Medical equipment and consumable management system based on Internet of Things technology

Abstract

The invention relates to the technical field of medical management, in particular to a medical equipment and consumable management system based on the Internet of Things technology, which comprises a data acquisition module, a data prediction module, an urgency and gap index calculation module and a dispatching module, the data acquisition module is used for acquiring equipment operation data, consumable data and environment data of each hospital area; the data prediction module is used for operating an equipment maintenance demand prediction strategy and a material demand prediction strategy, and predicting an equipment fault probability and a material demand; the urgency and gap index calculation module is used for calculating an equipment maintenance urgency index and a material gap index of each courtyard area, and calculating the equipment maintenance urgency index and the material gap index of each courtyard area; and the dispatching and allocating module is used for calculating and outputting an equipment dispatching scheme and a material allocating scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of medical management, and particularly to a medical device and consumable management system based on Internet of Things technology. Background Art

[0002] In traditional medical device and consumable management systems, resource scheduling mainly relies on manual experience and static plans. Equipment maintenance is usually based on fixed cycles or after-failure repair requests, and consumable replenishment is completed through simple estimation of historical consumption. In a multi-campus scenario, each campus manages equipment and consumables independently, lacking a unified data sharing and collaboration mechanism.

[0003] Disadvantages of the prior art: Only record the basic equipment usage duration or consumable inventory levels, lacking real-time monitoring and correlation analysis of environmental factors (such as humidity, peak usage periods), with single-dimensional data; rely on linear models or manual experience to predict equipment failures and consumable demands, unable to adapt to complex dynamic changes; lack a global perspective resource scheduling algorithm, with high costs and long cycles for resource allocation between campuses, difficult to balance emergency demands and economic benefits; the equipment maintenance teams and consumable inventories between campuses operate in isolation, and cross-campus scheduling in emergency situations relies on manual coordination, with low efficiency, and the allocation costs and clinical demand priorities are not quantified, prone to "high-cost, low-utility" scheduling schemes. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention proposes a medical device and consumable management system based on Internet of Things technology. The present invention integrates equipment operation data, consumable inventory data, and environmental data to provide multi-source inputs for prediction and scheduling, with comprehensive data collection; comprehensively considers equipment usage intensity, failure probability, and environmental humidity to dynamically generate a maintenance priority sequence to avoid resource waste; realizes real-time data synchronization across campuses, supports global resource scheduling, and feeds back the scheduling results to the data collection port to continuously optimize the prediction model and scheduling strategy, forming a "collection - prediction - scheduling - feedback" closed loop; temperature and humidity tags and peak period tags directly participate in the calculation to enhance the system's response ability to complex environments.

[0005] To achieve the above object, the technical solution of the present invention is as follows: Medical device and consumable management system based on Internet of Things technology. The system includes a data collection module, a data prediction module, an urgency and gap index calculation module, and a scheduling and allocation module. The data collection module is used to collect equipment operation data, consumable data, and environmental data of each hospital area; the data prediction module is used to run equipment maintenance demand prediction strategies and material demand prediction strategies to predict equipment failure probabilities and material demands; the urgency and gap index calculation module is used to calculate the equipment maintenance urgency index of each hospital area and the material gap index of each hospital area, and calculate the equipment maintenance urgency index and material gap index of each hospital area; the scheduling and allocation module is used to calculate and output equipment scheduling plans and material allocation plans.

[0006] A further improvement of the present invention is that the data collection module includes a patient equipment operation data collection unit, a consumable data collection unit, and an environmental data collection unit; the equipment operation data collection unit is used to collect the equipment usage duration, equipment failure frequency, and equipment maintenance cycle standard values of each hospital area; the consumable data collection unit is used to collect the material inventory, daily material consumption, and material replenishment cycle standard values of each hospital area; the environmental data collection unit is used to generate an environmental humidity tag value and a peak period tag value according to the environmental humidity and the equipment usage peak period. If the environmental humidity is greater than 70%, the environmental humidity tag value is 1, otherwise it is 0. If the current day is the equipment usage peak period, the peak period tag value is 1, otherwise it is 0.

