Dynamic demand response scheduling method for pharmaceutical logistics

Abstract

The present invention discloses a dynamic demand-responsive scheduling method for pharmaceutical logistics, belonging to the field of logistics optimization. The method comprises the following steps: matching and analyzing pharmaceutical logistics targets with real-time sensor data to obtain response synchronization evaluation results; locating demand deviations, wherein the demand deviations have asynchronous timestamps; performing time window analysis based on the asynchronous timestamps and the demand deviations to determine the deviation time window, performing logistics scheduling compensation, and obtaining scheduling compensation characteristics; obtaining scheduling response variables, wherein the scheduling response variables include path adjustment parameters and vehicle scheduling parameters; and searching for an optimal scheduling strategy using the scheduling compensation characteristics and the scheduling response variables with the goal of minimizing demand deviations. The optimal scheduling strategy is used as the scheduling response strategy, and the scheduling response strategy is used to control pharmaceutical logistics scheduling. By dynamically responding to pharmaceutical logistics demand deviations and optimizing the scheduling strategy in real time, the technical effect of improving pharmaceutical logistics scheduling efficiency and ensuring the quality of drug transportation is achieved.

Description

Technical Field

[0001] The present invention relates to the field of logistics optimization, and in particular to a dynamic demand response scheduling method for pharmaceutical logistics. Background Art

[0002] Pharmaceutical logistics plays a vital role in ensuring the timely and safe delivery of medicines to medical institutions and patients. However, during actual transportation, pharmaceutical logistics faces numerous uncertainties, such as traffic conditions, weather changes, and temperature fluctuations. These factors can cause actual transportation demand to deviate from pre-set plans, thereby impacting the timeliness and quality of drug delivery. Existing pharmaceutical logistics scheduling relies primarily on pre-established static scheduling plans, lacking the ability to respond to dynamic demand changes in real time. Once deviations from expectations occur, such as vehicle delays or temperature anomalies, timely adjustments are difficult to make, resulting in reduced transportation efficiency and potentially even compromising drug safety. Summary of the Invention

[0003] This application provides a dynamic demand response scheduling method for pharmaceutical logistics, aiming to solve the technical problems in the existing technology that pharmaceutical logistics scheduling is not responsive in a timely manner and is difficult to adapt to dynamic demand changes.

[0004] The dynamic demand response scheduling method for pharmaceutical logistics disclosed in the present application includes: matching and analyzing pharmaceutical logistics targets with real-time sensor data to obtain response synchronization evaluation results; locating demand deviations based on the response synchronization evaluation results, where the demand deviations have asynchronous timestamps; performing time window analysis based on the demand deviations based on the asynchronous timestamps to determine the deviation time windows; performing logistics scheduling compensation based on the deviation time windows to obtain scheduling compensation characteristics; obtaining scheduling response variables, where the scheduling response variables include path adjustment parameters and vehicle scheduling parameters; with the goal of minimizing demand deviations, utilizing scheduling compensation characteristics and matching evaluation with scheduling response variables to search for the optimal scheduling strategy, using the optimal scheduling strategy as the scheduling response strategy, and using the scheduling response strategy for scheduling control of pharmaceutical logistics.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] By matching and analyzing pharmaceutical logistics targets with real-time sensor data, response synchronization evaluation results are obtained, and the degree of synchronization between actual transportation conditions and targets is assessed, laying the foundation for subsequent demand deviation positioning. Demand deviations are located based on the response synchronization evaluation results, and demand deviations have asynchronous timestamps, which enable accurate recording of the time point when the deviation occurs. Based on the asynchronous timestamps, time window analysis is performed based on demand deviations to determine the deviation time window, providing a basis for subsequent scheduling compensation. Logistics scheduling compensation is performed based on the deviation time window, and scheduling compensation characteristics are obtained to guide the optimization of scheduling decisions. Scheduling response variables are obtained, including path adjustment parameters and vehicle scheduling parameters, providing decision space for subsequent search for the optimal scheduling strategy. With the goal of minimizing demand deviations, the scheduling compensation characteristics are used to match and evaluate the scheduling response variables to search for the optimal scheduling strategy, and the optimal scheduling strategy is used as the scheduling response strategy. The scheduling response strategy is used for scheduling control of pharmaceutical logistics and guides the dynamic scheduling control of pharmaceutical logistics. This technical solution solves the technical problems of the existing technology of pharmaceutical logistics scheduling that are not timely and difficult to adapt to dynamic demand changes. By dynamically responding to pharmaceutical logistics demand deviations and optimizing the scheduling strategy in real time, the technical effect of improving pharmaceutical logistics scheduling efficiency and ensuring the quality of drug transportation is achieved.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of a method for dynamic demand response scheduling of pharmaceutical logistics is provided for the embodiment of the present application;

[0009] Figure 2 A flow chart of obtaining scheduling compensation characteristics in a dynamic demand response scheduling method for pharmaceutical logistics is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0010] The overall idea of the technical solution provided by this application is as follows:

[0011] The embodiments of the present application provide a dynamic demand response scheduling method for pharmaceutical logistics. By real-time monitoring of various parameter indicators in the pharmaceutical logistics process, it dynamically identifies demand deviations that occur, and performs precise time positioning and intelligent scheduling compensation based on this, so as to minimize the impact of deviations, improve the efficiency of pharmaceutical logistics scheduling, and ensure the quality of drug transportation.

