Intelligent storage and dispensing management system for hospital special medicines

Abstract

The invention relates to the technical field of construction risk assessment, in particular to an intelligent storage and dispensing management system for hospital special medicines, which comprises an environment monitoring module used for collecting environment parameters in a special medicine storage environment; the inventory management module is used for collecting management information of special medicines; and the data processing module is used for constructing the collected environment parameters and management information into a time sequence data set according to a fixed timestamp, and preprocessing time sequence data. According to the invention, through the interaction influence management module and the environment adjustment module, the storage conformity of each medicine can be calculated based on various environment factors, and the environment parameters are adjusted when necessary, so that the decision is more intelligent and accurate; and the adjustment loss is calculated and compared with the preset loss threshold, so that the necessity of environmental parameter adjustment can be effectively evaluated, and a hospital is helped to optimize medicine storage management under the condition of limited resources, thereby reducing economic loss.

Description

Technical Field

[0001] The present invention relates to the technical field of construction risk assessment, and particularly to an intelligent storage and dispensing management system for special drugs in hospitals. Background Art

[0002] In the existing medical system, the storage and management of special drugs face many challenges. These drugs usually have special pharmacological properties and storage requirements, such as sensitivity to environmental conditions such as temperature, humidity, and light. Therefore, traditional manual management methods often fail to meet these high standards, easily leading to drug failure, waste, and safety hazards.

[0003] Especially in the mixed storage of different types of special drugs, it is impossible to ensure that each drug is maintained under the optimal storage conditions, thereby causing the degradation of the active ingredients of special drugs, increasing the risk of drug failure, and resulting in resource waste. And it is impossible to adjust the environmental parameters intelligently according to a variety of mixed special drugs to reasonably optimize the common storage conditions of different special drugs and reduce the risk of economic losses.

[0004] Therefore, an intelligent storage and dispensing management system for special drugs in hospitals is proposed to solve the above-mentioned problems. Summary of the Invention

[0005] Technical Problems to be Solved

[0006] In view of the above-mentioned drawbacks of the prior art, the present invention provides an intelligent storage and dispensing management system for special drugs in hospitals, which can effectively solve the problem in the prior art that it is impossible to perform global optimal environmental parameter adjustment for mixed special drugs.

[0007] Technical Solutions

[0008] To achieve the above object, the present invention is realized through the following technical solutions:

[0009] The present invention provides an intelligent storage and dispensing management system for special drugs in hospitals. The technical solutions adopted by the present invention are as follows: It includes an environmental monitoring module for collecting environmental parameters in the storage environment of special drugs; and also includes:

[0010] An inventory management module for collecting management information of special drugs;

[0011] A data processing module that constructs a time series data set from the collected environmental parameters and management information according to a fixed time stamp, and preprocesses the time series data;

[0012] The interaction impact management module calculates the storage compliance of special drugs in the current storage environment based on the time series dataset; compares the storage compliance of special drugs with their corresponding preset compliance thresholds to determine whether it is necessary to adjust the environmental parameters in the storage environment;

[0013] If any special drug triggers an adjustment of the environmental parameters, based on the adjustment value of the environmental parameters, calculate the impact score on other special drugs; and calculate the adjustment loss based on the impact score, and compare the adjustment loss with the preset loss threshold to determine whether to adjust the environmental parameters; if the adjustment loss is greater than the loss threshold, do not trigger an adjustment alarm; otherwise, trigger an adjustment alarm;

[0014] The environmental adjustment module is used to adjust the environmental parameters in the drug storage environment;

[0015] The drug dispensing review module is used to receive the drug dispensing application, check the inventory according to the drug information of the application to ensure that the inventory is sufficient and the drugs are not expired; after confirmation, issue the drugs applied for with the shortest expiration date; and generate a drug dispensing record to update the inventory information.

[0016] Among them, the environmental parameters include temperature, humidity, light intensity, gas composition, hygiene level, and air pressure;

[0017] The management information includes drug basic information, storage conditions, and inventory information.

