Energy consumption optimization method for drug storage system
By building a twin simulation model and multi-objective optimization algorithm, the problems of low temperature control and high energy consumption of drug storage systems are solved, and more efficient drug storage and energy consumption management are achieved, reducing operating costs.
Patent Information
- Application Number
- CN202411462731.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-19
- Publication Date
- 2024-12-27
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing drug storage system has problems with low precision in temperature control and high system energy consumption and operating costs.
By building a twin simulation model, temperature field simulation and storage location optimization are carried out, drug storage location set is determined, and multi-objective optimization algorithm and cluster analysis are combined to achieve fine control of the temperature field and energy consumption optimization.
It improves the precision of temperature control, reduces storage energy consumption and operating costs, ensures that drugs are stored within the appropriate temperature range, and improves drug quality and system efficiency.
Smart Images
Figure CN118981906B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of drug storage, and in particular to an energy consumption optimization method for a drug storage system. Background Art
[0002] With the development of medical technology and the increase in the types of drugs, the control requirements for the storage environment of drugs have become more complex and diverse. The energy consumption management of drug storage systems has become increasingly important. Existing drug storage systems are mostly based on closed-loop control for overall environmental management, which has technical problems such as low temperature control precision, high system energy consumption and high operating costs. Summary of the invention
[0003] The present invention provides an energy consumption optimization method for a drug storage system to solve the technical problems of low temperature control precision, high system energy consumption and high operating costs in the prior art, and achieve the technical effects of improving temperature control precision and reducing storage energy consumption and operating costs.
[0004] The energy consumption optimization method for a drug storage system provided by the present invention comprises:
[0005] Based on the target storage space, a twin simulation model is constructed; a standard drug model is constructed, and the temperature field of the drug storage space is simulated to obtain the initial temperature field; an interactive drug storage database is used to obtain the storage feature set of the target drug set, wherein the storage feature set includes an appearance volume indicator set, a packaging material indicator set, an environmental requirement indicator set, and a storage quantity indicator set; based on the initial temperature field and the storage feature set, the twin simulation model is updated and initialized to obtain a constant temperature twin simulation model; based on the constant temperature twin simulation model, the storage position is step-optimized to determine the drug storage position set.
[0006] In a feasible implementation, a standard drug model is constructed, and a temperature field simulation is performed on the drug storage space to obtain an initial temperature field, including:
[0007] Based on the historical storage records of the target storage space, typical drug samples are obtained; the appearance volume indicators and thermal properties of the typical drug samples are extracted to establish the standard drug model; the standard drug model is synchronized to the twin simulation model to perform array position distribution; based on the array position distribution results, a temperature field simulation is performed on the twin simulation model to obtain the initial temperature field of the drug storage space.
[0008] In a feasible implementation, based on the initial temperature field and the stored feature set, updating and initializing the twin simulation model to obtain a constant temperature twin simulation model includes:
[0009] Based on the storage feature set, the environmental demand indicator set is extracted, and the environmental demand indicator set is clustered and analyzed to generate multiple storage temperature intervals; based on the multiple temperature intervals, the initial temperature field is partitioned to obtain initial partitioning results; according to the storage feature set, the shape volume indicator set and the storage quantity indicator set are extracted, and combined with the clustering analysis results, multiple partition space requirements corresponding to the multiple storage temperature intervals are calculated and obtained; the multiple partition space requirements are compared with the initial partitioning results, and the partition adjustment of the initial partitioning results is performed to obtain adjusted partitioning results.
[0010] In a feasible implementation, based on the initial temperature field and the stored feature set, updating and initializing the twin simulation model to obtain a constant temperature twin simulation model further includes:
[0011] The adjusted partition results are used as multiple storage distribution spaces, and the cluster analysis results are called to perform random position allocation to obtain an initial storage position set; according to the storage feature set, a target drug model set is constructed; the initial storage position set and the target drug model set are combined to initialize the drug distribution in the twin simulation model; the initial temperature field is used as an invariant to initialize the temperature of the twin simulation model to obtain the constant temperature twin simulation model.