[0007] A further improvement of the present invention is that the data prediction module includes an equipment maintenance demand prediction unit and a material demand prediction unit; the equipment maintenance demand prediction unit is used to run equipment maintenance demand prediction strategies to construct an equipment failure probability prediction model for each hospital area and predict equipment failure probabilities; the material demand prediction unit is used to run material demand prediction strategies to construct a material consumption prediction model for each hospital area and predict material demands.

[0008] A further improvement of the present invention is that the urgency and gap index calculation module includes an equipment maintenance urgency calculation unit and a material gap index calculation unit; the equipment maintenance urgency calculation unit is used to calculate the equipment maintenance urgency index of each hospital area and generate an equipment maintenance queue; the material gap index calculation unit is used to calculate the material gap index of each hospital area.

[0009] A further improvement of the present invention lies in that the scheduling and allocation module includes an equipment maintenance scheduling unit and a material allocation unit; the equipment maintenance scheduling unit is used to run an equipment maintenance scheduling strategy, aiming to minimize the total maintenance cost, construct an equipment scheduling objective function and set equipment scheduling constraint conditions; the material allocation unit is used to run a material allocation strategy, aiming to minimize the allocation cost and shortage loss, construct a material allocation objective function and set material allocation constraint conditions.

[0010] A further improvement of the present invention lies in that the equipment maintenance demand prediction unit runs an equipment maintenance demand prediction strategy; the equipment maintenance demand prediction strategy includes the following specific steps: S11: Taking 24 hours as the data collection time interval, collecting the equipment usage duration, equipment failure frequency, environmental humidity tag value, and peak period tag value of each hospital area every 24 hours and combining them in the form of a feature vector to construct an equipment failure probability prediction model. S12: The equipment failure probability prediction model takes the set of all feature vectors of a single hospital area as input, takes the predicted value of the equipment failure probability corresponding to each group of feature vectors of a single hospital area as output, takes the actual value of the equipment failure probability corresponding to each group of feature vectors of a single hospital area as the prediction target, and takes minimizing the mean square error between the predicted value and the actual value of the equipment failure probability of a single hospital area as the training target, and stops training until the mean square error between the predicted value and the actual value of the equipment failure probability of a single hospital area reaches convergence; output the equipment failure probability predicted by the equipment failure probability prediction model; the equipment failure probability prediction model is any one of a deep neural network model or a deep belief network model.

[0011] A further improvement of the present invention lies in that the material demand prediction unit runs a material demand prediction strategy; the material demand prediction strategy includes the following specific steps: S21: Taking 24 hours as the data collection time interval, collecting the daily average consumption of materials and peak period tag value of each hospital area every 24 hours and combining them in the form of a feature vector to construct a material consumption prediction model. S22: The material consumption prediction model takes the set of feature vectors combined by the daily average consumption of all materials and peak period tag value of a single hospital area as input, takes the predicted value of the material demand corresponding to each group of feature vectors of a single hospital area as output, takes the actual value of the material demand corresponding to each group of feature vectors of a single hospital area as the prediction target, and takes minimizing the sum of squares of the difference between the predicted value and the actual value of the material demand of a single hospital area as the training target, and stops training until the sum of squares of the difference between the predicted value and the actual value of the equipment failure probability of a single hospital area reaches convergence; output the material demand predicted by the material consumption prediction model; the material consumption prediction model is any one of a support vector machine, a random forest, and a neural network model.

[0012] A further improvement of the present invention is that the calculation formula of the equipment maintenance urgency index is as follows: ; wherein, represents the equipment maintenance urgency index of campus i, represents the equipment usage duration of campus i, represents the equipment failure probability of campus i, represents the standard value of the equipment maintenance cycle, is the environmental humidity label value.

[0013] A further improvement of the present invention is that the calculation formula of the material gap index is as follows: ; wherein, represents the material gap index of campus i, represents the material inventory of campus i, represents the material demand of campus i, is the standard value of the material replenishment cycle, represents the theoretical inventory demand of the material.

