Medical instrument intelligent management system and method based on Internet of Things monitoring

Abstract

The invention provides a medical instrument intelligent management system based on Internet of Things monitoring and a method thereof. The system comprises an Internet of Things monitoring middle table, an identity acquisition module, an Internet of Things label identification module, an instrument state identification module, an association binding module and a data uplink module. The identity acquisition module acquires user identity information; the internet-of-things label identification module identifies an internet-of-things label of a target medical device, and obtains warehouse-in and warehouse-out information of the target medical device; the instrument state recognition module collects an original instrument image of the target medical instrument when the target medical instrument is put in storage, and obtains state information of the target medical instrument based on the original instrument image; the association binding module associates and binds the user identity information, the warehouse-in and warehouse-out information and the state information to obtain instrument use record information of the target medical instrument; and the data uploading module is used for uploading the instrument use record information to a block chain for storage based on the user identity information. The inventory data accuracy and the state management accuracy of the medical apparatus are improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an intelligent management system for medical equipment based on Internet of Things monitoring. Background Art

[0002] Medical devices play a vital role in modern healthcare, and their management is crucial to ensuring medical quality, patient safety, and hospital operational efficiency.

[0003] At present, the management of medical devices is mainly achieved through manual management methods. That is, the entry and exit of medical devices, usage records, etc. still rely on manual paper records or simple electronic spreadsheet entry. However, with the increase in entry and exit and usage records, manual entry is prone to human errors, such as data entry errors, missing records, etc., which leads to inaccurate inventory data of medical devices. When medical devices are put into storage after use, the status of medical devices after use is mainly recorded by doctors' experience. However, during use, medical devices may be improperly operated (such as excessive bending, collision) and cause minor damage that is difficult to detect with the naked eye, resulting in inaccurate status management of medical devices. Summary of the invention

[0004] The present invention provides an intelligent management system for medical devices based on Internet of Things monitoring and a method thereof, aiming to improve the accuracy of inventory data and status management of medical devices.

[0005] In a first aspect, the present invention provides an intelligent management system for medical devices based on Internet of Things monitoring, comprising an Internet of Things monitoring middle station, an identity collection module, an Internet of Things tag recognition module, an equipment status recognition module, an association binding module and a data uplink module; the Internet of Things monitoring middle station is respectively connected to the identity collection module, the Internet of Things tag recognition module, the equipment status recognition module, the association binding module and the data uplink module to manage each module;

[0006] The IoT monitoring middle platform is used to respond to the outbound request of the target medical device and the inbound request of the target medical device;

[0007] The identity collection module is used to collect user identity information;

[0008] The Internet of Things tag recognition module is used to identify the Internet of Things tag of the target medical device and obtain the storage and outbound information of the target medical device;

[0009] The device status recognition module is used to collect the original device image of the target medical device when it is put into storage, and obtain the status information of the target medical device based on the original device image;

[0010] The association and binding module is used to associate and bind the user identity information, the storage information and the status information to obtain the device usage record information of the target medical device;

[0011] The data uploading module is used to upload the device usage record information to the blockchain for storage based on the user identity information.

[0012] In a second aspect, the present invention further provides an intelligent management method for medical devices based on Internet of Things monitoring, which is applied to the intelligent management system for medical devices based on Internet of Things monitoring described in the first aspect, and the intelligent management method for medical devices based on Internet of Things monitoring includes:

[0013] Responding to the outbound delivery request of the target medical device, collecting user identity information, and identifying the IoT tag of the target medical device to obtain the outbound delivery information of the target medical device;

[0014] Responding to the storage request of the target medical device, identifying the Internet of Things tag of the target medical device, and obtaining the storage information of the target medical device;

[0015] Collecting an original device image of the target medical device when it is put into storage, and acquiring status information of the target medical device based on the original device image;

[0016] Associating and binding the user identity information, the outbound information, the inbound information, and the status information to obtain the device usage record information of the target medical device;

[0017] The device usage record information is uploaded to the blockchain for storage based on the user identity information.

[0018] According to the intelligent management method of medical devices based on Internet of Things monitoring provided by an embodiment of the present invention, the device type of the target medical device includes a diagnosis type, a treatment type and a monitoring type;

[0019] The collecting of the original device image of the target medical device when it is put into storage includes:

[0020] If the device type is a diagnostic type, a multi-peak grayscale histogram of the target medical device when it is put into storage is collected as the original device image; if the device type is a therapeutic type, a skewed grayscale histogram of the target medical device when it is put into storage is collected as the original device image; if the device type is a monitoring type, a unimodal grayscale histogram of the target medical device when it is put into storage is collected as the original device image;

[0021] The acquiring the status information of the target medical device based on the original device image includes:

[0022] Obtaining a target grayscale pixel threshold of the original device image;

[0023] Segmenting the original instrument image based on the target grayscale pixel threshold to obtain a foreground image and a background image of the original instrument image;

[0024] Eliminating the background image in the original device image to obtain a target device image;

[0025] Inputting the target device image into a defect detection model to obtain a defect detection result output by the defect detection model; the defect detection model is trained based on sample images and their corresponding defect labels;

[0026] The status information of the target medical device is determined based on the defect detection result.

[0027] According to the intelligent management method of medical equipment based on Internet of Things monitoring provided by an embodiment of the present invention, the step of obtaining the target grayscale pixel threshold of the original equipment image includes:

[0028] For a target medical device of a diagnostic type, the original device image is segmented based on each wave peak in the original device image to obtain a plurality of device sub-images; each of the device sub-images represents a sub-image corresponding to a wave peak;

[0029] For the first instrument sub-image with the maximum peak value, taking the pixel corresponding to the peak in the first instrument sub-image as the center and the first size range as the sliding window, obtain the first pixel point; determine the first grayscale pixel threshold based on the grayscale pixel value of the first pixel point;

[0030] For the second instrument sub-image with the minimum peak value, taking the pixel corresponding to the peak in the second instrument sub-image as the center and the second size range as the sliding window, obtain the second pixel point; determine the second grayscale pixel threshold based on the grayscale pixel value of the second pixel point;

[0031] For a third instrument sub-image other than the first instrument sub-image and the second instrument sub-image, taking the pixel corresponding to the wave peak in the third instrument sub-image as the center and the third size range as the sliding window, a third pixel point is obtained; and a third grayscale pixel threshold is determined based on the grayscale pixel value of the third pixel point;

[0032] The target grayscale pixel threshold is determined based on the first grayscale pixel threshold, the second grayscale pixel threshold and the third grayscale pixel threshold; the first size range is larger than the third size range, and the third size range is larger than the second size range.

