Intelligent management method for medical instrument, electronic equipment and storage medium

Abstract

The invention relates to the field of artificial intelligence, in particular to an intelligent management method for a medical instrument, electronic equipment and a storage medium, and aims to predict the residual life of the instrument. The intelligent management method for the medical instruments provided by the invention comprises the following steps: acquiring attribute information, position information and a current use state of each instrument in a target hospital; according to the instrument use requests initiated by the departments of the target hospital and the attribute information, the position information and the current use state of each instrument, the instruments are scheduled; obtaining maintenance records in the latest N time periods, and counting fault data of each instrument in each time period; and predicting the residual life of each instrument based on the fault data in the latest N time periods and a pre-trained fault prediction model. According to the invention, automatic scheduling of the instrument and prediction of the residual life of the instrument are realized.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to an intelligent management method, electronic equipment and storage medium for medical instruments. Background Art

[0002] In the current medical environment, medical instruments are an important support for medical services, and their performance and status are directly related to the accuracy of diagnosis and the effectiveness of treatment. With the continuous increase in the types and number of medical instruments, effective management and regulation are particularly important.

[0003] Existing medical instrument management systems generally lack intelligent prediction and maintenance functions. Traditional systems can only view the historical usage data, historical maintenance records and current status of the instrument, but cannot predict the remaining life of the instrument through in-depth analysis of these data. This means that the system cannot predict the occurrence of instrument shortages in advance. This situation will not only affect the continuity and quality of medical services, but may also pose a threat to the life safety of patients. Summary of the invention

[0004] In order to solve the above problems in the prior art, the present invention proposes an intelligent management method, electronic device and storage medium for medical instruments, which realize the prediction of the remaining life of the medical instruments.

[0005] In a first aspect of the present invention, a smart management method for medical instruments is provided, the method comprising:

[0006] Obtain the attribute information, location information and current usage status of each instrument in the target hospital;

[0007] Scheduling instruments according to instrument use requests initiated by various departments of the target hospital, as well as the attribute information, location information, and current use status of each instrument;

[0008] Obtain maintenance records within the most recent N time periods, and count the fault data of each instrument within each of the time periods;

[0009] Predicting the remaining life of each instrument based on the fault data in the most recent N time periods and the pre-trained fault prediction model;

[0010] in,

[0011] The attribute information includes: instrument type, instrument model, instrument number, manufacturer and instrument parameters;

[0012] The current use state includes: idle state, use state or maintenance state;

[0013] The N and the time period are both preset values;

[0014] The maintenance records include: the time of each failure, the type of failure, the maintenance time and the maintenance cost;

[0015] The fault data includes: the total number of faults, average maintenance cost and average maintenance time corresponding to various fault types.

[0016] Preferably, the use request includes: the type of target instrument, priority, request period and request location;

[0017] The step of “scheduling instruments according to instrument use requests initiated by various departments of the target hospital, as well as the attribute information, location information and current use status of each instrument” includes:

[0018] Receive the usage request initiated by each department of the target hospital and add it to the request queue;

[0019] Selecting one of the usage requests with the highest priority from the request queue at a time;

[0020] Allocate an instrument to the usage request according to the selected usage request and the attribute information, the location information and the usage status of each instrument;

[0021] The selected usage request is deleted from the request queue.

[0022] Preferably, the step of "assigning an instrument to the use request according to the selected use request and the attribute information, the location information and the use status of each instrument" includes:

[0023] According to the type of the target instrument, matching the instrument type in the attribute information of each instrument to obtain one or more target instrument models;

[0024] According to the target instrument model, one or more target instruments are found;

[0025] Among the found target instruments, an instrument that is in the idle state during the request period and is closest to the requested location is selected, and the attribute information and the location information of the instrument are sent to the requester.