[0014] A further improvement of the present invention is that the equipment maintenance scheduling unit runs the equipment maintenance scheduling strategy, and the equipment maintenance scheduling strategy includes the following specific steps: S31. Taking the minimization of the total maintenance cost as the goal, construct the equipment scheduling objective function as: ; wherein, represents the maintenance duration of the equipment scheduled from campus j to campus i, represents the labor cost per unit time of equipment scheduling; represents the distance between campus j and campus i; represents the transportation cost per unit distance of equipment scheduling; S32. Set the constraint conditions: ; wherein, represents the total maintenance duration of the equipment scheduled from campus j to each campus, is the daily available maintenance duration limit of campus j.

[0015] A further improvement of the present invention is that the material allocation unit runs the material allocation strategy, and the material allocation strategy includes the following specific steps: S41. Taking the minimization of the allocation cost and the shortage loss as the goal, construct the material allocation objective function as: ; wherein, is the amount of materials dispatched from hospital j to hospital i, Indicates the transportation cost per unit of material transfer, is the gap index weight factor; S42. Set constraints: ;in, represents the total amount of materials dispatched from campus j to each campus, is the material inventory of campus j, is the minimum inventory reserve quantity of hospital area j.

[0016] The technical effects of the present invention are as follows: The present invention integrates equipment operation data, consumable inventory data and environmental data to provide multi-source input for prediction and scheduling, and achieves comprehensive data collection. It dynamically generates a maintenance priority sequence based on equipment usage intensity, failure probability and environmental humidity to avoid waste of resources. It achieves real-time synchronization of data across campuses, supports global resource scheduling, and feeds back scheduling results to the data collection port, continuously optimizing prediction models and scheduling strategies to form a "collection-prediction-scheduling-feedback" closed loop. Temperature and humidity tags and peak period tags are directly involved in the calculation to enhance the system's responsiveness to complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 It is a structural schematic diagram of the medical equipment and consumables management system based on Internet of Things technology of the present invention. DETAILED DESCRIPTION

[0018] Example 1 This embodiment proposes a medical equipment and consumables management system based on the Internet of Things technology. By integrating equipment operation data, consumables inventory data and environmental data, it provides multi-source input for prediction and scheduling, and comprehensive data collection. It dynamically generates a maintenance priority sequence by comprehensively considering equipment usage intensity, failure probability and environmental humidity to avoid waste of resources. It realizes real-time synchronization of data across hospital campuses, supports global resource scheduling, and feeds back scheduling results to the data collection port, continuously optimizing the prediction model and scheduling strategy to form a "collection-prediction-scheduling-feedback" closed loop. Temperature and humidity tags and peak period tags are directly involved in the calculation to enhance the system's responsiveness to complex environments.

[0019] like Figure 1As shown, a medical device and consumable management system based on Internet of Things technology. The system includes a data collection module, a data prediction module, an urgency and gap index calculation module, and a scheduling and allocation module. The data collection module is used to collect equipment operation data, consumable data, and environmental data of each hospital area; the data prediction module is used to run an equipment maintenance demand prediction strategy and a material demand prediction strategy to predict the equipment failure probability and material demand; the urgency and gap index calculation module is used to calculate the equipment maintenance urgency index of each hospital area and the material gap index of each hospital area, and calculate the equipment maintenance urgency index and the material gap index of each hospital area; the scheduling and allocation module is used to calculate and output an equipment scheduling plan and a material allocation plan.

[0020] In this embodiment, the data collection module includes a patient equipment operation data collection unit, a consumable data collection unit, and an environmental data collection unit. The equipment operation data collection unit is used to collect the equipment usage duration, equipment failure frequency, and equipment maintenance cycle standard value of each hospital area; the consumable data collection unit is used to collect the material inventory, daily material consumption, and material replenishment cycle standard value of each hospital area; the environmental data collection unit is used to generate an environmental humidity label value and a peak period label value according to the environmental humidity and the equipment usage peak period. If the environmental humidity is greater than 70%, the environmental humidity label value is 1, otherwise it is 0. If the current day is the equipment usage peak period, the peak period label value is 1, otherwise it is 0.

[0021] In this embodiment, the data prediction module includes an equipment maintenance demand prediction unit and a material demand prediction unit. The equipment maintenance demand prediction unit is used to run an equipment maintenance demand prediction strategy to construct an equipment failure probability prediction model for each hospital area and predict the equipment failure probability; the material demand prediction unit is used to run a material demand prediction strategy to construct a material consumption prediction model for each hospital area and predict the material demand.

