Iot-based medical consumable usage compliance monitoring method and system

Abstract

The application discloses an internet-of-things-based medical consumable use compliance monitoring method and system, and relates to the technical field of medical internet-of-things-based consumable intelligent supervision and risk prevention and control.The method comprises the following steps: S1, continuously collecting temperature and humidity data of a medical consumable storage area by an environmental sensor to generate an environmental state signal; S2, identifying electronic tag information of an operated consumable by an RFID reader-writer to generate a consumable identity signal and a verification signal; S3, receiving the environmental state signal, the consumable identity signal and the authorization verification signal by a central processing platform, and performing real-time matching of the signals with a pre-stored compliance rule library; S4, generating a compliance execution signal to trigger an intelligent cabinet to unlock the consumable or generating a secondary violation alarm signal and freezing the consumable access permission; and S5, monitoring a consumable package opening state by a weight sensor to form a use start signal.The application can solve the problem of real-time compliance monitoring in the use process of medical consumables.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical Internet of Things consumable intelligent supervision and risk prevention and control, and particularly relates to a medical consumable use compliance monitoring method and system based on the Internet of Things. BACKGROUND

[0002] Current medical consumable management generally adopts a combination of barcode scanning and manual recording. Although high-value consumables have introduced radio frequency identification technology to achieve basic traceability, there are three defects: first, environmental compliance relies on regular manual inspection, which cannot realize real-time sensing of risks such as temperature and humidity exceeding the standard, failure of sterile packaging, and the like, and expired consumables are easily misused; second, there is a lack of dynamic authorization verification in the consumable use link, and the matching of the operator's identity and the patient's operation demand relies on paper document checking, which is prone to errors such as unauthorized use and mismatching of operation type and consumable specifications; and third, the time limit for use after opening is completely recorded manually by medical staff, and in emergency situations, the use time limit is easily ignored, leading to contaminated consumables being implanted into the patient's body.

[0003] Existing Internet of Things management solutions mostly focus on inventory positioning and warehouse in-out automation, such as intelligent shelves that monitor inventory through weighing sensors and radio frequency identification cabinets that realize rapid inventory, but they fail to deeply integrate system data such as operation scheduling, patient identification, and operator permissions. A few solutions that attempt to monitor behavior need to deploy a large number of visual sensors in the operating room, which face privacy disputes and high-cost promotion obstacles.

[0004] More importantly, existing technologies lack the ability to analyze the four-dimensional compliance elements of "environment-people-patient-time" in the consumable use chain, and most of the violations are traced back after the event rather than being blocked in the process, which exposes hospitals to the risks of medical insurance refusal, infection accidents, and legal disputes. In the field of high-value implantable consumables, the proportion of secondary operations due to non-standard operation is as high as thirty-seven percent of clinical accidents, and there is an urgent need to establish a real-time and proactive prevention and control system covering the entire life cycle of consumables. SUMMARY

[0005] In view of the above shortcomings of the prior art, the present application aims to provide a medical consumable use compliance monitoring method and system based on the Internet of Things, which solves the problem of real-time compliance monitoring in the use process of medical consumables. The present application generates multi-source signals by real-time collection of consumable storage environment, identity and operator information through environmental sensors, radio frequency identification and biometric devices, dynamically verifies the consumable-patient-operation correlation by integrating hospital system data on the central processing platform, and decides to unlock or freeze the consumables based on the compliance rule base; combined with the weight sensing and countdown mechanism to monitor the time limit for use after opening, a closed-loop management of violation alarm, permission control and sterilization disposal is formed, realizing proactive compliance intervention in the whole process from storage to consumption.

[0006] The present application provides a medical consumable use compliance monitoring method based on the Internet of Things, comprising:

[0007] S1: Continuously collect temperature and humidity data of the medical consumable storage area through environmental sensors to generate an environmental state signal;

[0008] S2: Identify the electronic tag information of the operated consumables through the RFID reader and generate a consumable identity signal, and obtain the operator identity information through the biometric identification device to form an authorization verification signal;

[0009] S3: The central processing platform receives the environmental state signal, the consumable identity signal and the authorization verification signal, and matches them with the pre-stored compliance rule library in real time. If it is detected that the environment is out of standard, the consumables are expired or the operator is unauthorized, a violation alarm signal is generated; otherwise, a consumable-patient-surgery association verification signal is generated;

[0010] S4: According to the association verification signal, if the consumable type, specification and current surgery demand match and the operation time window is valid, a compliance execution signal is generated to trigger the intelligent cabinet to unlock the consumables; if they do not match or are out of time, a secondary violation alarm signal is generated and the consumable access permission is frozen;

[0011] S5: The use start signal is formed by monitoring the opening state of the consumable package through the weight sensor, and the effective use time is monitored by starting the countdown timer. If the consumable activation feedback signal is not detected after the timeout, a time limit violation signal is generated and pushed to the supervision terminal.