[0026] Preferably, each instrument type corresponds to a pre-trained fault prediction model;

[0027] The step of “predicting the remaining life of each instrument based on the fault data in the most recent N time periods and the pre-trained fault prediction model” includes:

[0028] Normalizing the fault data of a certain instrument in the most recent N time periods to obtain normalized data in the i-(N-1)th, i-(N-2)th, ..., i-1th, and i-th time periods; wherein i represents the most recent time period;

[0029] The normalized data in the i-(N-1)th, i-(N-2), ..., i-1th, and i-th time periods are sequentially input into the pre-trained fault prediction model corresponding to the instrument type to which the instrument belongs in chronological order to obtain an output result; the output result includes: the fault data of the instrument in the next time period;

[0030] According to the output results, the total number of failures, the total maintenance cost and the total duration of failures within the next time period are calculated;

[0031] Compare the total number of failures, the total maintenance cost and the total duration of failures with the preset failure number threshold, maintenance cost threshold and failure duration threshold respectively, to determine whether the instrument will be scrapped within the next time period;

[0032] If the vehicle will not be scrapped, the output result is input into the pre-trained fault prediction model again, and the total number of faults, total maintenance cost and total fault duration in the next time period are calculated according to the new output result to determine whether the vehicle will be scrapped;

[0033] The process is repeated until it is determined that the instrument will be scrapped within a certain time period in the future, thereby obtaining the remaining life of the instrument.

[0034] Preferably, each of the fault prediction models adopts an LSTM neural network;

[0035] The training steps of the fault prediction model include:

[0036] For each instrument type, collecting a preset number of historical maintenance records corresponding to the time period;

[0037] For each instrument type, historical failure data within each time period is counted based on the collected historical maintenance records;

[0038] For each instrument type, the historical fault data are respectively organized into a time series, thereby constructing a data set for the instrument type;

[0039] For each instrument type, the data in the corresponding data set is divided into a training set and a validation set in chronological order;

[0040] Based on the training set and the validation set of each instrument type, training the fault prediction model corresponding to the instrument type;

[0041] in,

[0042] The historical maintenance records include: the time of each failure, failure type, maintenance time and maintenance cost;

[0043] The historical fault data includes: the total number of times, average maintenance cost and average maintenance time corresponding to various fault types.

[0044] Preferably, the training step of the fault prediction model further includes:

[0045] Regularly collect newly generated maintenance records and update the training data set;

[0046] The fault prediction model is retrained according to the updated training data set.

[0047] Preferably, the method further comprises:

[0048] In response to a user-triggered instruction to view all instruments, all instrument types of the target hospital and the number of instruments corresponding to each instrument type are displayed on the human-computer interaction interface;

[0049] In response to a location viewing instruction for a certain type of instrument triggered by a user, the location information of each instrument of the type is displayed in the form of a map on the human-computer interaction interface;

[0050] In response to a property viewing instruction for a certain instrument triggered by a user, displaying the property information of the instrument on the human-computer interaction interface;

[0051] In response to a maintenance record viewing instruction for a certain instrument triggered by a user, displaying the historical maintenance record of the instrument on the human-computer interaction interface;

[0052] In response to a life viewing instruction for a certain instrument triggered by a user, the remaining life of the instrument is displayed on the human-computer interaction interface.

[0053] Preferably, the method further comprises:

[0054] Count the average idle time corresponding to each instrument type in the most recent time period;

[0055] Calculate the average remaining life corresponding to each instrument type based on the remaining life of each instrument;

[0056] A procurement plan is generated according to the average idle time and the average remaining life.

[0057] According to a second aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method described above.

[0058] According to a third aspect of the present invention, a computer-readable storage medium is provided, storing a computer program that can be loaded by a processor and execute the method described above.

[0059] The present invention has the following beneficial effects:

[0060] The present invention selects idle instruments that meet the requirements for each department according to the use request, realizing automatic dispatch of instruments. The pre-trained fault prediction model is used to predict the remaining life of the instrument, providing the possibility for making an instrument purchase plan in advance.

[0061] When dispatching equipment, priority is given to the highest priority requests, and proximity is used as the principle when the equipment type requirements are met in order to save the greatest number of lives.

