Inventory early warning method, system and equipment for consumption data of medical apparatus and instruments, and medium
By detecting anomalies, filling in missing values, and normalizing medical device consumption data, combined with time series analysis, dynamic early warning thresholds are established, solving the problem of inventory backlog or shortage in traditional inventory management, and achieving efficient and timely supply of inventory.
Patent Information
- Application Number
- CN202511025764.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-24
- Publication Date
- 2025-11-18
AI Technical Summary
Traditional inventory management methods struggle to accurately predict the demand for medical devices, leading to frequent inventory build-ups or shortages and low management efficiency.
By acquiring medical device consumption data, anomaly detection algorithms are used to remove unqualified data, missing value filling and normalization are performed, time series feature data are extracted, early warning threshold offset and dynamic early warning threshold are determined, and inventory management early warning strategies are formulated based on data inventory, including inventory replenishment and turnover early warning.
This improved the accuracy and efficiency of inventory management, reduced the number of shipments and costs, and ensured the timeliness and rationality of medical device supply.
Smart Images
Figure CN120975704A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] Embodiments of the present disclosure relate to the field of medical data processing, and in particular, to a medical instrument consumption data inventory early warning method, system, device and medium. BACKGROUND
[0002] Medical instrument supply chain management is an important part of medical informatization, involving consumable procurement, inventory turnover and distribution efficiency. Inventory management helps to realize information sharing and collaborative work of each link of the supply chain, and improves the overall efficiency of the supply chain; in addition, reasonable inventory distribution can reduce the number of transportation and distance, thereby reducing transportation costs. With the increase of types and amount of medical consumables, traditional inventory management methods, such as manual inventory and static inventory threshold setting, have been difficult to meet the demand for accurate prediction, resulting in frequent inventory accumulation or shortage.
[0003] However, using the prior art, the inventory management efficiency is low. SUMMARY
[0004] The embodiments described herein provide a medical instrument consumption data inventory early warning method, system, device and medium, which overcome the above problems.
[0005] In a first aspect, according to the content of the present disclosure, a medical instrument consumption data inventory early warning method is provided, comprising:
[0006] obtaining medical instrument consumption data, and using a preset anomaly detection algorithm to eliminate data in the medical instrument consumption data that does not meet a preset detection requirement, to obtain first consumption data; performing missing value filling on continuous missing data in the first consumption data to obtain second consumption data; and performing normalization processing on the second consumption data by a preset normalization algorithm to obtain third consumption data;
[0007] determining a preset historical period, and extracting time series feature data corresponding to the preset historical period from the third consumption data;
[0008] determining a warning threshold offset based on the time series feature data corresponding to the preset historical period in the third consumption data, the warning threshold offset being determined according to an average consumable consumption amount, a consumable consumption fluctuation data and a preset risk sensitivity corresponding to the time series feature data;
[0009] determining a dynamic warning threshold based on the warning threshold offset, a historical consumption amount of the medical instrument consumption data corresponding to the preset historical period, and a predicted consumption amount of the medical instrument consumption data corresponding to a preset future period;
[0010] determine a corresponding inventory processing early warning strategy based on the database inventory of the medical instrument consumption data and the dynamic early warning threshold;
[0011] The inventory processing early warning strategy includes an inventory replenishment early warning strategy and an inventory turnover early warning strategy, and the inventory turnover early warning strategy is used to describe inventory data allocation adjustment on the medical instrument consumption data.
[0012] In a second aspect, according to the present disclosure, a medical instrument consumption data inventory early warning system is provided, comprising:
[0013] The processing module is configured to obtain medical instrument consumption data, and remove data that does not meet preset detection requirements from the medical instrument consumption data by using a preset anomaly detection algorithm to obtain first consumption data; fill in missing values in the first consumption data to obtain second consumption data; and perform normalization processing on the second consumption data by using a preset normalization algorithm to obtain third consumption data;
[0014] The extraction module is configured to determine a preset historical period, and extract time series feature data corresponding to the preset historical period from the third consumption data;
[0015] The first determination module is configured to determine an early warning threshold offset based on the time series feature data corresponding to the preset historical period in the third consumption data, wherein the early warning threshold offset is determined according to an average consumable consumption amount, a consumption amount fluctuation data, and a preset risk sensitivity corresponding to the time series feature data;
[0016] The second determination module is configured to determine a dynamic early warning threshold based on the early warning threshold offset, historical consumption amount of the medical instrument consumption data corresponding to the preset historical period, and predicted consumption amount of the medical instrument consumption data corresponding to a preset future period;
[0017] The third determination module is configured to determine a corresponding inventory processing early warning strategy based on the database inventory of the medical instrument consumption data and the dynamic early warning threshold;
[0018] The inventory processing early warning strategy includes an inventory replenishment early warning strategy and an inventory turnover early warning strategy, and the inventory turnover early warning strategy is used to describe inventory data allocation adjustment on the medical instrument consumption data.