[0012] First, by matching and analyzing real-time sensor data with pre-set logistics targets, the synchronization of the transportation process with the plan is assessed, allowing timely detection of demand deviations. Next, asynchronous timestamps are used to precisely locate these deviations and divide them into time windows, providing a basis for subsequent compensation. Based on the deviation time windows and the scheduling response variables, intelligent scheduling strategy search and optimization are performed to generate the optimal scheduling response strategy. This strategy is then applied to actual logistics scheduling control, dynamically guiding real-time adjustments to the transportation process to ensure the safe and efficient delivery of medicines to their destinations.

[0013] Through the dynamic demand response scheduling method of pharmaceutical logistics, we can actively adapt to various dynamic changes in the pharmaceutical logistics process, compensate for demand deviations in a timely manner, and improve the timeliness and accuracy of scheduling decisions, thereby effectively improving the efficiency of pharmaceutical logistics and ensuring the safety of drug transportation.

[0014] After introducing the basic principles of the present application, the following will specifically introduce the non-limiting implementation methods of the present application in conjunction with the drawings in the specification.

[0015] like Figure 1 As shown, the embodiment of the present application provides a dynamic demand response scheduling method for pharmaceutical logistics, which includes:

[0016] S1: Match and analyze pharmaceutical logistics goals with real-time sensor data to obtain response synchronization evaluation results.

[0017] Specifically, pharmaceutical logistics objectives, such as drug storage temperature ranges and expected delivery times, are first acquired as reference standards for evaluating whether the logistics process meets these requirements. Simultaneously, sensor devices are connected to capture real-time data reflecting logistics status, such as drug storage temperature data from temperature sensors and logistics vehicle location data from GPS positioning devices. After obtaining pharmaceutical logistics objectives and real-time sensor data, a matching analysis is performed. For example, the real-time temperature is compared with the target temperature range to determine if there is any deviation; the arrival time is predicted based on the real-time location and road conditions to determine whether the expected delivery time is met. The matching analysis results reflect the degree to which the current logistics status meets the pharmaceutical logistics objectives, forming a response synchronization evaluation result.

[0018] By obtaining the response synchronization evaluation results, possible deviations in the logistics process can be discovered in a timely manner, providing a decision-making basis for subsequent scheduling responses and laying a foundation for dynamic scheduling.

[0019] S2: Locate the demand deviation according to the response synchronization evaluation result, where the demand deviation has an asynchronous timestamp.

[0020] Specifically, after completing the matching analysis between pharmaceutical logistics targets and real-time sensor data, if the response synchronization evaluation results indicate a misalignment between the two, this indicates a demand deviation—meaning that the actual logistics status has deviated from the expected target. The response synchronization evaluation results are then used to pinpoint the specific demand item where the deviation occurred. For example, if the response synchronization evaluation results indicate that the temperature does not meet requirements, this is identified as a temperature deviation; if the response synchronization evaluation results indicate that the estimated arrival time is delayed, this is identified as a timeliness deviation. Pinpointing demand deviations facilitates subsequent targeted scheduling and compensation. Furthermore, due to the dynamic nature of logistics processes, the timing of demand deviations varies. To accurately record the time of demand deviation, an asynchronous timestamp is added to each identified demand deviation. This asynchronous timestamp records the specific moment the deviation occurred and serves as a reference for subsequent deviation time window analysis. For example, the timestamp for a temperature deviation represents a specific moment in the logistics process, while the timestamp for a timeliness deviation represents the difference between the estimated arrival time and the required time limit. Using the asynchronous timestamp, the timestamps for each demand deviation are correlated and differentiated.

[0021] By locating demand deviations, specific problems in the logistics process are clearly identified, and the time when the problem occurred is recorded through a timestamp, providing data support for subsequent time window analysis and scheduling decisions, laying the foundation for dynamic response deviations.

[0022] S3: Based on the asynchronous timestamp, perform time window analysis according to the demand deviation to determine the deviation time window.

[0023] Specifically, asynchronous timestamps are used to further analyze demand deviations in the time dimension. First, a deviation time window is introduced to represent the time range in which the demand deviation occurs as a time window. By analyzing the asynchronous timestamp of the demand deviation, the starting and ending boundaries of the deviation time window are determined. For example, for temperature deviation, the starting moment of the deviation is identified based on the asynchronous timestamp, and then the end moment of the deviation is determined in combination with the preset temperature recovery time threshold, thereby obtaining the deviation time window of the temperature deviation; for example, for time deviation, the asynchronous timestamp records the estimated delay time, then the starting point of the deviation time window is the current moment, and the end point is the estimated arrival time.

[0024] By determining the deviation time window, more specific time constraints and goals are provided for subsequent scheduling decisions. The time range of the demand deviation impact is clarified, which helps to assess the severity of the deviation, lays a time foundation for achieving accurate dynamic scheduling, and formulates targeted scheduling strategies.

[0025] S4: Perform logistics scheduling compensation according to the deviation time window to obtain scheduling compensation characteristics.

