Medical cold chain transportation capacity planning system and method

Abstract

The invention relates to the technical field of cold-chain logistics. The invention discloses a medical cold chain transport capacity planning system and method, and the system comprises a plurality of refrigerated transport units, each refrigerated transport unit comprises a refrigerated container which can maintain a plurality of different preset temperature intervals, and a transport task management module which is used for receiving and processing the drug information and transport requests of a plurality of drugs, the system comprises a transport task management module used for managing drug information and carrying out priority grading on the drug information and transport requests, a transport capacity demand prediction module used for predicting future transport capacity demands according to historical data, and a path optimization module used for carrying out transport arrangement according to the information provided by the transport task management module in combination with various comprehensive data. According to the medical cold-chain transportation capacity planning system provided by the invention, efficient, safe and reliable medical cold-chain transportation is realized by integrating a plurality of modules such as task management, capacity demand prediction, path optimization, real-time monitoring, alarm and user interface, and the high requirement of the modern medical industry on cold-chain logistics is met.

Description

Technical Field

[0001] The present invention relates to the field of cold chain logistics technology, and in particular to a system and method for planning medical cold chain transportation capacity. Background Art

[0002] With the development of the medical industry, the demand for the transportation of high-value and highly sensitive items such as medicines, vaccines, and biological products is increasing. These items need to maintain specific temperature conditions during transportation to ensure their quality and safety. Traditional cold chain logistics systems have many deficiencies in transportation task management, capacity scheduling, and route optimization, and are difficult to meet the high requirements of modern medical cold chain logistics.

[0003] The traditional system lacks an effective priority management mechanism in task reception and processing, resulting in urgent and important tasks not being processed in a timely manner. Incomplete and inaccurate task information can easily lead to errors and delays in the transportation process. The lack of capacity demand forecasting function based on historical data leads to unreasonable capacity allocation, and there is often an oversupply or undersupply of capacity. The inability to plan and prepare enough refrigerated transport units in advance affects the smooth completion of transportation tasks. Traditional path optimization methods mainly rely on static data and fail to fully consider real-time traffic conditions and emergencies, resulting in inaccurate and inflexible path planning. The lack of dynamic path adjustment function makes it impossible to respond to emergencies such as traffic congestion and road closures in a timely manner during transportation. Summary of the invention

[0004] In order to address the deficiencies of the prior art, the present invention discloses a medical cold chain transportation capacity planning system and method that can predict logistics capacity according to existing conditions, perform route planning according to the prediction results, and appropriately arrange logistics capacity.

[0005] The present invention discloses a medical cold chain transportation capacity planning system, which includes: a refrigerated transportation unit, the refrigerated transportation units include a plurality of refrigerated transportation units, each refrigerated transportation unit includes a refrigerated box, the refrigerated box can maintain a plurality of different preset temperature ranges, a transportation task management module, which is used to receive and process drug information and transportation requests of a plurality of drugs, and to perform priority scoring on the drug information and transportation requests, a capacity demand prediction module, which is used to predict the capacity demand in a future period of time according to historical demand data, and a path optimization module, which is used to plan the transportation path and schedule the time for the refrigerated transportation unit based on the information and transportation requests provided by the transportation task management module and in combination with a variety of comprehensive data.

[0006] Furthermore, the drug information and transportation request in the transportation task management module include: the type of drug to be transported j : Type of drug in the jth transport request, quantity Q j: The quantity of drugs in the jth transport request and the required refrigeration temperature range T(D j ): The refrigerated temperature range required for the drug in the jth transport request, the destination information L j : The destination of the drug in the jth transport request, J: the index number of the request, the drug information and the priority score P of the transport request j The formula is: ,in: P j : The priority score of the jth transport request, f(D j ) is a function used to evaluate the drug type D j The importance or urgency of j )) is a function used to estimate the required refrigeration temperature range T(D j )’s particularity or difficulty, h(L j ) is a function that evaluates the destination L j distance or complexity, w1, w2, w3, w4 are weight parameters used to balance the influence of various factors, and .

[0007] Furthermore, the specific formula for the capacity demand forecasting module to forecast the capacity demand in the future period based on historical demand data is: , Q^ t+1 : The predicted demand at future time t+1, Q t-i+1 : The actual demand in the past i-th time period, P t-i+1 : Priority score of the past i-th time period, V i : The weight of the i-th time period, and , weight V i It decreases as i increases. γ: weight parameter, used to balance the impact of cost and priority, and collects the actual demand Q in the past period of time. t , priority score P j and the actual future demand Q^ t+1 , use a linear regression model to fit historical data and determine the value of γ, where, .

[0008] Furthermore, the route optimization module combines comprehensive data including road network data G = (V, E), real-time traffic conditions Ttraffic (e) and the current status of each refrigerated transport unit S RTU ={s1, s2, ..., sn}, the optimal or suboptimal transportation path and schedule are calculated by minimizing the total transportation cost function C(T): , C ij : The cost of edge (i, j), xij : Binary variable, path T contains edge (i, j), then x ij =1, otherwise x ij =0, when calculating the route, transportation requests with higher priority scores are given priority. The specific formula is:

[0009] C(T) is the total transportation cost function, Q^ t+1 is the predicted demand at future time t+1, P j is the priority score of the jth transport request, J is the set of all transport requests, and λ1 and λ2 are weight parameters used to balance the impact of cost, future demand, and priority score.

