A temperature-controlled storage and autonomous mobile delivery system for a self-service vending apparatus

Abstract

The application provides a temperature control storage and autonomous mobile distribution system for a self-service vending device, and relates to the technical field of automatic distribution vehicles. The system obtains current environmental state parameters, real-time pose data and order space-time characteristic data through an environment and pose perception module and an order space-time analysis module; couples environmental thermal impedance and space trajectory by using a thermodynamics and trajectory joint planning module, and jointly generates global path planning parameters and a dynamic temperature adjustment curve; and then performs time sequence follow-up control on the thermal management circuit based on the remaining driving time and thermal inertia feedforward by using a time sequence follow-up cooperative controller, and synchronously schedules the chassis driving. The application breaks the limitation of navigation and temperature control separation, realizes precise temperature control delivery, and significantly reduces the overall vehicle energy consumption.

Description

Technical Field

[0001] This invention relates to the field of automated delivery vehicle technology, and in particular to a temperature-controlled storage and autonomous mobile delivery system for self-service vending machines. Background Technology

[0002] With the application of autonomous driving technology, vending machines are gradually evolving towards mobility. Mobile vending equipment typically consists of a drive-by-wire chassis and a cargo compartment. For example, Chinese patent document CN109131279A discloses an autonomous mobile vending vehicle and its obstacle avoidance method. This document describes a technical solution that uses a processor to output control signals to drive the drive-by-wire chassis of an unmanned vehicle for cruising. In scenarios involving temperature-controlled delivery, existing equipment typically installs independent cooling or heating components inside the cargo compartment. Before departure, staff manually set a fixed temperature threshold. During vehicle operation, the temperature control component relies solely on the status feedback from temperature sensors to maintain the set temperature.

[0003] The demands on product delivery experiences in current new retail scenarios are constantly increasing. A single mobile vending machine needs to be capable of simultaneously delivering goods with different temperature attributes. The temperature control system for the vending machine is evolving towards independent adjustment of multiple temperature zones. Meanwhile, due to the randomness of road conditions and user needs, the vehicle chassis requires frequent dynamic route scheduling. Researchers are beginning to focus on how to achieve hardware interconnection and system-level integration between the chassis's motion control and the vending machine's thermal management module to improve the overall operational efficiency of the entire device.

[0004] The existing equipment treats the vehicle navigation module and the cargo compartment temperature control module as two completely separate control subsystems. No underlying communication mechanism is established between the chassis travel time and the cargo compartment thermal load status. The global constant temperature control strategy adopted by the temperature control component wastes a significant amount of electrical energy during long-distance empty runs. This ineffective consumption of battery power severely shortens the vehicle's effective driving range. Furthermore, the commodity temperature change curve is not synchronized with the actual arrival time of the vehicle on the timeline. The equipment's spatial location data and thermodynamic data are not integrated for coordinated control. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a temperature-controlled storage and autonomous mobile delivery system for self-service vending equipment. This invention solves the problem that the prior art has problems such as high energy consumption and poor delivery temperature matching due to the disconnect between navigation and thermal management.

[0006] To achieve the above objectives, the present invention provides the following solution: A temperature-controlled storage and autonomous mobile delivery system for self-service vending machines includes: An autonomous mobile chassis is used to move within a preset work area according to the received driving instructions from the underlying system. A variable-temperature storage matrix is ​​mounted on the autonomous mobile chassis. The variable-temperature storage matrix includes two variable-temperature accommodating chambers with independent thermal management circuits. Each variable-temperature accommodating chamber is used to classify and store goods with different target delivery temperature attributes. The environment and pose perception module is used to collect current environmental state parameters and real-time pose data of the autonomous mobile chassis in real time. The order spatiotemporal analysis module is used to acquire target delivery orders, extract target delivery location and time constraints from the target delivery orders, and generate order spatiotemporal feature data based on the target delivery location and the time constraints; The thermodynamics and trajectory joint planning module is used to acquire the current environmental state parameters and the order spatiotemporal characteristic data, and generate joint scheduling instructions. The joint scheduling instructions include: global path planning parameters for the autonomous mobile chassis and dynamic temperature adjustment curves for the variable temperature accommodating chamber. The timing-following cooperative controller is used to acquire the joint scheduling command and the real-time pose data, and calculate the remaining driving time based on the real-time pose data and the global path planning parameters during the autonomous mobile chassis's movement. The timing-following cooperative controller is also used to perform timing-following control on the corresponding independent thermal management loop based on the remaining driving time and the dynamic temperature adjustment curve, and generate the underlying driving command to send to the autonomous mobile chassis.

