A Spatial Optimization Scheduling Method and System for Medical Device Distribution Based on the Internet of Things

Abstract

This invention discloses a spatial optimization scheduling method and system for medical device distribution based on the Internet of Things (IoT), relating to the field of medical device storage. The method includes collecting historical warehouse outbound data and extracting the outbound frequency and co-occurrence frequency of medical devices; obtaining the location coordinates and exit positions from the warehouse map information; constructing a comprehensive cost function, which consists of the weighted distance cost from the device to the exit and the collaborative handling cost between related devices; and solving for the minimum value of the cost function through an optimization algorithm to generate the optimal storage distribution strategy. This invention solves the problems of low picking efficiency and scattered storage of related devices caused by static layout in traditional medical warehousing, significantly shortening the handling path of frequently used devices and reducing the operational complexity of collaborative outbound scenarios.

Description

Technical Field

[0001] This invention relates to the field of medical device storage, and more specifically, to a method and system for optimizing the spatial distribution scheduling of medical devices based on the Internet of Things. Background Technology

[0002] In modern medical warehousing management, with the rapid increase in the types and quantities of medical devices, efficient allocation of storage space has become a key challenge for improving hospital logistics efficiency. Traditional warehouse layouts typically rely on manual experience or static rules for location allocation, lacking dynamic analysis of historical outbound data. This results in frequently used devices being placed far from exits, or related devices being stored in a scattered manner. These problems not only lengthen picking routes and increase operation time but can also lead to management chaos due to frequent handling. Furthermore, focusing on a single factor without comprehensively considering the balance between device usage frequency and collaborative needs makes it difficult to adapt to complex and ever-changing real-world scenarios. Therefore, how to collect data in real time based on IoT technology, build multi-dimensional optimization models, and quickly generate dynamic storage strategies has become a core issue that urgently needs to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for optimizing the spatial distribution of medical devices based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A spatial optimization scheduling method for the distribution of medical devices based on the Internet of Things includes the following steps: By collecting historical outbound data from the warehouse, two sets of core indicators were obtained: the outbound frequency of any single medical device and the co-occurrence frequency of any two medical devices being outbound simultaneously. Collect warehouse map information, including the coordinates of all available storage locations and the location of the exit; Constructing a comprehensive cost function : ; in Indicates medical devices With exports distance, Indicates medical devices Outbound frequency, Indicates medical devices With medical devices Spatial distance, Indicates medical devices With medical devices co-occurrence frequency, , These are the weighting coefficients; An optimization algorithm is used to obtain a distribution strategy that minimizes the comprehensive cost function, and medical devices are stored based on the distribution strategy.

[0005] In some embodiments, historical outbound data of the warehouse is collected using IoT devices; warehouse map information is collected using IoT devices.

[0006] In some embodiments, the Internet of Things device includes any one or more of the following: radio frequency identification medical device, Bluetooth Low Energy module, ultra-wideband positioning device, or infrared sensing device.

[0007] In some embodiments, the optimization algorithm is an annealing algorithm.

[0008] In some embodiments, the annealing algorithm includes the following steps: 1) Obtain the initial placement plan for medical devices; 2) Set the initial temperature T and the minimum temperature T min And the cooling coefficient r; 3) Calculate the cost function value L1 corresponding to the initial placement scheme; 4) At the current temperature T, perform a perturbation operation on the current placement scheme to generate a new placement scheme and calculate its corresponding cost function value L2; 5) If L2 is less than L1, then replace the current placement scheme with the new placement scheme; 6) If L2 is greater than L1, then accept the new placement scheme with probability p = exp[−(L2−L1) / T]; 7) Repeat steps 4) to 6) until the preset number of iterations is reached or the state convergence condition is met; 8) Update the current temperature T to r×T and decrease the temperature; 9) Repeat steps 4) through 8) until the current temperature T is lower than the minimum temperature T. min ; 10) Output the placement scheme with the minimum cost function value during the annealing process as the optimal scheme.

[0009] In some embodiments, the perturbation operation is: Calculate the current contribution of each medical device to the total cost function: ;in, j Remove all medical devices i Other medical devices; Select Largest medical device i and medical devices i Exchange positions with another medical device, which is a non-medical device.i Any medical device other than that.

[0010] In some embodiments, the method further includes constructing a structural map, the construction process of which is as follows: The nodes represent each type of medical device, and the characteristic of each node i is its outbound frequency. ; Construct a connection between two medical devices that share a common outbound case, with the edge characterized by its co-occurrence frequency. .

