Vaccine cold storage AGV operation navigation method and cold storage AGV

Abstract

The application provides a vaccine cold storage AGV operation navigation method and a cold storage AGV, and the operation navigation method comprises the following steps: S1, dividing the cold storage area by temperature collection points as boundaries, and determining the temperature distribution in each area; S2, acquiring AGV position data and temperature data at the current position in real time; S3, determining a starting point and a target point, establishing a multi-objective function with the minimum temperature change, the lowest path length and the lowest energy consumption, and solving the multi-objective function to obtain an optimal dynamic planning path. The application plans the path according to the temperature distribution in each area, avoids the temperature sudden change area, reduces the temperature fluctuation of the vaccine in the transportation process, and reduces the damage rate. Moreover, the path planning of the multi-objective function considers the adaptability of the distance, the temperature and the energy consumption, improves the operation efficiency, the dynamic energy consumption distribution strategy reduces the energy loss in the low-temperature environment, prolongs the endurance time of the AGV, reduces the charging frequency of the AGV in and out of the cold storage, and thus reduces the disturbance to the temperature field in the cold storage.

Description

Technical Field

[0001] This invention belongs to the field of vaccine cold storage technology, specifically relating to a vaccine cold storage AGV and its operation navigation method. Background Technology

[0002] As special biological products, vaccines have extremely strict temperature requirements for storage and transportation. Conventional vaccines must be stored in a constant temperature environment of 2-8℃, while special types of vaccines (such as inactivated polio vaccines) must be stored in a frozen environment below -20℃. This requirement poses a severe challenge to the environmental control capabilities of vaccine cold chain logistics.

[0003] With the deep application of automation technology in the logistics field, Automated Guided Vehicles (AGVs), with their advantages of automation, precision, and continuous operation, have become the core equipment for automated vaccine transfer in vaccine cold storage, effectively replacing the problems of low efficiency and high risk of human intervention in the traditional manual transfer mode. Chinese Patent CN201711338501.0, which discloses "Automated Control Method and Storage Management Device Based on Vaccine Storage and Management," is a typical application in this field. This technology uses an ATA central management and distribution system as the core control system to command and schedule AGV handling vehicles, conveyors, three-coordinate stacking mechanisms, and other equipment to work collaboratively, completing the entire process of automated management of vaccine warehousing and outbound operations. It also establishes a comprehensive traceability mechanism, achieving standardization and informatization of vaccine storage management, and significantly improving the efficiency and reliability of the traditional manual management mode.

[0004] However, in current vaccine storage scenarios, multi-temperature cold storage has become the mainstream configuration to meet the storage needs of different types of vaccines. These cold storage facilities typically contain multiple temperature zones, including a 2-8℃ refrigeration zone, a -20℃ or lower freezing zone, and a 0-4℃ transition zone, with temperature differences exceeding 20℃ between these zones. This drastic temperature gradient environment leads to performance degradation of AGV sensors (LiDAR, vision modules), condensation and icing interfering with positioning. Furthermore, traditional navigation algorithms (Dijkstra, A*) do not incorporate temperature factors into path planning, focusing solely on minimizing distance, which can easily cause equipment malfunctions or excessive temperature fluctuations during vaccine transport. While existing cold chain AGVs possess basic low-temperature adaptability, they lack dynamic path optimization mechanisms for the differences between multiple temperature zones, resulting in decreased navigation accuracy (positioning errors exceeding ±10mm in low-temperature environments), low multi-vehicle coordination efficiency, and high energy consumption, making it difficult to meet the efficient and safe transportation requirements of vaccine storage. Summary of the Invention

[0005] To address the technical problems existing in the prior art, the first aspect of the present invention is to provide a navigation method for a vaccine cold storage AGV. The second aspect, based on the same inventive concept, also provides a cold storage AGV for the aforementioned navigation method.

