A cold chain warehouse environment temperature control method and system

Abstract

This invention relates to the field of temperature control technology, specifically to a method and system for temperature control in cold chain warehousing environments. The method includes acquiring handling instructions and cargo SKU attributes; parsing and generating a three-dimensional dynamic displacement trajectory of the mobile equipment and extracting heat load characteristic parameters; calculating a basic temperature adjustment amount based on the trajectory's predicted timestamp and combining it with the heat load parameters to output a feedforward control signal; extracting the handover and dwelling points of relay equipment to calculate transient compensation adjustment amounts to output targeted control signals; and comparing actual and theoretical temperature time series to extract response hysteresis and local extreme value errors for closed-loop adaptive correction. This invention achieves spatial heat load feedforward pre-adjustment through pre-analysis of the material flow line, and combines transient targeted compensation at handover and dwelling points with a closed-loop adaptive correction mechanism to ensure the stable operation of the cold chain system.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, specifically to a method and system for temperature control in cold chain warehousing environments. Background Technology

[0002] With the widespread adoption of automation technology, modern cold chain warehousing systems extensively utilize intelligent logistics equipment such as AGV forklifts, RGV trolleys, and stacker cranes for high-frequency operations. In these complex warehousing environments, the frequent movement of large quantities of goods and the shuttling operation of relay equipment generate continuous and nonlinear dynamic thermal disturbances within a three-dimensional controlled space. This multi-source heterogeneous transient thermal load impact from discrete logistics operations places extremely high demands on the environmental temperature control system's ability to suppress disturbances and its steady-state control accuracy under complex operating conditions. However, current cold chain warehousing temperature control mostly adopts conventional passive feedback control strategies based on static thresholds, which suffers from a significant lag in the output of control signals compared to dynamic thermal disturbances in the space. Because traditional temperature control logic is independent of the warehousing and logistics scheduling system, the system only passively triggers the temperature control actuator to adjust after the temperature sensor detects a local temperature deviation. This inherent lag in the control principle means that the speed and spatial distribution of cold energy delivery cannot keep up with the high-frequency movement of AGV forklifts, RGV trolleys, and their loaded goods. This easily leads to extreme temperature overshoot and heat island effects at local controlled nodes, resulting in large system lag and poor robustness. Under conditions of frequent logistics disturbances, feedback adjustment based on temperature deviations may experience response delays, causing local temperatures to deviate from the preset allowable temperature range.

[0003] Therefore, a method and system for controlling the temperature of cold chain storage environments are proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for controlling the temperature of cold chain storage environments, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for controlling the temperature of a cold chain storage environment, comprising: Obtain the handling instructions and the SKU attributes of the goods, parse the handling instructions to generate the dynamic displacement trajectory of the mobile device in the three-dimensional spatial grid, and extract the thermal load characteristic parameters of the mapping system thermal disturbance variables based on the SKU attributes; Based on the dynamic displacement trajectory, the time stamp of the thermal disturbance variable entering the target space grid is predicted. The basic temperature adjustment is calculated by decoupling the thermal load characteristic parameters and the lead time constant through the feedforward compensation model, and the feedforward control signal is output to the temperature control actuator of the corresponding grid. The access path of the relay equipment is obtained, and the spatially overlapping nodes with the dynamic displacement trajectory are extracted as handover and dwelling points. Combining the expected dwelling time with the heat load characteristic parameters, the transient temperature compensation adjustment amount to suppress local dynamic changes is calculated, and the targeted control signal is output. The actual temperature time series of the controlled grid is collected and compared with the theoretical reference temperature series. The response hysteresis time difference and local extreme temperature error are extracted, and the lead time constant and transient temperature compensation adjustment amount in the next control cycle are corrected.

[0006] Preferably, the mobile device includes an AGV forklift for performing ground handling and an RGV trolley for performing rail handling; If the mobile device executing the handling command is determined to be an AGV forklift, a ground driving trajectory is generated based on the two-dimensional map of the cold chain storage area and the dynamic obstacle avoidance algorithm. Combined with the current height of the goods and the expected lifting height, the ground driving trajectory is mapped to the first dynamic displacement trajectory in the three-dimensional spatial grid. If the mobile device executing the handling command is determined to be an RGV vehicle, a fixed track travel trajectory is extracted based on a preset warehouse track topology network, and combined with the fixed elevation parameters of the track where the RGV vehicle is located, the fixed track travel trajectory is mapped to a second dynamic displacement trajectory in the three-dimensional spatial grid.

[0007] Preferably, the SKU attributes include goods type information, single item weight, three-dimensional dimensions of goods packaging, area-to-thermal resistance of outer packaging material, and target storage temperature; and the initial temperature of the goods upon entry into the warehouse and the real-time external ambient temperature are obtained. Based on the cargo type information, the specific heat capacity coefficient of the corresponding cargo is retrieved from the thermal property database; the temperature difference value is calculated by subtracting the initial temperature upon entry into the warehouse from the target storage temperature; and based on the temperature difference value, the specific heat capacity coefficient, and the mass of a single cargo item, the total physical heat load required for the cargo to reach the target storage temperature is calculated; the effective heat exchange surface area is calculated based on the three-dimensional dimensions of the cargo packaging; the internal and external environmental temperature difference is obtained by subtracting the real-time external ambient temperature from the initial temperature upon entry into the warehouse; and the heat exchange rate between the cargo and the external environment per unit time is calculated based on the internal and external environmental temperature difference, the effective heat exchange surface area, and the area specific thermal resistance of the outer packaging material; the total physical heat load and the heat exchange rate per unit time are combined to generate heat load characteristic parameters.

[0008] Preferably, the feedforward control signal is used to perform basic environmental pre-adjustment before the mobile device reaches the target space grid. The basic environmental pre-adjustment process includes mapping the coordinate space of the target space grid to a controlled grid covered by the physical wind field of the actual temperature control actuator through a preset fluid dynamics mapping matrix, and adjusting the initial air supply volume and the step opening degree of the local temperature control actuator covering the controlled grid according to the basic temperature adjustment amount. The targeted control signal performs instantaneous thermal disturbance compensation at the handover and dwelling point. Based on the fluid dynamics mapping matrix, the dwelling space of the handover and dwelling point and the target cargo location is associated with the corresponding targeted temperature control actuator. The stepless speed regulation PWM operation parameters of the corresponding temperature control actuator are set according to the transient temperature compensation adjustment amount, thereby increasing the instantaneous environmental regulation power during the dwelling period.