[0007] The present invention discloses the following technical effects: This invention provides a temperature-controlled storage and autonomous mobile delivery system for self-service vending machines. By deeply coupling the spatial orientation trajectory of the autonomous mobile chassis with the thermodynamic state of the variable-temperature storage matrix, a dynamic temperature adjustment curve is generated using environmental state parameters and order spatiotemporal characteristic data. During delivery, the independent thermal management loop is time-series controlled, effectively overcoming the limitations of existing technologies where navigation and thermal management are isolated. This solution further integrates underlying control mechanisms such as thermal impedance map mapping, energy consumption boundary verification, calculus-based heat compensation, and thermal inertia feedforward compensation. This not only accurately matches the temperature gradient of the goods, ensuring that the goods reach the optimal delivery temperature upon arrival at the target delivery location, but also completely eliminates the ineffective battery loss caused by traditional static global constant temperature control. While significantly reducing overall energy consumption and greatly extending the effective operating mileage of the equipment, this system achieves adaptive fault-tolerant scheduling and a high-quality product delivery experience under real-world conditions. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 Provided for embodiments of the present invention; Figure label: 1- Autonomous mobile chassis, 2- Variable temperature storage matrix, 3- Environment and pose perception module, 4- Order spatiotemporal analysis module, 5- Thermodynamics and trajectory joint planning module, 6- Timing follow-up collaborative controller. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0012] like Figure 1 As shown, the present invention provides a temperature-controlled storage and autonomous mobile delivery system for self-service vending machines, comprising: Autonomous mobile chassis 1 is used to move within a preset working area according to the received driving instructions from the underlying system; The variable temperature storage matrix 2 is mounted on the autonomous mobile chassis 1. The variable temperature storage matrix 2 includes two variable temperature accommodating chambers with independent thermal management circuits. Each variable temperature accommodating chamber is used to classify and store goods with different target delivery temperature attributes. The environment and pose perception module 3 is used to collect the current environmental state parameters and the real-time pose data of the autonomous mobile chassis 1 in real time. Order spatiotemporal analysis module 4 is used to acquire target delivery orders, extract target delivery location and time constraints from the target delivery orders, and generate order spatiotemporal feature data based on the target delivery location and the time constraints. Thermodynamics and trajectory joint planning module 5 is used to acquire the current environmental state parameters and the order spatiotemporal characteristic data, and generate joint scheduling instructions. The joint scheduling instructions include: global path planning parameters for the autonomous mobile chassis 1 and dynamic temperature adjustment curves for the variable temperature accommodating chamber. The timing-following cooperative controller 6 is used to acquire the joint scheduling command and the real-time pose data, and calculate the remaining driving time based on the real-time pose data and the global path planning parameters during the movement of the autonomous mobile chassis 1. The timing-following cooperative controller 6 is also used to perform timing-following control on the corresponding independent thermal management loop based on the remaining driving time and the dynamic temperature adjustment curve, and generate the underlying driving command to send to the autonomous mobile chassis 1.

[0013] Furthermore, the autonomous mobile chassis 1 and the independent thermal management circuit are specifically described as follows: This embodiment provides an autonomous mobile chassis 1 equipped with a variable-temperature storage matrix 2, which provides the basic energy node for overall operation through a distributed power management component. The distributed power management component is physically fixed within the bottom anti-collision layer of the autonomous mobile chassis 1, serving as the central hub for the convergence and distribution of the vehicle's electrical energy. It extracts the real-time output voltage and remaining charge of the built-in power battery, dividing the total electrical energy into driving energy and temperature control energy according to the power consumption level. In specific power distribution processing, the distributed power management component uses 48 volts as the baseline power distribution threshold. When the real-time output voltage of the power battery is higher than or equal to 48 volts, 70% of the total output power is allocated as driving energy to the chassis's power wheel assembly, and the remaining 30% is allocated to the variable-temperature storage matrix 2. When the real-time output voltage is lower than 48 volts, the distributed power management component triggers an energy protection matching mechanism, forcibly limiting the driving energy to 40% of the total power. The aforementioned driving energy directly provides the power source for the continuous physical movement of the chassis's power wheel assembly within the preset operating area.

[0014] This embodiment utilizes a drive-by-wire controller to receive underlying driving commands and perform the virtual command-to-physical drive signal mapping. The drive-by-wire controller receives the underlying driving commands in real-time from an external timing-based coordinating controller 6 via an onboard CAN bus communication interface at a baud rate of 500 kilobits per second. The underlying driving commands include a set of spatial coordinate points on the previously planned path and the desired vehicle speed. The drive-by-wire controller extracts the difference between the desired speed and the current actual speed, and extracts the yaw angle of the target spatial coordinate point relative to the current heading. It then maps the difference into corresponding drive torque parameters and the yaw angle into corresponding steering angle parameters. In the calculation of the drive torque parameters, the drive-by-wire controller reads a pre-installed motor test bench calibration table within a non-volatile flash memory chip soldered to the motherboard, looks up the table based on the difference, outputs the precise torque value, and locks the maximum torque output threshold at 120 Newton-meters at the program's underlying level. The chassis power wheel assembly is connected to the power supply harness of the distributed power management component and the signal harness of the drive-by-wire controller, respectively. It directly consumes the allocated drive power and strictly executes the calculated drive torque parameters and steering angle parameters to complete the autonomous mobile chassis 1's movement and lane change within the preset working area.

[0015] In this embodiment, a variable-temperature storage matrix 2 is mounted on the top bearing surface of the autonomous mobile chassis 1. The variable-temperature storage matrix 2 is rigidly anchored to the autonomous mobile chassis 1 around its bottom via four shock-absorbing flange seats, forming the core structure for achieving temperature-controlled logistics in this embodiment. The variable-temperature storage matrix 2 is rigidly divided into two completely physically isolated variable-temperature storage chambers by a polymer vacuum insulation panel. Each of the two chambers is equipped with an independent thermal management circuit, preventing heat crosstalk between them. The variable-temperature storage chambers are categorized and stored according to the inherent heat capacity characteristics and target delivery temperature attributes of the goods being sold. The target delivery temperature attribute refers to the absolute temperature baseline value that the goods must possess at the moment of delivery to the consumer. In this embodiment, the target delivery temperature attribute of the first variable-temperature storage chamber is constantly set at 5 degrees Celsius to store refrigerated goods, and the target delivery temperature attribute of the second variable-temperature storage chamber is constantly set at 65 degrees Celsius to store insulated goods, thereby achieving a single vehicle operating in a dual-temperature zone simultaneously and independently.

