Medical equipment intelligent scheduling method and device, equipment and storage medium

Abstract

The invention discloses a medical equipment intelligent scheduling method and device, equipment and a storage medium. The method comprises the steps that historical equipment use data of all first medical equipment in a target area are acquired, and the historical equipment use data at least comprise log data, positioning data and abnormal marks; for each first medical device, determining a first efficiency index and a first rule index corresponding to each first medical device according to historical device use data of the medical device; and based on a scheduling optimization strategy, according to the first efficiency index and the first rule index corresponding to each first medical device and the current to-be-inspected queue of the first medical device, dynamically allocating a patient inspection task so as to improve the utilization efficiency of the medical devices and the patient experience.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a method, apparatus, device and storage medium for intelligent scheduling of medical equipment. Background Art

[0002] Current medical equipment scheduling generally relies on manual scheduling and static data analysis, resulting in significant efficiency bottlenecks. Traditional methods employ fixed scheduling models, leading to severe imbalances in equipment resource utilization. While existing technologies can analyze equipment idle periods after the fact, they are unable to respond to real-time demand fluctuations such as equipment failures and emergency interventions, and they lack a globally visualized resource scheduling system.

[0003] Recent attempts at improvement have largely focused on optimizing a single dimension, yet have yet to overcome fundamental technical limitations. Solutions like queuing theory and time-slot appointment algorithms still rely on historical data modeling, making them incapable of responding to emergencies and preventing real-time scheduling, severely hindering improvements in medical service quality. Summary of the Invention

[0004] The present invention provides a method, apparatus, device and storage medium for intelligent scheduling of medical equipment to improve the utilization efficiency of medical equipment and patient experience.

[0005] According to one aspect of the present invention, a method for intelligent scheduling of medical equipment is provided. The method comprises:

[0006] Acquire historical device usage data of each first medical device in the target area, wherein the historical device usage data includes at least log data, positioning data, and abnormality marks;

[0007] For each of the first medical devices, determining a first efficiency indicator and a first regularity indicator corresponding to each of the first medical devices based on historical device usage data of the medical device;

[0008] Based on the scheduling optimization strategy, patient examination tasks are dynamically allocated according to the first efficiency index and the first regularity index corresponding to each of the first medical devices and the current queue of the first medical devices to be examined.

[0009] According to another aspect of the present invention, a medical equipment intelligent scheduling device is provided. The device comprises:

[0010] A usage data acquisition module is used to acquire historical device usage data of each first medical device in the target area, wherein the historical device usage data at least includes log data, positioning data and abnormality marks;

[0011] an equipment index determination module, configured to determine, for each of the first medical devices, a first efficiency index and a first regularity index corresponding to each of the first medical devices based on historical equipment usage data of the medical devices;

[0012] The medical equipment scheduling module is used to dynamically allocate patient examination tasks based on a scheduling optimization strategy, according to the first efficiency index, the first regularity index corresponding to each of the first medical equipment, and the current queue of the first medical equipment to be examined.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the medical equipment intelligent scheduling method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the medical equipment intelligent scheduling method described in any embodiment of the present invention when executed.

[0018] The technical solution of an embodiment of the present invention obtains historical device usage data for each first medical device within a target area. For each first medical device, a first efficiency indicator and a first regularity indicator corresponding to each first medical device are determined based on the historical device usage data. Based on a scheduling optimization strategy, patient examination tasks are dynamically assigned based on the first efficiency indicator and first regularity indicator corresponding to each first medical device, as well as the current queue of the first medical device to be examined, thereby improving the utilization efficiency of medical devices and the patient experience.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a flow chart of a medical equipment intelligent scheduling method provided according to the first embodiment of the present invention;

[0022] Figure 2 This is a structural diagram of a medical equipment intelligent scheduling device provided according to the second embodiment of the present invention;

[0023] Figure 3 It is a structural diagram of an electronic device for implementing the medical equipment intelligent scheduling method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Example 1

[0027] Figure 1 This is a flowchart of a medical equipment intelligent scheduling method provided in the first embodiment of the present invention. This embodiment is applicable to the intelligent scheduling management of the entire process of hospital diagnostic equipment. The method can be executed by a medical equipment intelligent scheduling device, which can be implemented in the form of hardware and / or software. The medical equipment intelligent scheduling device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0028] S101. Obtain historical device usage data of each first medical device in a target area.

