A temperature control and scheduling method and system for a temperature-controlled logistics box based on a heat budget

Abstract

The application relates to the technical field of logistics management, and discloses a temperature control and scheduling method and system for temperature-controlled logistics boxes based on thermal budget, which comprises the following steps: step 1, multi-source data acquisition; step 2, thermal budget calculation and control optimization; step 3, cloud collaborative scheduling and dynamic path planning; and step 4, strategy execution, state feedback and fault-tolerant control. Through the temperature control and scheduling integrated scheme for temperature-controlled logistics boxes based on thermal budget, the temperature accurate regulation and control capability and the dynamic scheduling efficiency of the temperature-controlled logistics boxes are significantly improved, the problems of large temperature fluctuation, scheduling response lag and high energy consumption of the traditional temperature-controlled logistics boxes in multi-scene transportation are solved, the stable maintenance of the temperature in the box and the optimal configuration of logistics resources are realized based on the dynamic algorithm and multi-node collaborative scheduling based on thermal budget, and the temperature control precision and transportation energy efficiency are effectively balanced.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, and more specifically discloses a temperature control and scheduling method and system for temperature-controlled logistics boxes based on thermal budget. Background Technology

[0002] With the rapid development of cold chain logistics and smart delivery, higher requirements have been placed on the refined and intelligent management of temperature-controlled logistics processes. As the core link connecting production, warehousing, distribution and consumption, the temperature sensitivity of logistics directly affects the quality, safety and compliance of goods such as medicines and fresh food.

[0003] The prior art patent document with authorization announcement number CN121391081A discloses "a cold chain collaborative management method and system based on cloud-edge collaboration", which includes acquiring temperature, humidity and inventory data and global distribution point path information from multi-node sensors; based on the temperature and inventory data, performing edge data aggregation, noise filtering and temperature threshold monitoring to obtain local inventory adjustment requirements; based on the local inventory adjustment requirements and the global distribution point path information, performing global path optimization analysis, multi-path variation cross set generation and comprehensive evaluation and ranking to obtain a global delivery path set; and performing node conflict detection, comprehensive decision-making and adaptive adjustment on the global delivery path set to obtain the final optimal path allocation.

[0004] The patent document with authorization announcement number CN113986415B discloses an "intelligent temperature control method and logistics service system in the logistics service process," which includes: an insulated box for storing packages corresponding to logistics orders with temperature control requirements; the insulated box is associated with a temperature measuring device and a communication module; the temperature measuring device is used to detect the temperature inside the insulated box, and the communication module is used to upload the temperature measurement results to a temperature control management device; a first client is used to associate with the packing task executor, obtain the temperature measuring device identifier and the logistics order identifier, and upload them to the temperature control management device; the temperature control management device is used to establish a binding relationship between the temperature measuring device identifier and the logistics order identifier, and provide temperature attribute information for the logistics order based on the temperature information inside the box detected by the temperature measuring device and the binding relationship.

[0005] While existing technologies can achieve precise temperature control of logistics boxes through the collaboration of sensors and controllers, providing temperature data support by combining the heat transfer characteristics of the items or order-box binding, and reducing temperature loss during delivery and assisting in liability determination through mother-daughter box design, thus ensuring the quality of temperature-controlled items and the logistics service experience to a certain extent, current technologies do not incorporate thermal budgeting into the core of temperature control management. They lack quantitative assessment and dynamic adaptation mechanisms for temperature control capabilities, do not accurately model temperature disturbances during the unpacking process, and lack an optimal order-box matching scheme under multiple constraints such as comprehensive temperature zone, timeliness, and energy consumption. Furthermore, they are insufficient in dynamic rescheduling responses to abnormal scenarios such as traffic delays and temporary order adjustments, and lack effective degradation control strategies to deal with communication interruptions and parameter anomalies. As a result, they cannot achieve deep collaboration between temperature control and scheduling, and cannot fully meet the refined and intelligent quality assurance requirements of cold chain logistics. Summary of the Invention