[0016] In this embodiment, the independent thermal management loop relies on a bidirectional heat pump heat exchanger as the output end for cooling or heating. The bidirectional heat pump heat exchanger is fixed in the back chamber of the variable-temperature storage matrix 2 and receives dynamic temperature control curves from the thermodynamics and trajectory joint planning module 5 in real time via an Ethernet interface, enabling directional and quantitative output of cooling and heating loads. This control process extracts the target power percentage corresponding to the current time node on the dynamic temperature control curve as the underlying calculation data. This target power percentage is multiplied by the maximum rated power of the bidirectional heat pump heat exchanger to obtain the required absolute control power. Based on this absolute control power, the corresponding cooling or heating load is output. Under cooling load output conditions, when the obtained target power percentage is 80% and the maximum rated power is 800 watts, the bidirectional heat pump heat exchanger directly starts the variable-frequency compressor at the 640-watt absolute control power node and drives the four-way reversing valve to switch to the cooling cycle mode, continuously producing low-temperature cooling loads for the corresponding variable-temperature storage chamber.

[0017] In this embodiment, the intracavity fluid distribution for the generated cooling or heating load is completed through a servo flow regulation component and a heat transfer fluid microcirculation component. The servo flow regulation component extracts the real-time temperature gradient change rate characterized on the dynamic temperature regulation curve and calculates the current required circulation flow rate of the refrigerant or heat transfer fluid based on this real-time temperature gradient change rate. In the specific flow locking calculation, the servo flow regulation component extracts the absolute value of the real-time temperature gradient change rate. When the absolute value of the change rate is greater than 3 degrees Celsius per minute, the circulation flow rate threshold is set to 15 liters per minute; when the absolute value of the change rate is less than or equal to 3 degrees Celsius per minute, the circulation flow rate threshold is set to 6 liters per minute, and the circulation flow rate is locked by adjusting the stepper motor rotation angle of the electronic expansion valve. The heat-conducting fluid micro-circulation component consists of a copper heat exchange coil laid on the side wall of the variable-temperature accommodating chamber, an inlet manifold, a return manifold, and a booster pump. The inlet manifold is connected to the output port of the servo flow regulating component, and the return manifold is connected to the suction port of the bidirectional heat pump heat exchange component. The heat-conducting fluid micro-circulation component distributes the generated cooling or heating load evenly into each corresponding variable-temperature accommodating chamber based on a set circulation flow rate of 15 liters or 6 liters per minute, thus completing a closed loop of physical heat exchange within the chamber.

[0018] Furthermore, the specific implementation process of the environment and pose perception module 3 and the order spatiotemporal analysis module 4 is as follows: In this embodiment, the environmental detection unit in the environment and pose perception module 3 collects current environmental state parameters in real time. The environmental detection unit is physically integrated around the top outer shell of the autonomous mobile chassis 1 and includes a temperature probe, a humidity probe, and a barometer. The current environmental state parameters refer to external environmental physical quantities that directly affect the heat dissipation of the variable-temperature containment chamber. In the specific data acquisition process, the environmental detection unit acquires the absolute ambient temperature, relative humidity, and atmospheric pressure values ​​around the chassis according to a preset data acquisition cycle of 50 milliseconds. The unit limits the effective acquisition threshold for the absolute ambient temperature value to between -40 degrees Celsius and +85 degrees Celsius. When an abnormal value exceeding this range is detected, a hardware-level reset procedure is automatically triggered. After processing by the underlying analog-to-digital converter chip, the collected physical quantities directly generate a thermal input reference for characterizing the external thermal environment, i.e., the current environmental state parameters.

[0019] This embodiment utilizes a pose measurement unit to acquire real-time pose data of the autonomous mobile chassis 1. The pose measurement unit consists of an inertial measurement unit (IMU) fixed at the geometric center of the chassis and a lidar scanner mounted on top. The real-time pose data characterizes the absolute position coordinates of the autonomous mobile chassis 1 in 3D physical space and the orientation angle of the vehicle's front. During data acquisition, the IMU outputs the chassis's 3-axis acceleration and 3-axis angular velocity at a frequency of 200 times per second, while the lidar scanner acquires the surrounding point cloud distance data at a rotation speed of 10 revolutions per second. The processing chip inside the pose measurement unit extracts these two sets of heterogeneous data, performs fusion calculations using a Kalman filter algorithm, updates the state using the pose estimate from the previous moment and the point cloud distance data from the current moment, removes high-frequency noise, and outputs smooth real-time pose data with a positioning error consistently within 0.05 meters, accurately reflecting the chassis's current dynamic motion state and spatial dwell position.