[0011] In some embodiments, the size of the nodes in the structure graph is set to be proportional to the outbound frequency. The length of the edge between nodes is expressed as inversely proportional to the co-occurrence frequency. This generates a visual map, and based on the visual map, the initial placement scheme for the optimization algorithm is set.

[0012] This invention also discloses an IoT-based spatial optimization scheduling system for medical device distribution, comprising the following modules: The data acquisition module is used to collect historical outbound data from the warehouse and obtain two sets of core indicators: the outbound frequency of any one medical device and the co-occurrence frequency of any two medical devices being outbound at the same time. The map modeling module is used to collect warehouse map information, including the coordinates of all available storage locations and the location of the exit. The cost function building module is used to construct the comprehensive cost function. : ; in Indicates medical devices With exports distance, Indicates medical devices Outbound frequency, Indicates medical devices With medical devices Spatial distance, Indicates medical devices With medical devices co-occurrence frequency, , These are the weighting coefficients.

[0013] An optimization module is used to obtain a distribution strategy that minimizes the comprehensive cost function using an optimization algorithm, and to store medical devices based on the distribution strategy.

[0014] In some embodiments, the system further includes a structure map construction module for constructing a structure map, the construction process of which is as follows: The nodes represent each type of medical device, and the characteristic of each node i is its outbound frequency. ; Construct a connection between two medical devices that share a common outbound case, with the edge characterized by its co-occurrence frequency. .

[0015] The advantages of this invention compared to existing technologies lie in its construction of a comprehensive cost function that jointly optimizes the outbound frequency, co-occurrence frequency, and spatial distance of medical devices. This significantly shortens the picking path for high-frequency devices while reducing the handling costs of related devices. Specifically, by dynamically balancing the individual movement efficiency of devices with collaborative needs, this invention can adaptively adjust the distribution of storage locations, reducing overall logistics time. Furthermore, real-time data collection via IoT devices ensures accurate synchronization between the warehouse map and outbound records, avoiding errors from manual data entry. The use of an annealing algorithm combined with a perturbation strategy efficiently searches for the global optimum in a complex solution space, preventing getting trapped in local optima. The visual design of the structural graph provides an intuitive basis for generating initial solutions, shortening the algorithm's convergence time. These improvements collectively achieve dynamic and intelligent management of warehouse layout, making it particularly suitable for large-scale, high-turnover medical warehousing scenarios, effectively improving emergency response capabilities and resource utilization. Attached Figure Description

[0016] Figure 1 This is the overall flowchart of the present invention; Figure 2 This is a flowchart of the data acquisition and indicator calculation process of this invention; Figure 3 This is a flowchart of the simulated annealing optimization algorithm of the present invention; Figure 4 This is a flowchart of the structural map construction process of the present invention. Detailed Implementation

[0017] The specific embodiments of the present invention will now be described with reference to the accompanying drawings.

[0018] This invention discloses a spatial optimization scheduling method and system for medical device distribution based on the Internet of Things (IoT). The aim is to improve warehouse operational efficiency by optimizing the storage location of medical devices in a warehouse, thereby reducing the overall cost of outbound and storage processes. This method comprehensively considers the outbound frequency and co-occurrence frequency of medical devices, as well as the physical layout of the warehouse, and combines advanced optimization algorithms to generate the optimal storage strategy.

[0019] like Figure 1 The diagram shown is an overall flowchart of the present invention, which includes the following steps: By collecting historical outbound data from the warehouse, two sets of core indicators were obtained: the outbound frequency of any single medical device and the co-occurrence frequency of any two medical devices being outbound simultaneously. Collect warehouse map information, including the coordinates of all available storage locations and the location of the exit; Constructing a comprehensive cost function : ; in Indicates medical devices With exports distance, Indicates medical devices Outbound frequency, Indicates medical devices With medical devices Spatial distance, Indicates medical devices With medical devices co-occurrence frequency, , These are the weighting coefficients; An optimization algorithm is used to obtain a distribution strategy that minimizes the overall cost function, and medical devices are stored based on this distribution strategy. More specifically: like Figure 2 As shown, the first step is to collect relevant warehouse data using IoT devices to support the subsequent optimization process. This data is mainly divided into two categories: historical outbound data and warehouse map information.