[0062] In order to ensure the accuracy of the model, a fault prediction model is pre-trained for each instrument type. The fault data in the last N time periods are input into the corresponding fault prediction model to obtain the fault data of the next time period, and then the instrument is judged whether it will be scrapped based on the fault data of the next time period. If it will not be scrapped in the next period, it will continue to predict whether it will be scrapped in the next period, and repeat the execution to predict the remaining life of the instrument.

[0063] By organizing historical failure data into time series and training the failure prediction model, we can learn the dependency relationship between failure data in different time periods, thereby improving the accuracy of life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the main steps of the first embodiment of the intelligent management method for medical instruments in the present invention;

[0065] Figure 2 It is a schematic diagram of the main steps of Embodiment 2 of the intelligent management method for medical instruments in the present invention;

[0066] Figure 3 It is a schematic diagram of the main steps of Embodiment 3 of the intelligent management method for medical instruments in the present invention;

[0067] Figure 4 Schematic diagram of the training steps of the fault prediction model in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0069] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 described embodiments are 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 creative work are within the scope of protection of the present invention.

[0070] It should be noted that, in the description of the present invention, the terms "first" and "second" are only for the convenience of description, and do not indicate or imply the relative importance of the devices, elements or parameters, and therefore cannot be understood as limiting the present invention. In addition, the term "and / or" in the present invention is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally indicates that the associated objects before and after are in an "or" relationship.

[0071] Figure 1 Schematic diagram of the main steps of the first embodiment of the intelligent management method for medical instruments in the present invention. Figure 1 As shown, the management method of this embodiment includes steps A10-A40:

[0072] Step A10: Obtain attribute information, location information and current usage status of each instrument in the target hospital.

[0073] The attribute information includes: instrument type, instrument model, instrument number, manufacturer and instrument parameters; the current use status includes: idle state, use state or maintenance state; instrument types include: CT machine, color Doppler ultrasound diagnostic instrument, portable ECG monitor, etc. Each instrument type may correspond to one or more different instrument models, and each instrument model may correspond to one or more different instruments.

[0074] Step A20: Schedule the instruments according to the instrument use requests initiated by the departments of the target hospital, as well as the attribute information, location information and current use status of each instrument.

[0075] The usage request includes: the type of target instrument, priority, request period and request location.

[0076] Step A20 may specifically include steps A21-A24:

[0077] Step A21: Receive usage requests initiated by departments of the target hospital and add them to the request queue.

[0078] Step A22: Select one usage request with the highest priority from the request queue each time.

[0079] The use request can be issued by the client device, and the management method of this embodiment runs on the server device. Regarding the setting of priority, the hospital can set the corresponding rules in advance. For example, the highest priority can be set for rescuing critically ill patients, a higher priority can be set for use requests issued when preparing for surgery, and the lowest priority can be set for ordinary outpatient physical examinations. When issuing a use request through a client device, each department can set the priority according to the above rules, or the department can enter the purpose of use on the client device, and then the client program will determine the corresponding priority according to the pre-determined rules.

[0080] Step A23: Allocate an instrument to the use request according to the selected use request and the attribute information, location information and use status of each instrument. This step may specifically include steps A231-A233:

[0081] Step A231: According to the type of the target instrument, match the instrument type in the attribute information of each instrument to obtain one or more target instrument models.

[0082] For example, when the type of the target instrument is a CT machine, multiple different CT machine models may be matched, and each model may have one or more CT machines.

[0083] Step A232: Find one or more target instruments according to the target instrument model.

[0084] Step A233: Among the found target instruments, select an instrument that is idle during the requested period and closest to the requested location, and send the attribute information and location information of the instrument to the requester.

[0085] For some portable devices, such as ECG monitors, the requester will bring the target device back to the requested location for use. For some devices that cannot be easily moved, such as CT machines, the requester can push the patient to the location of the target device for examination. Therefore, the requested location can be understood as the location of the patient's ward or outpatient clinic. Finding the nearest device helps to quickly complete the relevant examination.