[0019] In a third aspect, a computer device is provided, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the medical instrument consumption data inventory early warning method in any one of the above embodiments when executing the computer program.
[0020] In a fourth aspect, a computer-readable storage medium is provided, and the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the inventory early warning method for medical instrument consumption data in any one of the above embodiments are implemented.
[0021] The inventory early warning method for medical instrument consumption data provided by the embodiments of the present application acquires medical instrument consumption data, and adopts a preset anomaly detection algorithm to eliminate data in the medical instrument consumption data that does not meet preset detection requirements, to obtain first consumption data. The method performs missing value filling on continuous missing data in the first consumption data, to obtain second consumption data. The method performs normalization processing on the second consumption data by using a preset normalization algorithm, to obtain third consumption data. The method determines a preset historical time period, and extracts time series feature data corresponding to the preset historical time period from the third consumption data. The method determines an early warning threshold offset based on the time series feature data corresponding to the preset historical time period in the third consumption data, and the early warning threshold offset is determined according to average consumable consumption corresponding to the time series feature data, consumption fluctuation data, and a preset risk sensitivity. The method determines a dynamic early warning threshold based on the early warning threshold offset, historical consumption corresponding to the preset historical time period in the medical instrument consumption data, and predicted consumption corresponding to a preset future time period in the medical instrument consumption data. The method determines a corresponding inventory processing early warning strategy based on the database inventory of the medical instrument consumption data and the dynamic early warning threshold. The inventory processing early warning strategy includes an inventory replenishment early warning strategy and an inventory turnover early warning strategy, and the inventory turnover early warning strategy is used to describe inventory data allocation adjustment on the medical instrument consumption data. In this way, the database inventory is determined based on the determined dynamic early warning threshold, to effectively determine an adaptive inventory processing early warning strategy, and thus the inventory management efficiency is improved.
[0022] The above description is only a summary of the technical solutions of the embodiments of the present application. In order to more clearly understand the technical means of the embodiments of the present application, the embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, the specific embodiments of the present application are described below. BRIEF DESCRIPTION OF DRAWINGS
[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly described below. It should be noted that the drawings described below only relate to some embodiments of the present disclosure, but not limit the present disclosure, wherein:
[0024] Figure 1 is a flowchart of an inventory early warning method for medical instrument consumption data provided by the present disclosure.
[0025] Figure 2 is a structural diagram of an inventory early warning system for medical instrument consumption data provided by the present disclosure.
[0026] Figure 3 is a structural schematic diagram of a computer device provided by the present disclosure.
[0027] It should be noted that the elements in the drawings are schematic and not drawn to scale. DETAILED DESCRIPTION
[0028] In order to make the purposes, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions of the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without any inventive effort also belong to the scope of protection of the present disclosure.
[0029] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this present subject matter belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. As used herein, the statement that two or more parts are "connected" or "coupled" together refer to an indirect or direct connection or coupling.
[0030] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. A person of ordinary skill in the art will readily recognize from the disclosure herein, given the total volume of this application that one or more passages that are described as an embodiment is / are also an embodiment of another embodiment.
[0031] The term "and / or", merely describes an associated relationship between associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of existence of A, existence of A and B, and existence of B. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects. Terms such as "first" and "second" are merely used to distinguish one component (or part of a component) from another component (or another part of a component).
[0032] In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more (including two), and similarly, "a plurality of groups" means two or more groups (including two groups).
[0033] In order for those skilled in the art to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings.
[0034] Figure 1 is a flowchart of a medical instrument consumption data inventory warning method provided by an embodiment of the present disclosure, as shown in Figure 1 The specific process of the medical instrument consumption data inventory warning method includes the following steps.
[0035] S110, obtain medical instrument consumption data, and use a preset anomaly detection algorithm to eliminate data in the medical instrument consumption data that does not meet a preset detection requirement, to obtain first consumption data; perform missing value filling on continuous missing data in the first consumption data, to obtain second consumption data; and perform normalization processing on the second consumption data by using a preset normalization algorithm, to obtain third consumption data.
[0036] The medical instrument consumption data is used data of the medical instrument at a historical time, which can be obtained by real-time acquisition of consumable use data through an IoT (Internet of Things) device and a HIS (Hospital Information System), or by extracting a consumable list of each operation through an RFID (Radio Frequency Identification) tag / two-dimensional code scanning device and analyzing actual consumable specifications and quantities. The medical instrument consumption data can include department use amount, operation type associated consumption amount, and expiration date information.
