A composite material intelligent warehouse liquid cooling constant temperature and WMS linkage system and method

Abstract

The application discloses a composite material intelligent warehouse liquid cooling constant temperature and WMS linkage system and method, which comprises WMS operation plan version acquisition, material temperature control image query, partition liquid cooling circuit state acquisition, local thermal disturbance prediction, liquid cooling pre-control advance quantity calculation and parameter adjustment, closed loop correction during operation execution, temperature control load rate calculation and reverse scheduling, adjusted operation plan receiving and parameter updating and temperature abnormality correlation record; the application predicts local thermal disturbance and implements liquid cooling pre-control before operation by reading the WMS operation plan version, converts the traditional feedback type temperature control into feedforward type temperature control, effectively solves the problem of temperature control lag, significantly reduces the temperature peak value caused by operation, and avoids performance degradation of the composite material due to excessive temperature fluctuation.

Description

Technical Field

[0001] This invention relates to the fields of intelligent warehousing of composite materials, feedforward control of warehouse management system operation plan, zoned liquid cooling and constant temperature, material temperature control profiling, and temperature control load reverse scheduling, specifically a liquid cooling and constant temperature system and method for intelligent warehouse of composite materials linked with WMS. Background Technology

[0002] Composite materials, such as carbon fiber prepregs, films, resin-based composites, and honeycomb cores, are widely used in high-end manufacturing fields such as aerospace, automotive manufacturing, wind power, and rail transportation due to their excellent mechanical properties, lightweight characteristics, and corrosion resistance. However, these composite materials are extremely sensitive to temperature fluctuations and exposure time in the storage environment. Excessively high temperatures or large temperature fluctuations may cause premature curing of the resin matrix, decreased film adhesion, shortened shelf life of prepregs, and even irreversible degradation of material properties, seriously affecting the quality and reliability of the final product.

[0003] Existing temperature-controlled warehouses or cold storage facilities mostly employ traditional feedback control methods, which adjust based on real-time temperature data from temperature sensors. This control method typically activates the refrigeration or heating system only after the temperature has deviated from the set value, exhibiting significant lag. When warehouse doors are opened, equipment enters, or large-scale material handling occurs, substantial localized thermal disturbances are generated. Feedback control cannot anticipate these thermal disturbances and take preventative measures, leading to significant temperature peaks in the work area that exceed the permissible temperature fluctuation range for composite materials.

[0004] With the development of intelligent warehousing technology, modern warehouse management systems (WMS) have achieved refined management and automated scheduling of work plans. WMS systems can pre-determine detailed work plans for inbound / outbound operations, transfers, and inventory counts, specifying work times, work areas, materials involved, and equipment. However, existing temperature control systems are typically independent of the WMS system. The temperature control system only receives temperature alarm signals and does not read the WMS work plan version in advance, making it impossible to use the information in the work plan to predict and pre-control impending thermal disturbances.

[0005] Furthermore, different types and batches of composite materials have different temperature control requirements. For example, carbon fiber prepregs typically need to be stored in a low-temperature environment below -18°C with minimal allowable temperature fluctuations; while adhesive films may require storage in a constant-temperature environment of around 5°C, with a relatively low heat sensitivity coefficient. Existing composite material storage typically employs a uniform temperature control strategy, failing to establish personalized temperature control profiles for different material types and batches. This makes it impossible to implement differentiated control based on the material's heat sensitivity characteristics, resulting in energy waste or inadequate temperature control.

[0006] When the temperature control system is overloaded and unable to effectively control the temperature, existing systems typically only issue alarm signals and cannot proactively suggest adjustments to the work schedule to the WMS system. This leads to situations where, in extreme cases, even when the temperature control system is already overloaded, the WMS system continues to execute the work according to the original plan, further exacerbating the risk of temperature runaway. Furthermore, when temperature anomalies occur, existing systems usually only record the temperature curve, without correlating the anomalies with the work plan version, material batch, and liquid cooling circuit parameters. This makes it difficult to accurately determine the cause of the anomaly and provides no data support for optimizing subsequent temperature control strategies. Summary of the Invention

