Intelligent logistics scheduling and unmanned distribution system for zongzi

Abstract

The present application relates to unmanned delivery technical field, particularly to a kind of zongzi intelligent logistics scheduling and unmanned delivery system, including order data acquisition module, edible window modeling module, risk assessment module, joint scheduling decision module, environmental control module, transport control module and unmanned delivery execution module.The present application constructs edible window parameters, and the edible window parameters are combined with distribution scheduling process, to determine more suitable delivery time range, to avoid the problem of taste drop when received, improve the actual food quality retention capability of zongzi;By generating quality risk parameters, and based on the quality risk parameters, the cabin environment of unmanned delivery equipment is adjusted, to reduce the probability of hot zongzi returning to moisture, packaging moisture and taste hardening;By generating stability risk parameters and adjusting;Improve the structural stability and final delivery integrity of zongzi in the transportation process.

Description

Technical Field

[0001] This invention relates to the field of unmanned delivery technology, and in particular to an intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings). Background Technology

[0002] With the development of instant retail, community delivery, and unmanned delivery technologies, food delivery is gradually shifting from traditional manual pickup and delivery methods to intelligent scheduling and unmanned delivery. For regular boxed meals, beverages, or standard packaged foods, existing delivery systems typically schedule deliveries based on order time, delivery distance, delivery efficiency, and capacity allocation. They also improve overall delivery efficiency by using insulated boxes, refrigerated boxes, or zoned warehouses to keep food warm or transport it in a categorized manner.

[0003] However, zongzi (sticky rice dumplings) are a food with distinct characteristics, and their delivery is affected by both changes in their quality and the structure of their packaging. On one hand, freshly cooked hot zongzi continuously release steam after being removed from the pot. If the temperature and humidity in the delivery container are not properly controlled, condensation can easily occur, leading to problems such as damp bamboo leaves, softened packaging, and condensation on the surface of the gift box. Simultaneously, glutinous rice products gradually harden during storage and transportation, resulting in a harder texture and reduced flavor. This means that although the zongzi arrives within the specified time, the actual eating experience is poor. On the other hand, zongzi typically have irregular shapes such as triangular, square, or long, and are generally wrapped in bamboo leaves and tied with rope. Under conditions of vibration, stacking, compression, or sudden stops and turns during transportation, problems such as loose bamboo leaves, shifted knots, deformed zongzi, and scattered contents of the gift box can easily occur.

[0004] To address this, a smart logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) is proposed. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides an intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings).

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: an intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings), comprising: an order data acquisition module for collecting zongzi status data, packaging status data, and delivery task data corresponding to zongzi orders to be delivered; a suitable food window modeling module for generating suitable food window parameters characterizing the optimal delivery time range of the target zongzi; a risk assessment module for generating quality risk parameters characterizing changes in the delivery quality of the target zongzi, and stability risk parameters characterizing the structural stability of the target zongzi during delivery; and a joint scheduling decision module for determining the matching of delivery resources corresponding to the target zongzi order. The system generates the following results: dispatch timing, group buying strategy, warehouse allocation strategy, delivery priority, and route selection. An environmental control module adjusts the internal environment of the unmanned delivery equipment. A transportation control module adjusts the loading support method, limiting method, stacking method, and driving control parameters during transportation of the target rice dumplings. An unmanned delivery execution module completes the unmanned delivery of the target rice dumplings. The joint scheduling decision module uses the suitable food window parameters, quality risk parameters, and stability risk parameters as common inputs to ensure that the target rice dumplings are delivered within the suitable food window and maintain quality and structural stability during the delivery process.

[0007] As a preferred technical solution of the present invention, the order data acquisition module collects one or more of the following zongzi status data: cooking time, zongzi temperature, filling type, and individual weight; packaging status data: one or more of the following zongzi type, zongzi shape parameters, binding method, and box support method; and delivery task data: one or more of the following zongzi status data: delivery address, expected delivery time, order structure, and order quantity.

[0008] As a preferred technical solution of the present invention, the suitable eating window modeling module calls a preset suitable eating parameter library for zongzi based on the cooking time, current temperature, filling type and packaging form of the target zongzi, determines the basic suitable eating time range, and modifies the basic suitable eating time range by combining the quality risk parameters and stability risk parameters in the delivery process, so as to generate suitable eating window parameters.