[0020] This embodiment utilizes a sensing data timestamp synchronization unit to perform clock-level data alignment on the asynchronously acquired data. Clock-level data alignment refers to using a high-precision temperature-compensated crystal oscillator built into the underlying hardware motherboard to assign absolute time stamps with the same reference time axis to data collected by different sensors, thereby eliminating data timing misalignment caused by differences in the acquisition frequencies of different hardware. In the specific data processing flow, the sensing data timestamp synchronization unit extracts the current environmental state parameters and the real-time pose data, and reads the microsecond-level absolute timestamp value generated by the temperature-compensated crystal oscillator. Subsequently, the unit compares the timestamps of the two sets of data, determines data frames with an absolute timestamp difference of less than 5 microseconds as concurrent data at the same physical moment, and bundles them into a single synchronization data packet. After alignment, the packet is output to the thermodynamics and trajectory joint planning module 5 and the timing follow-up cooperative controller 6 as the reference input source for subsequent path planning and thermodynamic calculations.

[0021] This embodiment relies on an onboard edge computing gateway fixed within the equipment compartment of the autonomous mobile chassis 1. By executing program instructions stored in its internal memory, the order information extraction unit and delivery constraint conversion unit in the order spatiotemporal parsing module 4 complete data parsing. The order information extraction unit receives the target delivery order in a standardized data exchange text format from the cloud scheduling server via a wireless cellular network, and extracts the target delivery location and time constraints in string form by retrieving specific key-value pairs within the text. Next, the delivery constraint conversion unit converts the target delivery location into spatial coordinates in a global coordinate system and converts the time constraints into a countdown timestamp. In the specific mapping and conversion process, the delivery constraint conversion unit retrieves a locally stored high-precision vector map and an associated geographic point of interest (POI) database, matches the textual description of the target delivery location with the POIs, directly maps and outputs precise longitude and latitude coordinate values, and subtracts the current system motherboard's absolute millisecond value from the expected delivery absolute clock value in the time constraints to generate a countdown timestamp in seconds. In this embodiment, the maximum delivery distance boundary of the control system is limited to 5000 meters. If the straight-line distance between the generated spatial coordinates and the current location exceeds this distance threshold, the target delivery order will be directly returned to the cloud scheduling server.

[0022] This embodiment continues to utilize the spatiotemporal feature generation unit in the vehicle edge computing gateway to fuse the spatial coordinates with the countdown timestamp to generate the order spatiotemporal feature data. The order spatiotemporal feature data refers to composite data features that can be directly read by the chassis control algorithm and simultaneously contain physical spatial endpoints and time margin constraints. In the specific feature fusion operation, the spatiotemporal feature generation unit extracts the longitude and latitude coordinate values ​​from the spatial coordinates and extracts the countdown timestamp value. This unit establishes the three extracted values ​​into a 3D feature vector. When performing feature weighting, the weight coefficient for spatial distance is fixed at 0.6, and the weight coefficient for time urgency is fixed at 0.4. Subsequently, a minimax linear projection algorithm is used for normalization scaling, subtracting the preset minimum value from the current value of each feature and dividing by the range, uniformly compressing and mapping the values ​​to the boundary range of -1 to +1. The 3D feature vector after the above extreme value normalization process constitutes the order spatiotemporal feature data, which is used to intuitively and quantitatively characterize the comprehensive execution difficulty of this delivery task in terms of spatial span and time constraints.

[0023] Furthermore, the specific implementation process of the thermodynamics and trajectory joint planning module 5 is as follows: This embodiment relies on an onboard multi-core industrial control processor to perform the underlying calculations of the thermodynamics and trajectory joint planning module 5. The road network topology construction unit extracts the spatial coordinates of the target delivery location from the order's spatiotemporal feature data, selects available road segments within a 5000-meter radius of the current location from the local high-precision map database, and generates an initial road network topology map containing 200 discrete traffic nodes. Subsequently, the environmental thermal impedance mapping unit obtains the current environmental state parameters and converts them into spatially distributed thermal impedance weights. The thermal impedance weight refers to an evaluation index of additional heat loss caused by the external physical environment on the corresponding road segment. In specific data processing, the absolute environmental temperature value in the current environmental state parameters is extracted, and the absolute difference after subtracting the standard reference temperature of 25 degrees Celsius is multiplied by a fixed heat convection coefficient of 0.4 to obtain the thermal impedance weight of the road segment. This weight is then superimposed onto the corresponding lines in the initial road network topology map to generate a weighted road network topology map with thermodynamic properties. Next, the path optimization unit uses a heuristic best-priority graph search algorithm based on the weighted road network topology map to find the connected path with the minimum total weight and spatial distance, and generates global path planning parameters containing 15 continuous latitude and longitude coordinates for the autonomous mobile chassis 1.

[0024] This embodiment utilizes the chamber background heat capacity extraction subunit within the adjustment curve calculation unit to obtain the physical specific heat capacity parameters of the temperature-controlled chamber and the initial temperature of the goods being sold. The physical specific heat capacity parameters characterize the physical constants of heat absorbed or released by the internal medium and goods within the chamber. This subunit retrieves the physical specific heat capacity parameters for refrigerated goods from the local solid-state drive, maintaining a constant reading of 4200 joules per kilogram in Kelvin, and simultaneously acquires the initial temperature of 12 degrees Celsius measured by the chamber's internal probe. Next, the environmental heat dissipation assessment subunit establishes a heat dissipation analysis model of the temperature-controlled chamber during its movement based on the current environmental state parameters. Specifically, this subunit extracts the absolute ambient temperature and atmospheric pressure values, combines them with the heat conduction cross-sectional area of ​​the temperature-controlled chamber surface (2 square meters) and the insulation layer thickness (0.05 meters) embedded in the program, and calculates the baseline heat transfer rate under the internal and external temperature difference conditions, thereby establishing a heat dissipation analysis model characterizing the chamber's loss of 150 joules of physical heat per second.