[0010] Furthermore, the medical cold chain transportation capacity planning system also includes a dispatch center module. The dispatch center module commands the refrigerated transport unit to perform the transportation task based on the calculation results of the path optimization module. The constraints of the dispatch center module are: the capacity limit of each refrigerated box, for each refrigerated transport unit k and its refrigerated box b: , : Total drug quantity of all shipping requests assigned to cold storage box b, J b : The set of all transport requests assigned to refrigerated container b, C b : Maximum capacity of refrigerator b, temperature range matching: Each drug D j It must be placed in a place that meets its temperature requirements T(D j ) in the refrigerated box, the arrival time window: each destination L j There is an arrival time window [a j , b j ].

[0011] Furthermore, the workflow of the dispatch center module includes: task reception and allocation, the dispatch center module receives transportation task requests from different sources, analyzes each task and then allocates the tasks, path optimization, the dispatch center module collects various relevant data, calculates the optimal transportation path and time schedule through the path optimization module and sends the transportation path and time schedule to each refrigerated transportation unit, real-time monitoring and alarm, the dispatch center module monitors the indicators of the refrigerated transportation unit in real time, and transmits the indicator data to the dispatch center module, task execution and tracking, the refrigerated transportation unit executes the transportation task according to the issued path and time schedule, the dispatch center module tracks the execution of the task in real time, and dynamically adjusts the transportation path and time schedule according to real-time data and emergencies, data analysis and reporting, analyzes the data and generates a transportation report.

[0012] Furthermore, the path optimization module includes: a real-time traffic data update submodule, which is used to regularly obtain the latest traffic status data and integrate it into the road network data G; a dynamic path adjustment submodule, which is used to dynamically adjust the transportation path according to the real-time traffic conditions and emergencies during the transportation process to ensure the smooth completion of the transportation task.

[0013] Furthermore, the dispatch center module also includes: a task priority management submodule for setting the priority of the transportation task according to the importance and urgency of the medicine, and a resource allocation submodule for reasonably allocating transportation resources according to the availability of the refrigerated transport unit and the task priority.

[0014] Furthermore, the medical cold chain transportation capacity planning system also includes: a monitoring module for real-time monitoring of the operating status of each refrigerated transport unit, an alarm module for timely issuing an alarm notification when an abnormal situation is detected, a user interface module for providing a user-friendly operation interface to facilitate users to input transportation requests, view transportation status and historical records, and a storage temperature monitoring module. The storage temperature monitoring module monitors the temperature in the refrigerated transport unit in real time. The storage temperature monitoring module includes a temperature sensing submodule, a data acquisition submodule, a data transmission submodule and an alarm submodule installed in the refrigerated transport unit.

[0015] The present invention discloses a method for planning medical cold chain transportation capacity, which uses any one of the medical cold chain transportation capacity planning systems described above, including: S1: task reception and preliminary analysis, receiving transportation task requests from different sources through a dispatch center module, and recording detailed information of each task, S2: transportation task management, preliminary processing of received transportation tasks, analyzing the priority of each task, and calculating the priority score of each task, S3: capacity demand prediction, collecting historical demand data to predict the capacity demand in the future, and deploying and preparing refrigerated transportation units according to the prediction results, S4: task allocation, evaluating available refrigerated transportation units, and dispatching refrigerated transportation units according to the transportation tasks. The priority score of the task and the current status of the refrigerated transport unit are used to assign the task to the refrigerated transport unit. S5: Path optimization: collect various data, calculate the optimal transportation path and time schedule, and send the calculated path and time schedule to the corresponding refrigerated transport unit. S6: Real-time monitoring and alarm: monitor the indicators in the refrigerated transport unit and transmit the real-time data to the dispatch center module. S7: Task execution and tracking: the refrigerated transport unit executes the transportation task according to the issued path and time schedule. The dispatch center module tracks the execution of the task and adjusts the transportation strategy according to the real-time situation. S8: Data analysis and reporting: analyze historical transportation data, optimize transportation management, and generate transportation reports.

[0016] Beneficial effects of the present invention: The medical cold chain transportation capacity planning system of the present invention realizes efficient transportation management and optimization by integrating refrigerated transportation units, transportation task management modules, transportation capacity demand prediction modules and path optimization modules. The system can support multi-temperature zone transportation to ensure the safe transportation of drugs under appropriate temperature conditions. At the same time, through priority scoring and path optimization, it improves transportation efficiency and resource utilization, and reduces transportation time and costs. Through capacity demand prediction, the system can plan and allocate transportation capacity in advance, avoid waste of resources and insufficient transportation capacity, and improve the overall service quality and customer satisfaction.

