the timestamp corresponding to the gradient field. Generally, a 30-second correlation time period is formed by extending 15 seconds forward and backward from the gradient field timestamp. The device status acquisition frequency is the same as the environmental sampling frequency. The 30-second correlation time period can cover the typical delay between device action and environmental response, while avoiding data interference from irrelevant time periods.

[0042] In this invention, the acquisition timestamp and normalization integration mechanism ensure that the spatial distribution field of each frame represents the true distribution within the carriage at the same moment, eliminating spurious gradients introduced by differences in sampling time and sensor scale. The correlation mechanism between the gradient field change sequence and the equipment operating status data enables the calculation of the collaborative imbalance index to be based on the complete process of environmental response caused by equipment adjustment. These refined spatiotemporal alignment and correlation mechanisms lay a reliable data foundation for subsequent precise energy consumption collaborative optimization.

[0043] Specifically, the consistency verification module performs physical consistency verification on the spatial distribution field at each time node in the gradient field change sequence based on the deterministic relationship between gas density and temperature, humidity and oxygen concentration. It uses the corresponding constraints between the density gradient field and each spatial distribution field as verification conditions and calculates the constraint deviation of each monitoring point at the corresponding time node.

[0044] It is understandable that, under conditions such as door opening for loading and unloading, cargo stacking and obstruction, local short circuit of return air, sensor drift, or communication jitter in the controlled atmosphere refrigerated truck compartment, environmental data at some monitoring points may appear reasonable but inconsistent with physical laws. This can cause the gradient field to be skewed by anomalies, further affecting the analysis results of the collaborative judgment module on the degree of coupling and energy consumption trends.

[0045] Under normal transportation conditions with constant air pressure in the carriage, there is a deterministic correlation between the gas mixture density and temperature, humidity, and oxygen concentration. In practice, density calculations are performed based on the ideal gas law and the proportions of the gas mixture components. For each monitoring point, the saturated water vapor pressure at that temperature is determined based on the measured temperature, and the actual water vapor partial pressure is calculated by combining this with the relative humidity, thus obtaining the dry air partial pressure. Simultaneously, the average molecular weight of the gas mixture is calculated based on the measured oxygen and carbon dioxide concentrations, taking into account the differences between the baseline molecular weight of dry air and the molecular weights of water vapor, oxygen, and carbon dioxide. Substituting the dry air partial pressure, water vapor partial pressure, and average molecular weight of the gas mixture into the gas law, and combining this with the temperature at that point, the gas mixture density at that monitoring point can be obtained.

[0046] After completing the density calculation of all monitoring points, the spatial organization method and interpolation method consistent with the gradient field construction unit are used to map these discrete density data into a three-dimensional spatial coordinate system. Then, a density gradient field covering the entire carriage is generated through spatial interpolation, so that the density gradient field is consistent with the temperature gradient field, humidity gradient field and oxygen concentration gradient field in spatial dimension.

[0047] The corresponding constraints between the density gradient field and each spatial gradient field are used as verification conditions to calculate the constraint deviation for each monitoring point at that time node. It should be understood that since there are definite physical relationships between density and temperature, humidity, and oxygen concentration, when the measured temperature value at a monitoring point is abnormally high, the theoretical density calculated for that point will be correspondingly low. If the density at that point appears normal or high in the density gradient field, a directional contradiction arises between the two. Similarly, abnormal humidity or concentration will also lead to similar inconsistencies. Therefore, for each monitoring point, the density change direction calculated from the temperature gradient, the density change direction calculated from the humidity gradient, and the density change direction calculated from the oxygen concentration gradient are calculated separately. These three directions are then compared with the actual change direction at that point in the density gradient field to obtain the consistency value of the three directions. The constraint deviation value is determined by taking the maximum value, which reflects the overall deviation of the monitoring point's data from physical laws.

[0048] When the constraint deviation exceeds the consistency threshold, the system identifies the environmental data of the corresponding monitoring point at the corresponding acquisition timestamp as abnormal acquisition information. The consistency threshold is determined as follows: under the conditions of stable vehicle operation, typical loading mode, and calibrated sensors, several complete gradient field change sequences are collected, and the distribution range of constraint deviation under normal operating conditions is statistically analyzed; then, control data are collected under two controllable disturbances: door opening impact and slight sensor drift. The deviation distribution of the two types of data is compared to reach the 95% boundary position, thereby determining the threshold range that can distinguish between physical consistency and physical inconsistency. The consistency threshold can be adaptively adjusted according to the operating conditions, but the threshold is still based on experimental data to ensure that its source is traceable and interpretable.