[0037] When the preset anomaly detection algorithm is used to eliminate data in the medical instrument consumption data that does not meet the preset detection requirement, the data exceeding 3 times the standard deviation in the medical instrument consumption data can be identified and eliminated by using the Z-score method. For the continuous missing data in the first consumption data, linear interpolation or forward filling can be used for missing value filling. For the second consumption data of multiple dimensions, Min-Max normalization can be performed to eliminate dimension differences. In addition, the third consumption data can be associated with features, such as establishing a mapping relationship between operation types and consumable use amounts.
[0038] S120, determine a preset historical period, and extract time series feature data corresponding to the preset historical period from the third consumption data.
[0039] The preset historical period can be a time period in the historical time, for example, the last 30 days. When the features are extracted from the third consumption data, the features can be extracted according to the weekly / monthly consumption trend and the sudden consumption event, to obtain the time series feature data.
[0040] S130, determine the early warning threshold offset based on the time series feature data corresponding to the preset historical period in the third consumption data.
[0041] The early warning threshold offset is determined according to the average consumable consumption corresponding to the time series feature data, the consumption fluctuation data, and a preset risk sensitivity.
[0042] The early warning threshold offset can be dynamically determined and adjusted by sliding window analysis of the third consumption data, such as selecting the consumption data of the last 30 days to calculate the weighted moving average, calculating the standard deviation and mean of the data in the window, and defining the fluctuation coefficient.
[0043] In some embodiments, the early warning threshold offset is determined based on the time series feature data corresponding to the preset historical period in the third consumption data, including:
[0044] The average consumable consumption corresponding to the time series feature data within the preset historical period is calculated; the consumption fluctuation factor corresponding to the time series feature data is determined according to the average consumable consumption corresponding to the time series feature data within the preset historical period, and the consumption fluctuation data corresponding to the time series feature data is determined according to the consumption fluctuation factor corresponding to the time series feature data and the average consumable consumption corresponding to the time series feature data within the preset historical period; the preset risk sensitivity is determined based on the data type corresponding to the medical instrument consumption data; the early warning threshold offset is determined according to the preset risk sensitivity, the consumption fluctuation data corresponding to the time series feature data, and the average consumable consumption corresponding to the time series feature data within the preset historical period.
[0045] The average consumable consumption corresponding to the time series feature data within the preset historical period is the average value of the usage amount of all data in the time series feature data within the preset historical period. The consumption fluctuation factor corresponding to the time series feature data is the standard deviation of all data in the time series feature data, which can be obtained by calculating the square root of the average distance of each data in the sequence from the mean. The consumption fluctuation data corresponding to the time series feature data is the ratio of the consumption fluctuation factor and the average consumable consumption.
[0046] The early warning threshold offset is represented by the following formula (1).
[0047] Δ=μ·K·Cv (1)
[0048] In formula (1), μ represents the average consumable consumption corresponding to the time series feature data within the preset historical period; K represents the preset risk sensitivity, which can be preset according to the data type of the medical instrument consumption data. When K is larger, the offset is larger, and the threshold is more conservative. When K is smaller, the offset is smaller, which is conducive to focusing on more abnormal fluctuations; Cv represents the fluctuation coefficient, i.e., the consumption fluctuation data corresponding to the time series feature data.
[0049] S140, determining the dynamic early warning threshold based on the early warning threshold offset, the historical consumption data corresponding to the preset historical period of the medical instrument consumption data, and the predicted consumption data corresponding to the preset future period of the medical instrument consumption data.
[0050] Wherein, the weighted average of the historical consumption data corresponding to the preset historical period of the medical instrument consumption data is obtained, the mean is multiplied by the future time length to predict the predicted consumption data 1 in the future period, at the same time, the ARIMA model is used to predict the predicted consumption data 2 in the future period, and the maximum value of the predicted consumption data 1 and the predicted consumption data 2 is selected as the final predicted consumption data. The sum of the final predicted consumption data and the early warning threshold offset is obtained to obtain the dynamic early warning threshold.
[0051] For example, assuming that the actual consumable consumption is X per day, the window size can be set as W=30 days; the moving weighted average is calculated by multiplying the consumption data of the past 30 days by the weight, then adding the products and dividing by the total weight; the predicted consumption data 1 is obtained by combining the moving weighted average with N (the Nth day), and the predicted consumption data 2 is obtained by the ARIMA model prediction of the Nth day consumption, the maximum value of the predicted consumption data 1 and the predicted consumption data 2 is taken, and the dynamic early warning threshold is obtained by adding the early warning threshold offset. The AIC / BIC can be used to automatically select the optimal order of the ARIMA model, and the past W days of consumption can be used to predict the Nth day consumption.