[0006] The present invention mainly provides a temperature control and scheduling method and system for temperature-controlled logistics boxes based on thermal budget, which can solve the problems mentioned in the background art.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution, more specifically, a temperature control and scheduling method for temperature-controlled logistics boxes based on thermal budget, comprising: S1. Collect the environmental conditions inside and outside the temperature-controlled logistics box, equipment operating parameters, energy consumption data, as well as positioning information and box opening status data to form comprehensive basic data support; S2. Receive the prior information of docking point sequence, arrival time, and estimated unpacking time from the cloud, and combine it with the collected basic data to complete the thermal correlation quantification calculation and optimize the control strategy to adapt to the current scenario. S3: The cloud receives status data reported by the edge terminal, performs optimal matching of orders and boxes based on multi-dimensional constraints, plans reasonable delivery routes, and triggers rolling rescheduling operations for abnormal situations; S4. Perform relevant operations according to the cloud scheduling plan and the optimized temperature control strategy, collect and feedback the system operating status in real time, and activate the degradation control mechanism when communication interruption or abnormal parameter conditions occur.

[0008] Furthermore, in S1, the temperature and humidity inside the box and the temperature outside the box are collected by a temperature and humidity sensor, the opening status is collected by a door magnetic / photosensitive / accelerometer sensor, the positioning information is collected by a positioning module, and the battery state of charge data related to the energy consumption of the device is collected by the battery SOC.

[0009] Furthermore, in S2, a two / three-node adaptive thermal model is adopted, and thermal correlation quantification is completed by combining recursive least squares online parameter identification and extended Kalman filter state estimation. Temperature control capability is quantified through thermal budget index. Risk constraint model predictive control and temperature corridor strategy are used to optimize control strategy. Opening disturbance modeling is completed based on prior information.

[0010] Furthermore, in S3, the multi-dimensional constraints include temperature zone, timeliness, thermal budget, battery SOC, and thermal risk index. The optimal matching between orders and containers is solved by the Hungarian algorithm or minimum cost flow. A vehicle route planning model with time windows is adopted and a thermal risk penalty term is introduced to plan the delivery route. Abnormal situations that trigger rolling rescheduling include traffic delays causing ETA deviation to exceed the threshold, a sudden drop in the container's thermal budget, temporary order additions, or order cancellations.

[0011] Furthermore, in S4, the real-time feedback of system operating status includes thermal budget, thermal risk index, thermal model parameters, and battery SOC, and the degradation control mechanism includes disconnection fault tolerance and heat preservation priority power consumption limitation strategy.

[0012] According to another aspect of the present invention, a temperature control and scheduling system for temperature-controlled logistics boxes based on thermal budget is provided. This system is implemented based on the above-mentioned method for temperature control and scheduling of temperature-controlled logistics boxes based on thermal budget, specifically including: a data acquisition module, a thermal module and a thermal budget calculation module, an edge prediction control module, and a cloud-based collaborative scheduling module. The data acquisition module acquires environmental parameters inside and outside the box, equipment operating status, location information, and box opening / closing status data, and completes preliminary data verification. The thermal module and thermal budget calculation module adapt thermally related parameters based on the acquired data, and completes the quantification of temperature control capabilities and risk level determination. The edge prediction control module receives prior information from the cloud, combines it with local data to optimize control strategies, and has disturbance response and local emergency control functions. The cloud-based collaborative scheduling module receives status data and quantitative indicators uploaded from the edge terminal, completes order-box matching, path planning, and scheduling plan adjustment under abnormal scenarios, and achieves closed-loop collaboration with the edge terminal.

[0013] Furthermore, the data acquisition module includes: an environment and status perception module, and a device and energy consumption status acquisition module; Environment and Status Sensing Module: Collects temperature and humidity data inside and outside the enclosure through temperature and humidity sensors, captures opening status information through door magnetic / photosensitive / accelerometer sensors, and obtains real-time positioning data through GPS to ensure the comprehensiveness and real-time nature of environmental and status data; Equipment and Energy Consumption Status Acquisition Module: Acquires equipment operating parameters of the temperature-controlled logistics box, synchronously collects battery state of charge energy consumption related data, and completes basic data collection before preliminary data verification.

