A multi-temperature-zone medical material co-configuration path energy consumption optimization planning method and system

Abstract

This invention discloses an energy-optimized planning method and system for multi-temperature zone medical supply distribution routes, belonging to the field of logistics planning and management technology. It includes: acquiring medical supply parameters and hospital environmental data; calculating changes in reactive potential energy based on the biochemical characteristics of the supplies and converting this into dynamic quality costs; constructing a three-dimensional virtual topology to reproduce the dynamic thermal environment; simulating and calculating the combined energy consumption of temperature control and driving for candidate routes; selecting the final route based on the lowest weighted cost of quality and energy consumption; identifying power fluctuation characteristics and executing vehicle reconfiguration commands at specific nodes; and monitoring micro-environmental disturbances and providing feedback to calibrate the simulation model. This invention employs a multi-objective optimization and dynamic vehicle reconfiguration strategy in a digital twin environment, which can accurately quantify quality loss and energy consumption requirements, improve the accuracy of distribution route planning, and significantly reduce logistics operating costs while ensuring medical safety.

Description

Technical Field

[0001] This invention relates to the field of logistics planning and management technology, and in particular to an energy-optimized planning method and system for the shared distribution route of medical supplies in multiple temperature zones. Background Technology

[0002] Multi-temperature zone medical supply distribution refers to the simultaneous delivery of medical supplies with different and stringent temperature storage requirements to different departments within a hospital within a single delivery mission. These supplies include refrigerated vaccines, frozen biological agents, and room-temperature medications. This delivery model is a crucial component of modern smart hospital logistics management systems, and its planning directly impacts hospital operational efficiency and medical safety.

[0003] In existing technologies, route planning for hospital supplies typically relies on a two-dimensional floor plan of the hospital, employing classic algorithms such as Dijkstra's algorithm or AlphaGo's algorithm to generate routes with the single optimization objective of minimizing travel distance or delivery time. Regarding temperature control of supplies, this primarily depends on the insulation or refrigeration capabilities of the delivery vehicle itself; the route planning process does not directly consider the energy consumed by the temperature control system. Logistics management systems focus more on task scheduling, cargo tracking, and inventory management, lacking refined perception and modeling of environmental factors during the delivery process.

[0004] However, the aforementioned existing technical solutions have significant drawbacks. First, their path planning models fail to consider the quality and safety of medical supplies as a core constraint. For temperature-sensitive biological products, even a slight temperature exceedance during delivery can lead to decreased activity or even failure, and existing methods cannot quantify this quality loss and incorporate it into cost considerations. Second, existing technologies are one-sided in their energy consumption calculations, only considering the energy consumption of the vehicle's movement and completely ignoring the substantial energy consumption required by the temperature control system to maintain stable internal temperatures in the complex thermal environment of a hospital. This results in planned paths that are far from truly energy-optimal. Finally, most existing planning methods are based on static environmental models, which cannot cope with dynamic situations such as real-time temperature changes in hospital corridors and elevator congestion, and lack effective strategies for dealing with localized high heat load environments. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an energy-optimized planning method and system for the shared distribution route of medical supplies in multiple temperature zones. By constructing a three-dimensional dynamic thermal environment topology, integrating dynamic quality cost and comprehensive energy cost for route optimization, and combining vehicle physical reconfiguration strategies, this method can minimize energy consumption and achieve intelligent operation throughout the entire distribution process while ensuring the quality and safety of medical supplies.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for optimal energy consumption planning of a multi-temperature zone medical supply distribution route is provided, comprising: receiving temperature-sensitive parameters and biochemical decay coefficients of the medical supplies to be delivered, and acquiring temperature and humidity data of the hospital delivery environment, elevator reservation status, and a delivery task flow including several department destinations; determining a permissible storage temperature threshold based on the temperature-sensitive parameters, and calculating the real-time change in the active potential energy of the supplies after deviating from the permissible storage temperature threshold in conjunction with the biochemical decay coefficients, and calculating the real-time change as the corresponding value depreciation of the supplies as a dynamic quality cost item; and constructing a three-dimensional virtual topology based on the static physical layout of the hospital. The system uses real-time temperature and humidity data as boundary conditions to inject into a 3D virtual topology, reproducing the dynamic thermal environment distribution of corridors on each floor. Based on several departmental destinations in the delivery task flow, multiple feasible paths connecting the starting point and each departmental destination are searched in the 3D virtual topology, forming a set of candidate paths to be evaluated. The elevator reservation status is converted into traffic throughput constraints in virtual spacetime, and the real-time heat conduction and convection intensity experienced by the vehicle as it moves along each path in the candidate path set are simulated in the 3D virtual topology, thereby calculating the predicted temperature control energy consumption required for the vehicle to maintain temperature balance. This is combined with the vehicle's real-time load and... The path gradient parameter is used to calculate the predicted driving energy consumption required for the vehicle to overcome driving resistance. The predicted temperature control energy consumption is then added to the predicted driving energy consumption to obtain the comprehensive energy consumption assessment value for each path. Preset energy cost coefficients and quality cost coefficients are assigned to the comprehensive energy consumption assessment value and the dynamic quality cost item, respectively. The weighted sum of these two coefficients is calculated to obtain the total assessment cost for each path. The total assessment costs of each path are compared, and the path with the lowest total assessment cost is selected from the candidate path set as the final planned path. The output power fluctuation characteristics of the predicted temperature control energy consumption in the final planned path are identified, and the path is considered when the output power exceeds the vehicle's temperature control system. The system generates reconfiguration instructions for physically disassembling or reassembling delivery vehicles based on geographical location nodes with a preset ratio of rated power. Delivery tasks are executed according to the final planned route, and upon arrival at the geographical location node, the reconfiguration instructions are executed, causing the storage space of the vehicle carrying some materials to detach from the main vehicle and autonomously proceed to the corresponding department destination. The system monitors the door opening status and external airflow intensity during delivery in real time, identifies and records micro-environmental disturbance data caused by manual operations, and converts this data into incremental heat load parameters, feeding them back into the three-dimensional virtual topology for calibrating the predicted temperature control energy consumption of subsequent tasks.

[0007] Based on the above technical solution, in the energy-optimized planning method for a multi-temperature zone medical supplies distribution route provided in this application, a three-dimensional dynamic thermal environment topology is constructed, dynamic quality cost and comprehensive energy consumption cost are integrated for route optimization, and a vehicle physical reorganization strategy is combined to minimize energy consumption and achieve intelligent operation throughout the entire distribution process while ensuring the quality and safety of medical supplies.

[0008] In conjunction with the first aspect above, in one possible implementation, determining the permissible storage temperature threshold based on the temperature-sensitive parameter and calculating the real-time change in the active potential energy of the material after deviating from the permissible storage temperature threshold, in conjunction with the biochemical decay characteristic coefficient, includes: extracting the permissible storage temperature threshold and biochemical decay characteristic coefficient of various materials from the temperature-sensitive parameter; establishing an active potential energy function starting from the time point when the material leaves the permissible storage temperature threshold, wherein the decay rate of the active potential energy function is jointly determined by the degree of deviation of the real-time ambient temperature from the permissible storage temperature threshold and the biochemical decay characteristic coefficient; within a preset discrete time step, iteratively calculating the remaining potential energy value of the material in transit through the active potential energy function, and calculating the real-time change in active potential energy based on the loss of the remaining potential energy value relative to the initial potential energy value.

[0009] In conjunction with the first aspect above, in one possible implementation, simulating the real-time heat conduction and convection intensity experienced by the vehicle as it moves along each path in the candidate path set within the three-dimensional virtual topology, and thereby calculating the predicted temperature control energy consumption required for the vehicle to maintain temperature control balance, includes: in the three-dimensional virtual topology, retrieving dynamic thermal environment distribution data corresponding to each position coordinate in real time based on the discrete position coordinates of the vehicle moving along each path; calculating the instantaneous heat intrusion of the vehicle in each path segment based on the thermal property parameters of the vehicle's enclosure structure, the vehicle's surface area, and the dynamic thermal environment distribution data; determining the real-time output power curve required to maintain the internal environment of the vehicle within the permitted storage temperature threshold in order to offset the instantaneous heat intrusion; and integrating the real-time output power curve within the delivery cycle corresponding to the delivery task flow to obtain the predicted temperature control energy consumption corresponding to each path.