[0049] After identifying the abnormal data, the abnormal data is processed using a constraint-consistent replacement method based on adjacent monitoring points. The system determines the set of adjacent monitoring points of the abnormal point based on the location identifier of the monitoring point, and then selects several monitoring points that are spatially closest to the abnormal point from the same layer and adjacent layers as candidate points. Monitoring points whose constraint deviation does not exceed the consistency threshold are used as valid replacement sources. The corresponding environmental data items of the abnormal point are replaced according to the method of calculating spatial Euclidean distance. The replaced data still retains the original collection timestamp and monitoring point location identifier.

[0050] In this invention, the consistency verification module establishes a reliable filtering mechanism based on physical constraints at the gradient field level, preventing abnormal acquisition information from directly entering the effective gradient field. This ensures that the input data used by the collaborative judgment module to analyze the degree of multi-gradient coupling and energy consumption trends has physical consistency, thereby making the continuous control commands generated by the subsequent collaborative control module closer to the real needs. This avoids over-adjustment or erroneous adjustment driven by unnecessary anomalies, ultimately forming a more stable closed-loop collaborative optimization link.

[0051] It is understandable that the rate of change of the temperature gradient field directly characterizes the energy consumption of the refrigeration system, while the temperature gradient reflects the spatial distribution of cooling capacity. Spatially coupling the temperature gradient with the oxygen concentration gradient is to determine whether refrigeration regulation synchronously drives the spatial rearrangement of the controlled atmosphere system. Only by combining these two analyses can we determine whether energy is effectively converted into environmental control effects. Simultaneously, the stratification, retention, and remixing of oxygen concentration within the carriage will manifest in the oxygen concentration gradient field as gradient differences between the edge and center regions, as well as gradient migration along the airflow direction. There is no direct physical correspondence between humidity changes and refrigeration energy consumption; therefore, the humidity field is not suitable for determining the synergistic imbalance index. When the temperature gradient and oxygen concentration gradient exhibit similar gradient directions or synchronous gradient migration trajectories in space, a strong spatial coupling can be considered. Conversely, if the temperature gradient field has undergone significant gradient rearrangement while the oxygen concentration gradient remains in its original distribution or shows opposite migration, it indicates a mismatch between refrigeration regulation and controlled atmosphere response, which can easily lead to unnecessary energy consumption or localized preservation risks due to failure to achieve the preservation target.

[0052] In implementation, the collaborative judgment module first reads the temperature gradient field and oxygen concentration gradient field from the effective gradient field at the same acquisition time stamp, extracts their respective gradient intensity distribution and gradient direction distribution, and calculates the spatial coupling degree accordingly. The spatial coupling degree is determined using gradient vector similarity: for each spatial grid point, let the temperature gradient vector be T(x,y,z) and the oxygen concentration gradient vector be G(x,y,z). The dot product of the two vectors is calculated and divided by the product of their magnitudes to obtain the cosine of the angle between them, cosθ(x,y,z). The closer this value is to 1, the more consistent the two gradient directions are; the closer it is to 0 or a negative value, the more orthogonal or opposite the directions are. To comprehensively reflect the coupling degree of the entire workshop, the cosine of the angle between all grid points is weighted and averaged with their gradient intensity products as weights to obtain the spatial coupling degree coefficient C for that time node, with a value ranging from -1 to 1. The purpose of weighted averaging is to allow regions with larger gradient intensities to contribute more to the coupling degree, avoiding random noise from uniform temperature or concentration regions dominating the calculation results.

[0053] The collaborative judgment module calculates the rate of change of the temperature gradient field within the corresponding time period based on the gradient field change sequence, which is used to characterize the intensity of the temperature gradient field response caused by refrigeration regulation within that time period. The rate of change of the temperature gradient field, V, is obtained by the difference between the temperature gradient fields at adjacent time nodes. To avoid misjudgments caused by transient or occasional disturbances during door opening, a moving average can be applied to V within a short time window, for example, by taking a moving average of three consecutive calculated V values, so that the rate of change reflects a stable response trend rather than instantaneous fluctuations.