[0014] Furthermore, the thermal module and thermal budget calculation module include: a thermal model update module and a thermal budget and risk calculation module; Thermal model update module: It adopts a two / three-node adaptive thermal model, combined with recursive least squares online parameter identification and extended Kalman filter state estimation technology, to complete the dynamic adaptation of thermal-related parameters and the real-time update of the thermal model. Thermal Budget and Risk Calculation Module: Quantifies the temperature control capability of temperature-controlled logistics boxes through thermal budget indicators, and combines collected environmental, equipment and energy consumption data to accurately determine the thermal risk level.

[0015] Furthermore, the edge prediction control module includes: a disturbance prior mapping module, a risk constraint MPC optimization module, and an execution and degradation control module; Disturbance Prior Mapping Module: Receives prior information such as docking point sequence, arrival time, and estimated unpacking time from the cloud, and combines it with locally collected data to complete the modeling and mapping of unpacking disturbances; Risk-constrained MPC optimization module: It adopts risk-constrained model predictive control and temperature corridor strategy to optimize the temperature control strategy in a targeted manner to adapt to the current logistics and distribution scenario; Execution and Degradation Control Module: Executes cloud-based scheduling plans and optimized temperature control strategies, monitors system operating status in real time, and activates degradation control mechanisms such as disconnection fault tolerance and heat preservation priority power consumption limitation when communication interruption or abnormal parameter conditions occur.

[0016] Furthermore, the cloud-based collaborative scheduling module includes: an order-box matching module, a path planning and risk optimization module, and a rolling rescheduling triggering module; Order-box matching module: Taking into account constraints such as temperature range, timeliness, thermal budget, battery SOC, and thermal risk index, the module solves the optimal matching scheme between orders and boxes using either the Hungarian algorithm or the minimum cost flow algorithm. Route planning and risk optimization module: It adopts a vehicle route planning model with time windows and introduces a hot risk penalty term to optimize route planning and generate a reasonable delivery route that takes into account both efficiency and safety. Rolling rescheduling trigger module: Real-time monitoring of abnormal situations in the logistics and delivery process. When traffic delays cause ETA deviation to exceed the threshold, the hot budget at the container end drops sharply, or there are temporary additional orders or order cancellations, rolling rescheduling operations are automatically triggered.

[0017] The beneficial effects of this invention, a temperature control and scheduling method and system for temperature-controlled logistics boxes based on thermal budgeting, are as follows: By adopting an integrated scheme for temperature control and scheduling of temperature-controlled logistics boxes based on thermal budgeting, the precise temperature control capability and dynamic scheduling efficiency of temperature-controlled logistics boxes are significantly improved, solving the problems of large temperature fluctuations, delayed scheduling response, and high energy consumption in traditional temperature-controlled logistics boxes during multi-scenario transportation. Simultaneously, the dynamic algorithm based on thermal budgeting and multi-node collaborative scheduling achieve stable maintenance of the box temperature and optimal allocation of logistics resources, effectively balancing temperature control accuracy and transportation energy efficiency. Furthermore, the integration of temperature monitoring and intelligent scheduling ensures real-time transmission of temperature data and accurate issuance of scheduling instructions, reducing temperature deviations and resource waste during transportation. While improving the reliability of temperature-controlled logistics box transportation, it retains the system's characteristics of "highly efficient adaptation and dynamic collaboration," conforming to the development trend of intelligent and low-carbon modern cold chain logistics, and providing a stable and practical technical path for upgrading the efficiency of temperature-controlled logistics in multiple scenarios. Attached Figure Description

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and specific implementation methods.

[0019] Figure 1 This is a schematic diagram of the system framework; Figure 2 This is a flowchart illustrating the method. Detailed Implementation

[0020] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0021] According to one aspect of the invention, such as Figures 1-2 As shown, a temperature control and scheduling method and system for temperature-controlled logistics boxes based on thermal budget is provided, including: Step 1: Multi-source data acquisition Collect environmental conditions inside and outside the temperature-controlled logistics box, equipment operating parameters, energy consumption data, as well as location information and box opening status data to form comprehensive basic data support; Specifically, the system collects the temperature and humidity inside the enclosure and the outside temperature through temperature and humidity sensors, collects the enclosure opening status through door magnetic / photosensitive / accelerometer sensors, collects positioning information through a positioning module, and collects battery state-of-charge data related to the device's energy consumption through battery SOC.