[0017] The present invention provides a medical cold chain transportation capacity planning system and method, which realizes efficient, safe and reliable medical cold chain transportation by integrating multiple modules such as task management, capacity demand prediction, route optimization, real-time monitoring, alarm and user interface, and meets the high requirements of the modern medical industry for cold chain logistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a method for planning medical cold chain transportation capacity in an embodiment of the present application.

[0019] Figure 2 This is a system schematic diagram of a medical cold chain transportation capacity planning system in an implementation manner of the present application.

[0020] Figure 3 This is a system schematic diagram of another medical cold chain transportation capacity planning system in an embodiment of the present application.

[0021] Figure 4 This is a system schematic diagram of another medical cold chain transportation capacity planning system in an embodiment of the present application.

[0022] Figure 5 This is a system schematic diagram of another medical cold chain transportation capacity planning system in an embodiment of the present application.

[0023] Figure 6 This is a system schematic diagram of another medical cold chain transportation capacity planning system in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the specific implementation modes of the present invention will be clearly and completely described below.

[0025] The present invention discloses a medical cold chain transportation capacity planning system, which includes a refrigerated transportation unit, which includes several refrigerated transportation units, each refrigerated transportation unit includes a refrigerated box, and the refrigerated box can maintain multiple different preset temperature ranges, a transportation task management module, which is used to receive and process drug information and transportation requests of multiple drugs, and to prioritize the drug information and transportation requests, a capacity demand prediction module, which is used to predict the capacity demand in the future period based on historical demand data, and a path optimization module, which is used to plan the transportation path and schedule the refrigerated transportation unit based on the information and transportation requests provided by the transportation task management module and in combination with a variety of comprehensive data. The medical cold chain transportation capacity planning system of the present invention realizes efficient transportation management and optimization by integrating the refrigerated transportation unit, the transportation task management module, the capacity demand prediction module and the path optimization module. The system can support multi-temperature zone transportation, ensure the safe transportation of drugs under suitable temperature conditions, and at the same time improve the transportation efficiency and resource utilization through priority scoring and path optimization, and reduce transportation time and cost. Through capacity demand forecasting, the system can plan and allocate capacity in advance, avoid waste of resources and insufficient capacity, and improve overall service quality and customer satisfaction.

[0026] As an implementation method, the drug information and transportation request in the transportation task management module include: Type of drug to be transported j : The type of drug in the jth transport request; Quantity Q j : The number of drugs in the jth transport request; Required refrigeration temperature range T(D j ): The refrigerated temperature range required for the drug in the jth transport request; Destination Information j : The destination of the drug in the jth transport request; J: requested index number; Priority score for drug information and shipping requests j The formula is: ; in: P j : The priority score of the jth transport request; f(D j ) is a function used to evaluate the drug type D j the importance or urgency of the g(T(D j )) is a function used to estimate the required refrigeration temperature range T(D j )’s particularity or difficulty; h(L j) is a function that evaluates the destination L j distance or complexity; w1, w2, w3, w4 are weight parameters used to balance the influence of various factors, and .

[0027] Q j represents the number of drugs in the jth transport request. The larger the number, the greater the transport capacity required and the higher the priority. For example, if Q j If Q is larger, j The value of will be larger, thereby increasing P j .

[0028] Importance of drug type, f(D j ) can be based on the type of drug D j to assess their importance or urgency. For example, some drugs may have a higher priority because of their therapeutic effect or scarcity. j is an emergency medicine, then f(D j ) can be assigned a higher value. f(D j ) is assigned based on historical experience and doctor experience.

[0029] The specificity of the required refrigeration temperature range, g(T(D j )) is used to evaluate the required refrigeration temperature range T(D j ) is more difficult or challenging to maintain. Some temperature ranges may be more difficult to maintain and require special equipment or techniques. For example, if T(D j ) is a very strict temperature range such as -80°C to -60°C, then g(T(D j )) is assigned a higher value.

[0030] The distance or complexity of the destination, h(L j ) is used to evaluate the destination L j The longer the distance or the more complicated the transportation, the more resources and time may be required. For example, if L j is a remote area or a place with inconvenient transportation, then h(L j ) is assigned a higher value. Example

[0031] Shipping Request: D1 is an emergency medicine, quantity Q1=50, required refrigeration temperature range T(D1)=[2°C, 8°C], destination L1 is a city center; D2 is a common drug, quantity Q2 = 100, required refrigeration temperature range T(D2) = [15°C, 25°C], destination L2 is a remote area; According to historical experience, the weight parameters are w1=0.4, w2=0.3, w3=0.2, w4=0.1, and the function is assigned values ​​based on historical experience, doctor experience, etc.: f(D1)=0.9, f(D2)=0.5; g(T(D1))=0.8, g(T(D2))=0.5; h(L1)=0.3, h(L2)=0.8; The priority score is: P1=0.4·0.9+0.3·50+0.2·0.8+0.1·0.3=0.36+15+0.16+0.03=15.55; P2=0.4·0.5+0.3·100+0.2·0.5+0.1·0.8=0.2+30+0.1+0.08=30.38.