[0054] After obtaining the spatial coupling degree C and the rate of change of the temperature gradient field V, the coordination judgment module further determines the matching relationship between the two and calculates the coordination imbalance index I based on this. It should be understood that in the ideal state of good system coordination, the rate of change of the temperature gradient field should be positively correlated with the spatial coupling degree; that is, when the refrigeration system is working effectively, the change of the temperature gradient field should be accompanied by a synchronous spatial response of the oxygen concentration gradient. Conversely, if the rate of change of the temperature gradient field is high but the spatial coupling degree is low, it indicates that the temperature gradient field changes drastically but has not effectively driven the rearrangement of the oxygen concentration gradient. An energy wave exists through the proportionality coefficient γ between the rate of change of the temperature gradient field and the spatial coupling degree, such that V_target = γ·C represents the expected rate of change of the temperature gradient field. The absolute value of the deviation between the actual V and the expected value is calculated as the coordination imbalance index I = |V - γ·C|. This index is positive; the larger the value, the more severe the coordination imbalance.

[0055] The mapping coefficient γ is determined based on a finite number of experimental calibrations. During the system commissioning phase, several periods of stable system operation are selected, and the corresponding spatial coupling degree and temperature gradient field change rate sequences are collected. The V / C ratio at each moment is calculated, and the average of these ratios is taken as γ. For example, if the average C is 0.75 and the average V is 0.12℃ / s over several favorable periods, then γ = 0.12 / 0.75 = 0.16. If γ differs under different operating conditions, a segmented calibration can be performed to establish the mapping relationship between γ and the operating parameters, allowing γ to adaptively adjust according to the current operating conditions.

[0056] Through the above process, the coordination determination module can obtain the coordination imbalance index I at each time point. This index combines the degree of spatial coupling and the rate of change of the temperature gradient field, and can sensitively reflect the coordination status of the refrigeration and controlled atmosphere systems.

[0057] In practice, a low-risk level indicates that the spatial coupling between the current temperature gradient and oxygen concentration gradient is well matched with the rate of change of the temperature gradient field, and the system has high coordination, requiring no or only minor adjustments to maintain operation; a medium-risk level indicates that the matching relationship between the two has deviated to some extent, and there may be a local coordination imbalance trend, requiring appropriate intervention and adjustment; a high-risk level indicates that the matching relationship between the two is seriously out of balance, the change of the temperature gradient field has failed to effectively drive the oxygen concentration gradient response, or the oxygen concentration gradient is overly sensitive to temperature control disturbances, and strong coordination control measures must be taken immediately.

[0058] The specific values ​​for low-risk and high-risk thresholds need to be determined through a limited number of tests to ensure that the classification accurately reflects the system's collaborative state. The calibration process typically takes place during system development or on-site debugging: First, effective gradient field sequences are continuously collected under typical operating conditions of the controlled atmosphere refrigerated truck, and historical collaborative states are recorded simultaneously. The collected collaborative imbalance indices are statistically analyzed to calculate the distribution range of each collaborative state. Values ​​that can distinguish different states with a success rate of 95% are selected as the corresponding thresholds. For example, if statistics show that the collaborative imbalance index is less than 0.25 under good collaborative conditions and greater than 0.6 under severe imbalance conditions, then the low-risk threshold can be set to 0.25, and the high-risk threshold to 0.6. Furthermore, the risk thresholds can be adaptively adjusted according to seasonal, cargo type, and other operating conditions, but the final thresholds must ensure physical interpretability and engineering applicability.

[0059] In this invention, the collaborative control module can select control strategies of different intensities based on the level of risk, thereby achieving hierarchical collaborative optimization. Simultaneously, the risk level classification avoids frequent control command jitter caused by minor fluctuations in the collaborative imbalance index, making the gradual change in the carriage's operating state along continuous control commands smoother, ultimately forming a stable and reliable closed-loop collaborative optimization link, thereby reducing unnecessary energy consumption.

[0060] Specifically, the collaborative control module determines the corresponding adjustment range and adjustment rate based on the collaborative risk level, and generates continuous control commands based on the adjustment range and adjustment rate; when the collaborative risk level is low, the collaborative control module maintains the current continuous control commands. When the collaborative risk level is medium risk level, the collaborative control module generates continuous control commands containing medium frequency adjustment or medium opening adjustment. The medium frequency adjustment is used to adjust the operating frequency of the refrigeration actuator, and the medium opening adjustment is used to adjust the valve opening of the gas regulation actuator. When the collaborative risk level is high, the collaborative control module generates continuous control commands that include advanced frequency adjustment and / or advanced opening adjustment, with the advanced frequency adjustment being greater than the intermediate frequency adjustment and the advanced opening adjustment being greater than the intermediate opening adjustment.