[0006] In this embodiment of the invention, the operation and navigation method of the AGV for vaccine cold storage includes the following steps:

[0007] S1, divide the cold storage area into zones based on temperature acquisition points, and determine the temperature distribution in each zone;

[0008] S2, real-time acquisition of AGV position data and current temperature data;

[0009] S3. Determine the starting point and target point, establish a multi-objective function that minimizes temperature change, path length, and energy consumption, and solve the multi-objective function to obtain the optimal path through dynamic programming.

[0010] The cold storage AGV of this invention, used in the above-mentioned operation navigation method, includes an AGV body, a low-temperature resistant navigation component and a data processing module installed on the AGV body; the low-temperature resistant navigation component detects the position of the AGV and its real-time energy consumption and transmits them to the data processing module; the data processing module controls the operation of the AGV based on the starting point and target point, the position of the AGV, and the real-time energy consumption, using the aforementioned operation navigation method.

[0011] Compared with the prior art, the advantages of the superior technical solution of the present invention include:

[0012] 1. This invention plans routes based on the temperature distribution in each region, avoids areas with sudden temperature changes, reduces temperature fluctuations in vaccines during transportation, lowers the damage rate, and meets pharmaceutical cold chain safety standards.

[0013] 2. The multi-objective path planning takes into account the adaptability of distance, temperature and energy consumption, improves operation efficiency, reduces energy loss in low temperature environment, extends AGV endurance, and reduces the frequency of AGV entering and leaving the cold storage for charging, thereby reducing the disturbance to the internal temperature field of the cold storage.

[0014] 3. The temperature compensation unit of the cold storage AGV effectively solves the problem of sensor failure caused by low temperature, ensuring accurate and controllable vaccine transportation path; at the same time, combined with the operation navigation method, it can be adapted to the complex environment of multi-temperature cold storage, without the need for manual intervention and adjustment, reducing operation and maintenance costs. Attached Figure Description

[0015] Figure 1 This is a flowchart of the AGV operation navigation method for vaccine cold storage in an embodiment. Detailed Implementation

[0016] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0017] Example 1

[0018] This embodiment provides a method for AGV operation navigation in vaccine cold storage, such as... Figure 1 As shown, in a preferred embodiment, the navigation method includes the following steps:

[0019] S1, divide the cold storage area into zones based on temperature acquisition points, and determine the temperature distribution in each zone;

[0020] S2, real-time acquisition of AGV position data and current temperature data;

[0021] S3. Determine the starting point and target point, establish a multi-objective function that minimizes temperature change, path length, and energy consumption, and solve the multi-objective function to obtain the optimal path through dynamic programming.

[0022] In step S1 of this invention, the method for dividing the cold storage area by temperature acquisition points and determining the temperature distribution within each area is as follows:

[0023] S11. Define the three-dimensional spatial region of the cold storage, using... The coordinates represent any point within the cold storage;

[0024] S12. Divide the area based on the temperature acquisition points: Divide the cold storage area into multiple smaller areas according to the location of the temperature acquisition points.

[0025] Temperature acquisition point location ,

[0026] in, The number of the collection point. =1,2,3……n; For the first Spatial coordinates of each collection point;

[0027] S13. Calculate the temperature field in each region according to the heat conduction equation. Specifically, the heat conduction equation considering temperature changes and air convection is:

[0028]

[0029] in, Indicates at time Spatial location Temperature at that location; It is the velocity vector of airflow, representing the convective effect of airflow inside the cold storage. The temperature gradient vector represents the rate and direction of temperature change in space. The Laplace operator for temperature; It is the thermal diffusivity (which can be found in parameter manuals or determined based on empirical values);

[0030]

[0031] in, Let the coordinates of any point to be found be: For the first velocity vectors at each measuring point for to the measuring point distance, This is the distance weighting coefficient (usually set to 2).

[0032] Based on the heat conduction equation, the temperature field is solved using existing numerical methods: 1) Finite difference method: the spatial domain is discretized into a grid, and the temperature field is solved based on the discretized equation; 2) Finite element method: the cold storage is decomposed into multiple small units, and the temperature distribution of each unit is solved step by step.