[0029] The target area may refer to an area where medical equipment intelligent scheduling is required, such as a branch of a hospital, all campuses of a hospital, or all hospitals in a region. The first medical equipment may refer to the medical equipment to be intelligently scheduled. In the present invention, comprehensive intelligent scheduling can be performed for a certain type of medical equipment.

[0030] Historical device usage data may refer to historical usage data of the first medical device. For example, historical device usage data includes at least log data, location data, and abnormality flags. Log data records the start and end times of each examination, patient ID, and emergency department identification; location data records patient arrival time and movement path, etc.; and abnormality flags record periods of device failure and patient missed appointments, etc.

[0031] Specifically, through the scheduling platform of medical equipment, using the communication channel pre-built in the target area, the historical equipment usage data of each first medical equipment is collected, and the collected historical equipment usage data is cleaned to reduce the interference of other abnormal data.

[0032] S102 : For each first medical device, determine a first efficiency indicator and a first regularity indicator corresponding to each first medical device according to historical device usage data of the medical device.

[0033] The first efficiency index may refer to an efficiency index of the first medical device, preferably, the first efficiency index may refer to the device utilization rate of the first medical device. The first regularity index may refer to a regularity index of the first medical device, preferably, the first regularity index may refer to a service interval coefficient index of the first medical device.

[0034] It should be noted that, in other embodiments, the first efficiency indicator may also refer to the average waiting time of patients, and the first regularity indicator may also refer to the periodic fluctuation rate of the first medical device, etc.

[0035] Specifically, for each first medical device, a first efficiency index and a first regularity index are calculated based on historical device usage data. For example, the first efficiency index of the first medical device is determined based on the actual service time and device power-on time in the historical device usage data, and the first regularity index of the first medical device is determined based on the adjacent examination intervals and the average interval time in the historical device usage data.

[0036] Exemplarily, determining the first efficiency index and first regularity index corresponding to each first medical device based on the historical device usage data of the medical device includes: determining a device service curve corresponding to the first medical device based on the historical device usage data of the first medical device; and determining the first efficiency index and first regularity index corresponding to each first medical device based on the device service curve. The device service curve may be a service usage distribution curve of the first medical device. Exemplarily, the device service curve includes a service interval distribution curve and a periodic fluctuation curve.

[0037] Specifically, based on the historical equipment usage data of the first medical device, the timestamp of each service of the first medical device is determined (such as the time series of the CT machine completing the examination: 9:00, 9:25, 10:10...), the time interval between adjacent examination tasks is calculated (25 minutes, 45 minutes...), and a service interval distribution curve is generated. The usage data of the first medical device is sliced ​​by hour / day / week, and the periodic fluctuations are analyzed. Key features such as daily fluctuations, weekly fluctuations, and sudden fluctuations can be extracted, and the periodic fluctuation curve corresponding to the first medical device can be obtained. The total working time and the total available time of the first medical device are determined through the service interval distribution curve, and the first efficiency index of the first medical device is determined. The standard deviation of the intervals of the first medical device's most recent 10 examinations is determined based on the periodic fluctuation curve to determine the first regularity index.

[0038] S103 : Based on the scheduling optimization strategy, dynamically assign patient examination tasks according to the first efficiency index and the first regularity index corresponding to each of the first medical devices, and the current queue of the first medical devices to be examined.

[0039] Specifically, based on the first efficiency index, first regularity index, and weight coefficients of each index corresponding to each first medical device, a comprehensive loss function of the first medical device is calculated. Then, based on the comprehensive loss function and the current queue to be examined, the newly arrived patient examination tasks are comprehensively scheduled among the first medical devices. For example, the comprehensive loss function = α * first efficiency index + β * first regularity index. The weight coefficients α and β can be dynamically adjusted according to actual conditions. For example, the values ​​of α and β can be shown in Table 1:

[0040]

[0041] Table 1

[0042] Exemplarily, the scheduling optimization strategy includes at least minimizing waiting time and maximizing equipment utilization, and the constraints include at least the maximum capacity of the equipment, emergency priority, and doctor working hours.