[0009] As a preferred technical solution of the present invention, the quality risk parameter is generated by a subset of parameters, including the temperature of the rice dumpling, the temperature inside the delivery equipment compartment, the humidity inside the delivery equipment compartment, the air permeability of the packaging, the estimated transportation time, the type of filling, and the heat preservation capacity, after being processed by preset weighting and coupling. The stability risk parameter is generated by evaluating the rice dumpling shape parameters, the binding method, the individual weight, the packing support method, and the degree of road disturbance corresponding to the candidate path, and is divided into at least two risk levels.

[0010] As a preferred embodiment of the present invention, the joint scheduling decision module selects delivery equipment matching the target zongzi order from a preset unmanned delivery equipment capability profile based on the food window parameter, quality risk parameter, and stability risk parameter. The unmanned delivery equipment capability profile includes one or more of the following: compartmentalization capability, heat preservation capability, dehumidification capability, ventilation capability, maximum stacking capability, and vibration reduction capability. When the quality risk parameter is higher than a preset threshold, the joint scheduling decision module increases the delivery priority of the target zongzi order and restricts the target zongzi order from being combined with other orders with longer estimated transportation times. When the stability risk parameter is higher than a preset threshold, the joint scheduling decision module restricts the target zongzi order from being combined with high-stack orders, or outputs a warehouse-based delivery strategy.

[0011] As a preferred technical solution of the present invention, the environmental control module switches or coordinates between heat preservation control, ventilation control and dehumidification control according to the quality risk parameters. When the risk of steam condensation is high, the ventilation intensity and dehumidification intensity are increased; when the risk of glutinous rice retrogradation is high, the heat preservation intensity is increased and the ventilation intensity is decreased; when both the risk of steam condensation and the risk of glutinous rice retrogradation are high, an alternating heat preservation and ventilation / dehumidification control method is adopted.

[0012] As a preferred technical solution of the present invention, the transportation control module selects the loading support method and the limiting method according to the stability risk parameters. For target rice dumplings with high stability risk, at least one of the following methods is adopted: flexible support, segmented support, four-corner limiting, or lateral elastic limiting, and the number of stacking layers is limited. The path selection result is jointly determined by the joint scheduling decision module based on the expected time of the candidate path and the degree of road condition disturbance. The degree of road condition disturbance includes the number of speed bumps, the length of the ramp, the number of sharp bends, the frequency of stops and starts, and historical vibration data. The unmanned delivery execution module obtains the current location, expected arrival time, cabin temperature and humidity, and transportation vibration information in real time during the delivery process. When the expected arrival time exceeds the food window parameter or the transportation vibration information exceeds the preset threshold, at least one of the joint scheduling decision module, the environmental control module, and the transportation control module is triggered to make adjustments.

[0013] Compared with the prior art, the beneficial effects that this invention can achieve are: 1. By constructing a suitable food window parameter and combining the suitable food window parameter with the delivery scheduling process, this invention can determine a more suitable delivery time range based on the cooking status, temperature, filling type, and packaging form of the zongzi, thereby avoiding the problem of decreased taste upon arrival caused by using only the shortest delivery time as the scheduling basis, and improving the ability to maintain the actual edible quality of the zongzi.

[0014] 2. This invention generates quality risk parameters and adjusts the cabin environment of the unmanned delivery equipment in a coordinated manner based on these parameters. This allows for the simultaneous suppression of steam condensation and delay of glutinous rice retrogradation during the delivery of zongzi (sticky rice dumplings), reducing the probability of hot zongzi becoming damp, packaging getting damp, and the texture hardening, thereby improving the quality stability of zongzi during unmanned delivery.

[0015] 3. By generating stability risk parameters and adjusting the loading support method, limiting method, stacking method, path selection and driving control parameters according to the stability risk parameters, this invention can reduce the probability of situations such as loosening of zongzi leaves, displacement of knots, deformation of zongzi, pressure on gift boxes and displacement of internal components, thereby improving the structural stability of zongzi during transportation and the integrity of final delivery. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0017] To make the technical means, creative features, objectives, and effects of this invention easier to understand, the invention is further described below with reference to specific embodiments. However, the following embodiments are merely preferred embodiments of this invention and not all of them. Other embodiments obtained by those skilled in the art based on the embodiments described herein without creative effort are all within the protection scope of this invention.

