Medical equipment inventory intelligent allocation and supervision system based on the Internet of Things

Abstract

The present invention discloses an intelligent allocation and supervision system for medical device inventory based on the Internet of Things, including a quantum perception module, a holographic quantum communication module, an edge computing gateway module, a monitoring module, a DNA storage and quantum blockchain module, and an inventory optimization strategy generation module; the quantum perception module obtains the medical device inventory environment through a quantum sensor and monitors the medical device inventory environment in real time; the holographic quantum communication module transmits data during the communication process through quantum entanglement; the edge computing gateway module performs data processing and decision-making on the device side through edge computing technology. The present invention realizes real-time perception and secure transmission through quantum perception and holographic quantum communication, combines edge computing to reduce transmission delay, adopts DNA storage and quantum blockchain to realize ultra-high density storage and traceability, and finally the inventory optimization strategy generation module realizes the optimization strategy of intelligent allocation and management based on data analysis and demand forecasting.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent allocation and supervision of medical device inventory, and in particular to an intelligent allocation and supervision system for medical device inventory based on the Internet of Things. Background Art

[0002] The medical device inventory intelligent allocation and supervision system based on the Internet of Things refers to a system that uses the Internet of Things technology to connect, monitor and coordinate various medical devices and related equipment to achieve intelligent management, allocation and supervision of medical device inventory. This system integrates advanced technologies such as sensors, communication equipment, data processing technology and cloud computing, aiming to improve the efficiency, accuracy and safety of medical device inventory, while achieving real-time monitoring and intelligent allocation of inventory status.

[0003] The prior art has the following deficiencies:

[0004] 1. Traditional inventory management systems often cannot achieve real-time monitoring of minor changes in the inventory environment of medical devices, resulting in low sensitivity to environmental changes. Conventional communication methods have security risks in the data transmission process and are vulnerable to network attacks, affecting the confidentiality of medical device inventory data. In addition, cloud data processing has latency issues, which reduces the system's response speed, especially in terms of processing real-time data. In addition, traditional data storage methods are difficult to meet the needs of ultra-high-density storage and traceability of medical device information, and there are risks in information security and credibility;

[0005] 2. Inventory management often lacks intelligence and flexibility in facing future demand and reducing inventory risks, and cannot make timely adjustments based on real-time changing demand and risks, thus affecting the efficiency and accuracy of the entire inventory management.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide an intelligent inventory allocation and supervision system for medical devices based on the Internet of Things to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent allocation and supervision system for medical device inventory based on the Internet of Things, comprising a quantum perception module, a holographic quantum communication module, an edge computing gateway module, a monitoring module, a DNA storage and quantum blockchain module, and an inventory optimization strategy generation module;

[0009] The quantum perception module uses quantum sensors to obtain the inventory environment of medical devices and conducts real-time monitoring of the inventory environment of medical devices;

[0010] Holographic quantum communication module, which transmits data during the communication process through quantum entanglement;

[0011] The edge computing gateway module uses edge computing technology to process data and make decisions on the device side;

[0012] The monitoring module is used to obtain multi-source data information when the edge computing gateway is running, including real-time performance indicator information and operation status indicator information when the edge computing gateway is running. After obtaining, the real-time performance indicator information and operation status indicator information of the edge computing gateway are processed and comprehensively analyzed to generate a performance anomaly evaluation coefficient, and the data processing performance of the edge computing gateway is evaluated by the performance anomaly evaluation coefficient;

[0013] The DNA storage and quantum blockchain module uses DNA storage and combines it with quantum blockchain to store medical device information in the form of DNA sequences.

[0014] The inventory optimization strategy generation module generates optimization strategies for intelligent inventory allocation and management based on data analysis results and demand forecasts to meet future demand and reduce inventory risks.

[0015] Preferably, the specific implementation process of obtaining the inventory environment of medical devices through quantum sensors and performing real-time monitoring of the inventory environment of medical devices is as follows:

[0016] V101, excite the quantum sensor to make it in an excited state to sense the medical device inventory environment;

[0017] V102. Placing the excited quantum sensor in a medical device inventory environment so that the excited quantum sensor interacts with the environment;

[0018] V103, measure the quantum state and obtain the quantum state information after interaction with the environment;

[0019] V104, transmits the measured information to the edge computing gateway module for real-time data processing;

[0020] V105. Use the processed information to monitor small changes in the medical device inventory environment in real time.

[0021] Preferably, the specific process of data transmission during communication through quantum entanglement is as follows:

[0022] S101. At the initial stage of communication, two distant quantum entangled states are established;

[0023] S102. Use quantum entanglement for key distribution to ensure that both parties have the same encryption key and any attempt to monitor the key will be detected immediately.

[0024] S103. During the communication process, the established quantum entangled state is used to transmit information through the state of the quantum bit;

[0025] S104. Correcting errors caused by environmental interference in quantum states through quantum error correction;

[0026] S105. Use the established quantum communication channel to transmit medical device inventory data in real time;

[0027] S106. Regularly refresh the quantum keys used to enhance the security of the system.