[0033] According to the intelligent management method of medical equipment based on Internet of Things monitoring provided by an embodiment of the present invention, the step of obtaining the target grayscale pixel threshold of the original equipment image includes:

[0034] For a target medical device of a treatment type, segmenting the original device image according to a preset area size to obtain a plurality of first image areas;

[0035] Determine a second image area based on the density of pixels in each of the first image areas;

[0036] Determine a grayscale pixel threshold of each second image region based on a grayscale pixel value corresponding to a pixel point in each second image region;

[0037] The target grayscale pixel threshold is determined based on the grayscale pixel threshold of each of the second image regions.

[0038] According to the intelligent management method of medical equipment based on Internet of Things monitoring provided by an embodiment of the present invention, the step of obtaining the target grayscale pixel threshold of the original equipment image includes:

[0039] For a target medical device of the monitoring type, taking each pixel corresponding to a quarter peak value in the original device image as the center and the first preset radius as the diffusion radius, a fourth pixel point is obtained; and a fourth grayscale pixel threshold is determined based on the grayscale pixel value of the fourth pixel point;

[0040] Taking each pixel corresponding to one-half peak value in the original device image as the center and the second preset radius as the diffusion radius, a fifth pixel point is obtained; and a fifth grayscale pixel threshold is determined based on the grayscale pixel value of the fifth pixel point;

[0041] Taking each pixel corresponding to the three-quarter peak value in the original device image as the center and the third preset radius as the diffusion radius, a sixth pixel point is obtained; and a sixth grayscale pixel threshold is determined based on the grayscale pixel value of the sixth pixel point;

[0042] The target grayscale pixel threshold is determined based on the fourth grayscale pixel threshold, the fifth grayscale pixel threshold and the sixth grayscale pixel threshold; the first preset radius is smaller than the second preset radius, and the second preset radius is smaller than the third preset radius.

[0043] According to the intelligent management method of medical equipment based on Internet of Things monitoring provided by an embodiment of the present invention, the use record information of the equipment is uploaded to the blockchain for storage based on the user identity information, including:

[0044] Performing a hash operation on the device usage record information to obtain a structure hash value; the structure hash value includes a first hash value corresponding to the user identity information, a second hash value corresponding to the outbound information, a third hash value corresponding to the inbound information, and a fourth hash value corresponding to the status information;

[0045] Generate an initial record structure based on the device usage record information and the structure hash value;

[0046] Based on the first hash value, query the blockchain to obtain a hash tree corresponding to the user identity information in the blockchain; the hash tree includes a root node, a parent node, and a child node;

[0047] Encrypting the user identity information based on the root hash value of the root node, and generating a first target record structure based on the encrypted user identity information and the initial record structure;

[0048] The first target record structure is stored in the hash tree based on the structure hash value.

[0049] According to the intelligent management method of medical equipment based on Internet of Things monitoring provided by an embodiment of the present invention, storing the first target record structure in the hash tree based on the structure hash value includes:

[0050] Using the fourth hash value as a first traversal index to traverse the hash tree, and obtaining a parent node of the first target record structure in the hash tree;

[0051] Using the second hash value and the third hash value as a second traversal index, traverse the child nodes of the parent node to obtain a target child node in the parent node;

[0052] The first target record structure is concatenated with the second target record structure in the target subnode and stored in the target subnode.

[0053] In a third aspect, the present invention further provides an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing any of the above-mentioned intelligent management methods for medical devices based on Internet of Things monitoring.

[0054] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, wherein a computer software program is stored in the storage medium. When the computer software program is executed by a processor, the intelligent management method of medical devices based on Internet of Things monitoring as described above is implemented.

[0055] In a fifth aspect, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned intelligent management methods for medical devices based on Internet of Things monitoring.

[0056] The intelligent management system for medical devices based on Internet of Things monitoring provided by the embodiment of the present invention automatically records the in-and-out information of target medical devices when they are in and out of the warehouse through the Internet of Things tag recognition module, without the need for manual entry, thereby avoiding human errors caused by manual entry and improving the accuracy of inventory data of medical devices. At the same time, the original device image of the target medical device when it is in the warehouse is collected through the device status recognition module, and the status information of the target medical device is recognized based on the original device image. The device image can better highlight the detailed features of the target medical device, so it can accurately identify minor damage that is difficult to detect with the naked eye, thereby improving the accuracy of medical device status management. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a structural diagram of the intelligent management system of medical equipment based on Internet of Things monitoring provided by the present invention;

[0058] Figure 2 It is a flow chart of the intelligent management method of medical equipment based on Internet of Things monitoring provided by the present invention;

[0059] Figure 3 An embodiment diagram of an electronic device provided by an embodiment of the present invention;

[0060] Figure 4 An embodiment diagram of a computer-readable storage medium provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0062] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0063] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0064] Optional, see Figure 1 As shown, Figure 1 It is a structural diagram of the medical device intelligent management system based on Internet of Things monitoring provided by the present invention, and the medical device intelligent management system based on Internet of Things monitoring includes an Internet of Things monitoring middle station, an identity collection module, an Internet of Things tag recognition module, an equipment status recognition module, an association binding module and a data uplink module. In an embodiment of the present invention, the Internet of Things monitoring middle station is respectively connected with the identity collection module, the Internet of Things tag recognition module, the equipment status recognition module, the association binding module and the data uplink module to manage each module.

[0065] Optionally, when using medical equipment, users (such as doctors, nurses, etc.) need to trigger a warehouse-out request in the interactive screen of the medical equipment intelligent management system. When returning medical equipment, users need to trigger a warehouse-in request in the interactive screen of the medical equipment intelligent management system.

[0066] Furthermore, in the embodiments of the present invention, each medical device has a corresponding Internet of Things tag on its surface, wherein the Internet of Things tag is such as a near field communication (NFC) tag, a radio frequency identification (RFID) tag, a Bluetooth low energy (BLE) tag, etc. The Internet of Things tag contains basic information of the medical device, and the basic information includes the device model, device specifications, production batch, purchase date, device type and other information. At the same time, the Internet of Things tag of the medical device and the device model, device specifications, production batch, purchase date, device type and other information contained therein need to be registered in the intelligent management system of medical devices.

[0067] Furthermore, after the user triggers an outbound request for the target medical device in the interactive screen of the medical device intelligent management system, the Internet of Things monitoring center responds to the outbound request for the target medical device and controls the identity collection module to collect user identity information, wherein the user identity information includes user identity ID, user name, user department, user work permit and other information.

[0068] Furthermore, the IoT tag identification module identifies the IoT tag of the target medical device and obtains the outbound information of the target medical device, wherein the outbound information includes outbound time, outbound handler, device name, device type, device number and other information.

[0069] Furthermore, after the user triggers an entry request for the target medical device in the interactive screen of the medical device intelligent management system, the IoT monitoring center responds to the entry request of the target medical device, controls the IoT tag identification module to identify the IoT tag of the target medical device, and obtains the entry information of the target medical device, wherein the entry information includes the entry time, the entry acceptance person, the device name, the device type, the device number and other information.