[0022] In this embodiment, the urgency and gap index calculation module includes an equipment maintenance urgency calculation unit and a material gap index calculation unit. The equipment maintenance urgency calculation unit is used to calculate the equipment maintenance urgency index of each hospital area and generate an equipment maintenance queue; the material gap index calculation unit is used to calculate the material gap index of each hospital area.

[0023] In this embodiment, the scheduling and allocation module includes an equipment maintenance scheduling unit and a material allocation unit. The equipment maintenance scheduling unit is used to run an equipment maintenance scheduling strategy, with the goal of minimizing the total maintenance cost, construct an equipment scheduling objective function, and set equipment scheduling constraints; the material allocation unit is used to run a material allocation strategy, with the goal of minimizing the allocation cost and stockout loss, construct a material allocation objective function, and set material allocation constraints.

[0024] In this embodiment, the device maintenance requirement prediction unit runs a device maintenance requirement prediction strategy; the device maintenance requirement prediction strategy includes the following specific steps: S11. Taking 24 hours as the data collection time interval, collect the device usage duration, device failure frequency, environmental humidity tag value, and peak period tag value of each campus every 24 hours and combine them in the form of a feature vector to construct a device failure probability prediction model; S12. The device failure probability prediction model takes the set of all feature vectors of a single campus as input, takes the predicted value of the device failure probability corresponding to each group of feature vectors of a single campus as output, takes the actual value of the device failure probability corresponding to each group of feature vectors of a single campus as the prediction target, and takes minimizing the mean square error between the predicted value and the actual value of the device failure probability of a single campus as the training target, and stops training until the mean square error between the predicted value and the actual value of the device failure probability of a single campus reaches convergence; output the device failure probability predicted by the device failure probability prediction model; the device failure probability prediction model is any one of a deep neural network model or a deep belief network model.

[0025] In this embodiment, the material demand prediction unit runs a material demand prediction strategy; the material demand prediction strategy includes the following specific steps: S21. Taking 24 hours as the data collection time interval, collect the daily average consumption of materials and the peak period tag value of each campus every 24 hours and combine them in the form of a feature vector to construct a material consumption prediction model; S22. The material consumption prediction model takes the set of feature vectors combined by the daily average consumption of all materials and the peak period tag value of a single campus as input, takes the predicted value of the material demand corresponding to each group of feature vectors of a single campus as output, takes the actual value of the material demand corresponding to each group of feature vectors of a single campus as the prediction target, and takes minimizing the sum of the squares of the difference between the predicted value and the actual value of the material demand of a single campus as the training target, and stops training until the sum of the squares of the difference between the predicted value and the actual value of the device failure probability of a single campus reaches convergence; output the material demand predicted by the material consumption prediction model; the material consumption prediction model is any one of a support vector machine, a random forest, and a neural network model.

[0026] In this embodiment, the calculation formula of the device maintenance urgency index is: ; Wherein, represents the device maintenance urgency index of campus i, represents the device usage duration of campus i, represents the device failure probability of campus i, Represents the standard value of the equipment maintenance cycle, Is the environmental humidity label value.

[0027] In this embodiment, the calculation formula of the material notch index is: ; Wherein, Represents the material notch index of campus i, Represents the material inventory of campus i, Represents the material demand of campus i, Is the standard value of the material replenishment cycle, Represents the theoretical inventory demand of materials.

[0028] In this embodiment, the equipment maintenance scheduling unit runs the equipment maintenance scheduling strategy, and the equipment maintenance scheduling strategy includes the following specific steps: S31. Taking minimizing the total maintenance cost as the goal, construct the equipment scheduling objective function as: ; Wherein, Represents the maintenance duration of the equipment scheduled from campus j to campus i, Represents the labor cost per unit time of equipment scheduling; Represents the distance between campus j and campus i; Represents the transportation cost per unit distance of equipment scheduling; S32. Set the constraint conditions: ; Wherein, Represents the total maintenance duration of the equipment scheduled from campus j to each campus, Is the daily available maintenance duration limit of campus j.