[0028] Preferably, the real-time performance indicator information of the edge computing gateway during operation includes system response delay information, and the operating status indicator information of the edge computing gateway during operation includes data queue length growth information and data packet loss rate increase information. After collection, the system response delay information is processed by the monitoring module to generate a system response delay index, and the data queue length growth information and the data packet loss rate increase information are processed to generate a data queue length abnormal growth index and a data packet loss rate variation index.

[0029] Preferably, the logic of obtaining the system response delay index is as follows:

[0030] A1. In time period G, for tasks during the runtime of the edge computing gateway, record the timestamp of the edge computing gateway task completion response, the timestamp of the task submission, the timestamp of the task completion, and the timestamp of the task start execution to form a task time list;

[0031] A2. Calculate the total time from task submission to response completion. The calculation expression is: T Total =T ResCom -T TaskSub , where T Total It represents the total time from task submission to response completion, T ResCom Indicates the timestamp of the task completion response, T TaskSub Indicates the timestamp of task submission;

[0032] A3. Calculate the actual execution time of the task. The calculation expression is: T Exec =T TaskCom -T TaskStart , where T Exec Indicates the actual execution time of the task, T TaskCom Indicates the timestamp of task completion, T TaskStart Indicates the timestamp when the task starts executing;

[0033] A4. Calculate the waiting time of the computing task in the queue. The calculation expression is: T Queue = T TaskStart - T TaskSub , where T Queue represents the waiting time of the task in the queue;

[0034] A5. Calculate the system response delay index. The calculation expression is: where YC 响应 represents the system response delay index.

[0035] Preferably, the logic for obtaining the data queue length abnormal growth index is as follows:

[0036] B1. During the time period G, collect n data points. Each data point represents the data queue length at the corresponding moment. Calculate the average value of the data queue length. The calculation expression is: where t0 and t1 represent the start and end times of the time period G, t0 < t1, and the function s(t) represents the data queue length at time t;

[0037] B2. Calculate the data queue length abnormal growth index. The calculation expression is: where σ d represents the variance of the n data queue lengths, V d represents the cubic variance of the n data queue lengths, DL 长度 represents the data queue length abnormal growth index.

[0038] Preferably, the logic for obtaining the packet loss rate variation index is as follows:

[0039] C1. Collect the packet loss rate information of the edge computing gateway during data processing within the time G. Sample at the selected time interval to obtain a series of packet loss rate data nodes, and label the packet loss rate data nodes as x w , where w represents the number of the packet loss rate data node, w = 1, 2, 3, 4,..., m, and m is a positive integer;

[0040] C2. Calculate the average value and standard deviation of the packet loss rate of the edge computing gateway during data processing within the time G through the packet loss rate data nodes. The calculation expressions are:

[0041]

[0042] C3. Calculate the packet loss rate variation index. The calculation expression is: where SJ 丢失Indicates the packet loss rate variation index.

[0043] Preferably, the system response delay index YC when the edge computing gateway performs data processing is obtained 响应 , Data queue length abnormal growth index DL 长度 , Packet Loss Rate Variation Index SJ 丢失 After that, the system response delay index YC 响应 , Data queue length abnormal growth index DL 长度 , Packet Loss Rate Variation Index SJ 丢失 Generate a performance anomaly evaluation coefficient η, monitor the performance of the edge computing gateway when processing data through the performance anomaly evaluation coefficient η, and generate an abnormal signal or a normal signal.

[0044] Preferably, the specific steps of using DNA storage in combination with quantum blockchain to store medical device information in the form of DNA sequences are as follows:

[0045] S201, encoding medical device information into a DNA sequence;

[0046] S202, actually synthesizing the encoded DNA sequence;

[0047] S203, storing the synthesized DNA sequence in a specially designed medium;

[0048] S204. When data needs to be retrieved, DNA extraction is performed to extract the DNA from the storage medium, and the DNA sequence is read using DNA sequencing to restore the original medical device information;

[0049] S205. Generate a quantum hash value from the original data of the medical device information, and write the generated quantum hash value into the quantum blockchain;

[0050] S206. Use smart contracts in blockchain to implement logic and operations;

[0051] S207. By implementing access control mechanisms in smart contracts, ensure that only authorized users can retrieve, modify or add medical device information, and use encryption and privacy protection technologies to ensure patient privacy and data security.

[0052] Preferably, based on data analysis results and demand forecasts, the specific steps for generating an optimization strategy for intelligent inventory allocation and management to meet future demand and reduce inventory risks are as follows:

[0053] H101, collect real-time data from quantum perception module, holographic quantum communication module, edge computing gateway module, DNA storage and quantum blockchain module;

[0054] H102. Use machine learning and data mining algorithms to analyze historical inventory data to identify potential inventory demand patterns and trends, and combine the small change monitoring data from the quantum sensing module to analyze environmental changes related to inventory demand;

[0055] H103. Based on historical data and analysis results, use forecasting models to predict future medical device inventory demand;

[0056] H104. Use the traceability and transparency of quantum blockchain to track inventory history and assess inventory risk;

[0057] H105. Combine demand forecasting and inventory risk assessment to develop an optimization strategy for intelligent inventory allocation;

[0058] H106. Apply the generated optimization strategy to actual inventory allocation, use the quantum communication module to ensure the timely communication and execution of the strategy, monitor the execution status through the holographic communication module, monitor inventory changes in real time, and feed back to the data analysis module to continuously optimize the model and strategy;

[0059] H107. Collect feedback information during the implementation process and adjust the forecasting model and inventory optimization strategy based on the feedback information to achieve continuous optimization of the system.