[0012] In an embodiment of the present application, in step S1, the environmental sensor includes a multi-point temperature and humidity probe deployed inside the consumable storage cabinet. When at least one probe continuously generates an abnormal environmental state signal, the central processing platform automatically locates the out-of-standard area coordinates and generates a partition alarm signal. At the same time, the intelligent risk control equipment is linked to adjust the environmental parameters of the area until the environmental state signal returns to the compliance threshold range, and the adjustment process is recorded to form an environmental repair trajectory signal and stored in the audit database.

[0013] In an embodiment of the present application, in step S2, the biometric identification device synchronously collects the operator behavior characteristic data to form an operation risk coefficient when generating the authorization verification signal. If the coefficient exceeds the preset risk threshold, a multi-factor identity review process is triggered: the operator's historical compliance record is called to generate a behavior comparison signal, and a biological review signal is generated by combining real-time face liveness detection. After double verification, the authorization verification signal is updated and a high-risk operation identifier is marked.

[0014] In an embodiment of the present application, the construction process of the pre-stored compliance rule library in step S3 includes: parsing the hospital consumable management specification text to generate a basic rule signal, fusing historical violation case data to generate a risk pattern signal, and training the basic rule signal and the risk pattern signal through a machine learning engine to output a dynamically updated decision tree model, which receives environmental state signals, consumable identity signals and authorization verification signals in real time and outputs matching results.

[0015] In an embodiment of the present application, when the consumable-patient-surgery association verification signal is generated in step S4, the central processing platform pushes a consumable binding request signal to the operating room terminal; the operating nurse generates a patient identification feedback signal by scanning the patient wristband code, and generates a time window validity signal by confirming the surgery process stage; the platform receives the above signals and performs triple cross verification with the consumable identity signal, and only when the three are logically consistent, the association verification is determined to be passed.

[0016] In an embodiment of the present application, after the secondary violation alarm signal is triggered in step S4, the system automatically freezes the access permission of the same batch of consumables and generates a violation trace instruction: backtracking the temperature and humidity change curve signal of the consumable during storage, the operator contact history signal and the flow path positioning signal, constructing a violation evidence chain and storing it into a blockchain storage node, and sending an encrypted audit report signal to the quality management department.

[0017] In an embodiment of the present application, when the weight sensor monitors the opening state of the consumable package in step S5, a weight decay rate model is established: when the measured weight change rate exceeds the model warning threshold, it is determined as an abnormal opening behavior and a forced termination signal is generated; the signal triggers the intelligent cabinet to lock the unused consumables, and simultaneously broadcasts an alarm voice signal to the operating room.

[0018] In an embodiment of the present application, the starting conditions of the countdown timer monitoring the effective use time length include: after receiving the use start signal, detecting whether the consumable is in the sterile operation area positioning signal range; if it is in the sterile area, the standard timing mode is started, and if it is in the non-sterile area, the emergency accelerated timing mode is started, and the two modes correspond to different timeout judgment thresholds.

[0019] In an embodiment of the present application, after the compliance execution signal triggers the intelligent cabinet to be unlocked, a consumable use responsibility binding signal is generated synchronously: the operator biometric information, patient identification information and surgery time stamp are encrypted and written into the consumable radio frequency identification tag, forming an unalterable use trace record; the record generates a final consumption confirmation signal to close the monitoring process through the handheld terminal scanning after the consumable is used.