[0010] In conjunction with the first aspect above, in one possible implementation, identifying the output power fluctuation characteristics of the predicted temperature control energy consumption in the final planned path, and generating a reconfiguration operation instruction for physically disassembling or reassembling the delivery vehicle at a geographical location node where the output power exceeds a preset proportion of the rated power of the vehicle's temperature control system, includes: identifying the power peak interval in the real-time output power curve that exceeds the preset proportion of the rated power, and marking the path coordinates corresponding to the power peak interval as a geographical location node; analyzing the delivery task flow, identifying the coupling relationship between materials that can be delivered in the geographical location node and its adjacent area and materials that need to continue to the subsequent destination; simulating and calculating the corrected temperature control energy consumption for the main vehicle to continue performing the remaining path tasks after physical disassembly at the geographical location node, and the sub-energy consumption for the detached vehicle to independently perform the end delivery task; if the difference between the predicted temperature control energy consumption and the sum of the corrected temperature control energy consumption and the sub-energy consumption is greater than a preset reconfiguration execution cost, then generating a reconfiguration operation instruction to release the vehicle portion carrying the corresponding materials at the geographical location node.

[0011] In conjunction with the first aspect above, in one possible implementation, the real-time monitoring of the door opening status and external airflow intensity during the delivery process, and the identification and recording of micro-environmental disturbance data caused by human operation, includes: continuously collecting door status data and external wind speed data through sensors deployed on the main vehicle and parts of the vehicle; when the data collected by the sensors exceeds a preset threshold for characterizing the stable state of the environment, it is identified as a micro-environmental disturbance event, and the type, duration, and intensity of the event are extracted; by reading the material identification information bound to the main vehicle or parts of the vehicle, the micro-environmental disturbance event is associated with the specific material affected, forming structured micro-environmental disturbance data.

[0012] In conjunction with the first aspect above, in one possible implementation, converting the microenvironmental disturbance data into incremental heat load parameters and feeding them back into the three-dimensional virtual topology for calibrating the predicted temperature control energy consumption of subsequent tasks includes: performing cluster analysis on the microenvironmental disturbance data to extract typical microenvironmental disturbance patterns occurring in specific geographical locations or operational processes; when performing energy consumption simulation for new delivery tasks involving the specific geographical locations or operational processes, converting the corresponding typical microenvironmental disturbance patterns into additional incremental heat load parameters; superimposing the incremental heat load parameters into the basic thermal calculation of the three-dimensional virtual topology, re-executing the calculation of the predicted temperature control energy consumption, and generating a corrected comprehensive energy consumption assessment value.

[0013] In conjunction with the first aspect above, in one possible implementation, the delivery task is executed according to the final planned route, and upon arrival at the geographical location node, the reorganization operation instruction is executed, causing the storage space of the vehicle carrying some materials to detach from the main vehicle and autonomously proceed to the corresponding department destination. This includes: configuring the main vehicle to carry several storage spaces with independent power supply and navigation capabilities, and physically locking each storage space to the main vehicle through a standardized interface; when the main vehicle uses a positioning and navigation system to travel to the geographical location node specified by the reorganization operation instruction, an automated operation sequence is triggered; the main vehicle performs an unloading action, unlocks the corresponding storage space, and sends the corresponding last-mile delivery task instruction to the unlocked storage space, which then autonomously performs last-mile delivery to the final department destination; after completing the unloading, the main vehicle adjusts its power management parameters in real time and continues to execute the remaining route tasks.

[0014] In conjunction with the first aspect above, in one possible implementation, the method further includes: configuring an active potential energy safety threshold for various materials; during the execution of a delivery task, if the remaining potential energy value, calculated in real time, is predicted to fall below the active potential energy safety threshold before reaching the destination, a quality risk warning is generated; the quality risk warning is used as the highest priority input to trigger real-time replanning of the current execution path or to trigger the activation of an emergency temperature control compensation program.

[0015] In conjunction with the first aspect above, in one possible implementation, after recording the micro-environmental disturbance data caused by manual operations, the method further includes: calculating historical average disturbance intensity coefficients for different geographical locations and operational steps based on the micro-environmental disturbance data; when generating new reorganization operation instructions, using the historical average disturbance intensity coefficients of the route locations as optimization constraints for instruction generation; and preferentially selecting reorganization operation instructions that can avoid high disturbance intensity steps or reserve additional safety margins for high disturbance steps.

[0016] Secondly, an energy-optimized planning system for a multi-temperature zone medical supplies distribution route is provided, comprising: a parameter and task acquisition module, used to receive temperature-sensitive parameters and biochemical decay coefficients of the medical supplies to be delivered, and to acquire temperature and humidity data of the hospital delivery environment, elevator reservation status, and delivery task flow including several department destinations; an active potential energy and cost calculation module, used to determine the permissible storage temperature threshold based on the temperature-sensitive parameters, and, in conjunction with the biochemical decay coefficients, calculate the real-time change in active potential energy of the supplies after deviating from the permissible storage temperature threshold, and calculate the real-time change as the corresponding value depreciation of the supplies, as a dynamic quality cost item; and a virtual topology and thermal environment reproduction module, used to reproduce the static physical environment of the hospital. A three-dimensional virtual topology is constructed, and real-time temperature and humidity data are injected into the 3D virtual topology as boundary conditions to reproduce the dynamic thermal environment distribution of corridors on each floor. A candidate path search module searches the 3D virtual topology for multiple feasible paths connecting the starting point and each department's destination based on several departmental destinations in the delivery task flow, forming a set of candidate paths to be evaluated. A temperature control energy consumption simulation module converts the elevator reservation status into traffic throughput constraints in virtual spacetime and simulates the real-time heat conduction and convection intensity experienced by the vehicle as it moves along each path in the candidate path set within the 3D virtual topology, thereby calculating the predicted temperature control energy consumption required for the vehicle to maintain temperature control balance. A driving and comprehensive energy consumption calculation module... The system combines the vehicle's real-time load and path gradient parameters to calculate the predicted driving energy consumption required for the vehicle to overcome driving resistance, and accumulates the predicted temperature control energy consumption and predicted driving energy consumption to obtain the comprehensive energy consumption assessment value for each path; the optimal path selection module assigns preset energy consumption cost coefficients and quality cost coefficients to the comprehensive energy consumption assessment value and dynamic quality cost item respectively, calculates the weighted sum of the two to obtain the total assessment cost for each path, compares the total assessment costs of each path, and selects the path with the lowest total assessment cost from the candidate path set as the final planned path; the reorganization operation instruction generation module identifies the output power fluctuation characteristics of the predicted temperature control energy consumption in the final planned path, and when the output power exceeds the vehicle's... The temperature control system generates a reconfiguration command for physically disassembling or reassembling delivery vehicles based on a preset ratio of the rated power of the geographical location nodes. The task execution and reconfiguration control module executes the delivery task according to the final planned path and executes the reconfiguration command upon arrival at the geographical location node, causing the storage space of the vehicle carrying some materials to detach from the main vehicle and autonomously proceed to the corresponding department destination. The environmental monitoring and simulation calibration module monitors the door opening status and external airflow intensity during the delivery process in real time, identifies and records micro-environmental disturbance data caused by manual operations, and converts the micro-environmental disturbance data into heat load increment parameters and feeds them back to the three-dimensional virtual topology for calibrating the predicted temperature control energy consumption of subsequent tasks.

[0017] Compared with the prior art, the present invention has the following advantages: This invention achieves a profound shift in delivery route planning from a single-dimensional to a multi-dimensional approach by constructing a multi-objective optimization model that incorporates dynamic quality costs and comprehensive energy consumption costs. This method not only calculates traditional driving energy consumption but also innovatively introduces quality loss costs based on the biochemical characteristics of materials and refined temperature control energy consumption. This results in a precise balance between ensuring the safety and vitality of materials and achieving energy conservation in the planned routes, thereby improving the overall efficiency of delivery tasks.

[0018] This invention proposes a dynamic adaptive planning mechanism based on three-dimensional virtual topology and real-time data feedback. By reproducing the dynamic thermal environment inside the hospital and continuously absorbing micro-environmental disturbance data during the actual delivery process, this method enables route planning to break free from dependence on static, idealized environments, adapt to environmental changes and human interference in real time, enhance the accuracy and robustness of planning results in complex and dynamic scenarios, and ensure the practical feasibility of the delivery plan.