[0070] Furthermore, when responding to the storage request of the target medical device, the IoT monitoring center needs to control the device status recognition module to collect the original device image of the target medical device when it is stored, wherein the original device image is a grayscale image of the target medical device. The device status recognition module obtains the status information of the target medical device based on the original device image, wherein the status information represents the integrity of the target medical device.

[0071] Furthermore, the association and binding module associates and binds the user identity information, the storage information and the status information to obtain the device usage record information of the target medical device, wherein the association and binding rules are set according to the actual situation and can also be determined according to the type of the target medical device combined with the mapping table in the system.

[0072] Furthermore, the data uploading module uploads the device usage record information to the blockchain for storage based on the user identity information.

[0073] The embodiment of the present invention automatically records the in-and-out information of the target medical device when it enters and leaves the warehouse through the Internet of Things tag recognition module, without the need for manual entry, thereby avoiding human errors caused by manual entry and improving the accuracy of the inventory data of medical devices. At the same time, the original device image of the target medical device when it enters the warehouse is collected through the device status recognition module, and the status information of the target medical device is recognized based on the original device image. The device image can better highlight the detailed features of the target medical device, so it can accurately identify minor damage that is difficult to detect with the naked eye, thereby improving the accuracy of the status management of medical devices.

[0074] Optional, see Figure 2 , Figure 2 It is a flow chart of the intelligent management method of medical devices based on Internet of Things monitoring provided by the present invention. The execution subject of the intelligent management method of medical devices based on Internet of Things monitoring in the embodiment of the present invention is the medical device management system. Therefore, the intelligent management method of medical devices based on Internet of Things monitoring includes:

[0075] Step 10, respond to the outbound request of the target medical device, collect user identity information, and identify the IoT tag of the target medical device to obtain the outbound information of the target medical device.

[0076] Optionally, when using medical devices, the user needs to trigger a request for outbound delivery in the interactive screen of the medical device management system. When returning medical devices, it is necessary to trigger a request for inbound delivery in the interactive screen of the medical device management system. Furthermore, in the embodiment of the present invention, each medical device has a corresponding Internet of Things tag on its surface, wherein the Internet of Things tag is such as a near field communication (NFC) tag, a radio frequency identification (RFID) tag, a Bluetooth low energy (BLE) tag, etc. The Internet of Things tag contains basic information of the medical device, including the device model, device specifications, production batch, purchase date, device type and other information.

[0077] Optionally, the IoT tag of the medical device and the information it contains, such as the device model, device specification, production batch, purchase date, device type, etc., need to be registered in the medical device management system.

[0078] Therefore, after the user triggers the outbound request for the target medical device in the interactive screen of the medical device management system, the outbound request for the target medical device is responded to and the user identity information is collected, where the user identity information includes user identity ID, user name, user department, user work permit and other information.

[0079] Furthermore, the medical device management system identifies the IoT tag of the target medical device and obtains the outbound information of the target medical device, wherein the outbound information includes outbound time, outbound handler, device name, device type, device number and other information.

[0080] Step 20, respond to the warehousing request of the target medical device, identify the IoT tag of the target medical device, and obtain the warehousing information of the target medical device.

[0081] Furthermore, after the user triggers an entry request for the target medical device in the interactive screen of the medical device management system, the user responds to the entry request for the target medical device, identifies the IoT tag of the target medical device, and obtains the entry information of the target medical device, wherein the entry information includes the entry time, the entry acceptance person, the device name, the device type, the device number and other information.

[0082] Step 30, collecting the original device image of the target medical device when it is put into storage, and obtaining the status information of the target medical device based on the original device image.

[0083] Furthermore, when responding to the storage request of the target medical device, the medical device management system needs to collect the original device image of the target medical device when it is stored, wherein the original device image is a grayscale image of the target medical device. Furthermore, the medical device management system obtains the status information of the target medical device based on the original device image, wherein the status information represents the integrity of the target medical device, as described in detail in steps 301 to 305.

[0084] Step 40, the user identity information, outbound information, inbound information and status information are associated and bound to obtain the device usage record information of the target medical device.

[0085] Furthermore, the medical device management system associates and binds the user identity information, outbound information, inbound information and status information to obtain the device usage record information of the target medical device, wherein the association and binding rules are set according to the actual situation, and can also be determined according to the type of the target medical device combined with the mapping table in the system, such as the association and binding rules are bound in order of user identity information-inbound and outbound information and status information.

[0086] Therefore, in one embodiment, the device usage record information of the target medical device can be expressed as device usage record information R = {U, O, I, S}, where U represents user identity information, O represents outbound information, I represents inbound information, and S represents status information.

[0087] Step 50: Upload the device usage record information to the blockchain for storage based on the user identity information.

[0088] Furthermore, the medical device management system uploads the device usage record information to the blockchain for storage based on the user identity information, as described in steps 501 to 505.

[0089] The embodiment of the present invention automatically records the in-and-out information of the target medical device through the Internet of Things tag, without manual entry, thereby avoiding human errors caused by manual entry and improving the accuracy of the inventory data of medical devices. At the same time, by collecting the original device image of the target medical device when it enters the warehouse, and identifying the status information of the target medical device based on the original device image, because the device image can better highlight the detailed features of the target medical device, it can accurately identify minor damage that is difficult to detect with the naked eye, thereby improving the accuracy of the status management of medical devices.

[0090] In one embodiment, the device types of the target medical device include diagnostic type, therapeutic type and monitoring type, wherein diagnostic type medical devices include ultrasonic diagnostic equipment, magnetic resonance imaging equipment, computer tomography scanner, etc.; therapeutic type medical devices include laser therapy equipment, microwave therapy equipment, surgical instruments, etc.; monitoring type medical devices include wearable heart rate monitors, pulse oximeters, pacemakers, etc.

[0091] Furthermore, in order to ensure the accuracy of the status information of the target medical device, for target medical devices of different device types, the original device images of the target medical devices when they are put into storage are collected using different collection methods, wherein the collection methods include multi-peak grayscale histogram, skewed grayscale histogram and unimodal grayscale histogram.

[0092] Among them, since diagnostic medical devices often have complex internal structures and components made of a variety of different materials. For example, an ultrasonic diagnostic instrument has a probe (containing a variety of different acoustic materials and electronic components), a signal processing circuit, a display module, etc. Therefore, for target medical devices of diagnostic type, a multi-peak grayscale histogram of the target medical device when it is put into storage is collected as the original device image.

[0093] Since therapeutic medical devices usually have one or several key functional components, such as the laser emission module of a medical laser therapy device, which is the core component, other components play an auxiliary or supporting role. Therefore, for target medical devices of therapeutic type, the skewed grayscale histogram of the target medical device when it is put into storage is collected as the original device image.