[0060] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0061] The present invention uses quantum perception and holographic quantum communication to systematically perceive changes in the medical device inventory environment in real time, ensure safe transmission, improve the supervision system's protection of inventory real-time and communication security, push data processing and decision-making to the device end, reduce transmission delays with the cloud, and improve system response speed, especially in processing real-time data. It is more efficient, uses DNA storage and quantum blockchain to store medical device information in the form of DNA sequences, ensures data security and transparency through quantum blockchain, achieves ultra-high-density storage and traceability, improves data security and credibility, combines inventory optimization strategy generation with data analysis and demand forecasting, generates optimization strategies for intelligent inventory allocation and management, and flexibly adjusts according to real-time changing needs and risks, thereby improving the intelligence and flexibility of inventory management.

[0062] The present invention obtains multi-source data information during the operation of the edge computing gateway, including real-time performance indicators and operating status indicators, to effectively evaluate the operating status of the system, which is beneficial to the real-time performance monitoring and problem diagnosis of the system. Through the performance anomaly evaluation coefficient, the system can respond quickly when the performance of the edge computing gateway is abnormal, thereby improving the stability and reliability of the system. In particular, in terms of processing real-time data, the introduction of the monitoring module makes the system more efficient, helps to promptly discover and solve potential problems, and further improves the performance and availability of the entire medical device inventory supervision system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0064] Figure 1 This is a module schematic diagram of the medical device inventory intelligent allocation and supervision system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0065] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0066] The present invention provides Figure 1 The IoT-based intelligent allocation and supervision system for medical device inventory shown includes a quantum perception module, a holographic quantum communication module, an edge computing gateway module, a monitoring module, a DNA storage and quantum blockchain module, and an inventory optimization strategy generation module;

[0067] The quantum perception module uses quantum sensors to obtain the inventory environment of medical devices and conducts real-time monitoring of the inventory environment of medical devices;

[0068] The specific implementation process of obtaining the inventory environment of medical devices through quantum sensors and conducting real-time monitoring of the inventory environment of medical devices is as follows:

[0069] V101, excite the quantum sensor to make it in an excited state to sense the medical device inventory environment;

[0070] V102. Placing the excited quantum sensor in a medical device inventory environment so that the excited quantum sensor interacts with the environment;

[0071] In this process, the quantum state becomes entangled with tiny changes in the environment, enabling sensitive perception of tiny changes.

[0072] V103, measure the quantum state and obtain the quantum state information after interaction with the environment;

[0073] This step can use advanced technologies such as quantum non-destructive measurement to obtain information with minimal interference. V104, pass the measured information to the edge computing gateway module for real-time data processing;

[0074] This can include steps such as denoising, feature extraction, and real-time analysis to ensure that the information obtained from the quantum sensor is accurate and reliable.

[0075] V105. Use the processed information to monitor small changes in the medical device inventory environment in real time;

[0076] This can include real-time monitoring of parameters such as temperature, humidity, pressure, and immediate response to abnormal conditions.

[0077] Quantum state sensing technology can be used to obtain the inventory environment of medical devices through quantum sensors and conduct real-time monitoring of the inventory environment of medical devices. Quantum state sensing technology is a sensing technology based on the principles of quantum physics. It uses the properties of quantum states to achieve high-precision measurement of environmental parameters. In the quantum sensing module, quantum state sensing technology is used. Its core features include the following aspects:

[0078] 1. Quantum state perception utilizes the concept of quantum superposition, that is, a quantum system can be in one of multiple possible states at the same time, which enables quantum perception to perceive multiple environmental parameters at the same time, improving the comprehensiveness and comprehensiveness of perception;

[0079] 2. Entangled states are often used in quantum state perception, that is, there is an entangled relationship between two or more quantum particles. The change of the state of one particle will immediately affect other entangled particles. This entangled relationship can be used to achieve real-time monitoring, because any slight environmental changes will be immediately reflected in the state of the entangled particles;

[0080] 3. Quantum state perception relies on quantum measurement, which is a process of obtaining information about a quantum system through interaction. The characteristics of quantum measurement allow the measurement error to be minimized in the measurement, thus improving the accuracy of perception.

[0081] 4. The quantum interference effect may be used in quantum state perception to enhance the perception signal and improve the measurement sensitivity through quantum interference. This can be used to detect tiny environmental changes and make the system more sensitive to tiny changes.

[0082] Quantum state sensing technology makes full use of the unique properties of quantum mechanics and achieves high-precision perception of environmental parameters through means such as quantum superposition, entanglement, measurement and interference. This technology can be used in the medical device inventory intelligent allocation and supervision system to monitor small changes in parameters such as device status, temperature, and humidity in real time, thereby improving the system's perception accuracy and real-time performance.