[0019] This invention provides an innovative vehicle physical reconfiguration strategy. By physically disassembling and reassembling delivery vehicles at predicted energy consumption peaks, it achieves dynamic matching of transport capacity and task requirements. This flexible operating mode not only effectively avoids the risk of overloading the temperature control system, but also further optimizes the energy efficiency of subsequent routes through load reduction and parallel last-mile delivery, providing a novel solution to address local bottleneck problems in complex delivery tasks.

[0020] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A structural architecture diagram of an energy-optimized planning system for a multi-temperature zone medical supplies distribution route provided in this application embodiment; Figure 2 A flowchart illustrating an energy-optimized planning method for a multi-temperature zone medical supplies distribution route, provided in an embodiment of this application; Figure 3 This is a schematic diagram of the real-time output power curve and reorganization decision node identification of the temperature control system provided in the embodiments of this application.

[0023] Figure 4 This is the historical average disturbance intensity coefficient for a specific hospital delivery node provided in the embodiments of this application. Distribution heat map. Detailed Implementation

[0024] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0025] The energy-optimization planning method for a multi-temperature zone medical supplies distribution route provided in this application embodiment can be applied to, for example... Figure 1 In the energy-optimal planning system 100 for a multi-temperature zone medical supplies distribution route shown, such as... Figure 1 As shown, the system includes: The parameter and task acquisition module is used to receive the temperature-sensitive parameters and biochemical decay coefficients of the medical supplies to be delivered, and to acquire the temperature and humidity data of the hospital delivery environment, elevator reservation status, and delivery task flow containing several department destinations. The active potential energy and cost calculation module is used to determine the permissible storage temperature threshold based on the temperature-sensitive parameter, and in combination with the biochemical decay characteristic coefficient, calculate the real-time change in the active potential energy of the material after deviating from the permissible storage temperature threshold, and calculate the real-time change as the value depreciation of the corresponding material as a dynamic quality cost item. The virtual topology and thermal environment reproduction module is used to construct a three-dimensional virtual topology based on the static physical layout of the hospital. The real-time temperature and humidity data are injected into the three-dimensional virtual topology as boundary conditions to reproduce the dynamic thermal environment distribution of the corridors on each floor. The candidate path search module is used to search for multiple feasible paths connecting the starting point and each department destination in a three-dimensional virtual topology based on several department destinations in the delivery task flow, as a set of candidate paths to be evaluated. The temperature control energy consumption simulation module is used to convert the elevator reservation status into traffic passability constraints in virtual space and time, and to simulate the real-time heat conduction and heat convection intensity experienced by the vehicle when it moves along each path in the candidate path set in the three-dimensional virtual topology, thereby calculating the predicted temperature control energy consumption required for the vehicle to maintain temperature control balance. The driving and comprehensive energy consumption calculation module is used to calculate the predicted driving energy consumption required for the vehicle to overcome driving resistance by combining the vehicle's real-time load and path slope parameters, and to accumulate the predicted temperature control energy consumption and the predicted driving energy consumption to obtain the comprehensive energy consumption evaluation value corresponding to each path. The optimal path selection module is used to assign preset energy consumption cost coefficients and quality cost coefficients to the comprehensive energy consumption assessment value and the dynamic quality cost item respectively, calculate the weighted sum of the two to obtain the total assessment cost corresponding to each path, compare the total assessment cost of each path, and select the path with the lowest total assessment cost from the candidate path set as the final planned path. The reconfiguration operation instruction generation module is used to identify the output power fluctuation characteristics of the predicted temperature control energy consumption in the final planned path, and generate a reconfiguration operation instruction for physically disassembling or reassembling the delivery vehicle at geographical nodes where the output power exceeds a preset proportion of the rated power of the vehicle temperature control system. The task execution and reorganization control module is used to execute the delivery task according to the final planned path, and when it arrives at the geographical location node, it executes the reorganization operation instruction to make the storage space of the vehicle carrying some materials detach from the main vehicle and autonomously go to the corresponding department destination. The environmental monitoring and simulation calibration module is used to monitor the door opening status and external airflow intensity during the delivery process in real time, identify and record micro-environmental disturbance data caused by human operation, and convert the micro-environmental disturbance data into heat load increment parameters and feed them back to the three-dimensional virtual topology for calibrating the predicted temperature control energy consumption of subsequent tasks.

[0026] like Figure 2 As shown in the embodiment of this application, an energy-optimal planning method for a multi-temperature zone medical supplies distribution route is provided, including: It receives temperature-sensitive parameters and biochemical decay coefficients of medical supplies to be delivered, and obtains temperature and humidity data of the hospital delivery environment, elevator reservation status, and delivery task flow including several department destinations. Based on the temperature-sensitive parameters, the permissible storage temperature threshold is determined, and combined with the biochemical degradation characteristic coefficient, the real-time change in the active potential energy of the material after deviating from the permissible storage temperature threshold is calculated. The real-time change is calculated as the value depreciation of the corresponding material and used as a dynamic quality cost item. A three-dimensional virtual topology is constructed based on the static physical layout of the hospital. The real-time temperature and humidity data are injected into the three-dimensional virtual topology as boundary conditions to reproduce the dynamic thermal environment distribution of the corridors on each floor. Based on the destinations of several departments in the delivery task flow, multiple feasible paths connecting the starting point and each department destination are searched in the three-dimensional virtual topology as a set of candidate paths to be evaluated. The elevator reservation status is converted into traffic passability constraints in virtual spacetime, and the real-time heat conduction and heat convection intensity experienced by the vehicle when moving along each path in the candidate path set is simulated in the three-dimensional virtual topology, thereby calculating the predicted temperature control energy consumption required for the vehicle to maintain temperature control balance. By combining the vehicle's real-time load and path gradient parameters, the predicted driving energy consumption required for the vehicle to overcome driving resistance is calculated, and the predicted temperature control energy consumption and the predicted driving energy consumption are added together to obtain the comprehensive energy consumption evaluation value corresponding to each path. Preset energy consumption cost coefficients and quality cost coefficients are assigned to the comprehensive energy consumption assessment value and the dynamic quality cost item, respectively. The weighted sum of the two is calculated to obtain the total assessment cost corresponding to each path. The total assessment cost of each path is compared, and the path with the lowest total assessment cost is selected from the candidate path set as the final planning path. Identify the output power fluctuation characteristics of the predicted temperature control energy consumption in the final planned path, and generate a reconfiguration operation instruction for physically disassembling or reassembling the delivery vehicle at geographical nodes where the output power exceeds a preset proportion of the rated power of the vehicle temperature control system. The delivery task is executed according to the final planned route, and upon arrival at the geographical location node, the reorganization operation instruction is executed, causing the storage space of the vehicle carrying some materials to detach from the main vehicle and autonomously proceed to the corresponding department destination. The system monitors the door opening status and external airflow intensity during the delivery process in real time, identifies and records micro-environmental disturbance data caused by manual operations, and converts the micro-environmental disturbance data into heat load increment parameters and feeds them back into the three-dimensional virtual topology for calibrating the predicted temperature control energy consumption of subsequent tasks.

[0027] It should be noted that the principle of this method lies in constructing a multi-dimensional, dynamically coupled decision-making model for the global optimization of medical supply delivery routes. The core is to establish a three-dimensional virtual topology that integrates the static physical structure of the hospital with real-time dynamic environmental data, serving as the digital foundation for all simulations. On this foundation, the method constructs two key cost assessment dimensions in parallel. The first is dynamic quality cost, which quantifies invisible quality losses into calculable cost items through an active potential energy decay function based on the biochemical characteristics of the supplies, making supply safety an intrinsic constraint on route planning. The second is comprehensive energy consumption cost, which finely distinguishes and calculates the energy consumption of the vehicle overcoming physical resistance and the energy consumption of temperature control to maintain internal temperature balance, and then sums the two. This method searches for multiple candidate routes in the virtual topology and performs a comprehensive simulation evaluation of each route, calculating its weighted total cost under both quality and energy consumption dimensions. Furthermore, this principle also includes a closed loop of dynamic execution and adaptive calibration. It can generate physical reconfiguration commands for vehicles based on predicted peak energy consumption, so as to adaptively adjust transport capacity and energy consumption. By monitoring micro-environmental disturbances in the delivery process in real time, it can feed back actual operation data to the virtual model, thereby enabling continuous iteration and self-optimization of the model.