[0007] The purpose of this invention is to provide a liquid cooling and constant temperature control system and method for intelligent warehouses of composite materials, and to solve the problems existing in the prior art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for linking liquid cooling and temperature control with a WMS in a composite material intelligent warehouse, comprising: S1. Obtain the operation plan version of the composite material intelligent warehouse from the warehouse management system (WMS). The operation plan version includes storage area identifier, material batch, composite material type, operation type, estimated door opening time, operation equipment, planned start time, operation priority, and plan version number. The composite material type includes at least one of carbon fiber prepreg, film, resin-based composite material, and honeycomb core material. S2. Based on the composite material type and batch, query the material temperature control profile to determine the target temperature, allowable temperature fluctuation range, thermal sensitivity coefficient, maximum exposure time, and historical temperature response parameters of the corresponding storage area. S3. Obtain the status of the partitioned liquid cooling circuit of the corresponding storage area. The partitioned liquid cooling circuit includes liquid cooling plate or cooling coil, partition valve, variable frequency pump, heat exchange unit and temperature sensor. The status of the partitioned liquid cooling circuit includes liquid supply temperature, liquid cooling flow rate, valve opening degree, pump frequency, cooling margin and liquid cooling system response time. S4. Based on the work plan version, material temperature control profile, external ambient temperature, and historical temperature response parameters, predict local thermal disturbances, wherein the local thermal disturbances satisfy equation (1): in, The predicted local thermal disturbance value, For the estimated opening time, For the heat load of the operating equipment, This is the difference between the ambient temperature and the target temperature. The thermal sensitivity coefficient of the material. This represents the current cooling headroom; S5. Determine the liquid cooling pre-control advance amount based on local thermal disturbance, allowable temperature fluctuation range and liquid cooling system response time. The liquid cooling pre-control advance amount satisfies equation (2): In equation (2), This is for the lead time of liquid cooling pre-control. For the response time of the liquid cooling system, This is a preset proportional coefficient. The predicted local thermal disturbance value, Allowable temperature fluctuation range; And adjust the partitioned liquid cooling loop parameters of the corresponding storage area at the time corresponding to the liquid cooling pre-control advance amount before the planned start time, wherein the adjustment of the partitioned liquid cooling loop parameters satisfies equation (3): in, For the first Adjusted liquid cooling flow rate for each zone For the first Initial liquid cooling flow rate for each zone, For the first Minimum allowable liquid cooling flow rate for each zone, For the first The maximum permissible liquid cooling flow rate for each zone; S6. During the operation, collect the real-time temperature of the corresponding storage area, and perform closed-loop correction of the partition liquid cooling loop parameters based on the target temperature, the allowable temperature fluctuation range and the real-time temperature. S7. When the temperature control load rate of the corresponding storage area exceeds the load threshold or the predicted temperature peak exceeds the allowable temperature fluctuation range, a job adjustment suggestion is sent to the WMS, wherein the temperature control load rate satisfies equation (4): in, For the first The frequency of the variable frequency pump in each zone, For the first The valve opening degree of each zone, For the first The supply and return liquid temperature difference of each zone For the first Cooling capacity of each zone; S8. Receive the adjusted work plan version number returned by WMS, re-predict local thermal disturbances based on the adjusted work plan version number and update the partition liquid cooling circuit parameters. S9. When a temperature anomaly occurs, the temperature anomaly time window is associated with the recent work plan version, material batch, and zone liquid cooling circuit parameters to generate a temperature anomaly association record. The association score of the temperature anomaly association record satisfies equation (5): In equation (5), The correlation score between temperature anomaly event e and the z-th partition is given. , , For preset weighting coefficients, This represents the degree of overlap between the temperature anomaly time window and the z-th partition operation time window. This represents the degree of matching between the material batch involved in the temperature anomaly event and the material batch stored in the z-th partition. This represents the degree of change in the parameters of the liquid cooling circuit in the z-th zone before and after the temperature anomaly occurred.

[0009] Furthermore, the material temperature control profile also includes the material's entry time, cumulative exposure time, recommended storage area, and temperature control priority.

[0010] Furthermore, the adjustment of the parameters of the partitioned liquid cooling circuit includes at least one of reducing the liquid supply temperature, increasing the liquid cooling flow rate, increasing the valve opening, or increasing the pump frequency.

[0011] Furthermore, the proposed job adjustment includes postponing low-priority jobs, merging job windows, shifting job areas, shortening door opening time, replacing job equipment, estimating temperature risk level, estimating recovery time, suggesting alternative storage areas, and suggesting adjusted job start times.

[0012] Furthermore, the temperature anomaly association record includes the anomaly start time, anomaly end time, peak temperature, material batch, work plan version number, adjusted work plan version number, partitioned liquid cooling circuit parameters, and associated work type.

[0013] Furthermore, the types of operations include receiving, issuing, transferring, inventory counting, and re-inspection.

[0014] Furthermore, the operating equipment includes forklifts, AGVs, robotic arms, and manual handling equipment.

[0015] A liquid-cooled temperature control and WMS linkage system for intelligent warehouses of composite materials includes: The work plan acquisition module is used to obtain the work plan version of the composite material smart warehouse from the warehouse management system (WMS). The work plan version includes storage area identifier, material batch, composite material type, work type, estimated door opening time, work equipment, planned start time, work priority, and plan version number. The temperature control profile query module is used to query the temperature control profile of the material based on the composite material type and material batch, and determine the target temperature, allowable temperature fluctuation range, heat sensitivity coefficient, maximum exposure time and historical temperature response parameters of the corresponding storage area; The liquid cooling status acquisition module includes a liquid cooling plate or cooling coil, a partition valve, a variable frequency pump, a heat exchange unit, and a temperature sensor. The liquid cooling status acquisition module is used to acquire the status of the partition liquid cooling circuit in the corresponding storage area. The status of the partition liquid cooling circuit includes the liquid supply temperature, liquid cooling flow rate, valve opening degree, pump frequency, cooling margin, and liquid cooling system response time. The thermal disturbance prediction module is used to predict local thermal disturbances based on the work plan version, material temperature control profile, external ambient temperature, and historical temperature response parameters. The pre-control calculation module is used to determine the liquid cooling pre-control lead based on local thermal disturbance, allowable temperature fluctuation range and liquid cooling system response time, and adjust the partition liquid cooling loop parameters of the corresponding storage area at the time corresponding to the liquid cooling pre-control lead before the planned start time. The closed-loop correction module is used to collect the real-time temperature of the corresponding storage area during operation and perform closed-loop correction on the parameters of the partition liquid cooling circuit based on the target temperature, the allowable temperature fluctuation range and the real-time temperature. The reverse scheduling module is used to send job adjustment suggestions to WMS when the temperature control load rate of the corresponding storage area exceeds the load threshold or the predicted temperature peak exceeds the allowable temperature fluctuation range. The planning update module is used to receive the adjusted work plan version number returned by WMS, re-predict local thermal disturbances and update the partition liquid cooling loop parameters based on the adjusted work plan version number; The anomaly association module is used to associate the temperature anomaly time window with the recent work plan version, material batch and partition liquid cooling circuit parameters when a temperature anomaly occurs, and generate a temperature anomaly association record.