[0018] Example: Figure 1-2 As shown, an intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) is applicable to various zongzi product delivery scenarios, including freshly cooked hot zongzi, insulated zongzi awaiting delivery, refrigerated zongzi, vacuum-packed zongzi, and gift box zongzi. It is used to solve problems that easily occur during the delivery of zongzi, such as shortened eating window, steam condensation and dampness, hardening of glutinous rice, loosening of bamboo leaves, deformation of gift boxes due to pressure, and difficulty in uniformly scheduling different types of zongzi. It ensures that the target zongzi are delivered within the appropriate eating time range and maintain good appearance integrity and taste during transportation.

[0019] The system can be deployed between store kitchens, packaging stations, forward warehouses, cloud scheduling platforms, and unmanned delivery equipment. The cloud scheduling platform is used to complete model calculations, task allocation, and path selection. Store kitchens and packaging stations are used to generate and upload data related to the status of zongzi (sticky rice dumplings). Unmanned delivery equipment is used to perform loading, cabin environment adjustment, and last-mile delivery. Unmanned delivery equipment can be unmanned delivery vehicles, low-speed delivery robots, or last-mile delivery carriers with independent cargo warehouses.

[0020] The entire system includes an order information collection module, a suitable food window modeling module, a quality risk assessment module, a joint scheduling decision-making module, an in-cabin environment control module, a stable transportation control module, and an unmanned delivery execution module.

[0021] The order information collection module connects to the store order system, steaming station, weighing station, packaging station, image acquisition unit, map service interface, and the vehicle-mounted sensing unit of the unmanned delivery equipment. After a user places an order, the order information collection module first obtains the order number, order time, delivery address, expected delivery time, product name, product quantity, and order type. Subsequently, the steaming station automatically records the cooking time of each batch of zongzi; the infrared thermometer above the packaging station collects the surface temperature of the zongzi; the weighing platform collects the individual weight or the total weight of the gift box; the image acquisition unit collects images of the zongzi's shape and knots; and the packaging station controller records the packaging form, packing method, and support method.

[0022] To ensure a unified data foundation for subsequent scheduling and control, the order information collection module generates a set of status parameters for each zongzi to be delivered, including cooking time, zongzi temperature, filling type, packaging form, zongzi shape parameters, binding method, individual weight, and boxing support method. Among these, the filling type can be directly matched from the product database; the packaging form can be categorized as individual bag packaging, individual box packaging, multi-piece gift box packaging, vacuum packaging, and refrigerated packaging; and the boxing support method can be categorized as loose placement, tray compartment placement, four-corner limiting placement, and flexible base placement.

[0023] The parameters for the shape and binding method of the zongzi are not manually entered, but automatically identified by an image acquisition unit. An industrial camera is set up above the packaging station to take pictures of the zongzi after the initial packaging is completed. The image recognition program extracts the length, width, height, number of sharp corners, aspect ratio of the outer contour, and center of gravity offset features of the zongzi, thereby determining whether it belongs to a triangular zongzi, a square zongzi, a long zongzi, or an irregularly shaped zongzi. For the binding method, the image recognition program further extracts the number of knots, the distance between the knots, the center position of the knots, and the offset of the rope to determine whether the zongzi is bound with a single rope, double ropes crossed, or multiple loops, and identifies whether there are loose knots, skewed knots, or outward turning of the leaves. In this way, the system collects not just ordinary takeout order information, but physical and packaging parameters that are directly related to the quality of zongzi delivery.

[0024] After receiving the data uploaded by the order information collection module, the optimal eating window modeling module first calls the pre-established optimal eating parameter library for zongzi. The parameter library can be configured according to dimensions such as filling type, packaging form, whether it is eaten hot, and whether it is packaged in a gift box. For example, for freshly cooked meat zongzi, a shorter optimal eating period can be preset; for red bean paste zongzi, alkaline water zongzi, or vacuum-packed refrigerated zongzi, different basic eating periods are corresponding. The system takes the cooking time as the starting point, combines the current temperature of the zongzi and the packaging form, and obtains the basic optimal eating end time of the target zongzi. Here, optimal eating does not simply refer to the temperature, but rather to the comprehensive state of having a better taste, a lower probability of moisture absorption, and maintaining the integrity of the packaging within a short period of time after delivery.