[0009] Preferably, the process of generating the feedforward control signal is as follows: calculate the expected travel time of the goods in the target space grid based on the dynamic displacement trajectory and the equipment speed; multiply the heat exchange rate per unit time in the heat load characteristic parameters by the expected travel time to obtain the dynamic heat load impact amount and use it as the basic temperature adjustment amount; The slope of the regulating power output required to reach the target regulating power before the expected timestamp is calculated by combining the advance time constant; the proportion of the basic temperature regulation amount dissipated to the surrounding adjacent grids under the action of fluid is calculated using the fluid dynamics mapping matrix, and the basic temperature regulation amount is spatially allocated and corrected to obtain the target regulating medium delivery amount; based on the target regulating medium delivery amount and the regulating power output slope, the energy efficiency mapping matrix of the local temperature control actuator is called to match the step opening degree of the regulating valve and the corresponding fan speed; the fan speed is converted into the initial air volume and encapsulated in the protocol to generate a feedforward control signal.

[0010] Preferably, the generation process of the targeted control signal is as follows: extract the heat exchange rate per unit time from the heat load characteristic parameters, multiply it by the expected residence time at the handover point, calculate the total residence heat exchange of the goods during the handover period, and use it as the transient temperature compensation adjustment amount; after performing an equivalent conversion between physical dimensions and thermal work equivalents on the extracted heat exchange rate per unit time, use it as the instantaneous adjustment power increment required for the local area of ​​the handover point. The instantaneous adjustment power increment is superimposed with the current system base adjustment power of the corresponding temperature control actuator to obtain the total target power, and input into the power-PWM duty cycle mapping function to calculate the target continuously variable PWM operating parameters that meet the total target power requirement; the target continuously variable PWM operating parameters are protocol-encapsulated with the corresponding dwell execution time window to generate a targeted control signal.

[0011] Preferably, the process of extracting the response hysteresis time difference involves extracting the theoretical temperature change inflection point and the corresponding theoretical timestamp in the theoretical reference temperature sequence that generates temperature regulation in response to the control signal, identifying the corresponding actual temperature change inflection point and actual timestamp in the actual temperature time sequence by calculating the temperature change rate, and calculating the response hysteresis time difference by subtracting the actual timestamp from the theoretical timestamp. The process of extracting the local extreme temperature error involves extracting the theoretical maximum temperature extreme value generated by the dynamic heat load impact during the cargo handover and residence period of the theoretical reference temperature sequence, and simultaneously extracting the actual maximum temperature extreme value of the actual temperature time sequence within the handover and residence time window. The difference between the actual maximum temperature extreme value and the theoretical maximum temperature extreme value is then calculated to obtain the local extreme temperature error.

[0012] A cold chain storage environment temperature control system, comprising: Movement analysis module: acquires handling instructions and SKU attributes of goods, parses the handling instructions to generate dynamic displacement trajectory of mobile device in three-dimensional space grid, and extracts heat load characteristic parameters of mapping system thermal disturbance variables based on the SKU attributes; Feedforward control module: Based on the dynamic displacement trajectory, predict the timestamp of the thermal disturbance variable entering the target space grid, combine the thermal load characteristic parameters and the lead time constant to calculate the basic temperature adjustment through the feedforward compensation model, and output the feedforward control signal to the temperature control actuator of the corresponding grid; Targeted compensation module: Obtains the access path of the relay equipment, extracts the spatially overlapping nodes with the dynamic displacement trajectory as the handover and dwell points, calculates the transient temperature compensation adjustment amount to suppress local dynamic changes by combining the expected dwell time and the heat load characteristic parameters, and outputs the targeted control signal; Closed-loop adaptive module: Collects the actual temperature time series of the controlled grid, compares it with the theoretical reference temperature series, extracts the response hysteresis time difference and local extreme temperature error, and corrects the lead time constant and transient temperature compensation adjustment amount in the next control cycle.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention proposes a feedforward compensation mechanism based on dynamic displacement trajectory and cargo SKU thermal property parameters, overcoming the inherent defect of traditional passive feedback control systems where the control signal output lags significantly behind spatial dynamic thermal disturbance variables. By pre-analyzing logistics handling instructions to predict the timestamp of the mobile device entering the target spatial grid and converting specific cargo thermal load characteristics into basic temperature regulation values ​​for advance output, the temperature control system achieves an advanced and proactive response to spatial thermal load impacts, thereby ensuring the alignment of the temperature control medium delivery with the dynamic disturbance source in both time and space dimensions.

[0014] 2. This invention addresses the problem of local transient thermal disturbances caused by handover operations of automated equipment by constructing a targeted compensation control strategy based on spatially overlapping nodes. By extracting the handover and dwell points in the access path of the relay equipment and calculating the instantaneous compensation amount by combining the expected dwell time and thermal load parameters, a targeted control signal can be output to the temperature control actuator in the target area. This instantaneously increases the local environmental regulation power during the cargo dwell period, thereby suppressing the temperature extreme overshoot phenomenon caused by discrete logistics operations within a specific grid and avoiding the generation of local heat island effects.

[0015] 3. This invention introduces a closed-loop adaptive correction mechanism based on temperature time series comparison, constructing a feedback loop in which control parameters are dynamically updated according to the actual operating state. By collecting the actual temperature time series of the controlled grid and comparing it with the theoretical reference series, the hysteresis time difference and local extreme temperature error of the system response are automatically extracted. These real dynamic deviation data are used to continuously correct the lead time constant and compensation adjustment in the next control cycle, realizing the self-calibration of the control model according to changes in environmental physical conditions and equipment status, and avoiding the continuous control deviation caused by the use of fixed static parameters in traditional systems during long-term operation. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a cold chain warehousing environment temperature control method according to the present invention; Figure 2 This is a schematic diagram of the dynamic displacement trajectory generation process of the present invention; Figure 3 This is a schematic diagram of the process for generating heat load characteristic parameters according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 This invention provides a method and system for controlling the temperature of a cold chain storage environment, the technical solution of which is as follows: A method for controlling the temperature of a cold chain storage environment, comprising: Obtain the handling instructions and the SKU attributes of the goods, parse the handling instructions to generate the dynamic displacement trajectory of the mobile device in the three-dimensional spatial grid, and extract the thermal load characteristic parameters of the mapping system thermal disturbance variables based on the SKU attributes; Based on the dynamic displacement trajectory, the time stamp of the thermal disturbance variable entering the target space grid is predicted. The basic temperature adjustment is calculated by decoupling the thermal load characteristic parameters and the lead time constant through the feedforward compensation model, and the feedforward control signal is output to the temperature control actuator of the corresponding grid. The access path of the relay equipment is obtained, and the spatially overlapping nodes with the dynamic displacement trajectory are extracted as handover and dwelling points. Combining the expected dwelling time with the heat load characteristic parameters, the transient temperature compensation adjustment amount to suppress local dynamic changes is calculated, and the targeted control signal is output. The actual temperature time series of the controlled grid is collected and compared with the theoretical reference temperature series. The system response hysteresis time difference and local extreme temperature error are extracted, and the system lead time constant and transient temperature compensation adjustment amount in the next control cycle are corrected.