[0016] An electronic device includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the method.

[0017] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method.

[0018] Compared with the prior art, the beneficial effects of the present invention are: This invention reads the WMS work plan version, predicts local thermal disturbances and implements liquid cooling pre-control before the start of the operation, transforming the traditional feedback temperature control into a feedforward temperature control, effectively solving the problem of temperature control lag, significantly reducing the temperature peak caused by the operation, and avoiding performance degradation of composite materials due to excessive temperature fluctuations.

[0019] This invention establishes a material temperature control profile and adopts differentiated temperature control strategies for different composite material types and batches. It can precisely adjust liquid cooling parameters according to the thermal sensitivity of the material, thereby improving energy utilization efficiency and avoiding unnecessary energy waste while ensuring the quality of material storage.

[0020] This invention achieves bidirectional closed-loop linkage between the WMS and the liquid cooling control system through a temperature control load reverse scheduling and adjusted plan version feedback mechanism. When the temperature control system load is too high, it can proactively propose operation adjustment suggestions to the WMS. After the WMS adjusts the operation plan, the temperature control system can promptly re-predict and control according to the new plan, forming a complete closed-loop control loop and improving the system's stability and reliability.

[0021] This invention uses a temperature anomaly association recording function to link temperature anomalies with work plan versions, material batches, and liquid cooling circuit parameters. This allows for accurate tracing of the causes of temperature anomalies, providing data support for subsequent temperature control strategy optimization and work process improvement, and helping to continuously improve the temperature control management level of the warehouse. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the overall process of the present invention; Figure 3 This is a schematic diagram of the system structure of the WMS, temperature controller, partitioned liquid cooling circuit and sensor of the present invention; Figure 4 This is a timing diagram illustrating the work plan feedforward prediction and liquid cooling pre-control of the present invention; Figure 5 This is a schematic diagram of the data field structure of the correlation analysis record between temperature anomalies and recent operations in this invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] The executing entities of this invention include, but are not limited to, temperature controllers, WMS system servers, zoned liquid cooling loop controllers, temperature sensor networks, and material master data servers in intelligent warehouses for composite materials, or collaborative combinations of the above devices. The entire execution process is based on software algorithms and process control, and does not involve limitations on specific hardware configurations.

[0025] Please see Figure 1-5 This invention provides a method for linking liquid cooling temperature control and WMS in a smart warehouse for composite materials, comprising: Step 1: Obtain the WMS job plan version This step is the starting point of the entire feedforward control process. It obtains the latest work plan version from the WMS system and establishes a data interface between the temperature control system and the WMS system.

[0026] Specific execution details: The temperature control system establishes a communication connection with the WMS system through a standardized application programming interface (API). This interface supports real-time data transmission and version control, ensuring that the temperature control system can obtain updated work plans from the WMS system in a timely manner.

[0027] The WMS system proactively pushes the latest work plan version to the temperature control system at preset time intervals (e.g., every minute) or when the work plan changes. Each work plan version has a unique version number to identify its uniqueness and timeliness. When the WMS system adjusts the work plan, it generates a new version number and pushes the new version to the temperature control system.

[0028] The temperature control system receives job plan versions pushed by the WMS system, and parses and stores these job plan versions. The job plan version includes the following fields: plan_version_id: Job plan version number, a new version is generated after each WMS adjustment; zone_id: Storage zone identifier, used to specify which storage zone the job will be executed in; batch_id: Material batch, used to identify the batch of composite materials involved in the operation; material_type: Composite material type, including carbon fiber prepreg, film, resin-based composite material, honeycomb core material, etc.; work_type: Job type, including inbound, outbound, transfer, inventory count, re-inspection, etc.; door_time: Estimated door opening time, in minutes; device_type: Operating equipment, including forklifts, AGVs, robotic arms, manual handling equipment, etc.; start_time: The planned start time, used to calculate the pre-control start time; Priority: Task priority is used to determine whether a task can be postponed or adjusted.

[0029] The temperature control system verifies the validity of received work plan versions, checking for complete fields, correct data format, and reasonable start times. For invalid work plan versions, the temperature control system sends an error message to the WMS system, requesting resending.