[0083] Holographic quantum communication module, which transmits data during the communication process through quantum entanglement;

[0084] This step is to ensure data security and guarantee the instant transmission of medical device inventory data. The specific process of data transmission during communication through quantum entanglement is as follows:

[0085] S101. At the initial stage of communication, two distant quantum entangled states are established;

[0086] This might involve passing a pair of entangled qubits to either end of the communication.

[0087] S102. Use quantum entanglement for key distribution to ensure that both parties have the same encryption key and any attempt to monitor the key will be detected immediately.

[0088] S103. During the communication process, the established quantum entangled state is used to transmit information through the state of the quantum bit;

[0089] This ensures the security of information transmission, as any eavesdropping on the transmitted information will result in the destruction of the entangled state and be detected.

[0090] S104. Correcting errors caused by environmental interference in quantum states through quantum error correction;

[0091] This helps improve the reliability and stability of communications;

[0092] The working principle of quantum error correction technology is based on specially designed quantum error detection codes, which encode quantum bits into multi-bit states. The detection codes embedded in the system can not only monitor the state of quantum bits, but also detect potential errors. Once an error is detected, the system transmits information through additional quantum bits and uses the reserved bits in the error correction code to transmit correction information. Then, the system performs appropriate quantum gate operations, corrects the errors according to the detected error types, and finally restores the state of the system. This periodic error correction operation can cope with the instability of the external environment and ensure that the system maintains a high degree of stability and reliability in quantum communication and computing, effectively reducing errors caused by environmental interference, thereby ensuring the accuracy of information transmission.

[0093] S105. Use the established quantum communication channel to transmit medical device inventory data in real time;

[0094] The characteristics of quantum entanglement ensure the immediacy and security of transmission.

[0095] S106. Regularly refresh the quantum key used to enhance the security of the system;

[0096] This process is to reduce the potential risks caused by using the same key for a long time.

[0097] The edge computing gateway module uses edge computing technology to process data and make decisions on the device side;

[0098] This step is to reduce the data transmission delay with the cloud. Edge computing is a computing model that aims to perform calculations close to the data source rather than relying on centralized cloud computing resources. By introducing edge computing technology, the system can process data at a location close to the medical device inventory sensing module instead of transmitting all data to the cloud for processing. The core concept of edge computing is to push data processing to the source of data generation, that is, to process data on the device side where the medical device inventory sensing module is located. This means that the perceived data does not have to be transmitted to the remote cloud server in full, but is processed on the device near the sensing module. Data transmission delay refers to the time required for data to be transmitted from the source to the destination. By processing data on the device side, the need to transmit data to the remote cloud can be reduced, thereby reducing transmission time and improving system response speed. This is crucial for real-time monitoring and rapid decision-making, especially in the medical device inventory intelligent allocation system.

[0099] The edge computing gateway plays a key role in the intelligent inventory allocation and supervision system of medical devices. Its role is to move the computing and decision-making process from the cloud to the edge of the IoT device. The edge computing gateway processes the environmental perception data from the quantum perception module in real time, makes local decisions, and coordinates secure data transmission with the holographic quantum communication module by moving computing power to the vicinity of the device. The edge computing gateway processes the tiny environmental changes obtained by the quantum perception module in real time and makes local decisions, such as adjusting inventory strategies or triggering alarms in a timely manner. The edge computing gateway reduces the need to transmit all data to the cloud, thereby reducing the delay in data transmission and ensuring the real-time nature of medical device inventory data;

[0100] Poor data processing performance of edge computing gateways can lead to a series of serious consequences, including:

[0101] The decline in data processing performance will result in the edge computing gateway being unable to process real-time data from the quantum sensing module in a timely manner, thereby reducing the real-time performance of the system, which may lead to delayed alarms, inaccurate predictions of inventory demand, and other problems.

[0102] The edge computing gateway is responsible for local decision-making. If the data processing performance deteriorates, it may lead to inaccurate decisions or inability to respond appropriately based on real-time data, which may affect the decision-making effectiveness of the entire inventory intelligent allocation system.

[0103] Poor data processing performance may lead to errors in the data processing process, resulting in a decline in data quality. Erroneous data may cause the system to misjudge the inventory status, thereby affecting the accuracy of intelligent allocation.

[0104] The monitoring module is used to obtain multi-source data information when the edge computing gateway is running, including real-time performance indicator information and operation status indicator information when the edge computing gateway is running. After obtaining, the real-time performance indicator information and operation status indicator information of the edge computing gateway are processed and comprehensively analyzed to generate a performance anomaly evaluation coefficient, and the data processing performance of the edge computing gateway is evaluated by the performance anomaly evaluation coefficient;

[0105] The real-time performance indicator information of the edge computing gateway during operation includes system response delay information. After collection, the system response delay information is processed by the monitoring module to generate a system response delay index;

[0106] When the system response delay is high, it may cause a series of problems, especially for the edge computing gateway module, including:

[0107] Reduced real-time performance: High system response latency may prevent the edge computing gateway from processing real-time data from the quantum sensing module in a timely manner. This will lead to a decrease in the real-time performance of the system, manifested as delayed alarms and inaccurate predictions of inventory demand. In medical device inventory management, real-time performance is critical for timely deployment and response to emergencies.