[0025] The quality risk assessment module is used to generate a condensation and retrogradation coupled risk index and a package stability level. The condensation and retrogradation coupled risk index reflects the combined risks that hot rice dumplings face during transportation, namely steam condensation and glutinous rice retrogradation. This combined risk does not assess the two issues separately, but rather considers them as two mutually constraining aspects. Its calculation method can be expressed as follows: ; in, As a risk indicator for condensation regeneration coupling, For the condensation risk sub-item, For the risk of regeneration, , and These are the weighting coefficients.

[0026] Condensation risk sub-item The temperature of the rice dumpling, the temperature inside the container, the humidity inside the container, the air permeability of the packaging, and the expected transportation time are all considered together.

[0027] Resurrection Risk Sub-item The amount is mainly determined by the type of filling, the firmness of the glutinous rice, the expected transportation time, and the current heat preservation capacity.

[0028] Product term It is used to characterize the coupled effect when the risk of condensation and the risk of regeneration increase simultaneously.

[0029] The stability level of the package is assessed by the quality risk assessment module based on the parameters of the rice dumpling shape, the binding method, the weight of each individual, the packing support method, and the degree of road disturbance corresponding to the candidate delivery route, and is divided into three levels: low risk, medium risk, and high risk.

[0030] When the zongzi (sticky rice dumpling) is regular in shape, tightly bound, supported by compartments or a tray, and the candidate path is relatively flat, it is judged as low risk; when the zongzi is a heated triangular zongzi, the knot is skewed, the individual weight is large, and there are speed bumps, slopes, or sharp bends, it is judged as medium or high risk; when it is a gift box zongzi with weak box rigidity, many internal parts, and large path disturbance, it is preferentially judged as high risk.

[0031] After obtaining the basic feeding window end time, the feeding window modeling module modifies the basic feeding window end time based on the condensation retrograde coupling risk index and the encapsulation stability level. The modified feeding window end time satisfies the following: ; in, This is the revised end time for eating. The end of the basic dietary period As a risk indicator for condensation regeneration coupling, This refers to a quantitative value for package stability or a numerical value corresponding to the risk level. and This is a correction factor.

[0032] When the risk index of condensation regeneration coupling increases or the stability level of the package rises, the revised end time of the suitable feeding period is advanced, so that the joint scheduling decision module can adjust the departure time, grouping strategy, warehouse allocation strategy and candidate route selection results accordingly.

[0033] In terms of resource matching, the system pre-maintains a set of unmanned delivery equipment capability profiles. The capability profiles record whether each delivery equipment has an independent compartment, whether it has heat preservation capabilities, whether it has dehumidification capabilities, whether it has active ventilation capabilities, the maximum number of loading layers, the type of support components, and the vibration reduction level. When the risk of condensation and regeneration coupling of the target rice dumpling is high, unmanned delivery vehicles with temperature and humidity linkage control capabilities are given priority. When the stability level of the package is high, delivery equipment with compartmentalized support or independent pallet compartments is given priority. When both types of risks are high at the same time, the system restricts single delivery or adopts near-end relay delivery.

[0034] Determining the departure time is not simply a matter of ordering and delivering immediately. Instead, it takes into account both the revised end time of the food preparation period and the current estimated arrival time. If the current estimated arrival time is too late, the system will immediately trigger priority departure. If the current departure is too early and the user will receive the product significantly earlier than the food preparation period, a short stop at the store's warming station or short-term buffer station will be allowed, and the vehicle will depart when it is close to the food preparation window.

[0035] The group-buying strategy is mainly generated based on the risk status. For hot rice dumpling orders with a high risk of condensation and regeneration, the system restricts them from being combined with long-distance orders to prevent the window of consumption from being compressed due to waiting or detours. For gift box rice dumplings or irregularly shaped rice dumplings with high package stability, the system restricts them from being combined with high-stacked orders to avoid loading compression. If the same order contains both freshly cooked hot rice dumplings and gift box rice dumplings, the warehouse allocation strategy will distribute them to different compartments to avoid hot steam affecting the appearance of the gift box or the dryness of the contents.