[0025] This embodiment utilizes a dynamic heat load integral subunit to perform thermodynamic calculus calculations, combining the aforementioned physical specific heat capacity parameters and heat dissipation analysis model. During this calculation, the countdown timestamp dimension, defined by the order's spatiotemporal characteristic data, serves as the upper and lower limits of the definite integral. The 150 joules of heat lost per second are accumulated over time to obtain the cumulative heat loss value for the entire journey. Subsequently, the heat compensation boundary solution subunit subtracts the initial temperature of 12 degrees Celsius from the pre-set target delivery temperature attribute of 5 degrees Celsius to obtain the temperature difference value. This difference is multiplied by the aforementioned physical specific heat capacity parameter to obtain the basic compensation cooling capacity. This is then added to the cumulative heat loss value to calculate the total target heat compensation value of 85,000 joules. The time-series reverse derivation subunit extracts the pre-stored cooling energy efficiency ratio constant (specifically set to 2.5) from the chassis firmware, divides the 85,000 joules by 2.5 to convert it into an actual energy consumption boundary of 34,000 joules, and then distributes the heat pump power in reverse along the time axis from the end point to the beginning point to generate the initial adjustment curve. The temperature control gradient smoothing subunit uses a first-order inertial low-pass filter algorithm to perform arithmetic mean smoothing on abrupt nodes in the initial adjustment curve where the power change amplitude exceeds 15%, generating a dynamic temperature adjustment curve for stable temperature control.

[0026] Specifically, the expression for the cumulative heat loss value is: ; in, This represents the cumulative heat loss value. The total duration of the travel period limited by the countdown timestamp; The reference thermal conductivity of the outer shell material of the variable temperature accommodating chamber; The thermal conductivity cross-sectional area of ​​the surface of the variable-temperature accommodating chamber; This refers to the thickness of the insulation layer; It is a numerical function of absolute ambient temperature that changes over time; The initial temperature of the goods for sale; This embodiment utilizes an energy consumption boundary verification unit to estimate the system's expected total energy consumption for the entire vehicle. The verification unit extracts a constant per unit mileage from the historical rolling energy consumption log built into the autonomous mobile chassis 1. This constant is multiplied by the expected mileage calculated from the global path planning parameters to obtain the absolute value of drive power consumption. Simultaneously, the power values ​​at each node in the dynamic temperature control curve are integrated and summed to obtain the absolute value of temperature control power consumption. The two are then added together to obtain the expected total energy consumption for the entire vehicle. This embodiment sets the current remaining battery power threshold to 6000 watt-hours. If the expected total energy consumption of the entire vehicle reaches 6500 watt-hours, it is considered to have exceeded the limit. To ensure the system does not get stuck, the energy consumption boundary verification unit sets a maximum replanning limit of 3 times. When the limit is exceeded, the path optimization unit is triggered to replan. If the expected total energy consumption of the entire vehicle exceeds 6000 watt-hours after 3 consecutive replannings, a chassis speed reduction and power-saving fallback strategy is triggered. This strategy reduces the expected arrival speed in the global path planning parameters to decrease the absolute value of drive power consumption, thereby forcibly ensuring that the overall energy consumption falls back to within the safe boundary.

[0027] The expression for the expected total energy consumption of the vehicle is: ; in, This refers to the total expected energy consumption of the entire vehicle. This is the unit mileage power consumption constant extracted from historical energy consumption rolling ledger records; This represents the total number of continuous latitude and longitude coordinate points in the global path planning parameters. and The first The longitude and latitude coordinates of each access node; The maximum rated power of the hardware for the heat exchange components of the bidirectional heat pump; For the corresponding time node on the dynamic temperature regulation curve Target power percentage; This embodiment utilizes a joint scheduling instruction encapsulation unit to execute the merged output of underlying instructions when it is determined that the expected total energy consumption of the vehicle does not exceed the current remaining power threshold (or a power reserve strategy has been triggered). In this operation, the joint scheduling instruction encapsulation unit performs instruction protocol-level synchronous encapsulation of global path planning parameters and dynamic temperature adjustment curves. This protocol-level synchronous encapsulation involves extracting the binary values ​​of the two sets of data, adding an 8-byte synchronization identifier to the header of the standard control area network communication data frame, writing trajectory parameters and temperature curve parameters in parallel across different data segments, and adding a 4-byte cyclic redundancy check code to the tail of the message, thereby outputting a tightly formatted joint scheduling instruction. Finally, this embodiment uses the vehicle data bus interface to send the joint scheduling instruction to each execution hardware controller at a communication transmission rate of 1 megabit per second.