[0012] In a feasible implementation, based on the constant temperature twin simulation model, step optimization of storage locations is performed to determine a drug storage location set, including:
[0013] Acquire the temperature control constraints of the target scene, and perform step-by-step decomposition on the temperature control constraints to obtain a step-by-step constraint series; based on the constant-temperature twin simulation model, combine the multi-objective optimization algorithm with the first step-by-step constraint of the step-by-step constraint series, iteratively optimize the initial storage position to obtain an optimized storage position; set the optimized storage position as an invariant, perform simulation on the constant-temperature twin simulation model, analyze the temperature distribution, and obtain an optimized temperature field; determine whether the optimized temperature field and the optimized storage position satisfy the first step-by-step constraint, perform iterative optimization, and obtain the drug storage position set, wherein the drug storage position set corresponds to a drug storage temperature field.
[0014] In a feasible implementation, determining whether the optimized temperature field and the optimized storage position satisfy the first step constraint and performing iterative optimization includes:
[0015] If the optimized temperature field and the optimized storage position satisfy the first step constraint, the second step constraint is used as the optimization target, the constant temperature twin simulation model is initialized with the optimized temperature field and the optimized storage position, and the next step optimization iteration is performed; based on the fact that the optimized temperature field and the optimized storage position do not satisfy the first step constraint, the initial temperature field of the constant temperature twin simulation model is updated according to the optimized temperature field, and the optimized storage position is iteratively optimized.
[0016] In a feasible implementation, the method further includes:
[0017] An interactive drug dispensing system obtains a drug dispensing task list; locates multiple target drugs according to the drug dispensing task list and obtains a target dispensing drug sequence; parses the drug dispensing task list, obtains multiple drug dispensing paths and collects path environment temperature information sets of the drug dispensing paths; constructs a heat exchange numerical model based on the path environment temperature information set, performs dispensing simulation, and obtains a predicted temperature loss set according to the dispensing simulation results; updates the environmental demand indicator set according to the predicted temperature loss set, and performs prospective pre-cooling optimization on the drug storage temperature field of the drug storage location set.
[0018] In a feasible implementation, the method further includes:
[0019] Configure energy consumption optimization constraints, which include immediate update constraints, periodic update constraints, and near-expiry update constraints; perform update judgment based on the energy consumption optimization constraints, and if any one of the energy consumption optimization constraints is met, interact with the drug storage database, update the target drug set, and perform iterative optimization.
[0020] The present invention discloses an energy consumption optimization method for a drug storage system, including: constructing a twin simulation model based on a target storage space; constructing a standard drug model, simulating the temperature field of the drug storage space, and obtaining an initial temperature field; interacting with a drug storage database to obtain a storage feature set of a target drug set, including an appearance volume index set, a packaging material index set, an environmental demand index set, and a storage quantity index set; updating the initialized twin simulation model based on the initial temperature field and the storage feature set to obtain a constant temperature twin simulation model; performing step optimization of storage positions based on the constant temperature twin simulation model to determine a drug storage position set. The energy consumption optimization method for a drug storage system disclosed in the present invention solves the technical problems of low temperature control precision, high system energy consumption and operating costs, and achieves the technical effects of improving temperature control precision and reducing storage energy consumption and operating costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 It is a schematic flow chart of the energy consumption optimization method for a drug storage system of the present invention;
[0022] Figure 2 It is a schematic diagram of the process of performing stepwise optimization of storage positions in the energy consumption optimization method for a drug storage system of the present invention. DETAILED DESCRIPTION
[0023] The technical solution provided in the embodiments of the present invention is to solve the technical problems of low temperature control precision, high system energy consumption and high operating cost in the prior art. The overall idea adopted is as follows:
[0024] First, a twin simulation model is built based on the target storage space.
[0025] Then, a standard drug model is constructed: a standard drug model is constructed, the temperature field of the drug storage space is simulated, and the initial temperature field is obtained.
[0026] Then, the storage feature set is obtained: the interactive drug storage database is used to obtain the storage feature set of the target drug set, including the shape volume indicator set, the packaging material indicator set, the environmental requirement indicator set, and the storage quantity indicator set.
[0027] Next, the twin simulation model is initialized: based on the initial temperature field and the stored feature set, the initialized twin simulation model is updated to obtain a constant temperature twin simulation model.