[0019] Example 1: The system acquires handling instructions and the SKU attributes of the goods, parses the handling instructions to generate a dynamic displacement trajectory of the mobile device in a three-dimensional spatial grid, and extracts the thermal load characteristic parameters of the mapping system thermal disturbance variables based on the SKU attributes; the dynamic displacement trajectory is either a first dynamic displacement trajectory or a second dynamic displacement trajectory. See Figure 2 The mobile equipment includes AGV forklifts for ground handling and RGV trolleys for rail handling; the three-dimensional spatial grid is used to divide the cold chain storage space into independent spatial units with horizontal index, horizontal index, and vertical index. If the mobile device executing the handling command is determined to be an AGV forklift, a ground driving trajectory is generated based on the two-dimensional map of the cold chain storage area and the dynamic obstacle avoidance algorithm. Combined with the current height of the goods and the expected lifting height, the ground driving trajectory is mapped to the first dynamic displacement trajectory in the three-dimensional spatial grid. If the mobile device executing the handling command is determined to be an RGV vehicle, a fixed track travel trajectory is extracted based on a preset warehouse track topology network, and combined with the fixed elevation parameters of the track where the RGV vehicle is located, the fixed track travel trajectory is mapped to a second dynamic displacement trajectory in the three-dimensional spatial grid.

[0020] Specifically, after obtaining the handling instruction, the physical device identification code in the handling instruction is parsed and searched in the device attribute database of the warehouse management server; if the movement type associated with the physical device identification code is trackless chassis, the mobile device executing the handling instruction is determined to be an AGV forklift performing ground handling; if the movement type associated with the physical device identification code is rail chassis, the mobile device executing the handling instruction is determined to be an RGV trolley performing rail handling. If the mobile device executing the handling command is determined to be an AGV forklift, a two-dimensional map of the cold chain storage area is retrieved from the warehouse management server. This map is scanned by a LiDAR sensor using synchronous positioning and mapping technology and converted into a two-dimensional grid coordinate system containing obstacle markers. Using the current two-dimensional coordinates of the AGV forklift as the starting point and the target two-dimensional coordinates indicated by the handling command as the ending point, a basic route is calculated on the cold chain storage area's two-dimensional map using a global shortest path algorithm. During the driving phase, real-time distance parameters of dynamic obstacles are obtained using an onboard ranging radar, and these parameters are input into a dynamic obstacle avoidance algorithm. The dynamic obstacle avoidance algorithm performs trajectory sampling for multiple prediction windows within the dynamic constraint speed range of the AGV forklift, calculates the distance cost of each sampled trajectory deviating from the basic route and the collision risk cost of approaching obstacles, and selects the set of coordinate points with the minimum total cost to generate a ground driving trajectory. This ground driving trajectory contains a two-dimensional planar coordinate sequence with timestamps. The current height of the cargo is obtained by reading the values ​​from the photoelectric displacement sensor installed on the AGV forklift lifting mechanism, and recorded as the base height parameter. ;Analyze the layer number of the target shelf in the handling instruction, match the corresponding physical vertical distance in the warehouse shelf size database as the expected lifting height, and record it as the maximum height constraint parameter. The unit side length parameter of the three-dimensional spatial mesh is set as follows: The cold chain storage space is divided into independent spatial units with horizontal index, horizontal index, and vertical index.

[0021] Using the expected lifting height as a threshold constraint, based on the constant lifting speed parameter of the AGV forklift... and the time variable after the lifting action is triggered. The real-time vertical height of the cargo during the interaction between travel and lifting is calculated using the amplitude constraint formula. : ; when The calculated value is less than When the calculated value reaches or exceeds the specified value, the cargo is determined to be in a dynamic upward phase, where its real-time vertical height increases linearly with time. When the system determines that the goods have reached the designated shelf level, it automatically cuts off the height variable, keeping the real-time vertical height constant. ; The two-dimensional planar coordinates at each time stamp in the ground driving trajectory are spliced ​​with the real-time vertical height calculated at the corresponding time node to generate a three-dimensional spatial coordinate sequence with physical real size; each coordinate value in the three-dimensional spatial coordinate sequence is divided by the unit side length parameter and rounded down to obtain a spatial path connected by a series of discrete three-dimensional grid index coordinates, and this spatial path is used as the first dynamic displacement trajectory. If the mobile device executing the handling instruction is determined to be an RGV vehicle, a preset warehouse track topology network is obtained. The preset warehouse track topology network is pre-constructed by the warehouse management server. The construction method is as follows: extract the coordinates of the physical intersection points of all physical guide rails in the cold chain storage area as vertices of the graph data structure, extract the distance between the guide rails connecting adjacent intersection points as the edge weights, and generate a weighted directed graph; take the track intersection point where the RGV vehicle is currently stationed as the starting vertex, take the unloading point pointed to by the handling instruction as the target vertex, use the minimum cost search algorithm to traverse the weighted directed graph, find the vertex sequence with the shortest total weight distance, and extract the fixed track travel trajectory based on the vertex sequence. The fixed track travel trajectory includes the two-dimensional plane coordinate sequence of the track nodes and their connecting lines.