[0030] Step Two: Material Temperature Control Profile Inquiry This step retrieves the corresponding material temperature control profile from the material master data server based on the composite material type and batch in the work plan version, and obtains the personalized temperature control parameters for that material.

[0031] Specific execution details: The temperature control system sends a query request to the material master data server based on the material_type and batch_id fields in the work plan version. The material master data server stores detailed information and temperature control profiles for all incoming composite materials.

[0032] After receiving a query request, the material master data server retrieves the corresponding material temperature control profile from the database and returns the query results to the temperature control system. Material temperature control profiles are established according to composite material type and material batch, with each material batch corresponding to a unique temperature control profile.

[0033] The material temperature control profile includes the following fields: target_temperature: target temperature, which is the optimal storage temperature for this material; allowable_fluctuation: The range of temperature fluctuations allowed, i.e., the maximum permissible deviation relative to the target temperature; thermal_sensitivity: thermal sensitivity coefficient, used to quantify the sensitivity of a material to temperature changes; max_exposure_time: Maximum exposure time, i.e. the longest time that the material is allowed to be exposed to a non-optimal temperature environment; accumulated_exposure_time: Cumulative exposure time, which is the cumulative exposure time of this material batch in non-optimal temperature environments since it entered the warehouse; thermal_priority: Temperature control priority, used to determine which work area's temperature control should be prioritized when multiple operations are performed simultaneously; response_parameter: Historical temperature response parameter, which is the temperature response data of the storage area where the material is located under similar operating conditions in the past.

[0034] The temperature control system receives a profile of the material's temperature and adjusts its subsequent thermal disturbance prediction and liquid cooling parameter adjustment strategies based on the material's cumulative exposure time and temperature control priority. For materials with a cumulative exposure time close to the maximum exposure time or a high temperature control priority, the system will implement more stringent temperature control measures to ensure material safety.

[0035] Step 3: Acquisition of the status of the zoned liquid cooling circuit This step involves collecting real-time operating status parameters of the liquid cooling circuits in each storage area to understand the current cooling capacity and operating status of the liquid cooling system.

[0036] Specific execution details: Each storage area is equipped with one or more independent zoned liquid cooling circuits. Each zoned liquid cooling circuit consists of a liquid cooling plate or cooling coil, zoned valves, a variable frequency pump, a heat exchange unit, and a temperature sensor. The liquid cooling plate or cooling coil is installed on the walls, ceiling, or shelves of the storage area, and heat is removed by the circulating coolant; the zoned valves are used to control the on / off state and flow rate of the coolant; the variable frequency pump is used to drive the coolant circulation; the heat exchange unit is used to transfer the heat in the coolant to the external environment; and the temperature sensor is used to measure the supply liquid temperature, return liquid temperature, and air temperature of the storage area.

[0037] The zoned liquid cooling loop controller collects the operating status parameters of its loop in real time and transmits these parameters to the central temperature controller via industrial Ethernet. The collected status parameters include: supply_temperature: The supply temperature of the coolant, which is the temperature of the coolant entering the liquid cooling plate or cooling coil. return_temperature: return temperature, which is the temperature of the coolant flowing out of the liquid cooling plate or cooling coil; flow_rate: Liquid cooling flow rate, which is the volume of coolant passing through the liquid cooling plate or cooling coil per unit time; valve_opening: Valve opening degree, i.e. the degree to which the zone valve is open; pump_frequency: Pump frequency, i.e., the operating frequency of the variable frequency pump; supply_return_diff: Supply and return liquid temperature difference, that is, the difference between the return liquid temperature and the supply liquid temperature; cooling_margin: Cooling margin, which is the additional cooling capacity that the liquid cooling system can currently provide; system_response_time: The response time of the liquid cooling system, that is, the time required from issuing a control command to the liquid cooling system reaching a new steady state.

[0038] The central temperature controller preprocesses the collected status parameters of the partitioned liquid cooling circuits, including data filtering, outlier detection, and data calibration, to ensure the accuracy and reliability of the data.

[0039] Step 4: Prediction of Local Thermal Disturbance This step involves establishing a mathematical model based on the job plan version, material temperature control profile, external ambient temperature, and historical temperature response parameters to predict potential local thermal disturbances that may occur during the upcoming job.

[0040] Specific execution details: The temperature control system obtains the current ambient temperature from an external weather station or temperature sensor outside the warehouse, and calculates the difference between the ambient temperature and the target temperature of the material, EnvDiff.

[0041] The device_type field in the work plan version determines the device heat load (DeviceHeat). Different types of equipment have different thermal radiation and convection characteristics, resulting in different heat loads. The temperature control system queries the corresponding equipment type's heat load value from a pre-established database of equipment heat loads.

[0042] Based on the thermal_sensitivity field in the material's temperature control profile, obtain the MaterialSensitivity, the material's thermal sensitivity coefficient. A higher thermal sensitivity coefficient indicates that the material is more sensitive to temperature changes and requires more stringent temperature control.

[0043] The current cooling margin (CoolingMargin) is obtained from the cooling_margin field in the partitioned liquid cooling loop status parameters. A larger cooling margin indicates a stronger current cooling capacity of the liquid cooling system and the ability to withstand greater thermal disturbances.