[0028] Finally, storage location optimization is performed: Based on the constant-temperature twin simulation model, step optimization of the storage location is performed to determine the drug storage location set.
[0029] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.
[0030] Embodiment 1
[0031] Figure 1 The figure is a flow chart of the energy consumption optimization method for a drug storage system according to the present invention, wherein the method comprises:
[0032] Build a twin simulation model based on the target storage space.
[0033] Specifically, the target storage space is a specific storage space for storing drugs, such as a storage box or warehouse that can adjust the ambient temperature and humidity. The twin simulation model is used to flow and distribute the ambient airflow and temperature in the storage space.
[0034] Optionally, when building a twin simulation model, first, define the physical model according to the actual size and shape of the storage space. Including the length, width and height of the space, and then refine the physical model, including configuring the partitions or shelves inside the target storage space, the location of the environmental conditioning equipment, the location and orientation of the air outlet, etc., and then, use the computational fluid dynamics (CFD) method or other appropriate simulation technology to establish a numerical model of the target storage space, and finally, combine the physical model with the numerical model to obtain the twin simulation model. This model should be able to simulate the flow and distribution of drugs in the storage space, as well as changes in environmental parameters such as temperature and humidity.
[0035] Optionally, building a twin simulation model also includes defining the airflow circulation speed in the target storage space, the output parameters of the air outlet or environmental conditioning equipment, etc. Among them, the airflow circulation speed affects the temperature and humidity distribution in the storage space. For example, a higher circulation speed may result in a more uniform temperature and humidity distribution, but may also increase energy consumption. The output parameters of the air outlet or environmental conditioning equipment include the size and position of the air outlet, and the output temperature and humidity of the environmental conditioning equipment. These parameters can affect the airflow pattern and environmental conditions in the storage space. In addition, the packaging size, shape, and storage location of the drug also affect the airflow pattern in the storage space. For example, if the drug is stored in the middle of the shelf, the airflow may be hindered, resulting in an uneven distribution of temperature and humidity.
[0036] A standard drug model was constructed, and the temperature field of the drug storage space was simulated to obtain the initial temperature field.
[0037] Specifically, firstly, representative drugs are selected, and based on the drug type, the corresponding drug characteristic parameters are called to build a standard model that can reflect the thermal characteristics and appearance image of the drug packaging. Then, the standard drug model is used as the storage object, and the standard drug model is arranged and distributed in the drug storage space, and then the simulation calculation is performed based on CFD to obtain the initial temperature distribution.
[0038] In some embodiments, a standard drug model is constructed, a temperature field simulation is performed on the drug storage space, and an initial temperature field is obtained, including:
[0039] Based on the historical storage records of the target storage space, typical drug samples are obtained; the appearance volume indicators and thermal properties of the typical drug samples are extracted to establish the standard drug model; the standard drug model is synchronized to the twin simulation model to perform array position distribution; based on the array position distribution results, a temperature field simulation is performed on the twin simulation model to obtain the initial temperature field of the drug storage space.
[0040] Specifically, first, based on the historical storage records of the target storage space, representative drug samples are selected to determine the typical drug samples required for the standard drug model. Then, the shape volume indicators and thermal properties of the selected drug samples are extracted, where the shape volume indicators include length, width, and height, and the thermal property data include density, specific heat capacity, thermal conductivity, etc. Then, based on the shape volume indicators, CAD software such as AutoCAD and SolidWorks are used to establish a geometric model of the drug. Finite element analysis software (such as ANSYS, COMSOL, etc.) is used to establish a thermal conduction model of the drug based on thermal properties, and the thermal parameters of the drug are input to set boundary conditions and initial conditions.
[0041] Specifically, the established standard drug model is imported into the twin simulation platform, and the position and arrangement of the drugs in the storage space are determined according to the layout of the storage space and the actual storage location of the drugs, and the array position distribution of the drugs is performed.
[0042] Specifically, CFD software (such as Fluent, OpenFOAM) or FEA software (such as ANSYS, COMSOL) is used to perform numerical simulation of the temperature field based on the array position distribution of the drug, analyze the temperature distribution, and then obtain the initial temperature field data in the drug storage space. The initial temperature field data reflects how the temperature is distributed in the drug storage space when the standard drug model is stored. Based on the initial temperature field data, the storage position of the drug can be further optimized to ensure the temperature stability of the drug during the storage process.