[0033] In step S3 of this invention, a multi-objective function is established that minimizes temperature change, path length, and energy consumption. The method for solving the multi-objective function to obtain the optimal path through dynamic programming is as follows:

[0034]

[0035] Divide the starting point and the target point into Each segment It is a positive integer. The segment node number;

[0036] in, From node To the node Temperature change; This represents the maximum range of temperature variation within the cold storage, used for temperature term normalization. From node To the node Temperature influence coefficient; It is a node To the node Path length; This represents the total length of the paths within the cold storage facility, used for path item normalization. It is a node To the node The energy consumption of the path is for the nodes. To node Energy consumption; This is the maximum energy consumption value for AGV operation, used for energy consumption normalization; These are the weighting coefficients for temperature change, path length, and energy consumption, respectively.

[0037]

[0038] in, and These are nodes and nodes Temperature;

[0039]

[0040] in, and It is a node and nodes The coordinates;

[0041]

[0042] in, and These are nodes and nodes The battery charge of the AGV.

[0043] In another preferred embodiment of the present invention, the navigation method further includes a dynamic temperature adjustment mechanism, which sets a temperature influence coefficient and adjusts the coefficient to increase when the temperature of a certain area changes drastically.

[0044] .

[0045] in, The reference temperature influence coefficient (e.g., 0.1). The time influence coefficient. This is the spatial influence coefficient. For the node To the node Maximum temperature within the interval For the node To the node Minimum temperature of the interval, time period For the selected reference time period, For this time period, Time period Maximum internal temperature Time period The lowest internal temperature.

[0046] In this invention, the process of solving the multi-objective function must follow four main categories: physical constraints, parameter constraints, path topology constraints, and boundary constraints for multi-objective optimization. The specific constraints are as follows:

[0047] 1. Parameter value constraints

[0048] 1) Weighting coefficient constraints

[0049] Weighting factors for temperature change, path length, and energy consumption: It must meet the following requirements: ,and (The weights are non-negative, representing the relative importance of each objective.)

[0050] 2) Temperature influence coefficient constraint

[0051] Reference temperature influence coefficient Temperature influence coefficient Both are non-negative real numbers, and their ranges can be determined according to the actual scenario (e.g., 0 < 0). (≤5), to avoid the objective function being distorted due to excessively large coefficients.

[0052] 3) Segmentation number constraint

[0053] Number of segments from the starting point to the target point It is a positive integer, and the segment node number is... Satisfying 1≤ This ensures the integrity of the path segments.

[0054] 2. Physical quantity constraints

[0055] 1) Path-related physical quantities

[0056] Path length (Length is a non-negative scalar, negative paths do not exist), Energy consumption (Energy consumption is non-negative, and cost is always positive in scenarios without energy recovery).

[0057] 2) Temperature change constraint

[0058] The value must be a real number and must conform to the temperature range of the actual scenario. For example, in a navigation scenario, the ambient temperature may vary between -40℃ and 60℃. Temperature values ​​that do not conform to physical laws should be avoided.

[0059] 3. Path topology constraints

[0060] 1) Node connectivity constraints

[0061] Segmentation Node arrive The path is a feasible path, meaning there is a physically connected passage between the two points, and there are no broken roads or obstacles blocking the way.

[0062] 2) Constraints on starting point and target point

[0063] The starting point and target point of the path are fixed known points. Their spatial coordinates are not changed during the solution process. The segmented path must start from the starting point, pass through each segment node in sequence, and finally reach the target point.

[0064] 4. Boundary Constraints for Multi-Objective Optimization

[0065] Total path length , This represents the maximum allowed path length.

[0066] Total energy consumption , Maximum permissible energy consumption;

[0067] Total temperature change , The maximum allowable temperature change;

[0068] AGV battery power constraints: .