[0018] Among them, the method for obtaining the storage compliance is:

[0019] Collect the time series dataset within a fixed time, calculate the coefficient of variation of the special drug, calculate the environmental fluctuation coefficient based on the coefficient of variation, and calculate the storage compliance using the environmental fluctuation coefficient. The calculation formula is:

[0020] ;

[0021] In the formula, is the compliance of the jth special drug; n is the total number of environmental parameters; is the weight coefficient of the ith environmental parameter; is the status value of the ith environmental parameter; is the sensitivity coefficient of the jth special drug to the ith environmental parameter; is the current inventory quantity of the jth special drug; is the environmental fluctuation coefficient of the jth special drug.

[0022] Among them, the calculation formula of the environmental fluctuation coefficient is:

[0023] ; In the formula, is the stability coefficient of the j-th special drug; is the influence coefficient of the i-th environmental parameter; is the risk rating of the i-th environmental parameter; is the interaction influence coefficient of the j-th special drug; is the coefficient of variation of the j-th special drug.

[0024] Among them, the acquisition method of the influence score of the special drug is as follows:

[0025] Select several experimental groups by creating different types of special drugs, set dynamic environmental parameters for the experimental groups, and record the performance changes of the special drugs; calculate the sensitivity coefficient based on the absolute change of the environmental parameter and the performance change of the special drug. The calculation formula is:

[0026] ; In the formula, is the sensitivity coefficient of the j-th special drug to the i-th environmental parameter; is the performance change of the j-th special drug before and after the change of the i-th adjusted environmental parameter; is the absolute change of the i-th environmental parameter; is the actual value of the i-th adjusted environmental parameter;

[0027] The calculation formula of the influence score is:

[0028] ; In the formula, is the influence score of the j-th special drug; is the number of adjusted environmental parameters; is the adjustment value of the i-th adjusted environmental parameter; K is the number of environmental parameters that interact with the i-th adjusted environmental parameter; is the interaction influence intensity between the i-th adjusted environmental parameter and the k-th interacting environmental parameter.

[0029] Among them, the creation method of the experimental group is as follows:

[0030] Define test variables, including the dosage and packaging form of the special drug;

[0031] Set the evaluation weight and value range for each test variable based on historical data;

[0032] Construct multiple particles based on the particle swarm optimization algorithm to form an initial particle swarm;

[0033] Define the evaluation value function of the initial particle swarm based on the experimental test objectives, and calculate the evaluation value of each particle; update the velocity and position according to the historical best position of the particle and the best position of the initial particle swarm until the preset number of iterations is reached or the updated evaluation value no longer improves significantly, then stop the particle swarm optimization algorithm; select the particle with the global best position as the output experimental group to obtain the optimal test variables.

[0034] Among them, the calculation formula of the adjustment loss is:

[0035] ;

[0036] In the formula, is the adjustment loss; m is the total number of special drugs in the storage environment; is the economic cost of the jth special drug; is the time sensitivity coefficient; is the time change; Q represents the number of environmental parameters related to the jth special drug.

[0037] Among them, the adjustment method of the environmental parameters is:

[0038] Collect the time series data, storage compliance, impact score, adjustment value and adjustment loss before adjustment, and fuse them to construct a basic data set;

[0039] Use the genetic algorithm to process the basic data set to construct G individuals; combine the G individuals into an initial population, define the objective function of the initial population, and use the objective function to calculate the evaluation value of each individual in the initial population;

[0040] Use tournament selection to select P individuals from the initial population as the parent generation;

[0041] According to the crossover probability, randomly select two individuals from the parent generation for uniform crossover operation to generate new individuals, that is, the offspring;

[0042] Combine the offspring with the individuals in the parent generation whose evaluation values are greater than the preset evaluation threshold to form a new population;

[0043] If the preset upper limit of the number of iterations is not reached, continue the selection and crossover operations; when the upper limit of the number of iterations is reached, output the individual with the highest evaluation value in the new population to obtain the corresponding adjustment value and adjustment loss to be fitted; use this adjustment value to regulate the environmental parameters.