[0022] The following steps are performed: First, read the guide rail level number where the RGV trolley is currently running in the transport instruction. Then, query the building structure height configuration table to obtain the constant vertical distance between the guide rail level and the ground, which is used as the fixed elevation parameter of the track. Next, add a third dimension value to all two-dimensional plane coordinates in the fixed track trajectory. This third dimension value is the value of the fixed elevation parameter of the track. These coordinates are then stitched together to generate a three-dimensional spatial coordinate sequence on a fixed horizontal plane. Following the same grid transformation logic as the AGV forklift, set the same unit side length parameter. Divide each coordinate value in the three-dimensional spatial coordinate sequence on the fixed horizontal plane by the unit side length parameter and round down to the nearest integer, transforming it into a spatial path composed of discrete three-dimensional grid index coordinates. This spatial path is used as the second dynamic displacement trajectory. A unified scheduling time base and stopping cycle time are obtained to generate a timestamp for the second dynamic displacement trajectory. Simultaneously, an instruction state machine is established. When trajectory replanning or task cancellation is detected, the feedforward control signal already issued to the corresponding grid is revoked or overwritten. Different trajectory analysis strategies are employed for AGV forklifts performing ground handling and RGV trolleys performing rail handling, reflecting the physical operating characteristics of different types of mobile equipment. For AGV forklifts, a combination of a 2D ground map, dynamic obstacle avoidance algorithms, and real-time vertical lifting height calculation of goods is used to map position changes during planar travel and vertical lifting interactions into dynamic displacement trajectories in a 3D spatial grid. For RGV trolleys, a 3D trajectory mapping is completed by combining a pre-set warehouse rail topology network and fixed rail elevation parameters. This classification and mapping mechanism based on physical equipment type can accurately track the 3D spatial coordinate sequence of goods within the warehouse space, conforming to actual working conditions. See Figure 3 The SKU attributes include goods type information, single item weight, three-dimensional dimensions of goods packaging, area-to-thermal resistance of outer packaging material, and target storage temperature; the initial temperature of goods upon entry into the warehouse and the real-time external ambient temperature are obtained. Based on the cargo type information, the specific heat capacity coefficient of the corresponding cargo is retrieved from the thermal property database; the temperature difference value is calculated by subtracting the initial temperature upon entry into the warehouse from the target storage temperature; and based on the temperature difference value, the specific heat capacity coefficient, and the mass of a single cargo item, the total physical heat load required for the cargo to reach the target storage temperature is calculated; the effective heat exchange surface area is calculated based on the three-dimensional dimensions of the cargo packaging; the internal and external environmental temperature difference is obtained by subtracting the real-time external ambient temperature from the initial temperature upon entry into the warehouse; and the heat exchange rate between the cargo and the external environment per unit time is calculated based on the internal and external environmental temperature difference, the effective heat exchange surface area, and the area specific thermal resistance of the outer packaging material; the total physical heat load and the heat exchange rate per unit time are combined to generate heat load characteristic parameters.

[0023] Specifically, the system receives handling instructions and parses the cargo identification code in the handling instructions. Based on the cargo identification code, it retrieves the SKU attribute of the cargo from the product master data table of the warehouse management server. The SKU attribute includes pre-entered static parameters: cargo type information, single cargo weight, length, width and height values ​​in the three-dimensional dimensions of the cargo packaging, area thermal resistance of the outer packaging material, and target storage temperature. Simultaneously, the actual surface temperature of the goods is obtained by scanning with an array of infrared temperature sensors located at the nodes of the inbound conveyor belt. This temperature is then mapped using a preset surface-core temperature compensation coefficient and used as the initial inbound temperature. Furthermore, the real-time external ambient temperature is collected by an environmental temperature sensor located within the controlled grid where the goods are currently situated. The preset surface-core temperature compensation coefficient is obtained through pre-determined offline experimental calibration. Specifically, a standard sample of the corresponding goods is selected, and its external surface temperature and internal true core temperature are simultaneously collected under the test environment. The deviation data between the two under different temperature difference conditions are then calculated and numerically fitted. The thermal property database is a mapping data table built in a local storage medium. The cargo type information is extracted as a search keyword, and the specific heat capacity coefficient associated with it is queried and extracted from the mapping data table. The temperature difference is calculated by subtracting the initial temperature upon entry into the warehouse from the target storage temperature. Based on this temperature difference, the specific heat capacity coefficient, and the mass of each item, the total physical heat load required for the goods to reach the target storage temperature is calculated using the following logic: The temperature difference is obtained by subtracting the initial temperature upon entry into the warehouse from the target storage temperature and taking the absolute value. The total physical heat load is then calculated using a formula based on a thermodynamic theoretical model. ; in, Represents the total physical heat load. This represents the specific heat capacity coefficient obtained through retrieval. This represents the mass of the single item. The temperature difference; The effective heat exchange surface area is calculated by extracting the length, width, and height values ​​from the three-dimensional dimensions of the cargo packaging, and then calculating the effective heat exchange surface area using the surface area expansion formula. ; in This represents the effective heat exchange surface area. , , These represent the three dimensions of the cargo packaging: length, width, and height, respectively. The temperature difference between the real-time external ambient temperature and the initial temperature upon entering the warehouse is obtained by subtracting the absolute value of the difference. Based on the heat transfer model, the heat transfer rate per unit time is calculated by substituting the temperature difference between the internal and external environments, the effective heat exchange surface area, and the area specific thermal resistance of the outer packaging material into the following formula: ; in, This represents the heat exchange rate per unit time. This represents the temperature difference between the internal and external environments. The area-specific thermal resistance of the outer packaging material is used as an example. The calculated total physical heat load and the heat exchange rate per unit time are used as floating-point feature elements and concatenated into an array according to a preset feature order to construct a one-dimensional feature vector. The feature vector is used as the heat load feature parameter. The preset feature order is that the total physical heat load is used as the first feature element of the one-dimensional feature vector, and the heat exchange rate per unit time is used as the second feature element.

[0024] By combining pre-recorded static SKU attributes with temperature data dynamically collected by sensors, and by deriving the total physical heat load and heat exchange rate per unit time of the cargo according to thermodynamic and heat transfer calculation models, the absolute heat requirement of the cargo to reach the target temperature and the dynamic process of heat exchange with the external environment are objectively reflected, so that the combined heat load characteristic parameters have clear physical support. Based on the predicted timestamp of the thermal disturbance variable entering the target space grid according to the dynamic displacement trajectory, the basic temperature adjustment amount is calculated by decoupling the thermal load characteristic parameters and the lead time constant through a feedforward compensation model, and a feedforward control signal is output to the temperature control actuator of the corresponding grid; the target space grid is the grid that the thermal disturbance variable enters according to the predicted dynamic displacement trajectory, and its coordinate space is mapped to a controlled grid covered by the physical wind field of the actual temperature control actuator through a preset fluid dynamics mapping matrix; The feedforward control signal is used to perform basic environmental pre-adjustment before the mobile device reaches the target space grid. The basic environmental pre-adjustment process includes mapping the coordinate space of the target space grid to a controlled grid covered by the physical wind field of the actual temperature control actuator through a preset fluid dynamics mapping matrix, and adjusting the initial air supply volume and the step opening degree of the local temperature control actuator covering the controlled grid according to the basic temperature adjustment amount. The targeted control signal performs instantaneous thermal disturbance compensation at the handover and dwelling point. Based on the fluid dynamics mapping matrix, the dwelling space of the handover and dwelling point and the target cargo location is associated with the corresponding targeted temperature control actuator. The stepless speed regulation PWM operation parameters of the corresponding temperature control actuator are set according to the transient temperature compensation adjustment amount, thereby increasing the instantaneous environmental regulation power during the dwelling period.