[0020] The system uses IoT devices to record the real-time outbound movement of medical devices from the warehouse, obtaining outbound records for each type of medical device and combinations of medical devices that are simultaneously outbound. Applicable IoT devices include RFID medical devices, Bluetooth Low Energy (BLE) modules, ultra-wideband (UWB) positioning devices, or infrared sensors. These devices can accurately track the location and movement trajectory of medical devices, ensuring data accuracy. For example, RFID tags can be attached to medical devices, and readers can record the time and frequency of their entry and exit from the warehouse; UWB positioning devices provide higher location accuracy and are suitable for large warehouses.

[0021] Based on the collected data, two sets of core indicators were calculated: Outbound frequency This reflects the number of times each medical device i is dispatched within a specific time period. Medical devices with high dispatch frequency should be stored as close to the exit as possible to minimize handling time. For example, if a medical device is dispatched 5 times a day, then its... .

[0022] Co-occurrence frequency This represents the number of times any two medical devices i and j occur simultaneously in the same outbound event. Medical devices with high co-occurrence frequencies should be stored as close as possible to facilitate simultaneous retrieval. For example, if medical devices A and B are simultaneously outbound 10 times within a month, then... .

[0023] For example, suppose there are three medical devices in a warehouse: A, B, and C. In the past 30 days, A was shipped out 15 times, B 8 times, and C 12 times; A and B were shipped out simultaneously 6 times, A and C simultaneously 4 times, and B and C simultaneously 2 times. Then, the calculation shows... , , , , , .

[0024] The warehouse's physical layout information is also collected via IoT devices, including the coordinates of all available storage locations and the location of the exit. This information provides the foundation for subsequent distance calculations and optimization of storage solutions. For example, assuming the warehouse is a 5x5 grid, and the exit is located at coordinates... Each storage location is represented by coordinates. This is represented by x and y, where x and y are the row and column numbers, respectively. The relative position of each storage location and exit can be accurately determined using UWB positioning devices or infrared sensors.

[0025] To evaluate the merits of medical device storage solutions, this invention proposes a comprehensive cost function L, the expression of which is: ; in Indicates medical devices With exports distance, Indicates medical devices Outbound frequency, Indicates medical devices With medical devices Spatial distance, Indicates medical devices With medical devices co-occurrence frequency, , These are the weighting coefficients; The overall cost function consists of two parts, each targeting a different optimization objective: Part One Focus is placed on placing frequently dispatched medical devices as close to the exit as possible. Dispatch frequency. With distance The product reflects the transportation cost, multiplied by the weight. This can highlight the importance of the objective. For example, if a medical device is frequently dispatched but stored deep within the warehouse, the handling time and cost will increase significantly.

[0026] Part Two The aim is to reduce the distance between medical devices that frequently leave the warehouse together. Co-occurrence frequency. With distance The product reflects the transportation cost when using the items simultaneously, multiplied by the weight. The priority of this need is then emphasized. For example, if two medical devices are frequently used together but are far apart, the operator will need to make multiple trips, which is inefficient.

[0027] Through this design, the integrated cost function can balance the efficiency of issuing a single medical device and the efficiency of retrieval of multiple medical devices.

[0028] Weighting coefficient and The value range of is usually [0,1], and satisfies This ensures that the costs of the two parts remain balanced in the total. The specific values ​​can be adjusted according to the warehouse's operational needs. If fast outbound processing is prioritized, you can set... , This emphasizes the weight of the first part.

[0029] If you prioritize the efficiency of simultaneous outbound shipments, you can set... Increase the proportion of the second part.

[0030] In practical applications, the optimal value can be determined through experiments or historical data analysis. For example, if statistics show that the frequency of outbound shipments accounts for 60% of the efficiency, then an initial setting can be used. .

[0031] To find the comprehensive cost function The minimum storage solution employs the simulated annealing algorithm. This algorithm was chosen for its global search capability and its ability to handle complex optimization problems.

[0032] like Figure 3 As shown, the simulated annealing algorithm approximates the optimal solution step by step by simulating the physical annealing process. Its specific steps are as follows: 1. Generate initial placement scheme: Medical devices can be randomly assigned to storage locations, or an initial plan can be generated based on simple rules (such as arranging them from the exit to the outside according to the frequency of outbound shipments).

[0033] 2. Set parameters: initial temperature T It needs to be high enough (e.g., 1000) to accept poor solutions in the early stages of the search and increase the scope of exploration.