[0036] The candidate route selection results are not the ordinary shortest path. The map service interface returns multiple alternative routes to the system. In addition to the length and estimated time, each route also includes the number of speed bumps, gradient range, number of sharp bends, frequency of traffic light stops and starts, and historical vehicle vibration data. The system converts these factors into road condition disturbance levels. For orders with high stability risks, the joint scheduling decision module prioritizes routes with lower road condition disturbances. For orders with very tight deadlines, routes with shorter time but still within an acceptable disturbance range are prioritized while meeting time constraints. A balance is struck between time and stability, rather than simply choosing the shortest path.

[0037] The cabin environment control module controls the insulation, dehumidification, and ventilation components of the unmanned delivery equipment based on the output of the joint scheduling decision module. In terms of hardware, each cargo compartment or each compartment of the delivery equipment is equipped with a temperature sensor and a humidity sensor. In some implementations, a miniature fan, heating element, dehumidification unit, and controllable damper are also provided. The on-board controller reads the temperature and humidity data of each compartment according to a fixed sampling period and selects different control modes based on the condensation reversion coupling risk index.

[0038] When the risk of condensation is dominant, the controller increases the ventilation frequency and dehumidification intensity while limiting the heating intensity to prevent the continuous accumulation of moisture. When the risk of reflux is dominant, the controller increases the heat preservation intensity and reduces the ventilation frequency to delay the hardening of the glutinous rice. When both risks are high at the same time, the controller adopts pulse-type linkage control, that is, it maintains moderate heat preservation for a period of time and briefly turns on ventilation and dehumidification for another period of time to release some moisture without significantly reducing the temperature of the rice dumpling.

[0039] The stable transportation control module is responsible for translating the package stability level into loading and driving actions. The loading station is equipped with various replaceable support components, including a flexible base, pallet compartment components, four-corner limiting frames, lateral elastic limiting components, and gift box anti-slip pads. When the target zongzi is a hot triangular zongzi with a high stability level, the system selects a flexible base and lateral light limiting to avoid pressure on the top. When the target zongzi is a gift box zongzi, the system selects four-corner limiting frames and anti-slip pads, and limits it to single-layer placement. When the target zongzi is a regular boxed refrigerated zongzi with low risk, a regular pallet can be used and stacking within two layers is allowed. The clamping force is controlled by an actuator with pressure detection function. The actuator collects the force value in real time during the clamping process to prevent excessive clamping that could damage the zongzi leaves or cause the knots to shift.

[0040] To reduce the impact of transportation, the onboard controller also adjusts driving parameters according to the stability level. For high-stability-risk orders, the system reduces the starting acceleration, turning angle, and braking deceleration. For medium-risk orders, the system only limits the emergency braking threshold and turning speed. For low-risk orders, the system drives according to the normal delivery parameters. In this way, the system not only selects a more stable route, but also directly reduces the additional disturbances caused by driving actions at the vehicle end.

[0041] The unmanned delivery execution module is used to execute delivery tasks and transmit the process data back. After loading, the execution module reads the departure time, route information, compartment environment control parameters, and driving control parameters, and then begins delivery. During the journey, the vehicle continuously transmits its current location, estimated arrival time, compartment temperature, compartment humidity, driving vibration intensity, and current compartment status back to the cloud scheduling platform. If the estimated arrival time approaches the revised end time of the optimal feeding period, the cloud scheduling platform can trigger dynamic rescheduling, such as increasing the priority of the task, shortening unnecessary stops, switching to an alternative route, or, if conditions permit, having a closer last-mile delivery robot take over to complete the final delivery. If the humidity inside the compartment rises abnormally, the compartment environment control module immediately increases the dehumidification and ventilation frequency. If the vehicle vibration exceeds the limit, the stable transportation control module reduces the driving speed and limits subsequent acceleration and deceleration.