[0032] As an implementation method, the specific formula for the capacity demand forecasting module to forecast the capacity demand in the future period based on the historical demand data is: ; Q^ t+1 : The predicted demand at future time t+1; Q t-i+1 : The actual demand in the past i-th time period; P t-i+1 : The priority score of the past i-th time period; V i : The weight of the i-th time period, and , weight V i It decreases as i increases; γ: weight parameter, used to balance the impact of cost and priority, and collect the actual demand Q in the past period of time t , priority score P j and the actual future demand Q^ t+1 , use a linear regression model to fit historical data and determine the value of γ; in, .

[0033] Priority score P t-i+1 , represents the priority score of the past i-th time period, which is calculated by the transportation task management module based on the drug type, quantity, required refrigeration temperature range and destination information.

[0034] Weight V i, represents the weight of the i-th time period, and decreases as i increases to reflect the importance of recent demand data. i increases, representing the weight of the i-th time period. For example, the weight of the first time period is closer to the present and shorter in time than the weight of the eighth time period.

[0035] The weight parameter γ is used to balance the impact of actual demand and priority score. If γ is large, the priority score has a greater impact on the prediction results; if γ is small, the actual demand has a greater impact on the prediction results. The parameter γ controls the priority score P t-i+1 The weight in the prediction. A larger γ value means that the priority score has a greater impact on the prediction result, while a smaller γ value means that the actual demand Q t-i+1 The impact on the forecast results is greater. Adjust the sensitivity of the forecast: By adjusting γ, the sensitivity of the forecast model to high-priority tasks can be changed. A higher γ will make the model more inclined to consider high-priority tasks, which may increase the allocation of resources to cope with these tasks. Determining the value of γ by linear regression method can effectively balance the impact of actual demand and priority score on the forecast results. This helps the dispatch center better allocate resources and ensure that high-priority tasks receive appropriate attention, while also reasonably considering changes in actual demand. This can improve the accuracy of the forecast and the overall efficiency of the system. Example

[0036] There are actual demand and priority score data for the past five time periods, which are: Qt-1=100, Pt-1=15.55; Qt-2=120, Pt-2=30.38; Qt-3=110, Pt-3=10.20; Qt-4=115, Pt-4=25.10; Qt-5=105, Pt-5=20.00; The weights are v1=0.4, v2=0.3, v3=0.2, v4=0.1, v5=0.05, and γ=0.1. The predicted demand at the future time t+1 is: Q^ t+1 =0.4·(100+0.1·15.55)+0.3·(120+0.1·30.38)+0.2·(110+0.1·10.20)+0.1·(115+0.1·25.10)+0.05·(105+0.1·20.00);=116.8384; The transportation demand forecasting module predicts future demand based on historical demand data. These forecast data can be used as input for the transportation task management module to help it better receive and process new transportation requests.

[0037] For example, if the forecast shows that demand will increase significantly in a certain period of time in the future, the transportation task management module can prepare more refrigerated transport units in advance to cope with the upcoming peak demand.

[0038] When calculating the optimal route, the route optimization module needs to consider the current transportation tasks and possible future tasks. The forecast data provided by the capacity demand forecast module can help the route optimization module estimate future demand more accurately, thereby reserving sufficient capacity in route planning.

[0039] For example, if forecasts show that demand for a particular route will increase in the future, the route optimization module can prioritize this route to ensure that there is enough capacity to meet future demand.

[0040] As an implementation method, the route optimization module combines the road network data G = (V, E), the real-time traffic conditions Ttraffic (e) and the current state S of each refrigerated transport unit. RTU ={s1, s2, ..., sn}, the optimal or suboptimal transportation path and schedule are calculated by minimizing the total transportation cost function C(T): ; C ij :The cost of edge (i, j) indicates the cost from node i to node j, which can be distance, time, fuel consumption, etc. The specific value of the cost can be obtained in many ways. Obtain the straight-line distance or road distance between two nodes through map data. Obtain the estimated travel time between two nodes through real-time traffic data. Calculate the cost of edge (i, j) based on the vehicle's fuel efficiency and travel distance.

[0041] X ij : Binary variable, path T contains edge (i, j), then x ij =1, otherwise x ij =0; When calculating the route, transportation requests with higher priority scores are given priority. The specific formula is: ;

[0042] C(T): Total transportation cost function C(T) represents the total transportation cost of path T, including distance or time cost; Q^ t+1 : The predicted demand at future time t+1; P j: The priority score of the jth transport request; J: the collection of all transport requests; λ1 and λ2: weight parameters used to balance the impact of cost, future demand, and priority score. Based on business experience and understanding of the importance of different factors, the values ​​of λ1 and λ2 are initially set. If you think that transportation cost is more important than future demand, you can set λ1 to be smaller; if you think that task priority is very important, you can set λ2 to be larger. Use these initial values ​​to perform actual path planning and transportation task execution to observe whether the results meet expectations. Based on the experimental results, gradually adjust the values ​​of λ1 and λ2 until satisfactory performance is achieved.