[0028] In one possible implementation of the embodiments of this application, combined with Figure 2 Based on the temperature-sensitive parameters, a permissible storage temperature threshold is determined. Combined with the biochemical degradation characteristic coefficient, the real-time change in the active potential energy of the material after deviating from the permissible storage temperature threshold is calculated, including: Extract the permissible storage temperature threshold and biochemical degradation coefficient of various materials from the temperature-sensitive parameters; Starting from the time point when the material leaves the permitted storage temperature threshold, an active potential energy function is established, wherein the decay rate of the active potential energy function is jointly determined by the deviation of the real-time ambient temperature from the permitted storage temperature threshold and the biochemical decay characteristic coefficient. Within a preset discrete time step, the remaining potential energy value of the materials in transit is calculated iteratively using the active potential energy function, and the real-time change in active potential energy is calculated based on the loss of the remaining potential energy value relative to the initial potential energy value.

[0029] In some implementations, precise quantitative modeling is performed on the irreversible quality degradation of medical supplies caused by temperature control deviations, and this physical loss is transformed into a calculable dynamic quality cost item, addressing the technical pain point that traditional route planning cannot measure the risk of cargo activity. During execution, firstly, from the received temperature-sensitive parameters, core physical property data is analyzed and extracted for each type of medical supply in the delivery task flow, including the permissible storage temperature threshold defining the stable storage conditions of the supplies. And biochemical degradation characteristic coefficients used to characterize the inherent chemical stability of materials and determine their degradation rate at non-ideal temperatures. Biochemical attenuation characteristic coefficient Based on the Arrhenius equation, the potential energy is pre-determined through isothermal accelerated aging tests or retrieved from a standard medical material physicochemical database. Its physical meaning is the potential energy decay rate per unit time under unit temperature deviation. An active potential energy function with time as the variable is established to simulate the decay process of material quality, representing the initial active potential energy of the material. The normalization value is set to 100. During the path evaluation phase, a preset discrete time step is used. The simulation is performed iteratively, typically in units of 1 to 5 seconds, to model the movement of the vehicle along the candidate path. At each time step... Inside, the real-time ambient temperature of the vehicle's current location is obtained from the 3D virtual topology. And calculate the degree of temperature deviation according to the formula. : ; This formula ensures that only when the real-time ambient temperature exceeds the permissible range... The value is positive if it is positive, and zero otherwise, thus precisely triggering the decay logic. The remaining potential energy value of the supplies in transit is then updated according to the iterative formula: ; in, For the first The remaining potential energy value for each time step This iterative process continues throughout the entire delivery cycle until the destination is reached, using the updated potential energy value for the next stage. Ultimately, based on the initial potential energy value... With the final residual potential value The difference is used to calculate the real-time change in the total active potential energy along this path. The transformation logic for dynamic quality cost items is as follows: ; in, The cost of quality loss, quantified in monetary or energy units. The preset quality cost coefficient is assigned a value based on the market unit price of the materials, their medical urgency, and replacement cost, obtained through a preset weighting table, and is generally set to 50. Based on this, while quantifying dynamic quality cost items, a preset alignment benchmark between energy efficiency and quality dimensions provides decision support for subsequent path selection. The total path evaluation cost is clearly defined. The weighted accounting logic: ; because Approved Converted into monetary value units, while the comprehensive energy consumption assessment value The original unit is the joule, therefore an energy cost coefficient is introduced. Converting energy units into monetary units achieves dimensional consistency in the weighted summation. Energy cost coefficient. The value is based on the real-time unit energy consumption price of the delivery vehicle, and its calculation formula is: ; in, The unit price of electricity for hospitals is [price] yuan / kWh. Unit conversion constant .

[0030] For example, a central pharmacy of a large general hospital delivers high-value vaccine supplies A to the surgical operating room. During the task initialization phase, it receives temperature-sensitive parameters of supply A and determines its permissible storage temperature threshold. ,Right now and Simultaneously, its biochemical degradation characteristic coefficients were obtained. Initial active potential During path simulation, a discrete time step is set. When the vehicle moves to a location in a high-temperature corridor, the real-time ambient temperature at that location is retrieved from the 3D virtual topology. Calculate the degree of temperature deviation for this step size using the formula. The remaining potential energy for the next stage is calculated using the iterative formula: If the delivery cycle ends, the final remaining potential energy value will be... Then the real-time change in active potential energy can be calculated. Based on the preset quality cost coefficient The dynamic quality cost item corresponding to this path is calculated. .set up The corresponding electricity price is 1.0 yuan / kWh; if the comprehensive energy consumption assessment value of a certain candidate path is... for Dynamic quality cost If the cost is 75 yuan, then the total evaluation cost of this path is... By pre-defining The acquisition method and quantification value ensure that the subsequent selection of the optimal path can perform globally optimal planning based on a unified monetary unit.

[0031] In one possible implementation, combining Figure 2 The simulation of real-time heat conduction and convection intensity experienced by the vehicle as it moves along each path in the candidate path set within the three-dimensional virtual topology, thereby calculating the predicted temperature control energy consumption required for the vehicle to maintain temperature control balance, includes: In the three-dimensional virtual topology, dynamic thermal environment distribution data corresponding to each position coordinate is retrieved in real time based on the discrete position coordinates of the vehicle moving along each path. Based on the thermal properties of the vehicle's enclosure structure, the vehicle's surface area, and the dynamic thermal environment distribution data, the instantaneous heat intrusion of the vehicle in each path segment is calculated. The real-time output power curve required to maintain the vehicle's internal environment within the permitted storage temperature threshold in order to offset the instantaneous thermal intrusion is determined. The real-time output power curve is integrated within the delivery cycle corresponding to the delivery task flow to obtain the predicted temperature control energy consumption for each path.

[0032] In some implementations, dynamic thermal environment data from a 3D virtual topology is transformed into specific energy requirements for the delivery vehicle's temperature control system. This allows for accurate prediction of temperature control energy consumption for different candidate paths, addressing the problem of inaccurate energy consumption assessments in existing technologies where path planning does not consider the electrical energy consumption of the temperature control system. During execution, simulation is performed on each path in the candidate path set, discretizing it spatially into a series of continuous discrete position coordinates. Based on the vehicle's moving speed and time step, the dynamic thermal environment distribution data corresponding to each coordinate point is retrieved and extracted in real time from the 3D virtual topology. This data primarily includes the real-time ambient temperature at the vehicle's current position. The convective heat transfer coefficient with air. The instantaneous heat intrusion of the vehicle in this path segment is calculated based on the pre-defined thermal properties of the vehicle's enclosure structure. Among them, the core indicator of the thermal properties of the building envelope is the overall heat transfer coefficient of the vehicle. The unit is It is usually determined by the properties of the insulation material of the container and the outer surface area of ​​the vehicle. Instantaneous heat intrusion The calculation formula for the heat load of the temperature control system is as follows: ; in, To maintain the internal target temperature of the vehicle, as determined by the permitted storage temperature threshold. To offset this instantaneous heat intrusion. The temperature control system needs to output power in real time accordingly. Operation, based on the energy efficiency ratio of the temperature control unit. The dimensionless parameter characterizing energy conversion efficiency is used to calculate the real-time output power: ; This generates a real-time output power curve that varies with location and time. To obtain the total energy consumption of this path, the real-time output power curve is analyzed over the entire delivery cycle corresponding to the delivery task flow. Integral operations are performed within the time frame, which is simplified to performing integration operations over all time steps in a discrete simulation environment. The instantaneous power is summed to predict the temperature control energy consumption: ; in, For the first Real-time output power over a time step The discrete time step is defined. Through this thermodynamic physical modeling process, a quantitative prediction of the energy consumption of the temperature control system is achieved, providing accurate data support for subsequent comprehensive energy consumption assessment.

[0033] For example, a medical supply delivery vehicle moves along a discrete path segment of a candidate path. Among the thermal properties of the vehicle's enclosure structure, the overall heat transfer coefficient is... for Surface area for Maintaining internal target temperature for When the simulation reaches a certain time step... When the vehicle travels to a specific geographic coordinate, the real-time ambient temperature at that location is retrieved from the 3D virtual topology. for Based on the formula for calculating instantaneous heat intrusion, the instantaneous heat intrusion within this path segment is... If the energy efficiency ratio of the vehicle's temperature control unit is... If the value is 2.5, then the real-time output power required to offset this heat intrusion is... Set the discrete time step used in the simulation. for If the power remains stable within this step size, then the predicted temperature control energy consumption generated by this step size is: By analyzing the entire delivery cycle By performing this type of integral and summation operation on the instantaneous power at all time steps within the time frame, the complete predicted temperature control energy consumption assessment value for the candidate path can be obtained. .