[0094] Since the structure and materials of monitoring-type medical devices are usually relatively simple, such as wearable heart rate monitors, which are mainly composed of a shell (usually a single material such as plastic), a sensor (mostly a certain type of biosensor) and a simple circuit. Therefore, for target medical devices of monitoring type, the unimodal grayscale histogram of the target medical device when it is put into storage is collected as the original device image.

[0095] In one embodiment, the description of step 301 to step 305 includes:

[0096] Step 301, obtaining a target grayscale pixel threshold of an original instrument image.

[0097] Optionally, the medical device management system obtains a target grayscale pixel threshold of the original device image, wherein for target medical devices of different device types, the method of obtaining their original device images is different, as described in steps 3011 to 30113.

[0098] Step 302 : segment the original instrument image based on the target grayscale pixel threshold to obtain a foreground image and a background image of the original instrument image.

[0099] Furthermore, the medical device management system uses the target grayscale pixel threshold as a guide, takes the pixel points in the original device image whose grayscale pixel values ​​are less than the target grayscale pixel threshold as background pixel points, and takes the pixel points whose grayscale pixel values ​​are greater than or equal to the target grayscale pixel threshold as foreground pixel points, and segments the original device image to obtain the foreground image and background image of the original device image, wherein the foreground image is the image composed of foreground pixels, and the background image is the image composed of background pixels.

[0100] Step 303: remove the background image in the original instrument image to obtain the target instrument image.

[0101] Step 304: input the target device image into the defect detection model to obtain the defect detection result output by the defect detection model.

[0102] Optionally, a defect detection model is embedded in the medical device management system of an embodiment of the present invention, wherein the defect detection model is obtained by training a preset neural network based on sample images and their corresponding defect labels, wherein the preset neural network model can be a feedforward neural network (FNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a long short-term memory network (LSTM), etc.

[0103] Furthermore, the medical device management system removes the background image in the original device image and retains only the foreground image in the original device image to obtain the target device image. Furthermore, the medical device management system inputs the target device image into the defect detection model to obtain the defect detection result output by the defect detection model, wherein the defect detection result includes the defect type and the probability value of the defect type.

[0104] Step 305: determine the status information of the target medical device based on the defect detection result.

[0105] Furthermore, the medical device management system determines the defect type and the probability value of the defect type in the defect detection result, and determines the status information of the target medical device according to the defect type and the probability value of the defect type. In one embodiment, when the probability value is less than 0.1, it indicates that the defect type is normal; when the probability value is greater than or equal to 0.1 and less than 0.3, it indicates that the defect type is slightly damaged and repairable; when the probability value is greater than or equal to 0.3, it indicates that the defect type is severely damaged and cannot be repaired.

[0106] In one embodiment, if the defect detection result is no defect, the status information of the target medical device is no damage. If the defect detection result is a scratch type, the probability value of the scratch type is 0.08, and the status information of the target medical device is normal. If the defect detection result is a hole type, the probability value of the hole type is 0.5, and the status information of the target medical device is severely damaged and cannot be repaired.

[0107] The embodiment of the present invention collects the original device image of the target medical device when it is put into storage, and identifies the status information of the target medical device based on the original device image. Since the device image can better highlight the detailed features of the target medical device, it can accurately identify minor damage that is difficult to detect with the naked eye, thereby improving the accuracy of medical device status management.

[0108] In one embodiment, for a target medical device of a diagnostic type, obtaining a target grayscale pixel threshold of an original device image includes steps 3011 to 3015:

[0109] Step 3011 , performing image segmentation on the original instrument image based on each wave peak in the original instrument image to obtain a plurality of instrument sub-images.

[0110] Specifically, since there are multiple peaks in the original instrument image, the medical instrument management system performs image segmentation on the original instrument image according to each peak in the original instrument image to obtain multiple instrument sub-images. In other words, each instrument sub-image represents a sub-image corresponding to a peak. The specific analysis is as follows:

[0111] In one embodiment, the original instrument image is I(x, y), where (x, y) represents the coordinates in the original instrument image. The peaks can be determined by a preset peak detection algorithm (such as a peak detection filter method, a wavelet transform method, etc.). Therefore, the n peak sets of the original instrument image obtained by peak detection are P = {p1, p2, ..., p n}, where each peak p i (i=1,2,...,n) has its corresponding coordinates and peak size information. The original instrument image is segmented by the peak, and the set of n instrument sub-images is obtained as I sub ={I1,I2,...,I n}, where each instrument sub-image I j (j=1,2,...,n) corresponds to a sub-image of a peak.

[0112] Step 3012, for the first instrument sub-image with the maximum peak value, taking the pixel corresponding to the peak in the first instrument sub-image as the center and the first size range as the sliding window, obtain the first pixel point; determine the first grayscale pixel threshold based on the grayscale pixel value of the first pixel point.

[0113] Optionally, the first size range in the embodiment of the present invention is larger than the third size range, and the third size range is larger than the second size range. In one embodiment, the first size range is such as 6*6, the second size range is such as 3*3, and the third size range is such as 5*5.

[0114] Furthermore, for the first instrument sub-image with the maximum peak value, the medical device management system obtains the pixel point corresponding to the peak in the first instrument sub-image, and obtains the first pixel point with the pixel point corresponding to the peak in the first instrument sub-image as the center and the first size range as the sliding window.

[0115] Furthermore, the medical device management system determines the first grayscale pixel threshold according to the grayscale pixel value of the first pixel point. The specific analysis process is as follows: the first device sub-image is I max , the pixel coordinates corresponding to the peak are (x max ,y max ). The first size range is set to W1*H1 (representing the width and height of the sliding window), then the set P1 of m first pixel points in the sliding window can be determined by the following method:

[0116]

[0117] Further, after obtaining the set of first pixel points, the set of grayscale pixel values ​​of m first pixel points is G1={g 11 ,g 12 ,...,g 1m}, where g 1m Represents the grayscale pixel value of the mth first pixel point. The first grayscale pixel threshold is calculated based on the grayscale pixel values ​​of the m first pixels, and the formula is as follows:

[0118]

[0119] Where T1 represents the first grayscale pixel threshold, g 1i represents the grayscale pixel value of the first pixel point i, w(g 1i ) represents the weight coefficient corresponding to the grayscale pixel value of the i-th first pixel point, e represents the base of the exponential function, α represents the preset adjustment parameter, Represents the mean of the grayscale pixel values ​​of the m first pixels.

[0120] Step 3013, for the second instrument sub-image with the minimum peak value, taking the pixel corresponding to the peak in the second instrument sub-image as the center and the second size range as the sliding window, obtain the second pixel point; determine the second grayscale pixel threshold based on the grayscale pixel value of the second pixel point.