[0022] First, temperature and humidity sensors deployed through the environmental and status sensing module are set up at detection points in key storage areas inside the box (such as the center and four corners) and on the outer surface of the box. Temperature and humidity data inside the box and ambient temperature data outside the box are collected every 10 seconds. The analog signals output by the sensors are converted into digital signals by A / D converters. Then, an internal verification algorithm is used to remove outliers that exceed the sensor's range (such as internal temperature -20℃~80℃, humidity 0%~100%RH, and external temperature -40℃~85℃) to ensure the accuracy and continuity of temperature and humidity data. Then, the door magnetic sensor detects the mechanical state of the door opening and closing, and outputs high / low level signals to indicate that the door is closed / open. Simultaneously, it combines a photosensitive sensor to sense changes in the light intensity inside the box (the judgment is triggered when the light intensity suddenly increases by ≥500 lux when the box is opened) and an acceleration sensor to capture the box shaking amplitude (assistive verification when the box tilts or vibrates by ≥3° / s when the box is opened). After performing logical AND operation on the data from these three sources, the opening status and duration of the opening are accurately captured. At the same time, the real-time latitude and longitude coordinates of the logistics box are collected by GPS every 30 seconds to form location trajectory data. In addition, the operating power of the temperature-controlled logistics box's refrigeration / heating module, compressor start-stop frequency, fan speed, and other equipment operating parameters are acquired in real time. Battery status of charge (SOC) data is collected through the battery management system (BMS). The remaining battery charge percentage is calculated every 5 seconds (accuracy ±2%) using the current integration method combined with open-circuit voltage calibration. Battery charging and discharging current, voltage, and temperature data are recorded simultaneously to complete the comprehensive collection of basic energy consumption data. Finally, the collected temperature and humidity, unpacking status, positioning information, equipment operating parameters, and battery SOC data are structured and integrated according to the format of "timestamp + data type + sensor identifier". The data is compared with the correlation data of different sensors at the same time dimension (e.g., the consistency between unpacking status and temperature change inside the box) through data verification algorithms (such as the deviation threshold method). Invalid information with missing data and logical contradictions is eliminated, and finally a standardized basic dataset is formed, which provides complete and reliable data support for subsequent thermal budget calculation and control strategy optimization.

[0023] Step 2: Thermal Budget Calculation and Control Optimization Receive prior information such as docking point sequence, arrival time, and estimated unpacking time from the cloud, and combine it with the collected basic data to complete thermal correlation quantification calculations and optimize control strategies to adapt to the current scenario; Specifically, a two- or three-node adaptive thermal model is adopted, and thermal correlation quantification is completed by combining recursive least squares online parameter identification and extended Kalman filter state estimation. Temperature control capability is quantified through thermal budget index. Risk constraint model predictive control and temperature corridor strategy are used to optimize control strategy. Opening disturbance modeling is completed based on prior information.

