Medical instrument storage information management method and system

Abstract

The invention relates to a medical instrument storage information management method and system, and relates to the technical field of information management. The method comprises the following steps: automatically acquiring medical instrument information of a plurality of medical instruments by using an image recognition technology and a multi-dimensional sensor, and preprocessing and standardizing the acquired medical instrument information to obtain preprocessed data; according to the preprocessed data, determining the current optimal inventory of the medical apparatus and instruments; calculating a preferential use index of each medical device according to the preprocessed data; monitoring the current storage environment of the medical instrument in real time, and calculating a corresponding environment risk index; automatically generating a current replenishment suggestion for the medical apparatus based on the historical data of replenishment of the medical apparatus and the prediction model; and according to the optimal stock amount, the preferential use index, the environmental risk index and the replenishment suggestion, optimizing a warehouse management strategy of the medical apparatus and instruments. According to the invention, the efficiency and accuracy of medical instrument warehouse management can be improved.

Description

Technical Field

[0001] The present invention relates to the field of information management technology, and in particular to a medical device storage information management method, system, electronic device and non-transitory computer-readable storage medium. Background Art

[0002] Nowadays, with the rapid development of the medical industry, the types and quantities of medical devices are increasing, and medical device storage management has become an important task for hospitals, clinics and related medical institutions. Existing medical device storage management usually relies on manual records and simple information systems. For example, by manually entering the purchase, storage, and delivery information of the equipment, and using a simple database or Excel spreadsheet for data management and storage. In some large hospitals, barcode or RFID technology may also be used to improve the efficiency and accuracy of equipment storage.

[0003] However, manual data entry is prone to errors and it is difficult to update inventory status in real time, resulting in inaccurate or delayed information. Furthermore, simple information systems have single functions and are difficult to cope with complex storage needs, such as device expiration date management, temperature and humidity monitoring, etc. Moreover, most storage information is simply used as data records and cannot play its information value, making it difficult to use the storage information to achieve effective storage management of medical devices.

[0004] Therefore, there is an urgent need for a more intelligent, automated and comprehensive information management method and system to improve the efficiency and accuracy of medical device warehouse management. Summary of the invention

[0005] In view of the technical problems existing in the prior art, the present invention provides a medical device warehouse information management method, system, electronic device and non-transitory computer-readable storage medium that can improve the efficiency and accuracy of medical device warehouse management.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] The present invention provides a method for managing medical device storage information, the method comprising:

[0008] Automatically collect medical device information of multiple medical devices using image recognition technology and multi-dimensional sensors, and pre-process and standardize the collected medical device information to obtain pre-processed data;

[0009] Determining the current optimal inventory quantity of the medical device according to the preprocessed data;

[0010] Calculating a priority use index for each of the medical devices based on the preprocessed data;

[0011] Monitor the current storage environment of the medical device in real time and calculate the corresponding environmental risk index;

[0012] Automatically generate current replenishment recommendations for the medical device based on historical data and a forecasting model for replenishment of the medical device;

[0013] The warehouse management strategy for the medical device is optimized based on the optimal inventory level, the priority usage index, the environmental risk index and the replenishment suggestion.

[0014] Optionally, determining the current optimal inventory of the medical device according to the preprocessed data includes:

[0015] Obtaining the average daily usage, replenishment cycle, demand fluctuation coefficient of the medical device, and the usage fluctuation adjustment factor of the average daily usage;

[0016] Obtaining seasonal factors, storage space utilization, and safety stock coefficients for warehousing the medical devices;

[0017] Obtaining an influence coefficient for indicating the influence of the ratio of the usage of the medical device described in the inventory management to the safety stock coefficient on the inventory quantity;

[0018] The current optimal inventory of the medical device is determined based on the average daily usage of the medical device, the replenishment cycle, the demand fluctuation coefficient, the usage fluctuation adjustment factor of the average daily usage, the seasonal factor for warehousing the medical device, the storage space utilization rate, the safety stock coefficient and the impact coefficient.

[0019] Optionally, the optimal inventory quantity is expressed as:

[0020]

[0021] Among them, O is the optimal inventory level, τ is the adjustment coefficient, U is the average daily usage of medical devices, t is the replenishment cycle of medical devices, V is the demand fluctuation coefficient, L1 is the safety stock coefficient, β is the seasonal factor, S is the storage space utilization rate, δ1 is the usage fluctuation adjustment factor, γ1 is the influence coefficient, ∈ and ζ are the first nonlinear adjustment item and the second nonlinear adjustment item respectively.

[0022] Optionally, calculating the priority use index of each medical device according to the preprocessed data includes:

[0023] Obtain the number of days until expiration, total shelf life, frequency of use, and unit price of the medical device;

[0024] Obtaining a logarithmic relationship between the validity period and the frequency of use of the medical device;

[0025] Normalizing the total shelf life of the medical device to obtain a normalized total shelf life of the device;

[0026] Obtaining a cost factor and a time decay factor for adjusting the priority use index;

[0027] The number of days until expiration, the total shelf life of the medical device, the frequency of use, and the unit price of the medical device are processed according to the cost factor, the time decay factor, and the logarithmic relationship, and the priority use index of each medical device is calculated in combination with the normalized total shelf life of the device.

[0028] Optionally, the priority usage index is expressed as:

[0029]

[0030] Where P is the priority use index, E is the number of days to expiration, M is the total shelf life of the device, λ1 is the time decay factor, R is the frequency of use, γ2 is the cost factor, C is the unit price of the device, k is the logarithmic relationship between the validity period and the frequency of use, η is the normalization coefficient, q is the power term, ξ and They are the third nonlinear adjustment term and the fourth nonlinear adjustment term respectively.

[0031] Optionally, the real-time monitoring of the current storage environment of the medical device and calculation of the corresponding environmental risk index include:

[0032] Obtaining the current temperature, current humidity and light intensity of the storage environment;

[0033] Obtaining a preset ideal temperature, an ideal humidity, a first nonlinear index, a second nonlinear index, a temperature normalization parameter, a humidity normalization parameter, a temperature adjustment index, a humidity adjustment index, a temperature correlation factor, a humidity correlation factor, a first nonlinear adjustment item, and a second nonlinear adjustment item;

[0034] According to the ideal temperature, the ideal humidity, the first nonlinear index, the second nonlinear index, the temperature normalization parameter, the humidity normalization parameter, the temperature adjustment index, the humidity adjustment index, the temperature correlation factor, the humidity correlation factor, the first nonlinear adjustment item and the second nonlinear adjustment item, the current temperature, current humidity and light intensity of the storage environment are processed to obtain the environmental risk index.

[0035] Optionally, the environmental risk index is expressed as:

[0036]

[0037] Among them, I is the environmental risk index, T is the current temperature, T0 is the ideal temperature, H is the current humidity, H0 is the ideal humidity, L2 is the light intensity, n is the first nonlinear index, m is the second nonlinear index, ρ is the temperature normalization parameter, σ is the humidity normalization parameter, α1 and α2 are the first nonlinear adjustment items and the second nonlinear adjustment items, θ is the temperature adjustment index, δ2 is the humidity adjustment index, λ is the temperature correlation factor, γ3 is the humidity correlation factor, ω1, ω2, and ω3 are the first weight, the second weight, and the third weight, respectively.