[0043] The priority path comprehensive objective function is used to consider the impact of total transportation cost, future demand and priority score in the path optimization process, and calculate the optimal or suboptimal transportation path and schedule.

[0044] Road network data G=(V, E); V: A collection of nodes, each node represents a geographical location, such as a warehouse, distribution center, customer address, etc.

[0045] E: edge set, each edge (i, j) represents a connection between two nodes and can be directed or undirected.

[0046] Sources of road network data: Road network data can be obtained from various map service providers. It can be obtained from GIS system. Geographic Information System GIS can provide detailed road network data, including node and edge information. Traffic department data. Traffic management departments usually have detailed traffic network data, which can be obtained through official channels. Road network data can be obtained using the API interface of the map service provider. Data download: Download data files from GIS system or traffic department. Store the obtained data in the database for subsequent query and use.

[0047] Real-time traffic conditions Ttraffic(e); Ttraffic(e): represents the real-time traffic condition of each edge (i, j), which can be the degree of traffic congestion, average speed, estimated travel time, etc.

[0048] Sources of real-time traffic conditions Ttraffic(e): Real-time traffic data can be obtained from various traffic information service providers. Sensor data acquisition: sensors installed on the road can monitor traffic flow and speed in real time. Traffic conditions reported by users through mobile applications. Real-time traffic data can be obtained using the API interface of traffic information service providers. Sensor data collected by IoT devices can monitor traffic conditions in real time. Traffic conditions reported by users are obtained through crowdsourcing platforms.

[0049] The current status S of each refrigerated transport unit RTU ={s1,s2,...,sn}; S RTU : The current state set of the refrigerated transport unit, each state sk includes information such as location, load, remaining capacity, current task, etc.

[0050] Source: Refrigerated transport units are usually equipped with GPS devices to obtain their location information in real time. Sensors installed on refrigerated transport units can monitor environmental parameters such as temperature and humidity. The company's internal logistics management system can record the mission and status information of each refrigerated transport unit. Real-time location information of refrigerated transport units is obtained through GPS devices, usually through API interfaces or data streams. Environmental parameters such as temperature and humidity are obtained through on-board sensors, and these data are usually transmitted to the central server through IoT devices. The mission and status information of each refrigerated transport unit is recorded through the company's internal logistics management system, and these data are usually stored in a database.

[0051] As an implementation method, the medical cold chain transportation capacity planning system also includes a dispatch center module. The dispatch center module commands the refrigerated transportation unit to perform the transportation task based on the calculation results of the path optimization module. The constraints of the dispatch center module are: The capacity limit of each refrigerated container, for each refrigerated transport unit k and its refrigerated container b: ; : Total drug quantity of all transport requests assigned to cooler b; J b : The set of all transport requests assigned to reefer box b; C b : Maximum capacity of refrigerator box b; Temperature range matching: Each drug D j It must be placed in a place that meets its temperature requirements T(D j ) in a refrigerator to ensure that each drug is placed in a refrigerator that can meet its temperature requirements to ensure the quality and safety of the drug.

[0052] Arrival time window: Each destination L j There is an arrival time window [a j , b j ], the transportation plan must ensure that the drug arrives within the time window. Ensure that each transportation task arrives at the destination within the specified time window to meet the customer's delivery requirements.

[0053] As an implementation method, the workflow of the dispatch center module includes: task reception and allocation. The dispatch center module receives transportation task requests from different sources, including customer orders, internal deployment, etc. Analyze the priority, demand, destination, delivery time and other information of each task. According to the priority and demand of the task, combined with the current status of the refrigerated transport unit such as location, load, availability, etc., reasonably allocate the task to the appropriate refrigerated transport unit.

[0054] Path optimization collects road network data, real-time traffic conditions, current status of refrigerated transport units, future demand forecasts and priority scores. Calculates the optimal or suboptimal transport path and schedule. Sends the calculated path and schedule to the corresponding refrigerated transport unit to guide it in performing the transport task.

[0055] Real-time monitoring and alarm, through temperature sensors, GPS and other equipment, real-time monitoring of key indicators such as temperature, location, speed, etc. in the refrigerated transport unit. Real-time data is transmitted to the central server through wireless communication technology. When key indicators exceed the set range, the system automatically sends an alarm to notify relevant personnel and take timely measures. For example, when the temperature exceeds the set range, the system can send SMS, email or APP push notification to the management personnel, and sound an alarm in the refrigerated transport unit.

[0056] Task execution and tracking: The refrigerated transport unit executes the transport task according to the route and time schedule issued. The dispatch center module tracks the execution of the task in real time, including transport progress, current location, estimated arrival time, etc. According to real-time data and emergencies, the transport route and time schedule are dynamically adjusted to ensure the smooth progress of the task.

[0057] Data analysis and reporting: Analyze historical transportation data to identify potential problems and improvement points, and optimize transportation management and scheduling strategies. Generate transportation reports regularly, including task completion status, cost analysis, performance evaluation, etc., for management reference and decision-making.