[0024] First, based on the collected temperature data of the air inside the container, the core cargo, and the container walls, along with the equipment operating parameters, a two- or three-node thermal model is constructed. The air temperature inside the container, the core cargo temperature, and the container wall temperature are defined as state vectors, as shown in the following formula: In the formula, The temperature of the air inside the chamber. For the core temperature of the cargo, The temperature is the temperature of the chamber wall. Combined with basic data such as the external temperature and cooling / heating control parameters, we substitute these into the discrete state equation formula, as shown below: In the formula, These are the equivalent thermal resistance / heat capacity parameters. For cooling / heating control parameters, As a perturbation value, the recursive least squares (RLS) algorithm is used to read the battery SOC and loading data in real time, dynamically update the equivalent thermal resistance / heat capacity parameters, and at the same time use the extended Kalman filter (EKF) to accurately estimate the core temperature of the cargo, which cannot be directly measured, to eliminate data noise interference and ensure that the thermal model is adapted to the current loading and environmental conditions in real time. Then, based on the updated thermal model and the collected energy consumption data and ambient temperature change trends, two thermal budget quantification methods are provided for engineering selection: When using the time margin type thermal budget calculation, the formula is as follows: In the formula, For the current moment, To predict when the internal temperature of the enclosure will first touch the upper or lower limit of the temperature range, based on the current moment and considering the maximum cooling / heating capacity and battery SOC constraint, the time when the internal temperature of the enclosure will first touch the upper or lower limit of the temperature range is predicted, thus obtaining the remaining time for the enclosure to maintain the compliant temperature range. Then, when using an energy margin-based thermal budget calculation, the following formula is used: In the formula, To predict the time domain, This is the upper limit temperature of the temperature range. The predicted temperature inside the chamber at time k. The sampling period is The equivalent heat capacity of the enclosure is used to calculate the temperature and temperature range margin by integrating within the predicted time domain. The equivalent heat capacity is then converted into energy margin. Both methods simultaneously integrate the temperature trajectory data within the future predicted time domain to calculate the minimum temperature margin as a thermal risk index, thereby completing the accurate determination of temperature control capability and risk level. The perturbation prior mapping involves timing the estimated unpacking time from the cloud with the arrival time at the docking point, and combining this with real-time unpacking status data collected by door magnetic / photosensitive / accelerometer sensors to define the unpacking indication quantity. The thermal perturbation vector is then incorporated into the model, and the open-box perturbation is modeled using the following formula: In the formula, Let K be the ambient temperature outside the chamber at time k. Let k be the opening indication value at time k. The heat exchange caused by opening the box at time k is used to transform the opening plan for future time periods into quantifiable prior information of disturbances, and synchronously correlate it with the external temperature data of the box, so as to provide a basis for early response for subsequent control strategy optimization. In addition, a temperature corridor is constructed, with the following formula as the optimization objective: In the formula, Control quantity at time k The corresponding energy consumption, To control the smoothing weighting coefficient, For risk weighting coefficients, The temperature exceedance risk term at time k is used, and then the collected battery SOC data is substituted into the following formula as the energy constraint: In the formula, Let k be the battery state of charge at time k. Energy conversion factor The sampling period is defined by the following temperature constraint formula: In the formula, This is the lower limit temperature of the temperature range. This is the upper limit temperature of the temperature range. Let k be the temperature inside the chamber at time k, and the following formula is introduced as an actuator constraint: In the formula, To control the minimum limit, Let k be the control quantity at time k. To control the maximum limit of the quantity, the risk of exceeding the limit is finally quantified using the following formula: In the formula, and The risks of exceeding the upper and lower limits of temperature are quantified respectively. A small-scale QP solver is called every 30 seconds to 5 minutes for rolling optimization, and the optimal sequence of cooling / heating control quantities is output to achieve a dynamic balance between energy saving, disturbance rejection and endurance safety. Finally, the optimized control strategy is integrated and verified with the unpacking disturbance model and thermal budget data. Based on the deviation between the real-time collected box temperature feedback and the prior information in the cloud, the output amplitude of the control quantity is finely adjusted to ensure that the control strategy is accurately adapted to the current delivery scenario. At the same time, the calculated thermal budget, thermal risk index and updated thermal model parameters are uploaded to the cloud to provide real-time and reliable quantitative support for subsequent order matching and route planning.

[0025] Step 3: Cloud-based collaborative scheduling and dynamic path planning The cloud receives status data reported by the edge device, performs optimal matching of orders and boxes based on multi-dimensional constraints, plans reasonable delivery routes, and triggers rolling rescheduling operations for abnormal situations; Specifically, the multi-dimensional constraints include temperature zone, timeliness, thermal budget, battery SOC, and thermal risk index. The optimal matching between orders and containers is solved by the Hungarian algorithm or minimum cost flow. A vehicle routing planning model with time windows is adopted and a thermal risk penalty term is introduced to plan the delivery route. Abnormal situations that trigger rolling rescheduling include traffic delays causing ETA deviation to exceed the threshold, a sudden drop in the container's thermal budget, temporary order additions, or order cancellations.