[0044] Among them, the selection method of the P individuals is:

[0045] Calculate the similarity between any two individuals in the initial population using the Euclidean distance; traverse all individuals in the initial population, calculate the similarity of each pair of individuals, and form a similarity matrix; based on a preset similarity threshold, determine the diversity degree of each pair of individuals in the similarity matrix;

[0046] If the similarity of a pair of individuals is less than the similarity threshold, retain this pair of individuals;

[0047] If the similarity of a pair of individuals is greater than the similarity threshold, reduce the selection probability of this pair of individuals;

[0048] Calculate the average similarity of the similarity matrix, and the calculation formula is:

[0049] ;

[0050] In the formula, is the average similarity of the similarity matrix; is the similarity between the i-th individual and the j-th individual;

[0051] Compare the average similarity of the similarity matrix with a preset matrix threshold. If the average similarity is greater than the matrix threshold, adjust the initial population;

[0052] Set the tournament scale B and the number of parental individuals P; randomly select B individuals from the initial population, calculate the evaluation values of these B individuals, and find the P individuals with the highest evaluation values among them as the parents; repeat the operation until the number of parental individuals reaches the preset quantity.

[0053] Among them, the way to reduce the selection probability is:

[0054] Set a selection weight for the individual, and the calculation formula for the selection weight is:

[0055] ; In the formula, is the selection weight, is the adjustment coefficient; O is the evaluation value of the individual;

[0056] The way to adjust the initial population is;

[0057] Regenerate C individuals, and find the individuals with low evaluation values and low similarities among the C individuals; sort the individuals in the initial population in descending order of evaluation values, and use the C individuals to replace the V individuals with low evaluation values in the initial population.

[0058] To sum up, due to the adoption of the above technical solutions, the beneficial effects of the present invention are:

[0059] 1. In the present invention, by real-time monitoring of key factors (such as temperature, humidity, light, etc.) in the drug storage environment and issuing an alarm in a timely manner when abnormalities are detected, it is ensured that drugs can be stored in the optimal environment, thereby extending the shelf life of drugs and reducing the risk of drug failure.

[0060] 2. In the present invention, through the inventory management module, the basic information and storage conditions of drugs are tracked in real time to ensure that the information of each drug can be accurately identified and traced; this transparent management method improves the efficiency and accuracy of drug management and reduces the occurrence of human errors.

[0061] 3. In the present invention, through the interaction impact management module and the environment adjustment module, the storage compliance of each drug can be calculated based on multiple environmental factors, and the environmental parameters can be adjusted when necessary, making the decision-making more intelligent and accurate; by comparing the calculated adjustment loss with the preset loss threshold, the necessity of adjusting environmental parameters can be effectively evaluated, helping hospitals optimize drug storage management under limited resources, thereby reducing economic losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0064] Embodiment

[0065] Referring to Figure 1 , a smart storage and drug dispensing management system for special drugs in hospitals is proposed in this case, including: an environmental monitoring module, an inventory management module, a data processing module, an interaction impact management module, an environment adjustment module, and a drug dispensing review module.

[0066] The environmental monitoring module is used to collect environmental parameters in the storage environment of special drugs and monitor the environmental parameters based on a preset environmental threshold range. When any environmental parameter exceeds the corresponding environmental threshold range, the system will automatically issue a notice to warn the management staff to handle it in a timely manner.

[0067] Environmental parameters include temperature, humidity, light intensity, gas composition, hygiene level, and air pressure. Temperature is a crucial factor in drug storage because most drugs maintain their efficacy within a specific temperature range. Too high or too low a temperature may cause chemical degradation, physical changes (such as precipitation or crystallization), or microbial growth. Humidity affects the stability and shelf life of drugs. Excessive humidity may cause drugs to absorb moisture, deteriorate, or become moldy, while too low humidity may cause some drugs to dry out and become ineffective. Ultraviolet and visible light may cause optical degradation of some drugs, thereby affecting the stability of their active ingredients. The gas composition in the drug storage environment, such as the concentrations of oxygen and carbon dioxide, directly affects the degree of oxidation and stability of drugs. Hygiene level is crucial in drug storage, capable of effectively preventing the entry of microorganisms and other contaminants, maintaining the purity and stability of drugs. A high level of hygiene can reduce the risk of drug contamination and degradation, thus ensuring the effectiveness and safety of drugs. Changes in air pressure in the drug storage environment may affect the volatility and stability of drugs, especially for gaseous and volatile liquid drugs.

[0068] During the storage of special drugs, strictly monitoring and controlling the above environmental parameters can maximize the shelf life of drugs, ensure that drugs are stored in the best condition, and thus guarantee the safety and effectiveness of drugs when used by patients.

