An instruction scheduling method and system for controlling multiple devices in a cold chain warehouse

Abstract

The present invention discloses a command scheduling method and system for controlling multiple devices in a cold chain warehouse, belonging to the field of cold chain warehouse control. The method comprises: collecting environmental data and device status data in the cold chain warehouse, preprocessing the data, and sending it to the cloud; the cloud constructs a device database in chronological order according to the timestamp of the message format, and processes the data in the device database through a cold chain warehouse collaborative control model; sorts all generated task requests according to predefined priorities, generates a task queue for the scheduling core, generates atomic operation instructions for specific devices through a rolling time domain optimization algorithm; sends the operation instruction message to the edge gateway, and sends the native command to the master control of the corresponding device through the communication module of the edge gateway. The present invention realizes intelligent monitoring and scheduling of device status, and can timely adjust and control according to environmental changes.

Description

Technical Field

[0001] The present invention relates to the field of cold chain warehouses, and in particular to an instruction scheduling method and system for controlling multiple devices in a cold chain warehouse. Background Art

[0002] The rapid development of the cold chain logistics industry has placed higher demands on the environmental perception capabilities and operational efficiency of cold chain warehouse management systems. Existing cold chain warehouse management systems have some deficiencies in environmental perception, including poor sensor data fusion and low environmental perception accuracy and reliability. Furthermore, current cold chain warehouse management systems also face significant challenges in equipment scheduling. The system lacks intelligent scheduling solutions and cannot promptly adjust and control the cold chain warehouse's internal environment based on changes. This hinders the provision of a stable temperature environment for goods, easily leading to deterioration and economic losses. Furthermore, existing equipment management systems often rely on manual operation, resulting in cumbersome operational processes and the risk of human error, increasing workload and time costs. Furthermore, inaccurate or missing data can affect the accuracy and reliability of management. Summary of the Invention

[0003] One of the purposes of the present invention is to provide an instruction scheduling method for controlling multiple devices in a cold chain warehouse, so as to solve the problem that the cold chain warehouse management system in the prior art lacks an intelligent scheduling solution in terms of equipment scheduling.

[0004] The present invention is implemented through the following technical solution, a command scheduling method for controlling multiple devices in a cold chain warehouse, comprising the following steps: S100, collecting environmental data in the cold chain warehouse through sensors deployed in the cold chain warehouse, and collecting device status data generated by the operation of the equipment in the cold chain warehouse, and preprocessing the collected data. The edge gateway encapsulates the preprocessed device data into a unified message format and sends it to the cloud; S200, the cloud constructs a device database in chronological order according to the timestamp of the message format, and processes the data in the device database through a cold chain warehouse collaborative control model. The control variables calculated by the cold chain warehouse collaborative control model generate scheduling decisions, and continuously monitor the real-time status of the equipment. When the device data is abnormal or does not meet the expected Set rules to trigger events immediately, and generate corresponding task requests based on the triggered events and combined with scheduling decisions; S300, sort all generated task requests according to predefined priorities, generate a task queue for the scheduling core, and generate atomic operation instructions for specific devices through a rolling time domain optimization algorithm based on the priority, dependency, real-time requirements and current status of each task; S400, encapsulate the atomic operation instructions into operation instruction messages, and send the operation instruction messages to the edge gateway. The edge gateway parses the target device and specific commands. The local executor of the edge gateway converts the general instructions into native commands that can be recognized by the target device according to the device abstract model and driver, and sends the native commands to the main control of the corresponding device through the communication module of the edge gateway.

[0005] Furthermore, the device status data is collected by establishing a connection with the device through the communication module set in the edge gateway, and is actively monitored or periodically polled. The edge gateway parses the collected data into a structured data format and performs preliminary unit conversion or error checking. At the same time, the collected data is preliminarily filtered in the edge gateway to remove obvious outliers or duplicate data, thereby completing the data preprocessing.