[0020] The present application also includes a medical consumable use compliance monitoring system based on Internet of Things, comprising:

[0021] The acquisition module continuously acquires the temperature and humidity data of the medical consumable storage area through the environment sensor to generate an environment state signal;

[0022] The coordination module identifies the electronic tag information of the operated consumable through the RFID reader and generates a consumable identity signal, and obtains the operator identity information through the biometric identification device to form an authorization verification signal;

[0023] The comparison module receives the environment state signal, the consumable identity signal and the authorization verification signal, and matches them with the pre-stored compliance rule library in real time, and if it is detected that the environment is out of standard, the consumable is expired or the operator is unauthorized, a violation alarm signal is generated; otherwise, a consumable-patient-surgery association verification signal is generated;

[0024] The rehearsal module generates a compliance execution signal to trigger the intelligent cabinet to unlock the consumable according to the association verification signal if the consumable type and specification match the current surgery demand and the operation time window is valid; if they do not match or are overdue, a secondary violation alarm signal is generated and the consumable access permission is frozen;

[0025] The analysis module monitors the opening state of the consumable package through the weight sensor to form a use start signal, starts a countdown timer to monitor the effective use time, and if the consumable activation feedback signal is not detected after the time is up, a time limit violation signal is generated and pushed to the supervision terminal.

[0026] The medical consumable use compliance monitoring method and system based on the Internet of Things provided by the application generate multi-source signals by real-time acquisition of consumable storage environment, identity and operator information through environment sensors, radio frequency identification and biometric identification devices, the central processing platform dynamically verifies the consumable-patient-surgery association based on hospital system data, and decides to unlock or freeze the consumable based on the compliance rule library; the opening time limit is monitored by combining the weight sensor and the countdown mechanism, forming a closed-loop management of violation alarm, permission control and sterilization disposal, and realizing active compliance intervention in the whole process from storage to consumption. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0028] Figure 1 The method flowchart of the medical consumable use compliance monitoring method based on the Internet of Things;

[0029] Figure 2 The working flowchart of the medical consumable use compliance monitoring method based on the Internet of Things;

[0030] Figure 3 Fig. 6 is a schematic diagram showing the workflow of the intelligent cabinet after being unlocked;

[0031] Figure 4 Fig. 9 is a system architecture diagram of the medical consumable usage compliance monitoring system based on the Internet of Things. DETAILED DESCRIPTION

[0032] The advantages and effects of the present application can be easily understood by the skilled in the art from the above description of the embodiments of the present application. The present application can also be implemented or applied by other different embodiments, and the details in the description can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0033] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present application, and only the components related to the present application are shown in the diagrams, not the number, shape and size of the components when actually implemented. The shapes, number and proportions of the components when actually implemented can be arbitrarily changed, and the layout pattern of the components can be more complex.

[0034] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application, however, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details, and in other embodiments, the known structures and devices are shown in the form of block diagrams instead of details to avoid making the embodiments of the present application difficult to understand.

[0035] Please refer to Figures 1-4The application discloses an Internet of Things-based medical consumable use compliance monitoring method and system. The Internet of Things-based medical consumable use compliance monitoring method comprises the following steps: S1, continuously collecting temperature and humidity data of a medical consumable storage area by an environmental sensor to generate an environmental state signal; S2, identifying electronic tag information of an operated consumable by an RFID reader and writer to generate a consumable identity signal, and obtaining operator identity information by a biological recognition device to form an authorization verification signal; S3, a central processing platform receives the environmental state signal, the consumable identity signal and the authorization verification signal, and matches them with a pre-stored compliance rule library in real time, and if it is detected that the environment is out of standard, the consumable is expired or the operator is unauthorized, a violation alarm signal is generated; otherwise, a consumable-patient-surgery association verification signal is generated; S4, according to the association verification signal, if the consumable type, specification and current surgery demand are matched and the operation time window is valid, a compliance execution signal is generated to trigger an intelligent cabinet to unlock the consumable; if they are not matched or the time is out, a secondary violation alarm signal is generated and the consumable access permission is frozen; S5, a weight sensor is used to monitor a consumable package opening state to form a use start signal, a countdown timer is started to monitor the effective use time, and if the time is out and no consumable activation feedback signal is detected, a time limit violation signal is generated and is pushed to a supervision terminal.