[0043] Exemplarily, the scheduling optimization strategy is based on which the patient examination tasks are dynamically allocated according to the first efficiency index and the first regularity index corresponding to each of the first medical devices and the current queue of the first medical devices to be examined, including:

[0044] For each of the first medical devices, determining a device comprehensive load factor corresponding to the first medical device according to the first efficiency index, the first regularity index, and the current queue to be inspected;

[0045] Based on a multi-objective optimization algorithm, with the goal of minimizing waiting time and maximizing equipment utilization, the task allocation weight corresponding to each of the first medical devices is determined according to the comprehensive equipment load coefficient corresponding to each of the first medical devices, so as to dynamically allocate patient examination tasks.

[0046] Specifically, the comprehensive equipment load coefficient of each first medical device is calculated using the current waiting queue, the first efficiency index, the first regularity index and the weight coefficients of each index. Then, with minimizing waiting time and maximizing equipment utilization, as well as the maximum carrying capacity, emergency priority, and doctor's working hours as constraints, an allocation plan that optimizes the global goal is selected based on the comprehensive equipment load coefficient of each first medical device.

[0047] Write the optimal time window into the equipment schedule, update the equipment status in the digital twin map, and clarify the idle status of each first-line medical device.

[0048] Exemplarily, the dynamically allocating patient examination tasks includes:

[0049] Determine the patient type corresponding to the medical patient; if the patient type is an emergency type, determine the third medical device closest to the medical patient from the first medical device, and readjust the patient examination task of the third medical device based on preset adjustment rules.

[0050] It should be noted that the patient type includes the emergency type and the routine type. The third medical device may refer to the first medical device that is closest to the medical patient.

[0051] Specifically, if the patient type is determined to be an emergency, the nearest third medical device to the patient is determined based on the patient's current location information. The emergency patient is given the highest priority and is given priority for medical examinations. The examination time for subsequent patient examinations at the third medical device is compressed, and the examination task for a non-emergency patient can also be transferred to another nearby first medical device.

[0052] Exemplarily, after dynamically allocating patient examination tasks, the method further includes:

[0053] According to the assigned patient examination task, the first position information of the medical patient and the second position information corresponding to the second medical device performing the patient examination task are determined; based on the pre-built digital twin map, the optimal examination route is generated for the medical patient according to the first position information and the second position information.

[0054] In the present invention, the second medical device can refer to the first medical device assigned to the patient for examination. Those skilled in the art can generate a three-dimensional digital twin map based on BIM or point cloud scanning technology, marking the device location of each medical device (e.g., CT room, MRI room). The patient can use a mobile device (e.g., portable ultrasound) to provide real-time feedback on their location via a UWB tag.

[0055] Specifically, the patient's primary location is acquired using hospital Wi-Fi or the patient's phone's GPS and associated with the examination request. Simultaneously, the secondary location of a second medical device is determined based on the 3D digital twin map. Based on this primary and secondary location information, an optimal examination route is generated for the patient and sent to the patient's mobile device, providing navigational guidance via the map.

[0056] Exemplarily, based on a pre-trained equipment failure prediction model, a fourth medical device that has failed and an equipment maintenance window corresponding to the fourth medical device are predicted from the first medical device; based on the equipment maintenance window, the patient examination task of the fourth medical device is reallocated.

[0057] Specifically, the device failure prediction model predicts device failure risks and identifies potential fourth medical devices that are about to fail, allowing for early planning of maintenance windows. When a sudden failure occurs, the patient examination task for the fourth medical device is automatically reassigned to a backup device, and the current status of the fourth medical device in the digital twin map is updated.

[0058] For example, medical staff can monitor the status of equipment and patient queues throughout the hospital through digital twin maps, and medical patients can receive real-time queue progress, examination room change notifications and navigation instructions through mobile devices.