[0025] The process of generating the feedforward control signal is as follows: calculate the expected travel time of the cargo in the target space grid based on the dynamic displacement trajectory and the equipment speed; multiply the heat exchange rate per unit time in the heat load characteristic parameters by the expected travel time to obtain the dynamic heat load impact amount and use it as the basic temperature adjustment amount. The slope of the regulating power output required to reach the target regulating power before the expected timestamp is calculated by combining the advance time constant; the proportion of the basic temperature regulation amount dissipated to the surrounding adjacent grids under the action of fluid is calculated using the fluid dynamics mapping matrix, and the basic temperature regulation amount is spatially allocated and corrected to obtain the target regulating medium delivery amount; based on the target regulating medium delivery amount and the regulating power output slope, the energy efficiency mapping matrix of the local temperature control actuator is called to match the step opening degree of the regulating valve and the corresponding fan speed; the fan speed is converted into the initial air volume and encapsulated in the protocol to generate a feedforward control signal.

[0026] Specifically, the coordinate space of the target spatial grid is mapped to a controlled grid covered by the physical wind field of the actual temperature control actuators through a preset fluid dynamics mapping matrix. The fluid dynamics mapping matrix is ​​obtained by acquiring the three-dimensional spatial coordinates of each temperature control actuator in the cold chain storage area and the measured environmental wind speed distribution data of each air outlet at different speeds. The air volume diffusion weight of each temperature control actuator to each three-dimensional spatial grid unit in the storage area is calculated by a computational fluid dynamics simulation model. The mapping matrix is ​​constructed by filling the air volume diffusion weight into the corresponding matrix elements with the equipment number of each temperature control actuator as the column vector of the matrix and the coordinate identifier of each spatial grid unit as the row vector of the matrix. After identifying the local temperature control actuators covering the controlled grid, the initial air volume and the step opening of the regulating valve of the local temperature control actuator are adjusted according to the value of the basic temperature adjustment amount. Based on the fluid dynamics mapping matrix, extract the column vector with the largest weight value from the row vectors of the corresponding handover and dwelling points and the dwelling space coordinates of the target cargo location, and associate the corresponding temperature control actuator as the target temperature control actuator; set the stepless speed regulation pulse width modulation operation parameters of the corresponding temperature control actuator according to the transient temperature compensation adjustment amount. The specific conversion logic is as follows: divide the transient temperature compensation adjustment amount by the maximum rated temperature regulation power of the corresponding target temperature control actuator to obtain the basic power ratio. After weighting and compensating the basic power ratio using the ambient temperature correction coefficient, convert it into a percentage value as the target duty cycle for controlling the duration ratio of high and low levels; increase the instantaneous ambient regulation power during the dwelling period by adjusting the duty cycle; The process of generating the feedforward control signal involves obtaining the current driving speed of the mobile device and its dynamic displacement trajectory as it enters the target space grid. The physical path length of the dynamic displacement trajectory within the target space grid is divided by the current driving speed to calculate the estimated travel time of the cargo within the target space grid. The heat exchange rate per unit time in the heat load characteristic parameters is multiplied by the estimated travel time to obtain the dynamic heat load impact amount released or absorbed by the cargo during its passage through the grid, which is then used as the base temperature regulation amount. In an empty chamber state, a step control command is input to the temperature control actuator, and data from the temperature sensor at the center point of the controlled grid is collected. The time interval from the moment the command is issued until the sensor temperature change rate is greater than zero for three consecutive sampling periods is recorded as the lead time constant. Based on the lead time constant, the basic temperature adjustment amount is divided by the expected travel time to calculate the required target adjustment power. The target adjustment power is then divided by the lead time constant to calculate the adjustment power output slope required to reach the target adjustment power before the expected timestamp. Using the fluid dynamics mapping matrix, the diffusion weight of the corresponding local temperature control actuator in the target space grid row and the diffusion weight in the adjacent grid rows are extracted. The ratio of the sum of the diffusion weights of the adjacent grids to the diffusion weight of the target space grid is used as the dissipation ratio of the basic temperature regulation amount to the adjacent grids under the action of fluid. The basic temperature regulation amount is multiplied by a compensation coefficient that includes 1 and the sum of the dissipation ratio to perform spatial allocation correction on the basic temperature regulation amount, thereby obtaining the target control medium delivery amount. Based on the target regulating medium delivery amount and the regulating power output slope, the energy efficiency mapping matrix of the local temperature control actuator is invoked. The energy efficiency mapping matrix is ​​generated by fitting the cooling and heating capacity and the overall power consumption under different combinations of regulating valve step opening and fan speed at different ambient temperatures. In the energy efficiency mapping matrix, all available parameter combinations that can simultaneously satisfy the target regulating medium delivery amount and the regulating power output slope are initially retrieved. The set with the smallest theoretical overall power consumption value is selected from the parameter combination set and matched as the regulating valve step opening and the corresponding fan speed. The fan speed is converted into the corresponding initial air volume, and the initial air volume and the regulating valve step opening value are converted into hexadecimal machine code. According to the industrial standard serial communication format, the start bit, the slave address code of the temperature control actuator, the write function code, the data register start address, the data area composed of the hexadecimal machine code, and the cyclic redundancy check code are sequentially concatenated to generate a binary data stream that can be directly parsed by the underlying programmable logic controller, and a feedforward control signal is generated. By combining dynamic displacement trajectory with expected travel time, the dynamic heat load impact of moving goods can be quantified; further, the airflow dissipation ratio is introduced for spatial distribution correction, which compensates for the natural loss of the control medium to the surrounding adjacent grids and avoids the problem of inadequate pre-regulation caused by heat loss.

[0027] The process of generating the targeted control signal is as follows: extract the heat exchange rate per unit time from the heat load characteristic parameters, multiply it by the expected residence time at the handover point, calculate the total residence heat exchange of the goods during the handover period, and use it as the transient temperature compensation adjustment amount; after performing an equivalent conversion between physical dimensions and thermal work equivalents on the extracted heat exchange rate per unit time, use it as the instantaneous adjustment power increment required for the local area of ​​the handover point. The instantaneous adjustment power increment is superimposed with the current system base adjustment power of the corresponding temperature control actuator to obtain the total target power, and input into the power-PWM duty cycle mapping function to calculate the target continuously variable PWM operating parameters that meet the total target power requirement; the target continuously variable PWM operating parameters are protocol-encapsulated with the corresponding dwell execution time window to generate a targeted control signal.