[0038] Optionally, the automatic collection of medical device information of multiple medical devices using image recognition technology and multi-dimensional sensors includes:

[0039] Identify the model, specification and packaging information of the medical device through a deep learning algorithm;

[0040] Collect identification code information of the medical device through RFID, and automatically collect environmental information of the storage environment where the medical device is located through temperature sensors, humidity sensors and light sensors;

[0041] The model, specification, packaging information, identification code information and environmental information of the medical device are associated to obtain the medical device information.

[0042] Optionally, the method further includes:

[0043] Construct a full-chain traceability system for the medical device information;

[0044] The blockchain technology of the full-chain traceability system records the entire process information of the production, transportation, warehousing, delivery and use of the medical devices.

[0045] Adopt HL7 standard and FHIR protocol to integrate with other hospital systems to achieve seamless data interaction.

[0046] The present invention also provides a medical device storage information management method system, the system comprising:

[0047] A data processing module, used to automatically collect medical device information of multiple medical devices using image recognition technology and multi-dimensional sensors, and preprocess and standardize the collected medical device information to obtain preprocessed data;

[0048] An inventory determination module, used to determine the current optimal inventory quantity of the medical device according to the preprocessed data;

[0049] A priority use module, used to calculate a priority use index of each of the medical devices according to the preprocessed data;

[0050] An environmental risk module, used to monitor the current storage environment of the medical device in real time and calculate the corresponding environmental risk index;

[0051] A replenishment suggestion module, configured to automatically generate current replenishment suggestions for the medical device based on historical data and a prediction model for replenishment of the medical device;

[0052] A warehouse management module is used to optimize the warehouse management strategy for the medical device according to the optimal inventory level, the priority usage index, the environmental risk index and the replenishment suggestion.

[0053] In addition, to achieve the above objectives, the present invention also proposes an electronic device, comprising: a memory for storing computer software programs; a processor for reading and executing the computer software programs, thereby implementing a medical device warehouse information management method as described above.

[0054] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, a medical device warehouse information management method as described above is implemented.

[0055] The beneficial effects of the present invention are:

[0056] (1) The present invention can automatically identify and collect information about medical devices, reducing reliance on manual operations, greatly reducing the risk of human input errors, and ensuring the real-time and accuracy of data. Compared with the traditional manual input method, the intelligent collection method not only improves work efficiency, but also reduces subsequent management problems caused by inaccurate information, and optimizes the entire process of warehouse information management.

[0057] (2) The present invention can calculate the priority use index based on factors such as the shelf life, frequency of use and cost of the device. It can intelligently recommend the priority use of medical devices that are about to expire, ensuring that these devices can be reasonably used within the shelf life and avoiding resource waste and medical risks caused by device expiration. Compared with traditional manual recording and inspection, the accuracy and efficiency of expiration date management are greatly improved.

[0058] (3) The present invention can effectively predict and warn of environmental abnormalities by real-time monitoring of factors such as temperature, humidity, and light in the storage environment and using a complex environmental risk index calculation formula. In this way, medical institutions can adjust the storage environment in a timely manner to ensure that medical devices are stored under appropriate conditions, avoid damage or failure of devices caused by environmental factors (such as abnormal temperature and humidity), and significantly improve the safety and reliability of medical device storage.

[0059] In summary, the present invention can not only significantly improve the intelligence level of medical device warehouse management, but also demonstrate great technical advantages in data processing, inventory optimization, environmental monitoring, intelligent decision-making and other aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flowchart of a medical device storage information management method provided by the present invention;

[0061] Figure 2 A schematic diagram of the structure of a medical device storage information management method system provided by the present invention;

[0062] Figure 3 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0063] Figure 4 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0065] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0066] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0067] See also Figure 1 , provides a flowchart of a medical device storage information management method of the present invention, comprising the following steps:

[0068] Step 201: Automatically collect medical device information of multiple medical devices using image recognition technology and multi-dimensional sensors, and preprocess and standardize the collected medical device information to obtain preprocessed data.

[0069] In some embodiments, step 201 may include:

[0070] Identify the model, specification and packaging information of the medical device through a deep learning algorithm;

[0071] Collect identification code information of the medical device through RFID, and automatically collect environmental information of the storage environment where the medical device is located through temperature sensors, humidity sensors and light sensors;

[0072] The model, specification, packaging information, identification code information and environmental information of the medical device are associated to obtain the medical device information.

[0073] Deep learning algorithms are advanced machine learning technologies based on artificial neural networks that can automatically learn complex patterns from large amounts of data. For the models, specifications, and packaging information of medical devices, deep learning algorithms can accurately identify and classify different device models, specifications, and packaging types by training on a large amount of sample data. This algorithm can analyze images, text, and even data in other formats to automatically identify and classify this information. In actual operation, deep learning algorithms may capture the appearance information of medical devices through cameras or other image acquisition devices, and then analyze these images to extract key features related to models, specifications, and packaging. Through the trained model, the system can accurately identify and record the detailed information of each medical device, reducing the errors and workload of manual entry.

[0074] RFID (Radio Frequency Identification) technology is a technology that transmits information contactlessly via radio waves. Each medical device can be attached with an RFID tag that stores the device's identification code information. RFID readers can quickly read this information and can efficiently identify it even at long distances or when there is no line of sight. In a warehouse environment, RFID readers can wirelessly scan and obtain the device's identification code information. These identification codes are usually unique and are used to identify each device and are associated with its model, specifications, and other information. The efficient and accurate identification capabilities of RFID technology have greatly improved the automation level of warehouse management and reduced the workload of manual scanning and entry.

[0075] In some embodiments, temperature, humidity, and light sensors are used to monitor the temperature, humidity, and light intensity in the storage environment in real time, respectively. These environmental conditions have a direct impact on the storage quality of medical devices and must be continuously monitored to ensure that the devices are stored under optimal conditions. These sensors can be distributed in various locations in the warehouse, automatically collecting environmental data and transmitting it to the central management system in real time. Based on these data, the management system can determine whether the current storage environment is suitable for the storage of medical devices, and can issue an early warning or adjust the storage conditions if necessary. Automated environmental information collection ensures the timeliness and accuracy of data and avoids damage to devices due to environmental deterioration.

[0076] The system can integrate the model, specification, packaging information and identification code information of medical devices collected through deep learning algorithms and RFID technology with the environmental information collected by sensors. This information integration enables all data of each medical device to be fully recorded and managed by the system. By associating the physical information of the device (such as model, specification, packaging, identification code) with the information of its storage environment, the system can comprehensively monitor the status of each device. For example, if the storage environment of a certain device changes (such as temperature exceeds the standard), the system can immediately identify the specific device affected and take corresponding measures based on its model, specification and other information, such as giving priority to use or adjusting the storage location. This information association greatly improves the accuracy and response speed of warehouse management, ensuring the safety and quality of medical devices throughout the storage cycle.

[0077] The present invention realizes the intelligent and automated storage management of medical devices by combining deep learning algorithms with Internet of Things technology. The deep learning algorithm is responsible for identifying the model, specifications and packaging information of the device, RFID technology quickly and accurately collects the identification code information of the device, and the sensor network monitors the storage environment in real time. This information is integrated together to form complete medical device information. The system can optimize the storage and use of the device based on this information, thereby improving storage efficiency, reducing human errors, and ensuring that the device is stored and used under optimal conditions.

[0078] Step 202: Determine the current optimal inventory quantity of the medical device based on the preprocessed data.