[0069] The objective function can be solved using existing dynamic programming algorithms or heuristic algorithms (such as genetic algorithms, ant colony algorithms, etc.), and the specific solution process will not be elaborated here.

[0070] Example 2

[0071] This embodiment provides a cold storage AGV for the navigation method of Embodiment 1. The cold storage AGV includes an AGV body, a navigation component and a data processing module installed on the AGV body. The navigation component detects the AGV's position and real-time energy consumption and transmits this information to the data processing module. The data processing module (which has an input interface for inputting a starting point and a target point) controls the AGV's operation based on the starting point, target point, AGV's position, and real-time energy consumption, using the navigation method of Embodiment 1.

[0072] In this embodiment, the low-temperature navigation component includes a lidar, a position sensor, and an energy consumption monitoring module. The lidar is used to construct a 3D map of the cold storage environment and detect obstacles, transmitting the detected structures to the data processing module. The position sensor detects the real-time position of the AGV, obtains the temperature information of the location based on the real-time position, and transmits it to the data processing module. The energy consumption monitoring module obtains the real-time energy consumption of the AGV (specifically through current and voltage sensors, fuel gauges, etc.) and transmits it to the data processing module.

[0073] Preferably, the low-temperature resistant navigation component further includes a vision sensor for recognizing shelf markings and QR code navigation landmarks. In another preferred embodiment, the AGV body is also equipped with a temperature compensation unit to protect electronic components from condensation and icing. Specifically, the temperature compensation unit includes an active heating circuit and an intelligent temperature control system. The active heating circuit adopts a distributed design, including a flexible heating film surrounding key electronic components (such as data processing modules and navigation sensor interface boards), and a ring heater designed for the optical windows of lidar and vision sensors. The intelligent temperature control system is based on a high-precision digital temperature sensor network (arranged at various heat-generating elements and key environmental points), with a microcontroller as its core, and performs PWM (pulse width modulation) control on the heating circuit according to a preset multi-level temperature control strategy (such as gradient heating and constant temperature maintenance). The core effect of this temperature compensation unit is to create and maintain a suitable local microenvironment for electronic components, which not only prevents performance degradation or failure of sensor lenses and circuit board surfaces due to condensation and icing, but also ensures that various chips and components operate within their rated temperature range.

[0074] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for AGV operation and navigation in vaccine cold storage, characterized in that, Includes the following steps: S1, divide the cold storage area into zones based on temperature acquisition points, and determine the temperature distribution in each zone; S2, real-time acquisition of AGV position data and current temperature data; S3. Determine the starting point and target point, establish a multi-objective function that minimizes temperature change, path length, and energy consumption, and solve the multi-objective function to obtain the optimal path through dynamic programming. In step S1, the cold storage area is divided with temperature acquisition points as boundaries, and the method for determining the temperature distribution in each area is as follows: S11. Define the three-dimensional spatial region of the cold storage, using... The coordinates represent any point within the cold storage; S12. Divide the area based on the temperature acquisition points: Divide the cold storage area into multiple smaller areas according to the location of the temperature acquisition points. Temperature acquisition point location , Where i is the number of the collection point, i=1,2,3……n; Let i be the spatial coordinates of the i-th acquisition point; S13. Calculate the temperature field in each region according to the heat conduction equation. Specifically, the heat conduction equation considering temperature changes and air convection is: ， in, Indicates the spatial location at time t. Temperature at that location; It is the velocity vector of airflow, representing the convective effect of airflow inside the cold storage. The temperature gradient vector represents the rate and direction of temperature change in space. Let be the Laplace operator for temperature; α is the thermal diffusivity. ， in, Let the coordinates of any point to be found be: Let i be the velocity vector of the i-th measuring point. for The distance to measurement point i, where k is the distance weighting coefficient; In step S3, a multi-objective function is established that minimizes temperature change, path length, and energy consumption. The method for solving the multi-objective function to obtain the optimal path through dynamic programming is as follows: ， Divide the starting point and the target point into m-1 segments, where m is a positive integer and j is the segment node number; in, It is the temperature change from node j to node j+1; This represents the maximum range of temperature variation within the cold storage facility. It is the temperature influence coefficient from node j to node j+1; It is the path length from node j to node j+1; This represents the total length of the paths within the cold storage facility. It is the energy consumption of the path from node j to node j+1, and it is the energy consumption from node j to node j+1. This represents the maximum energy consumption value for AGV operation. These are the weighting coefficients for temperature change, path length, and energy consumption, respectively. ， in, and These are the temperatures of node j and node j+1, respectively. ， in, and These are the coordinates of node j and node j+1; ， in and These are the battery capacities of the AGVs at nodes j and j+1, respectively. The navigation method also includes a dynamic temperature adjustment mechanism, which sets a temperature influence coefficient and increases the coefficient when the temperature in a certain area changes drastically. ， in, The reference temperature influence coefficient, The time influence coefficient. This is the spatial influence coefficient. The maximum temperature value is the interval from node j to node j+1. The minimum temperature value for the interval from node j to node j+1, over a time period. The selected reference time period is t, where t represents a specific time point within that time period. Time period Maximum internal temperature Time period The lowest internal temperature.