[0042] The implementation process of this invention is illustrated below using a complete business scenario: A store received an order during lunchtime containing two freshly cooked pork zongzi and a four-pack of red bean paste gift box zongzi. The delivery address was approximately 3.5 kilometers from the store. The kitchen's steaming equipment automatically recorded the cooking time of the pork zongzi into the system. The infrared thermometer at the packaging station measured the surface temperature of the pork zongzi at 73 degrees Celsius, and the weighing platform measured the weight of each zongzi at approximately 230 grams. The industrial camera identified the zongzi as triangular and bound with double ropes. The gift box zongzi was packaged in a cardboard box with a current surface temperature of 45 degrees Celsius. The dimensions and material of the gift box were automatically uploaded by the packaging station controller.

[0043] The map interface returned two alternative routes. The first route was shorter but included two speed bumps, a ramp, and several stop-and-start points. The second route was slightly longer but smoother overall. The suitable eating window modeling module generated a basic suitable eating end time based on the filling type, cooking time, and packaging of the fresh meat zongzi. The quality risk assessment module further calculated that the fresh meat zongzi had a high risk of condensation and retrogradation coupling. Based on its hot state, triangular shape, and double-strapped binding, its packaging stability was determined to be of medium to high risk. For gift box zongzi, due to its moisture-sensitive appearance and pressure sensitivity, it was also determined to have a high stability risk.

[0044] Based on this, the joint scheduling decision module outputs the following results: This order cannot be combined with other orders; fresh meat dumplings and gift box dumplings are placed in separate double compartments; an unmanned delivery vehicle with independent compartments, insulation, ventilation, and dehumidification capabilities is allocated; the departure time is set to immediate departure; the second route is selected as the priority; the fresh meat dumpling compartment adopts a medium insulation, intermittent ventilation, and simultaneous dehumidification mode; the gift box dumpling compartment adopts a low insulation and focused dehumidification mode; in terms of loading method, fresh meat dumplings use a flexible base with lateral light restraint, and gift box dumplings use a four-corner restraint frame with anti-slip pads, and are limited to single-layer placement.

[0045] After delivery begins, the vehicle controller collects temperature and humidity data for each compartment every thirty seconds and reads the vibration value output by the accelerometer in real time. When the humidity in the compartment containing the fresh meat dumplings approaches the threshold, the controller briefly increases the fan speed and starts the dehumidification unit. When the vehicle enters a side road in the community and detects increased road disturbance, the controller automatically reduces the vehicle speed and limits the turning angle. Finally, the fresh meat dumplings are delivered before the corrected end time for eating, and the dumplings still maintain a good hot taste when received by the user. The gift box dumplings did not have any issues with the outer packaging being damp or the contents being scattered.

[0046] As can be seen from the above process, this invention does not simply superimpose ordinary unmanned delivery, ordinary insulated boxes and ordinary route planning. Instead, it first establishes core judgments around the preservation of the zongzi's palatability, steam condensation, glutinous rice regeneration and package stability, and then links the judgment results to the departure timing, order-sharing strategy, warehouse allocation strategy, cabin control, loading method and driving mode, thereby forming a closed-loop scheduling and unmanned delivery solution for the specific food zongzi.

[0047] In other optional implementations, the order information collection module can also access historical sales data and user preference data from stores to fine-tune the optimal eating time range; the cabin environment control module can also set control cycles for different compartments; the stable transportation control module can also directly apply real-time road vibration feedback to the subsequent route reselection logic; none of the above changes alter the core concept of this invention: using the unique quality risk of zongzi to drive joint scheduling and delivery control.

[0048] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A smart logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings), characterized in that, include: The order data collection module is used to collect data on the status of zongzi (sticky rice dumplings) and the packaging status of zongzi orders to be delivered. The optimal food window modeling module is used to generate optimal food window parameters that characterize the range of the best delivery time for the target zongzi. The risk assessment module is used to generate quality risk parameters that characterize changes in the quality of the target zongzi during delivery, as well as stability risk parameters that characterize the structural stability of the target zongzi during delivery. The joint scheduling decision module is used to determine the matching results of delivery resources corresponding to the target zongzi order, and generate the departure timing, order consolidation strategy, warehouse allocation strategy, delivery priority and route selection results; The environmental control module is used to regulate the cabin environment of the unmanned delivery equipment; The transportation control module is used to adjust the loading support method, limiting method, stacking method, and driving control parameters of the target rice dumplings during transportation. The unmanned delivery execution module is used to complete the unmanned delivery of the target zongzi (sticky rice dumplings). The joint scheduling decision module uses the suitable food window parameters, quality risk parameters, and stability risk parameters as common inputs, which can enable the target rice dumplings to be delivered within the suitable food window and maintain the quality and structural stability during the delivery process.