[0044] The temperature control system calculates the predicted local thermal disturbance value according to equation (1). : in, The predicted local thermal disturbance value, For the estimated opening time, For the heat load of the operating equipment, This is the difference between the ambient temperature and the target temperature. The thermal sensitivity coefficient of the material. This represents the current cooling margin.

[0045] The temperature control system compares the predicted local thermal disturbance value DeltaT_pred with the material's allowable temperature fluctuation range DeltaT_allow. If DeltaT_pred is less than a certain percentage (e.g., 80%) of DeltaT_allow, it indicates that the thermal disturbance generated by the operation is within the normal regulation capacity of the liquid cooling system and no pre-control is required. If DeltaT_pred is greater than or equal to a certain percentage of DeltaT_allow, it indicates that the thermal disturbance generated by the operation may cause the temperature to exceed the allowable range, and liquid cooling pre-control is required.

[0046] Step 5: Calculation of liquid cooling pre-control lead time and parameter adjustment This step calculates the liquid cooling pre-control lead based on the predicted local thermal disturbance, the allowable temperature fluctuation range of the material, and the response time of the liquid cooling system, and adjusts the parameters of the partitioned liquid cooling loop at appropriate times.

[0047] Specific execution details: The temperature control system calculates the liquid cooling pre-control advance amount tau according to formula (2): In the formula, For the response time of the liquid cooling system, This is a preset proportional coefficient. The formula indicates that the liquid cooling pre-control lead time consists of two parts: one is the response time of the liquid cooling system itself, and the other is an additional lead time calculated based on the degree to which the thermal disturbance exceeds the allowable range. The greater the thermal disturbance, the longer the lead time required to ensure that the liquid cooling system has sufficient time to adjust to the appropriate operating state.

[0048] The temperature control system determines the pre-control start time based on the start_time field in the work plan version and the calculated liquid cooling pre-control lead time tau: When the system time reaches the pre-controlled start time, the temperature control system sends a control command to the partition liquid cooling loop controller of the corresponding storage area to adjust the partition liquid cooling loop parameters. Parameter adjustments include lowering the supply liquid temperature, increasing the liquid cooling flow rate, increasing the valve opening, or increasing the pump frequency.

[0049] The temperature control system calculates the adjusted liquid cooling flow rate Flow_z according to equation (3): in, For the first Adjusted liquid cooling flow rate for each zone For the first Initial liquid cooling flow rate for each zone, For the first Minimum allowable liquid cooling flow rate for each zone, For the first The maximum permissible liquid cooling flow rate for each zone.

[0050] In addition to adjusting the liquid cooling flow rate, the temperature control system can also simultaneously adjust parameters such as the liquid supply temperature, valve opening, and pump frequency based on predicted local thermal disturbances and the current state of the liquid cooling system. For example, when the predicted thermal disturbance is large, the liquid supply temperature can be reduced and the pump frequency increased at the same time to enhance the cooling effect.

[0051] Step Six: Closed-Loop Correction During Job Execution During the operation, this step involves closed-loop correction of the parameters of the partitioned liquid cooling circuit through real-time temperature feedback to compensate for prediction errors and changes in actual thermal disturbances.

[0052] Specific execution details: During the operation, temperature sensors installed in the work area collect real-time temperature data at a high frequency (e.g., once per second) and transmit the data to the central temperature controller.

[0053] The temperature control system compares the collected real-time temperature with the material's target temperature and allowable temperature fluctuation range to calculate the temperature deviation. If the temperature deviation is within the allowable fluctuation range, the temperature control system keeps the current liquid cooling circuit parameters unchanged; if the temperature deviation exceeds the allowable fluctuation range, the temperature control system uses a proportional-integral-derivative (PID) control algorithm or other advanced control algorithms to calculate the required liquid cooling parameter increment.

[0054] The temperature control system sends corrected control commands to the zoned liquid cooling circuit controller to adjust parameters such as liquid cooling flow rate, liquid supply temperature, valve opening degree, or pump frequency, so that the temperature in the working area returns to the allowable fluctuation range.

[0055] The temperature control system continuously monitors temperature changes in the work area until the work is completed and the temperature returns to near the target temperature. After the work is finished, the temperature control system gradually restores the parameters of the zoned liquid cooling circuit to normal operating conditions to save energy.

[0056] Step 7: Calculation of Temperature Control Load Rate and Reverse Dispatch This step calculates the temperature control load rate of the zoned liquid cooling system. When the load rate exceeds the threshold, a job adjustment suggestion is sent to the WMS system, enabling the temperature control system to reverse the scheduling of the WMS job plan.

[0057] Specific execution details: The temperature control system calculates the temperature control load rate Load_z of the z-th zone according to equation (4): in, For the first The frequency of the variable frequency pump in each zone, For the first The valve opening degree of each zone, For the first The supply and return liquid temperature difference of each zone For the first Cooling capacity of each zone.

[0058] The temperature control system compares the calculated temperature control load rate Load_z with the preset load threshold Load_threshold. Simultaneously, the system also compares the predicted temperature peak (i.e., the target temperature plus the predicted local thermal disturbance value DeltaT_pred) with the material's allowable temperature fluctuation range DeltaT_allow.