[0043] The interactive drug storage database obtains a storage feature set of a target drug set, wherein the storage feature set includes an appearance volume indicator set, a packaging material indicator set, an environmental requirement indicator set, and a storage quantity indicator set.
[0044] Specifically, in order to optimize the drug storage space, it is necessary to obtain the storage feature set of the target drug set from the drug storage database. First, connect to the drug storage database, which contains the inventory information of all drugs that need to be stored in the target scenario, including drug types, drug identification codes, inventory quantities, storage locations, storage requirements, etc. Then, extract the storage feature set from the acquired drug information, including the shape volume index set, packaging material index set, environmental requirement index set, and storage quantity index set.
[0045] Specifically, the length, width, and height fields are extracted from the drug storage database to construct an external volume indicator set; the packaging material fields, such as glass, plastic, metal, etc., are extracted to construct a packaging material indicator set; the suitable temperature and suitable humidity fields are extracted to construct an environmental requirement indicator set; the inventory quantity field is extracted to construct a storage quantity indicator set.
[0046] Through the above steps, the storage feature set of the target drug set is accurately obtained from the drug storage database, which provides necessary data support for subsequent drug storage optimization.
[0047] Based on the initial temperature field and the stored feature set, the twin simulation model is updated and initialized to obtain a constant-temperature twin simulation model.
[0048] In some embodiments, based on the initial temperature field and the stored feature set, updating and initializing the twin simulation model to obtain a constant temperature twin simulation model includes:
[0049] Based on the storage feature set, the environmental demand indicator set is extracted, and the environmental demand indicator set is clustered and analyzed to generate multiple storage temperature intervals; based on the multiple temperature intervals, the initial temperature field is partitioned to obtain initial partitioning results; according to the storage feature set, the shape volume indicator set and the storage quantity indicator set are extracted, and combined with the clustering analysis results, multiple partition space requirements corresponding to the multiple storage temperature intervals are calculated and obtained; the multiple partition space requirements are compared with the initial partitioning results, and the partition adjustment of the initial partitioning results is performed to obtain adjusted partitioning results.
[0050] Specifically, first, environmental demand indicators such as temperature range and humidity are extracted from the storage feature set, and clustering analysis is performed on the extracted environmental demand indicators using a clustering algorithm (such as K-means, hierarchical clustering, etc.) to generate multiple storage temperature intervals. Through the storage temperature intervals, the diverse storage requirements of various drugs can be divided into multiple temperature ranges that are relatively easy to understand and have a relatively fixed number.
[0051] Then, based on multiple temperature intervals, the initial temperature field is partitioned to obtain initial partitioning results, including mapping the temperature data in the initial temperature field to multiple stored temperature intervals, and according to the mapping results, dividing the initial temperature field into partitions of multiple temperature intervals with the center temperatures of the multiple stored temperature intervals to obtain initial partitioning results.
[0052] Furthermore, based on the storage feature set, the external volume indicators (such as the volume and size of the drugs) and the storage quantity (indicators such as the number and batches of drugs) in the storage feature set are extracted, and combined with the cluster analysis results, the partition space requirement corresponding to each storage temperature interval is calculated. The partition space requirement represents the total volume of drugs that need to be stored in each storage temperature interval.
[0053] Specifically, the partition space requirements are compared with the initial partition results to find the partitions with mismatched space requirements, and the initial partitions are adjusted according to the comparison results to reallocate the storage space so as to meet the space requirements of the storage temperature range, and obtain the adjusted partition results. Exemplarily, the partition boundaries of the initial partition results are adjusted to ensure that each partition can accommodate the corresponding number and volume of drugs.
[0054] The above method steps can accurately control the temperature distribution of the drug storage space by zoning and adjusting the temperature field. By adjusting the zoning results, it can better adapt to the storage needs of the drugs and help ensure that the drugs are stored in an appropriate temperature range.