[0036] As Figure 1As shown, the core process of the medical consumable usage compliance monitoring method based on the Internet of Things starts with continuous monitoring of the storage area by an environmental sensor, which includes a distributed deployment of temperature and humidity detection units that collect environmental parameters every 10 seconds and generate digitized environmental state signals. The signals are transmitted to the central processing platform through a low-power wide-area network. When the consumable is operated, a radio frequency identification reader fixed on the side of the storage cabinet door automatically scans the consumable electronic tag to extract its unique code, specification and model, and expiration date information to generate a structured consumable identity signal. A synchronously activated biometric identification device collects the operator's fingerprint or vein features and generates an encrypted authorization verification signal by comparing with the pre-stored authorized database. Both signals are uploaded after time stamp alignment by the edge computing gateway. After receiving the above signals, the central processing platform calls the pre-stored compliance rule library to perform three verifications: first, analyze the temperature and humidity values in the environmental state signal. If the continuous 3 sampling values exceed the storage threshold of the consumable, the environment is marked as exceeding the standard. Second, check the difference between the expiration date field in the consumable identity signal and the system clock. If it has expired, the consumable is marked as invalid. Finally, match the operator ID in the authorization verification signal with the consumable operation permission list. If it does not match, it is marked as unauthorized. Any marker trigger generates a violation alarm signal containing the violation type, location coordinates, and time point, which is pushed to the supervision terminal. If all verifications are passed, a data call request is initiated to the hospital information system to obtain the patient ID, surgery type and planned consumable list in the current operating room schedule, and a consumable-patient-surgery association verification signal is constructed. The association verification signal is used to drive the intelligent cabinet control decision: the central processing platform analyzes the consumable specification parameters in the surgery demand and performs similarity matching with the specification field in the consumable identity signal, while checking whether the current time is within the surgery preparation time window. When both conditions are met, a compliance execution signal carrying a dynamic unlocking password is sent to the electromagnetic lock of the intelligent cabinet, triggering the cabinet door to open and allowing access. If the specifications do not match or the surgery start time exceeds 30 minutes, a secondary violation alarm signal is generated and the intelligent cabinet freezes the consumable access permission for 72 hours. After the consumable is taken out, it enters the usage monitoring phase: the weight sensor embedded in the packaging base monitors the quality change in real time. When a sudden drop in quality exceeding 20% of the total packaging weight is detected, it is determined that the packaging has been opened, and a usage start signal is generated to activate the countdown timer. The timer loads the pre-set valid time according to the consumable type (e.g. 4 hours for sterile dressings), and continuously detects whether the contact sensor inside the consumable returns an activation feedback signal during this period. If the countdown timer reaches zero and no feedback is received, a time limit violation signal is generated and the overtime evidence chain is pushed to the operating room supervision terminal.

[0037] Further, the storage area is divided into several independent temperature control units, each unit is equipped with at least three temperature and humidity probes to form a redundant monitoring network. When the central processing platform detects that more than half of the probes in a unit continuously generate abnormal environmental state signals, it immediately starts the positioning analysis algorithm: calculate the variance of each probe data deviating from the standard value, select the coordinates of the probe with the largest variance as the core over-standard area, and generate a partition alarm signal containing the floor position, cabinet number, and unit partition code. This signal triggers a three-level control mechanism: the first level responds by sending a frequency conversion command to the intelligent wind control device in the area through the Internet of Things gateway to control the semiconductor cooling plate to start cooling and dehumidifying; the second level responds after 5 minutes of continuous over-standard, activates the spare storage bin transfer channel and generates a consumable migration path planning signal; the third level responds to special biochemical reagents that need to be saved, drives the mechanical arm to transfer the consumables to the emergency cold storage box. During the environmental regulation process, the central processing platform receives the operation parameters returned by the wind control device in real time, and constructs a closed-loop control model combined with the change trend of the environmental state signal: when the temperature and humidity data are within the threshold range, an environmental repair confirmation signal is generated to stop the control; if the control continues for 15 minutes without meeting the standard, it is upgraded to a major fault alarm and notifies the equipment maintenance team. The key events of the entire control process are encapsulated as environmental repair trajectory signals, including initial over-standard parameters, control measure sequence, recovery time point, and energy consumption data. After digital signature, the signal is stored in the blockchain audit database to ensure data tamper-proofing. The partition alarm signal also drives the update of the visual interface: on the hospital consumable management large screen, the over-standard area presents a red flashing identifier, and clicking can view the real-time temperature and humidity curve and repair progress; at the same time, it pushes a hierarchical warning message to the responsible person's mobile phone, sends a short message reminder for mild over-standard, and triggers a voice call alarm for severe over-standard.