[0028] Furthermore, the specific implementation process of the timing-following cooperative controller 6 is as follows: In this embodiment, a microprocessor in a timing-driven collaborative controller 6, fixed on the control motherboard of the autonomous mobile chassis 1, performs core calculations on pose error and driving progress. The pose error compensation unit within the microprocessor first acquires real-time pose data reported by the front-end environment and pose perception module 3, and then uses a Kalman filter algorithm to denoise and correct the coordinates of this real-time pose data. In the specific Kalman filter operation, the microprocessor sets the system process noise covariance constant to 0.05 and the observation noise covariance constant to 0.1. It uses the optimal estimated pose from the previous cycle to predict the current state, and calculates the Kalman gain based on the current sensor observations. This eliminates high-frequency coordinate drift noise caused by road bumps, outputting high-precision pose data with a constant absolute positioning error of less than 0.02 meters. Subsequently, the driving progress calculation unit extracts the current coordinate point from this high-precision pose data and compares it with 15 consecutive latitude and longitude coordinate points included in the global path planning parameters. It then calculates the difference between the already traveled trajectory and the planned total trajectory to obtain the remaining physical driving distance. The processing unit extracts the real-time average wheel speed of the current chassis, divides the remaining physical driving distance by the real-time average wheel speed, and generates the remaining driving time in seconds. When the remaining physical driving distance is 500 meters and the current average wheel speed is 5 meters per second, the deterministic remaining driving time is 100 seconds.

[0029] This embodiment utilizes a rendezvous node prediction unit to predict the deviation between the actual arrival time and the remaining travel time at the target delivery location, based on the current instantaneous speed and acceleration of the autonomous mobile chassis 1. The rendezvous node prediction unit directly reads the current instantaneous speed reported by the bottom-level wheel encoder belonging to the pose measurement unit and the instantaneous acceleration reported by the inertial measurement unit via the internal vehicle control bus. Based on the kinematic mapping relationship of uniformly accelerated linear motion, it calculates the absolute true time required to complete the remaining physical travel distance under the current acceleration state and defines this absolute true time as the actual arrival time. In the specific data subtraction process, the rendezvous node prediction unit subtracts the theoretical remaining travel time calculated in the previous segment from the actual arrival time, generating a time difference value, which is the deviation value. Under the deceleration condition of an instantaneous speed of 6 meters per second and an instantaneous acceleration of -0.5 meters per square second, the calculated actual arrival time is 110 seconds. Subtracting this from the theoretical remaining travel time of 100 seconds yields a deviation value of +10 seconds.

[0030] This embodiment uses a thermal inertia feedforward compensation unit to generate a feedforward time offset by combining the deviation value with the inherent thermal properties of the physical system. The inherent thermal hysteresis time refers to the constant time delay between the moment the bidirectional heat pump heat exchange component receives the power adjustment level signal and the moment the corresponding temperature sensor inside the variable-temperature containment chamber detects a corresponding decrease in the actual temperature. In this embodiment, this inherent thermal hysteresis time is hard-calibrated to a fixed 45 seconds in the factory firmware. The thermal inertia feedforward compensation unit reads this 45-second inherent thermal hysteresis time and algebraically adds it to the deviation value obtained in the previous steps. Adding the 45-second hysteresis constant to the deviation value of +10 seconds, the unit generates a fixed 55-second feedforward time offset. The core function of introducing this feedforward time offset is to issue cooling or heating commands in advance on the underlying time axis, thereby offsetting the time lag effect caused by the mechanical start-up of the heat pump compressor and the heat conduction of the copper pipes at the physical level.

[0031] This embodiment relies on a control parameter matching unit to retrieve and match the target power parameter for the current moment from the dynamic temperature adjustment curve based on the remaining driving time and the feedforward time offset. The dynamic temperature adjustment curve is a discrete data mapping table with timestamp as the horizontal axis and heat pump power percentage as the vertical axis. In the specific matching mapping process, the control parameter matching unit directly subtracts the feedforward time offset from the currently calculated remaining driving time to obtain a virtual execution timestamp with advance compensation. When the remaining driving time is 100 seconds and the feedforward time offset is 55 seconds, the two are subtracted to obtain a virtual execution timestamp of 45 seconds; the unit directly looks up the node with the horizontal axis of 45 seconds in the data table, extracts its corresponding vertical axis value, and when this value is 85%, it directly locks 85% as the target power parameter currently issued to the heat pump component. Meanwhile, to prevent array out of bounds when approaching the finish line, when the remaining driving time is less than the feedforward time offset, causing the virtual execution timestamp to be calculated as a negative number, the control parameter matching unit triggers the zero-value truncation protection boundary, forcibly locking the virtual execution timestamp to 0, and overwriting the target power parameter to 0%, directly shutting down the compressor to enter a pure physical heat insulation state.