[0069] The inventory management module is used to collect the management information of special drugs; the management information includes drug basic information, storage conditions, and inventory information. Drug basic information includes information such as the drug name, specification, manufacturer, expiration date, batch number, etc.; through the drug basic information, it can be ensured that the drug can be accurately identified and traced throughout the management process. The storage conditions record the specific storage conditions of the drug (such as temperature, humidity, light, etc.); recording these conditions helps managers ensure that the drug is stored in a suitable environment to maintain its effectiveness and safety. The inventory status includes information such as the drug's warehousing time, warehousing quantity, warehousing personnel, outbound time, outbound quantity, recipients (such as medical staff or patients), etc.; by real-time tracking the inventory quantity and flow records of drugs, it is ensured that the inventory status of each drug is always effectively managed throughout its life cycle, guaranteeing the accuracy, safety, and efficiency of drug management.

[0070] The data processing module preprocesses the collected environmental parameters and management information (including data cleaning and standardization) according to a fixed time stamp, and fuses the environmental parameters and management information at each time stamp to construct a time series data set.

[0071] The interaction impact management module calculates the storage compliance of each special drug in the current storage environment based on the time series dataset; compares the storage compliance of each special drug with its corresponding preset compliance threshold to determine whether it is necessary to adjust the environmental parameters in the storage environment; if the storage compliance is greater than the compliance threshold, it means that there is no need to adjust the environmental parameters, otherwise adjustment is required; if any special drug triggers an adjustment of the environmental parameters, based on the adjustment value of the environmental parameters, calculate the impact score on other special drugs. And calculate the adjustment loss based on the impact scores of all drugs, and compare the adjustment loss with the preset loss threshold to determine whether to adjust the environmental parameters.

[0072] If the adjustment loss is greater than the loss threshold, it means that the loss caused by the environmental adjustment will exceed the potential loss in the case of non-adjustment, and the resources and costs required for the adjustment are not cost-effective, so the adjustment alarm is not triggered;

[0073] If the adjustment loss is less than the loss threshold, it means that the adjustment measure can reduce the overall loss and increase the survival rate and performance of other drugs, so the adjustment alarm is triggered.

[0074] The environmental adjustment module is used to adjust the environmental parameters in the drug storage environment; the adjustment method is: if the adjustment alarm is triggered, collect the time series data, storage compliance, impact score, adjustment value and adjustment loss before the adjustment, and fuse and construct them into a basic dataset.

[0075] Use the genetic algorithm to process the basic dataset to construct G individuals (encoded with the adjustment values of the environmental parameters, and randomly create data combinations with different adjustment values); combine the G individuals into an initial population, and define the objective function of the initial population: ; where O is the evaluation value of the objective function (i.e., the evaluation value of the individual), that is, to minimize the overall adjustment loss; is the weight coefficient; calculate the evaluation value of each individual in the initial population using the objective function, is the impact score of the jth special drug; is the adjustment loss.

[0076] Use tournament selection to select P individuals from the initial population as the parent generation;

[0077] According to the crossover probability, randomly select two individuals from the parent generation for uniform crossover operation to generate new individuals, that is, the offspring;

[0078] Merge the offspring with the individuals in the parent generation whose evaluation values are greater than the preset evaluation threshold to form a new population;

[0079] If the preset upper limit of the number of iterations is not reached, continue with the selection and crossover operations; when the upper limit of the number of iterations is reached, output the individual with the highest evaluation value in the new population, and obtain the corresponding adjustment value and adjustment loss to be fitted; use this adjustment value to regulate the environmental parameters.