[0086] Step A24: Delete the selected usage request from the request queue.

[0087] Step A30: Obtain maintenance records within the latest N time periods, and collect statistics on the fault data of each instrument within each time period.

[0088] Among them, N is a preset value, such as 3, 4 or 5; the time period is also a preset value, such as half a year or a year; the maintenance record includes: the time of each failure, the type of failure, the maintenance time and the maintenance cost; the failure data includes: the total number of failures corresponding to various failure types, the average maintenance cost and the average maintenance time.

[0089] Step A40: predict the remaining life of each instrument based on the fault data in the most recent N time periods and the pre-trained fault prediction model.

[0090] In this embodiment, each instrument type corresponds to a pre-trained fault prediction model.

[0091] Step A40 may specifically include steps A41-A46:

[0092] Step A41, normalize the fault data of a certain instrument in the latest N time periods respectively to obtain the normalized data in the i-(N-1)th, i-(N-2)th, ..., i-1th, and i-th time periods.

[0093] Here, i represents the most recent time period.

[0094] Step A42: input the normalized data in the i-(N-1)th, i-(N-2)th, ..., i-1th, and i-th time periods into the pre-trained fault prediction model corresponding to the instrument type to which the instrument belongs in chronological order to obtain the output result.

[0095] The output results include: the fault data of the instrument in the next time period, that is, the total number of faults, average maintenance cost and average maintenance time corresponding to various fault types in the next time period.

[0096] Step A43: Calculate the total number of failures, total maintenance costs and total failure duration in the next time period based on the output results.

[0097] Step A44, compare the total number of failures, total maintenance cost and total failure duration with the preset failure number threshold, maintenance cost threshold and failure duration threshold respectively to determine whether the instrument will be scrapped within the next time period.

[0098] In this embodiment, if any one of the three indicators exceeds the corresponding threshold value, it is determined that the instrument will be scrapped within the next time period, that is, the instrument will end its service life.

[0099] Step A45: If the vehicle will not be scrapped, the output result is input into the pre-trained fault prediction model again to obtain a new output result, and then go to step A42.

[0100] Step A46: If it is determined that the instrument will be scrapped within a certain time period in the future, the remaining life of the instrument is obtained.

[0101] Figure 2 Schematic diagram of the main steps of the second embodiment of the intelligent management method for medical instruments in the present invention. Figure 2 As shown, the management method of this embodiment includes steps B10-B90:

[0102] Among them, steps B10-B40 correspond to steps A10-A40 in embodiment 1 respectively and are the same, and are not repeated here.

[0103] Step B50: In response to the user-triggered instruction to view all instruments, all instrument types of the target hospital and the number of instruments corresponding to each instrument type are displayed on the human-computer interaction interface.

[0104] Step B60: In response to a location viewing instruction for a certain type of instrument triggered by the user, the location information of each instrument of this type is displayed in the form of a map or a list on the human-computer interaction interface.

[0105] Step B70: In response to a property viewing instruction for a certain instrument triggered by a user, the property information of the instrument is displayed on the human-computer interaction interface.

[0106] Step B80: In response to a maintenance record viewing instruction for a certain instrument triggered by a user, the historical maintenance record of the instrument is displayed on the human-computer interaction interface.

[0107] Step B90: In response to a user-triggered instruction to view the life of a certain instrument, the remaining life of the instrument is displayed on the human-computer interaction interface.

[0108] Figure 3 Schematic diagram of the main steps of the third embodiment of the intelligent management method for medical instruments in the present invention. Figure 3 As shown, the management method of this embodiment includes steps C10-C70:

[0109] Among them, steps C10-C40 correspond to steps A10-A40 in embodiment 1 respectively and are the same, and are not repeated here.

[0110] Step C50: Count the average idle time corresponding to each instrument type in the most recent time period.