[0061] It is understandable that when the collaborative risk level is low, it indicates that the spatial coupling between the current temperature gradient and oxygen concentration gradient and the rate of change of the temperature gradient field are in good condition, the system has high coordination, and there is no need to trigger new adjustment actions. Therefore, the collaborative control module maintains the current continuous control command to avoid frequent actions of the actuators due to small fluctuations, thereby achieving the effect of optimizing energy consumption.

[0062] When the collaborative risk level is medium, it indicates that the matching relationship between the two has deviated to a certain extent, and there is a local collaborative imbalance trend, which requires appropriate intervention and adjustment. At this time, the collaborative control module generates continuous control commands containing intermediate frequency adjustment or intermediate opening adjustment. The intermediate frequency adjustment is used to adjust the operating frequency of the refrigeration actuator, and the intermediate opening adjustment is used to adjust the valve opening of the gas regulation actuator.

[0063] When the collaborative risk level is high, it indicates that the matching relationship between the two is seriously mismatched, and strong collaborative control measures must be taken immediately. At this time, the collaborative control module generates continuous control commands containing advanced frequency adjustment and / or advanced opening adjustment, with the advanced frequency adjustment being greater than the intermediate frequency adjustment and the advanced opening adjustment being greater than the intermediate opening adjustment.

[0064] The specific values ​​of the intermediate frequency adjustment and intermediate opening adjustment were determined through a limited number of tests. Operating conditions with different loads, ambient temperatures, and door opening frequencies were selected, and adjustment tests were conducted at different step lengths. The recovery of the temperature gradient field change rate and spatial coupling was observed, and the step size range that could achieve a stable approach to the target state with the minimum adjustment was recorded. Generally, the intermediate frequency adjustment was 2–3 Hz, and the intermediate opening adjustment was 5–8% as the baseline values. The determination of the advanced frequency adjustment and advanced opening adjustment was based on this, scaled up proportionally according to the risk level. 1.5–2 times the intermediate frequency adjustment was taken as the advanced frequency adjustment, and 2–3 times the intermediate opening adjustment was taken as the advanced opening adjustment, to ensure rapid suppression of deterioration trends under high imbalance conditions.

[0065] In this invention, the collaborative control module is equipped with a graded response adjustment mechanism. When the risk is low, it maintains the original state to avoid ineffective actions; when the risk is medium, it intervenes appropriately to prevent the imbalance from expanding; and when the risk is high, it intervenes strongly to ensure rapid recovery. Thus, while ensuring the preservation requirements, it achieves refined control of energy consumption.

[0066] Understandably, the central area is typically a cargo stacking zone, requiring a uniform and stable environment; therefore, its temperature and oxygen concentration gradients are relatively low. The edge areas, closer to heat exchange boundaries, retain moderate gradients due to heat or airflow exchange with the outside environment. If control is effective, the gradient intensity in the central area should be significantly lower than that in the edge areas. Conversely, if the gradient intensity in the central area is still higher than or close to that in the edge areas, it indicates that the control has not effectively improved the uniformity of the central area, potentially indicating problems such as localized stagnation or airflow short-circuiting.

[0067] In practical implementation, after the coordinated adjustment is executed, the spatial gradient fields are reconstructed. The average temperature gradient intensity of the central region and the average temperature gradient intensity of the edge regions of the carriage are extracted from the reconstructed temperature gradient field and compared. Simultaneously, the average concentration gradient intensity of the central region is extracted from the reconstructed oxygen concentration gradient field and compared with the average concentration gradient intensity of the edge regions. Through this comparison, the coordinated control module can determine whether the coordinated adjustment has reduced the gradient intensity of the central region of the carriage to below that of the edge regions. The front and rear areas of each floor and points near the carriage walls are defined as edge regions, and the central region is the area where the central monitoring point is located.

[0068] Specifically, when the collaborative control module determines that the flip verification result is unstable, it corrects the continuous control command based on the current degree of deviation. Correction methods include: increasing the adjustment amplitude (e.g., increasing the intermediate frequency adjustment to a high-level frequency adjustment, or the intermediate opening adjustment to a high-level opening adjustment), extending the adjustment time, adjusting the adjustment rate, or changing the adjustment direction. The corrected continuous control command is reissued and executed, triggering the sensor detection module to reconstruct the gradient field and perform flip verification again. This cycle continues until the verification meets the preset stability conditions. Through this iterative correction mechanism, the system can gradually approach the ideal collaborative state, avoiding repeated oscillations caused by insufficient or excessive control in a single operation, and achieving coordinated energy consumption optimization of the refrigeration and controlled atmosphere systems while ensuring preservation requirements.