[0058] As an implementation method, the path optimization module includes: a real-time traffic data update submodule, which is used to regularly obtain the latest traffic condition data and integrate it into the road network data G; regularly obtain the latest traffic condition data to ensure that path planning is based on the latest and most accurate information, avoiding erroneous planning due to outdated data.

[0059] The dynamic path adjustment submodule is used to dynamically adjust the transportation path according to real-time traffic conditions and emergencies during transportation. During transportation, the transportation path is dynamically adjusted according to real-time traffic conditions and emergencies such as road closures and weather changes to ensure the smooth completion of transportation tasks. Through dynamic adjustment, the system can quickly respond to unforeseen events, reduce the risk of transportation interruptions, and improve the reliability and safety of transportation. Through the collaborative work of the real-time traffic data update submodule and the dynamic path adjustment submodule, the path optimization module can significantly improve the efficiency, reliability and safety of medical cold chain transportation. Real-time data updates ensure the accuracy of path planning, and dynamic adjustments can flexibly respond to various emergencies during transportation to ensure the smooth completion of transportation tasks. These functions not only reduce transportation time and costs, but also improve customer satisfaction and the overall quality of transportation services.

[0060] As an implementation method, the dispatch center module also includes: a task priority management submodule, which is used to set the priority of the transportation task according to the importance and urgency of the medicine; by setting the priority of the transportation task according to the importance and urgency of the medicine, it is ensured that urgent and important tasks can be handled with priority, thereby improving the overall transportation efficiency and service quality. The priority management submodule can quickly identify and handle high-priority tasks, reduce waiting time, and improve the response speed of the system.

[0061] The resource allocation submodule is used to reasonably allocate transportation resources according to the availability of refrigerated transport units and task priorities. According to the availability of refrigerated transport units and task priorities, transportation resources are reasonably allocated to ensure that each task can get appropriate transportation support. By optimizing resource allocation, empty loads and ineffective transportation are reduced, transportation efficiency is improved, and operating costs are reduced. Through the collaborative work of the task priority management submodule and the resource allocation submodule, the dispatch center module can significantly improve the efficiency, reliability and customer satisfaction of medical cold chain transportation. Task priority management ensures that urgent and important tasks are given priority, and resource allocation ensures that each task can get appropriate transportation support. These functions not only improve transportation efficiency and resource utilization, but also enhance the system's flexibility and ability to respond to emergencies, ensuring the smooth completion of transportation tasks.

[0062] As an implementation method, the medical cold chain transportation capacity planning system also includes: a monitoring module for real-time monitoring of the operating status of each refrigerated transport unit; real-time monitoring of the operating status of each refrigerated transport unit, including key indicators such as temperature, location, speed, etc., to ensure transparency and traceability of data during transportation.

[0063] The alarm module is used to issue an alarm notification in time when an abnormal situation is detected. When an abnormal situation is detected, such as temperature exceeding the set range, the vehicle deviating from the predetermined path, etc., an alarm notification is issued in time to ensure that relevant personnel can take prompt measures. Through timely alarm, the problem can be solved quickly, reducing drug damage or transportation delays caused by abnormal situations, and reducing economic losses.

[0064] The user interface module is used to provide a user-friendly operation interface, which is convenient for users to input transportation requests, view transportation status and historical records, etc. It provides a user-friendly operation interface, which is convenient for users to input transportation requests, view transportation status and historical records, etc., to improve the user's operation experience and satisfaction.

[0065] The storage temperature monitoring module monitors the temperature in the refrigerated transport unit in real time to ensure that the storage conditions of the medicines meet the requirements. The storage temperature monitoring module includes a temperature sensing submodule, a data acquisition submodule, a data transmission submodule and an alarm submodule installed in the refrigerated transport unit. The temperature sensing submodule is installed in the refrigerated transport unit to collect temperature data in the refrigerated transport unit in real time. A high-precision and high-reliability temperature sensor can be used to adapt to the low-temperature environment of the refrigerated transport unit. Multiple temperature sensors can be installed according to the size and layout of the refrigerated transport unit to ensure the comprehensiveness and accuracy of the temperature data. The data acquisition submodule and the temperature sensor collect temperature data at regular intervals, such as once a minute, to ensure the real-time and continuity of the data. The data transmission submodule transmits the temperature data to the alarm submodule through wireless communication technology. When the temperature exceeds the set range, the system automatically sends an alarm to notify relevant personnel. The set temperature range is the temperature range of the medicines transported by the refrigerated transport unit.

[0066] The present invention discloses a method for planning medical cold chain transportation capacity, which uses any medical cold chain transportation capacity planning system as described above, including: S1: Task reception and preliminary analysis, receiving transport task requests from different sources through the dispatch center module, and recording the detailed information of each task; The dispatch center module receives transport task requests from different sources, including customer orders, internal deployment, etc. Record the detailed information of each task, including drug type, quantity, destination, delivery time, temperature requirements, etc.

[0067] S2: Transport task management, preliminary processing of received transport tasks, analysis of the priority of each task, and calculation of the priority score of each task; preliminary processing of received transport tasks to ensure that all information is complete and correct. Analyze the priority of each task, considering factors such as the importance and urgency of the drug, quantity, temperature requirements, distance and complexity of the destination. Calculate the priority score of each task for subsequent task allocation and path optimization.