[0006] Furthermore, the cold chain warehousing collaborative control model transforms the traditional discrete scheduling problem into a distributed control problem in continuous space through a set of mutually coupled equations; the set of equations describes the continuous changes in temperature, humidity and airflow fields inside the cold chain warehouse, as well as the interaction and evolution process between physical fields, and transforms the scheduling decisions of cold chain warehouse equipment into control items of the physical fields, thereby realizing optimized management of the entire cold chain warehouse environment. The data in the equipment database acts as control input in the set of equations to achieve global instruction optimization of the cold chain warehousing environment.

[0007] Furthermore, a set of equations is constructed based on partial differential equations to obtain: a temperature evolution equation, a humidity evolution equation, and a turbulence-corrected airflow equation. Among them, the temperature evolution equation describes the evolution process of the temperature field in the cold chain warehouse by calculating energy storage, convection, diffusion, source terms, and external disturbances. By capturing the various physical mechanisms of temperature changes in the cold chain warehouse, the equipment scheduling and airflow are coupled to achieve refined temperature control. By controlling the scheduling of equipment, the influence of different equipment is adjusted to optimize the temperature distribution in the cold chain warehouse; the humidity evolution equation is used to describe the evolution of the humidity field inside the cold chain warehouse, and the convection, diffusion of moisture, and the coupled influence of humidification / dehumidification equipment and door opening and closing events are combined as source and sink terms, combined with the airflow field and temperature on the humidity field. By optimizing the scheduling of equipment, the humidification / dehumidification equipment is adjusted to control the humidity changes in the warehouse; the turbulence-corrected airflow equation is constructed by simplifying and correcting the Navier-Stokes equations, and is used to describe the flow of air inside the warehouse. The airflow field and the temperature field are coupled by calculating how the resultant force acting on a unit volume of fluid causes a change in its momentum.

[0008] Furthermore, S300 also includes: by checking the preconditions and dependencies of the generated tasks, the tasks can be scheduled only when the preconditions are met, and avoiding deadlocks through deadlock detection or checking according to preset rules.

[0009] Furthermore, the instruction scheduling method also includes: S500, a feedback step, after the device completes the execution, the execution result, success / failure status, the latest operation data, and the status changes during the execution process are fed back to the edge gateway, and the edge gateway encapsulates the device feedback information into a unified feedback message and sends it to the cloud. The cloud updates the device execution result and the latest status based on the feedback message.

[0010] Furthermore, the temperature evolution equation is expressed as follows:

[0011] ,

[0012] in, is the air density, is the specific heat capacity, is the partial differential symbol, For time, is the airflow velocity field; is the Laplace operator, is the temperature field, represents the temperature gradient; is the thermal diffusivity, The fan heat consumption is Indicates the heat consumption of the fan. As the cooling source, represents the cooling source function, Heat source for logistics robots, represents the heat source function of the logistics robot, is the thermal conductivity coefficient, is the external temperature.

[0013] Furthermore, the humidity evolution equation is expressed as follows:

[0014] ,

[0015] in, is the humidity field, is the humidity diffusion coefficient, For humidification / dehumidification, represents the humidification / dehumidification function; is the humidity exchange rate coefficient, is the saturated humidity, It means that saturated humidity is a function of temperature T. The higher the temperature, the more water vapor the air can carry, and the greater the saturated humidity. For the opening and closing event of the cold storage door, Represents the opening and closing event function of the cold storage door. When the door is opened , when closed .

[0016] Furthermore, the turbulence-corrected airflow equation is expressed as follows:

[0017] ,in, is the kinematic viscosity of the airflow, is the gravitational acceleration vector, is the coefficient of thermal expansion, is the reference temperature, which is used to define the reference point of buoyancy. hour, The direction is opposite to the gravitational acceleration vector; when hour, The direction is the same as the gravitational acceleration vector.

[0018] On the other hand, the present invention provides an instruction scheduling system for controlling multiple devices in a cold chain warehouse, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the instruction scheduling method for controlling multiple devices in a cold chain warehouse as described above.