[0121] Furthermore, for the second instrument sub-image with the minimum peak value, the medical device management system obtains the pixel point corresponding to the peak in the second instrument sub-image, and obtains the second pixel point with the pixel point corresponding to the peak in the second instrument sub-image as the center and the second size range as the sliding window.

[0122] Furthermore, the medical device management system determines the second grayscale pixel threshold value based on the grayscale pixel value of the second pixel point. The specific analysis process is as follows: the second device sub-image is I min , the pixel coordinates corresponding to the peak are (x min ,y min ). The second size range is set to W2*H2, then the set P2 of k second pixel points in the sliding window can be determined by the following method:

[0123]

[0124] Further, after obtaining the set of second pixel points, the set of grayscale pixel values ​​of k second pixel points is G2={g 21 ,g 22 ,...,g 2k}, where g 2k Represents the grayscale pixel value of the kth second pixel point. The second grayscale pixel threshold is calculated based on the grayscale pixel values ​​of the k second pixels, and the formula is as follows:

[0125]

[0126] Where T2 represents the second grayscale pixel threshold, g 2i represents the grayscale pixel value of the i-th second pixel; C 2i Represents the local contrast of the second pixel of the ith pixel, and the size of its surrounding neighborhood is r; N r (g 2i ) represents the neighborhood pixel set with a radius of r of the i-th second pixel, (x', y') represents the coordinates corresponding to any pixel in the neighborhood pixel set, g 2(x',y') Represents the grayscale pixel value corresponding to the pixel point (x', y'), |N r (g 2i )| represents the sum of the grayscale pixel values ​​of all pixels in the neighborhood pixel set.

[0127] Step 3014, for a third instrument sub-image other than the first instrument sub-image and the second instrument sub-image, a third pixel point is obtained with the pixel point corresponding to the peak in the third instrument sub-image as the center and a third size range as the sliding window; and a third grayscale pixel threshold is determined based on the grayscale pixel value of the third pixel point.

[0128] Optionally, the third instrument sub-image in the embodiment of the present invention is an instrument sub-image other than the first instrument sub-image and the second instrument sub-image, that is, the third instrument sub-image is an instrument sub-image whose wave peak value is between the maximum wave peak value and the minimum wave peak value. The third size range includes the first target size range and the second target size range, and the first target size range is larger than the second target size range.

[0129] Therefore, the medical device management system determines the peak difference based on the maximum peak value and the minimum peak value.

[0130] Further, for the first target instrument sub-image in the third instrument sub-image, the peak value of the first target instrument sub-image is greater than or equal to the peak difference, and the medical instrument management system obtains the pixel point corresponding to the peak in the first target instrument sub-image, and takes the pixel point corresponding to the peak in the first target instrument sub-image as the center and the first target size range as the sliding window to obtain the first target pixel point. Further, the medical instrument management system calculates the mean of the grayscale pixel values ​​corresponding to all the first target pixel points to obtain the first target grayscale pixel threshold of the first target instrument sub-image.

[0131] Further, for the second target instrument sub-image in the third instrument sub-image, the peak value of the second target instrument sub-image is less than the peak difference, and the medical instrument management system obtains the pixel point corresponding to the peak in the second target instrument sub-image, and takes the pixel point corresponding to the peak in the second target instrument sub-image as the center and the second target size range as the sliding window to obtain the second target pixel point. Further, the medical instrument management system calculates the mean of the grayscale pixel values ​​corresponding to all the second target pixel points to obtain the second target grayscale pixel threshold of the second target instrument sub-image.

[0132] Further, the medical device management system determines the first target grayscale pixel threshold and the second target grayscale pixel threshold as the third grayscale pixel threshold.

[0133] Step 3015, determining a target grayscale pixel threshold based on the first grayscale pixel threshold, the second grayscale pixel threshold, and the third grayscale pixel threshold.

[0134] Furthermore, the medical device management system calculates the target grayscale pixel threshold according to the first grayscale pixel threshold, the second grayscale pixel threshold, the first target grayscale pixel threshold and the second target grayscale pixel threshold and their corresponding weight coefficients, and the specific calculation formula is as follows:

[0135] T(Gray)=T1*w1+T2*w2+T 31 *w 31 +T 32 *w 32 ;

[0136] w1+w2+w3+w4=1;

[0137] Among them, T(Gray) represents the target grayscale pixel threshold, T 31 represents the first target grayscale pixel threshold, T 32 represents the second target grayscale pixel threshold, w1,w2,w 31 ,w 32 Represents the weight coefficient.

[0138] The embodiment of the present invention can accurately obtain the target grayscale pixel threshold for a diagnostic target medical device, so that the target device image can be subsequently obtained according to the target grayscale pixel threshold, thereby accurately identifying the status information of the target medical device, thereby improving the accuracy of medical device status management.

[0139] In one embodiment, for a target medical device of a treatment type, obtaining a target grayscale pixel threshold of an original device image includes steps 3016 to 3019:

[0140] Step 3016: segment the original instrument image according to preset area sizes to obtain a plurality of first image areas.

[0141] Specifically, since there are different pixel densities in the original device image, the medical device management system divides the original device image according to the preset area size to obtain multiple first image areas, wherein the preset area size is set according to actual conditions, and the specific analysis is as follows:

[0142] In one embodiment, the original instrument image is I(x, y), wherein the size of the original instrument image is M*N (M is the number of pixels in the height direction, and N is the number of pixels in the width direction), and the preset region size is Z*C (Z is the segmentation size in the height direction, and C is the segmentation size in the width direction). Therefore, the number of first image regions obtained by segmenting the original instrument image I(x, y) is:

[0143] in, Indicates rounding down.

[0144] For the i-th first image region I 1i , whose coordinate range can be expressed as L1 to L2, where:

[0145] Among them, % represents the modulo operation.

[0146]

[0147] Step 3017: determine the second image area based on the pixel density of each first image area.

[0148] Furthermore, the medical device management system determines the second image area according to the pixel density of each first image area. The specific analysis process is as follows:

[0149] For each first image region I 1i , calculate the pixel density D i In one embodiment, each first image region I 1i The total number of pixels in is P 1i , calculate each first image region I 1i The pixel density D i The specific formula is as follows:

[0150]

[0151] Among them, d ij Represents the first image area I 1i Each pixel (x j ,y j ) in the entire first image area I 1i The density contribution value within, K(x,y) represents the two-dimensional Gaussian kernel function, σ represents the standard deviation of the Gaussian kernel, and e represents the base of the exponential function.

[0152] Furthermore, the medical device management system calculates the pixel density D of each first image area. i The value is compared with the density threshold θ. If the pixel density is greater than or equal to the density threshold θ, the first image area corresponding to the pixel density is determined as the second image area I 2k (Here k is equal to the number of pixels satisfying a density greater than or equal to a density threshold θ).