[0028] Specifically, the heat exchange rate per unit time is extracted from the heat load characteristic parameters; the rated lifting speed, rated lowering speed, and fork extension speed of the relay equipment are read, and the basic mechanical cycle time is calculated by combining the expected lifting height and storage depth of the goods; the relay equipment is an automated device that performs cross-regional handover and three-dimensional storage operations of goods in a cold chain warehousing system, including stacker cranes in automated warehouses or automated forklifts with fork extension mechanisms; simultaneously, the mass of a single item of goods is extracted and converted using a preset dynamic conversion formula: ; Perform calculations, where For the time to compensate for motion lag, The mass of the single item is... This is the equipment load damping coefficient. The dimensionless empirical calibration coefficient is obtained by measuring the actual motion hysteresis time of test cargo with a known mass gradient, substituting it into the dynamic conversion formula for inverse solution, and taking the arithmetic mean of the obtained coefficient value for calibration and solidification; the expected dwell time is obtained by adding the mechanical cycle time of the basic motion to the motion hysteresis compensation time. Multiply the heat exchange rate per unit time in the heat load characteristic parameters by the expected residence time to calculate the total residence heat exchange that the cargo dissipates or absorbs during the handover residence period. This amount is used as the transient temperature compensation adjustment amount to assess the overall heat load impact boundary. At the same time, after performing an equivalent conversion between physical dimensions and thermal work equivalents on the heat exchange rate per unit time in the heat load characteristic parameters, it is directly used as the instantaneous adjustment power increment required for the local area of ​​the handover residence point. The three-phase operating current and operating voltage of the corresponding target temperature control actuator's underlying controller are read in real time via the industrial control bus during the current working cycle. The operating current and operating voltage are multiplied, and then multiplied by the device's current power factor and the three-phase AC power conversion factor (for line voltage and line current, this factor is taken as...). The system's current basic regulation power is calculated; the instantaneous regulation power increment is superimposed with the system's basic regulation power to obtain the total target power that meets the total regulation power demand of the local area. The construction process of the power-PWM duty cycle mapping function is as follows: Under a standard test environment, with a control step size of 5%, the actual output temperature control power of the corresponding temperature control actuator is tested under different duty cycle commands from 10% to 100%, and multiple sets of power and duty cycle data coordinate points are recorded; the least squares method is used to perform polynomial curve fitting on the multiple sets of data coordinate points to generate a functional relationship with the adjustment power as the independent variable and the duty cycle value as the dependent variable, which is used as the power-PWM duty cycle mapping function; the total target power is input into the power-PWM duty cycle mapping function to calculate the corresponding target continuously variable PWM operating parameters; Obtain the corresponding dwell time window, convert the dwell time window into a timer countdown value, and convert the target continuously variable speed PWM operation parameters into the corresponding hexadecimal duty cycle control code; according to the underlying communication format, sequentially concatenate the start bit, the device address code of the target temperature control actuator, the register write instruction function code, the control instruction area containing the hexadecimal duty cycle control code, the time control area containing the timer countdown value, and the cyclic redundancy check code to generate a binary data stream that can be directly parsed by the underlying programmable logic controller, encapsulate it according to the protocol, and generate the target control signal; By converting the heat exchange rate per unit time into an equivalent value as the instantaneous adjustment power increment, the calculation logic of the underlying controller is simplified and the computing power consumption is reduced. At the same time, the power increment is superimposed with the current basic adjustment power and combined with the power-PWM mapping function to generate corresponding execution parameters and timing information for the temperature control actuator according to the dwell time window, and the underlying controller executes them according to the preset communication rules.

[0029] The actual temperature time series of the controlled grid is collected and compared with the theoretical reference temperature series. The response hysteresis time difference and the local extreme temperature error correction are used to correct the lead time constant and the transient temperature compensation adjustment amount in the next control cycle. The controlled grid is a grid that is mapped from the coordinate space of the target space grid through a preset fluid dynamics mapping matrix and is covered by the physical wind field of the actual temperature control actuator.

[0030] The process of extracting the response hysteresis time difference involves extracting the theoretical temperature change inflection point and the corresponding theoretical timestamp in the theoretical reference temperature sequence that generates temperature regulation in response to the control signal, identifying the corresponding actual temperature change inflection point and actual timestamp in the actual temperature time sequence by calculating the temperature change rate, and calculating the response hysteresis time difference by subtracting the actual timestamp from the theoretical timestamp. The process of extracting the local extreme temperature error involves extracting the theoretical maximum temperature extreme value generated by the dynamic heat load impact during the cargo handover and residence period of the theoretical reference temperature sequence, and simultaneously extracting the actual maximum temperature extreme value of the actual temperature time sequence within the handover and residence time window. The difference between the actual maximum temperature extreme value and the theoretical maximum temperature extreme value is then calculated to obtain the local extreme temperature error.

[0031] Specifically, the initial space temperature of the controlled grid at the beginning of the current control cycle and the basic temperature adjustment input of the local temperature control actuator are obtained, and the first-order thermodynamic hysteresis difference equation of the controlled grid is used: ; Discrete iterative calculations are performed to generate the theoretical reference temperature sequence, which consists of multiple discrete time points and their corresponding theoretical temperature values. For the first The theoretical temperature at each time step. For discrete time steps, The air heat capacity constant of the controlled grid, The input is the equivalent of cooling or heating power. The heat dissipation to the surrounding area is equivalent to the heat. At the same time, a platinum resistance temperature sensor deployed at the geometric center of the three-dimensional space of the controlled grid is used to perform real-time analog-to-digital conversion and record the actual temperature value in the current control cycle at a sampling frequency consistent with the discrete time step, thereby generating the actual temperature time series arranged in chronological order. The theoretical reference temperature sequence is traversed, and the first derivative of the theoretical temperature is calculated by subtracting the difference between two adjacent theoretical temperature values ​​and dividing by the discrete time step. The array index position corresponding to the first time the absolute value of the first derivative of the theoretical temperature exceeds the steady-state fluctuation threshold is found, and the theoretical temperature change inflection point and the corresponding theoretical timestamp are extracted at this position. In the actual temperature time sequence, the discrete temperature change rate is calculated by subtracting the difference between adjacent actual temperature sample data and dividing by the sampling period. The discrete temperature change rate of each discrete time node is compared sequentially, and the actual sampling node whose absolute value of the discrete temperature change rate is first greater than the steady-state fluctuation threshold is selected. The actual sampling node is identified as the corresponding actual temperature change inflection point, and the system clock time corresponding to the node is recorded as the actual timestamp. The difference between the actual timestamp and the theoretical timestamp is used to calculate the response hysteresis time difference. The system extracts the first action time of the underlying controller determining that the forks of the relay equipment begin to extend as the starting point, and the second action time of determining that the forks retract to their original position as the ending point. The time period between the starting point and the ending point is set as the cargo handover and residence period. Based on this, all theoretical temperature discrete values ​​generated by the dynamic thermal load impact during the cargo handover and residence period are extracted from the theoretical reference temperature sequence. The theoretical maximum temperature extreme value is found by using an array sorting algorithm. Simultaneously, the starting point and the ending point are used as array slice boundaries to extract the actual temperature value array within the handover and residence time window of the actual temperature time sequence. The actual temperature value array is traversed and the values ​​are compared to extract the actual maximum temperature extreme value. The difference between the actual maximum temperature extreme value and the theoretical maximum temperature extreme value is used to calculate the local extreme temperature error used to determine the intensity of local thermal shock.