[0069] On the other hand, the present invention also provides a method comprising: Step S1: Collect environmental data at several monitoring points inside the controlled atmosphere refrigerated truck, construct a spatial distribution field of temperature, humidity and oxygen concentration based on the environmental data, and form a gradient field change sequence in the time dimension. Step S2: Perform physical consistency verification on each spatial distribution field to determine abnormal acquisition information and output the valid gradient field; Step S3: Analyze the coupling degree between the temperature gradient and the oxygen concentration gradient in the effective gradient field, determine the synergistic imbalance index based on the coupling degree and the temperature gradient field change in the corresponding time period, and determine the synergistic risk level based on the synergistic imbalance index. Step S4: Generate continuous control commands based on the collaborative risk level, and adjust the refrigeration actuator and the gas regulation actuator in a coordinated manner so that the operating state of the carriage changes gradually along with the continuous control commands; Step S5: After performing the linkage adjustment, reconstruct each spatial gradient field and perform a flip verification on the gradient difference between the central region and the edge region of the carriage. If the flip verification result is unstable, correct the continuous control command and re-execute the cooperative control.

[0070] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An energy consumption co-optimization system for modified atmosphere refrigerated trucks, characterized in that, include: The sensing and detection module is used to collect environmental data at several monitoring points inside the modified atmosphere refrigerated truck. The environmental data includes temperature, humidity and oxygen concentration, and the module also acquires the operating status data of the actuators. Based on the environmental data, the module constructs a spatial distribution field of temperature, humidity and oxygen concentration, and forms a gradient field change sequence in the time dimension. The consistency verification module is used to perform physical consistency verification on each of the spatial distribution fields to determine abnormal acquisition information and output the valid gradient field. The coordination determination module is used to analyze the coupling degree between the temperature gradient and the oxygen concentration gradient in the effective gradient field, determine the coordination imbalance index based on the coupling degree and the temperature gradient field change in the corresponding time period, and determine the coordination risk level based on the coordination imbalance index. The collaborative control module is used to generate continuous control commands based on the collaborative risk level, and to coordinate and adjust the refrigeration actuator and the gas regulation actuator to regulate the operating status of the carriage. The coordinated control module, after executing the linkage adjustment, controls the sensing and detection module to reconstruct each spatial gradient field, performs a flip verification on the gradient difference between the central and edge regions of the carriage, and corrects the continuous control command and re-executes the coordinated control in response to the flip verification result being unstable.

2. The energy consumption co-optimization system for controlled atmosphere refrigerated trucks according to claim 1, characterized in that, The sensing and detection module includes: The environmental data acquisition unit includes a detection equipment group with several monitoring points deployed inside the controlled atmosphere refrigerated truck. The monitoring points are arranged in layers and zones according to the space area of ​​the truck compartment, and the temperature, humidity and oxygen concentration of each monitoring point are collected simultaneously. The equipment status acquisition unit is communicatively connected to the refrigeration actuators and gas regulation actuators of the controlled atmosphere refrigerated truck, and acquires the operating status data of each actuator in real time. The gradient field construction unit constructs a corresponding spatial distribution field inside the carriage based on the spatial coordinates of each monitoring point and the corresponding environmental data collected, and updates each spatial distribution field at a preset time interval to form a spatial gradient field change sequence.

3. The energy consumption co-optimization system for controlled atmosphere refrigerated trucks according to claim 2, characterized in that, The data collected by each monitoring point in the sensing and detection module carries a corresponding collection timestamp and monitoring point location identifier. The gradient field construction unit normalizes and integrates the environmental data of each monitoring point in the same time dimension based on the collection timestamp, and then combines it with the monitoring point location identifier to complete the construction of the spatial distribution field. In addition, the spatial distribution field of each time node in the gradient field change sequence is associated with the equipment operation status data of the corresponding time period.

4. The energy consumption co-optimization system for controlled atmosphere refrigerated trucks according to claim 3, characterized in that, The consistency verification module performs physical consistency verification on the spatial distribution field at each time node in the gradient field change sequence based on the deterministic gas density change law of gas density with temperature, humidity and oxygen concentration. It uses the corresponding constraints between the density gradient field and each spatial distribution field as verification conditions and calculates the constraint deviation of each monitoring point at the corresponding time node. In response to the constraint deviation exceeding the consistency threshold, the environmental data of the corresponding monitoring point at the corresponding acquisition timestamp is determined to be abnormal acquisition information, and the environmental data corresponding to the abnormal acquisition information is subjected to constraint consistency replacement processing based on adjacent monitoring points to output an effective gradient field.