[0079] In some embodiments, step 202 may include:

[0080] Obtaining the average daily usage, replenishment cycle, demand fluctuation coefficient of the medical device, and the usage fluctuation adjustment factor of the average daily usage;

[0081] Obtaining seasonal factors, storage space utilization, and safety stock coefficients for warehousing the medical devices;

[0082] Obtaining an influence coefficient for indicating the influence of the ratio of the usage of the medical device described in the inventory management to the safety stock coefficient on the inventory quantity;

[0083] The current optimal inventory of the medical device is determined based on the average daily usage of the medical device, the replenishment cycle, the demand fluctuation coefficient, the usage fluctuation adjustment factor of the average daily usage, the seasonal factor for warehousing the medical device, the storage space utilization rate, the safety stock coefficient and the impact coefficient.

[0084] In some embodiments, the optimal inventory level is expressed as:

[0085]

[0086] Among them, O is the optimal inventory level, τ is the adjustment coefficient, U is the average daily usage of medical devices, t is the replenishment cycle of medical devices, V is the demand fluctuation coefficient, L1 is the safety stock coefficient, β is the seasonal factor, S is the storage space utilization rate, δ1 is the usage fluctuation adjustment factor, γ1 is the influence coefficient, ∈ and ζ are the first nonlinear adjustment item and the second nonlinear adjustment item respectively.

[0087] In the specific implementation, O is the optimal inventory level, which indicates the optimal inventory level to ensure the continuous supply of medical devices within a specific replenishment cycle. This parameter is a crucial indicator in warehouse management and is directly related to the balance between the reliability of medical device supply and inventory cost control. By accurately calculating O, while ensuring the supply of medical devices, the optimal inventory level should be greater than the average daily usage multiplied by the replenishment cycle to ensure that there will be no out-of-stock during the replenishment cycle, minimize the occurrence of over- or under-stocking, and optimize inventory management efficiency.

[0088] τ is the adjustment coefficient, which is a coefficient used to adjust the output of the formula, and its value range is usually between 0 and 1. The calculation results can be fine-tuned according to actual operational needs. For example, when the demand for certain medical devices fluctuates greatly or the storage conditions are special, these changes can be adapted by adjusting τ, so that the O value is closer to actual demand. In some embodiments, 0.8≤τ≤1.2, the adjustment coefficient is usually between 0.8 and 1.2, and is used to moderately adjust the inventory to cope with demand fluctuations or supply chain changes.

[0089] U is the average daily usage of medical devices, which indicates the average daily consumption of medical devices within a certain period of time. It is a key indicator reflecting the level of demand for devices. It directly determines the daily consumption rate of medical devices and is an important basis for calculating inventory. Using this parameter, demand in future replenishment cycles can be predicted to ensure sufficient inventory. The range of U depends on the frequency of use of specific medical devices. For example, commonly used surgical instruments may correspond to higher U values, while special equipment may have lower U values. There is no unified standard and it can be determined based on historical data.

[0090] t is the replenishment cycle of medical devices, which means the time interval from the issuance of a replenishment request to the actual completion of replenishment, usually in days. The length of t determines the inventory required during the replenishment cycle. A longer replenishment cycle means that a higher inventory is required to cover the consumption during this period, and vice versa. Therefore, t is an important parameter that affects the O value. The value range of T is [1, 60]. The replenishment cycle depends on the supply chain efficiency and the type of device, usually between 1 day and two months.

[0091] V is the demand fluctuation coefficient, which indicates the volatility of demand for medical devices and is a coefficient that reflects the uncertainty of demand. The larger the V is, the stronger the volatility of demand is, and more safety stock needs to be maintained to cope with uncertain demand changes. By introducing V, the calculation of O can be made more consistent with the actual demand fluctuations, thereby improving the flexibility of inventory management. Usually 0≤V≤1, the closer the V value is to 1, the greater the demand fluctuation is; the closer it is to 0, the relatively stable demand is.

[0092] L1 is the safety stock factor, which indicates the minimum inventory level set to cope with sudden demand or supply delays. It is an important parameter for preventing risks in inventory management. L1 ensures that the demand for medical devices can still be met when the supply chain is disrupted or demand surges. A higher L1 value means that a larger safety stock is required, which has a direct impact on the O value. 0.1≤L1≤0.5, the L1 value is usually set at 10% to 50% of the average daily usage, depending on demand fluctuations and supply chain reliability.

[0093] β is a seasonal factor, a coefficient used to adjust inventory demand, reflecting the seasonal fluctuations in demand for medical devices. β can make the O value more adaptable when demand fluctuates seasonally by adjusting the impact of S (storage space utilization) in the formula. The higher β is, the greater the impact of seasonal fluctuations on inventory, and the inventory calculation results need to be adjusted accordingly. Usually 0≤β≤1.5, β=1 means no seasonal changes; β>1 means peak demand; β<1 means low demand.

[0094] S is the storage space utilization rate, which indicates the current utilization of storage space and reflects the relationship between the storage capacity of the warehouse and the existing inventory. S plays a role in adjusting the inventory in the calculation of O value. When the storage space utilization rate is high, it may be necessary to control the inventory to avoid exceeding the warehouse capacity; when the utilization rate is low, the inventory can be appropriately increased. S further refines the inventory management by combining with β. Usually 0.6≤S≤0.9, the efficient utilization rate is usually between 60% and 90%. Too high may lead to insufficient space, and too low will waste resources.

[0095] δ1 is the usage fluctuation adjustment factor, which is a factor used to adjust the impact of demand fluctuations and reflects the degree of fluctuation of the average daily usage U. The introduction of δ enables the formula to more accurately reflect the impact of demand fluctuations on inventory. In the case of large demand fluctuations, δ will increase the calculation result of the inventory to ensure sufficient inventory during the fluctuation period; in the case of small fluctuations, it can reduce unnecessary inventory and optimize inventory management. Usually 0.5≤δ1≤1.5, the adjustment factor is set according to the actual usage fluctuations to ensure that the inventory level can adapt to changes in demand.

[0096] γ1 is the influence coefficient. Considering the impact of the ratio of usage to safety stock coefficient on inventory management, γ1 balances the ratio between actual usage and safety stock through its relationship with U and L, ensuring that the calculation result of inventory is more in line with actual needs. For example, when U is large and L is small, γ1 can increase the value of O, and vice versa, ensuring flexibility and accuracy in inventory management. Usually 0≤γ1≤1, γ1=0 means that the factor has no influence; γ1=1 means that the factor has a complete influence.

[0097] ∈ and ζ are the first nonlinear adjustment item and the second nonlinear adjustment item, respectively. They are parameters used to adjust the nonlinear factors in the formula, and act on the logarithmic term and exponential term in the formula, respectively. The nonlinear adjustment item enhances the adaptability of the formula to demand fluctuations and changes in space utilization. When U (demand) is high or S (space utilization) changes significantly, ∈ and ζ can make the output of the formula smoother and more stable, avoiding drastic fluctuations in inventory due to excessive changes in demand or space. Through this adjustment, the calculation result of O can be more reliable and accurate in complex actual environments. Usually 0≤∈,ζ≤2. Adjust according to the specific nonlinear characteristics of the formula and actual needs, usually between 0 and 2, to ensure the appropriateness of the nonlinear adjustment.