[0029] In this embodiment, the material allocation unit runs the material allocation strategy, and the material allocation strategy includes the following specific steps: S41. Taking minimizing the allocation cost and shortage loss as the goal, construct the material allocation objective function as: ; Wherein, Is the amount of materials scheduled from campus j to campus i, Represents the transportation cost per unit quantity of material allocation, Is the notch index weight factor; S42. Set the constraint conditions: ; Wherein, Represents the total amount of materials scheduled from campus j to each campus, Is the material inventory of campus j, Is the minimum inventory retention of campus j.

[0030] It should be noted here that by integrating device operation data, consumable inventory data, and environmental data, multi-source inputs are provided for prediction and scheduling, and data collection is comprehensive; by synthesizing device usage intensity, failure probability, and environmental humidity, a maintenance priority sequence is dynamically generated to avoid resource waste; real-time synchronization of data across hospital campuses is achieved to support global resource scheduling, and the scheduling results are fed back to the data collection port to continuously optimize the prediction model and scheduling strategy, forming a "collection - prediction - scheduling - feedback" closed loop; temperature and humidity tags and peak period tags directly participate in the calculation to enhance the system's response ability to complex environments.

[0031] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not imply the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0032] It should be understood that determining B based on A does not mean determining B solely based on A, and B can also be determined based on A and / or other information.

[0033] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by the computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0034] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0035] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0036] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one way, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0037] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0038] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0039] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0040] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A medical device and consumable management system based on Internet of Things technology, characterized in that, The system includes a data collection module, a data prediction module, an urgency and gap index calculation module, and a scheduling and allocation module. The data collection module is used to collect equipment operation data, consumable data, and environmental data of each hospital area. The data prediction module is used to run an equipment maintenance demand prediction strategy and a material demand prediction strategy to predict the equipment failure probability and material demand. The urgency and gap index calculation module is used to calculate the equipment maintenance urgency index of each hospital area and the material gap index of each hospital area, and calculate the equipment maintenance urgency index and the material gap index of each hospital area. The scheduling and allocation module is used to calculate and output an equipment scheduling plan and a material allocation plan.

2. The medical device and consumable management system based on the Internet of Things technology according to claim 1, wherein The data collection module includes a patient equipment operation data collection unit, a consumable data collection unit, and an environmental data collection unit. The equipment operation data collection unit is used to collect the equipment usage duration, equipment failure frequency, and equipment maintenance cycle standard value of each hospital area. The consumable data collection unit is used to collect the material inventory, daily material consumption, and material replenishment cycle standard value of each hospital area. The environmental data collection unit is used to generate an environmental humidity tag value and a peak period tag value according to the environmental humidity and the equipment usage peak period. If the environmental humidity is greater than 70%, the environmental humidity tag value is 1, otherwise it is 0. If the current day is the equipment usage peak period, the peak period tag value is 1, otherwise it is 0.

3. The medical device and consumable management system based on the Internet of Things technology according to claim 2, wherein The data prediction module includes an equipment maintenance demand prediction unit and a material demand prediction unit. The equipment maintenance demand prediction unit is used to run an equipment maintenance demand prediction strategy to construct an equipment failure probability prediction model for each hospital area and predict the equipment failure probability. The material demand prediction unit is used to run a material demand prediction strategy to construct a material consumption prediction model for each hospital area and predict the material demand.

4. The medical device and consumable management system based on the Internet of Things technology according to claim 3, characterized in that, The urgency and gap index calculation module includes an equipment maintenance urgency calculation unit and a material gap index calculation unit. The equipment maintenance urgency calculation unit is used to calculate the equipment maintenance urgency index of each hospital area and generate an equipment maintenance queue. The material gap index calculation unit is used to calculate the material gap index of each hospital area.

5. The medical device and consumable management system based on the Internet of Things technology according to claim 4, wherein The scheduling and allocation module includes an equipment maintenance scheduling unit and a material allocation unit. The equipment maintenance scheduling unit is used to run an equipment maintenance scheduling strategy, with the goal of minimizing the total maintenance cost, construct an equipment scheduling objective function, and set equipment scheduling constraints. The material allocation unit is used to run a material allocation strategy, with the goal of minimizing the allocation cost and shortage loss, construct a material allocation objective function, and set material allocation constraints.