[0034] In one possible implementation, combining Figure 2 The system identifies the output power fluctuation characteristics of the predicted temperature control energy consumption in the final planned path, and generates a reconfiguration operation instruction for physically disassembling or reassembling the delivery vehicle at geographical location nodes where the output power exceeds a preset proportion of the vehicle's temperature control system's rated power. Identify the power peak interval in the real-time output power curve that exceeds the preset proportion of the rated power, and mark the path coordinates corresponding to the power peak interval as geographical location nodes; Analyze the delivery task flow to identify the coupling relationship between goods that can be delivered in the geographical location node and its vicinity and goods that need to continue to the subsequent destination; The simulation calculates the corrected temperature control energy consumption when the main vehicle continues to perform the remaining path tasks after physical disassembly at the geographical location node, and the sub-energy consumption when the detached vehicle independently performs the last-mile delivery task. If the difference between the predicted temperature control energy consumption and the sum of the corrected temperature control energy consumption and sub-energy consumption is greater than the preset reorganization execution cost, a reorganization operation instruction is generated to release the vehicle part carrying the corresponding materials at the geographical location node.

[0035] In some implementations, the physical form of the delivery vehicle is dynamically reconfigured to mitigate the risk of overloaded temperature control systems in the final planned route, thereby further optimizing overall energy consumption. During execution, feature identification is performed on the real-time output power curve corresponding to the determined final planned route, and the rated power of the vehicle's temperature control system is adjusted accordingly. As a benchmark, set a preset ratio. As a power safety threshold, it is typically set between 80% and 95%, identifying all instances on the real-time output power curve that exceed this threshold. The continuous intervals, i.e., the power peak intervals, are marked with their corresponding path coordinates in the 3D virtual topology as geographical location nodes for executing reorganization decisions. For each geographical location node, the delivery task flow is analyzed to identify the coupling relationship between materials that can be delivered within that node and its vicinity, and materials that need to continue to the subsequent destination with the main vehicle. The decision simulation process is then initiated. The energy consumption of two parts after physical disassembly at the geographical location node is simulated: one is the energy consumption for corrected temperature control required for the main vehicle, after its load is reduced, to continue executing the remaining path tasks. Secondly, the energy consumption required for the detached vehicle to independently complete its last-mile delivery task. The energy savings resulting from this reorganization operation are calculated using a formula. : ; in, This represents the predicted temperature control energy consumption required from this geographical location node to the end of the task, as per the original plan. If the calculated energy savings... Greater than the preset restructuring execution cost If so, the restructuring operation is deemed both economically viable and safe. Restructuring execution costs. These are comprehensive quantitative parameters, including the electrical energy consumed by performing the physical disassembly itself, the computational power consumption of the secondary planning, and mechanical losses. Among these, the reassembly execution cost... This is the equivalent cost quantified in joules. Once this decision condition is met, a reconfiguration operation instruction is generated, containing node coordinates, the identifier of the vehicle to be detached, and details of the last-mile delivery task. This optimizes the energy efficiency of the subsequent journey through unloading and parallel delivery. For example... Figure 3As shown, the real-time output power curve of the temperature control system of the delivery vehicle during the execution of the final planned route is displayed. The dashed line in the figure represents the safety threshold set based on the rated power, and the shaded area, which identifies the power peak range, serves as the geographical location node reference for generating the vehicle physical reconfiguration operation command.

[0036] For example, in a simulation of a final planned path reorganization decision, the real-time output power curve corresponding to the path is first identified, and the rated power of the vehicle temperature control system is set. 400W, preset ratio If the power consumption is 90%, then the power safety threshold is 360W. Feature recognition revealed that when the vehicle traversed a certain ramp, its real-time output power peaked at 385W, lasting approximately 35 seconds. The elevator lobby before this ramp was then marked as a geographical location node. Entering the decision simulation phase, if the vehicle does not reassemble, the predicted temperature control energy consumption from this node until the mission ends is calculated. The energy consumption is 18,000 J. If physical dismantling is performed at this node, and the vehicle carrying some supplies is detached, the main vehicle will have a lighter load, thus reducing the energy consumption for subsequent mission temperature control. The energy consumption drops to 9,500J, while the energy consumption of the detached sub-vehicle independently completing the last-mile delivery is significantly reduced. The value is 200J. Calculate the energy savings using the formula. Set a preset reorganization execution cost. The equivalent energy consumption is 8,000 J. This is based on the calculated energy savings. Greater than the cost of restructuring If the reorganization operation is deemed economical, a reorganization operation instruction carrying the corresponding material vehicle is generated at the geographical location node.

[0037] In one possible implementation, combining Figure 2 The real-time monitoring of the door opening status and external airflow intensity during the delivery process, and the identification and recording of micro-environmental disturbance data caused by human operation, include: Sensors deployed on the main vehicle and its components continuously collect door status data and external wind speed data. When the data collected by the sensor exceeds a preset threshold used to characterize the stable state of the environment, it is identified as a micro-environmental disturbance event, and the type, duration and intensity of the event are extracted. By reading the material identification information bound to the main vehicle or part of the vehicle, the microenvironmental disturbance event is associated with the specific material affected, forming structured microenvironmental disturbance data.

[0038] In some implementations, instantaneous disturbances to the temperature control microenvironment of the vehicle during manual operations such as material handover are captured and quantified in real time, and converted into structured data records to provide empirical input for subsequent simulation model calibration. Continuous environmental monitoring is performed using multiple sets of sensors integrated on the main vehicle and each separable vehicle component. These sensors include Hall effect sensors or mechanical switches for monitoring door opening status, and miniature hot-wire or impeller-type anemometers for measuring external airflow intensity. Door status data and external wind speed data streams are continuously collected at a preset high-frequency sampling rate. Preset thresholds characterizing stable environmental states are internally set; for example, when a door changes from closed to open, or when external wind speed data suddenly increases and exceeds a baseline value within a short period, it is automatically identified as a microenvironmental disturbance event, and the type, duration, and intensity of the event are accurately extracted. To associate physical events with specific business flows, material identification information bound to the currently operating main vehicle or vehicle component is read synchronously. This information is typically obtained by scanning electronic tags using an RFID reader. This implementation clearly defines the quantification logic of microenvironmental disturbances, and calculates the incremental heat load parameters introduced by a single disturbance using a formula: ; in, This represents the heat load increment, measured in joules. air density; The specific heat capacity of air at constant pressure; The opening area of ​​the vehicle's cargo box door; The average external airflow intensity; This refers to the average duration. The historical average ambient temperature for this geographical location; To maintain the temperature of targets inside the vehicle, as determined by the permitted storage temperature threshold. By integrating and storing event type, duration, intensity, geographic coordinates, timestamps, and a list of affected specific materials into structured microenvironmental disturbance data, the quantification feasibility of microenvironmental impacts in subsequent predictions is ensured.

[0039] For example, consider a scenario where a delivery vehicle arrives at the operating room on the 5th floor of Building C to hand over supplies A. During this process, manual operation causes the refrigerated container door to open and remain open for 25 seconds. A sensor deployed at the container door detects an external airflow intensity of 0.6 m / s. Identify and record this micro-environmental disturbance event, and define the area of ​​the container door opening. 0.15m 2 air density 1.2kg / m 3 specific heat capacity of air at constant pressure The historical average ambient temperature at this location is 1005 J / (kg·K). for The internal target temperature of material A is maintained. for Quantitative calculations are performed based on the heat load increment formula: The structured disturbance data was then associated with a specific material identifier and stored.

[0040] In one possible implementation, combining Figure 2 The microenvironmental disturbance data is converted into incremental heat load parameters and fed back into the three-dimensional virtual topology for calibrating the predicted temperature control energy consumption of subsequent tasks, including: Cluster analysis is performed on the microenvironmental disturbance data to extract typical microenvironmental disturbance patterns that occur in specific geographical locations or operational processes; When simulating energy consumption for new delivery tasks involving the specific geographical location or operational process, the corresponding typical microenvironmental disturbance mode is converted into additional heat load increment parameters. The incremental heat load parameters are superimposed onto the basic thermal calculation of the three-dimensional virtual topology, and the calculation of the predicted temperature control energy consumption is re-executed to generate a corrected comprehensive energy consumption assessment value.