[0019] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0020] 1. The present invention realizes real-time monitoring of the cold chain warehouse environment and equipment status by deploying multiple sensors and equipment in the cold chain warehouse and using edge gateways and the cloud for data processing and scheduling decisions. It overcomes the problem of the existing system's lack of real-time monitoring and display functions and improves the accuracy and timeliness of warehouse status monitoring.

[0021] 2. The present invention uses a cold chain warehousing collaborative control model to intelligently process and analyze the data in the equipment database, realize intelligent monitoring and scheduling of equipment status, and can make timely adjustments according to environmental changes, thereby ensuring the stability of the cargo storage environment, effectively preventing cargo deterioration, and improving the safety of cold chain logistics.

[0022] 3. The present invention uses automated scheduling decisions and atomic operation instruction execution, and introduces a rolling time domain optimization algorithm to perform intelligent scheduling based on factors such as device status, task priority, and real-time requirements, thereby achieving efficient resource utilization and a priority list of tasks, improving the overall operating efficiency of the system, reducing manual intervention, avoiding the occurrence of human errors, improving operational accuracy and efficiency, and reducing labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0024] Figure 1 This is a flow chart of the method provided in Example 1 of the present invention.

[0025] Figure 2 This is a timing diagram of the method provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0027] Example 1

[0028] This embodiment discloses a method for dispatching instructions for controlling multiple devices in a cold chain warehouse. Figure 1 1 shows a flow chart of the method in this embodiment, Figure 2 The method timing diagram in this embodiment is shown. Figure 1 It can be seen that this embodiment includes the following steps:

[0029] Step 1: Collect equipment data in the cold chain warehouse and preprocess the collected data.

[0030] Environmental data is obtained by periodically measuring the environment in the cold chain warehouse through various sensors (temperature, humidity, air pressure, etc.) deployed in the cold chain warehouse.

[0031] It also collects the current status reports of equipment (refrigeration units, fans, AGVs / AMRs, lighting, etc.) in the cold chain warehouse, including the equipment's own operating status data (such as operating mode, fault codes, location information, power level, etc.). For example, the location, status, and task progress reported by mobile devices such as AGVs / AMRs, or alarm / status signals reported by fire protection and security equipment.

[0032] The edge gateway establishes a connection with the device through the communication module installed in the edge gateway, and actively monitors or periodically polls the device data. The edge gateway parses the received raw binary or formatted data into a structured, processable data format and performs preliminary unit conversion and error checking. The edge gateway also performs preliminary filtering on the collected data to remove obvious outliers or duplicate data, reducing invalid data transmission.

[0033] Finally, the edge gateway encapsulates the standardized device data into a unified message format (including device ID, data type, timestamp, specific data value, etc.) and sends the message to the cloud.

[0034] Step 2: The cloud receives the message in a unified format and constructs a device database in chronological order based on the timestamps. The data in the device database is then processed using the cold chain warehouse collaborative control model to monitor the status of the equipment in the cold chain warehouse. Scheduling decisions are then made based on the control variables calculated by the cold chain warehouse collaborative control model. Furthermore, by continuously monitoring the real-time status of the equipment, an event is triggered if any anomalies in the device data are detected or if the data does not conform to the preset rules. Based on the triggered event and the scheduling decision, a corresponding task request is generated.

[0035] In this embodiment, the core of the cold chain warehouse collaborative control model is to transform the traditional discrete scheduling problem into a distributed control problem in a continuous space through the framework of partial differential equations. A set of partial differential equations is used to describe the continuous changes in the temperature, humidity and airflow fields inside the warehouse, as well as the complex interactions and evolution processes between these physical fields. At the same time, the scheduling decisions of the storage equipment are transformed into control items of these physical fields, thereby achieving optimal management of the entire storage environment. By treating the space of the cold chain warehouse as a continuous physical space, the temperature field is defined. , humidity field , air flow velocity field The evolution of these equations is described by a set of partial differential equations. In this set of partial differential equations, the changes in each physical field are affected by control items (such as refrigeration units, fans, humidifiers and other equipment). The data in the equipment database acts as control input in these equations to achieve global instruction optimization of the cold chain storage environment.