[0108] Inaccurate decision-making: High response latency may cause the edge computing gateway to be unable to obtain the latest real-time data in time when making decisions. This will affect the system's accurate judgment of inventory status, and then affect the formulation of intelligent allocation decisions. Inaccurate decisions may lead to unreasonable inventory allocation and fail to meet actual needs.

[0109] Degraded data quality: High response latency may lead to errors in data processing. This may cause the data processed by the system to be inaccurate, resulting in degraded data quality. Incorrect data may be used incorrectly to evaluate inventory status, which in turn affects the accuracy of intelligent allocation.

[0110] The logic for obtaining the system response delay index is as follows:

[0111] A1. In time period G, for tasks during the runtime of the edge computing gateway, record the timestamp of the edge computing gateway task completion response, the timestamp of the task submission, the timestamp of the task completion, and the timestamp of the task start execution to form a task time list;

[0112] It should be noted that for tasks during the runtime of the edge computing gateway, the timestamp of the edge computing gateway task completion response, the timestamp of task submission, the timestamp of task completion, and the timestamp of task start execution are recorded to form a task time list. These timestamps can be obtained in real time by deploying a real-time monitoring system and running a monitoring agent or agent program on the edge computing gateway to capture the timestamps of task submission, start execution, completion, and response in real time;

[0113] A2. Calculate the total time from task submission to response completion. The calculation expression is: T Total =T ResCom -T TaskSub , where T Total It represents the total time from task submission to response completion, T ResCom Indicates the timestamp of the task completion response, T TaskSub Indicates the timestamp of task submission;

[0114] A3. Calculate the actual execution time of the task. The calculation expression is: T Exec =T TaskCom -T TaskStart , where T Exec Indicates the actual execution time of the task, T TaskCom Indicates the timestamp of task completion, T TaskStart Indicates the timestamp when the task starts executing;

[0115] A4. Calculate the waiting time of the task in the queue. The calculation expression is: T Queue =T TaskStart -T TaskSub , where T Queue Indicates the time the task waits in the queue;

[0116] A5. Calculate the system response delay index. The calculation expression is: In the formula, YC 响应 Indicates the system response delay index;

[0117] The system response delay index covers all stages of task submission, queuing, execution, and completion. By analyzing the overall task response time, the performance of the edge computing gateway can be evaluated more comprehensively. The smaller the value of the system response delay index, the faster the system responds and the better the performance. Conversely, the slower the system responds and the worse the performance.

[0118] The health index information of the edge computing gateway during runtime includes the data queue length growth information and the packet loss rate increase information. After collection, the monitoring module processes the data queue length growth information and the packet loss rate increase information to generate the data queue length abnormal growth index and the packet loss rate variation index.

[0119] The relatively rapid growth of the data queue length of the edge computing gateway may lead to a series of serious consequences, specifically including:

[0120] Reduced real-time performance: A sharp increase in the data queue length may prevent the edge computing gateway from processing real-time data from the quantum sensing module in a timely manner, which will lead to a decline in the real-time performance of the system, delay the triggering of alarms and the update of real-time inventory requirements, and reduce the system's response speed to emergencies.

[0121] Inaccurate decision-making: The rapid growth of the data queue length may prevent the edge computing gateway from obtaining timely information when making decisions, which may lead to inaccurate prediction of inventory requirements by the system. Because of the delay and incompleteness of real-time data, the system may not be able to make accurate decisions based on the current state.

[0122] Misjudgment of inventory status: Errors in the data processing process may lead to a decline in data quality. Incorrect data may lead to misjudgment of the inventory status by the system. If the edge computing gateway makes decisions and allocations based on incorrect data, it may lead to inappropriate allocation of inventory, thereby affecting the accuracy of the entire intelligent allocation system.

[0123] Reduced system stability: An excessive data queue length may lead to over-occupation of system resources, thereby affecting the stability of the edge computing gateway. The system may become unstable, and may even lead to system crashes or inability to work properly, thus affecting the reliability of the entire supervision system.

[0124] The logic for obtaining the data queue length abnormal growth index is as follows:

[0125] B1. During the time period G, collect n data points, each data point representing the data queue length at the corresponding moment, and calculate the mean value of the data queue length. The calculation expression is: where, t0 and t1 represent the start and end times of the time period G, t0 < t1, and the function s(t) represents the data queue length at time t;

[0126] It should be noted that professional application performance monitoring tools are used. These tools can monitor the performance parameters of the edge computing gateway in real time, including the data queue length. For example, Prometheus is an open source monitoring and alerting tool that can collect performance indicators such as the data queue length of the edge computing gateway through Exporter and implement real-time monitoring. Grafana is usually integrated with data sources such as Prometheus to provide intuitive dashboards and real-time monitoring charts, which can be used to visualize the performance data of the edge computing gateway, including the data queue length.