5. The energy consumption co-optimization system for controlled atmosphere refrigerated trucks according to claim 4, characterized in that, The collaborative determination module analyzes the spatial coupling degree between the temperature gradient and the oxygen concentration gradient in the effective gradient field, and determines the collaborative imbalance index based on the matching relationship between the spatial coupling degree and the rate of change of the temperature gradient field in the corresponding time period.

6. The energy consumption co-optimization system for controlled atmosphere refrigerated trucks according to claim 5, characterized in that, The collaboration determination module determines the corresponding collaboration risk level based on the collaboration imbalance index. Specifically, if the collaborative imbalance index is lower than the low-risk threshold, it is determined to be a low-risk level; if the collaborative imbalance index is between the low-risk threshold and the high-risk threshold, it is determined to be a medium-risk level; and if the collaborative imbalance index is higher than the high-risk threshold, it is determined to be a high-risk level.

7. The energy consumption co-optimization system for controlled atmosphere refrigerated trucks according to claim 6, characterized in that, The collaborative control module determines the corresponding adjustment amplitude and adjustment rate based on the collaborative risk level, and generates continuous control commands based on the adjustment amplitude and adjustment rate, including: In response to a low-risk level, the collaborative control module maintains the current continuous control commands; In response to a medium-risk level, the collaborative control module generates continuous control commands containing intermediate frequency adjustment or intermediate opening adjustment. The intermediate frequency adjustment is used to adjust the operating frequency of the refrigeration actuator, and the intermediate opening adjustment is used to adjust the valve opening of the gas regulating actuator. In response to a high-risk level, the collaborative control module generates continuous control commands that include advanced frequency adjustment and / or advanced opening adjustment. Wherein, the advanced frequency adjustment amount is greater than the intermediate frequency adjustment amount, and the advanced opening adjustment amount is greater than the intermediate opening adjustment amount.

8. The energy consumption co-optimization system for controlled atmosphere refrigerated trucks according to claim 7, characterized in that, After executing the linkage adjustment, the collaborative control module compares the temperature gradient intensity of the central region of the carriage with the temperature gradient intensity of the edge region of the carriage in the reconstructed temperature gradient field, and compares the concentration gradient intensity of the central region of the carriage with the concentration gradient intensity of the edge region of the carriage in the reconstructed oxygen concentration gradient field, so as to determine whether the linkage adjustment reduces the gradient intensity of the central region of the carriage to below the gradient intensity of the edge region through flip verification.

9. The energy consumption co-optimization system for controlled atmosphere refrigerated trucks according to claim 8, characterized in that, The cooperative control module, in response to the determination that the flip verification result is unstable under the cooperative stability condition, corrects the continuous control command and re-executes the cooperative control. The cooperative stability condition is that, during the rollover verification, the temperature gradient intensity in the central region of the carriage does not decrease below the temperature gradient intensity in the edge region of the carriage, and / or, the oxygen concentration gradient intensity in the central region of the carriage does not decrease below the oxygen concentration gradient intensity in the edge region of the carriage.

10. A method for applying the energy consumption collaborative optimization system for a controlled atmosphere refrigerated truck according to any one of claims 1-9, characterized in that, include: Environmental data is collected at several monitoring points inside the controlled atmosphere refrigerated truck. Based on the environmental data, a spatial distribution field of temperature, humidity and oxygen concentration is constructed, and a change sequence of the gradient field is formed in the time dimension. Physical consistency verification is performed on each spatial distribution field to identify abnormal acquisition information and output the effective gradient field; The degree of coupling between the temperature gradient and the oxygen concentration gradient in the effective gradient field is analyzed. Based on the degree of coupling and the change of the temperature gradient field in the corresponding time period, a cooperative imbalance index is determined, and a cooperative risk level is determined based on the cooperative imbalance index. Continuous control commands are generated based on the collaborative risk level, and the refrigeration actuator and gas regulation actuator are adjusted in conjunction to make the carriage operation state gradually change according to the continuous control commands. After the linkage adjustment is executed, the spatial gradient fields are reconstructed, and the gradient difference between the central region and the edge region of the carriage is flipped for verification. If the flip verification result is unstable, the continuous control command is corrected and the cooperative control is re-executed.

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