[0052] S150, determining the corresponding inventory processing early warning strategy based on the database inventory of the medical instrument consumption data and the dynamic early warning threshold.
[0053] Wherein, the inventory processing early warning strategy includes: inventory replenishment early warning strategy and inventory turnover early warning strategy, and the inventory turnover early warning strategy is used to describe the inventory data allocation adjustment of the medical instrument consumption data. The inventory turnover early warning strategy can dynamically adjust the inventory according to the shelf life sensitivity (such as sterile consumables) and storage cost.
[0054] In some embodiments, based on the database inventory of the medical instrument consumption data and the dynamic early warning threshold, the corresponding inventory processing early warning strategy is determined, which includes:
[0055] If the database inventory of the medical instrument consumption data is less than the dynamic early warning threshold, the inventory processing early warning strategy is determined as the inventory replenishment early warning strategy; if the database inventory of the medical instrument consumption data is greater than or equal to the dynamic early warning threshold, the inventory processing early warning strategy is determined as the inventory turnover early warning strategy.
[0056] The inventory replenishment early warning strategy includes a regular replenishment mechanism and an emergency replenishment mechanism. When the inventory quantity is lower than the dynamic threshold value and the supplier response time is greater than the preset inventory use days, the emergency replenishment mechanism is triggered automatically. When the inventory quantity is lower than the dynamic threshold value and the supplier response time is less than or equal to the preset inventory use days, the regular replenishment mechanism is triggered automatically.
[0057] Therefore, the inventory processing early warning strategy corresponding to the database inventory of the medical instrument consumption data is effectively determined according to the dynamic early warning threshold value.
[0058] In this embodiment, the medical instrument consumption data is obtained, and a preset anomaly detection algorithm is used to eliminate data in the medical instrument consumption data that does not meet the preset detection requirement to obtain first consumption data. The missing value of the continuous missing data in the first consumption data is filled to obtain second consumption data. The second consumption data is normalized by a preset normalization algorithm to obtain third consumption data. A preset historical period is determined, and time series feature data corresponding to the preset historical period is extracted from the third consumption data. Based on the time series feature data corresponding to the preset historical period in the third consumption data, a warning threshold offset is determined. The warning threshold offset is determined according to the average consumption of consumables, the consumption fluctuation data and the preset risk sensitivity corresponding to the time series feature data. Based on the warning threshold offset, the historical consumption of the medical instrument consumption data corresponding to the preset historical period and the predicted consumption of the medical instrument consumption data corresponding to the preset future period, a dynamic early warning threshold is determined. Based on the database inventory of the medical instrument consumption data and the dynamic early warning threshold, a corresponding inventory processing early warning strategy is determined. The inventory processing early warning strategy includes an inventory replenishment early warning strategy and an inventory turnover early warning strategy, and the inventory turnover early warning strategy is used to describe the inventory data allocation adjustment of the medical instrument consumption data. In this way, the database inventory is determined by the determined dynamic early warning threshold, so as to effectively determine the adaptive inventory processing early warning strategy, and to improve the inventory management efficiency.
[0059] In some embodiments, the inventory processing early warning strategy is an inventory replenishment early warning strategy.
[0060] The method further includes:
[0061] The inventory consumption time corresponding to the medical instrument consumption data and the supply required time corresponding to the medical instrument consumption data are obtained, and the inventory consumption time corresponding to the medical instrument consumption data and the supply required time corresponding to the medical instrument consumption data are compared. If the inventory consumption time corresponding to the medical instrument consumption data is greater than the supply required time corresponding to the medical instrument consumption data, a replenishment work order information corresponding to the medical instrument consumption data is generated. The replenishment work order information corresponding to the medical instrument consumption data is sent to the first data procurement node.
[0062] The inventory consumption duration corresponding to the medical instrument consumption data is used to describe the number of days of inventory maintenance of the medical instrument consumption data; and the supply required duration corresponding to the medical instrument consumption data is used to describe the time duration of the arrival of the supplier of the medical instrument consumption data.
[0063] Correspondingly, if the inventory consumption duration corresponding to the medical instrument consumption data is less than or equal to the supply required duration corresponding to the medical instrument consumption data, the data usage priority of the medical instrument consumption data is obtained; based on the data usage priority of the medical instrument consumption data, the purchase order information corresponding to the medical instrument consumption data is generated; and the purchase order information corresponding to the medical instrument consumption data is sent to the second data procurement node.