[0026] Specifically, the time period requiring scheduling compensation is determined based on the start and end time boundaries of the deviation time window. Within this time period, the logistics scheduling strategy is adjusted to reduce or even eliminate the impact of the deviation. Logistics scheduling compensation can be implemented in a variety of ways, with flexible decisions based on the type of deviation and actual circumstances. For example, for temperature deviations, the refrigeration equipment parameters of the transport vehicle can be adjusted within the deviation time window, or a backup vehicle with more favorable temperature conditions can be selected. For time delay deviations, route planning can be optimized within the deviation time window to avoid congested sections or to increase the dispatch of faster-response transport capacity. By implementing scheduling compensation within the deviation time window, the impact of deviations on logistics quality and timeliness can be effectively reduced, improving the responsiveness and resilience of the supply chain. The results of scheduling compensation are characterized by a set of parameters, including multiple dimensions such as the type, intensity, and duration of the compensation measures. These quantitatively describe the key attributes of the compensation strategy and provide important input for subsequent scheduling optimization.

[0027] By formulating and implementing scheduling compensation measures for deviation time windows and proactively adjusting strategies, we can dynamically adapt to changes in demand and ensure logistics quality and efficiency.

[0028] S5: Obtaining scheduling response variables, where the scheduling response variables include path adjustment parameters and vehicle scheduling parameters.

[0029] Specifically, first, the scheduling response variables are obtained. Scheduling response variables reflect the flexibility and adjustable space of logistics in terms of route planning and capacity scheduling. By obtaining and analyzing scheduling response variables, the ability to cope with deviations and dynamically optimize scheduling can be better assessed. Scheduling response variables include route adjustment parameters and vehicle scheduling parameters. Among them, route adjustment parameters are related to the optimization of delivery routes and include a series of parameters such as the number of alternative routes, estimated travel time, and adjustment costs. By analyzing the route adjustment parameters, it is possible to determine whether the selection of an alternative route is feasible and the potential benefits. Vehicle scheduling parameters reflect the flexibility of capacity scheduling and include the number and type of available vehicles, the current location and status of the vehicles, scheduling time, and mileage costs. By analyzing the vehicle scheduling parameters, it is possible to assess the feasibility of increasing capacity or changing vehicle models and the potential benefits.

[0030] Through various channels such as on-board equipment and dispatching systems, dispatch response variables are obtained in real time, providing a detailed basis for optimizing dispatching decisions. By comprehensively considering path adjustment parameters and vehicle dispatching parameters, the dynamic response capability of logistics can be fully evaluated. On this basis, the optimal dispatching strategy combination is selected to minimize dispatching costs and maximize logistics efficiency while satisfying the deviation time window constraints.

[0031] S6: With the goal of minimizing the demand deviation, the scheduling compensation characteristics are used to match and evaluate the scheduling response variables to search for the optimal scheduling strategy, and the optimal scheduling strategy is used as the scheduling response strategy. The scheduling response strategy is used to perform scheduling control on pharmaceutical logistics.

[0032] Specifically, the scheduling compensation characteristics and scheduling response variables are matched and evaluated to find the optimal scheduling strategy combination. The matching evaluation process aims to minimize demand deviation. By quantitatively analyzing the relationship between scheduling compensation characteristics and scheduling response variables, the expected effects of different scheduling strategy combinations are evaluated, and the optimal strategy that can minimize the impact of deviation is found. For example, heuristic search, genetic algorithms, reinforcement learning, etc. are used to continuously iterate and optimize in the strategy search space. The deviation mitigation effects of different strategies are compared through simulation and evaluation until the optimal solution that meets the demand is found. The optimal scheduling strategy obtained by the search can effectively compensate for the deviations that have already occurred and can also deal with the risks of possible deviations. Subsequently, the optimal scheduling strategy is used as the scheduling response strategy to guide the actual pharmaceutical logistics scheduling execution process. By dynamically responding to changes in demand and continuously optimizing scheduling decisions, the quality and efficiency of pharmaceutical logistics can be significantly improved, and the risk of drug supply can be reduced.

[0033] Through the intelligent matching optimization of scheduling compensation characteristics and scheduling response variables, the optimal scheduling strategy searched can minimize demand deviation, improve the efficiency of pharmaceutical logistics scheduling, and ensure the quality of drug transportation.

[0034] Furthermore, the embodiment of the present application also includes:

[0035] Performing logistics demand analysis based on the pharmaceutical logistics target to obtain pharmaceutical logistics constraint conditions, wherein the constraint conditions have a constraint strength coefficient;

[0036] Connecting to the sensor device channel to obtain sensor data, including drug storage temperature data and logistics location tracking data;

[0037] According to the sensor data, logistics parameters are parsed according to data types to obtain logistics status parameters;

[0038] Performing matching analysis on the logistics state parameters using the constraint conditions to determine a constraint matching coefficient;

[0039] Taking the constraint strength coefficient as a target, a response evaluation is performed on the constraint matching coefficient to obtain the response synchronization evaluation result.