[0098] The present invention can accurately calculate the optimal inventory of medical devices under complex conditions. The formula comprehensively considers multiple key factors such as demand fluctuations, safety inventory, replenishment cycle, seasonal changes, and storage space utilization, and makes the results smoother and more stable through nonlinear adjustment items. The final calculated O value not only ensures the stable supply of medical devices, but also effectively controls inventory costs and storage space usage, and improves the overall efficiency and reliability of warehouse management.

[0099] Step 203: Calculate the priority use index of each of the medical devices based on the preprocessed data.

[0100] In some embodiments, step 203 may include:

[0101] Obtain the number of days until expiration, total shelf life, frequency of use, and unit price of the medical device;

[0102] Obtaining a logarithmic relationship between the validity period and the frequency of use of the medical device;

[0103] Normalizing the total shelf life of the medical device to obtain a normalized total shelf life of the device;

[0104] Obtaining a cost factor and a time decay factor for adjusting the priority use index;

[0105] The number of days until expiration, the total shelf life of the medical device, the frequency of use, and the unit price of the medical device are processed according to the cost factor, the time decay factor, and the logarithmic relationship, and the priority use index of each medical device is calculated in combination with the normalized total shelf life of the device.

[0106] In some embodiments, the priority usage index is expressed as:

[0107]

[0108] Where P is the priority use index, E is the number of days to expiration, M is the total shelf life of the device, λ1 is the time decay factor, R is the frequency of use, γ2 is the cost factor, C is the unit price of the device, k is the logarithmic relationship between the validity period and the frequency of use, η is the normalization coefficient, q is the power term, ξ and They are the third nonlinear adjustment term and the fourth nonlinear adjustment term respectively.

[0109] In specific implementation, P is the priority use index, which indicates the priority use degree of medical devices. It is an important indicator to determine which devices should be used first among various medical devices. The higher the priority use index, the more the device should be consumed and used first in the inventory. The calculation of P helps the warehouse management system optimize the use order of devices, reduce expiration waste and resource waste, and ensure the effective use of medical devices. Usually 0≤P≤1, the higher the P value, the more the device should be used first.

[0110] E is the number of days until expiration, indicating the number of days remaining until the medical device reaches the end of its shelf life. E is one of the key factors affecting the priority use index. As the E value decreases (i.e. the device is close to expiration), the priority use index P will increase accordingly, prompting the system to give priority to these devices that are about to expire to avoid waste. Usually 0≤E≤M, the value of E ranges from 0 (about to expire) to the total shelf life of the device (M). The smaller the E, the higher the P.

[0111] M is the total shelf life of the device, which refers to the total number of days from the manufacture of the medical device to the end of its shelf life. M is used to normalize the E value to help determine the position of a device in its life cycle. A larger M value indicates a longer shelf life for the device. In the formula, it indirectly affects the priority use index by affecting the calculation of the normalization and attenuation factors. Usually 30≤M≤3650, in days, about 1 month to 10 years, and the range of M depends on the type and characteristics of the medical device. Some consumables for short-term use may have a shorter shelf life, while durable medical equipment may have a longer shelf life.

[0112] λ1 is the time decay factor, which is used to adjust the effect of the frequency of use R on the priority use index. It determines the degree of influence of the frequency of use R on the P value. As the frequency of use increases, the decay factor controls the growth rate of P, so that high-frequency devices are given priority when they are close to expiration. The larger the λ1 value, the more obvious the decay effect, and low-frequency devices will be given priority later. Usually 0≤λ1≤0.1, when the λ1 value is small, the effect of time on the priority use index is slow; when the λ1 value is large, the priority of devices close to expiration increases rapidly.

[0113] R is the frequency of use, which indicates how often a medical device is used within a certain period of time, i.e., the rate at which the device is consumed. R directly affects the priority index of the device. Devices with high frequency of use tend to be consumed faster in inventory and need to be used first. The combined adjustment of R with λ and q ensures the significance of high-frequency devices in the P value, so that these devices can be used first when they are close to expiration. Usually 0≤R≤10, the R value depends on the frequency of use of the device. Commonly used devices have higher R values, while rarely used devices have lower R values.

[0114] γ2 is a cost factor, which is a factor that affects the impact of the unit price C of the device on the priority use index. Combined with the cost C of the device, it is used to adjust the priority use of high-value devices when they are close to expiration. Due to their higher economic value, high-cost devices usually need to be used more preferentially to avoid wasting resources. The larger the γ2 value, the higher the priority of high-cost devices in P. Usually 0≤γ2≤1, the higher the γ2 value, the greater the impact of cost on the priority use index, which is usually used for high-priced devices.

[0115] C is the unit price of the device, which indicates the unit price of the medical device and is the basic parameter for calculating the economic value. C directly reflects the economic value of the device and affects the P value together with other factors when calculating the priority use index. When high-priced devices are close to expiration, the P value is usually higher, suggesting that these high-value devices should be used first to reduce economic losses. Usually 10≤C≤100,000, in yuan, the C value can range from low-priced consumables to high-priced medical equipment, affecting the final calculation of P.

[0116] k is the logarithmic relationship between validity period and frequency of use. It is a coefficient used to adjust the relationship between E (number of days until expiration) and R (frequency of use). It reflects the nonlinear effect between them through the logarithmic relationship. k adjusts The relationship between ln(1+R) makes the effect of frequency of use on the priority index smoother and more reasonable. When R is large, the growth of ln(1+R) slows down, thereby avoiding too high P values ​​for high-frequency devices and ensuring that other devices can be reasonably allocated for use. Usually 0.1≤k≤1, when the k value is small, the effect of the logarithmic relationship is weak; when it is large, the effect of the logarithmic relationship is enhanced, making the effect of frequency of use on P more significant.

[0117] η is the normalization coefficient, which is used to normalize the calculation results of the formula to ensure that the priority use indexes of different devices can be compared on the same scale. By affecting the denominator of the P value, the overall range of the priority use index is controlled. It can balance the impact of differences in M ​​(shelf life) between different devices to ensure that the calculation results are more comparable. Usually 0.01≤η≤1, η is usually taken as a small value, which is used to normalize the priority use index of different devices to ensure that the P value is within a reasonable range.

[0118] q is a power term, which is the power term of the frequency of use R, and adjusts the impact of R on the priority index. The introduction of q makes the role of R in the formula more flexible. For high-frequency devices, the q value can amplify their impact in the priority index, ensuring that these devices can be used first at the appropriate time. The higher the q value, the more significant the impact of R. Usually 1≤q≤3, the q value controls the nonlinear impact of the frequency of use R on P. The larger the q, the more significant the impact of R on P.

[0119] ξ and They are the third nonlinear adjustment term and the fourth nonlinear adjustment term. ξ nonlinearly adjusts the weight of high-value devices in priority use to ensure that these devices can be used first when they are close to expiration. The balance between the unit price of the device and the frequency of use is further refined to avoid the underestimation of high-priced devices when they are used at low frequencies. Usually 0≤ξ≤2, when the ξ value is small, the nonlinear effect of the device cost on P is weak; when the ξ value is large, the cost impact is significantly enhanced. Usually The value affects the adjustment effect of ξ, ensuring that the P value can reasonably reflect the need for priority use of the device under different usage frequencies and costs.