[0080] Among them, the selection method for P individuals is as follows:

[0081] Calculate the similarity between any two individuals in the initial population using the Euclidean distance; traverse all individuals in the initial population, calculate the similarity of each pair of individuals, and form a similarity matrix; based on the preset similarity threshold, judge the diversity degree of each pair of individuals in the similarity matrix; if the similarity of a pair of individuals is less than the similarity threshold, it means that this pair of individuals are different in characteristics and belong to independent individuals, and keep this pair of individuals to increase their chances in subsequent selection operations, because diversity helps to improve the adaptability of the population, create more variable offspring, and ensure sufficient exploration. On the contrary, if the similarity of a pair of individuals is greater than the similarity threshold, it means that this pair of individuals have a high degree of overlap in characteristics, which may lead to information redundancy, then it is necessary to consider reducing the probability of these similar individuals entering the next generation in the selection operation; the way to reduce the probability is: adjust the selection probability of similar individuals, and the adjustment method is to set a selection weight for each individual, and the calculation formula of the selection weight is: ; in the formula, is the adjustment coefficient, used to control the influence of similarity on the selection weight; is the similarity between the i-th individual and the j-th individual; use the selection weight to reduce the probability of similar individuals being selected in the tournament selection.

[0082] Then calculate the average similarity of the similarity matrix, and the calculation formula is:

[0083] ;

[0084] In the formula, is the average similarity of the similarity matrix. The closer this value is to 1, the lower the diversity of the similarity matrix, and vice versa, it means high diversity.

[0085] Compare the average similarity of the similarity matrix with the preset matrix threshold. If the average similarity is less than the matrix threshold, it indicates that the average similarity of the similarity matrix meets the individual diversity; on the contrary, if the average similarity is greater than the matrix threshold, it indicates that the individuals are too similar and need to be adjusted; the adjustment method is:

[0086] Regenerate C individuals (the number of C can be set according to the actual situation, for example, 10% of the population size), and find the individuals with low evaluation values and low similarity among the C individuals; sort the individuals in the initial population in descending order of evaluation values, and use the C individuals to replace the V individuals with low evaluation values (such as 5%-10% of the individuals) in the initial population.

[0087] Set the tournament size B and the number of parental individuals P; randomly select B individuals from the initial population, calculate the evaluation values of these B individuals, and find the P individuals with the highest evaluation values among them as the parents; repeat the operation until the preset number of parental individuals is reached.

[0088] The drug dispensing review module is used to receive drug dispensing applications, check the inventory according to the drug information in the applications to ensure that the inventory is sufficient and the drugs are not expired; after confirmation, issue the drugs applied for with the shortest expiration date; then, based on the drugs already dispensed, generate a drug dispensing record and update the inventory information.

[0089] The formula for calculating the storage compliance is: ;

[0090] In the formula, is the compliance of the jth special drug; n is the total number of environmental parameters; is the weight coefficient of the ith environmental parameter, reflecting the influence degree of the ith environmental parameter in the calculation of the compliance of the jth special drug; is the status value of the ith environmental parameter, that is, the detection value of the ith environmental parameter at time t; is the sensitivity coefficient of the jth special drug to the ith environmental parameter, indicating the sensitivity of the jth special drug under a certain specific environmental parameter. The higher the value, the more severely the drug is affected under this environmental condition; the sensitivity coefficient can be obtained through clinical data or literature research; is the current inventory quantity of the jth special drug. The higher the quantity, the greater the weight of the importance of the special drug in the overall storage management; is the environmental fluctuation coefficient of the jth special drug, reflecting the stability of the jth special drug under the current environmental conditions.

[0091] Among them, the acquisition method of the environmental fluctuation coefficient is:

[0092] Calculate the coefficient of variation of the jth special drug. The calculation formula is: ; In the formula, is the coefficient of variation of the jth special drug; is the mean value of the jth special drug in the time series dataset, ; Among them is the number of data points of the jth special drug in the time series dataset; is the i-th data point of the j-th special drug in the time series dataset; is the standard deviation of the j-th special drug in the time series dataset, ;

[0093] Then, based on the coefficient of variation, calculate the environmental fluctuation coefficient, and the calculation formula is:

[0094] ; In the formula, is the stability coefficient of the j-th special drug, which reflects the stability of the special drug under various storage conditions. Its value range is [0, 1]. The closer the value is to 1, the higher the stability of the special drug, which is determined based on experimental data; is the influence coefficient of the i-th environmental parameter, which reflects the influence degree of the specific environmental parameter on the special drug, and is determined based on experimental data; is the risk rating of the i-th environmental parameter, which reflects the potential risk of the environmental factor to the drug under specific storage conditions, and is usually determined and assigned based on historical data; is the interaction influence coefficient of the j-th special drug, which reflects the interaction influence between different special drugs (for example, it may lead to a decrease in drug efficacy), ; Among them, is the number of drug combinations of the j-th special drug (that is, all other special drugs that have an impact on the j-th special drug and the possible combinations between the j-th special drug, for example, if there are drugs A, B, and C, then there are four combinations: AB, AC, BC, and ABC); is the interaction influence score of the i-th drug combination of the j-th special drug, which is determined by fitting based on experimental data. The interaction influence score is a score matrix, . Through multi-level calculations, comprehensively consider different influences, can evaluate the interaction between different variables, provide a more accurate description for complex phenomena, and then identify potential risks when the environment changes, so as to provide a basis for implementing effective countermeasures.