[0040] In one feasible implementation, a comprehensive logistics demand analysis is first conducted based on the specific needs of pharmaceutical logistics objectives. This analysis not only considers logistics requirements such as drug storage conditions and delivery deadlines, but also takes into account the specific characteristics of pharmaceutical logistics, such as the need for precise temperature and humidity control, restrictions on environmental factors such as vibration and light during transportation, and special requirements for transportation vehicles and packaging materials. Through this comprehensive analysis of pharmaceutical logistics objectives, the constraints of pharmaceutical logistics are extracted. Furthermore, a constraint intensity coefficient is introduced to assess the criticality of each constraint on pharmaceutical logistics quality and safety, and to assign a corresponding intensity coefficient. For example, the constraint intensity coefficient for drug storage temperature is higher than that for transportation vehicle type because the former has a more direct and critical impact on drug quality and effectiveness. This constraint intensity coefficient enables a refined and differentiated characterization of logistics needs, providing an important basis for subsequent intelligent matching analysis. Next, sensor channels are connected and, by integrating various sensor devices, multi-dimensional data from the logistics process is collected in real time to generate sensor data, including drug storage temperature data and logistics location tracking data. Among them, the drug storage temperature data is controlled at multiple points and monitored throughout the entire process to ensure its accuracy and completeness; the logistics positioning and tracking data is obtained through various positioning and tracking technologies such as GPS and Beidou, reflecting the transportation trajectory and key nodes of the monitored drugs.

[0041] Next, different types of sensor data are classified and preprocessed to remove noise and outliers, improving data quality. Subsequently, specialized feature extraction and parameter calculation algorithms are designed for each data type. For example, for medication storage temperature data, the average temperature is calculated, and multiple indicators such as temperature fluctuation and the percentage of time exceeding the threshold are analyzed. For logistics location tracking data, key parameters such as driving speed, path deviation, and estimated arrival time are calculated. Through intelligent data analysis, the raw sensor data is converted into a series of measurable and evaluable logistics status parameters, providing rich data support for intelligent decision-making. Subsequently, the logistics status parameters are mapped one-to-one with the constraints to establish a corresponding relationship between the two. Next, for each pair of constraints and logistics status parameters, a matching evaluation function is designed to quantitatively assess the degree to which the status parameters meet the constraints. For example, for a temperature constraint and an actual temperature parameter, the constraint matching coefficient for the temperature constraint is determined by considering factors such as the degree of deviation between the actual temperature and the target temperature range and the duration of the deviation. For a time constraint and an estimated arrival time parameter, the difference between the estimated time and the required time is evaluated to determine the constraint matching coefficient for the time constraint. By introducing the constraint matching coefficient, a quantitative correlation and comparison between logistics status and demand constraints is achieved, providing an important basis for deviation analysis and scheduling optimization. Then, using the constraint strength coefficient as a weight, the constraint matching coefficients of each constraint condition are weighted and integrated to obtain the response synchronization evaluation result. The response synchronization evaluation result is a comprehensive indicator that takes into account the varying importance of various logistics constraints and comprehensively and objectively reflects the degree of alignment between the real-time status of pharmaceutical logistics and expected requirements.

[0042] Furthermore, the embodiment of the present application also includes:

[0043] The demand deviation includes storage temperature deviation and logistics time tracking deviation. When the demand deviation is storage temperature deviation, the temperature threshold and time tolerance interval are obtained according to the constraint conditions;

[0044] Obtaining a time starting point according to the asynchronous timestamp of the demand deviation;

[0045] Obtaining the drug storage temperature data of the demand deviation, calculating the temperature cumulative deviation in combination with the temperature threshold, and determining the tolerance time threshold based on the time tolerance interval;

[0046] The deviation time window is acquired according to the time starting point and the tolerance time threshold.

[0047] In a preferred embodiment, first, it is clarified that the demand deviation in the pharmaceutical logistics process includes storage temperature deviation and logistics time tracking deviation. Among them, storage temperature deviation refers to the situation where the actual storage temperature of the medicine deviates from the preset temperature range. For storage temperature deviation, the temperature threshold and time tolerance interval are obtained according to the corresponding temperature constraint conditions. Among them, the temperature threshold defines the upper and lower limits of the acceptable temperature of the medicine, which is the benchmark for judging whether the temperature deviates; and the time tolerance interval stipulates the maximum tolerance time for the temperature deviation threshold, which reflects the tolerance of the medicine to temperature fluctuations. Secondly, for each detected temperature deviation, the time point of its occurrence is recorded to form an asynchronous timestamp, which serves as the starting point of time and identifies the starting moment of the deviation. It is the key anchor point for determining the deviation time window. By capturing the asynchronous timestamp, the precise positioning of the time when the deviation occurs is achieved, laying the foundation for subsequent time window analysis.

[0048] Next, drug storage temperature data is continuously acquired after a temperature deviation occurs and compared in real time with the temperature threshold. If the actual temperature continuously exceeds the temperature threshold, the temperature difference for the portion exceeding the threshold is accumulated to obtain a cumulative temperature deviation value, reflecting the severity and duration of the temperature deviation. Simultaneously, a tolerance time threshold is calculated, referring to a preset time tolerance interval. If the cumulative temperature deviation value returns to an acceptable range within the tolerance time, the impact of the deviation is considered tolerable. Setting a tolerance time threshold can avoid oversensitivity to occasional, short-term temperature fluctuations and improve the rationality of deviation judgment. Subsequently, based on the obtained temperature deviation starting time and the tolerance time threshold, the time interval formed between the starting time and the tolerance time threshold constitutes the deviation time window. This precisely defines the period of impact of the temperature deviation and intuitively presents the start and end boundaries of the temperature deviation. Extracting the deviation time window allows subsequent scheduling decisions to accurately target the critical time nodes affecting the deviation, applying control measures at the most needed times, and thus minimizing the adverse effects of the deviation.