[0041] In some implementations, deep mining of micro-environmental disturbance data accumulated from historical delivery tasks is used to construct a disturbance compensation model with predictive capabilities, thereby eliminating energy consumption prediction biases caused by neglecting human operational interference in traditional route planning. During execution, massive amounts of structured micro-environmental disturbance data stored in the database are retrieved, and K-means or density clustering algorithms are used to perform cluster analysis based on geographical coordinates, operation task type, and occurrence time. This analysis aims to extract statistically significant typical micro-environmental disturbance patterns from chaotic random events, such as identifying typical disturbance characteristics typically associated with the "door opening and handover" process at the "Surgical Operating Room on the 5th Floor of Building C". For each identified typical pattern, the corresponding additional heat load increment parameter is pre-calculated. Its quantification process is based on the law of conservation of energy, and the typical average external airflow intensity under this mode is... and typical average duration As the core input variable, the calculation formula is: ; Unlike the real-time recording of a single random event, here... This represents the expected heat load for that specific step in the simulation. When performing energy consumption simulations for new delivery tasks, once a geographical location or operational step in the candidate route successfully matches a library of established typical patterns, the corresponding... As a preset instantaneous heat load pulse, it is superimposed into the three-dimensional virtual topological fundamental heat balance equation at this node. Through this pre-compensation mechanism based on historical experience, a corrected comprehensive energy consumption assessment value that already includes the expected impact of human interference can be generated. This logical leap from "actual measurement records" to "model predictions" not only enhances the robustness of the path planning scheme in the complex and ever-changing scenarios of hospitals, but also eliminates the risk of insufficient disclosure through explicit statistical calculation logic.

[0042] For example, firstly, historical disturbance data of the "surgical operating room on the 5th floor of Building C" during the "door opening and handover" process is retrieved from the database and clustered to extract the typical average external airflow intensity under this pattern. for Typical average duration for Set air density for specific heat capacity of air at constant pressure for Vehicle door opening area for The historical average ambient temperature at this location for Internal target temperature for The preset heat load increment parameters are calculated based on the quantitative formula: When conducting energy consumption simulations for new delivery tasks involving this operating room, this... As the instantaneous heat load pulse is superimposed on the basic thermal calculation of the three-dimensional virtual topology, the integral operation is re-executed to generate a corrected comprehensive energy consumption assessment value. Thus, the prediction accuracy of the planning scheme under actual high disturbance environment is ensured through the pre-compensation of historical patterns.

[0043] In one possible implementation, combining Figure 2 The delivery task is executed according to the final planned route, and upon arrival at the geographical location node, the reorganization operation instruction is executed, causing the storage space of the vehicle carrying some materials to detach from the main vehicle and autonomously proceed to the corresponding departmental destination, including: The main vehicle is configured to carry several storage spaces with independent power supply and navigation capabilities, and each storage space is physically locked to the main vehicle through a standardized interface; When the main vehicle uses the positioning and navigation system to travel to the geographical location node specified by the reorganization operation instruction, an automated operation sequence is triggered; The main vehicle performs an unloading action, unlocks the corresponding storage space, and sends the corresponding last-mile delivery task instruction to the unlocked storage space, which then autonomously performs last-mile delivery to the final department destination. After the main vehicle completes the load reduction, it adjusts the power management parameters in real time and continues to execute the remaining path tasks.

[0044] In some implementations, the reconfiguration instructions at the planning level are transformed into precise and automated disassembly and collaborative operations of delivery vehicles in the physical world, achieving energy efficiency optimization throughout the delivery process by dynamically adjusting transport capacity. This implementation clearly defines the control logic for vehicle reconfiguration and task handover: First, the main vehicle is physically configured to carry several storage spaces with independent power supply units and autonomous navigation capabilities. These storage spaces, as detachable vehicle parts, are physically locked to the main vehicle through a standardized electromechanical interface, which also handles task data transmission and energy sharing. During the delivery task execution phase, the main vehicle uses a positioning and navigation system, such as SLAM technology based on LiDAR, to perceive its geographic coordinates in the physical environment corresponding to the three-dimensional virtual topology in real time. When the real-time coordinates match the geographic node coordinates specified by the reconfiguration instructions, a pre-programmed automated operation sequence is immediately triggered. This sequence begins with the main vehicle performing a detachment action to unlock the corresponding standardized interface, and simultaneously sending task instructions containing end-point path data and handover procedures to the onboard controller of the unlocked storage space, which then activates its own power system to autonomously proceed to the final departmental destination. Immediately after the main vehicle completes the unloading, the power management parameters need to be adjusted in real time to adapt to the new mass state. The calculation formula for the power adjustment logic is as follows: ; in, To adjust the motor output power requirements. The remaining total mass after unloading. It is the acceleration due to gravity. The current path slope, The rolling resistance coefficient, For driving speed, This improves the efficiency of the transmission system. Through this real-time power compensation and capacity release, the main vehicle can continue to perform the remaining path tasks with a better energy efficiency ratio, thereby ensuring the precise closed-loop execution of the reconfiguration command at the engineering level.

[0045] For example, in a delivery mission, the main vehicle arrives at the elevator lobby on the second floor of Building B to execute a reassembly operation. The main vehicle is physically equipped with several storage spaces locked via standardized interfaces. When it uses LiDAR to locate and perceive real-time geographic coordinates and enters the geographic location node specified in the instruction, an automated operation sequence is triggered. The main vehicle unlocks the corresponding interface and sends a final delivery instruction to the detached vehicle portion. Immediately after the unloading action is completed and the load is reduced, the power management parameters need to be adjusted in real time. Assume the remaining total mass of the main vehicle after unloading... for Current path slope for , Rolling resistance coefficient The value is 0.02, and the driving speed is... for Gravitational acceleration Pick Transmission system efficiency The value is 0.85. Based on the power correction logic formula, the corrected motor output power requirement is calculated. Through this real-time power compensation calculation, the main vehicle can precisely adjust the motor output torque to continue performing the remaining path tasks with better energy efficiency.

[0046] In one possible implementation, combining Figure 2 The method further includes: A safety threshold for active potential energy is configured for various materials. During the delivery task, if the remaining potential energy value, calculated in real time, is predicted to fall below the safety threshold for active potential energy before reaching the destination, a quality risk warning is generated. The quality risk warning is taken as the highest priority input, triggering real-time replanning of the current execution path or triggering the activation of the emergency temperature control compensation procedure.

[0047] In some implementation methods, a proactive, in-transit quality and safety assurance mechanism is established. This mechanism uses forward-looking prediction to prevent the reactive energy of medical supplies from falling below a critical point, thereby upgrading the static cost assessment model into a dynamic risk intervention system. During execution, at the task initialization stage, in addition to configuring basic temperature-sensitive parameters, a key reactive energy safety threshold is also configured for each type of medical supply in the delivery task flow. This threshold is typically set as the initial potential energy value of the material. A remaining energy level of 85% to 98% represents the minimum quality threshold for maintaining the clinical effectiveness of the supplies. During the delivery process, sensor data is continuously used to calculate the current remaining potential energy value of the supplies at each discrete time step using an iterative method. More importantly, a forward-looking quality risk assessment is performed in each calculation cycle: the remaining planned path from the current location to the final departmental destination is extracted, and the temperature control energy consumption simulation module is called to quickly simulate this future path, predicting the predicted remaining potential energy value of the materials when they reach the destination under the thermal environment exposure of the remaining path. The predictive logic calculation formula is: ; in, To predict the total number of steps to reach the destination, To predict the temperature deviation of each node in the future path. Once determined. If the predicted residual potential energy value is determined to have fallen below the safety threshold, a quality risk warning is immediately generated. This warning signal is set as the highest priority input in the system, capable of interrupting the normal energy-optimized execution logic and immediately triggering an emergency response decision. Based on preset strategy rules, one of the following operations will be performed: First, trigger real-time replanning of the current execution path, switching the objective function of the path planning algorithm from lowest overall cost to shortest delivery time or lowest heat load; second, when no better path exists, trigger the activation of the emergency temperature control compensation program, instructing the vehicle's temperature control system to enter an over-rated power operation mode, forcibly suppressing temperature fluctuations within the container at the expense of short-term energy efficiency, thereby preventing further rapid decline in active potential energy and ensuring the quality and safety of medical supplies.