[0095] The calculation formula for the influence score of the special drug is:

[0096] ; In the formula, is the influence score of the j-th special drug; is the number of adjusted environmental parameters; is the adjustment value of the i-th adjusted environmental parameter; K is the number of environmental parameters that interact with the i-th adjusted environmental parameter (such as the influence of temperature on humidity and light); is the interaction influence strength between the environmental parameter adjusted for the i-th time and the environmental parameter interacting with the k-th one, formulated based on experimental data. By introducing the interaction influence strength between environmental parameters into the calculation of the influence score of special drugs, the overall influence of different environmental conditions on special drugs can be effectively integrated, enabling the influence score to more comprehensively reflect the actual situation; and through the sensitivity coefficient, the response amount of special drugs to each individual environmental parameter can be obtained, clearly understanding the performance changes of each special drug under specific conditions, helping to optimize storage conditions and improve the accuracy of influence score calculation.

[0097] Among them, the acquisition method of the sensitivity coefficient is as follows:

[0098] Select different types of special drugs for testing, create multiple experimental groups, and each experimental group covers different environmental parameter settings (such as setting different temperature and humidity combinations); the creation method of the experimental group is as follows:

[0099] Define test variables, including the dosage and packaging form of special drugs;

[0100] Based on historical data, set the evaluation weight and value range for each test variable;

[0101] Based on the particle swarm optimization algorithm, construct multiple particles to form an initial particle swarm; each particle represents an experimental combination (that is, each particle has different test variables), and also includes a position (that is, the current experimental combination parameters of this particle) and a velocity (that is, the amplitude of each iteration of this particle to try new combinations);

[0102] Based on the experimental test objective (the performance change of special drugs), define the evaluation value function of the initial particle swarm, and calculate the evaluation value of each particle; update the velocity and position according to the historical best position of the particle and the best position of the initial particle swarm until reaching the preset number of iterations or the updated evaluation value no longer improves significantly, then stop the particle swarm optimization algorithm; select the particle with the global best position as the output experimental group to obtain the optimal test variables;

[0103] Set dynamically changing environmental parameters for the experimental group, and record the performance changes of special drugs (such as drug efficacy, stability); calculate the sensitivity coefficient based on the absolute change amount of environmental parameters and the performance changes of special drugs, and the calculation formula is: ;

[0104] In the formula, is the performance change of the j-th special drug before and after the change of the environmental parameter adjusted for the i-th time; is the absolute change amount of the i-th environmental parameter; is the actual value of the environmental parameter adjusted for the i-th time. By the relationship between the performance change of the special drug and the dynamic change of the environmental parameter, the response of the drug to a specific environment is quantitatively measured, and the sensitivity of the characteristics such as the stability and potency of the drug under specific conditions is clearly described; and by introducing the interaction intensity and actual value between environmental parameters, the complex relationship between different environmental parameters can be reflected, and then the performance of the special drug under different environmental conditions can be dynamically monitored; finally, through the sensitivity coefficient, it is possible to clearly identify which drugs are sensitive to environmental changes, so as to formulate corresponding storage and management strategies and serve as a warning indicator to provide early warnings of possible special drug failure or safety hazards.

[0105] The calculation formula for the adjustment loss is:

[0106] ;

[0107] In the formula, is the adjustment loss, that is, the total economic loss caused by the adjustment change of the environmental parameter in the storage environment of the special drug; m is the total number of special drugs in the storage environment; is the economic cost of the j-th special drug; is the time sensitivity coefficient, which reflects the degree to which the loss of the special drug accelerates over time after the change of the environmental parameter; is the time change amount, which represents the time length considered after the environmental parameter is adjusted; Q represents the number of environmental parameters related to the j-th special drug.