[0049] Furthermore, the embodiment of the present application also includes:

[0050] When the demand deviation is the logistics time tracking deviation, obtaining the logistics node time constraint according to the constraint condition;

[0051] Obtain the deviation time starting point according to the asynchronous timestamp of the demand deviation;

[0052] Determine the vehicle's travel speed based on the logistics location tracking data combined with time data;

[0053] Obtaining a node time deviation according to the vehicle driving speed and the logistics node time constraint;

[0054] The deviation time window is obtained according to the deviation time starting point and the node time deviation, wherein the deviation time end point of the deviation time window is a time point obtained by adding the node time deviation to the deviation time starting point.

[0055] In a preferred embodiment, when the demand deviation is a logistics time tracking deviation, first, according to the constraint conditions, the expected arrival time of each key logistics node is obtained as the logistics node time constraint, wherein the logistics nodes include shipping points, transfer stations, destinations, etc. The logistics node time constraint reflects the expected time requirements of the drug at each logistics node. By clarifying the time constraints of each logistics node, a judgment benchmark is provided for the subsequent quantitative calculation of time deviation. Unlike a single end-point time requirement, it realizes the refined control of the time of each link in the entire logistics process. Subsequently, the asynchronous timestamp when the time deviation occurs is obtained as the starting point of the deviation time. However, unlike the temperature deviation, the occurrence of time deviation is often not a certain time point, but gradually accumulates during the logistics process. Therefore, the starting point of the time deviation is selected when the expected time begins to be significantly affected, such as when delivery delays begin to occur, so as to more accurately lock the key influencing nodes of the time problem.

[0056] Next, the logistics vehicle's location tracking data and timestamp information are used to calculate the vehicle's speed in real time on different road sections. Specifically, the vehicle's GPS location data is obtained at different times, and the vehicle's speed is calculated by comparing the distance difference with the time difference. By continuously tracking vehicle speed changes, it is possible to accurately detect abnormal idling or congestion delays, providing an important basis for determining timeliness deviations. Subsequently, the real-time vehicle speed is compared with the time constraints of the logistics nodes to quantitatively assess whether the current vehicle speed can meet the time requirements of subsequent nodes. This results in an estimated node time deviation. Specifically, the node time deviation is equal to the actual estimated arrival time minus the constrained expected arrival time. A positive node time deviation indicates a risk of timeliness delay. The deviation start time is used as the timestamp of the logistics deviation, and the end time is the deviation start time plus the node time deviation, forming a forward-looking deviation time window. Compared to a simple time slice, the deviation time window can detect timeliness deviations in advance and predict the duration of the deviation impact, allowing for targeted adjustment of response strategies.

[0057] Further, such as Figure 2 As shown, the embodiment of the present application also includes:

[0058] Obtaining a deviation attribute of the deviation time window, wherein the deviation attribute is a deviation parameter category determined for the storage temperature deviation and the logistics aging tracking deviation;

[0059] According to the deviation attribute, obtaining the temperature deviation amount and time range, and the aging deviation amount and time range of the deviation time window;

[0060] According to the temperature deviation and time range, the aging deviation and time range, the compensation parameters and the compensation requirements are extracted to obtain the scheduling compensation characteristics.

[0061] In one feasible implementation, the deviation attributes for each deviation time window are first acquired. Deviation attributes characterize the type of deviation and are categorized into two main types: storage temperature deviation and logistics time tracking deviation. By identifying the attribute category of the deviation time window, corresponding compensation strategies and parameters can be selected, providing a basis for differentiated compensation schemes. Then, corresponding deviation quantitative indicators and time information are extracted for different deviation attributes. For storage temperature deviation, the temperature deviation amount and the time range of the deviation duration are acquired from the deviation time window. For logistics time tracking deviation, the time deviation amount (e.g., estimated delay time) and the time range of the deviation impact are acquired from the deviation time window. By extracting key indicators, the deviation risk reflected by the deviation time window is converted into specific quantitative indicators, facilitating subsequent targeted compensation scheduling. Subsequently, based on the acquired temperature deviation amount and time range, and time deviation amount and time range, the corresponding compensation parameters and required compensation amount are further extracted, thereby forming a complete scheduling compensation feature. Specifically, for temperature deviations, the compensation parameter is the power of the drug-carrying vehicle's refrigeration equipment, while the compensation demand is the duration or intensity of refrigeration required to restore the temperature. For time-to-delay deviations, the compensation parameter is the speed increase of the dispatched vehicle, while the compensation demand is the time reduction required to compensate for the delay. The design of these compensation parameters and demands takes into account factors such as the drug's properties and vehicle performance, striving to minimize the adverse effects of deviations through compensation strategies.

[0062] Through key mapping and transformation from deviation time windows to scheduling compensation characteristics, a bridge is built between deviation analysis and compensation decision-making. By accurately extracting compensation-related parameters and quantitatively analyzing compensation needs, the scheduling optimization process is guided more efficiently and accurately, minimizing the impact of transportation deviations on drug quality and timeliness, and ensuring the safe and efficient operation of medicines.