[0034] Minimum temperature It needs to be low enough (e.g., 0.01) to ensure that the algorithm converges to a better solution.

[0035] Cooling coefficient The value is usually between (0,1), such as 0.99, which indicates that the temperature decreases slowly with each iteration.

[0036] 3. Calculate the initial cost: Calculate the comprehensive cost function value based on the initial placement plan. .

[0037] 4. Perturbation generates new schemes: At the current temperature T Next, the current plan is perturbed, such as by randomly swapping the positions of two medical devices, and the cost function value of the new plan is calculated. .

[0038] 5. Rules for accepting the new proposal: like If the new proposal is superior to the current one, it should be accepted directly.

[0039] like Then, based on probability Accept the new solution. This probabilistic acceptance mechanism avoids getting trapped in local optima.

[0040] 6. Iteration and Convergence: Repeat the perturbation and acceptance steps at the current temperature until the preset number of iterations (e.g., 100 times) is reached or the convergence condition is met (e.g., no new solution is accepted for 50 consecutive iterations).

[0041] 7. Cooling down: Updated temperature Reduce the scope of exploration.

[0042] 8. Execute in a loop: Repeat the perturbation, acceptance, and cooling steps until the temperature is below .

[0043] 9. Output the optimal solution: The solution with the minimum cost function value is recorded throughout the process and used as the final storage strategy.

[0044] To improve efficiency, this invention also proposes an improved perturbation method: Calculate the contribution of each medical device to the total cost. : Where j represents all medical devices except medical device i, and d(i,j) is the spatial distance between the storage locations of medical device i and medical device j under the current placement scheme. choose The largest medical device i swaps its position with another medical device, which can be any medical device other than medical device i. That is, in each perturbation operation, the positions of medical device i and the other medical device are swapped. This prioritizes adjustments to the components that have the greatest impact on the total cost. For example, if medical device A... If the position of A is significantly higher than that of other instruments, then the position of A should be adjusted first.

[0045] like Figure 4 As shown, in order to more intuitively understand the relationship between medical devices and assist in optimization, this invention designs a method for constructing structural maps.

[0046] The construction process is as follows: Each node represents a type of medical device, and the characteristic of a node is its outbound frequency. In visualization, node size and... The higher the outbound frequency, the larger the node.

[0047] Draw lines connecting medical devices that co-occur, with the edges characterized by their co-occurrence frequency. The length of the side and Inversely proportional, the higher the co-occurrence frequency, the shorter the side, reflecting that the two should be closer.

[0048] For example, for medical devices A, B, and C, if If A has the largest node, then the edge between A and B is the shortest.

[0049] Visual maps are generated based on the structure graph, with node size and side length intuitively reflecting data characteristics. Managers can quickly identify key medical devices and their relationships using the map. Furthermore, the map can be used to set initial schemes for optimization algorithms, such as pre-arranging frequently co-occurring medical devices in adjacent storage locations to accelerate convergence.

[0050] The present invention also provides a supporting system, comprising the following modules: The data acquisition module is used to collect historical outbound data from the warehouse and obtain two sets of core indicators: the outbound frequency of any one medical device and the co-occurrence frequency of any two medical devices being outbound at the same time. The map modeling module is used to collect warehouse map information, including the coordinates of all available storage locations and the location of the exit. The cost function building module is used to construct the comprehensive cost function. : ; in Indicates medical devices With exports distance, Indicates medical devices Outbound frequency, Indicates medical devices With medical devices Spatial distance, Indicates medical devices With medical devices co-occurrence frequency, , These are the weighting coefficients.

[0051] An optimization module is used to obtain a distribution strategy that minimizes the comprehensive cost function using an optimization algorithm, and to store medical devices based on the distribution strategy.

[0052] In some embodiments, the system further includes a structure map construction module for constructing a structure map, the construction process of which is as follows: The nodes represent each type of medical device, and the characteristic of each node i is its outbound frequency. ; Construct a connection between two medical devices that share a common outbound case, with the edge characterized by its co-occurrence frequency. .

[0053] Through the above methods and systems, the present invention can effectively optimize the storage layout of medical devices, improve warehouse efficiency, and has high practical value.