[0153] Step 3018: Determine the grayscale pixel threshold of each second image region based on the grayscale pixel value corresponding to the pixel point in each second image region.

[0154] Furthermore, the medical device management system determines the grayscale pixel threshold of each second image area according to the grayscale pixel value corresponding to the pixel point in each second image area. The specific analysis process is as follows:

[0155] For each second image region I 2k , get the number of pixels whose grayscale pixel value is g H 2k (g), where g represents the grayscale pixel value, which usually ranges from 0 to 255. The specific calculation formula is as follows:

[0156]

[0157] H total (t) = H in(t)+H between (t);

[0158]

[0159] Among them, T 2k represents the grayscale pixel threshold in each second image region, Indicates that H total (t) maximum t value; H total (t) represents the total entropy, t represents the temporary threshold variable, H between (t) represents the inter-class entropy, H in (t) represents the intra-class entropy, P 2k (g) represents the second image area I 2k , the probability of a pixel with gray value g.

[0160] Step 3019, determining a target grayscale pixel threshold based on the grayscale pixel threshold of each second image region.

[0161] Furthermore, the medical device management system determines the target grayscale pixel threshold according to the grayscale pixel threshold of each second image area. The specific analysis process is as follows:

[0162] Get the number n2 of the second image regions, and for each second image region, the grayscale pixel threshold T 2k , the corresponding weight value is determined according to its area size and the number of pixels. The specific calculation formula is:

[0163]

[0164] Among them, w 2k represents the weight value of the grayscale pixel threshold of each second image region, S 2k represents the area of ​​each second image region (calculated in terms of the number of pixels); S total Represents the total number of pixels, P 2k Represents the total number of pixels in each second image area.

[0165] Further, according to the grayscale pixel threshold T of each second image area 2k and its corresponding weight value w 2k The target grayscale pixel threshold T(Gray) is calculated, and the specific formula is as follows:

[0166]

[0167] The embodiment of the present invention can accurately obtain the target grayscale pixel threshold for the target medical device of the treatment type, so that the target device image can be subsequently obtained according to the target grayscale pixel threshold, thereby accurately identifying the status information of the target medical device, thereby improving the accuracy of the status management of the medical device.

[0168] In one embodiment, for a target medical device of a monitoring type, obtaining a target grayscale pixel threshold of an original device image includes steps 30110 to 30113:

[0169] Step 30110, taking each pixel corresponding to one quarter peak value in the original device image as the center and the first preset radius as the diffusion radius, obtaining a fourth pixel point; determining a fourth grayscale pixel threshold based on the grayscale pixel value of the fourth pixel point;

[0170] Step 30111, taking each pixel corresponding to one-half peak value in the original device image as the center and the second preset radius as the diffusion radius, obtaining a fifth pixel point; determining a fifth grayscale pixel threshold based on the grayscale pixel value of the fifth pixel point;

[0171] Step 30112, taking each pixel corresponding to the three-quarter peak value in the original device image as the center and the third preset radius as the diffusion radius, obtaining a sixth pixel point; determining a sixth grayscale pixel threshold based on the grayscale pixel value of the sixth pixel point;

[0172] Step 30113, determine the target grayscale pixel threshold based on the fourth grayscale pixel threshold, the fifth grayscale pixel threshold and the sixth grayscale pixel threshold.

[0173] In the embodiment of the present invention, the first preset radius is smaller than the second preset radius, and the second preset radius is smaller than the third preset radius. In one embodiment, the first preset radius r1=3, the second preset radius r2=5, and the third preset radius r3=6.

[0174] Specifically, the medical device management system takes each pixel point corresponding to one quarter peak value in the original device image as the center, takes the first preset radius as the diffusion radius, obtains the fourth pixel point, and calculates the mean value based on the grayscale pixel value of each fourth pixel point to obtain the fourth grayscale pixel threshold.

[0175] Furthermore, the medical device management system takes each pixel point corresponding to one-half peak in the original device image as the center, uses the second preset radius as the diffusion radius, obtains the fifth pixel point, and calculates the mean based on the grayscale pixel value of each fifth pixel point to obtain the fifth grayscale pixel threshold.

[0176] Furthermore, the medical device management system takes each pixel point corresponding to the three-quarters peak value in the original device image as the center, and uses the third preset radius as the diffusion radius to obtain the sixth pixel point, and performs mean calculation based on the grayscale pixel value of each sixth pixel point to obtain the sixth grayscale pixel threshold.

[0177] Furthermore, the medical device management system performs weighted calculation according to the fourth grayscale pixel threshold, the fifth grayscale pixel threshold and the sixth grayscale pixel threshold and their corresponding weight coefficients to obtain a target grayscale pixel threshold. The specific calculation formula is as follows:

[0178] T(Gray)=T4*w4+T5*w5+T6*w6;

[0179] w4+2w5+2w6=1;

[0180] Among them, T (Gray) represents the target gray pixel threshold, T4 represents the fourth gray pixel threshold, T5 represents the target gray pixel threshold, T6 represents the sixth gray pixel threshold, and w4, w5, and w6 represent weight coefficients.

[0181] The embodiment of the present invention can accurately obtain the target grayscale pixel threshold for the monitoring type target medical device, so that the target device image can be subsequently obtained according to the target grayscale pixel threshold, thereby accurately identifying the status information of the target medical device, thereby improving the accuracy of the status management of the medical device.

[0182] In one embodiment, the description of steps 501 to 505 is as follows:

[0183] Step 501: Perform a hash operation on the equipment usage record information to obtain a structure hash value.

[0184] Step 502: Generate an initial record structure based on the device usage record information and the structure hash value.

[0185] Specifically, the equipment usage record information can be expressed as R={U, O, I, S}, where U represents user identity information, O represents outbound information, I represents inbound information, and S represents status information.

[0186] Furthermore, in order to ensure the integrity and verifiability of data, the embodiment of the present invention performs a hash operation on the device usage record information to obtain a structure hash value corresponding to the device usage record information. Therefore, the structure hash value includes a first hash value corresponding to the user identity information, a second hash value corresponding to the outbound information, a third hash value corresponding to the inbound information, and a fourth hash value corresponding to the status information. In one embodiment, the hash operation process is as follows: the first hash value h U =Hash(U), the second hash value h O=Hash(O), the third hash value h I =Hash(I), the fourth hash value h S =Hash(S), Hash() represents a hash function, such as the SHA-256 hash algorithm.

[0187] Furthermore, the medical device management system concatenates the device usage record information and the structure hash value to generate the initial record structure R c = {U,O,I,S,h U ,h O ,h I ,h S}.

[0188] Step 503: query the blockchain based on the first hash value to obtain a hash tree corresponding to the user identity information in the blockchain.