[0055] In some embodiments, based on the initial temperature field and the stored feature set, updating and initializing the twin simulation model to obtain a constant temperature twin simulation model further includes:
[0056] The adjusted partition results are used as multiple storage distribution spaces, and the cluster analysis results are called to perform random position allocation to obtain an initial storage position set; according to the storage feature set, a target drug model set is constructed; the initial storage position set and the target drug model set are combined to initialize the drug distribution in the twin simulation model; the initial temperature field is used as an invariant to initialize the temperature of the twin simulation model to obtain the constant temperature twin simulation model.
[0057] Specifically, first, based on the results of the adjusted partitions, multiple storage distribution spaces are determined, and the cluster analysis results are called to perform random location allocation of drugs, record the initial storage location of each drug, and form an initial storage location set. Exemplarily, it includes establishing an association relationship between multiple storage distribution spaces and multiple clusters in the cluster analysis results; then, the random location distribution of drugs in multiple corresponding clusters is performed in multiple storage distribution spaces. Among them, multiple drugs of the same type are distributed continuously to facilitate the preparation and use of stored drugs.
[0058] Specifically, the drug's external volume, packaging material, environmental requirements and other indicators are extracted from the stored feature set, and based on the same method and principle as building the standard drug model, a target drug model set is formed. The target drug model set includes monomer models of multiple drugs in the target drug set.
[0059] Specifically, the drugs in the target drug model set are mapped according to the initial storage location set, and the mapped drug models are synchronized to the twin simulation model to complete the drug distribution initialization. The drug distribution initialization includes calling, copying, translating, rotating, and fixing the monomer models of multiple drugs in the target drug model set.
[0060] Furthermore, the acquired initial temperature field data is mapped to the corresponding position in the twin simulation model to ensure that the temperature parameters of the initial temperature field are fixed in the entire twin simulation model and are not affected by external factors, so as to ensure that the initial temperature field serves as an invariant in the twin simulation model and obtain a constant temperature twin simulation model.
[0061] Through the above method steps, the update of the twin simulation model is simplified, because each simulation only needs to update one of the temperature field or the drug position, which helps to reduce the amount of calculation and simulation difficulty, thereby improving efficiency.
[0062] Based on the constant temperature twin simulation model, step optimization of storage locations is performed to determine a set of drug storage locations.
[0063] In some embodiments, Figure 2 As shown, based on the constant temperature twin simulation model, step optimization of storage positions is performed to determine a drug storage position set, including:
[0064] Acquire the temperature control constraints of the target scene, and perform step-by-step decomposition on the temperature control constraints to obtain a step-by-step constraint series; based on the constant-temperature twin simulation model, combine the multi-objective optimization algorithm with the first step-by-step constraint of the step-by-step constraint series, iteratively optimize the initial storage position to obtain an optimized storage position; set the optimized storage position as an invariant, perform simulation on the constant-temperature twin simulation model, analyze the temperature distribution, and obtain an optimized temperature field; determine whether the optimized temperature field and the optimized storage position satisfy the first step-by-step constraint, perform iterative optimization, and obtain the drug storage position set, wherein the drug storage position set corresponds to a drug storage temperature field.
[0065] Specifically, first, relevant data is obtained from the environmental control system of the target scene to ensure that the specific constraints of temperature control are clear, for example, the temperature control requirements, including the temperature fluctuation range. Then, the temperature control constraints are decomposed to form a series of step constraints, each of which represents a small range of temperature control targets, which is convenient for step-by-step optimization.
[0066] Specifically, in combination with the first step constraint, a multi-objective optimization algorithm (such as gradient descent, genetic algorithm or particle swarm optimization) is used to adjust the storage positions of multiple drugs to obtain an optimized storage position, wherein the optimized storage position is a drug storage position that satisfies the first step constraint.
[0067] Specifically, in the aforementioned optimization process, the temperature field in the constant temperature twin simulation model is regarded as a fixed parameter, and the drug storage position is iteratively optimized independently. However, in actual storage, the drug storage position will also have a reverse effect on the temperature field and change the temperature distribution of the temperature field. Therefore, it is necessary to update the temperature field in the constant temperature twin simulation model based on the optimized storage position obtained by optimization, and verify whether the optimized storage position in the updated temperature field still meets the storage requirements of multiple drugs in the target drug set.