[0111] If the average idle time exceeds the preset value, it means that the number of instruments of this type can fully meet the usage demand; if it is too short, it means that the number of instruments of this type cannot meet the usage demand.

[0112] Step C60: Calculate the average remaining life of each instrument type according to the remaining life of each instrument.

[0113] Step C70: Generate a purchase plan based on the average idle time and the average remaining life.

[0114] Specifically, based on the average idle time and the average remaining life, it is possible to know which instrument types will be in short supply in the current or future period, so that corresponding procurement plans can be formulated.

[0115] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art can understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0116] In the embodiment of the present invention, each fault prediction model adopts an LSTM neural network.

[0117] Figure 4 FIG. 1 is a schematic diagram of the training steps of the fault prediction model in an embodiment of the present invention. Figure 4 As shown, the training steps of this embodiment may include steps D10-D50:

[0118] Step D10, for each instrument type, collect historical maintenance records corresponding to a preset number of time periods.

[0119] Step D20: For each instrument type, historical fault data in each time period is counted based on the collected historical maintenance records.

[0120] Step D30: For each instrument type, the historical fault data are respectively organized into a time series, thereby constructing a data set for the instrument type.

[0121] Step D40: For each instrument type, the data in the corresponding data set is divided into a training set and a validation set in chronological order.

[0122] Specifically, the dataset is divided into 7:3 ratios, with the 7 earlier ones used as training sets and the 3 later ones used as validation sets.

[0123] Step D50: Based on the training set and the validation set of each instrument type, the fault prediction model corresponding to the instrument type is trained.

[0124] Among them, historical maintenance records include: the time of each failure, failure type, maintenance time and maintenance cost; historical failure data include: the total number of failure types, average maintenance cost and average maintenance time.

[0125] In an optional embodiment, the training step of the fault prediction model may include steps D50-D60:

[0126] Step D50: regularly collect newly generated maintenance records and update the training data set.

[0127] Step D60: retrain the fault prediction model according to the updated training data set.

[0128] Based on the above method embodiment, the present invention also provides an embodiment of an electronic device. The electronic device of this embodiment includes a memory and a processor. The memory stores a computer program that can be loaded by the processor and execute the method as described above.

[0129] Furthermore, based on the above method embodiment, the present invention also provides an embodiment of a computer-readable storage medium, in which the storage medium of this embodiment stores a computer program that can be loaded by a processor and execute the method as described above.

[0130] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0131] Those skilled in the art should be able to appreciate that the method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0132] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An intelligent management method for medical instruments, characterized in that: The method comprises: Obtain the attribute information, location information and current usage status of each instrument in the target hospital; Scheduling instruments according to instrument use requests initiated by various departments of the target hospital, as well as the attribute information, location information, and current use status of each instrument; Obtain maintenance records within the most recent N time periods, and count the fault data of each instrument within each of the time periods; Predicting the remaining life of each instrument based on the fault data in the most recent N time periods and the pre-trained fault prediction model; in, The attribute information includes: instrument type, instrument model, instrument number, manufacturer and instrument parameters; The current use state includes: idle state, use state or maintenance state; The N and the time period are both preset values; The maintenance records include: the time of each failure, the type of failure, the maintenance time and the maintenance cost; The fault data includes: the total number of faults, average maintenance cost and average maintenance time corresponding to various fault types.

2. The intelligent management method for medical instruments according to claim 1, characterized in that: The use request includes: the type of target instrument, priority, request period and request location; The step of "scheduling instruments according to instrument use requests initiated by various departments of the target hospital, as well as the attribute information, location information and current use status of each instrument" includes: Receive the usage request initiated by each department of the target hospital and add it to the request queue; Selecting one of the usage requests with the highest priority from the request queue at a time; Allocate an instrument to the usage request according to the selected usage request and the attribute information, the location information and the usage status of each instrument; The selected usage request is deleted from the request queue.