[0189] Furthermore, the medical device management system uses the first hash value h U Query the blockchain and obtain the first hash value h U The matching hash tree is the hash tree corresponding to the user identity information in the blockchain. The hash tree in the embodiment of the present invention can be understood as a binary tree, and therefore, the hash tree includes a root node, multiple parent nodes, and multiple child nodes.

[0190] Step 504: encrypt the user identity information based on the root hash value of the root node, and generate a first target record structure based on the encrypted user identity information and the initial record structure.

[0191] Furthermore, the medical device management system obtains the root hash value h of the root node root , with the root hash value h root is the private key, with the first hash value h U The public key digitally signs and encrypts the user identity information to obtain the encrypted user identity information. Therefore, the encrypted user identity information can be expressed as Here, Sign represents a digital signature algorithm, such as ECDSA (Elliptic Curve Digital Signature Algorithm).

[0192] Furthermore, the medical device management system merges the encrypted user identity information and the initial record structure to generate a first target record structure. Therefore, the first target record structure can be expressed as

[0193] Step 505: store the first target record structure into a hash tree based on the structure hash value.

[0194] Furthermore, the medical device management system stores the first target record structure into the hash tree according to the second hash value, the third hash value and the fourth hash value in the structure hash value, as specifically described in steps 5051 to 5053 .

[0195] The embodiment of the present invention stores the instrument use record information in the hash tree of the blockchain. Since any tampering with the instrument use record information will cause the hash value of the branch to change, data tampering can be prevented, ensuring the authenticity and security of the data. At the same time, the hierarchical structure of the hash tree is consistent with the storage structure of the blockchain, and the instrument use record information can be organized to facilitate storage management and retrieval in the blockchain network, which helps to quickly locate and query.

[0196] In one embodiment, the description of steps 5051 to 5053 is as follows:

[0197] Step 5051, traverse the hash tree using the fourth hash value as the first traversal index to obtain the parent node of the first target record structure in the hash tree;

[0198] Step 5052: traverse the child nodes of the parent node using the second hash value and the third hash value as a second traversal index to obtain a target child node in the parent node;

[0199] Step 5053: concatenate the first target record structure with the second target record structure in the target child node and store them in the target child node.

[0200] Specifically, the medical device management system first traverses the hash tree with the fourth hash value corresponding to the status information as the first traversal index, obtains the parent node in the hash tree with the same hash value as the fourth hash value, and determines the parent node as the parent node of the first target record structure in the hash tree. If the parent node with the same fourth hash value is not traversed in the hash tree, a new parent node is created under the root node, and the first target record structure is stored in the hash tree as a child node of the new parent node.

[0201] Furthermore, in the case where there is a parent node that is the same as the fourth hash value, the medical device management system uses the second hash value corresponding to the outbound information and the third hash value corresponding to the inbound information as the second traversal index, traverses the child nodes of the parent node, obtains the child nodes in the parent node that are the same as the second hash value and the third hash value, and determines the child node as the target child node of the first target record structure in the parent node. If no child node that is the same as the second hash value and the third hash value is traversed in the parent node, a new child node is created under the parent node, and the first target record structure is stored in the hash tree as the new child node.

[0202] Furthermore, in the case where there is a child node with the same second hash value and third hash value, the medical device management system concatenates the first target record structure R1 with the second target record structure R2 in the target child node and stores them in the target child node. Therefore, the record structure R total Can be expressed || indicates splicing.

[0203] The embodiment of the present invention stores the instrument use record information in the hash tree of the blockchain. Since any tampering with the instrument use record information will cause the hash value of the branch to change, data tampering can be prevented, ensuring the authenticity and security of the data. At the same time, the hierarchical structure of the hash tree is consistent with the storage structure of the blockchain, and the instrument use record information can be organized to facilitate storage management and retrieval in the blockchain network, which helps to quickly locate and query.

[0204] See also Figure 3 , Figure 3 FIG. 1 is an embodiment diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented:

[0205] Respond to the outbound request of the target medical device, collect user identity information, and identify the IoT tag of the target medical device to obtain the outbound information of the target medical device;

[0206] Respond to the storage request of the target medical device, identify the IoT tag of the target medical device, and obtain the storage information of the target medical device;

[0207] Collecting the original device image of the target medical device when it is put into storage, and obtaining the status information of the target medical device based on the original device image;

[0208] The user identity information, outbound information, inbound information and status information are associated and bound to obtain the device usage record information of the target medical device;

[0209] Based on the user's identity information, the device usage record information is uploaded to the blockchain for storage.

[0210] See also Figure 4 , Figure 4 Detailed description of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400, on which a computer program 311 is stored. When the computer program 311 is executed by a processor, the following steps are implemented:

[0211] Respond to the outbound request of the target medical device, collect user identity information, and identify the IoT tag of the target medical device to obtain the outbound information of the target medical device;

[0212] Respond to the storage request of the target medical device, identify the IoT tag of the target medical device, and obtain the storage information of the target medical device;

[0213] Collecting the original device image of the target medical device when it is put into storage, and obtaining the status information of the target medical device based on the original device image;

[0214] The user identity information, outbound information, inbound information and status information are associated and bound to obtain the device usage record information of the target medical device;

[0215] Based on the user's identity information, the device usage record information is uploaded to the blockchain for storage.

[0216] On the other hand, the present invention further provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the intelligent management method of medical devices based on Internet of Things monitoring provided by the above methods, and the method includes:

[0217] Respond to the outbound request of the target medical device, collect user identity information, and identify the IoT tag of the target medical device to obtain the outbound information of the target medical device;

[0218] Respond to the storage request of the target medical device, identify the IoT tag of the target medical device, and obtain the storage information of the target medical device;

[0219] Collecting the original device image of the target medical device when it is put into storage, and obtaining the status information of the target medical device based on the original device image;

[0220] The user identity information, outbound information, inbound information and status information are associated and bound to obtain the device usage record information of the target medical device;

[0221] Based on the user's identity information, the device usage record information is uploaded to the blockchain for storage.

[0222] The system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0223] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0224] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent management system for medical devices based on Internet of Things monitoring, characterized in that: It includes an Internet of Things monitoring center, an identity collection module, an Internet of Things tag recognition module, an equipment status recognition module, an association binding module and a data uplink module; the Internet of Things monitoring center is respectively connected to the identity collection module, the Internet of Things tag recognition module, the equipment status recognition module, the association binding module and the data uplink module to manage each module; The IoT monitoring middle platform is used to respond to the outbound request of the target medical device and the inbound request of the target medical device; The identity collection module is used to collect user identity information; The Internet of Things tag identification module is used to identify the Internet of Things tag of the target medical device and obtain the storage and out-of-warehouse information of the target medical device; The device status recognition module is used to collect the original device image of the target medical device when it is put into storage, and obtain the status information of the target medical device based on the original device image; The association and binding module is used to associate and bind the user identity information, the storage information and the status information to obtain the device usage record information of the target medical device; The data uploading module is used to upload the device usage record information to the blockchain for storage based on the user identity information.