[0038] As Figure 1As shown, the system synchronously loads the behavior analysis engine when collecting the operator's biometric features. When the operator contacts the biometric identification device, the pressure sensor records the pressing intensity change curve, and the infrared sensor captures the hand micro-tremor frequency, both of which are fused to generate a behavior feature data set; the central processing platform inputs this data set into the pre-trained risk assessment model, and outputs an operation risk coefficient in the interval of 0-1. If the coefficient exceeds the preset risk threshold of 0.7, the multi-factor identity review process is immediately triggered: first, the operator's consumable operation records in the past 30 days are called, and the average operation time, commonly used consumable types, typical operation period and other features are extracted to generate a behavior comparison signal; at the same time, a high-definition camera is started for live detection, requiring the operator to complete the blinking and turning head actions according to random instructions, and generate a biological review signal containing action continuity and facial micro-expression. Two signals are input into the decision engine for weighted scoring: the behavior comparison signal accounts for 60% of the weight, and the deviation of the current operation mode from the historical habit; the biological review signal accounts for 40% of the weight, and detects whether there are signs of coercive operation through the biological review signal. If the comprehensive score of the two signals exceeds 85 points, it is determined to be a real operation, the authorized verification signal is updated and a high-risk operation identifier is added. This identifier triggers three measures to strengthen supervision: first, the sampling frequency of the weight sensor is increased to 5 times per second during consumable use, and abnormal opening behavior is sensitively monitored; second, the video stream associated with this operation is automatically backed up to a secure storage area, and the saving period is extended to 3 years; third, an operation risk assessment report is generated and pushed to the department director terminal. For the situation that the review fails, the system performs step-by-step interception: a voice prompt is generated to require re-verification when it fails for the first time; the operator's account is frozen and a security patrol request is sent after it fails for the second time; the account is permanently locked and requires on-site identity verification to be unblocked when it fails for the third time. All high-risk operation records generate encrypted logs, which are stored in combination with the consumable identity signal and environmental state signal of the operation, forming a traceable responsibility evidence chain.

[0039] As Figure 2As shown, the construction of the pre-stored compliance rule library starts with the digital analysis of the hospital consumable management specifications: the central processing platform imports the standard operation process documents, extracts the key constraint conditions through the natural language processing engine, converts them into machine-readable rule metadata, and generates basic rule signals containing storage temperature thresholds, operator permission levels, consumable expiration date tolerances, and other elements. Historical violation case data is also loaded into the analysis module to extract environmental parameter features, operation time features, and consumable type features of typical violation events, identify high-frequency risk patterns through clustering algorithms, and generate risk pattern signals with risk level labels. The basic rule signals and risk pattern signals are input into the machine learning engine for feature fusion training: first, build an initial decision tree framework, and use the hard constraints in the basic rule signals as the root node splitting conditions; second, use the associated features in the risk pattern signals as the basis for leaf node expansion, for example, when the consumable identity signal shows an implantable device, automatically add a biological identification review level. During the training process, a reinforcement learning mechanism is introduced, and whenever the system generates a new violation alert signal, its context feature vector is extracted as a negative sample to inject into the training set, and the decision tree adjusts the node weights dynamically and generates a version update log. The final output dynamic decision tree model is deployed in the real-time analysis engine, which activates the stored compliance sub-model when receiving the environmental state signal, analyzes the temperature and humidity data stream to determine whether to trigger an environmental alert; when receiving the consumable identity signal, it calls the expiration date verification sub-model, calculates the remaining safety period based on the inventory turnover history; when receiving the authorization verification signal, it executes the permission matching sub-model, and implements hierarchical authorization according to the operator's title and the risk level of the consumable. The model automatically performs online incremental learning every 24 hours, and when the rule library version number changes, it pushes a lightweight model update package to all edge nodes.