[0064] The first data procurement node can be used to describe a procurement department under a regular time limit; and the second data procurement node can be used to describe a procurement department under an emergency time limit.
[0065] According to the gap amount and the historical supply stability of the supplier, an optimal supplier can be selected to generate an urgent order; and for different priority orders, a corresponding emergency degree can be set, for example, first emergency: the required consumable shortage of the operating room tomorrow's schedule, the system automatically contacts the supplier to start 24-hour urgent distribution; second emergency: the inventory can be maintained for 3-5 days, but the supplier responds slowly, and the system synchronously pushes a list of alternative suppliers for manual intervention.
[0066] Therefore, when the inventory replenishment early warning strategy is a regular replenishment mechanism, inventory replenishment is performed through a procurement department under a regular time limit, and when the inventory replenishment early warning strategy is an emergency replenishment mechanism, inventory is quickly replenished through a procurement department under an emergency time limit, so as to respond to inventory replenishment of different emergency degrees in a timely manner and further improve the efficiency of inventory management.
[0067] In some embodiments, the inventory processing early warning strategy is an inventory turnover early warning strategy.
[0068] The method of the embodiment further includes:
[0069] If the inventory consumption duration corresponding to the medical instrument consumption data is less than a preset time duration threshold, the historical usage information of the plurality of data usage nodes with respect to the medical instrument consumption data is obtained; and based on the inventory consumption duration corresponding to the medical instrument consumption data and the historical usage information of the plurality of data usage nodes with respect to the medical instrument consumption data, a medical allocation plan corresponding to each data usage node of the medical instrument consumption data is generated.
[0070] The historical use information of the plurality of data use nodes with respect to the medical instrument consumption data can be used to describe the expected consumption duration, i.e., the expected consumption days (inventory quantity / daily consumption quantity), of the medical instrument consumption data. The medical allocation plan of the medical instrument consumption data corresponding to each data use node can be generated according to the inventory consumption duration of the medical instrument consumption data and the historical use information of the plurality of data use nodes with respect to the medical instrument consumption data. For example, the system automatically marks consumables with an expiration date less than 30 days (i.e., a preset duration threshold) and preferentially allocates the consumables to recent surgical plans; if the remaining days of a consumable (i.e., the inventory consumption duration) are less than the expected consumption days, a "expiration date warning" is triggered to prompt preferential use or allocation.
[0071] In some embodiments, the method further includes:
[0072] The target consumption data corresponding to the target warehouse is identified from the medical instrument consumption data; the storage consumption data and the storage consumption requirement of the target consumption data are obtained, and it is determined whether the storage consumption data of the target consumption data meets the storage consumption requirement; if the storage consumption data of the target consumption data does not meet the storage consumption requirement, a transfer warehouse warning prompt of the target consumption data is generated.
[0073] The target consumption data corresponding to the target warehouse is the consumable that needs special storage, and the daily storage cost (i.e., the storage consumption data) thereof can be automatically calculated; if the cost exceeds a preset threshold (i.e., the storage consumption requirement), a "warehouse allocation suggestion" is triggered, so that a certain type of consumable can be dynamically allocated to different warehouses or storage areas according to the characteristics, storage cost and use demand of the consumable. Thus, the expiration waste and storage cost are dynamically balanced to reduce the expiration waste and control the storage cost.
[0074] The embodiment dynamically adapts to consumption fluctuations through a sliding window and an ARIMA model to reduce the false positive rate; integrates surgical scheduling, supplier response and other data to improve the timeliness of replenishment; the emergency replenishment trigger does not require manual review, and the response time is shortened to within 2 hours; through expiration-cost double-target optimization and safe inventory grading management, the expiration loss is reduced, and the storage cost is reduced.
[0075] Figure 2 A structural schematic diagram of a medical instrument consumption data inventory warning system provided by the embodiment is provided, and the medical instrument consumption data inventory warning system can include a processing module 210, an extraction module 220, a first determination module 230, a second determination module 240 and a third determination module 250.
[0076] The processing module 210 is configured to acquire the medical instrument consumption data, remove data that does not meet preset detection requirements from the medical instrument consumption data by using a preset anomaly detection algorithm, and obtain first consumption data; perform missing value filling on continuous missing data in the first consumption data, and obtain second consumption data; and perform normalization processing on the second consumption data by using a preset normalization algorithm, and obtain third consumption data.
[0077] The extraction module 220 is configured to determine a preset historical period, and extract time series feature data corresponding to the preset historical period from the third consumption data.
[0078] The first determination module 230 is configured to determine a warning threshold offset based on the time series feature data corresponding to the preset historical period in the third consumption data, wherein the warning threshold offset is determined according to an average consumable consumption amount corresponding to the time series feature data, consumable consumption fluctuation data, and a preset risk sensitivity.