[0032] The response hysteresis time difference is multiplied by a preset time compensation gain coefficient, and then inversely superimposed onto the current lead time constant to calculate the corrected lead time constant for the next control cycle, thereby triggering subsequent pre-adjustment actions in advance. Simultaneously, the local extreme temperature error is multiplied by a preset power compensation ratio coefficient to calculate the power correction compensation amount, which is then superimposed onto the current transient temperature compensation adjustment amount to generate the corrected transient temperature compensation adjustment amount for the next control cycle. The preset time compensation gain coefficient and the preset power compensation ratio coefficient are obtained during the initial debugging phase through step response test calibration using a standard thermal load.

[0033] This invention predicts the dynamic displacement trajectory of mobile equipment in a three-dimensional spatial grid by analyzing handling instructions, and extracts thermal load characteristic parameters by combining the SKU attributes of the goods. This enables feedforward prediction and proactive response to spatial thermal load impacts, overcoming the lag in control signal output in traditional passive feedback systems. Simultaneously, by extracting handover and dwell points and outputting targeted control signals, it suppresses transient thermal disturbances caused by local discrete operations, compensating for local temperature disturbances during goods handover, and adjusting subsequent control parameters based on the deviation between the measured temperature and the reference temperature.

[0034] Example 2: This application applies a cold chain warehousing environment temperature control method to a large-scale fresh food cold chain distribution center. To address the multi-source heterogeneous transient heat load shocks caused by frequent movement of large quantities of goods and the shuttle operation of relay equipment, the method includes the following steps: When high-end imported beef is received into the warehouse, the movement analysis module obtains the handling instructions and the SKU attributes of the batch of beef. These attributes include the type of beef, the weight of a single item (e.g., 20kg), the three-dimensional dimensions of the packaging, the area thermal resistance of the outer packaging material, and the target storage temperature (-18℃). When the mobile device executing the handling instructions is determined to be an AGV forklift, the module generates a ground travel trajectory based on a two-dimensional map of the cold chain storage area and a dynamic obstacle avoidance algorithm. Combining this with the current height and expected lifting height of the beef, the module maps the ground travel trajectory to the first dynamic displacement trajectory in a three-dimensional spatial grid. Simultaneously, the module calculates the total physical heat load and heat transfer rate per unit time based on the initial temperature upon receipt, the external ambient temperature, and the SKU attributes. Based on this, it extracts the heat load characteristic parameters of the mapped system's thermal disturbance variables. During the movement of beef by the AGV forklift, the feedforward control module predicts the timestamp of the beef cargo (thermal disturbance variable) entering the target spatial grid based on the generated first dynamic displacement trajectory. To achieve proactive response to spatial thermal load impacts, the module combines the beef's thermal load characteristic parameters with the advance time constant, and calculates the basic temperature adjustment amount through a decoupled feedforward compensation model. The module outputs a feedforward control signal to the temperature control actuator in the corresponding grid. This feedforward control signal is used for basic environmental pre-adjustment before the mobile device reaches the target spatial grid. By adjusting the initial airflow of the local temperature control actuator and the step opening of the regulating valve, the control medium is introduced in advance, thereby compensating for the dynamic thermal load impact during the cargo's passage. When the AGV forklift arrives at the designated area and needs to transfer the beef goods to the RGV trolley or other relay equipment performing track handling, the targeted compensation module intervenes. It acquires the access path of the relay equipment and extracts the spatially overlapping nodes with the dynamic displacement trajectory as handover and dwell points. To suppress local transient thermal disturbances caused by the automated equipment handover operation, it calculates the transient temperature compensation adjustment amount to suppress local dynamic abrupt changes, based on the expected dwell time and the heat load characteristics of the beef. Then, the module outputs a targeted control signal to the corresponding targeted temperature control actuator, setting its stepless speed-regulating PWM operating parameters. During the handover dwell period, it instantaneously increases the environmental regulation power, thereby avoiding temperature extreme overshoot and heat island effects in the local controlled grid. Throughout the operation of the temperature control system, the closed-loop adaptive module continuously collects the actual temperature time series of the controlled grid through sensors. The collected real temperature data is compared with the theoretical reference temperature series generated by the system's thermodynamic difference equations. Through calculation and comparison, the response hysteresis time difference (i.e., the difference between the actual temperature change inflection point and the theoretical timestamp) and the local extreme temperature error (i.e., the difference between the actual maximum temperature extreme value and the theoretical maximum temperature extreme value within the transition dwell time window) are extracted. Using these real dynamic deviation data, the system lead time constant and transient temperature compensation adjustment in the next control cycle are dynamically corrected, thereby constructing a feedback loop where control parameters are dynamically updated according to the actual operating state. This achieves system self-calibration and avoids persistent control deviations during long-term operation.

[0035] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the temperature of a cold chain storage environment, characterized in that, include: Obtain the handling instructions and the SKU attributes of the goods, parse the handling instructions to generate the dynamic displacement trajectory of the mobile device in the three-dimensional spatial grid, and extract the thermal load characteristic parameters of the mapping system thermal disturbance variables based on the SKU attributes; Based on the dynamic displacement trajectory, the time stamp of the thermal disturbance variable entering the target space grid is predicted. The basic temperature adjustment is calculated by decoupling the thermal load characteristic parameters and the lead time constant through the feedforward compensation model, and the feedforward control signal is output to the temperature control actuator of the corresponding grid. The access path of the relay equipment is obtained, and the spatially overlapping nodes with the dynamic displacement trajectory are extracted as handover and dwelling points. Combining the expected dwelling time with the heat load characteristic parameters, the transient temperature compensation adjustment amount to suppress local dynamic changes is calculated, and the targeted control signal is output. The actual temperature time series of the controlled grid is collected and compared with the theoretical reference temperature series. The lead time constant and the transient temperature compensation adjustment amount in the next control cycle are extracted by the response hysteresis time difference and the local extreme temperature error correction.