[0120] The present invention comprehensively considers multiple factors such as the number of days to expiration, shelf life, frequency of use, and device cost, and uses multiple nonlinear adjustment items and power items to make the calculated priority use index P more accurate and reasonable. Through this index, the warehouse management system can effectively determine which medical devices should be used first, thereby optimizing resource allocation and reducing expiration losses and economic waste. The design of each parameter in the formula ensures the comparability and priority rationality of different medical devices in actual applications, which helps to improve the efficiency and accuracy of warehouse management.

[0121] Step 204: monitor the current storage environment of the medical device in real time and calculate the corresponding environmental risk index.

[0122] In some embodiments, step 204 may include:

[0123] Obtaining the current temperature, current humidity and light intensity of the storage environment;

[0124] Obtaining a preset ideal temperature, an ideal humidity, a first nonlinear index, a second nonlinear index, a temperature normalization parameter, a humidity normalization parameter, a temperature adjustment index, a humidity adjustment index, a temperature correlation factor, a humidity correlation factor, a first nonlinear adjustment item, and a second nonlinear adjustment item;

[0125] According to the ideal temperature, the ideal humidity, the first nonlinear index, the second nonlinear index, the temperature normalization parameter, the humidity normalization parameter, the temperature adjustment index, the humidity adjustment index, the temperature correlation factor, the humidity correlation factor, the first nonlinear adjustment item and the second nonlinear adjustment item, the current temperature, current humidity and light intensity of the storage environment are processed to obtain the environmental risk index.

[0126] In some embodiments, the environmental risk index is expressed as:

[0127]

[0128] Among them, I is the environmental risk index, T is the current temperature, T0 is the ideal temperature, H is the current humidity, H0 is the ideal humidity, L2 is the light intensity, n is the first nonlinear index, m is the second nonlinear index, ρ is the temperature normalization parameter, σ is the humidity normalization parameter, α1 and α2 are the first nonlinear adjustment items and the second nonlinear adjustment items, θ is the temperature adjustment index, ξ2 is the humidity adjustment index, λ2 is the temperature correlation factor, γ3 is the humidity correlation factor, ω1, ω2, and ω3 are the first weight, the second weight, and the third weight, respectively.

[0129] In the specific implementation, I is the environmental risk index, which indicates the degree of influence of the current environmental conditions (temperature, humidity, light, etc.) on the storage safety of medical devices. The higher the environmental risk index, the greater the potential harm of the current environment to medical devices, and it may be necessary to regulate or take measures to ensure the safe storage of the devices. By calculating the I value, the warehouse management system can monitor the environmental conditions in real time and trigger the early warning or automatic adjustment system when necessary to maintain a suitable storage environment and ensure the quality and safety of medical devices.

[0130] T is the current temperature, which indicates the actual temperature in the current storage environment. Temperature is one of the key factors affecting the storage safety of medical devices. Too high or too low temperature may cause failure or performance degradation of medical devices. By calculating the deviation between T and the ideal temperature T0, the formula can quantify the impact of temperature deviation on storage safety. Usually 0℃≤T≤40℃, the storage temperature of most medical devices is between 0℃ and 40℃, and specific equipment may have more stringent requirements.

[0131] T0 is the ideal temperature, which refers to the ideal temperature under the best storage conditions of medical devices, that is, at this temperature, the device can maintain the best condition. As a benchmark value, any temperature deviation will be considered a risk factor. The formula evaluates the risk of temperature deviation by calculating the difference between the current temperature T and T0. Usually 15℃≤T0≤25℃, the ideal storage temperature of most medical devices is usually within this range to ensure stable performance of the device.

[0132] H is the current humidity, which indicates the actual humidity in the current storage environment. Humidity has a significant impact on the storage quality of many medical devices. Too high humidity may cause corrosion or mildew of the device, while too low humidity may cause the material to dry out or fail. The deviation of H from the ideal humidity H0 directly affects the environmental risk index. Usually 20% ≤ H ≤ 80%, the suitable storage humidity for most medical devices is between 20% and 80%, and extreme humidity conditions may damage the performance of the device.

[0133] H0 is the ideal humidity, which is the ideal humidity for medical devices under optimal storage conditions. As a benchmark value for humidity control, it is used to evaluate the rationality of the current humidity H. Any humidity change that deviates from H0 may increase the risk, so in the formula, the difference between H and H0 is calculated to reflect the impact of humidity on environmental risks. Usually 30% ≤ H0 ≤ 60%, medical devices are usually stored best within this humidity range, avoiding both over-drying and over-humidity.

[0134] L2 is the light intensity, which indicates the light intensity in the current storage environment. Light intensity is one of the key factors affecting the stability of medical devices (especially light-sensitive devices). Strong or unstable light may cause changes or deterioration in the material properties of the device. Therefore, L2 works together with temperature and humidity in the formula to comprehensively affect the environmental risk index. Usually 0≤L2≤1000, the unit is lx, and the light intensity depends on the type of device. For light-sensitive devices, the light intensity should be as close to 0l x as possible; ordinary devices can tolerate higher light.

[0135] n is the first nonlinear index, which is a nonlinear index used to adjust the temperature deviation, reflecting the nonlinear influence of temperature deviation on the risk index. By adjusting the n value, the influence of temperature deviation on the risk index can be controlled. When n is larger, the impact of temperature deviation on environmental risk is more significant; when n is smaller, the impact is more moderate. The introduction of n makes the calculation of risk index more flexible and can adapt to different temperature sensitivity requirements. Usually 1≤n≤3, the higher the n value, the more significant the impact of temperature deviation from the ideal value on the environmental risk index.

[0136] m is the second nonlinear index, which is used to adjust the humidity deviation and reflects the nonlinear influence of humidity deviation on the risk index. Similar to n, m controls the nonlinear influence of humidity deviation on the risk index. When the m value is large, the impact of humidity deviation on environmental risk increases significantly. The introduction of m enables the formula to flexibly respond to the needs of different humidity sensitivities and ensure that humidity changes are reasonably reflected. Usually 1≤m≤3, the higher the m value, the more significant the impact of humidity deviation from the ideal value on the environmental risk index.

[0137] ρ is the temperature normalization parameter, which is used to normalize the temperature deviation. By normalizing the difference between the current temperature T and the ideal temperature T0, the influence of temperature is made more reasonable and consistent in the formula. The introduction of ρ helps balance the risk assessment at different temperatures and ensures that the influence of temperature deviation in the calculation is in line with the actual situation. By adjusting ρ, it can adapt to different temperature ranges, making the formula more adaptable. Usually 1℃≤ρ≤10℃, the ρ value is used to adjust the influence of temperature differences to meet the needs of different temperature sensitivities.

[0138] σ is the humidity normalization parameter, which is used to normalize the humidity deviation. By normalizing the difference between the current humidity H and the ideal humidity H0, the influence of humidity is made more reasonable and consistent in the formula. σ makes the risk assessment under different humidity conditions more consistent and controllable, ensuring that the formula can accurately assess the risk under different humidity environments. The adjustment of σ can adapt to different humidity sensitivity requirements and improve the wide applicability of the formula. Usually 5%≤σ≤20%, the σ value is used to adjust the influence of humidity differences to meet the needs of different humidity sensitivities.