[0127] B2. Calculate the abnormal growth index of the data queue length. The calculation expression is: In the formula, σ d represents the variance of the lengths of n data queues, V d represents the cubic difference of the lengths of n data queues, DL 长度 Indicates the abnormal growth index of data queue length;

[0128] The data queue length abnormal growth index is used to indicate the abnormal growth of the data queue length in time period G. The larger the value of the data queue length abnormal growth index in time period G, the more obvious the abnormal growth of the queue length is. Conversely, the smaller the value, the less obvious the abnormal growth of the queue length is.

[0129] Large changes in the packet loss rate of the edge computing gateway may cause system performance degradation, which in turn affects the normal operation of the entire inventory intelligent allocation system. The following is a detailed explanation:

[0130] Impact on real-time performance: Large changes in packet loss rate may cause the edge computing gateway to be unable to process real-time data from the quantum sensing module in a timely manner. Real-time performance is critical for inventory monitoring systems, especially in situations involving medical device inventory. Delays may cause delays in pushing alarms for abnormal conditions, reducing the real-time performance of the system.

[0131] Impact on decision accuracy: Drastic changes in packet loss rates may cause the edge computing gateway to be unable to accurately process data, affecting the accuracy of decision making. In medical device inventory management, accurate data is the basis for making inventory requirements and intelligent allocation decisions. If the packet loss rate fluctuates greatly, it may cause errors in the edge computing gateway's estimation of inventory status, resulting in inaccurate decisions or failure to make appropriate responses based on real-time data.

[0132] Leading to erroneous data processing: Large changes in packet loss rates may lead to errors in data processing, thereby reducing data quality. Erroneous data may cause the system to make inaccurate judgments about inventory status, thereby affecting the accuracy of intelligent allocation. In the medical field, erroneous data may lead to misjudgment of patient needs, thus affecting the accurate configuration and allocation of medical devices.

[0133] The logic for obtaining the packet loss rate variation index is as follows:

[0134] C1. Collect the packet loss rate information within G time when the edge computing gateway processes data, sample it at the selected time interval, obtain a series of packet loss rate data nodes, and mark the packet loss rate data nodes as x w , w represents the number of the data node of the packet loss rate, w = 1, 2, 3, 4, ..., m, m is a positive integer;

[0135] It should be noted that professional network monitoring tools, such as Wireshark, Nagios or other similar tools, are used to monitor the data transmission between the edge computing gateway and other devices. These tools can provide detailed network data packet information, including indicators such as loss rate. Secondly, it should be noted that the selected time interval is not specifically limited here. You can select all the same time intervals, all different time intervals, or a combination of the two.

[0136] C2. The packet loss rate data node calculates the average packet loss rate and the standard deviation of the packet loss rate in G time when the edge computing gateway processes data. The calculation expression is:

[0137] C3. Calculate the packet loss rate variation index. The calculation expression is: In the formula, SJ 丢失 represents the packet loss rate variation index;

[0138] It can be seen from the packet loss rate variation index that the larger the performance value of the packet loss rate variation index generated within the G time when the edge computing gateway performs data processing, the larger the performance value, which indicates that the packet loss rate has experienced a larger fluctuation or change within the G time, and vice versa, which indicates that the packet loss rate has experienced a smaller fluctuation or change within the G time.

[0139] Get the system response delay index YC when the edge computing gateway processes data 响应 , Data queue length abnormal growth index DL 长度 , Packet Loss Rate Variation Index SJ 丢失 After that, the system response delay index YC 响应, Data queue length abnormal growth index DL 长度 , Packet Loss Rate Variation Index SJ 丢失 Generate the performance anomaly evaluation coefficient η based on the formula: Where ξ1, ξ2, ξ3 are the system response delay index YC respectively. 响应 , Data queue length abnormal growth index DL 长度 , Packet Loss Rate Variation Index SJ 丢失 The preset proportional coefficients, ξ1, ξ2, ξ3 are all greater than 0;

[0140] It can be seen from the calculation formula that the larger the system response delay index, the larger the abnormal growth index of the data queue length, and the larger the packet loss rate variation index when the edge computing gateway performs data processing, that is, the larger the performance value of the performance anomaly evaluation coefficient η generated when the edge computing gateway performs data processing, the greater the hidden danger of abnormality in the edge computing gateway data processing, and vice versa.

[0141] The performance anomaly evaluation coefficient generated within G time when the edge computing gateway processes data is compared and analyzed with the pre-set performance anomaly evaluation coefficient reference threshold. If the performance anomaly evaluation coefficient is greater than or equal to the performance anomaly evaluation coefficient reference threshold, an abnormal signal is generated through the detection unit. Once an abnormality occurs when the edge computing gateway processes data, the system generates a corresponding alarm after obtaining the abnormal signal, notifying relevant personnel or systems for further processing. An alarm mechanism can be implemented. Once the edge computing gateway detects a data processing abnormality, the system can generate an alarm notification, which is usually notified by sending an alarm message, email, SMS or other communication methods to notify the relevant system administrator or relevant operation and maintenance personnel. An automatic recovery mechanism can be implemented in the system, allowing the system to automatically try to re-execute the abnormal task to reduce the impact of the abnormality on the entire system. If the performance anomaly evaluation coefficient is less than the performance anomaly evaluation coefficient reference threshold, a normal signal is generated through the detection unit, and the data continues to be processed through the edge computing gateway.