[0068] Specifically, the optimized storage location is set as an invariant, the temperature distribution is regarded as a variable, and a simulation is performed to analyze the temperature distribution and obtain the optimized temperature field. Then, it is determined whether the optimized temperature field and the optimized storage location meet the first step constraint. If not, return to the previous step to further iterate and optimize the optimized storage location. If satisfied, continue to optimize the next step constraint until all step constraints are met.
[0069] In some implementations, determining whether the optimized temperature field and the optimized storage position satisfy the first step constraint and performing iterative optimization includes:
[0070] If the optimized temperature field and the optimized storage position satisfy the first step constraint, the second step constraint is used as the optimization target, the constant temperature twin simulation model is initialized with the optimized temperature field and the optimized storage position, and the next step optimization iteration is performed; based on the fact that the optimized temperature field and the optimized storage position do not satisfy the first step constraint, the initial temperature field of the constant temperature twin simulation model is updated according to the optimized temperature field, and the optimized storage position is iteratively optimized.
[0071] Specifically, if the current solution satisfies the first step constraint, the second step constraint can be used as a new optimization target. The constant temperature twin simulation model is initialized using the current optimized temperature field and optimized storage location, and the next optimization iteration is performed.
[0072] Specifically, if the current solution does not meet the first step constraint, it is necessary to update the initial temperature field of the constant temperature twin simulation model according to the optimized temperature field, and return to the above iterative optimization step, combining the multi-objective optimization algorithm with the first step constraint, and iteratively optimize the optimized storage location until the obtained optimized temperature field and optimized storage location meet the first step constraint.
[0073] Furthermore, the method further comprises:
[0074] An interactive drug dispensing system obtains a drug dispensing task list; locates multiple target drugs according to the drug dispensing task list and obtains a target dispensing drug sequence; parses the drug dispensing task list, obtains multiple drug dispensing paths and collects path environment temperature information sets of the drug dispensing paths; constructs a heat exchange numerical model based on the path environment temperature information set, performs dispensing simulation, and obtains a predicted temperature loss set according to the dispensing simulation results; updates the environmental demand indicator set according to the predicted temperature loss set, and performs prospective pre-cooling optimization on the drug storage temperature field of the drug storage location set.
[0075] Specifically, first, the interactive drug dispensing system obtains the current drug dispensing task list, which includes the type, quantity, distribution target location and distribution path of the drugs to be distributed in the drug dispensing task list. Then, according to the drug dispensing task list, multiple target drugs are located and the target distribution drug sequence is obtained.
[0076] Specifically, the drug distribution task list is parsed, multiple drug distribution paths are determined, and the path environment temperature information sets of these distribution paths are collected, including the temperature changes on each path. Exemplarily, the drug distribution path includes an indoor path (the path where the drug moves inside the building), an outdoor path (the path where the drug moves in an open environment), a mixed path (the path where the drug moves alternately indoors and outdoors) and the corresponding path lengths.
[0077] Optionally, depending on the conditions of the drug accessories, the drug distribution path also includes distribution with insulation and distribution without insulation.
[0078] Specifically, based on the path environment temperature information set, a heat exchange numerical model is constructed to simulate the temperature changes experienced by the drug during the distribution process to predict the temperature loss of the drug during the distribution process. Then, based on the distribution simulation results, the temperature changes of the drug before and after distribution are calculated to obtain the predicted temperature loss set, thereby evaluating the temperature loss of the drug during the distribution process. Among them, the predicted temperature loss set includes the temperature rise and the heat absorption.
[0079] Specifically, according to the temperature changes during the distribution process, the storage temperature of the medicine is optimized, including updating the environmental demand indicator set according to the predicted temperature loss set and adjusting the storage requirements of the medicine. Then, based on the adjusted storage requirements of the medicine, the temperature field of the medicine storage system is adjusted, and the storage temperature field of the medicine storage location set is prospectively pre-cooled and optimized to ensure that the medicine can maintain a suitable temperature during the distribution process.