[0139] α1 and α2 are the first and second nonlinear adjustment items. α1 is a nonlinear adjustment item used to adjust the impact of temperature deviation. Combined with the logarithmic function, it reflects the complex impact of temperature change on environmental risks. By combining with temperature deviation, the impact of temperature change on environmental risks is more diverse. The greater the temperature deviation from the ideal value, the more significant the nonlinear effect introduced by α1, which enhances the sensitivity of the formula to risks under extreme temperature conditions. Usually 0≤α1≤1, the higher the α1 value, the greater the nonlinear impact of temperature change on the risk index.

[0140] α2 is a nonlinear adjustment term used to adjust the impact of humidity deviation, combined with an exponential function to reflect the complex impact of humidity changes on environmental risks. The impact of humidity on environmental risks is adjusted through an exponential function. As humidity increases, the α2 adjustment term will introduce a more significant risk increment, ensuring that the formula can accurately reflect the threat of increased humidity to the storage safety of medical devices. Usually 0≤α2≤1, the higher the α2 value, the greater the nonlinear impact of humidity changes on the risk index.

[0141] θ is the temperature adjustment index, which is a constant used to adjust the effect of the temperature logarithmic function and affects the expression of temperature in the formula. It helps control the impact of temperature in risk calculations, especially when the temperature is close to or exceeds the ideal value. θ can smooth the impact of temperature changes on the risk index, making the formula more flexible in practical applications. δ2 is the humidity adjustment index. Usually 0℃≤θ≤10℃, θ is used to fine-tune the impact of temperature on the environmental risk index. Higher θ values ​​may reduce the impact of temperature. Usually 0≤δ2≤1, δ2 is used to adjust the attenuation effect of humidity. The higher the value, the smaller the impact of humidity on the environmental risk index.

[0142] λ2 is a temperature correlation factor, which is used to establish a correlation between light intensity L2 and temperature T, reflecting the additional impact of temperature on light risk. By introducing the temperature factor into the light risk, temperature and light jointly affect the environmental risk index. When the temperature rises, λ increases the contribution of light to the risk, ensuring that the formula can capture the interaction of multiple environmental factors. Usually 0≤λ2≤1, the λ2 value is used to reflect the degree of influence of temperature on light. The higher the value, the more significant the impact of temperature on light-related risks.

[0143] γ3 is the humidity correlation factor. It is a factor used to establish a correlation between light intensity L2 and humidity H, reflecting the additional impact of humidity on light risk. The impact of light on environmental risk is adjusted by humidity to ensure that the impact of light factors on risk is reasonably assessed when humidity is high or low. The introduction of γ3 enhances the formula's ability to handle the interaction of multiple factors. ω1, ω2, and ω3 are the first, second, and third weights, respectively. Usually 0≤γ3≤1, and the γ3 value is used to reflect the degree of influence of humidity on light. The higher the value, the more significant the impact of humidity on light-related risks.

[0144] Usually 0≤ω1,ω2,ω3≤1, and ω1+ω2+ω3=1, and the sum of the weights is 1, which is used to allocate the contribution of temperature, humidity and light to the overall environmental risk index. The specific value of the weight depends on the sensitivity of the medical device to these environmental factors.

[0145] The present invention accurately calculates the environmental risk index by comprehensively considering multiple environmental factors such as temperature, humidity, light intensity and their interrelationships. This index can reflect the threat level of the current storage environment to the safety of medical devices, and help the warehouse management system to monitor and optimize storage conditions in real time. The nonlinear index, normalization parameters and adjustment items in the formula increase the flexibility and adaptability of the calculation, ensuring that the environmental risk index can accurately reflect the complex actual environment, thereby providing effective support for the safe storage of medical devices.

[0146] Step 205: Automatically generate current replenishment recommendations for the medical devices based on historical data and a forecasting model for replenishment of the medical devices.

[0147] In some embodiments, the replenishment history data may include the use records of medical devices over a certain period of time, the replenishment frequency, the quantity of each replenishment, the replenishment cycle, seasonal changes, and replenishment requirements under specific circumstances (such as emergencies or peak demand in a specific season). These data are an important basis for understanding the demand pattern of medical devices.

[0148] The system can continuously collect and store these replenishment history data, which can reflect the consumption trends of different types of medical devices in different time periods. By analyzing these data, the system can identify common replenishment cycles and demand, and discover potential factors that affect replenishment demand (such as seasonal changes, hospital operations, etc.).

[0149] Among them, the forecasting model is a mathematical model built using statistics and machine learning algorithms based on historical replenishment data to predict the demand for specific medical devices in a certain period of time in the future. These models can consider multiple variables, such as past demand trends, current inventory levels, usage frequency, replenishment cycles, seasonal fluctuations, etc., to generate accurate demand forecasts.

[0150] The forecasting model in the system can estimate the demand for each medical device in the future by learning and analyzing historical data. For example, the demand for a certain medical device has increased significantly in winter every year in the past two years. The forecasting model can predict the demand for the device in the future winter and generate corresponding replenishment suggestions before winter comes.

[0151] In some embodiments, based on the output results of the prediction model, the system automatically generates replenishment recommendations for current medical devices. This process no longer requires manual judgment, but is completely completed automatically by the system. Specifically, the system automatically calculates the optimal replenishment time and replenishment quantity based on the predicted demand, current inventory levels, supplier delivery time, and other relevant factors. For example, if the prediction model shows that the demand for a certain medical device will increase significantly in the next two weeks, and the current inventory may not be able to meet the expected demand, the system will automatically recommend replenishment. Replenishment recommendations may include "replenishing 100 units of type X medical devices within three days" to ensure that inventory is always at a safe level. In addition, the system can also optimize the time nodes for replenishment based on the supplier's delivery time to avoid overstocking or replenishment delays.

[0152] Since the demand for medical devices may be affected by various unpredictable factors (such as sudden epidemics, equipment failures, etc.), the system will dynamically adjust replenishment recommendations based on actual usage and environmental changes. The forecasting model can be updated in real time and regenerate replenishment recommendations based on new data to ensure the flexibility and accuracy of the system.

[0153] Assuming that the usage of a certain medical device suddenly increases during a special period (such as a public health emergency), the system will immediately identify this anomaly and regenerate replenishment recommendations based on the latest data to avoid the risk of inventory depletion.

[0154] Through in-depth analysis of historical replenishment data and the application of forecasting models, the present invention can automatically generate replenishment recommendations for medical devices. The system uses historical data to identify demand patterns, and estimates future demand through forecasting models, automatically calculating the optimal replenishment time and quantity. This not only improves the efficiency of replenishment decisions and reduces errors in manual judgment, but also ensures that medical device inventory is always at a reasonable level, avoiding excessive or insufficient inventory. In addition, the system's dynamic adjustment capabilities make replenishment recommendations more flexible, able to respond to changes in demand in a timely manner, and ensure the continued stability of medical device supply.

[0155] Step 206: Optimize the warehouse management strategy for the medical device according to the optimal inventory level, the priority usage index, the environmental risk index and the replenishment suggestion.

[0156] It can be understood that the optimal inventory level (O) is the ideal inventory level calculated based on factors such as the average daily usage of medical devices, replenishment cycle, demand fluctuation coefficient, safety stock coefficient, etc. It ensures that while meeting daily needs, there will not be too much inventory backlog to avoid taking up too much storage space and resources. In warehouse management, the optimal inventory level provides a benchmark for formulating replenishment plans and inventory control. The management system will take O as the goal and maintain the inventory level within the optimal range to ensure that there are always enough devices in the warehouse to cope with regular needs and emergencies. If the inventory level is close to or below the optimal inventory level, the system will automatically trigger a replenishment suggestion.