2. The intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) according to claim 1, characterized in that, The order data acquisition module collects one or more of the following data regarding the status of zongzi: cooking time, zongzi temperature, filling type, and individual weight. The packaging status data includes one or more of the following: packaging form, zongzi shape parameters, binding method, and box support method. The delivery task data includes one or more of the following: delivery address, expected delivery time, order structure, and order quantity.

3. The intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) according to claim 2, characterized in that, The optimal eating window modeling module determines the basic optimal eating time range by calling a preset optimal eating parameter library based on the target zongzi's cooking time, current temperature, filling type, and packaging form. It then modifies this basic optimal eating time range by combining quality risk parameters and stability risk parameters during the delivery process to generate optimal eating window parameters.

4. The intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) according to claim 3, characterized in that, The quality risk parameters are generated by a subset of parameters, including the temperature of the rice dumpling, the temperature inside the delivery equipment compartment, the humidity inside the delivery equipment compartment, the air permeability of the packaging, the estimated transportation time, the type of filling, and the heat preservation capacity, after being processed by preset weighting and coupling.

5. The intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) according to claim 4, characterized in that, The stability risk parameters are generated based on the rice dumpling shape parameters, binding method, individual weight, packing support method, and the degree of road condition disturbance corresponding to the candidate path, and are divided into at least two risk levels.

6. The intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) according to claim 5, characterized in that, The joint scheduling decision module selects delivery equipment that matches the target zongzi order from a preset unmanned delivery equipment capability file based on the food window parameter, quality risk parameter, and stability risk parameter. The unmanned delivery equipment capability file includes one or more of the following: compartmentalization capability, heat preservation capability, dehumidification capability, ventilation capability, maximum stacking capability, and vibration reduction capability.

7. The intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) according to claim 6, characterized in that, When the quality risk parameter is higher than the preset threshold, the joint scheduling decision module increases the delivery priority of the target zongzi order and restricts the target zongzi order from being combined with other orders with longer expected transportation times. When the stability risk parameter is higher than the preset threshold, the joint scheduling decision module restricts the target zongzi order from being combined with high-stack orders, or outputs a warehouse distribution strategy.

8. The intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) according to claim 7, characterized in that, The environmental control module switches or coordinates between heat preservation control, ventilation control and dehumidification control according to the quality risk parameters. When the risk of steam condensation is high, the ventilation intensity and dehumidification intensity are increased. When the risk of glutinous rice retrogradation is high, increase the heat preservation intensity and reduce the ventilation intensity. When both the risk of steam condensation and the risk of glutinous rice reversion are high, an alternating heat preservation and ventilation / dehumidification control method is adopted.

9. The intelligent logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) according to claim 8, characterized in that, The transport control module selects the loading support method and the limiting method according to the stability risk parameters. For target rice dumplings with high stability risk, at least one of the following methods is adopted: flexible support, segmented support, four-corner limiting or lateral elastic limiting, and the number of stacking layers is limited.

10. A smart logistics scheduling and unmanned delivery system for zongzi (sticky rice dumplings) according to claim 9, characterized in that, The path selection result is jointly determined by the joint scheduling decision module based on the estimated time of the candidate path and the degree of road condition disturbance. The degree of road condition disturbance includes the number of speed bumps, the length of the slope, the number of sharp bends, the frequency of stops and starts, and historical vibration data. The unmanned delivery execution module acquires the current location, estimated arrival time, cabin temperature and humidity, and transportation vibration information in real time during the delivery process. When the estimated arrival time exceeds the food window parameter or the transportation vibration information exceeds the preset threshold, it triggers at least one of the joint scheduling decision module, environmental control module, and transportation control module to make adjustments.

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