[0059] When Load_z is greater than or equal to Load_threshold, or the predicted peak temperature is greater than or equal to target_temperature + DeltaT_allow, it indicates that the current cooling capacity of the liquid cooling system is insufficient to meet the temperature control requirements. Continuing to execute the operation according to the original plan may lead to temperature runaway. At this time, the temperature control system triggers the reverse scheduling mechanism and sends an operation adjustment suggestion to the WMS system.

[0060] The proposed adjustments to the assignment include the following: The list of tasks to be adjusted includes the task identifier, task type, original planned start time, and task priority; Suggested adjustments include postponing low-priority tasks, merging task windows, relocating task areas, shortening door opening times, and replacing task equipment. The expected temperature risk level is divided into three levels: low, medium, and high, which indicates the degree of temperature risk that may result from not adjusting the operation. Estimated recovery time, which is the time required for the liquid cooling system to return to normal load if the recommended adjustments to the operation are made. Recommend alternative storage areas; that is, if it is recommended to relocate the work area, provide a list of alternative storage areas with sufficient cooling capacity. The recommended adjusted start time is the new start time based on the recovery time of the liquid cooling system.

[0061] The temperature control system sends operational adjustment suggestions to the WMS system and awaits a response. Upon receiving the suggestions, the WMS system decides whether to adopt the suggestions and adjust the operational plan based on current business needs, operational priorities, and the actual warehouse conditions.

[0062] Step 8: Receiving the adjusted work plan and updating parameters This step receives the adjusted job plan version number returned by the WMS system and re-predicts thermal disturbances and updates liquid cooling parameters based on the adjusted job plan.

[0063] Specific execution details: After adjusting the work plan, the WMS system generates a new version of the work plan and returns the new plan version number (adjusted_plan_version_id) to the temperature control system. If the WMS system decides not to adjust the work plan, it will also send a confirmation message to the temperature control system explaining the reason for not adjusting.

[0064] After receiving the revised work plan version number from the WMS system, the temperature control system sends a request to the WMS system to obtain the complete revised work plan content.

[0065] The temperature control system analyzes and adjusts the work plan, and redetermines information such as the storage area, start time, expected door opening time, and work equipment.

[0066] According to the adjusted work plan, the temperature control system re-executes the process of steps four and five, predicts new local thermal disturbances, calculates new liquid cooling pre-control lead, and updates the parameters of the zoned liquid cooling loop.

[0067] If the adjusted work plan shifts the work to other storage areas, the temperature control system stops pre-controlling the original storage area and instead performs thermal disturbance prediction and liquid cooling parameter adjustment for the new work area.

[0068] The temperature control system records the adjusted work plan version number and the corresponding liquid cooling control parameters in the log file for later query and analysis.

[0069] Step Nine: Record Temperature Anomalies When a temperature anomaly occurs, this step associates the anomaly with the recent work plan version, material batch, and zonal liquid cooling circuit parameters to generate a temperature anomaly association record, providing data support for subsequent analysis and optimization.

[0070] Specific execution details: The temperature control system monitors the temperature data of each storage area in real time. When the temperature of a storage area exceeds the allowable fluctuation range and the duration exceeds the preset anomaly detection time, the temperature control system determines that a temperature anomaly event has occurred.

[0071] The temperature control system records basic information about abnormal temperature events, including the anomaly start time (anomaly_start_time), the anomaly end time (anomaly_end_time, recorded when the temperature returns to the allowable range), the peak temperature (peak_temperature), and the recovery time (the time from the start of the anomaly to the temperature returning to the allowable range).

[0072] The temperature control system retrieves all job plan versions in the storage area within the temperature anomaly time window (i.e., a period of time before and after the anomaly start time) and obtains the corresponding information such as plan_version_id, adjusted_plan_version_id, batch_id, material_type, and related_work_type.

[0073] The temperature control system retrieves historical data of the partitioned liquid cooling loop parameters of the storage area within the time window of temperature anomalies, including parameters such as supply_temperature, flow_rate, valve_opening, and pump_frequency.

[0074] The temperature control system calculates the correlation score Rel(e,z) between the temperature anomaly event e and the z-th partition according to equation (5): in, Indicates an abnormal temperature event With the The correlation score of each partition. Indicates the time window of temperature anomaly and the first The degree of overlap of the time windows of each partition's tasks Indicates the degree of matching between material batches. Indicates the time before and after the temperature anomaly occurred. The degree of variation in the parameters of the liquid cooling circuit in each zone.

[0075] The temperature control system uses correlation scores to determine the factors most relevant to abnormal temperature events. For example, if the correlation score mainly comes from... This indicates that the temperature anomaly was most likely caused by operational activities; if the correlation score mainly comes from... This indicates that the abnormal temperature may be caused by a malfunction in the liquid cooling system or improper parameter settings.