[0068] S3: Capacity demand forecasting, collect historical demand data to forecast capacity demand in the future, and deploy and prepare refrigerated transport units based on the forecast results; collect historical demand data, including past transport task volumes, seasonal changes, holiday impacts, etc. Forecast capacity demand in the future. Based on the forecast results, deploy and prepare refrigerated transport units in advance to ensure sufficient capacity to cope with future transport tasks.

[0069] S4: Task allocation, evaluate the available refrigerated transport units, and assign tasks to the refrigerated transport units based on the priority score of the transport task and the current status of the refrigerated transport unit. Evaluate the available refrigerated transport units, including vehicle status, location, load conditions, etc. Check the capacity and temperature range of each refrigerated box to ensure that the task requirements are met. Preliminarily assign tasks to refrigerated transport units based on the priority score of the transport task and the current status of the refrigerated transport unit. Ensure that the total number of drugs in each refrigerated box does not exceed its maximum capacity. Ensure that each drug is placed in a refrigerated box that can meet its temperature requirements.

[0070] S5: Path optimization, collect various data, calculate the optimal transportation path and schedule, and send the calculated path and schedule to the corresponding refrigerated transport unit; collect road network data, real-time traffic conditions, current status of refrigerated transport units, future demand forecasts and priority scores. Calculate the optimal transportation path and schedule. Consider a variety of comprehensive data, including distance, time, traffic conditions, risk factors, etc. Send the calculated path and schedule to the corresponding refrigerated transport unit to guide it to perform the transportation task.

[0071] S6: Real-time monitoring and alarm, monitor the indicators in the refrigerated transport unit, and transmit the real-time data to the dispatch center module; through temperature sensors, GPS and other equipment, monitor the temperature, location, speed and other key indicators in the refrigerated truck in real time. Transmit real-time data to the central server through wireless communication technology. When the key indicators exceed the set range, the system automatically sends an alarm to notify relevant personnel and take timely measures. For example, when the temperature exceeds the set range, the system can send SMS, email or APP push notification to the management personnel, and sound an alarm in the refrigerated truck.

[0072] S7: Task execution and tracking. The refrigerated transport unit executes the transport task according to the route and time schedule issued. The dispatch center module tracks the execution of the task and adjusts the transport strategy according to the real-time situation. The refrigerated transport unit executes the transport task according to the route and time schedule issued. The dispatch center module tracks the execution of the task in real time, including the transport progress, current location, and estimated arrival time. According to real-time data and emergencies, the transport route and time schedule are dynamically adjusted to ensure the smooth progress of the task.

[0073] S8: Data analysis and reporting, analyze historical transportation data, optimize transportation management, and generate transportation reports. Analyze historical transportation data to find potential problems and improvement points, optimize transportation management and scheduling strategies. Generate transportation reports regularly, including task completion status, cost analysis, performance evaluation, etc., for management reference and decision-making.

[0074] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A medical cold chain transportation capacity planning system, characterized in that: include: Refrigerated transport units, each of which includes a refrigerated box capable of maintaining a plurality of different preset temperature ranges; A transport task management module, which is used to receive and process drug information and transport requests for multiple drugs, and to prioritize the drug information and transport requests; The capacity demand forecasting module is used to forecast the capacity demand in the future based on historical demand data; The route optimization module is used to plan the transportation route and schedule the time for the refrigerated transport unit based on the information and transportation request provided by the transportation task management module and combined with various comprehensive data.

2. According to the medical cold chain transportation capacity planning system described in claim 1, it is characterized by: The drug information and transportation request in the transportation task management module include: Type of drug to be transported j : The type of drug in the jth transport request; Quantity Q j : The number of drugs in the jth transport request; Required refrigeration temperature range T(D j ): The refrigerated temperature range required for the drug in the jth transport request; Destination Information j : The destination of the drug in the jth transport request; J: requested index number; Priority score for drug information and shipping requests j The formula is: ； in: P j : The priority score of the jth transport request; f(D j ) is a function used to evaluate the drug type D j the importance or urgency of the g(T(D j )) is a function used to estimate the required refrigeration temperature range T(D j )’s particularity or difficulty; h(L j ) is a function that evaluates the destination L j distance or complexity; w1, w2, w3, w4 are weight parameters used to balance the influence of various factors, and .

3. According to the medical cold chain transportation capacity planning system described in claim 2, it is characterized by: The specific formula for the capacity demand forecasting module to predict the capacity demand in the future based on historical demand data is: ； Q^ t+1 : The predicted demand at future time t+1; Q t-i+1 : The actual demand in the past i-th time period; P t-i+1 : The priority score of the past i-th time period; V i : The weight of the i-th time period, and , weight V i It decreases as i increases; γ: weight parameter, used to balance the impact of cost and priority, and collect the actual demand Q in the past period of time t , priority score P j and the actual future demand Q^ t+1 , use a linear regression model to fit historical data and determine the value of γ; in, .