2. The navigation method according to claim 1, characterized in that, The constraints for solving multi-objective functions include the following: Parameter value constraints 1) Weighting coefficient constraints Weighting factors for temperature change, path length, and energy consumption: It must meet the following requirements: ,and ; 2) Temperature influence coefficient constraint Reference temperature influence coefficient The calibration range is determined based on the actual scenario; Temperature influence coefficient ; 3) Segmentation number constraint The number of segments m from the starting point to the target point is a positive integer, and the segment node index j satisfies This ensures the integrity of the path segments; Physical quantity constraints 1) Path-related physical quantities Path length Energy consumption ; 2) Temperature change constraint The value must be a real number and must conform to the temperature range of the actual scenario. For example, in a navigation scenario, the ambient temperature may vary between -40℃ and 60℃. Temperature values ​​that do not conform to physical laws should be avoided. Path topology constraints 1) Node connectivity constraints The path from segment node j to j+1 is a feasible path, meaning there is a physically connected channel between the two points, and there are no broken paths or obstacles blocking the way. 2) Constraints on starting point and target point The starting and target points of the path are fixed known points, and their spatial coordinates are not changed during the solution process. The segmented path must start from the starting point, pass through each segment node in sequence, and finally reach the target point. Boundary constraints for multi-objective optimization Total path length , This represents the maximum allowed path length. Total energy consumption , Maximum permissible energy consumption; Total temperature change , The maximum allowable temperature change; AGV battery power constraints: .

3. A cold storage AGV used in the operation navigation method according to claim 1 or 2, characterized in that, Includes the AGV body, as well as the navigation components and data processing module installed on the AGV body; The navigation component detects the AGV's position and real-time energy consumption and transmits this information to the data processing module. The data processing module controls the operation of the AGV based on the starting point and target point, the location of the AGV, and the real-time energy consumption, using the operation navigation method described in claim 1 or 2.

4. The cold storage AGV according to claim 3, characterized in that, The navigation components include a lidar, a position sensor, and an energy consumption monitoring module; The lidar is used to construct a three-dimensional map of the cold storage environment and detect obstacles, and transmit the detected structure to the data processing module. The position sensor detects the real-time position of the AGV, obtains the temperature information of the location based on the real-time position, and transmits it to the data processing module. The energy consumption monitoring module acquires the real-time energy consumption of the AGV and transmits it to the data processing module.

5. The cold storage AGV according to claim 3, characterized in that, The navigation component also includes a vision sensor for recognizing shelf markings and QR code navigation landmarks.

6. The cold storage AGV according to claim 3, characterized in that, The AGV body is also equipped with a temperature compensation unit, which is used to protect electronic components from condensation and icing.

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