[0142] The present invention obtains multi-source data information during the operation of the edge computing gateway, including real-time performance indicators and operating status indicators, to effectively evaluate the operating status of the system, which is beneficial to the real-time performance monitoring and problem diagnosis of the system. Through the performance anomaly evaluation coefficient, the system can respond quickly when the performance of the edge computing gateway is abnormal, thereby improving the stability and reliability of the system. In particular, in terms of processing real-time data, the introduction of the monitoring module makes the system more efficient, helps to promptly discover and solve potential problems, and further improves the performance and availability of the entire medical device inventory supervision system.

[0143] The DNA storage and quantum blockchain module uses DNA storage and combines it with quantum blockchain to store medical device information in the form of DNA sequences.

[0144] The quantum blockchain ensures data security and transparency, achieves ultra-high-density storage and traceability, and uses DNA storage in combination with the quantum blockchain to store medical device information in the form of DNA sequences. The specific steps are as follows:

[0145] S201, encoding medical device information into a DNA sequence;

[0146] This usually involves converting numbers and letters into corresponding DNA bases, for example, A, T, C and G can be used to represent different data information.

[0147] S202, actually synthesizing the encoded DNA sequence;

[0148] This can be achieved by chemical synthesis or DNA synthesis techniques.

[0149] S203, storing the synthesized DNA sequence in a specially designed medium;

[0150] This could include glass slides or other materials suitable for long-term storage, a step aimed at achieving ultra-high-density data storage that exploits the high compactness and stability of DNA molecules.

[0151] S204. When data needs to be retrieved, DNA extraction is performed to extract the DNA from the storage medium, and the DNA sequence is read using DNA sequencing to restore the original medical device information;

[0152] This process requires high-throughput sequencing technology to ensure accuracy and efficiency.

[0153] S205. Generate a quantum hash value from the original data of the medical device information, and write the generated quantum hash value into the quantum blockchain;

[0154] The original data of medical device information can be used to generate a quantum hash value by adopting a quantum hash algorithm to map the original data into a unique and irreversible quantum hash value. The generated quantum hash value can be written into the quantum blockchain by adopting quantum secure blockchain technology to ensure the security and transparency of the data. Each block contains the hash value of the previous block to form a chain structure to ensure the data cannot be tampered with.

[0155] S206. Use smart contracts in blockchain to implement logic and operations;

[0156] For example, access permission control, data sharing and auditing functions for medical device information can be implemented.

[0157] S207. By implementing access control mechanisms in smart contracts, ensure that only authorized users can retrieve, modify or add medical device information, and use encryption and privacy protection technologies to ensure patient privacy and data security.

[0158] Inventory optimization strategy generation module, based on data analysis results and demand forecasts, generates optimization strategies for intelligent inventory allocation and management to meet future demand and reduce inventory risks;

[0159] Based on data analysis results and demand forecasts, the specific steps to generate an optimization strategy for intelligent inventory allocation and management to meet future demand and reduce inventory risks are as follows:

[0160] H101, collect real-time data from quantum perception module, holographic quantum communication module, edge computing gateway module, DNA storage and quantum blockchain module;

[0161] Including environmental perception, communication security, edge computing processing results and medical device information.

[0162] H102. Use machine learning and data mining algorithms to analyze historical inventory data to identify potential inventory demand patterns and trends, and combine the small change monitoring data from the quantum sensing module to analyze environmental changes related to inventory demand;

[0163] Machine learning and data mining algorithms can be used to analyze historical inventory data to identify potential inventory demand patterns and trends. Here are some existing algorithms that can be used:

[0164] Machine Learning Algorithms:

[0165] Decision Trees: It constructs a tree structure to represent different decision paths. It is suitable for classification and regression tasks and can be used for inventory demand prediction and classification.

[0166] Support Vector Machines: used for classification and regression analysis. It has good adaptability to complex nonlinear relationships by finding the optimal decision boundary.

[0167] Random Forest: Improves accuracy and generalization performance by integrating multiple decision trees. It is suitable for processing large-scale data and complex feature relationships.

[0168] Neural Networks: Suitable for processing complex nonlinear relationships, learning and predicting through multiple layers of neurons.

[0169] Data Mining Algorithms:

[0170] Association Rule Mining: Used to discover associations in data, it can identify potential patterns in medical device inventory, such as which devices are often in demand at the same time.

[0171] Clustering Analysis: By dividing the data into similar groups, potential inventory demand patterns and groups can be discovered.

[0172] Time series analysis: It is used to process time-related data and can be used to predict the time trend of inventory demand.

[0173] Factor Analysis: Used to identify the key factors that lead to changes in inventory demand.

[0174] H103. Based on historical data and analysis results, use forecasting models (such as time series analysis, regression analysis, etc.) to predict future medical device inventory demand.

[0175] H104. Use the traceability and transparency of quantum blockchain to track inventory history and assess inventory risk;

[0176] Including expiration, damage, etc.