[0036] Specifically, the partial differential equations in the cold chain warehousing collaborative control model include: temperature evolution equation, humidity evolution equation and turbulence corrected airflow equation, among which,

[0037] The temperature evolution equation can be expressed as follows:

[0038]

[0039] ,

[0040] in, is the air density, which represents the air mass per unit volume, Specific heat capacity, which represents the amount of heat required to raise the temperature of a unit mass of air by one degree. is the partial differential symbol, For time, is the air flow velocity field, indicating the speed of air flow; is the Laplace operator, is the temperature field, represents the temperature gradient, which represents the rate of change of temperature in space; is the thermal diffusivity, which indicates the ability of heat to diffuse in the air. A high diffusivity means that heat can spread quickly in space. The fan heat consumption is Indicates the heat consumption power of the fan, which is used to describe the heat consumption effect brought by the airflow through the fan. When the fan is running, it not only pushes air into the warehouse, but also may generate heat due to mechanical losses, thereby affecting the temperature of the warehouse. The heat consumption power generated by the fan is usually related to the operating status of the fan (such as wind speed, fan power, etc.). As the cooling source, It represents the cooling source function, which indicates the influence of the cooling source on the temperature field. The cooling source generates cooling through the operation of the refrigeration unit and controls the temperature distribution in the warehouse. Heat source for logistics robots, Represents the heat source function of the logistics robot, which is used to describe the heat generated by the logistics robot (AGV / AMR). The movement of the logistics robot consumes energy and generates heat, which affects the temperature field in the warehouse. is the thermal conductivity coefficient, which indicates the ability of heat transfer between the wall of the cold chain warehouse and the external environment; The external temperature indicates the temperature of the environment outside the warehouse.

[0041] In this embodiment, the cooling source function can be expressed by the following formula:

[0042] ,

[0043] in, is the coefficient related to the refrigeration capacity of the refrigerator, is the refrigerator power output, Represents the power output of the refrigerator at time t, which is used to control the cooling intensity of the refrigerator. is the Dirac function, Indicates that the refrigerator acts on a specific location , i is the refrigerator.

[0044] In this embodiment, the heat source function of the logistics robot can be expressed by the following formula:

[0045] ,

[0046] in, is the speed coefficient of the logistics robot, is the speed of the logistics robot, j is the logistics robot, is the local kernel function, is the position of logistics robot j at time t, It represents the distribution of heat sources in space. The characteristic of the heat source of a logistics robot is that it changes with movement. When the logistics robot moves in the warehouse, it generates heat, affecting the temperature of the surrounding area.

[0047] It should be noted that Indicates the rate of change of temperature over time, representing the change in thermal energy storage in the warehouse. Represents the convection term, which is used to describe the convection effect of airflow on the temperature field. The airflow generated by the fan transfers the temperature from one location to another by pushing the air flow. In cold chain warehousing, the influence of fans and airflow on temperature distribution is crucial. Airflow can remove heat or bring in cold air throughout the warehouse. The heat diffusion term represents the diffusion effect of heat, describing the natural expansion or propagation of heat in space and reflecting the natural flow of heat from high-temperature areas to low-temperature areas. This equation describes the evolution of the temperature field within the warehouse through a series of physical terms (energy storage, convection, diffusion, source terms, external disturbances, etc.). It accurately captures the various physical mechanisms of temperature variation in cold chain warehouses and, through its source and convection terms, tightly couples equipment scheduling and airflow dynamics, laying the foundation for refined temperature control. Furthermore, by controlling the scheduling of equipment such as chillers, fans, and automated guided vehicles (AGVs), the impact of these terms can be adjusted to optimize the temperature distribution within the warehouse, thereby maintaining the ideal temperature range.