[0063] Furthermore, the embodiment of the present application also includes:

[0064] Obtaining response parameters of schedulable targets based on the scheduling response variables, wherein the schedulable targets include backup logistics vehicles and adjustable routes, and the response parameters include the position, speed, and temperature of the backup logistics vehicles, and the adjustment distance and adjustment time of the adjustable routes, wherein the response parameters correspond to the route adjustment parameters and vehicle scheduling parameters;

[0065] Fitting a relationship function between the response parameter and the scheduling compensation feature, and performing a matching evaluation between the scheduling compensation feature and the response parameter based on the relationship function to obtain a matching degree;

[0066] The missing compensation coefficient is calculated based on the matching degree, and the demand deviation minimization is converted into the minimum missing compensation coefficient as the goal for iterative search until the demand deviation requirement or the number of iterations is met, and the schedulable target with the minimum current demand deviation and its response parameters are combined into the optimal scheduling strategy.

[0067] In a preferred embodiment, response parameters of schedulable targets are first obtained based on the scheduling response variables, namely, the target objects currently available for scheduling optimization and their key parameters. These schedulable targets primarily include backup logistics vehicles and adjustable routes. For backup logistics vehicles, state parameters such as their current location, driving speed, and cargo hold temperature are obtained. For adjustable routes, attribute parameters such as distance change and time cost of possible adjustment options are obtained. These parameters directly correspond to the route adjustment and vehicle scheduling variables, providing a choice of decision space for subsequent scheduling optimization. After obtaining the scheduling compensation characteristics and response parameters, a mapping relationship is established between the two, and a matching evaluation is conducted based on this relationship. First, a relationship function between the response parameters and the scheduling compensation characteristics is constructed through data fitting, characterizing the quantitative relationship between different response parameter combinations and their corresponding compensation effects. Then, based on this relationship function, the compensation effect of each possible scheduling decision (i.e., a set of response parameters) is evaluated to determine the extent to which it can compensate for the compensation gap, and a matching index is calculated. The higher the matching index, the more effectively the scheduling decision compensates for the current deviation, and the better the scheduling effect. This matching index allows for a more accurate and efficient evaluation and comparison of the advantages and disadvantages of different scheduling strategies. Afterwards, based on the degree of matching, the compensation gap coefficient is calculated to measure the size of the compensation gap of the scheduling strategy for the current deviation. The smaller the coefficient, the better the scheduling strategy is in compensating for the deviation, and the fewer the remaining uncompensated deviations. Therefore, the original demand deviation minimization problem is transformed into a compensation gap coefficient minimization problem. Guided by this goal, the intelligent optimization algorithm is used to continuously evaluate and adjust the scheduling strategy through iterative search until a set of response parameter combinations with the smallest compensation gap coefficient is found, or the preset number of iterations is reached. The optimal parameter combination is finally determined, that is, the optimal scheduling strategy with the smallest current demand deviation. Through the construction of intelligent search algorithms and objective functions, the optimal solution can be quickly locked in among a large number of scheduling strategy combinations, greatly improving the efficiency and accuracy of scheduling optimization.

[0068] Furthermore, the embodiment of the present application also includes:

[0069] Taking the response parameter as an independent variable and the adjustment compensation characteristic as a dependent variable, fitting the relationship function, wherein the relationship function includes a path adjustment parameter relationship function and a vehicle scheduling parameter relationship function;

[0070] According to the path adjustment parameter relationship function, the adjustment distance and the adjustment time of the adjustable path are converted into compensation amounts to obtain a path time-sensitive characteristic compensation amount;

[0071] According to the vehicle scheduling parameter relationship function, the position, speed, and temperature of the backup logistics vehicle are converted into compensation amounts to obtain a temperature deviation characteristic compensation amount;

[0072] The path aging feature compensation amount and / or the temperature deviation feature compensation amount are used as the numerator, and the corresponding parameter compensation feature in the scheduling compensation feature is used as the denominator to calculate the proportion and obtain the matching degree.

[0073] In a preferred embodiment, a function fitting method is first used to model the quantitative relationship between response parameters and scheduling compensation characteristics. The response parameters are used as independent variables, and the scheduling compensation characteristics as dependent variables, to construct a mapping function between the two. Considering that compensation scheduling is primarily achieved through route adjustment and vehicle scheduling, the mapping functions are accordingly divided into two categories: route adjustment parameter relationship functions and vehicle scheduling parameter relationship functions. This mathematical function characterizes the relationship between different scheduling strategies (i.e., different response parameter combinations) and their corresponding compensation effects, laying the foundation for a model that quantitatively analyzes the compensation effects. Subsequently, the response parameters are converted into corresponding compensation characteristics using the route adjustment parameter relationship functions and vehicle scheduling parameter relationship functions, respectively. For route adjustment, the key parameters are the distance change and time cost of the adjusted route. These relationship functions are used to map these into the compensation for time deviation caused by route adjustment, i.e., the path time deviation compensation. For vehicle scheduling, the key parameters are the location, speed, and temperature of the backup vehicles. These relationship functions are used to map these into the compensation for temperature deviation caused by vehicle scheduling, i.e., the temperature deviation compensation. By calculating the compensation amount, on the one hand, the adjustment range of the scheduling strategy is taken into account, and on the other hand, the sensitivity of the compensation characteristics to the deviation is also taken into account, which can objectively reflect the compensation effect of the scheduling strategy.