[0026] First, the cloud receives real-time status data such as thermal budget, battery SOC, thermal risk index, and thermal model parameters reported by the edge device. Combined with information such as the temperature zone requirements and time limits of the orders to be assigned, the order-box matching process is initiated. A cost matrix is ​​constructed using the quantitative indicators output by the thermal budget and risk calculation module. The matching cost of high-risk orders with boxes with sufficient thermal margin and good SOC is set to the minimum. Then, the Hungarian algorithm is used to solve the cost matrix to accurately match orders with boxes. This avoids assigning temperature-sensitive orders to boxes with insufficient thermal budget or limited power from the source, ensuring basic delivery safety. Then, based on the optimal matching result of order and container, the route planning and risk optimization module is invoked. The vehicle route planning with time window (VRP-TW) model is used as the basic framework, incorporating delivery distance and the arrival / service time window constraints required by the order into the core optimization objectives. At the same time, a hot risk penalty term is innovatively introduced, and the total route cost is quantified by the following formula: In the formula, and To preset the penalty coefficient, The sum of thermal risk indices of the boxes covered by the route is quickly solved using heuristic algorithms (such as ALNS, large neighborhood search, tabu search) to generate the optimal delivery route that balances delivery efficiency, time compliance, and temperature safety, and to clarify the order of each stop, the estimated arrival time, and the estimated unpacking time. In addition, the ETA deviation caused by traffic delays is calculated by using the location data of the logistics box and road condition information. If the deviation exceeds the preset threshold, or if abnormal information such as a sudden drop in hot budget, temporary order addition / cancellation is received from the edge terminal, the rolling rescheduling mechanism is immediately triggered to re-execute the order-box matching and route planning process. After the new scheduling plan is generated, the updated stop sequence, estimated arrival time and unpacking time and other prior information are sent to the edge terminal. The execution and degradation control module synchronously adjusts the local temperature control strategy to achieve dynamic closed-loop coordination between scheduling and temperature control. Finally, the optimized scheduling plan (including order allocation results, route details, and operation parameters of each station) is accurately sent to the edge controller of the corresponding cold chain delivery vehicle through a stable communication link. The cloud continuously receives real-time status data from the edge terminal to dynamically verify the feasibility of the scheduling plan. If it is found that the thermal risk index of a certain box continues to rise and the thermal budget decays rapidly during delivery, the subsequent route order is immediately fine-tuned to prioritize the delivery of orders carried by that box, or to arrange for the order handover to a nearby box with sufficient thermal margin, so as to ensure temperature control safety and scheduling efficiency throughout the process and solve the technical pain point of the separation between traditional scheduling and temperature control.

[0027] Step 4: Strategy Execution, Status Feedback, and Fault Tolerance Control The system executes relevant operations according to the cloud-based scheduling plan and the optimized temperature control strategy, collects and feeds back the system's operating status in real time, and activates the degradation control mechanism when communication is interrupted or parameters are abnormal. Specifically, the real-time feedback of system operating status includes thermal budget, thermal risk index, thermal model parameters, and battery SOC. The degradation control mechanism includes disconnection fault tolerance and heat preservation priority power consumption limitation strategy.

[0028] First, it receives the scheduling plan (including docking order and unpacking time) and optimized temperature control strategy from the cloud, drives the refrigeration / heating modules, fans and other actuators to operate according to the optimal control quantity, and uses the above temperature constraint formula as the operating constraint to compare the temperature inside the box with the temperature corridor boundary in real time, and dynamically adjusts the actuator power (for example, in the refrigeration scenario, when the temperature is close to the lower edge of the corridor (2℃), the refrigeration power is reduced, and when it is close to the upper edge (8℃), it is moderately increased to ensure that the temperature is always within the compliant range, so as to protect the quality of goods and avoid ineffective energy consumption). The status feedback continuously captures the system's operating status through a full-link data acquisition mechanism. Every 30 seconds, it integrates the thermal budget, thermal risk index, updated thermal model parameters, and battery SOC data. After being structured in the format of "timestamp + device identifier + quantitative indicator", it is uploaded to the cloud through a stable communication link. After receiving the data, the cloud-based collaborative scheduling module combines the current path information from the path planning and risk optimization modules to verify the matching degree between the status data and the scheduling plan. If it finds that the thermal budget is lower than the safety threshold or the SOC is rapidly decaying, it immediately provides an early warning signal to the rolling rescheduling trigger module to support dynamic adjustment decisions. In addition, when a communication signal interruption is detected (e.g., no network in a remote area) or abnormal thermal model parameters (e.g., exceeding the normal fluctuation range), a degradation control mechanism is activated: In the disconnection state, the local system executes a heat preservation priority strategy based on the last received thermal budget data and the current SOC, limiting the maximum operating power of the cooling / heating modules to avoid excessive power consumption leading to insufficient battery life. At the same time, it strictly follows the temperature constraint formula mentioned above to ensure that the temperature inside the chamber does not exceed the upper and lower limits of the temperature zone. When parameters are abnormal, risk constraint MPC optimization is paused, and the system switches to a temperature control mode based on historically optimal parameters. Meanwhile, it continuously attempts to re-identify parameters through the thermal model update module. During the disconnection period, all operating data is temporarily stored locally and automatically re-transmitted to the cloud after communication is restored to ensure that no data is lost. Finally, after all delivery tasks are completed, the execution and degradation control module stops the temperature control strategy, packages and archives the data such as the entire temperature trajectory, heat budget change curve, SOC consumption record, and degradation mechanism triggering status, and uploads it to the cloud to form a complete delivery compliance report. The cloud-based collaborative scheduling module performs a final verification of the data. After confirming that there are no temperature violations or irregular scheduling situations, the closed loop of this delivery process is completed. All data is retained long-term for logistics traceability and system optimization, ensuring that temperature-controlled logistics is fully traceable, controllable, and verifiable.