[0079] The second determination module 240 is configured to determine a dynamic warning threshold based on the warning threshold offset, historical consumption amounts of the medical instrument consumption data corresponding to the preset historical period, and predicted consumption amounts of the medical instrument consumption data corresponding to a preset future period.
[0080] The third determination module 250 is configured to determine a corresponding inventory processing warning strategy based on the database inventory of the medical instrument consumption data and the dynamic warning threshold, wherein the inventory processing warning strategy includes an inventory replenishment warning strategy and an inventory turnover warning strategy, and the inventory turnover warning strategy is used to describe inventory data allocation adjustment of the medical instrument consumption data.
[0081] In this embodiment, optionally, the first determination module 230 is specifically configured to:
[0082] determine an average consumable consumption amount corresponding to the preset historical period of the time series feature data, determine a consumable consumption fluctuation factor corresponding to the time series feature data according to the average consumable consumption amount corresponding to the preset historical period of the time series feature data, and determine consumable consumption fluctuation data corresponding to the time series feature data according to the consumable consumption fluctuation factor corresponding to the time series feature data and the average consumable consumption amount corresponding to the preset historical period of the time series feature data, determine a preset risk sensitivity based on a data type corresponding to the medical instrument consumption data, and determine a warning threshold offset corresponding to the database inventory of the medical instrument consumption data according to the preset risk sensitivity, the consumable consumption fluctuation data corresponding to the time series feature data, and the average consumable consumption amount corresponding to the preset historical period of the time series feature data.
[0083] In this embodiment, optionally, the third determination module 250 is specifically configured to:
[0084] If the database inventory of the medical instrument consumption data is less than the dynamic early warning threshold, the inventory processing early warning strategy is determined as the inventory replenishment early warning strategy; if the database inventory of the medical instrument consumption data is greater than or equal to the dynamic early warning threshold, the inventory processing early warning strategy is determined as the inventory turnover early warning strategy.
[0085] In the embodiment, the inventory processing early warning strategy is the inventory replenishment early warning strategy.
[0086] Further comprising: a comparison module, a generation module and a sending module.
[0087] The comparison module is configured to acquire the inventory consumption duration corresponding to the medical instrument consumption data and the supply required duration corresponding to the medical instrument consumption data, and compare the inventory consumption duration corresponding to the medical instrument consumption data with the supply required duration corresponding to the medical instrument consumption data.
[0088] The generation module is configured to generate the replenishment work order information corresponding to the medical instrument consumption data if the inventory consumption duration corresponding to the medical instrument consumption data is greater than the supply required duration corresponding to the medical instrument consumption data.
[0089] The sending module is configured to send the replenishment work order information corresponding to the medical instrument consumption data to the first data procurement node.
[0090] In the embodiment, the generation module is further configured to acquire the data use priority of the medical instrument consumption data if the inventory consumption duration corresponding to the medical instrument consumption data is less than or equal to the supply required duration corresponding to the medical instrument consumption data.
[0091] The generation module is further configured to generate the procurement order information corresponding to the medical instrument consumption data based on the data use priority of the medical instrument consumption data.
[0092] The sending module is further configured to send the procurement order information corresponding to the medical instrument consumption data to the second data procurement node.
[0093] In the embodiment, the inventory processing early warning strategy is the inventory turnover early warning strategy.
[0094] The generation module is further configured to acquire the historical use information of the plurality of data use nodes with respect to the medical instrument consumption data if the inventory consumption duration corresponding to the medical instrument consumption data is less than the preset duration threshold.
[0095] The generation module is further configured to generate the medical allocation plan of the medical instrument consumption data corresponding to each data use node based on the inventory consumption duration corresponding to the medical instrument consumption data and the historical use information of the plurality of data use nodes with respect to the medical instrument consumption data.
[0096] The generation module is further configured to generate the medical allocation plan of the medical instrument consumption data corresponding to each data use node based on the inventory consumption duration corresponding to the medical instrument consumption data and the historical use information of the plurality of data use nodes with respect to the medical instrument consumption data.
[0097] In the embodiment, the method further comprises: identifying the target consumption data corresponding to the target warehouse from the medical instrument consumption data.
[0098] The identifying module is configured to identify target consumption data corresponding to the target warehouse from the medical instrument consumption data.
[0099] The obtaining module is further configured to obtain storage consumption data and storage consumption requirements of the target consumption data.
[0100] The determining module is configured to determine whether the storage consumption data of the target consumption data meets the storage consumption requirements.