2. The method for controlling the temperature of a cold chain storage environment according to claim 1, characterized in that, The mobile equipment includes AGV forklifts for performing ground handling and RGV trolleys for performing rail handling; If the mobile device executing the handling command is determined to be an AGV forklift, a ground driving trajectory is generated based on the two-dimensional map of the cold chain storage area and the dynamic obstacle avoidance algorithm. Combined with the current height of the goods and the expected lifting height, the ground driving trajectory is mapped to the first dynamic displacement trajectory in the three-dimensional spatial grid. If the mobile device executing the handling command is determined to be an RGV vehicle, a fixed track travel trajectory is extracted based on a preset warehouse track topology network, and combined with the fixed elevation parameters of the track where the RGV vehicle is located, the fixed track travel trajectory is mapped to a second dynamic displacement trajectory in the three-dimensional spatial grid.

3. The method for controlling the temperature of a cold chain storage environment according to claim 1, characterized in that, The SKU attributes include goods type information, single item weight, three-dimensional dimensions of goods packaging, area-to-thermal resistance of outer packaging material, and target storage temperature; the initial temperature of goods upon entry into the warehouse and the real-time external ambient temperature are also obtained. Based on the cargo type information, the specific heat capacity coefficient of the corresponding cargo is retrieved from the thermal property database; the temperature difference value is calculated by subtracting the initial temperature upon entry into the warehouse from the target storage temperature; and based on the temperature difference value, the specific heat capacity coefficient, and the mass of a single cargo item, the total physical heat load required for the cargo to reach the target storage temperature is calculated; the effective heat exchange surface area is calculated based on the three-dimensional dimensions of the cargo packaging; the internal and external environmental temperature difference is obtained by subtracting the real-time external ambient temperature from the initial temperature upon entry into the warehouse; and the heat exchange rate between the cargo and the external environment per unit time is calculated based on the internal and external environmental temperature difference, the effective heat exchange surface area, and the area specific thermal resistance of the outer packaging material; the total physical heat load and the heat exchange rate per unit time are combined to generate heat load characteristic parameters.

4. The method for controlling the temperature of a cold chain storage environment according to claim 1, characterized in that, The feedforward control signal is used to perform basic environmental pre-adjustment before the mobile device reaches the target space grid. The basic environmental pre-adjustment process includes mapping the coordinate space of the target space grid to a controlled grid covered by the physical wind field of the actual temperature control actuator through a preset fluid dynamics mapping matrix, and adjusting the initial air supply volume and the step opening degree of the local temperature control actuator covering the controlled grid according to the basic temperature adjustment amount. The targeted control signal performs instantaneous thermal disturbance compensation at the handover and dwelling point. Based on the fluid dynamics mapping matrix, the dwelling space of the handover and dwelling point and the target cargo location is associated with the corresponding targeted temperature control actuator. The stepless speed regulation PWM operation parameters of the corresponding temperature control actuator are set according to the transient temperature compensation adjustment amount, thereby increasing the instantaneous environmental regulation power during the dwelling period.

5. The method for controlling the temperature of a cold chain storage environment according to claim 4, characterized in that, The process of generating the feedforward control signal is as follows: calculate the expected travel time of the cargo in the target space grid based on the dynamic displacement trajectory and the equipment speed; multiply the heat exchange rate per unit time in the heat load characteristic parameters by the expected travel time to obtain the dynamic heat load impact amount and use it as the basic temperature adjustment amount. The slope of the regulation power output required to reach the target regulation power before the expected time stamp is calculated by combining the advance time constant; the proportion of the basic temperature regulation amount dissipated to the surrounding adjacent grids under the action of fluid is calculated by using the fluid dynamics mapping matrix; and the basic temperature regulation amount is spatially allocated and corrected to obtain the target regulation medium delivery amount. Based on the target control medium delivery amount and the adjustment power output slope, the energy efficiency mapping matrix of the local temperature control actuator is invoked to match the step opening degree of the regulating valve and the corresponding fan speed; the fan speed is converted into the initial air volume and encapsulated in the protocol to generate a feedforward control signal.

6. The method for controlling the temperature of a cold chain storage environment according to claim 4, characterized in that, The process of generating the targeted control signal is as follows: extract the heat exchange rate per unit time from the heat load characteristic parameters, multiply it by the expected residence time at the handover point, calculate the total residence heat exchange of the goods during the handover period, and use it as the transient temperature compensation adjustment amount; after performing an equivalent conversion between physical dimensions and thermal work equivalents on the extracted heat exchange rate per unit time, it is used as the instantaneous adjustment power increment required for the local area of ​​the handover point. The instantaneous adjustment power increment is superimposed with the current system base adjustment power of the corresponding temperature control actuator to obtain the total target power, and input into the power-PWM duty cycle mapping function to calculate the target continuously variable PWM operating parameters that meet the total target power requirement; the target continuously variable PWM operating parameters are protocol-encapsulated with the corresponding dwell execution time window to generate a targeted control signal.

7. The method for controlling the temperature of a cold chain storage environment according to claim 1, characterized in that, The process of extracting the response hysteresis time difference involves extracting the theoretical temperature change inflection point and the corresponding theoretical timestamp in the theoretical reference temperature sequence that generates temperature regulation in response to the control signal, identifying the corresponding actual temperature change inflection point and actual timestamp in the actual temperature time sequence by calculating the temperature change rate, and calculating the response hysteresis time difference by subtracting the actual timestamp from the theoretical timestamp. The process of extracting the local extreme temperature error involves extracting the theoretical maximum temperature extreme value generated by the dynamic heat load impact during the cargo handover and residence period of the theoretical reference temperature sequence, and simultaneously extracting the actual maximum temperature extreme value of the actual temperature time sequence within the handover and residence time window. The difference between the actual maximum temperature extreme value and the theoretical maximum temperature extreme value is then calculated to obtain the local extreme temperature error.

8. A temperature control system for cold chain storage environment, characterized in that, include: Movement analysis module: acquires handling instructions and SKU attributes of goods, parses the handling instructions to generate dynamic displacement trajectory of mobile device in three-dimensional space grid, and extracts heat load characteristic parameters of mapping system thermal disturbance variables based on the SKU attributes; Feedforward control module: Based on the dynamic displacement trajectory, predict the timestamp of the thermal disturbance variable entering the target space grid, combine the thermal load characteristic parameters and the lead time constant to calculate the basic temperature adjustment through the feedforward compensation model, and output the feedforward control signal to the temperature control actuator of the corresponding grid; Targeted compensation module: Obtains the access path of the relay equipment, extracts the spatially overlapping nodes with the dynamic displacement trajectory as the handover and dwell points, calculates the transient temperature compensation adjustment amount to suppress local dynamic changes by combining the expected dwell time and the heat load characteristic parameters, and outputs the targeted control signal; Closed-loop adaptive module: Collects the actual temperature time series of the controlled grid, compares it with the theoretical reference temperature series, and extracts the response hysteresis time difference and the local extreme temperature error correction in the next control cycle, as well as the transient temperature compensation adjustment amount.

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