[0108] By comprehensively evaluating the loss after the adjustment of the environmental parameter through the influence score, sensitivity coefficient, economic cost, and time sensitivity coefficient of the special drug, the complexity of the regulation in reality can be truly reflected; and by introducing the time change amount, the influence of time change on the loss can be considered; as time goes by, the loss may increase, so the loss prediction can be adjusted according to the time lag of the environmental change to make the decision more timely and effective; especially for the interaction between special drugs and different environmental parameters, this makes the loss assessment not only limited to the influence of a single environmental parameter, but can consider the comprehensive influence of multiple environmental factors on the drug performance, which helps to understand the complex relationship between the complex environment and the drug performance.

[0109] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent storage and dispensing management system for special drugs in hospitals, characterized in that: It includes an environmental monitoring module for collecting environmental parameters in the storage environment of special drugs; it also includes: Inventory management module, used to collect management information of special medicines; The data processing module constructs the collected environmental parameters and management information into a time series data set according to a fixed timestamp and preprocesses the time series data; The interactive impact management module calculates the storage compliance of special drugs in the current storage environment based on the time series data set; compares the storage compliance of special drugs with the corresponding preset compliance threshold to determine whether the environmental parameters in the storage environment need to be adjusted; If any special drug triggers the adjustment of environmental parameters, the impact score on other special drugs is calculated based on the adjustment value of the environmental parameters; and the adjustment loss is calculated based on the impact score, and the adjustment loss is compared with the preset loss threshold to determine whether to adjust the environmental parameters; if the adjustment loss is greater than the loss threshold, the adjustment alarm is not triggered; otherwise, the adjustment alarm is triggered; An environmental adjustment module is used to adjust environmental parameters in the drug storage environment; The drug dispensing review module is used to receive drug dispensing applications, check the inventory based on the drug information applied for, and ensure that the inventory is sufficient and the drugs are not expired; after confirmation, the applied drugs with the shortest validity period are issued; and the drug dispensing records are generated and the inventory information is updated.

2. The intelligent storage and dispensing management system for special drugs in a hospital as claimed in claim 1, characterized in that: The environmental parameters include temperature, humidity, light intensity, gas composition, hygiene level and air pressure; Management information includes basic drug information, storage conditions and inventory information.

3. The intelligent storage and dispensing management system for special drugs in a hospital as claimed in claim 2, characterized in that: The storage compliance is obtained in the following manner: Collect time series data sets within a fixed time, calculate the coefficient of variation of special drugs, calculate the environmental fluctuation coefficient based on the coefficient of variation, and use the environmental fluctuation coefficient to calculate storage compliance. The calculation formula is: ； In the formula, is the compliance of the jth special drug; n is the total number of environmental parameters; is the weight coefficient of the i-th environmental parameter; is the state value of the i-th environmental parameter; is the sensitivity coefficient of the jth special drug to the ith environmental parameter; is the current inventory quantity of the jth special drug; is the environmental fluctuation coefficient of the jth special drug.

4. The intelligent storage and dispensing management system for special drugs in a hospital as claimed in claim 3, characterized in that: The calculation formula of the environmental fluctuation coefficient is: ; In the formula, is the stability coefficient of the jth special drug; is the influence coefficient of the i-th environmental parameter; is the risk rating of the i-th environmental parameter; is the interaction coefficient of the jth special drug; is the coefficient of variation of the j-th special drug.

5. The intelligent storage and dispensing management system for special drugs in a hospital as claimed in claim 4, characterized in that: The influence points of the special drugs are obtained as follows: Select different types of special drugs to create several experimental groups, set dynamically changing environmental parameters for the experimental groups, and record the performance changes of special drugs; calculate the sensitivity coefficient based on the absolute changes in environmental parameters and the performance changes of special drugs. The calculation formula is: ; In the formula, is the sensitivity coefficient of the jth special drug to the ith environmental parameter; is the performance change of the jth special drug before and after the i-th adjusted environmental parameter change; is the absolute change of the i-th environmental parameter; is the actual value of the i-th adjusted environmental parameter; The calculation formula for impact points is: ; In the formula, is the impact score of the jth special drug; The number of environmental parameters to be adjusted; is the adjustment value of the i-th adjusted environmental parameter; K is the number of environmental parameters that interact with the i-th adjusted environmental parameter; The interaction strength between the i-th adjusted environmental parameter and the k-th interactive environmental parameter.