[0032] The expression for the virtual execution timestamp after advance compensation is: ; in, This is a virtual execution timestamp that has undergone advance compensation; The remaining physical travel distance is the difference between the traveled trajectory and the planned total trajectory. This represents the current real-time average wheel speed of the chassis. This is the inherent thermal hysteresis constant of the heat exchange components of a bidirectional heat pump; This represents the deviation between the actual arrival time and the theoretical remaining travel time. The function is selected to determine the maximum value of the underlying zero-value truncation protection boundary. This embodiment achieves bidirectional closed-loop control of the independent thermal management loop and chassis power through the lower-level subunit in the instruction coordination distribution unit. The time interval division subunit divides the remaining driving time into three different follow-up control intervals according to the urgency of time: the time period greater than 120 seconds is defined as the first follow-up control interval, the time period from 60 seconds to 120 seconds is defined as the second follow-up control interval, and the time period less than 60 seconds is defined as the third follow-up control interval. When the follow-up power adjustment subunit detects that it is currently in the first follow-up control interval, it directly retrieves the aforementioned matched 85% target power parameter and sends it to the bidirectional heat pump heat exchange component for high-power cooling; when entering the third follow-up control interval, it triggers the end frequency reduction logic, forcibly reducing the target power parameter to 20% to perform low-power operation. Meanwhile, the driving speed compensation subunit reads the real-time electronic control parameters reported by the distributed power management component mentioned above through the communication data line. When the total discharge current of the power battery reaches 40 amps and the cooling power occupies the dominant load, in order to prevent the main wiring harness of the vehicle from overheating due to overload, the subunit simultaneously modifies the underlying driving command sent to the drive-by-wire controller, forcibly limiting the maximum allowable expected vehicle speed from 8 meters per second to 5 meters per second. This reduces the driving power consumption at the physical level, ensuring that the variable temperature storage matrix 2 obtains sufficient temperature control power while maintaining the stable movement of the chassis.

[0033] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0034] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A temperature-controlled storage and autonomous mobile delivery system for self-service vending machines, characterized in that, include: An autonomous mobile chassis is used to move within a preset work area according to the received driving instructions from the underlying system. A variable-temperature storage matrix is ​​mounted on the autonomous mobile chassis. The variable-temperature storage matrix includes two variable-temperature accommodating chambers with independent thermal management circuits. Each variable-temperature accommodating chamber is used to classify and store goods with different target delivery temperature attributes. The environment and pose perception module is used to collect current environmental state parameters and real-time pose data of the autonomous mobile chassis in real time. The order spatiotemporal analysis module is used to acquire target delivery orders, extract target delivery location and time constraints from the target delivery orders, and generate order spatiotemporal feature data based on the target delivery location and the time constraints; The thermodynamics and trajectory joint planning module is used to acquire the current environmental state parameters and the order spatiotemporal characteristic data, and generate joint scheduling instructions. The joint scheduling instructions include: global path planning parameters for the autonomous mobile chassis and dynamic temperature adjustment curves for the variable temperature accommodating chamber. The timing-following cooperative controller is used to acquire the joint scheduling command and the real-time pose data, and calculate the remaining driving time based on the real-time pose data and the global path planning parameters during the autonomous mobile chassis's movement. The timing-following cooperative controller is also used to perform timing-following control on the corresponding independent thermal management loop based on the remaining driving time and the dynamic temperature adjustment curve, and generate the underlying driving command to send to the autonomous mobile chassis.

2. The temperature-controlled storage and autonomous mobile delivery system for self-service vending equipment according to claim 1, characterized in that, The autonomous mobile chassis includes: Drive-by-wire controller, distributed power management components, and chassis drive wheel assembly; The drive-by-wire controller is used to receive the underlying driving command and convert it into corresponding drive torque parameters and steering angle parameters; The distributed power management component is used to allocate the driving power required for driving the autonomous mobile chassis. The chassis drive wheel assembly is electrically connected to the drive-by-wire controller and the distributed power management component, respectively, and is used to utilize the drive power and drive the autonomous mobile chassis to move within the preset working area according to the drive torque parameters and the steering angle parameters.

3. The temperature-controlled storage and autonomous mobile delivery system for self-service vending equipment according to claim 1, characterized in that, The independent thermal management loop includes: Bidirectional heat pump heat exchange components, servo flow regulation components, and heat transfer fluid microcirculation components; The bidirectional heat pump heat exchange component is used to respond to the control of the time-following cooperative controller and output a cooling load or heating load with corresponding power. The servo flow regulation component is used to adjust the circulation flow rate of the refrigerant or heat transfer fluid according to the dynamic temperature regulation curve. The heat-conducting fluid micro-circulation component is connected to the bidirectional heat pump heat exchange component and the servo flow regulation component, respectively, and is used to distribute the cooling load or heating load in the corresponding variable temperature accommodating chambers with the circulation flow rate.

4. A temperature-controlled storage and autonomous mobile delivery system for self-service vending equipment according to claim 1, characterized in that, The environment and pose perception module includes: Environmental detection unit, pose measurement unit, and sensing data timestamp synchronization unit; The environmental detection unit is used to collect current environmental status parameters in real time; The pose measurement unit is used to collect real-time pose data of the autonomous mobile chassis. The sensing data timestamp synchronization unit is used to perform clock-level data alignment between the current environmental state parameters and the real-time pose data, and then output the data to the thermodynamic and trajectory joint planning module and the timing follow-up cooperative controller.

5. A temperature-controlled storage and autonomous mobile delivery system for self-service vending machines according to claim 1, characterized in that, The order spatiotemporal analysis module includes: Order information extraction unit, delivery constraint conversion unit, and spatiotemporal feature generation unit; The order information extraction unit is used to obtain the target delivery order and extract the target delivery location and time constraints from the target delivery order; The delivery constraint conversion unit is used to convert the target delivery location into spatial coordinates in the global coordinate system and to convert the time constraint into a countdown timestamp. The spatiotemporal feature generation unit is used to fuse the spatial coordinates with the countdown timestamp to generate the order spatiotemporal feature data.