[0101] The generating module is further configured to generate a warehouse transfer warning prompt of the target consumption data if the storage consumption data of the target consumption data does not meet the storage consumption requirements.
[0102] The inventory warning system for medical instrument consumption data provided by the present disclosure can execute the above method embodiments, and the specific implementation principles and technical effects can be referred to the above method embodiments, which will not be described here in detail.
[0103] The present application also provides a computer device. For details, please refer to Figure 3 , Figure 3 The present application also provides a computer device. For details, please refer to
[0104] The computer device comprises a memory 310 and a processor 320 which are connected to each other through a system bus. It should be noted that only the computer device with the memory 310 and the processor 320 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art can understand that the computer device herein is a device capable of automatically performing numerical calculation and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.
[0105] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The computer device can interact with the user through a keyboard, a mouse, a remote controller, a touchpad or a voice control device.
[0106] The memory 310 includes at least one type of readable storage medium, including non-volatile memory or volatile memory, for example, flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. The RAM can include static RAM or dynamic RAM. In some embodiments, the memory 310 can be an internal storage unit of the computer device, for example, a hard disk or a memory of the computer device. In other embodiments, the memory 310 can also be an external storage device of the computer device, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, or a flash card, etc. equipped on the computer device. Of course, the memory 310 can include both an internal storage unit and an external storage device of the computer device. In the present embodiment, the memory 310 is generally used to store an operating system and various application software installed on the computer device, for example, program codes of the above-described method, etc. In addition, the memory 310 can also be used to temporarily store various data that has been output or will be output.
[0107] The processor 320 is generally used to perform the overall operation of the computer device. In the present embodiment, the memory 310 is used to store program codes or instructions, which include computer operation instructions, and the processor 320 is used to execute the program codes or instructions stored in the memory 310 or process data, for example, run the program codes of the above-described method.
[0108] In this article, the bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus system can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is shown in the figure, but it does not mean that there is only one bus or only one type of bus.
[0109] Another embodiment of the present application also provides a computer readable medium, which can be a computer readable signal medium or a computer readable medium. The processor in the computer reads the computer readable program code stored in the computer readable medium, so that the processor can perform the function actions specified in each step or combination of steps in the above method; generate the device implementing the function actions specified in each block or combination of blocks in the block diagram.
[0110] The computer readable medium includes but is not limited to electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any appropriate combination of the foregoing, for storing program codes or instructions, which include computer operation instructions, and processors for executing the program codes or instructions of the above method stored in the memory.
[0111] The definition of the memory and the processor can refer to the description of the foregoing computer device embodiment, which will not be repeated here.
[0112] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiment described above is only schematic, for example, the division of modules or units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.
[0113] The function units or modules in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software function unit.
[0114] If the integrated unit is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.
[0115] In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application can be implemented by means of both hardware and software, and any combination thereof. In the device claim enumerating several means, several of these means can be embodied by one and the same item of hardware. The mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage. The use of relative terms such as "first", "second" and "third", etc. does not connote any prioritization, but such terms are used to distinguish a certain feature from another feature with the same name. The steps of the methods described in the above embodiments should not be understood as necessarily limited in their sequence, except when this is explicitly specified.
[0116] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; even though the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims
1. A method for inventory alerting of medical instrument consumable data, characterized by, The method comprises: obtaining medical instrument consumption data, and removing data in the medical instrument consumption data that does not meet preset detection requirements by using a preset anomaly detection algorithm to obtain first consumption data; performing missing value filling on continuous missing data in the first consumption data to obtain second consumption data; and performing normalization processing on the second consumption data by using a preset normalization algorithm to obtain third consumption data; determining a preset historical period, and extracting time series feature data corresponding to the preset historical period from the third consumption data; based on the time series feature data corresponding to the preset historical period in the third consumption data, determining a warning threshold offset, the warning threshold offset being determined according to average consumable consumption amount, consumption amount fluctuation data corresponding to the time series feature data, and a preset risk sensitivity; based on the warning threshold offset, historical consumption amount of the medical instrument consumption data corresponding to the preset historical period, and predicted consumption amount of the medical instrument consumption data corresponding to a preset future period, determining a dynamic warning threshold; based on the database inventory of the medical instrument consumption data and the dynamic warning threshold, determining a corresponding inventory processing warning strategy; wherein the inventory processing warning strategy comprises an inventory replenishment warning strategy and an inventory turnover warning strategy, and the inventory turnover warning strategy is used to describe inventory data allocation adjustment of the medical instrument consumption data.