[0048] For example, when a large general hospital was delivering high-value vaccine supplies (A) to the operating room on the 5th floor of Building C, it configured an active potential energy safety threshold for them during the task initialization phase. Unit, the initial potential energy value of the vaccine The value is 100 units. During the delivery process, when the vehicle reaches the node connecting the corridor, the remaining potential energy value of the current materials is calculated according to the formula. Unit. At this point, a prospective quality risk assessment is performed, extracting the remaining path from the current point to the operating room and simulating it, predicting that in the remaining 15 minutes, i.e. Step length Predicted residual potential energy of materials upon arrival at their destination under path thermal environment exposure. Based on the predictive logic calculation formula: If the average temperature deviation of the road section is predicted in the future for Then the calculation yields Calculations show that the predicted value will fall below the safety threshold of 95 units. Once determined... It immediately generates a high-priority quality risk warning, interrupts the optimal energy consumption plan, and triggers an emergency temperature control compensation program, instructing the vehicle's temperature control system to enter an over-rated power operation mode to forcibly suppress temperature fluctuations inside the container and ensure that the active potential energy of the materials does not fall below the safety red line when they arrive at their destination.

[0049] In one possible implementation, combining Figure 2 Following the recording of microenvironmental disturbance data caused by human intervention, the method further includes: Based on the microenvironmental disturbance data, historical average disturbance intensity coefficients are calculated for different geographical locations and operational procedures; When generating a new reorganization operation instruction, the historical average disturbance intensity coefficient of the route location is used as an optimization constraint for instruction generation. Prioritize generating reconfiguration instructions that can avoid high-disturbance-intensity stages or reserve additional safety margins for high-disturbance stages.

[0050] In some implementations, continuously recorded microenvironmental disturbance data is transformed into proactive risk avoidance strategies. By quantifying human-induced operational disturbances at specific geographical locations, the generation logic of reorganized work instructions is optimized, thereby proactively avoiding known quality risks while pursuing optimal energy efficiency. During execution, structured microenvironmental disturbance data recorded in historical delivery tasks is periodically statistically analyzed. The data is categorized and aggregated according to the dual dimensions of "geographical location - operational process," and the historical average disturbance intensity coefficient is calculated for each unique combination. This coefficient aims to quantify the inherent temperature control risk level of a specific space node, and its calculation formula is as follows: ; in, The historical average disturbance intensity coefficient, with the physical unit being watts, is used to visually represent the average increase in additional heat load introduced per second when an operation is performed at this location; The sum of heat load increments introduced by all disturbance events at this geographic location node within a preset statistical period; This is the total duration of the statistical period. When generating reorganization operation instructions for new delivery tasks, this historical average disturbance intensity coefficient is used. It is incorporated into the decision-making model as a key optimization constraint. The focus is on evaluating candidate geographic locations as dismantling points, which will... This is transformed into a risk cost penalty item, in terms of theoretical energy savings. The energy deviation value positively correlated with this coefficient is subtracted. This means that instructions that can be executed at nodes with low disturbance intensity will be prioritized to avoid the risk of thermal shock from busy handover areas. If the disturbance coefficients of candidate nodes are all higher than the preset safety threshold, a safety margin will be automatically added to the reassembly operation instructions. For example, the vehicle part will be temporarily pre-cooled before disassembly. By quantifying historical disturbance patterns into instruction generation constraints, the quality assurance capability of the delivery solution in actual complex manual operation environments is significantly improved. Figure 4 As shown, the historical average disturbance intensity coefficient of the hospital's internal delivery area is displayed. The spatial distribution heatmap. By quantifying the historical disturbance characteristics of different coordinate points, the system can automatically avoid high-disturbance-risk areas (high in dark) when generating reorganization instructions, thereby optimizing the quality and reliability of the delivery plan.

[0051] For example, firstly, statistical analysis is performed on the disturbance data accumulated from historical delivery tasks regarding the "door opening and handover" process at the "surgical operating room on the 5th floor of Building C". Within a set one-month statistical period, 50 disturbance events were identified at this location, and the total heat load increment generated at this location and its corresponding process was calculated. The value is 1,447,200 J. The total duration of this statistical period is set. The duration is 72,000 s. The calculation formula is based on the historical average disturbance intensity coefficient: When generating reconfiguration instructions for a new delivery task and evaluating the operating room entrance as a potential dismantling node, this 20.1W disturbance intensity is converted into a risk cost penalty. Theoretically, performing reconfiguration at this node could save energy. The estimated benefit is 8,300J, but the risk bias due to high disturbances will be deducted, reducing the overall benefit of this node to 6,300J. If another candidate node, such as the one on the second floor of Building B... With a minimum investment of 50,000 units and higher overall returns, the reorganization operation instructions will be generated on the second floor of Building B first, thereby effectively avoiding areas with high disturbance risk.

[0052] It should be noted that all equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A multi-temperature zone medical material co-configuration path energy consumption optimization planning method, characterized in that, The method includes: It receives temperature-sensitive parameters and biochemical decay coefficients of medical supplies to be delivered, and obtains temperature and humidity data of the hospital delivery environment, elevator reservation status, and delivery task flow including several department destinations. Based on the temperature-sensitive parameters, the permissible storage temperature threshold is determined, and combined with the biochemical degradation characteristic coefficient, the real-time change in the active potential energy of the material after deviating from the permissible storage temperature threshold is calculated. The real-time change is calculated as the value depreciation of the corresponding material and used as a dynamic quality cost item. A three-dimensional virtual topology is constructed based on the static physical layout of the hospital. The real-time temperature and humidity data are injected into the three-dimensional virtual topology as boundary conditions to reproduce the dynamic thermal environment distribution of the corridors on each floor. Based on the destinations of several departments in the delivery task flow, multiple feasible paths connecting the starting point and each department destination are searched in the three-dimensional virtual topology as a set of candidate paths to be evaluated. The elevator reservation status is converted into traffic passability constraints in virtual spacetime, and the real-time heat conduction and heat convection intensity experienced by the vehicle when moving along each path in the candidate path set is simulated in the three-dimensional virtual topology, thereby calculating the predicted temperature control energy consumption required for the vehicle to maintain temperature control balance. By combining the vehicle's real-time load and path gradient parameters, the predicted driving energy consumption required for the vehicle to overcome driving resistance is calculated, and the predicted temperature control energy consumption and the predicted driving energy consumption are added together to obtain the comprehensive energy consumption evaluation value corresponding to each path. Preset energy consumption cost coefficients and quality cost coefficients are assigned to the comprehensive energy consumption assessment value and the dynamic quality cost item, respectively. The weighted sum of the two is calculated to obtain the total assessment cost corresponding to each path. The total assessment cost of each path is compared, and the path with the lowest total assessment cost is selected from the candidate path set as the final planning path. Identify the output power fluctuation characteristics of the predicted temperature control energy consumption in the final planned path, and generate a reconfiguration operation instruction for physically disassembling or reassembling the delivery vehicle at geographical nodes where the output power exceeds a preset proportion of the rated power of the vehicle temperature control system. The delivery task is executed according to the final planned route, and upon arrival at the geographical location node, the reorganization operation instruction is executed, causing the storage space of the vehicle carrying some materials to detach from the main vehicle and autonomously proceed to the corresponding department destination. The system monitors the door opening status and external airflow intensity during the delivery process in real time, identifies and records micro-environmental disturbance data caused by manual operations, and converts the micro-environmental disturbance data into heat load increment parameters and feeds them back into the three-dimensional virtual topology for calibrating the predicted temperature control energy consumption of subsequent tasks.

2. The energy-optimal planning method for a multi-temperature zone medical supplies distribution route according to claim 1, characterized in that, Based on the temperature-sensitive parameters, a permissible storage temperature threshold is determined. Combined with the biochemical degradation characteristic coefficient, the real-time change in the active potential energy of the material after deviating from the permissible storage temperature threshold is calculated, including: Extract the permissible storage temperature threshold and biochemical degradation coefficient of various materials from the temperature-sensitive parameters; Starting from the time point when the material leaves the permitted storage temperature threshold, an active potential energy function is established, wherein the decay rate of the active potential energy function is jointly determined by the deviation of the real-time ambient temperature from the permitted storage temperature threshold and the biochemical decay characteristic coefficient. Within a preset discrete time step, the remaining potential energy value of the materials in transit is calculated iteratively using the active potential energy function, and the real-time change in active potential energy is calculated based on the loss of the remaining potential energy value relative to the initial potential energy value.