[0059] The technical solution of the embodiment of the present invention obtains the historical equipment usage data of each first medical device in the target area. For each first medical device, the first efficiency index and the first regularity index corresponding to each first medical device are determined based on the historical equipment usage data of the medical device. Based on the scheduling optimization strategy, according to the first efficiency index and the first regularity index corresponding to each first medical device, and the current queue of the first medical device to be inspected, the patient examination tasks are dynamically allocated, so as to reduce the equipment idle rate through dynamic load balancing, reduce the average waiting time of medical patients, improve the efficiency of emergency response, and thus improve the utilization efficiency of medical equipment and patient experience.

[0060] Example 2

[0061] Figure 2 This is a schematic diagram of the structure of a medical equipment intelligent scheduling device provided by the second embodiment of the present invention. Figure 2 As shown, the device includes:

[0062] A usage data acquisition module 201 is configured to acquire historical device usage data of each first medical device in a target area, wherein the historical device usage data includes at least log data, positioning data, and abnormality marks;

[0063] The device index determination module 202 is configured to determine, for each of the first medical devices, a first efficiency index and a first regularity index corresponding to each of the first medical devices based on the historical device usage data of the medical device;

[0064] The medical equipment scheduling module 203 is used to dynamically allocate patient examination tasks based on a scheduling optimization strategy, according to the first efficiency index and the first regularity index corresponding to each of the first medical equipment, and the current queue of the first medical equipment to be examined.

[0065] The technical solution of the embodiment of the present invention obtains the historical equipment usage data of each first medical device in the target area. For each first medical device, the first efficiency index and the first regularity index corresponding to each first medical device are determined based on the historical equipment usage data of the medical device. Based on the scheduling optimization strategy, according to the first efficiency index and the first regularity index corresponding to each first medical device, and the current queue of the first medical device to be inspected, the patient examination tasks are dynamically allocated, so as to reduce the equipment idle rate through dynamic load balancing, reduce the average waiting time of medical patients, improve the efficiency of emergency response, and thus improve the utilization efficiency of medical equipment and patient experience.

[0066] Optionally, the device indicator determination module 202 is specifically configured to:

[0067] determining, based on historical device usage data of the first medical device, a device service curve corresponding to the first medical device, wherein the device service curve includes a service interval distribution curve and a period fluctuation curve;

[0068] According to the equipment service curve, a first efficiency index and a first regularity index corresponding to each of the first medical devices are determined.

[0069] Optionally, the scheduling optimization strategy at least includes minimizing waiting time and maximizing equipment utilization, and the constraints at least include maximum equipment capacity, emergency priority, and doctor working hours.

[0070] Optionally, the medical equipment scheduling module 203 is configured to:

[0071] For each of the first medical devices, determining a device comprehensive load factor corresponding to the first medical device according to the first efficiency index, the first regularity index, and the current queue to be inspected;

[0072] Based on a multi-objective optimization algorithm, with the goal of minimizing waiting time and maximizing equipment utilization, the task allocation weight corresponding to each of the first medical devices is determined according to the comprehensive equipment load coefficient corresponding to each of the first medical devices, so as to dynamically allocate patient examination tasks.

[0073] Optionally, the medical equipment scheduling module 203 is further configured to:

[0074] Determining a patient type corresponding to the medical patient, wherein the patient type includes an emergency type and a routine type;

[0075] In the case that the patient type is an emergency type, a third medical device closest to the medical patient is determined from the first medical device, and the patient examination task of the third medical device is readjusted based on a preset adjustment rule.

[0076] Optionally, the device further includes an inspection route determination module, configured to:

[0077] After the patient examination task is dynamically assigned, determining first position information of the medical patient and second position information corresponding to a second medical device that performs the patient examination task according to the assigned patient examination task;

[0078] Based on a pre-built digital twin map, an optimal examination route is generated for the medical patient according to the first location information and the second location information.

[0079] Optionally, the device further includes an emergency processing module, configured to:

[0080] Based on a pre-trained device failure prediction model, predicting a fourth medical device that has failed from the first medical devices, and a device maintenance window corresponding to the fourth medical device;

[0081] Based on the equipment maintenance window, the patient examination task of the fourth medical equipment is reallocated.