[0080] Through the above method steps, combined with multi-objective optimization algorithm and temperature field simulation, pre-cooling optimization is performed according to the temperature loss during the dispensing process, possible temperature changes are responded to in advance, and the temperature suitability of the drug in the entire process is ensured, thereby ensuring the quality and safety of the drug.
[0081] Furthermore, the method further comprises:
[0082] Configure energy consumption optimization constraints, which include immediate update constraints, periodic update constraints, and near-expiry update constraints; perform update judgment based on the energy consumption optimization constraints, and if any one of the energy consumption optimization constraints is met, interact with the drug storage database, update the target drug set, and perform iterative optimization.
[0083] Specifically, first, configure energy consumption optimization constraints, including immediate update constraints, periodic update constraints, and near-expiry update constraints. Among them, the immediate update constraint requires that the storage information be updated immediately after the drug is dispensed. This means that every time the drug is taken out or put into the storage system, the storage location and quantity of the drug need to be updated. This ensures the accuracy of the storage information and helps you better track the storage status of the drug.
[0084] The periodic update constraint sets a preset update cycle and requires regular updates within this cycle. It is used to regularly check the storage status of drugs to promptly discover and solve possible problems.
[0085] Among them, the near-expiry update constraint requires that the drug be updated when it is identified that the drug is about to expire. This can ensure that the drugs that are about to expire are removed in time to prevent expired drugs from affecting the efficiency of the storage system and the quality of the drugs. Specifically, based on the information in the drug storage database, when it is identified that there are near-expiry drugs that have been removed, an update is performed.
[0086] Optionally, a judgment is made based on the configured energy consumption optimization constraints, including checking the storage status of the drugs, such as whether any drugs have been dispensed, whether the preset update cycle has been reached, or whether any drugs are about to expire. If any energy consumption optimization constraint is met, the drug storage database interacts, involving querying the storage information of the drug, updating the storage status of the drug, or removing drugs that are about to expire. After interacting with the drug storage database, the target drug set is updated. This includes adding new drugs, or removing drugs that have been dispensed or expired from the target drug set. Then, iterative optimization is performed based on the updated target drug set.
[0087] The above-mentioned method and steps can help to effectively reduce energy consumption and improve the efficiency and sustainability of the drug storage system while ensuring the quality of the drugs.
[0088] In summary, the energy consumption optimization method for a drug storage system provided by the present invention has the following technical effects:
[0089] Through the target storage space, a twin simulation model is constructed; a standard drug model is constructed to simulate the temperature field of the drug storage space and obtain the initial temperature field; the interactive drug storage database is used to obtain the storage feature set of the target drug set, including the shape volume index set, packaging material index set, environmental demand index set, and storage quantity index set; based on the initial temperature field and storage feature set, the initialized twin simulation model is updated to obtain the constant temperature twin simulation model; based on the constant temperature twin simulation model, the storage position is step-optimized to determine the drug storage position set. This achieves the technical effect of improving the precision of temperature control and reducing storage energy consumption and operating costs.
[0090] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.
Claims
1. An energy consumption optimization method for a drug storage system, characterized in that: The method comprises: Based on the target storage space, a twin simulation model is constructed, wherein the physical model is defined according to the actual size and shape of the storage space, including the length, width and height of the space; the physical model is refined, including the configuration of partitions or shelves inside the target storage space, the location of the environmental conditioning equipment, and the location and orientation of the air outlet; the numerical model of the target storage space is established using the computational fluid dynamics (CFD) method; the physical model and the numerical model are combined to obtain the twin simulation model; Construct a standard drug model, simulate the temperature field of the drug storage space, and obtain the initial temperature field; Interacting with the drug storage database, obtaining a storage feature set of the target drug set, wherein the storage feature set includes an appearance volume index set, a packaging material index set, an environmental requirement index set, and a storage quantity index set; Based on the initial temperature field and the stored feature set, updating and initializing the twin simulation model to obtain a constant-temperature twin simulation model; Based on the constant temperature twin simulation model, performing step optimization of storage positions to determine a drug storage position set; Wherein, based on the initial temperature field and the stored feature set, updating and initializing the twin simulation model to obtain a constant temperature twin simulation model includes: Based on the storage feature set, extracting the environmental demand indicator set, and performing cluster analysis on the environmental demand indicator set to generate multiple storage temperature intervals; Based on multiple temperature intervals, the initial temperature field is partitioned to obtain initial partition results; According to the storage feature set, the shape volume index set and the storage quantity index set are extracted, and combined with the cluster analysis result, multiple partition space requirements corresponding to the multiple storage temperature intervals are calculated and obtained; Compare the plurality of partition space requirements with the initial partition result, perform partition adjustment on the initial partition result, and obtain an adjusted partition result; Wherein, based on the initial temperature field and the stored feature set, updating and initializing the twin simulation model to obtain a constant temperature twin simulation model further includes: Using the adjusted partition results as multiple storage distribution spaces, calling the cluster analysis results to perform random position allocation, and obtaining an initial storage position set; constructing a target drug model set according to the stored feature set; Initializing drug distribution in the twin simulation model by combining the initial storage location set with the target drug model set; Taking the initial temperature field as an invariant, the temperature of the twin simulation model is initialized to obtain the constant temperature twin simulation model.