3. The energy-optimal planning method for a multi-temperature zone medical supplies distribution route according to claim 1, characterized in that, The real-time heat conduction and convection intensity experienced by the vehicle as it moves along each path in the candidate path set within the three-dimensional virtual topology are simulated to calculate the predicted temperature control energy consumption required for the vehicle to maintain temperature control balance, including: In the three-dimensional virtual topology, dynamic thermal environment distribution data corresponding to each position coordinate is retrieved in real time based on the discrete position coordinates of the vehicle moving along each path. Based on the thermal properties of the vehicle's enclosure structure, the vehicle's surface area, and the dynamic thermal environment distribution data, the instantaneous heat intrusion of the vehicle in each path segment is calculated. The real-time output power curve required to maintain the vehicle's internal environment within the permitted storage temperature threshold in order to offset the instantaneous thermal intrusion is determined. The real-time output power curve is integrated within the delivery cycle corresponding to the delivery task flow to obtain the predicted temperature control energy consumption for each path.

4. The energy-optimal planning method for a multi-temperature zone medical supplies distribution route according to claim 3, characterized in that, Identify the output power fluctuation characteristics of the predicted temperature control energy consumption in the final planned path, and at geographical location nodes where the output power exceeds a preset proportion of the vehicle's temperature control system's rated power, generate a reconfiguration operation instruction for physically disassembling or reassembling the delivery vehicle, including: Identify the power peak interval in the real-time output power curve that exceeds the preset proportion of the rated power, and mark the path coordinates corresponding to the power peak interval as geographical location nodes; Analyze the delivery task flow to identify the coupling relationship between goods that can be delivered in the geographical location node and its vicinity and goods that need to continue to the subsequent destination; The simulation calculates the corrected temperature control energy consumption when the main vehicle continues to perform the remaining path tasks after physical disassembly at the geographical location node, and the sub-energy consumption when the detached vehicle independently performs the last-mile delivery task. If the difference between the predicted temperature control energy consumption and the sum of the corrected temperature control energy consumption and sub-energy consumption is greater than the preset reorganization execution cost, a reorganization operation instruction is generated to release the vehicle part carrying the corresponding materials at the geographical location node.

5. The energy-optimal planning method for a multi-temperature zone medical supplies distribution route according to claim 1, characterized in that, The real-time monitoring of the door opening status and external airflow intensity during the delivery process, and the identification and recording of micro-environmental disturbance data caused by human operation, include: Sensors deployed on the main vehicle and its components continuously collect door status data and external wind speed data. When the data collected by the sensor exceeds a preset threshold used to characterize the stable state of the environment, it is identified as a micro-environmental disturbance event, and the type, duration and intensity of the event are extracted. By reading the material identification information bound to the main vehicle or part of the vehicle, the microenvironmental disturbance event is associated with the specific material affected, forming structured microenvironmental disturbance data.

6. The energy-optimal planning method for a multi-temperature zone medical supplies distribution route according to claim 1, characterized in that, The microenvironmental disturbance data is converted into incremental heat load parameters and fed back into the three-dimensional virtual topology for calibrating the predicted temperature control energy consumption of subsequent tasks, including: Cluster analysis is performed on the microenvironmental disturbance data to extract typical microenvironmental disturbance patterns that occur in specific geographical locations or operational processes; When simulating energy consumption for new delivery tasks involving the specific geographical location or operational process, the corresponding typical microenvironmental disturbance mode is converted into additional heat load increment parameters. The incremental heat load parameters are superimposed onto the basic thermal calculation of the three-dimensional virtual topology, and the calculation of the predicted temperature control energy consumption is re-executed to generate a corrected comprehensive energy consumption assessment value.

7. The energy-optimal planning method for a multi-temperature zone medical supplies distribution route according to claim 1, characterized in that, The delivery task is executed according to the final planned route, and upon arrival at the geographical location node, the reorganization operation instruction is executed, causing the storage space of the vehicle carrying some materials to detach from the main vehicle and autonomously proceed to the corresponding departmental destination, including: The main vehicle is configured to carry several storage spaces with independent power supply and navigation capabilities, and each storage space is physically locked to the main vehicle through a standardized interface; When the main vehicle uses the positioning and navigation system to travel to the geographical location node specified by the reorganization operation instruction, an automated operation sequence is triggered; The main vehicle performs an unloading action, unlocks the corresponding storage space, and sends the corresponding last-mile delivery task instruction to the unlocked storage space, which then autonomously performs last-mile delivery to the final department destination. After the main vehicle completes the load reduction, it adjusts the power management parameters in real time and continues to execute the remaining path tasks.

8. The energy-optimal planning method for a multi-temperature zone medical supplies distribution route according to claim 2, characterized in that, The method further includes: A safety threshold for active potential energy is configured for various materials. During the delivery task, if the remaining potential energy value, calculated in real time, is predicted to fall below the safety threshold for active potential energy before reaching the destination, a quality risk warning is generated. The quality risk warning is taken as the highest priority input, triggering real-time replanning of the current execution path or triggering the activation of the emergency temperature control compensation procedure.

9. The energy-optimal planning method for a multi-temperature zone medical supplies distribution route according to claim 4, characterized in that, Following the recording of microenvironmental disturbance data caused by human intervention, the following is also included: Based on the microenvironmental disturbance data, historical average disturbance intensity coefficients are calculated for different geographical locations and operational procedures; When generating a new reorganization operation instruction, the historical average disturbance intensity coefficient of the route location is used as an optimization constraint for instruction generation. Prioritize generating reconfiguration instructions that can avoid high-disturbance-intensity stages or reserve additional safety margins for high-disturbance stages.

10. An energy-optimal planning system for a multi-temperature zone medical supplies distribution route, characterized in that, The system is used for an energy-optimal planning method for a multi-temperature zone medical supplies distribution route as described in any one of claims 1-9, and the system comprises: The parameter and task acquisition module is used to receive the temperature-sensitive parameters and biochemical decay coefficients of the medical supplies to be delivered, and to acquire the temperature and humidity data of the hospital delivery environment, elevator reservation status, and delivery task flow containing several department destinations. The active potential energy and cost calculation module is used to determine the permissible storage temperature threshold based on the temperature-sensitive parameter, and in combination with the biochemical decay characteristic coefficient, calculate the real-time change in the active potential energy of the material after deviating from the permissible storage temperature threshold, and calculate the real-time change as the value depreciation of the corresponding material as a dynamic quality cost item. The virtual topology and thermal environment reproduction module is used to construct a three-dimensional virtual topology based on the static physical layout of the hospital. The real-time temperature and humidity data are injected into the three-dimensional virtual topology as boundary conditions to reproduce the dynamic thermal environment distribution of the corridors on each floor. The candidate path search module is used to search for multiple feasible paths connecting the starting point and each department destination in a three-dimensional virtual topology based on several department destinations in the delivery task flow, as a set of candidate paths to be evaluated. The temperature control energy consumption simulation module is used to convert the elevator reservation status into traffic passability constraints in virtual space and time, and to simulate the real-time heat conduction and heat convection intensity experienced by the vehicle when it moves along each path in the candidate path set in the three-dimensional virtual topology, thereby calculating the predicted temperature control energy consumption required for the vehicle to maintain temperature control balance. The driving and comprehensive energy consumption calculation module is used to calculate the predicted driving energy consumption required for the vehicle to overcome driving resistance by combining the vehicle's real-time load and path slope parameters, and to accumulate the predicted temperature control energy consumption and the predicted driving energy consumption to obtain the comprehensive energy consumption evaluation value corresponding to each path. The optimal path selection module is used to assign preset energy consumption cost coefficients and quality cost coefficients to the comprehensive energy consumption assessment value and the dynamic quality cost item respectively, calculate the weighted sum of the two to obtain the total assessment cost corresponding to each path, compare the total assessment cost of each path, and select the path with the lowest total assessment cost from the candidate path set as the final planned path. The reconfiguration operation instruction generation module is used to identify the output power fluctuation characteristics of the predicted temperature control energy consumption in the final planned path, and generate a reconfiguration operation instruction for physically disassembling or reassembling the delivery vehicle at geographical nodes where the output power exceeds a preset proportion of the rated power of the vehicle temperature control system. The task execution and reorganization control module is used to execute the delivery task according to the final planned path, and when it arrives at the geographical location node, it executes the reorganization operation instruction to make the storage space of the vehicle carrying some materials detach from the main vehicle and autonomously go to the corresponding department destination. The environmental monitoring and simulation calibration module is used to monitor the door opening status and external airflow intensity during the delivery process in real time, identify and record micro-environmental disturbance data caused by human operation, and convert the micro-environmental disturbance data into heat load increment parameters and feed them back to the three-dimensional virtual topology for calibrating the predicted temperature control energy consumption of subsequent tasks.

Images