[0074] The scheduling strategy's matching index is then calculated by calculating the ratio of the compensation amount to the compensation characteristic. Specifically, for time deviation, the path time deviation compensation amount is used as the numerator, and the time deviation compensation characteristic is used as the denominator. The ratio of the two represents the scheduling strategy's compensation for time deviation. For temperature deviation, the temperature deviation compensation amount is divided by the temperature compensation characteristic. The ratio represents the scheduling strategy's compensation effectiveness for temperature deviation. The matching calculation fully considers the differences in compensation requirements for different deviation types. Using independent ratio calculations allows for a more accurate and targeted matching assessment. In practical applications, a scheduling strategy may involve both route adjustment and vehicle scheduling, necessitating a comprehensive consideration of both time and temperature matching. For example, a simple addition or weighted average of the two matching values can be used to generate a comprehensive matching index. Alternatively, the weight coefficients of the two matching indexes can be dynamically adjusted based on the urgency of the time and temperature deviations under the current circumstances, with the more severe the deviation, the greater the weight of the index. This results in a dynamically weighted comprehensive matching index.

[0075] Furthermore, the embodiment of the present application also includes:

[0076] Perform compensation time analysis according to the optimal scheduling strategy and construct a compensation time window;

[0077] According to the compensation time window and the deviation time window, matching and fitting are performed with the end time window to determine the window fitting degree;

[0078] The window fitting degree is used as a strategy verification parameter, and when the strategy verification parameter meets the fitting requirement, the optimal scheduling strategy is determined.

[0079] In one feasible implementation, the compensation completion time for each deviation node is first estimated based on the route adjustments and vehicle scheduling solutions determined by the optimal scheduling policy. This is used to construct a complete compensation time window. This window covers the entire time span from the current moment to the moment when all deviations are compensated, characterizing the dynamic evolution of the deviation impact over time after the scheduling policy is implemented. The introduction of the compensation time window organically combines the static decision-making of the scheduling policy with its dynamic execution, providing a temporal basis for policy validation. The compensation time window is then compared and fitted with the original deviation time window. The compensation time window reflects the expected improvement and elimination of the deviation impact under the optimal scheduling policy, while the deviation time window describes the natural persistence and diffusion of the deviation impact. The two windows are matched at the endpoint and the degree of overlap is evaluated to obtain a window fit. A higher fit indicates a better dynamic deviation compensation effect of the optimal scheduling policy, and thus a higher adaptability and reliability of the optimal scheduling policy. The window fit is then used as a quantitative policy validation parameter, representing the dynamic adaptability of the optimal scheduling policy. When this parameter meets the preset fitting requirements (e.g., above a certain threshold), the current optimal scheduling strategy is considered reliable and effective and can be implemented. Conversely, if the strategy verification parameter does not meet the standard, it means that the strategy may have risks and uncertainties during dynamic execution and requires further adjustment and optimization. By establishing a closed-loop strategy optimization mechanism through strategy verification parameters and setting clear judgment criteria, scheduling decisions can be adaptively corrected and improved, continuously enhancing the dynamic performance of the strategy.

[0080] In summary, the pharmaceutical logistics dynamic demand response scheduling method provided by the embodiments of the present application has the following technical effects:

[0081] By matching and analyzing pharmaceutical logistics goals with real-time sensor data, response synchronization evaluation results are obtained to identify changes in logistics demand, providing a basis for timely detection of demand deviations. Demand deviations are located based on the response synchronization evaluation results. Demand deviations have asynchronous timestamps, quantifying the time dimension of the deviations and laying the foundation for subsequent time window analysis and scheduling compensation. Based on asynchronous timestamps, time window analysis is performed based on demand deviations to determine the deviation time window. The deviation impact is then correlated with the time dimension, providing a more granular decision-making reference for scheduling compensation. Logistics scheduling compensation is performed based on the deviation time window, and scheduling compensation characteristics are obtained to guide subsequent scheduling optimization. Scheduling response variables, including route adjustment parameters and vehicle scheduling parameters, are obtained, providing the basis for intelligent search for the optimal scheduling strategy. With the goal of minimizing demand deviations, the scheduling compensation characteristics are matched and evaluated with the scheduling response variables to search for the optimal scheduling strategy. The optimal scheduling strategy is then used as the scheduling response strategy for scheduling control of pharmaceutical logistics, forming a complete dynamic scheduling response closed loop for pharmaceutical logistics. This can effectively respond to various dynamic changes during transportation, improve pharmaceutical logistics scheduling efficiency, and ensure the quality of drug transportation.

[0082] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0083] Furthermore, the terms "first" or "second" as described above may not only represent an order relationship but may also represent a specific concept and / or refer to the selectability of multiple elements, either individually or in combination. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, if such modifications and variations fall within the scope of this application and its equivalents, this application is intended to include such modifications and variations.