[0040] As Figure 2As shown, after the process of generating the consumable-patient-surgery association verification signal is started, the central processing platform sends an encrypted binding request signal to the wall-mounted terminal in the target operating room, which contains the consumable unique code, specification picture and recommended use period. The operating nurse scans the two-dimensional code on the patient's wristband using the terminal's built-in code scanner to analyze the patient's hospital number and current surgery code to generate a patient identification feedback signal; at the same time, the touch screen selects the operation process stage option (such as anesthesia completion, incision establishment, suturing), and generates a surgery stage signal with a time stamp. After the platform receives the above two signals, it performs three cross-verification: the first verification is whether the surgery adaptation type field in the consumable identity signal matches the patient's surgery code, for example, cardiovascular stent consumables need to correspond to the heart intervention surgery code; the second verification is the compatibility of the consumable specification parameters and the patient's body size data, which calls the patient's height and weight in the electronic medical record to calculate the applicable specification range; the third verification is to confirm the validity of the operation time window, if the current time is within 60 minutes before the key operation period indicated by the surgery stage signal, it is determined to be valid. Only when all three verifications return positive results, the platform generates an association verification signal with a green pass identification, which triggers two responses: one sends an unlock instruction to the smart cabinet and attaches a patient name voice broadcast prompt, the other pushes the consumable characteristic warning information to the anesthesia monitoring system. If any verification fails, a red block signal is generated and differentiated treatment is performed: when the surgery type does not match, send an emergency replacement suggestion list to the chief surgeon terminal; when the specification is not compatible, start the 3D printing equipment to quickly generate an adaptive connector; when the time limit is overdue, release the access authority of the standby consumable cabinet. All verification processes generate audit trails, which are automatically aligned with the patient surgery video timeline for storage.

[0041] As Figure 3The execution steps after generating the compliance execution signal are specifically illustrated, and the violation tracing instruction after the secondary violation alarm signal triggers includes a three-level evidence collection process: the first level traces back the storage environment data, extracts the temperature and humidity change curve signal of the batch of consumables since storage from the blockchain database, and focuses on analyzing the abnormal fluctuation period and the exceeding standard cumulative time length; the second level traces the operator contact history, calls all personnel identity signals and operation time length data of the biological identification log of the operator of the consumables, and marks the abnormal short contact or high frequency contact record; the third level restores the transfer path, reconstructs the motion trajectory signal of the consumables from the storage cabinet to the operating table through the warehouse positioning beacon and the radio frequency identification record of the operating room access control system. The evidence chain construction engine inputs the evidence collection data: the environmental analysis module calculates the influence coefficient of the temperature and humidity exceeding the standard on the performance of the consumables, the operation audit module evaluates the possibility of unauthorized operation, and the path detection module identifies the risk points of irregular transfer. The weighted fusion of the output results of the three modules generates a violation evidence score, and if the score exceeds the threshold, it is determined as an effective violation evidence chain. The evidence chain is split into several data packets, each packet is attached with a digital fingerprint and distributed to multiple storage nodes of the hospital blockchain network, forming a distributed tamper-proof storage. The synchronous generated encrypted audit report signal contains three parts of core content: the first part is the visual evidence graph, which shows the spatial superposition view of the temperature and humidity curve, the operation time axis and the transfer path; the second part lists the responsibility associated parties, and automatically labels the direct operator, environment administrator and operation responsible person according to the operation record; the third part outputs the disposal suggestion matrix, which recommends administrative sanctions, system permission adjustment or process reengineering measures according to the violation type. The report is transmitted to the quality management department host through the quantum encryption channel, triggers the automatic case filing system and assigns the investigation task number.

[0042] As Figure 4As shown, the present application relates to a medical consumable usage compliance monitoring system based on Internet of Things, comprising a collection module, which continuously collects temperature and humidity data of a medical consumable storage area through an environmental sensor to generate an environmental state signal; a coordination module, which identifies electronic tag information of an operated consumable through an RFID reader and generates a consumable identity signal, and simultaneously obtains operator identity information through a biometric identification device to form an authorization verification signal; a comparison module, which receives the environmental state signal, the consumable identity signal and the authorization verification signal by a central processing platform of the comparison module, and performs real-time matching with a pre-stored compliance rule library, and if it is detected that the environment is out of standard, the consumable is expired or the operator is unauthorized, a violation alarm signal is generated; otherwise, a consumable-patient-surgery association verification signal is generated; a rehearsal module, which generates a compliance execution signal to trigger the intelligent cabinet to unlock the consumable according to the association verification signal if the consumable type, specification and current surgery demand match and the operation time window is valid; if they do not match or the time is up, a secondary violation alarm signal is generated and the consumable access permission is frozen; an analysis module, which monitors the opening state of the consumable packaging through a weight sensor to form a usage start signal, starts a countdown timer to monitor the effective usage time, and if the time is up without detection, a time limit violation signal is generated and pushed to a supervision terminal.