[0157] The priority index (P) reflects which medical devices should be used first in the inventory. It takes into account factors such as the shelf life of the device, the number of days until expiration, the frequency of use, and the unit price to avoid waste due to expired or excessive backlog of devices. In warehouse management, the priority index is used to guide the order in which medical devices are shipped out of the warehouse. The system will give priority to devices with higher P values, especially those that are close to their expiration date or have higher economic value. Through this strategy, it is possible to reduce the waste of devices, optimize inventory turnover, and ensure that medical devices in the warehouse are always in the best possible state of use.

[0158] The environmental risk index (I) is calculated based on current environmental conditions such as temperature, humidity, and light intensity, and reflects the potential impact of the storage environment on medical devices. A higher environmental risk index means that the storage environment may not be conducive to the long-term storage of the device. The warehouse management system regulates the environmental conditions of the warehouse by monitoring the environmental risk index in real time. If the I value exceeds the safety threshold, the system can automatically issue an alarm or take measures (such as adjusting temperature and humidity, shading, etc.) to improve the environment. In addition, for those devices that are more vulnerable to damage under specific environmental conditions, the system can prioritize scheduling to ensure that they are stored under appropriate conditions.

[0159] Replenishment suggestions provide the optimal replenishment time and quantity based on historical data and forecasting models, ensuring that future demand is met without excessive or insufficient inventory. The system can reasonably arrange the time and quantity of replenishment based on the replenishment suggestions to avoid insufficient inventory in the warehouse. Replenishment suggestions combined with the calculation of the optimal inventory level can balance inventory levels and ensure that the warehouse is always in an efficient state during operation. In addition, the system will dynamically adjust replenishment suggestions based on actual demand and market changes to ensure that the warehousing strategy is flexible to respond to demand fluctuations.

[0160] By combining optimal inventory levels, priority usage indexes, environmental risk indexes, and replenishment recommendations, warehouse management strategies can be fully optimized to ensure reasonable inventory levels, maximize storage space, reduce waste, and respond to demand changes in a timely manner.

[0161] Through reasonable inventory management, the system can maintain the balance and rationality of inventory; through priority use and environmental monitoring, the system ensures the quality and efficiency of equipment; and through the implementation of optimized replenishment suggestions, the system can flexibly respond to changes in demand. This multi-dimensional optimization strategy not only improves the efficiency of warehouse management, but also ensures the safety and availability of medical equipment, providing strong support for medical institutions.

[0162] In some embodiments, the present invention may further include:

[0163] Construct a full-chain traceability system for the medical device information;

[0164] The whole process information of production, transportation, storage, delivery and use of the medical device is recorded through the blockchain technology of the whole chain traceability system;

[0165] Adopt HL7 standard and FHIR protocol to integrate with other hospital systems to achieve seamless data interaction.

[0166] Among them, the full-chain traceability system is a management system that can track the entire life cycle of medical devices. It covers every link from the production, transportation, warehousing, outbound to the final use of the device. Through this system, medical institutions can fully grasp the circulation process and status of each medical device, ensuring that the source of the device can be checked, the destination can be traced, and the responsibility can be investigated. In actual operation, the full-chain traceability system will assign a unique identification code to each medical device and record detailed information of the device at each link. For example, when the device is shipped from the manufacturer, the system records the production date, production batch, manufacturer information, etc.; during transportation, the system records logistics information, transportation conditions (such as temperature, humidity), etc.; when entering the warehouse, the warehouse location and warehousing time are recorded; when leaving the warehouse and using it, the exit time and the department of use are recorded. This comprehensive traceability ensures that the entire life cycle of medical devices is carried out under strict monitoring, reduces the risk of counterfeit and inferior products entering the market, and improves the transparency of quality management.

[0167] It can be understood that in the full-chain traceability system of medical devices, blockchain technology ensures the data security and authenticity of all key nodes. For example, when medical devices are marked in the production process, relevant information (such as production date, batch number, certificate of conformity, etc.) will be written into the blockchain; when the device goes through logistics transportation, warehouse storage and other links, this information will also be recorded separately on the blockchain. Ultimately, medical institutions can query the complete traceability record of the device through the blockchain, ensuring that the source and use of the device can be tracked under any circumstances, preventing data from being tampered with, and enhancing the credibility and transparency of the data.

[0168] The present invention adopts the HL7 standard and FHIR protocol to ensure that the full-chain traceability system of medical devices can be seamlessly integrated with other hospital information systems (such as HIS, electronic medical record system, LIS laboratory information system, PACS imaging system, etc.). This means that when medical devices enter the hospital's storage system, relevant information (such as the device's identification code, traceability record, etc.) can be automatically synchronized with other hospital systems through the HL7 and FHIR protocols, thereby achieving data sharing in clinical use, patient management, etc. For example, when a doctor prescribes a surgical instrument in the electronic medical record system, he can directly access the data of the full-chain traceability system to view the status of the instrument, traceability records and other information to ensure the safety and compliance of the instrument.

[0169] By constructing a full-chain traceability system, the present invention enables medical institutions to trace the entire process of each medical device from production to use. The application of blockchain technology ensures the immutability and transparency of data and enhances the credibility of device management. The integration of HL7 standards and FHIR protocols enables the full-chain traceability system to seamlessly connect with other information systems of the hospital to achieve efficient sharing and circulation of data. This system not only improves the transparency and security of medical device management, but also simplifies the data interaction process between various hospital systems, optimizes the use and management process of medical devices, and helps to improve the overall operational efficiency and service quality of the hospital.

[0170] See also Figure 2 , Figure 2 A schematic diagram of the structure of a medical device storage information management method system provided by the present invention.

[0171] like Figure 2 As shown, a medical device storage information management method system proposed in an embodiment of the present invention includes:

[0172] The data processing module 301 is used to automatically collect medical device information of multiple medical devices using image recognition technology and multi-dimensional sensors, and pre-process and standardize the collected medical device information to obtain pre-processed data;

[0173] An inventory determination module 302, for determining the current optimal inventory quantity of the medical device according to the preprocessed data;

[0174] A priority use module 303, used to calculate a priority use index of each medical device according to the preprocessed data;

[0175] The environmental risk module 304 is used to monitor the current storage environment of the medical device in real time and calculate the corresponding environmental risk index;

[0176] A replenishment suggestion module 305, configured to automatically generate a current replenishment suggestion for the medical device based on historical data and a prediction model for replenishment of the medical device;

[0177] The warehouse management module 306 is used to optimize the warehouse management strategy for the medical device according to the optimal inventory level, the priority usage index, the environmental risk index and the replenishment suggestion.

[0178] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0179] Automatically collect medical device information of multiple medical devices using image recognition technology and multi-dimensional sensors, and pre-process and standardize the collected medical device information to obtain pre-processed data;

[0180] Determining the current optimal inventory quantity of the medical device according to the preprocessed data;

[0181] Calculating a priority use index for each of the medical devices based on the preprocessed data;

[0182] Monitor the current storage environment of the medical device in real time and calculate the corresponding environmental risk index;

[0183] Automatically generate current replenishment recommendations for the medical device based on historical data and a forecasting model for replenishment of the medical device;

[0184] The warehouse management strategy for the medical device is optimized based on the optimal inventory level, the priority usage index, the environmental risk index and the replenishment suggestion.