6. A temperature-controlled storage and autonomous mobile delivery system for self-service vending equipment according to claim 1, characterized in that, The thermodynamics and trajectory joint planning module includes: The system includes a road network topology construction unit, an environmental thermal impedance mapping unit, a path optimization unit, an adjustment curve calculation unit, an energy consumption boundary verification unit, and a joint scheduling instruction encapsulation unit. The road network topology construction unit is used to generate an initial road network topology map containing multiple access nodes based on the target delivery location in the order spatiotemporal feature data. The environmental thermal impedance mapping unit is used to obtain the current environmental state parameters, convert them into spatially distributed thermal impedance weights, and superimpose them onto the initial road network topology map to generate a weighted road network topology map with thermodynamic properties. The path optimization unit is used to generate the global path planning parameters for the autonomous mobile chassis based on the weighted road network topology map. The adjustment curve calculation unit is used to obtain the current environmental state parameters and, in conjunction with the order spatiotemporal feature data, generate the dynamic temperature adjustment curve for the variable temperature accommodating chamber. The energy consumption boundary verification unit is used to estimate the expected total energy consumption of the system based on the global path planning parameters and the dynamic temperature adjustment curve, and to trigger the path optimization unit to regenerate the global path planning parameters when the expected total energy consumption of the system exceeds the current remaining power threshold. The joint scheduling instruction encapsulation unit is used to encapsulate the global path planning parameters and the dynamic temperature adjustment curve at the instruction protocol level and output the joint scheduling instruction when the expected total energy consumption of the vehicle does not exceed the current remaining power threshold.

7. A temperature-controlled storage and autonomous mobile delivery system for self-service vending machines according to claim 6, characterized in that, The adjustment curve calculation unit includes: Sub-units for extracting chamber background heat capacity, environmental heat dissipation assessment, dynamic heat load integration, heat compensation boundary solution, time-series inverse deduction, and temperature control gradient smoothing; The chamber background heat capacity extraction subunit is used to obtain the physical specific heat capacity parameters of the corresponding variable temperature accommodating chamber and the initial temperature of the product being sold. The environmental heat dissipation assessment subunit is used to establish a heat dissipation analysis model of the variable temperature accommodating chamber during its movement based on the current environmental state parameters. The dynamic heat load integral subunit is used to combine the physical specific heat capacity parameter and the heat dissipation analysis model to perform calculus calculations on the time dimension defined by the order spatiotemporal characteristic data to obtain the cumulative heat loss value. The heat compensation boundary solving subunit is used to calculate the target total heat compensation value within the travel cycle based on the initial temperature and the cumulative heat loss value. The time-series inverse deduction subunit is used to perform time-series inverse solution based on the target total heat compensation value to generate an initial adjustment curve for ensuring that the variable temperature accommodating chamber is at the target delivery temperature attribute when it arrives at the target delivery position. The temperature control gradient smoothing subunit is used to perform abrupt change point elimination processing on the initial adjustment curve using a low-pass filtering algorithm to generate the dynamic temperature adjustment curve.

8. A temperature-controlled storage and autonomous mobile delivery system for self-service vending equipment according to claim 1, characterized in that, The timing-following cooperative controller includes: The unit includes a pose error compensation unit, a driving progress calculation unit, a rendezvous node prediction unit, a thermal inertia feedforward compensation unit, a control parameter matching unit, and an instruction coordination and distribution unit. The pose error compensation unit is used to acquire the real-time pose data and use the Kalman filter algorithm to denoise and correct the coordinates of the real-time pose data, and output high-precision pose data. The driving progress calculation unit is used to obtain the joint scheduling instruction and calculate the remaining driving time based on the high-precision pose data and the global path planning parameters during the autonomous mobile chassis's movement. The rendezvous node prediction unit is used to predict the deviation between the actual arrival time at the target delivery location and the remaining travel time based on the current instantaneous speed and acceleration of the autonomous mobile chassis. The thermal inertia feedforward compensation unit is used to generate a feedforward time offset based on the inherent thermal hysteresis time of the independent thermal management loop and the deviation value. The control parameter matching unit is used to retrieve and match the target power parameter at the current moment from the dynamic temperature adjustment curve based on the remaining driving time and the feedforward time offset. The instruction coordination and distribution unit is used to perform timing follow-up control on the corresponding independent thermal management circuit according to the target power parameters, and synchronously generate the underlying driving instruction to send to the autonomous mobile chassis.

9. A temperature-controlled storage and autonomous mobile delivery system for self-service vending equipment according to claim 8, characterized in that, The instruction coordination distribution unit includes: The time-series interval division subunit, the follow-up power adjustment subunit, and the driving speed compensation subunit; The time interval division subunit is used to divide the remaining driving time into at least two different follow-up control intervals; The servo power adjustment subunit is used to retrieve the target power parameters in different servo control intervals to perform the timing servo control on the corresponding independent thermal management loop; The driving speed compensation subunit is used to synchronously adjust the underlying driving command according to the current vehicle energy consumption status when the follow-up power adjustment subunit executes the timing follow-up control, so as to maintain or adjust the driving speed of the autonomous moving chassis.

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