4. According to the medical cold chain transportation capacity planning system described in claim 3, it is characterized by: The route optimization module combines comprehensive data including road network data G = (V, E), real-time traffic conditions Ttraffic (e) and the current status of each refrigerated transport unit S RTU ={s1, s2, ..., sn}, the optimal or suboptimal transportation path and schedule are calculated by minimizing the total transportation cost function C(T): ； C ij : The cost of edge (i, j); x ij : Binary variable, path T contains edge (i, j), then x ij =1, otherwise x ij =0; When calculating the route, transportation requests with higher priority scores are given priority. The specific formula is: ； C(T) is the total transportation cost function; Q^ t+1 is the predicted demand at future time t+1; P j is the priority score of the jth transport request; J is the set of all transportation requests; λ1 and λ2 are weight parameters used to balance the impact of cost, future demand, and priority score.

5. According to the medical cold chain transportation capacity planning system of claim 4, it is characterized by: The medical cold chain transportation capacity planning system also includes a dispatch center module. The dispatch center module commands the refrigerated transport unit to perform the transportation task based on the calculation results of the path optimization module. The constraints of the dispatch center module are: The capacity limit of each refrigerated container, for each refrigerated transport unit k and its refrigerated container b: ； : Total drug quantity of all transport requests assigned to cooler b; J b : The set of all transport requests assigned to reefer box b; C b : Maximum capacity of refrigerator box b; Temperature range matching: Each drug D j Must be placed in a place that meets its temperature requirements T(D j ) in a refrigerator; Arrival time window: Each destination L j There is an arrival time window [a j , b j ].

6. According to the medical cold chain transportation capacity planning system of claim 5, it is characterized by: The workflow of the dispatch center module includes: Task reception and allocation,The dispatch center module receives transportation task requests from different sources, analyzes each task and then allocates the task; Path optimization: the dispatch center module collects various relevant data, calculates the optimal transportation path and schedule through the path optimization module, and sends the transportation path and schedule to each refrigerated transportation unit; Real-time monitoring and alarm, the dispatch center module monitors the indicators of the refrigerated transport unit in real time and transmits the indicator data to the dispatch center module; Task execution and tracking: The refrigerated transport unit executes the transport task according to the route and time schedule issued. The dispatch center module tracks the execution of the task in real time and dynamically adjusts the transport route and time schedule according to real-time data and emergencies. Data analysis and reporting: analyze the data and generate transportation reports.

7. A medical cold chain transportation capacity planning system according to claim 4, characterized in that: The path optimization module includes: The real-time traffic data update submodule is used to regularly obtain the latest traffic status data and integrate it into the road network data G; The dynamic path adjustment submodule is used to dynamically adjust the transportation path according to real-time traffic conditions and emergencies during the transportation process to ensure the smooth completion of the transportation task.

8. A medical cold chain transportation capacity planning system according to claim 6, characterized in that: The dispatch center module also includes: The task priority management submodule is used to set the priority of the transportation task according to the importance and urgency of the drug; The resource allocation submodule is used to reasonably allocate transportation resources according to the availability and task priority of refrigerated transport units.

9. A medical cold chain transportation capacity planning system according to claim 1, characterized in that: The medical cold chain transportation capacity planning system also includes: Monitoring module, used to monitor the operating status of each refrigerated transport unit in real time; An alarm module is used to issue an alarm notification in time when an abnormal situation is detected; A user interface module is used to provide a user-friendly operation interface for users to input transportation requests and view transportation status and history records; The storage temperature monitoring module monitors the temperature in the refrigerated transport unit in real time. The storage temperature monitoring module includes a temperature sensor submodule, a data acquisition submodule, a data transmission submodule and an alarm submodule installed in the refrigerated transport unit.

10. A method for planning medical cold chain transportation capacity, characterized in that Using any one of the medical cold chain transportation capacity planning systems as described in claims 1-9 above, comprising: S1: Task reception and preliminary analysis: receiving transportation task requests from different sources through the dispatch center module and recording detailed information of each task; S2: Transportation task management, preliminary processing of received transportation tasks, analysis of the priority of each task, and calculation of the priority score of each task; S3: Capacity demand forecasting, collecting historical demand data to forecast capacity demand in the future, and deploying and preparing refrigerated transport units based on the forecast results; S4: Task allocation, evaluate the available refrigerated transport units, and assign tasks to the refrigerated transport units according to the priority scores of the transport tasks and the current status of the refrigerated transport units; S5: Path optimization, collect various data, calculate the optimal transportation path and schedule, and send the calculated path and schedule to the corresponding refrigerated transportation unit; S6: Real-time monitoring and alarm, monitoring indicators in the refrigerated transport unit and transmitting real-time data to the dispatch center module; S7: Task execution and tracking. The refrigerated transport unit executes the transport task according to the route and time schedule issued. The dispatch center module tracks the execution of the task and adjusts the transport strategy according to the real-time situation. S8: Data analysis and reporting: analyze historical transportation data, optimize transportation management, and generate transportation reports.

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