[0029] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention are also within the protection scope of the present invention.

Claims

1. A method for temperature control and scheduling of temperature-controlled logistics boxes based on thermal budget, characterized in that, The method includes: S1. Collect the environmental conditions inside and outside the temperature-controlled logistics box, equipment operating parameters, energy consumption data, as well as positioning information and box opening status data to form comprehensive basic data support; S2. Receive the prior information of docking point sequence, arrival time, and estimated unpacking time from the cloud, and combine it with the collected basic data to complete the thermal correlation quantification calculation and optimize the control strategy to adapt to the current scenario. S3: The cloud receives status data reported by the edge terminal, performs optimal matching of orders and boxes based on multi-dimensional constraints, plans reasonable delivery routes, and triggers rolling rescheduling operations for abnormal situations; S4. Perform relevant operations according to the cloud scheduling plan and the optimized temperature control strategy, collect and feedback the system operating status in real time, and activate the degradation control mechanism when communication interruption or abnormal parameter conditions occur.

2. The temperature control and scheduling method for a temperature-controlled logistics box based on thermal budget as described in claim 1, characterized in that: In step S1, the temperature and humidity inside the box and the temperature outside the box are collected by a temperature and humidity sensor, the opening status of the box is collected by a door magnetic / photosensitive / accelerometer, the positioning information is collected by a positioning module, and the battery state of charge data related to the energy consumption of the device is collected by the battery SOC.

3. The temperature control and scheduling method for a temperature-controlled logistics box based on thermal budget as described in claim 1, characterized in that: In S2, a two / three-node adaptive thermal model is adopted, and thermal correlation quantification is completed by combining recursive least squares online parameter identification and extended Kalman filter state estimation. Temperature control capability is quantified by thermal budget index. Risk constraint model predictive control and temperature corridor strategy are used to optimize control strategy. Opening disturbance modeling is completed based on prior information.

4. The temperature control and scheduling method for a temperature-controlled logistics box based on thermal budget as described in claim 1, characterized in that: In S3, the multi-dimensional constraints include temperature zone, timeliness, thermal budget, battery SOC, and thermal risk index. The optimal matching between orders and containers is solved by the Hungarian algorithm or minimum cost flow. A vehicle route planning model with time windows is adopted and a thermal risk penalty term is introduced to plan the delivery route. Abnormal situations that trigger rolling rescheduling include traffic delays causing ETA deviation to exceed the threshold, a sudden drop in the container's thermal budget, temporary order additions, or order cancellations.

5. The temperature control and scheduling method for a temperature-controlled logistics box based on thermal budget as described in claim 1, characterized in that: In S4, the real-time feedback of system operating status includes thermal budget, thermal risk index, thermal model parameters, and battery SOC. The degradation control mechanism includes disconnection fault tolerance and heat preservation priority power consumption limitation strategy.