[0185] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0186] Automatically collect medical device information of multiple medical devices using image recognition technology and multi-dimensional sensors, and pre-process and standardize the collected medical device information to obtain pre-processed data;

[0187] Determining the current optimal inventory quantity of the medical device according to the preprocessed data;

[0188] Calculating a priority use index for each of the medical devices based on the preprocessed data;

[0189] Monitor the current storage environment of the medical device in real time and calculate the corresponding environmental risk index;

[0190] Automatically generate current replenishment recommendations for the medical device based on historical data and a forecasting model for replenishment of the medical device;

[0191] The warehouse management strategy for the medical device is optimized based on the optimal inventory level, the priority usage index, the environmental risk index and the replenishment suggestion.

[0192] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0193] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0195] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0197] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0198] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for managing medical device storage information, characterized in that: The method comprises: Automatically collect medical device information of multiple medical devices using image recognition technology and multi-dimensional sensors, and pre-process and standardize the collected medical device information to obtain pre-processed data; Determining the current optimal inventory quantity of the medical device according to the preprocessed data; Calculating a priority use index for each of the medical devices based on the preprocessed data; Monitor the current storage environment of the medical device in real time and calculate the corresponding environmental risk index; Automatically generate current replenishment recommendations for the medical device based on historical data and a forecasting model for replenishment of the medical device; The warehouse management strategy for the medical device is optimized based on the optimal inventory level, the priority usage index, the environmental risk index and the replenishment suggestion.

2. The medical device storage information management method according to claim 1, characterized in that: Determining the current optimal inventory of the medical device according to the preprocessed data includes: Obtaining the average daily usage, replenishment cycle, demand fluctuation coefficient, and usage fluctuation adjustment factor of the average daily usage of the medical device; Obtaining seasonal factors, storage space utilization, and safety stock coefficients for warehousing the medical devices; Obtaining an influence coefficient for indicating the influence of the ratio of the usage of the medical device described in the inventory management to the safety stock coefficient on the inventory quantity; The current optimal inventory of the medical device is determined based on the average daily usage of the medical device, the replenishment cycle, the demand fluctuation coefficient, the usage fluctuation adjustment factor of the average daily usage, the seasonal factor for warehousing the medical device, the storage space utilization rate, the safety stock coefficient and the impact coefficient.

3. The medical device storage information management method according to claim 2, characterized in that: The optimal inventory quantity is expressed as: Among them, O is the optimal inventory level, τ is the adjustment coefficient, U is the average daily usage of medical devices, t is the replenishment cycle of medical devices, V is the demand fluctuation coefficient, L1 is the safety stock coefficient, β is the seasonal factor, S is the storage space utilization rate, δ1 is the usage fluctuation adjustment factor, γ1 is the influence coefficient, ∈ and ζ are the first nonlinear adjustment item and the second nonlinear adjustment item respectively.

4. The medical device storage information management method according to claim 3, characterized in that: Calculating the priority use index of each medical device according to the preprocessed data includes: Obtain the number of days until expiration, total shelf life, frequency of use, and unit price of the medical device; Obtaining a logarithmic relationship between the validity period and the frequency of use of the medical device; Normalizing the total shelf life of the medical device to obtain a normalized total shelf life of the device; Obtaining a cost factor and a time decay factor for adjusting the priority use index; The number of days until expiration, the total shelf life of the medical device, the frequency of use, and the unit price of the medical device are processed according to the cost factor, the time decay factor, and the logarithmic relationship, and the priority use index of each medical device is calculated in combination with the normalized total shelf life of the device.

5. The medical device storage information management method according to claim 4, characterized in that: The priority use index is expressed as: Where P is the priority use index, E is the number of days to expiration, M is the total shelf life of the device, λ1 is the time decay factor, R is the frequency of use, γ2 is the cost factor, C is the unit price of the device, k is the logarithmic relationship between the validity period and the frequency of use, η is the normalization coefficient, q is the power term, ξ and They are the third nonlinear adjustment term and the fourth nonlinear adjustment term respectively.

6. The medical device storage information management method according to claim 5, characterized in that: The real-time monitoring of the current storage environment of the medical device and calculation of the corresponding environmental risk index include: Obtaining the current temperature, current humidity and light intensity of the storage environment; Obtaining a preset ideal temperature, an ideal humidity, a first nonlinear index, a second nonlinear index, a temperature normalization parameter, a humidity normalization parameter, a temperature adjustment index, a humidity adjustment index, a temperature correlation factor, a humidity correlation factor, a first nonlinear adjustment item, and a second nonlinear adjustment item; According to the ideal temperature, the ideal humidity, the first nonlinear index, the second nonlinear index, the temperature normalization parameter, the humidity normalization parameter, the temperature adjustment index, the humidity adjustment index, the temperature correlation factor, the humidity correlation factor, the first nonlinear adjustment item and the second nonlinear adjustment item, the current temperature, current humidity and light intensity of the storage environment are processed to obtain the environmental risk index.

7. The medical device storage information management method according to claim 6, characterized in that: The environmental risk index is expressed as: Among them, I is the environmental risk index, T is the current temperature, T0 is the ideal temperature, H is the current humidity, H0 is the ideal humidity, L2 is the light intensity, n is the first nonlinear index, m is the second nonlinear index, ρ is the temperature normalization parameter, σ is the humidity normalization parameter, α1 and α2 are the first nonlinear adjustment items and the second nonlinear adjustment items, θ is the temperature adjustment index, δ2 is the humidity adjustment index, λ2 is the temperature correlation factor, γ3 is the humidity correlation factor, ω1, ω2, and ω3 are the first weight, the second weight, and the third weight, respectively.

8. The medical device storage information management method according to claim 7, characterized in that: The automatic collection of medical device information of multiple medical devices using image recognition technology and multi-dimensional sensors includes: Identify the model, specification and packaging information of the medical device through a deep learning algorithm; Collect identification code information of the medical device through RFID, and automatically collect environmental information of the storage environment where the medical device is located through temperature sensors, humidity sensors and light sensors; The model, specification, packaging information, identification code information and environmental information of the medical device are associated to obtain the medical device information.

9. The medical device storage information management method according to any one of claims 1 to 8, characterized in that: The method further comprises: Construct a full-chain traceability system for the medical device information; The whole process information of production, transportation, storage, delivery and use of the medical device is recorded through the blockchain technology of the whole chain traceability system; Adopt HL7 standard and FHIR protocol to integrate with other hospital systems to achieve seamless data interaction.

10. A medical device storage information management method system, characterized in that: The system comprises: A data processing module, used to automatically collect medical device information of multiple medical devices using image recognition technology and multi-dimensional sensors, and preprocess and standardize the collected medical device information to obtain preprocessed data; An inventory determination module, used to determine the current optimal inventory quantity of the medical device according to the preprocessed data; A priority use module, used to calculate a priority use index of each of the medical devices according to the preprocessed data; An environmental risk module, used to monitor the current storage environment of the medical device in real time and calculate the corresponding environmental risk index; A replenishment suggestion module, configured to automatically generate current replenishment suggestions for the medical device based on historical data and a prediction model for replenishment of the medical device; A warehouse management module is used to optimize the warehouse management strategy for the medical device according to the optimal inventory level, the priority usage index, the environmental risk index and the replenishment suggestion.

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