[0048] The humidity evolution equation can be expressed as follows:

[0049]

[0050] ,

[0051] in, is the humidity field, which represents the humidity distribution in the warehouse; is the humidity diffusion coefficient, which indicates the diffusion ability of moisture in space; For humidification / dehumidification, Represents the humidification / dehumidification function, which is used to describe the effect of a humidifier or dehumidifier on adding or removing moisture from the air and is linked to the scheduling decision of the humidifier / dehumidifier; is the humidity exchange rate coefficient, which indicates the rate of humidity exchange between the outside air and the warehouse. is the saturated humidity, It means that saturated humidity is a function of temperature T. The higher the temperature, the more water vapor the air can carry, so the saturated humidity is also greater. For the opening and closing event of the cold storage door, Represents the opening and closing event function of the cold storage door. When the door is opened , when closed ,This function is closely related to the scheduling of logistics robots, ,because the driving route of logistics robots may involve the ,opening and closing of cold storage, affecting the air exchange ,process.

[0052] In this embodiment, the humidification / dehumidification function can be expressed by the following formula:

[0053] ,

[0054] in, is the efficiency coefficient of the humidifier / dehumidifier; is the humidification / dehumidification capacity (the mass of moisture added / removed per unit time). For a humidifier, this is a positive value; for a dehumidifier, this is a negative value. Indicates humidifier / dehumidifier At the moment Humidification / dehumidification capacity; For humidifier / dehumidifier Position in space; is the Dirac function of the humidifier / dehumidifier, indicating the humidifier / dehumidifier Influences the humidity field at its physical location.

[0055] It's important to note that this equation describes the evolution of the humidity field within a cold chain warehouse, accounting for moisture convection and diffusion, as well as source and sink terms caused by humidification / dehumidification equipment and door opening and closing events. The influence of airflow and temperature on the humidity field is a key coupling point. Furthermore, the operation of the humidification / dehumidification equipment and the door opening events caused by logistics robots directly influence the dynamics of the humidity field as control variables. This makes the entire model a highly interconnected system. By optimizing these factors, humidity fluctuations within the warehouse can be effectively controlled.

[0056] The turbulence-corrected airflow equation can be expressed as follows:

[0057] ,

[0058] in, is the kinematic viscosity of the airflow, is the gravitational acceleration vector, is the coefficient of thermal expansion, is the reference temperature, which is used to define the reference point of buoyancy. hour, The direction is opposite to the gravity acceleration vector (upward, causing buoyancy); when hour, The direction is the same as the gravity acceleration vector (downward, causing sinking).

[0059] It should be noted that this equation describes the flow of air inside the warehouse. The flow of air is very important for maintaining the temperature of the cold chain warehouse, and it is also the key medium for connecting the temperature field and the humidity field through the convection term. The equation shown in this embodiment is a simplification and correction based on the Navier-Stokes equation. This equation describes how the resultant force acting on a unit volume of fluid causes a change in its momentum, taking into account the inertia, pressure gradient force, viscous force and buoyancy of the fluid. At the same time, since the airflow in the warehouse is usually turbulent, directly solving the original Navier-Stokes equation is computationally intensive. Therefore, turbulence correction and simplification are used in this embodiment. Specifically, the left side of the equation Represents the inertial response of air flow, including changes over time and space; Represents the pressure gradient term, which is used to describe the fluid being pushed by the pressure difference, and the air flows from high pressure to low pressure; represents the viscous diffusion term, which is used to describe the viscous dissipation or momentum diffusion term and characterizes the smoothing effect of the internal friction of the fluid on the velocity field; The buoyancy term, derived through the Boussinesq approximation, describes the effect of buoyancy on airflow due to temperature differences. This is the core coupling point between the airflow and temperature fields. Under the Boussinesq approximation, changes in fluid density are considered only in the gravity term, while the density in other terms is treated as a constant. This equation tightly couples the buoyancy term with the temperature field, optimizing fan scheduling decisions by obtaining the velocity field throughout the warehouse. The velocity field also serves as the core input into the convection terms of the temperature and humidity field equations, resulting in a highly coupled and dynamic model.

[0060] As can be seen from the above, in the cold chain warehousing collaborative control model, there is a close coupling relationship between control variables and equipment scheduling. The control variables in the model determine the operating status of the equipment, while equipment scheduling affects the changes in these control variables, providing strong support for the generation of scheduling decisions.