6. A temperature control and scheduling system for temperature-controlled logistics boxes based on thermal budget, characterized in that, This system is based on a thermal budget-based temperature control and scheduling method for temperature-controlled logistics boxes as described in any one of claims 1-5. Specifically, it includes: a data acquisition module, a thermal module and thermal budget calculation module, an edge prediction control module, and a cloud-based collaborative scheduling module. The data acquisition module acquires environmental parameters inside and outside the box, equipment operating status, location information, and box opening / closing status data, and performs preliminary data verification. The thermal module and thermal budget calculation module adapt thermally relevant parameters based on the acquired data, quantifying temperature control capabilities and determining risk levels. The edge prediction control module receives prior information from the cloud and optimizes control strategies by combining local data, possessing disturbance response and local emergency control functions. The cloud-based collaborative scheduling module receives status data and quantitative indicators uploaded from the edge terminal, completes order-box matching, path planning, and scheduling plan adjustments under abnormal scenarios, achieving closed-loop collaboration with the edge terminal.

7. The temperature control and scheduling system for a temperature-controlled logistics box based on thermal budget as described in claim 6, characterized in that: The data acquisition module includes: an environment and status perception module, and a device and energy consumption status acquisition module; Environment and Status Sensing Module: Collects temperature and humidity data inside and outside the enclosure through temperature and humidity sensors, captures opening status information through door magnetic / photosensitive / accelerometer sensors, and obtains real-time positioning data through GPS to ensure the comprehensiveness and real-time nature of environmental and status data; Equipment and Energy Consumption Status Acquisition Module: Acquires equipment operating parameters of the temperature-controlled logistics box, synchronously collects battery state of charge energy consumption related data, and completes basic data collection before preliminary data verification.

8. The temperature control and scheduling system for a temperature-controlled logistics box based on thermal budget as described in claim 6, characterized in that: The thermal module and thermal budget calculation module include: a thermal model update module and a thermal budget and risk calculation module; Thermal model update module: It adopts a two / three-node adaptive thermal model, combined with recursive least squares online parameter identification and extended Kalman filter state estimation technology, to complete the dynamic adaptation of thermal-related parameters and the real-time update of the thermal model. Thermal Budget and Risk Calculation Module: Quantifies the temperature control capability of temperature-controlled logistics boxes through thermal budget indicators, and combines collected environmental, equipment and energy consumption data to accurately determine the thermal risk level.

9. A temperature control and scheduling system for a temperature-controlled logistics box based on thermal budget as described in claim 6, characterized in that: The edge prediction control module includes: a disturbance prior mapping module, a risk constraint MPC optimization module, and an execution and degradation control module. Disturbance Prior Mapping Module: Receives prior information such as docking point sequence, arrival time, and estimated unpacking time from the cloud, and combines it with locally collected data to complete the modeling and mapping of unpacking disturbances; Risk-constrained MPC optimization module: It adopts risk-constrained model predictive control and temperature corridor strategy to optimize the temperature control strategy in a targeted manner to adapt to the current logistics and distribution scenario; Execution and Degradation Control Module: Executes cloud-based scheduling plans and optimized temperature control strategies, monitors system operating status in real time, and activates degradation control mechanisms such as disconnection fault tolerance and heat preservation priority power consumption limitation when communication interruption or abnormal parameter conditions occur.

10. A temperature control and scheduling system for a temperature-controlled logistics box based on thermal budget as described in claim 6, characterized in that: The cloud-based collaborative scheduling module includes: an order-container matching module, a path planning and risk optimization module, and a rolling rescheduling triggering module; Order-box matching module: Taking into account constraints such as temperature range, timeliness, thermal budget, battery SOC, and thermal risk index, the module solves the optimal matching scheme between orders and boxes using either the Hungarian algorithm or the minimum cost flow algorithm. Route planning and risk optimization module: It adopts a vehicle route planning model with time windows and introduces a hot risk penalty term to optimize route planning and generate a reasonable delivery route that takes into account both efficiency and safety. Rolling rescheduling trigger module: Real-time monitoring of abnormal situations in the logistics and delivery process. When traffic delays cause ETA deviation to exceed the threshold, the hot budget at the container end drops sharply, or there are temporary additional orders or order cancellations, rolling rescheduling operations are automatically triggered.

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