[0043] As Figure 4As shown, when the weight sensor monitors the opening state of the consumable package, the system pre-establishes a weight attenuation benchmark model: for different types of consumable packages, collect weight change data of thousands of standard openings in a laboratory environment, extract the characteristic parameters of the sudden drop in mass and the duration of time, and generate a benchmark parameter matrix corresponding to the package type. In actual monitoring, the sensor collects weight data streams at a frequency of 20 times per second, and when the instantaneous mass drop value exceeds 15% of the total weight of the package, the abnormal opening analysis engine is started: first, calculate the actual attenuation rate, which is the first derivative of the mass change per unit time; second, query the benchmark model to obtain the standard attenuation rate interval for this package; finally, compare the actual rate with the standard interval to generate a deviation coefficient. If the deviation coefficient exceeds 2.0, it is determined to be an abnormal opening behavior, at which time a three-level response mechanism is triggered: the first level generates an orange warning signal and displays the "abnormal opening" prompt on the smart cabinet screen; the second level starts the forced termination program when the deviation coefficient reaches 3.0, sends an emergency locking instruction to the smart cabinet electromagnetic lock to freeze the unopened consumables, and cuts off the power supply of the position; the third level activates the package self-destruction mechanism to release the dye marker agent for high-risk implants. After the forced termination signal is generated, a synchronous broadcast alarm voice signal is generated, which is optimized by the operating room sound field modeling engine: a directional sound beam is used to broadcast detailed alarm content in the operating table area, and a general warning tone is played in the auxiliary area to avoid interfering with the operation of the surgeon. All abnormal opening event generation processes record signals, including weight change curves, deviation coefficient calculation processes, and response measure sequences, which are automatically synchronized with the surgical video stream and stored to support later drag-time axis playback analysis. The system aggregates abnormal data monthly to train the benchmark model, dynamically adjusting the attenuation rate standard interval for each type of package.

[0044] The medical consumable use compliance monitoring method and system based on the Internet of Things can solve the problem of real-time compliance monitoring during the use of medical consumables.

[0045] Therefore, the medical consumable use compliance monitoring method and system based on the Internet of Things can solve the problem of real-time compliance monitoring during the use of medical consumables.

[0046] The above embodiments are only illustrative of the principles of the present application and its efficacy, and are not intended to limit the present application. Any modification or change made by any person skilled in the art without departing from the spirit and scope of the present application shall be covered by the claims of the present application.

Claims

1. A method for monitoring the compliance of medical consumables use based on the Internet of Things, characterized in that, include: S1: The temperature and humidity data of the medical consumables storage area are continuously collected by environmental sensors to generate an environmental status signal. In step S1, the environmental sensors include multiple temperature and humidity probes deployed inside the consumables storage cabinet. When at least one probe continuously generates an abnormal environmental status signal, the central processing platform automatically locates the coordinates of the area exceeding the standard and generates a zone alarm signal. At the same time, the intelligent risk control equipment is linked to adjust the environmental parameters of the area until the environmental status signal returns to the compliant threshold range, and the adjustment process is recorded to form an environmental repair trajectory signal and stored in the audit database. S2: Identify the electronic tag information of the consumables being operated through an RFID reader / writer to generate a consumables identification signal, and at the same time obtain the operator's identity information through a biometric device to form an authorization verification signal; S3: The central processing platform receives the environmental status signal, consumable identity signal and authorization verification signal, and matches them with the pre-stored compliance rule base in real time. If the environmental standard is exceeded, the consumable is expired or the operator is not authorized, a violation alarm signal is generated. Otherwise, a consumable-patient-surgery association verification signal is generated; The construction process of the pre-stored compliance rule base mentioned in step S3 includes: parsing the hospital consumables management specification text to generate basic rule signals, integrating historical violation case data to generate risk pattern signals, using a machine learning engine to perform feature association training on the basic rule signals and risk pattern signals, and outputting a dynamically updated decision tree model. This model receives environmental status signals, consumables identity signals, and authorization verification signals in real time and outputs matching results. S4: Based on the associated verification signal, if the consumable type and specifications match the current surgical needs and the operation time window is valid, a compliance execution signal is generated to trigger the smart cabinet to unlock the consumable; if they do not match or time out, a secondary violation alarm signal is generated and the consumable access permission is frozen; when the consumable-patient-surgery associated verification signal is generated in step S4, the central processing platform pushes a consumable binding request signal to the operating room terminal; the surgical nurse generates a patient identification feedback signal by scanning the patient's wristband code and confirms that the time window is valid during the surgical process; after receiving the above signal, the platform performs triple cross-verification with the consumable identity signal, and only when the three are logically consistent is the associated verification determined to be successful; S5: The weight sensor monitors the opening status of the consumable packaging to generate a usage start signal, and starts a countdown timer to monitor the effective usage time. If no activation feedback signal is detected after the timeout, a timeout violation signal is generated and pushed to the monitoring terminal. The start conditions for the countdown timer to monitor the effective usage time include: after receiving the usage start signal, detecting whether the consumable is within the positioning signal range of the sterile operation area; if it is in the sterile area, the standard timing mode is started; if it is in the non-sterile area, the emergency accelerated timing mode is started. The two modes correspond to different timeout judgment thresholds.