[0061] Step 3: All generated task requests enter the scheduling core's task queue and are sorted according to predefined priorities. For each task, its preconditions and dependencies are checked. The task is scheduled only if the preconditions are met. (For example, a task to lower the temperature of a refrigerator requires ensuring the refrigerator is operating normally. Or an AGV / AMR movement task requires confirming that the path is unobstructed and the target cold storage door is open.) Deadlocks are also avoided through simplified deadlock detection or checks based on pre-defined rules.

[0062] Finally, based on the priority, dependency, real-time requirements of each task and the current status of the device, atomic operation instructions for specific devices are generated through the rolling time domain optimization algorithm to obtain an accurate, executable, atomic device control instruction sequence.

[0063] Step 4: Encapsulate the atomic operation instruction into an operation instruction message (including instruction ID, target device ID, command type, parameters, priority, timeout, etc.), and send the operation instruction message to the edge gateway.

[0064] After receiving the operation instruction message, the edge gateway parses the target device and specific commands. The local executor module of the edge gateway converts the general instructions into native commands that can be recognized by the target device based on the device abstract model and driver, and sends the native commands to the main control center of the corresponding device through the communication module of the edge gateway.

[0065] Step 5: After the device completes execution, it sends feedback to the edge gateway, including the execution result, success / failure status, latest operating data, and status changes during execution. The edge gateway encapsulates the device feedback information into a unified feedback message and sends it to the cloud. The cloud updates the device execution result and latest status based on the feedback message, thus forming a closed loop.

[0066] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for dispatching instructions to control multiple devices in a cold chain warehouse, characterized in that: The instruction scheduling method includes: S100: Sensors deployed in the cold chain warehouse collect environmental data from the cold chain warehouse and device status data generated by the equipment in the cold chain warehouse. The collected data is pre-processed and the edge gateway encapsulates the pre-processed device data into a unified message format and sends it to the cloud. S200, the cloud builds a device database in chronological order according to the timestamp of the message format, and processes the data in the device database through the cold chain warehouse collaborative control model. The control variables calculated by the cold chain warehouse collaborative control model generate scheduling decisions and continuously monitor the real-time status of the equipment. When the equipment data is abnormal or does not meet the preset rules, an event is immediately triggered. Based on the triggered event, the corresponding task request is generated in combination with the scheduling decision; S300, sorting all generated task requests according to predefined priorities, generating a task queue for the scheduling core, and generating atomic operation instructions for specific devices using a rolling time domain optimization algorithm based on the priority, dependency, real-time requirements, and current status of each task; S400: Encapsulate the atomic operation instruction into an operation instruction message, and send the operation instruction message to the edge gateway. The edge gateway parses the target device and specific commands. The local executor of the edge gateway converts the general instructions into native commands that the target device can recognize based on the device abstract model and driver. The native commands are then sent to the main control of the corresponding device through the communication module of the edge gateway. The cold chain warehousing collaborative control model transforms the traditional discrete scheduling problem into a distributed control problem in continuous space through a set of coupled equations. The continuous changes of temperature, humidity and airflow fields inside the cold chain warehouse, as well as the interaction and evolution process between physical fields are described by a set of equations. Transform the scheduling decisions of cold chain warehouse equipment into control items of the physical field to achieve optimized management of the entire cold chain warehouse environment. The data in the equipment database acts as the control input in the equation group to achieve global instruction optimization of the cold chain storage environment. The equation group is constructed based on partial differential equations and includes: temperature evolution equation, humidity evolution equation and turbulence-corrected airflow equation, wherein, The temperature evolution equation describes the evolution of the temperature field in a cold chain warehouse by calculating energy storage, convection, diffusion, source terms, and external disturbances. By capturing the various physical mechanisms of temperature change in a cold chain warehouse, it couples equipment scheduling with airflow to achieve refined temperature control. By controlling equipment scheduling and adjusting the impact of different equipment, the temperature distribution within the cold chain warehouse is optimized. The humidity evolution equation describes the evolution of humidity fields within cold chain warehouses. It considers moisture convection and diffusion, as well as events caused by humidification / dehumidification equipment and door opening and closing as source and sink terms, and combines the coupled effects of airflow and temperature on the humidity field. By optimizing equipment scheduling and adjusting humidification / dehumidification equipment, humidity changes within the warehouse are controlled. The turbulence-corrected airflow equation is constructed by simplifying and modifying the Navier-Stokes equations. It is used to describe the flow of air inside the warehouse. By calculating how the resultant force acting on a unit volume of fluid causes a change in its momentum, the airflow field is coupled with the temperature field.