6. The intelligent storage and dispensing management system for special drugs in a hospital as claimed in claim 5, characterized in that: The experimental group is created as follows: Define test variables, including dosage and packaging formats for specific drug products; Set evaluation weights and value ranges for each test variable based on historical data; Construct multiple particles based on the particle swarm optimization algorithm to form an initial particle swarm; Based on the experimental test objectives, the evaluation value function of the initial particle swarm is defined, and the evaluation value of each particle is calculated; the speed and position are updated according to the historical best position of the particle and the best position of the initial particle swarm, until the preset number of iterations is reached or the updated evaluation value is no longer significantly improved, the particle swarm optimization algorithm is stopped; the particle with the global best position is selected as the output experimental group to obtain the optimal test variable.

7. The intelligent storage and dispensing management system for special drugs in a hospital as claimed in claim 5, characterized in that: The calculation formula for the adjusted loss is: ； In the formula, is the loss adjustment; m is the total quantity of special drugs in the storage environment; is the economic cost of the jth special drug; is the time sensitivity coefficient; is the time variation; Q represents the number of environmental parameters related to the jth special drug.

8. The intelligent storage and dispensing management system for special drugs in a hospital as claimed in claim 2, characterized in that: The environmental parameters are adjusted as follows: Collect the time series data before adjustment, store the compliance, impact score, adjustment value and adjustment loss, and integrate them to build the basic data set; The basic data set is processed using a genetic algorithm to construct G individuals; the G individuals are merged into an initial population, the objective function of the initial population is defined, and the evaluation value of each individual in the initial population is calculated using the objective function; Use tournament selection to select P individuals from the initial population as parents; According to the crossover probability, two individuals are randomly selected from the parent generation for uniform crossover operation to generate new individuals, i.e., offspring; Merge the individuals in the offspring and parent generations whose evaluation values ​​are greater than the preset evaluation threshold to form a new population; If the preset upper limit of iterations is not reached, the selection and crossover operations will continue; when the upper limit of iterations is reached, the individual with the highest evaluation value in the new population will be output, and the corresponding adjustment value and adjustment loss to be fitted will be obtained; the environmental parameters will be regulated using the adjustment value.

9. The intelligent storage and dispensing management system for special drugs in a hospital as claimed in claim 8, characterized in that: The selection method of the P individuals is: The similarity between any two individuals in the initial population is calculated using the Euclidean distance; all individuals in the initial population are traversed, the similarity of each pair of individuals is calculated, and a similarity matrix is ​​formed; based on a preset similarity threshold, the degree of diversity of each pair of individuals in the similarity matrix is ​​determined; If the similarity between a pair of individuals is less than the similarity threshold, the pair of individuals is retained; If the similarity of a pair of individuals is greater than the similarity threshold, reduce the selection probability of the pair of individuals; Calculate the average similarity of the similarity matrix. The calculation formula is: ； In the formula, is the average similarity of the similarity matrix; is the similarity between the i-th individual and the j-th individual; The average similarity of the similarity matrix is ​​compared with the preset matrix threshold. If the average similarity is greater than the matrix threshold, the initial population is adjusted; Set the tournament size B and the number of parent individuals P; randomly select B individuals from the initial population, calculate the evaluation values ​​of these B individuals, and find the P individuals with the highest evaluation values ​​as parents; repeat the operation until the preset number of parent individuals is reached.

10. The intelligent storage and dispensing management system for special drugs in a hospital as claimed in claim 1, characterized in that: The selection probability is reduced in the following manner: Set the selection weight for the individual, and the calculation formula for the selection weight is: ; In the formula, To select weights, is the adjustment coefficient; O is the individual assessment value; The initial population is adjusted in the following way; Regenerate C individuals and find out the individuals with low evaluation values ​​and low similarity among the C individuals; arrange the individuals in the initial population in descending order according to the evaluation values, and use C individuals to replace the V individuals with low evaluation values ​​in the initial population.

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