2. The method for monitoring the compliance of medical consumables use based on the Internet of Things according to claim 1, characterized in that, In step S2, when generating the authorization verification signal, the biometric device simultaneously collects the operator's behavioral feature data to form an operation risk coefficient. If the coefficient exceeds the preset risk threshold, a multi-factor identity verification process is triggered: the operator's historical compliance records are retrieved to generate a behavior comparison signal, and a biometric verification signal is generated by combining real-time face liveness detection. After the dual verification is passed, the authorization verification signal is updated and a high-risk operation identifier is marked.

3. The method for monitoring the compliance of medical consumables use based on the Internet of Things according to claim 1, characterized in that, After the secondary violation alarm signal is triggered in step S4, the system automatically freezes the access permissions of the same batch of consumables and generates a violation tracing instruction: traces back the temperature and humidity change curve signal, operator contact history signal and circulation path location signal of the consumable during storage, constructs a violation evidence chain and stores it in the blockchain evidence storage node, and sends an encrypted audit report signal to the quality management department.

4. The method for monitoring the compliance of medical consumables use based on the Internet of Things according to claim 1, characterized in that, In step S5, when the weight sensor monitors the unsealed status of the consumable packaging, a weight decay rate model is established: when the measured weight change rate exceeds the model's warning threshold, it is determined to be an abnormal unsealing behavior and a forced stop signal is generated; this signal triggers the smart cabinet to lock the unused consumables and broadcasts an alarm voice signal to the operating room.

5. The method for monitoring the compliance of medical consumables use based on the Internet of Things according to claim 1, characterized in that, After the compliance execution signal triggers the smart cabinet to unlock, a consumable usage responsibility binding signal is generated simultaneously: the operator's biometric information, patient identification information and surgical timestamp are encrypted and written into the consumable RFID tag to form an unalterable usage traceability record; after the consumable is used, the record is scanned by a handheld terminal to generate a final consumption confirmation signal to close the monitoring process.

6. A medical consumables usage compliance monitoring system using the Internet of Things-based medical consumables usage compliance monitoring method according to any one of claims 1-5, characterized in that, include: The acquisition module continuously collects temperature and humidity data of the medical consumables storage area through environmental sensors and generates environmental status signals. The coordination module identifies the electronic tag information of the consumables being operated through an RFID reader / writer, and generates a consumable identity signal and an authorization verification signal. The comparison module, whose central processing platform receives the environmental status signal, consumable identity signal and authorization verification signal, and performs real-time matching with the pre-stored compliance rule base to generate a violation alarm signal or a consumable-patient-surgery association verification signal; The pre-operation module generates a compliance execution signal if the type and specifications of consumables match the current surgical requirements and the operation time window is valid, based on the associated verification signal; otherwise, it generates a secondary violation alarm signal. The analysis module uses a weight sensor to monitor the opening status of the consumable packaging to generate a usage start signal, starts a countdown timer to monitor the effective usage time, and if no violation is detected within the time limit, it generates a time-lapse violation signal and pushes it to the monitoring terminal.

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