3. The intelligent management method for medical instruments according to claim 2, characterized in that: The step of "allocating an instrument to the use request according to the selected use request and the attribute information, the location information and the use status of each instrument" includes: According to the type of the target instrument, matching the instrument type in the attribute information of each instrument to obtain one or more target instrument models; According to the target instrument model, one or more target instruments are found; Among the found target instruments, an instrument that is in the idle state during the request period and is closest to the requested location is selected, and the attribute information and the location information of the instrument are sent to the requester.

4. The intelligent management method for medical instruments according to claim 1, characterized in that: Each instrument type corresponds to a pre-trained fault prediction model; The step of "predicting the remaining life of each instrument based on the fault data in the most recent N time periods and the pre-trained fault prediction model" includes: Normalizing the fault data of a certain instrument in the most recent N time periods respectively to obtain normalized data in the i-(N-1)th, i-(N-2)th, ..., i-1th, and i-th time periods; wherein i represents the most recent time period; The normalized data in the i-(N-1)th, i-(N-2), ..., i-1th, and i-th time periods are sequentially input into the pre-trained fault prediction model corresponding to the instrument type to which the instrument belongs in chronological order to obtain an output result; the output result includes: the fault data of the instrument in the next time period; According to the output results, the total number of failures, the total maintenance cost and the total duration of failures within the next time period are calculated; Compare the total number of failures, the total maintenance cost and the total duration of failures with the preset failure number threshold, maintenance cost threshold and failure duration threshold respectively, to determine whether the instrument will be scrapped within the next time period; If the vehicle will not be scrapped, the output result is input into the pre-trained fault prediction model again, and the total number of faults, total maintenance cost and total fault duration in the next time period are calculated according to the new output result to determine whether the vehicle will be scrapped; The process is repeated until it is determined that the instrument will be scrapped within a certain time period in the future, thereby obtaining the remaining life of the instrument.

5. The intelligent management method for medical instruments according to claim 4, characterized in that: Each of the fault prediction models adopts an LSTM neural network; The training steps of the fault prediction model include: For each instrument type, collecting a preset number of historical maintenance records corresponding to the time period; For each instrument type, historical failure data within each time period is counted based on the collected historical maintenance records; For each instrument type, the historical fault data are respectively organized into a time series, thereby constructing a data set of the instrument type; For each instrument type, the data in the corresponding data set is divided into a training set and a validation set in chronological order; Based on the training set and the validation set of each instrument type, training the fault prediction model corresponding to the instrument type; in, The historical maintenance records include: the time of each failure, the failure type, the maintenance time and the maintenance cost; The historical fault data includes: the total number of times, average maintenance cost and average maintenance time corresponding to various fault types.

6. The intelligent management method for medical instruments according to claim 5, characterized in that: The training step of the fault prediction model also includes: Regularly collect newly generated maintenance records and update the training data set; The fault prediction model is retrained according to the updated training data set.

7. The intelligent management method for medical instruments according to claim 1, characterized in that: The method further comprises: In response to a user-triggered instruction to view all instruments, all instrument types of the target hospital and the number of instruments corresponding to each instrument type are displayed on the human-computer interaction interface; In response to a location viewing instruction for a certain type of instrument triggered by a user, the location information of each instrument of the type is displayed in the form of a map on the human-computer interaction interface; In response to a property viewing instruction for a certain instrument triggered by a user, displaying the property information of the instrument on the human-computer interaction interface; In response to a maintenance record viewing instruction for a certain instrument triggered by a user, displaying the historical maintenance record of the instrument on the human-computer interaction interface; In response to a life viewing instruction for a certain instrument triggered by a user, the remaining life of the instrument is displayed on the human-computer interaction interface.

8. The intelligent management method for medical instruments according to claim 1, characterized in that: The method further comprises: Count the average idle time corresponding to each instrument type in the most recent time period; Calculate the average remaining life corresponding to each instrument type based on the remaining life of each instrument; A procurement plan is generated according to the average idle time and the average remaining life.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 8.

Images