6. The medical device and consumable management system based on the Internet of Things technology according to claim 5, characterized in that, The equipment maintenance demand prediction unit runs an equipment maintenance demand prediction strategy. The equipment maintenance demand prediction strategy includes the following specific steps: S11. Taking 24 hours as the data collection time interval, collecting the equipment usage duration, equipment failure frequency, environmental humidity tag value, and peak period tag value of each hospital area every 24 hours and combining them in the form of a feature vector, and constructing an equipment failure probability prediction model. S12. The equipment failure probability prediction model takes the set of all feature vectors of a single hospital area as input, takes the predicted value of the equipment failure probability corresponding to each group of feature vectors of a single hospital area as output, takes the actual value of the equipment failure probability corresponding to each group of feature vectors of a single hospital area as the prediction target, and takes minimizing the mean square error between the predicted value and the actual value of the equipment failure probability of a single hospital area as the training target, and stops training when the mean square error between the predicted value and the actual value of the equipment failure probability of a single hospital area converges; outputs the equipment failure probability predicted by the equipment failure probability prediction model; the equipment failure probability prediction model is any one of a deep neural network model and a deep belief network model.

7. The medical device and consumable management system based on Internet of Things technology according to claim 6, characterized in that, The material demand prediction unit runs a material demand prediction strategy; the material demand prediction strategy includes the following specific steps: S21, taking 24 hours as the data collection time interval, collecting the average daily material consumption and peak label value of each hospital area every 24 hours and combining them into a feature vector to build a material consumption prediction model; S22. The material consumption prediction model uses as input a set of feature vectors of the combination of the average daily consumption of all materials in a single hospital area and the peak label value, uses as output the predicted value of the material demand corresponding to each set of feature vectors in a single hospital area, uses as prediction targets the actual value of the material demand corresponding to each set of feature vectors in a single hospital area, and uses as training targets the minimization of the sum of squares of the difference between the predicted value and the actual value of the material demand in a single hospital area, and stops training when the sum of squares of the difference between the predicted value and the actual value of the equipment failure probability in a single hospital area converges; outputs the material demand predicted by the material consumption prediction model; the material consumption prediction model is any one of a support vector machine, a random forest, and a neural network model.

8. The medical device and consumable management system based on the Internet of Things technology according to claim 7, wherein The calculation formula of the equipment maintenance urgency index is: ； Among them, represents the equipment maintenance urgency index of campus i, represents the equipment usage duration of campus i, represents the equipment failure probability of campus i, represents the standard value of the equipment maintenance cycle, is the environmental humidity label value.

9. The medical device and consumable management system based on Internet of Things technology according to claim 8, characterized in that, The calculation formula of the material gap index is: ； Among them, represents the material gap index of campus i, represents the material inventory of campus i, represents the material demand of campus i, is the standard value of the material replenishment cycle, represents the theoretical inventory demand of materials.

10. The medical device and consumable management system based on the Internet of Things technology according to claim 9, wherein, The equipment maintenance scheduling unit runs an equipment maintenance scheduling strategy, and the equipment maintenance scheduling strategy includes the following specific steps: S31. Taking minimizing the total maintenance cost as the goal, the equipment scheduling objective function is constructed as follows: ； Among them, represents the maintenance duration of the equipment scheduled from campus j to campus i, represents the labor cost per unit time of equipment scheduling; represents the distance between campus j and campus i of the equipment; represents the transportation cost per unit distance of equipment scheduling; S32. Set constraint conditions: ; where represents the total maintenance duration dispatched from campus j to each campus, is the single-day available maintenance duration limit for campus j.

11. The medical device and consumable management system based on the Internet of Things technology according to claim 10, characterized in that, The material allocation unit executes a material allocation strategy, and the material allocation strategy includes the following specific steps: S41. Taking minimizing the transfer cost and out-of-stock loss as the goal, the material transfer objective function is constructed as follows: ； Among them, is the amount of materials dispatched from campus j to campus i, represents the transportation cost of the unit quantity of material allocation, is the weight factor of the gap index; S42. Set constraint conditions: ; where represents the total amount of materials dispatched from campus j to each campus, is the material inventory of campus j, is the minimum inventory reserve of campus j.

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