[0054] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A spatial optimization scheduling method for the distribution of medical devices based on the Internet of Things, characterized in that, Includes the following steps: By collecting historical outbound data from the warehouse, two sets of core indicators were obtained: the outbound frequency of any single medical device and the co-occurrence frequency of any two medical devices being outbound simultaneously. Collect warehouse map information, including the coordinates of all available storage locations and the location of the exit; Constructing a comprehensive cost function : ； in Indicates medical devices With exports distance, Indicates medical devices Outbound frequency, Indicates medical devices With medical devices Spatial distance, Indicates medical devices With medical devices co-occurrence frequency, , These are the weighting coefficients; An optimization algorithm is used to obtain a distribution strategy that minimizes the comprehensive cost function, and medical devices are stored based on the distribution strategy. The optimization algorithm is the annealing algorithm; The annealing algorithm includes the following steps: 1) Obtain the initial placement plan for medical devices; 2) Set the initial temperature T and the minimum temperature T min And the cooling coefficient r; 3) Calculate the cost function value L1 corresponding to the initial placement scheme; 4) At the current temperature T, perform a perturbation operation on the current placement scheme to generate a new placement scheme and calculate its corresponding cost function value L2; 5) If L2 is less than L1, then replace the current placement scheme with the new placement scheme; 6) If L2 is greater than L1, then accept the new placement scheme with probability p = exp[−(L2−L1) / T]; 7) Repeat steps 4) to 6) until the preset number of iterations is reached or the state convergence condition is met; 8) Update the current temperature T to r×T and decrease the temperature; 9) Repeat steps 4) through 8) until the current temperature T is lower than the minimum temperature T. min ; 10) Output the placement scheme with the minimum cost function value during the annealing process as the optimal scheme.

2. The IoT-based spatial optimization scheduling method for medical device distribution according to claim 1, characterized in that, in, Use IoT devices to collect historical outbound data from the warehouse; use IoT devices to collect warehouse map information.

3. The IoT-based spatial optimization scheduling method for medical device distribution according to claim 2, characterized in that, The IoT device includes any one or more of the following: radio frequency identification medical devices, Bluetooth low power modules, ultra-wideband positioning devices, or infrared sensing devices.

4. The IoT-based spatial optimization scheduling method for medical device distribution according to claim 1, characterized in that, The disturbance operation is as follows: Calculate the current contribution of each medical device to the total cost function: ;in, j Remove all medical devices i Other medical devices; Select Largest medical device i and medical devices i Exchange positions with another medical device, which is a non-medical device. i Any medical device other than that.

5. The IoT-based spatial optimization scheduling method for medical device distribution according to claim 1, characterized in that, The method also includes the construction of a structural map, the process of which is as follows: The nodes represent each type of medical device, and the characteristic of each node i is its outbound frequency. ; Construct a connection between two medical devices that share a common outbound case, with the edge characterized by its co-occurrence frequency. .

6. The IoT-based spatial optimization scheduling method for medical device distribution according to claim 5, characterized in that, In the aforementioned structural diagram, the size of the nodes is set to be proportional to the outbound frequency. The length of the edge between nodes is expressed as inversely proportional to the co-occurrence frequency. This generates a visual map, and based on the visual map, the initial placement scheme for the optimization algorithm is set.

7. A spatially optimized scheduling system for the distribution of medical devices based on the Internet of Things (IoT) for implementing the method of claim 1, characterized in that, Includes the following modules: The data acquisition module is used to collect historical outbound data from the warehouse and obtain two sets of core indicators: the outbound frequency of any one medical device and the co-occurrence frequency of any two medical devices being outbound at the same time. The map modeling module is used to collect warehouse map information, including the coordinates of all available storage locations and the location of the exit. The cost function building module is used to construct the comprehensive cost function. : ； in Indicates medical devices With exports distance, Indicates medical devices Outbound frequency, Indicates medical devices With medical devices Spatial distance, Indicates medical devices With medical devices co-occurrence frequency, , These are the weighting coefficients; An optimization module is used to obtain a distribution strategy that minimizes the comprehensive cost function using an optimization algorithm, and to store medical devices based on the distribution strategy.

8. The IoT-based spatial optimization scheduling system for medical device distribution according to claim 7, characterized in that, The system also includes a structure map construction module for constructing structure maps. The construction process of the structure map is as follows: The nodes represent each type of medical device, and the characteristic of each node i is its outbound frequency. ; Construct a connection between two medical devices that share a common outbound case, with the edge characterized by its co-occurrence frequency. .

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