2. A medical device intelligent management method based on Internet of Things monitoring, applied to the medical device intelligent management system based on Internet of Things monitoring as claimed in claim 1, characterized in that: The intelligent management method of medical devices based on Internet of Things monitoring includes: Responding to the outbound delivery request of the target medical device, collecting user identity information, and identifying the IoT tag of the target medical device to obtain the outbound delivery information of the target medical device; Responding to the storage request of the target medical device, identifying the Internet of Things tag of the target medical device, and obtaining the storage information of the target medical device; Collecting an original device image of the target medical device when it is put into storage, and acquiring status information of the target medical device based on the original device image; Associating and binding the user identity information, the outbound information, the inbound information, and the status information to obtain the device usage record information of the target medical device; The device usage record information is uploaded to the blockchain for storage based on the user identity information.

3. The intelligent management method of medical equipment based on Internet of Things monitoring according to claim 2 is characterized in that: The device types of the target medical devices include diagnostic type, therapeutic type and monitoring type; The collecting of the original device image of the target medical device when it is put into storage includes: If the device type is a diagnostic type, a multi-peak grayscale histogram of the target medical device when it is put into storage is collected as the original device image; if the device type is a therapeutic type, a skewed grayscale histogram of the target medical device when it is put into storage is collected as the original device image; if the device type is a monitoring type, a unimodal grayscale histogram of the target medical device when it is put into storage is collected as the original device image; The acquiring the status information of the target medical device based on the original device image includes: Obtaining a target grayscale pixel threshold of the original device image; Segmenting the original instrument image based on the target grayscale pixel threshold to obtain a foreground image and a background image of the original instrument image; Eliminating the background image in the original device image to obtain a target device image; Inputting the target device image into a defect detection model to obtain a defect detection result output by the defect detection model; the defect detection model is trained based on sample images and their corresponding defect labels; The status information of the target medical device is determined based on the defect detection result.

4. The intelligent management method of medical equipment based on Internet of Things monitoring according to claim 3 is characterized in that: The step of obtaining a target grayscale pixel threshold of the original device image includes: For a target medical device of a diagnostic type, the original device image is segmented based on each wave peak in the original device image to obtain a plurality of device sub-images; each of the device sub-images represents a sub-image corresponding to a wave peak; For the first instrument sub-image with the maximum peak value, taking the pixel corresponding to the peak in the first instrument sub-image as the center and the first size range as the sliding window, obtain the first pixel point; determine the first grayscale pixel threshold based on the grayscale pixel value of the first pixel point; For the second instrument sub-image with the minimum peak value, taking the pixel corresponding to the peak in the second instrument sub-image as the center and the second size range as the sliding window, obtain the second pixel point; determine the second grayscale pixel threshold based on the grayscale pixel value of the second pixel point; For a third instrument sub-image other than the first instrument sub-image and the second instrument sub-image, taking the pixel corresponding to the wave peak in the third instrument sub-image as the center and the third size range as the sliding window, a third pixel point is obtained; and a third grayscale pixel threshold is determined based on the grayscale pixel value of the third pixel point; The target grayscale pixel threshold is determined based on the first grayscale pixel threshold, the second grayscale pixel threshold and the third grayscale pixel threshold; the first size range is larger than the third size range, and the third size range is larger than the second size range.

5. The intelligent management method of medical equipment based on Internet of Things monitoring according to claim 3 is characterized in that: The step of obtaining a target grayscale pixel threshold of the original device image includes: For a target medical device of a treatment type, segmenting the original device image according to a preset area size to obtain a plurality of first image areas; Determine a second image area based on the density of pixels in each of the first image areas; Determine a grayscale pixel threshold of each second image region based on a grayscale pixel value corresponding to a pixel point in each second image region; The target grayscale pixel threshold is determined based on the grayscale pixel threshold of each of the second image regions.

6. The intelligent management method of medical equipment based on Internet of Things monitoring according to claim 3 is characterized in that: The step of obtaining a target grayscale pixel threshold of the original device image includes: For a target medical device of the monitoring type, taking each pixel corresponding to a quarter peak value in the original device image as the center and the first preset radius as the diffusion radius, a fourth pixel point is obtained; and a fourth grayscale pixel threshold is determined based on the grayscale pixel value of the fourth pixel point; Taking each pixel corresponding to one-half peak value in the original device image as the center and the second preset radius as the diffusion radius, a fifth pixel point is obtained; and a fifth grayscale pixel threshold is determined based on the grayscale pixel value of the fifth pixel point; Taking each pixel corresponding to the three-quarter peak value in the original device image as the center and the third preset radius as the diffusion radius, a sixth pixel point is obtained; and a sixth grayscale pixel threshold is determined based on the grayscale pixel value of the sixth pixel point; Determining the target grayscale pixel threshold based on the fourth grayscale pixel threshold, the fifth grayscale pixel threshold and the sixth grayscale pixel threshold; The first preset radius is smaller than the second preset radius, and the second preset radius is smaller than the third preset radius.

7. The intelligent management method of medical equipment based on Internet of Things monitoring according to any one of claims 2 to 6, characterized in that: The step of uploading the device usage record information to a blockchain for storage based on the user identity information includes: Performing a hash operation on the device usage record information to obtain a structure hash value; the structure hash value includes a first hash value corresponding to the user identity information, a second hash value corresponding to the outbound information, a third hash value corresponding to the inbound information, and a fourth hash value corresponding to the status information; Generate an initial record structure based on the device usage record information and the structure hash value; Based on the first hash value, query the blockchain to obtain a hash tree corresponding to the user identity information in the blockchain; the hash tree includes a root node, a parent node, and a child node; Encrypting the user identity information based on the root hash value of the root node, and generating a first target record structure based on the encrypted user identity information and the initial record structure; The first target record structure is stored in the hash tree based on the structure hash value.

8. The intelligent management method of medical equipment based on Internet of Things monitoring according to claim 7 is characterized in that: The storing the first target record structure into the hash tree based on the structure hash value includes: Using the fourth hash value as a first traversal index to traverse the hash tree, and obtaining a parent node of the first target record structure in the hash tree; Using the second hash value and the third hash value as a second traversal index, traverse the child nodes of the parent node to obtain a target child node in the parent node; The first target record structure is concatenated with the second target record structure in the target subnode and stored in the target subnode.

9. An electronic device, comprising: A memory and a processor, characterized in that a computer software program is stored in the memory, and when the processor reads and executes the computer software program, the intelligent management method of medical equipment based on Internet of Things monitoring as described in any one of claims 2 to 8 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, the intelligent management method of medical equipment based on Internet of Things monitoring as described in any one of claims 2 to 8 is implemented.

Images