[0076] The temperature control system generates temperature anomaly association records and stores these records in the database. Each temperature anomaly association record contains the following fields: anomaly_id: A unique identifier for an abnormal temperature event; anomaly_start_time: The time when the anomaly started; anomaly_end_time: Abnormal end time; peak_temperature: Peak temperature; recovery_time: Recovery time; zone_id: The identifier of the storage zone where the exception occurred; plan_version_id: Original job plan version number; adjusted_plan_version_id: The version number of the adjusted job plan; batch_id: The batch of materials involved; material_type: The type of composite material involved; related_work_type: Related job type; supply_temperature: The liquid supply temperature during abnormal periods; flow_rate: Liquid cooling flow rate during abnormal periods; valve_opening: Valve opening degree during abnormal periods; pump_frequency: Pump frequency during abnormal periods; correlation_score: correlation score; possible_cause: Possible causes of the exception.

[0077] The temperature control system regularly performs statistical analysis on temperature anomaly correlation records to identify frequently occurring anomaly patterns and root causes, and based on this, optimizes the weighting coefficients of the thermal disturbance prediction model, adjusts the control parameters of the liquid cooling system, and improves the WMS operation plan formulation strategy.

[0078] Example: In one embodiment, the temperature control system obtains the work plan version from the WMS and reads the material temperature control profile of the composite material batch from the material master data. The liquid cooling controller collects the supply liquid temperature, liquid cooling flow rate, valve opening, pump frequency, and cooling headroom.

[0079] Equation (1) Prediction of local thermal disturbance: Equation (2) Liquid cooling pre-control lead time: Formula (3) Liquid cooling flow rate setting: Equation (4) Temperature control load rate: Equation (5) Temperature Anomaly Correlation Score: For example, a batch of carbon fiber prepreg is scheduled to be shipped from Zone A, with the door expected to open 8 minutes later. The system predicts that local thermal disturbances are approaching the upper limit of allowable temperature fluctuations and adjusts the liquid supply temperature and liquid cooling flow rate before the schedule begins. If the temperature control load rate exceeds the threshold during the operation, the system sends a suggestion to the WMS to postpone low-priority operations and recalculates the control parameters after the WMS returns the adjusted schedule.

[0080] Those skilled in the art can configure parameters, thresholds, weights, and time windows according to specific business systems, equipment models, interface protocols, and test data. Equivalent substitutions made without departing from the concept of this invention should all fall within the protection scope of this invention.

[0081] In one embodiment, the system initiates liquid cooling pre-control before the planned start time, so that the temperature peak during the operation is lower than the temperature peak without feedforward pre-control, and shortens the time for the temperature to recover to the target range.

[0082] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for linking liquid cooling temperature control with a WMS in a smart warehouse for composite materials, characterized in that: include: S1. Obtain the operation plan version of the composite material intelligent warehouse from the warehouse management system (WMS). The operation plan version includes storage area identifier, material batch, composite material type, operation type, estimated door opening time, operation equipment, planned start time, operation priority, and plan version number. The composite material type includes at least one of carbon fiber prepreg, film, resin-based composite material, and honeycomb core material. S2. Based on the composite material type and batch, query the material temperature control profile to determine the target temperature, allowable temperature fluctuation range, thermal sensitivity coefficient, maximum exposure time, and historical temperature response parameters of the corresponding storage area. S3. Obtain the status of the partitioned liquid cooling circuit of the corresponding storage area. The partitioned liquid cooling circuit includes liquid cooling plate or cooling coil, partition valve, variable frequency pump, heat exchange unit and temperature sensor. The status of the partitioned liquid cooling circuit includes liquid supply temperature, liquid cooling flow rate, valve opening degree, pump frequency, cooling margin and liquid cooling system response time. S4. Based on the work plan version, material temperature control profile, external ambient temperature, and historical temperature response parameters, predict local thermal disturbances, wherein the local thermal disturbances satisfy equation (1): in, The predicted local thermal disturbance value, For the estimated opening time, For the heat load of the operating equipment, This is the difference between the ambient temperature and the target temperature. The thermal sensitivity coefficient of the material. This represents the current cooling headroom; , , , , These are the preset thermal disturbance prediction weighting coefficients; S5. Determine the liquid cooling pre-control advance amount based on local thermal disturbance, allowable temperature fluctuation range and liquid cooling system response time. The liquid cooling pre-control advance amount satisfies equation (2): In equation (2), This is for the lead time of liquid cooling pre-control. For the response time of the liquid cooling system, This is a preset proportional coefficient. Allowable temperature fluctuation range; And adjust the partitioned liquid cooling loop parameters of the corresponding storage area at the time corresponding to the liquid cooling pre-control advance amount before the planned start time, wherein the adjustment of the partitioned liquid cooling loop parameters satisfies equation (3): in, For the first Adjusted liquid cooling flow rate for each zone For the first Initial liquid cooling flow rate for each zone, For the first Minimum allowable liquid cooling flow rate for each zone, For the first The maximum permissible liquid cooling flow rate for each zone , This is the preset liquid cooling flow rate adjustment coefficient; For the first The temperature control load rate of each zone, the Calculate according to formula (4); S6. During the operation, collect the real-time temperature of the corresponding storage area, and perform closed-loop correction of the partition liquid cooling loop parameters based on the target temperature, the allowable temperature fluctuation range and the real-time temperature. S7. When the temperature control load rate of the corresponding storage area exceeds the load threshold or the predicted temperature peak exceeds the allowable temperature fluctuation range, a job adjustment suggestion is sent to the WMS, wherein the temperature control load rate satisfies equation (4): in, For the first The frequency of the variable frequency pump in each zone, For the first The valve opening degree of each zone, For the first The supply and return liquid temperature difference of each zone For the first Cooling capacity of each zone , , , This is the preset weighting coefficient for the temperature control load rate; S8. Receive the adjusted work plan version number returned by WMS, re-predict local thermal disturbances based on the adjusted work plan version number and update the partition liquid cooling circuit parameters. S9. When a temperature anomaly occurs, the temperature anomaly time window is associated with the recent work plan version, material batch, and zone liquid cooling circuit parameters to generate a temperature anomaly association record. The association score of the temperature anomaly association record satisfies equation (5): in, Indicates an abnormal temperature event With the The correlation score of each partition. Indicates the time window of temperature anomaly and the first The degree of overlap of the time windows of each partition's tasks Indicates the degree of matching between material batches. Indicates the time before and after the temperature anomaly occurred. The degree of variation of liquid cooling circuit parameters in each zone , , These are the preset association score weighting coefficients.