2. The method of claim 1, wherein, The method further comprises: calculating the average consumable consumption amount corresponding to the preset historical period of the time series feature data; determining a consumption amount fluctuation factor corresponding to the time series feature data according to the average consumable consumption amount corresponding to the preset historical period of the time series feature data, and determining the consumption amount fluctuation data corresponding to the time series feature data according to the consumption amount fluctuation factor corresponding to the time series feature data and the average consumable consumption amount corresponding to the preset historical period of the time series feature data; determining a preset risk sensitivity based on the data type corresponding to the medical instrument consumption data; determining the warning threshold offset according to the preset risk sensitivity, the consumption amount fluctuation data corresponding to the time series feature data, and the average consumable consumption amount corresponding to the preset historical period of the time series feature data.
3. The method of claim 1, wherein, The method further comprises: if the database inventory of the medical instrument consumption data is less than the dynamic warning threshold, determining that the inventory processing warning strategy is the inventory replenishment warning strategy; if the database inventory of the medical instrument consumption data is greater than or equal to the dynamic warning threshold, determining that the inventory processing warning strategy is the inventory turnover warning strategy.
4. The method of claim 3, wherein, The inventory processing warning strategy is the inventory replenishment warning strategy. The method further comprises: acquire the inventory consumption duration corresponding to the medical instrument consumption data and the supply required duration corresponding to the medical instrument consumption data, and compare the inventory consumption duration corresponding to the medical instrument consumption data with the supply required duration corresponding to the medical instrument consumption data; if the inventory consumption duration corresponding to the medical instrument consumption data is greater than the supply required duration corresponding to the medical instrument consumption data, generate the restocking work order information corresponding to the medical instrument consumption data; send the restocking work order information corresponding to the medical instrument consumption data to the first data procurement node.
5. The method of claim 4, wherein, The method further comprises: if the inventory consumption duration corresponding to the medical instrument consumption data is less than or equal to the supply required duration corresponding to the medical instrument consumption data, acquire the data usage priority of the medical instrument consumption data; based on the data usage priority of the medical instrument consumption data, generate the procurement order information corresponding to the medical instrument consumption data; send the procurement order information corresponding to the medical instrument consumption data to the second data procurement node.
6. The method of claim 3, wherein, The inventory processing early warning strategy is the inventory turnover early warning strategy. The method further comprises: if the inventory consumption duration corresponding to the medical instrument consumption data is less than a preset duration threshold, acquire the historical usage information of a plurality of data usage nodes with respect to the medical instrument consumption data; based on the inventory consumption duration corresponding to the medical instrument consumption data and the historical usage information of a plurality of data usage nodes with respect to the medical instrument consumption data, generate a medical allocation plan corresponding to each of the data usage nodes for the medical instrument consumption data.
7. The method of claim 6, wherein, The method further comprises: identify target consumption data corresponding to a target warehouse from the medical instrument consumption data; acquire storage consumption data and storage consumption requirements of the target consumption data, and determine whether the storage consumption data of the target consumption data meets the storage consumption requirements; if the storage consumption data of the target consumption data does not meet the storage consumption requirements, generate a warehouse transfer early warning prompt for the target consumption data.
8. A medical instrument consumable data inventory alert system, comprising: comprise: a processing module configured to acquire medical instrument consumption data, and use a preset anomaly detection algorithm to eliminate data in the medical instrument consumption data that does not meet preset detection requirements, to obtain first consumption data; perform missing value filling on continuous missing data in the first consumption data, to obtain second consumption data; and perform normalization processing on the second consumption data by using a preset normalization algorithm, to obtain third consumption data; a determination module configured to determine a preset historical period, and extract time series feature data corresponding to the preset historical period from the third consumption data; a first determination module configured to determine, based on the time series feature data corresponding to the preset historical period in the third consumption data, a warning threshold offset, the warning threshold offset being determined according to an average consumable consumption amount corresponding to the time series feature data, consumable consumption fluctuation data, and a preset risk sensitivity; a second determining module configured to determine a dynamic early warning threshold based on the early warning threshold offset, historical consumption corresponding to the medical instrument consumption data in the preset historical period, and predicted consumption corresponding to the medical instrument consumption data in a preset future period; a third determining module configured to determine a corresponding inventory processing early warning strategy based on the database inventory of the medical instrument consumption data and the dynamic early warning threshold; wherein the inventory processing early warning strategy comprises an inventory replenishment early warning strategy and an inventory turnover early warning strategy, and the inventory turnover early warning strategy is used to describe inventory data allocation adjustment on the medical instrument consumption data.
9. A computer device, comprising: The medical instrument consumption data inventory early warning method comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the medical instrument consumption data inventory early warning method according to any one of claims 1-7.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the medical instrument consumption data inventory early warning method according to any one of claims 1-7.