2. The instruction scheduling method for controlling multiple devices in a cold chain warehouse according to claim 1 is characterized in that: The device status data is collected by establishing a connection with the device through the communication module set in the edge gateway, and is actively monitored or periodically polled. The edge gateway parses the collected data into a structured data format and performs preliminary unit conversion or error checking. At the same time, the collected data is preliminarily filtered in the edge gateway to remove obvious outliers or duplicate data, thereby completing the preprocessing of the data.

3. The instruction scheduling method for controlling multiple devices in a cold chain warehouse according to claim 1 is characterized in that: The S300 further includes: checking the preconditions and dependencies of the generated tasks, and scheduling the tasks only when the preconditions are met, and avoiding deadlocks by deadlock detection or checking according to preset rules.

4. The instruction scheduling method for controlling multiple devices in a cold chain warehouse according to claim 1 is characterized in that: The instruction scheduling method further includes: S500, a feedback step, wherein after the device completes the execution, the execution result, success / failure status, the latest operation data, and the status change during the execution are fed back to the edge gateway. The edge gateway encapsulates the device feedback information into a unified feedback message and sends it to the cloud. The cloud updates the device execution results and latest status based on the feedback message.

5. The instruction scheduling method for controlling multiple devices in a cold chain warehouse according to claim 1 is characterized in that: The temperature evolution equation is expressed by the following formula: ， in, is the air density, is the specific heat capacity, is the partial differential symbol, For time, is the airflow velocity field; is the Nabla operator symbol, is the temperature field, represents the temperature gradient; is the thermal diffusivity, Heat consumption of the fan, Indicates the heat consumption of the fan. As the cooling source, represents the cooling source function, Heat source for logistics robots, represents the heat source function of the logistics robot, is the thermal conductivity coefficient, is the external temperature.

6. The instruction scheduling method for controlling multiple devices in a cold chain warehouse according to claim 1 is characterized in that: The humidity evolution equation is expressed by the following formula: ， in, is the humidity field, is the humidity diffusion coefficient, For humidification / dehumidification, represents the humidification / dehumidification function; is the humidity exchange rate coefficient, is the saturated humidity, It means that saturated humidity is a function of temperature T. The higher the temperature, the more water vapor the air can carry, and the greater the saturated humidity. For the opening and closing event of the cold storage door, Represents the opening and closing event function of the cold storage door. When the door is opened , when closed ; For time, is the air flow velocity field, is the humidity gradient.

7. The instruction scheduling method for controlling multiple devices in a cold chain warehouse according to claim 1 is characterized in that: The turbulence-corrected airflow equation is expressed by the following formula: ， in, is the kinematic viscosity of the airflow, is the gravitational acceleration vector, is the coefficient of thermal expansion, is the reference temperature, which is used to define the reference point of buoyancy. hour, The direction is opposite to the gravitational acceleration vector; when hour, The direction is the same as the gravitational acceleration vector; For time, is the air flow velocity field, is the convective acceleration of the airflow velocity field, is the air density, represents the pressure gradient, Represents the divergence of the airflow velocity gradient.

8. A command dispatching system for controlling multiple devices in a cold chain warehouse, characterized in that: The instruction scheduling system includes: processor; A memory storing a computer program, which, when executed by a processor, implements the instruction scheduling method for controlling multiple devices in a cold chain warehouse as described in any one of claims 1 to 7.

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