2. The method for linking liquid cooling and temperature control with WMS in a composite material intelligent warehouse according to claim 1, characterized in that: The material temperature control profile also includes the material's entry time, cumulative exposure time, recommended storage area, and temperature control priority.

3. The method for linking liquid cooling and temperature control with WMS in a composite material intelligent warehouse according to claim 1, characterized in that: The adjustment of the parameters of the partitioned liquid cooling circuit includes at least one of the following: reducing the liquid supply temperature, increasing the liquid cooling flow rate, increasing the valve opening, or increasing the pump frequency.

4. The method for linking liquid cooling and temperature control with WMS in a composite material intelligent warehouse according to claim 1, characterized in that: The proposed job adjustment includes postponing low-priority jobs, merging job windows, shifting job areas, shortening door opening time, replacing job equipment, estimating temperature risk level, estimating recovery time, suggesting alternative storage areas, and suggesting adjusted job start times.

5. The method for linking liquid cooling and temperature control with WMS in a composite material intelligent warehouse according to claim 1, characterized in that: The temperature anomaly association record includes the anomaly start time, anomaly end time, peak temperature, material batch, work plan version number, adjusted work plan version number, zoned liquid cooling circuit parameters, and associated work type.

6. The method for linking liquid cooling and temperature control with WMS in a composite material intelligent warehouse according to claim 1, characterized in that: The types of operations include receiving, issuing, transferring, inventory counting, and re-inspection.

7. The method for linking liquid cooling and temperature control with WMS in a composite material intelligent warehouse according to claim 1, characterized in that: The operating equipment includes forklifts, AGVs, robotic arms, and manual handling equipment.

8. A liquid-cooled temperature control and WMS linkage system for intelligent warehouses of composite materials, characterized in that, include: The work plan acquisition module is used to obtain the work plan version of the composite material smart warehouse from the warehouse management system (WMS). The work plan version includes storage area identifier, material batch, composite material type, work type, estimated door opening time, work equipment, planned start time, work priority, and plan version number. The temperature control profile query module is used to query the temperature control profile of the material based on the composite material type and material batch, and determine the target temperature, allowable temperature fluctuation range, heat sensitivity coefficient, maximum exposure time and historical temperature response parameters of the corresponding storage area; The liquid cooling status acquisition module includes a liquid cooling plate or cooling coil, a partition valve, a variable frequency pump, a heat exchange unit, and a temperature sensor. The liquid cooling status acquisition module is used to acquire the status of the partition liquid cooling circuit in the corresponding storage area. The status of the partition liquid cooling circuit includes the liquid supply temperature, liquid cooling flow rate, valve opening degree, pump frequency, cooling margin, and liquid cooling system response time. The thermal disturbance prediction module is used to predict local thermal disturbances based on the work plan version, material temperature control profile, external ambient temperature, and historical temperature response parameters. The pre-control calculation module is used to determine the liquid cooling pre-control lead based on local thermal disturbance, allowable temperature fluctuation range and liquid cooling system response time, and adjust the partition liquid cooling loop parameters of the corresponding storage area at the time corresponding to the liquid cooling pre-control lead before the planned start time. The closed-loop correction module is used to collect the real-time temperature of the corresponding storage area during operation and perform closed-loop correction on the parameters of the partition liquid cooling circuit based on the target temperature, the allowable temperature fluctuation range and the real-time temperature. The reverse scheduling module is used to send job adjustment suggestions to WMS when the temperature control load rate of the corresponding storage area exceeds the load threshold or the predicted temperature peak exceeds the allowable temperature fluctuation range. The planning update module is used to receive the adjusted work plan version number returned by WMS, re-predict local thermal disturbances and update the partition liquid cooling loop parameters based on the adjusted work plan version number; The anomaly association module is used to associate the temperature anomaly time window with the recent work plan version, material batch and partition liquid cooling circuit parameters when a temperature anomaly occurs, and generate a temperature anomaly association record.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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