2. The energy consumption optimization method for a drug storage system according to claim 1, characterized in that: Construct a standard drug model, simulate the temperature field of the drug storage space, and obtain the initial temperature field, including: Based on the historical storage records of the target storage space, obtain typical drug samples; Extracting the appearance volume index and thermal characteristics of the typical drug sample to establish the standard drug model; Synchronizing the standard drug model to the twin simulation model to perform array position distribution; Based on the array position distribution results, a temperature field simulation is performed on the twin simulation model to obtain the initial temperature field of the drug storage space.
3. The energy consumption optimization method for a drug storage system according to claim 1, characterized in that: Based on the constant temperature twin simulation model, step optimization of storage locations is performed to determine a drug storage location set, including: Acquire the temperature control constraints of the target scene, and perform stepwise decomposition on the temperature control constraints to acquire a stepwise constraint series; Based on the constant temperature twin simulation model, the initial storage position is iteratively optimized in combination with a multi-objective optimization algorithm and the first step constraint of the step constraint series to obtain an optimized storage position; The optimized storage position is set as a constant, a simulation is performed on the constant temperature twin simulation model, the temperature distribution is analyzed, and an optimized temperature field is obtained; It is determined whether the optimized temperature field and the optimized storage position satisfy the first step constraint, and iterative optimization is performed to obtain the drug storage position set, wherein the drug storage position set corresponds to a drug storage temperature field.
4. The energy consumption optimization method for a drug storage system according to claim 3, characterized in that: Determining whether the optimized temperature field and the optimized storage position satisfy the first step constraint and performing iterative optimization includes: If the optimized temperature field and the optimized storage position satisfy the first step constraint, the second step constraint is used as the optimization target, the constant temperature twin simulation model is initialized with the optimized temperature field and the optimized storage position, and the next step optimization iteration is performed; Based on the fact that the optimized temperature field and the optimized storage position do not satisfy the first step constraint, the initial temperature field of the constant temperature twin simulation model is updated according to the optimized temperature field, and the optimized storage position is iteratively optimized.
5. The energy consumption optimization method for a drug storage system according to claim 1, characterized in that: The method further comprises: Interactive drug dispensing system, obtains drug dispensing task list; According to the drug dispensing task list, multiple target drugs are located and a target dispensing drug sequence is obtained; parsing the drug dispensing task list, acquiring multiple drug dispensing routes and collecting a set of route environment temperature information of the drug dispensing routes; Based on the path environment temperature information set, a heat exchange numerical model is constructed to perform a distribution simulation, and a predicted temperature loss set is obtained according to the distribution simulation result; According to the predicted temperature loss set, the environmental demand indicator set is updated, and the drug storage temperature field of the drug storage location set is prospectively pre-cooled and optimized.
6. The energy consumption optimization method for a drug storage system according to claim 1, characterized in that: The method further comprises: Configure energy consumption optimization constraints, wherein the energy consumption optimization constraints include immediate update constraints, periodic update constraints, and near-period update constraints; An update judgment is performed according to the energy consumption optimization constraints. If any one of the energy consumption optimization constraints is satisfied, the drug storage database is interacted with, the target drug set is updated, and iterative optimization is performed.