[0082] The medical equipment intelligent scheduling device provided in the embodiment of the present invention can execute the medical equipment intelligent scheduling method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0083] Example 3

[0084] Figure 3 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0085] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0086] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0087] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the medical device intelligent scheduling method.

[0088] In some embodiments, the medical device intelligent scheduling method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the medical device intelligent scheduling method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the medical device intelligent scheduling method in any other appropriate manner (for example, by means of firmware).

[0089] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0090] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0091] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0092] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0093] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0094] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0095] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0096] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A medical equipment intelligent scheduling method, characterized in that: include: Acquire historical device usage data of each first medical device in the target area, wherein the historical device usage data includes at least log data, positioning data, and abnormality marks; For each of the first medical devices, determining a first efficiency indicator and a first regularity indicator corresponding to each of the first medical devices based on historical device usage data of the first medical device; Based on the scheduling optimization strategy, patient examination tasks are dynamically allocated according to the first efficiency index and the first regularity index corresponding to each of the first medical devices and the current queue of the first medical devices to be examined.

2. The method according to claim 1, characterized in that Determining a first efficiency indicator and a first regularity indicator corresponding to each of the first medical devices based on historical device usage data of the first medical devices includes: determining, based on historical device usage data of the first medical device, a device service curve corresponding to the first medical device, wherein the device service curve includes a service interval distribution curve and a period fluctuation curve; According to the equipment service curve, a first efficiency index and a first regularity index corresponding to each of the first medical devices are determined.

3. The method according to claim 1, characterized in that The scheduling optimization strategy at least includes minimizing waiting time and maximizing equipment utilization, and the constraints at least include the maximum load capacity of the equipment, emergency priority, and doctor working hours.

4. The method according to claim 3, characterized in that The method of dynamically allocating patient examination tasks based on the scheduling optimization strategy according to the first efficiency index and the first regularity index corresponding to each of the first medical devices and the current queue of the first medical devices to be examined includes: For each of the first medical devices, determining a device comprehensive load factor corresponding to the first medical device according to the first efficiency index, the first regularity index, and the current queue to be inspected; Based on a multi-objective optimization algorithm, with the goal of minimizing waiting time and maximizing equipment utilization, the task allocation weight corresponding to each of the first medical devices is determined according to the comprehensive equipment load coefficient corresponding to each of the first medical devices, so as to dynamically allocate patient examination tasks.

5. The method according to claim 1 or 4, characterized in that The dynamic allocation of patient examination tasks includes: Determining a patient type corresponding to the medical patient, wherein the patient type includes an emergency type and a routine type; In the case that the patient type is an emergency type, a third medical device closest to the medical patient is determined from the first medical device, and the patient examination task of the third medical device is readjusted based on a preset adjustment rule.

6. The method according to claim 1, characterized in that After dynamically allocating the patient examination tasks, the method further includes: Determining, according to the assigned patient examination task, first position information of the medical patient and second position information corresponding to a second medical device performing the patient examination task; Based on a pre-built digital twin map, an optimal examination route is generated for the medical patient according to the first location information and the second location information.

7. The method according to claim 1, characterized in that The method further comprises: Based on a pre-trained device failure prediction model, predicting a fourth medical device that has failed from the first medical devices, and a device maintenance window corresponding to the fourth medical device; Based on the equipment maintenance window, the patient examination task of the fourth medical equipment is reallocated.

8. A medical equipment intelligent scheduling device, characterized in that: include: A usage data acquisition module is used to acquire historical device usage data of each first medical device in the target area, wherein the historical device usage data at least includes log data, positioning data and abnormality marks; an equipment index determination module, configured to determine, for each of the first medical devices, a first efficiency index and a first regularity index corresponding to each of the first medical devices based on historical equipment usage data of the medical devices; The medical equipment scheduling module is used to dynamically allocate patient examination tasks based on a scheduling optimization strategy, according to the first efficiency index, the first regularity index corresponding to each of the first medical equipment, and the current queue of the first medical equipment to be examined.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the medical equipment intelligent scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the medical equipment intelligent scheduling method according to any one of claims 1 to 7 when executed.

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