Claims

1. A dynamic demand response scheduling method for pharmaceutical logistics, characterized by: The pharmaceutical logistics dynamic demand response scheduling method includes: Match and analyze pharmaceutical logistics goals with real-time sensor data to obtain response synchronization evaluation results; Locating a demand deviation according to the response synchronization evaluation result, the demand deviation having an asynchronous timestamp; Based on the asynchronous timestamp, performing time window analysis according to the demand deviation to determine the deviation time window; Performing logistics scheduling compensation according to the deviation time window to obtain scheduling compensation characteristics; Obtaining a dispatch response variable, wherein the dispatch response variable includes a path adjustment parameter and a vehicle dispatch parameter; With the goal of minimizing the demand deviation, the scheduling compensation characteristics are used to match and evaluate the scheduling response variables to search for an optimal scheduling strategy, and the optimal scheduling strategy is used as a scheduling response strategy. The scheduling response strategy is used to perform scheduling control of pharmaceutical logistics; Wherein, obtaining the response synchronization evaluation result includes: Performing logistics demand analysis based on the pharmaceutical logistics target to obtain pharmaceutical logistics constraint conditions, wherein the constraint conditions have a constraint strength coefficient; Connecting to the sensor device channel to obtain sensor data, including drug storage temperature data and logistics location tracking data; According to the sensor data, logistics parameters are parsed according to data types to obtain logistics status parameters; Performing matching analysis on the logistics state parameters using the constraint conditions to determine a constraint matching coefficient; Taking the constraint strength coefficient as a target, performing response evaluation on the constraint matching coefficient to obtain the response synchronization evaluation result; The step of performing time window analysis based on the asynchronous timestamp and the demand deviation to determine the deviation time window includes: The demand deviation includes storage temperature deviation and logistics time tracking deviation. When the demand deviation is storage temperature deviation, the temperature threshold and time tolerance interval are obtained according to the constraint conditions; Obtaining a time starting point according to the asynchronous timestamp of the demand deviation; Obtaining the drug storage temperature data of the demand deviation, calculating the temperature cumulative deviation in combination with the temperature threshold, and determining the tolerance time threshold based on the time tolerance interval; The deviation time window is acquired according to the time starting point and the tolerance time threshold.

2. The pharmaceutical logistics dynamic demand response scheduling method according to claim 1, characterized in that: The determining of the deviation time window further includes: When the demand deviation is the logistics time tracking deviation, obtaining the logistics node time constraint according to the constraint condition; Obtain the deviation time starting point according to the asynchronous timestamp of the demand deviation; Determine the vehicle's travel speed based on the logistics location tracking data combined with time data; Obtaining a node time deviation according to the vehicle driving speed and the logistics node time constraint; The deviation time window is obtained according to the deviation time starting point and the node time deviation, wherein the deviation time end point of the deviation time window is a time point obtained by adding the node time deviation to the deviation time starting point.

3. The pharmaceutical logistics dynamic demand response scheduling method according to claim 2, characterized in that: Performing logistics scheduling compensation according to the deviation time window to obtain scheduling compensation characteristics includes: Obtaining a deviation attribute of the deviation time window, wherein the deviation attribute is a deviation parameter category determined for the storage temperature deviation and the logistics aging tracking deviation; According to the deviation attribute, obtaining the temperature deviation amount and time range, and the aging deviation amount and time range of the deviation time window; According to the temperature deviation and time range, the aging deviation and time range, the compensation parameters and the compensation requirements are extracted to obtain the scheduling compensation characteristics.

4. The pharmaceutical logistics dynamic demand response scheduling method according to claim 3, characterized in that: With the goal of minimizing the demand deviation, the optimal scheduling strategy is searched by using the scheduling compensation characteristics and the scheduling response variable matching evaluation, including: Obtaining response parameters of schedulable targets based on the scheduling response variables, wherein the schedulable targets include backup logistics vehicles and adjustable routes, and the response parameters include the position, speed, and temperature of the backup logistics vehicles, and the adjustment distance and adjustment time of the adjustable routes, wherein the response parameters correspond to the route adjustment parameters and vehicle scheduling parameters; Fitting a relationship function between the response parameter and the scheduling compensation feature, and performing a matching evaluation between the scheduling compensation feature and the response parameter based on the relationship function to obtain a matching degree; The missing compensation coefficient is calculated based on the matching degree, and the demand deviation minimization is converted into the minimum missing compensation coefficient as the goal for iterative search until the demand deviation requirement or the number of iterations is met, and the schedulable target with the minimum current demand deviation and its response parameters are combined into the optimal scheduling strategy.

5. The pharmaceutical logistics dynamic demand response scheduling method according to claim 4, characterized in that: Fitting a relationship function between the response parameter and the scheduling compensation feature, and performing a matching evaluation between the scheduling compensation feature and the response parameter based on the relationship function to obtain a matching degree, including: Taking the response parameter as an independent variable and the adjustment compensation characteristic as a dependent variable, fitting the relationship function, wherein the relationship function includes a path adjustment parameter relationship function and a vehicle scheduling parameter relationship function; According to the path adjustment parameter relationship function, the adjustment distance and the adjustment time of the adjustable path are converted into compensation amounts to obtain a path time-sensitive characteristic compensation amount; According to the vehicle scheduling parameter relationship function, the position, speed, and temperature of the backup logistics vehicle are converted into compensation amounts to obtain a temperature deviation characteristic compensation amount; The path aging feature compensation amount and / or the temperature deviation feature compensation amount are used as the numerator, and the corresponding parameter compensation feature in the scheduling compensation feature is used as the denominator to calculate the proportion and obtain the matching degree.

6. The pharmaceutical logistics dynamic demand response scheduling method according to claim 4, characterized in that: The pharmaceutical logistics dynamic demand response scheduling method further includes: Perform compensation time analysis according to the optimal scheduling strategy and construct a compensation time window; According to the compensation time window and the deviation time window, matching and fitting are performed with the end time window to determine the window fitting degree; The window fitting degree is used as a strategy verification parameter, and when the strategy verification parameter meets the fitting requirement, the optimal scheduling strategy is determined.

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