[0177] H105. Combine demand forecasting and inventory risk assessment to develop an optimization strategy for intelligent inventory allocation;

[0178] Consider the real-time processing capabilities of the edge computing gateway and generate a deployment plan that adapts to real-time changes.

[0179] H106. Apply the generated optimization strategy to actual inventory allocation, use the quantum communication module to ensure the timely communication and execution of the strategy, monitor the execution status through the holographic communication module, monitor inventory changes in real time, and feed back to the data analysis module to continuously optimize the model and strategy;

[0180] The generated optimization strategy is applied to actual inventory allocation to implement operations such as adjusting inventory location and quantity.

[0181] H107. Collect feedback information during the implementation process (including actual inventory usage, allocation effect, etc.), and adjust the forecasting model and inventory optimization strategy based on the feedback information to achieve continuous optimization of the system.

[0182] The present invention uses quantum perception and holographic quantum communication to systematically perceive changes in the medical device inventory environment in real time, ensure safe transmission, improve the supervision system's protection of inventory real-time and communication security, push data processing and decision-making to the device end, reduce transmission delays with the cloud, and improve system response speed, especially in processing real-time data. It is more efficient, uses DNA storage and quantum blockchain to store medical device information in the form of DNA sequences, ensures data security and transparency through quantum blockchain, achieves ultra-high-density storage and traceability, improves data security and credibility, combines inventory optimization strategy generation with data analysis and demand forecasting, generates optimization strategies for intelligent inventory allocation and management, and flexibly adjusts according to real-time changing needs and risks, thereby improving the intelligence and flexibility of inventory management.

[0183] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0184] It should be understood that the term "and / or" herein is merely a description of the association relationship of associated objects, indicating that there may be three relationships, for example, A and / or B, which may represent: A exists alone, A and B exist at the same time, and B exists alone, wherein A and B may be singular or plural. In addition, the character " / " herein generally indicates that the associated objects before and after are in an "or" relationship, but may also represent an "and / or" relationship, which may be specifically understood with reference to the context. In this application, "at least one" refers to one or more, and "multiple" refers to two or more. "At least one of the following (individuals)" or similar expressions thereof refers to any combination of these items, including any combination of single items (individuals) or plural items (individuals). For example, at least one of a, b, or c (one) may represent: a, b, c, ab, ac, bc, or abc, wherein a, b, c may be single or multiple.

[0185] It should be understood that in various embodiments of the present application, the size of the sequence number of each process does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0186] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0187] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The intelligent allocation and supervision system of medical equipment inventory based on the Internet of Things is characterized by: It includes quantum perception module, holographic quantum communication module, edge computing gateway module, monitoring module, DNA storage and quantum blockchain module, and inventory optimization strategy generation module; The quantum perception module uses quantum sensors to obtain the inventory environment of medical devices and conducts real-time monitoring of the inventory environment of medical devices; Holographic quantum communication module, which transmits data during the communication process through quantum entanglement; The edge computing gateway module uses edge computing technology to process data and make decisions on the device side; The monitoring module is used to obtain multi-source data information when the edge computing gateway is running, including real-time performance indicator information and operation status indicator information when the edge computing gateway is running. After obtaining, the real-time performance indicator information and operation status indicator information of the edge computing gateway are processed and comprehensively analyzed to generate a performance anomaly evaluation coefficient, and the data processing performance of the edge computing gateway is evaluated by the performance anomaly evaluation coefficient; The DNA storage and quantum blockchain module uses DNA storage and combines it with quantum blockchain to store medical device information in the form of DNA sequences. The inventory optimization strategy generation module generates optimization strategies for intelligent inventory allocation and management based on data analysis results and demand forecasts to meet future demand and reduce inventory risks. The specific steps are as follows: H101, collect real-time data from quantum perception module, holographic quantum communication module, edge computing gateway module, DNA storage and quantum blockchain module; H102. Use machine learning and data mining algorithms to analyze historical inventory data to identify potential inventory demand patterns and trends, and combine the small change monitoring data from the quantum sensing module to analyze environmental changes related to inventory demand; H103. Based on historical data and analysis results, use forecasting models to predict future medical device inventory demand; H104. Track inventory history and assess inventory risk; H105. Combine demand forecasting and inventory risk assessment to develop an optimization strategy for intelligent inventory allocation; H106. Apply the generated optimization strategy to actual inventory allocation, use the quantum communication module to ensure the timely communication and execution of the strategy, monitor the execution status through the holographic communication module, monitor inventory changes in real time, and feed back to the data analysis module to continuously optimize the model and strategy; H107. Collect feedback information during the implementation process and adjust the forecasting model and inventory optimization strategy based on the feedback information to achieve continuous optimization of the system.

2. The medical device inventory intelligent allocation and supervision system based on the Internet of Things according to claim 1 is characterized in that: The specific implementation process of obtaining the inventory environment of medical devices through quantum sensors and conducting real-time monitoring of the inventory environment of medical devices is as follows: V101, excite the quantum sensor to make it in an excited state to sense the medical device inventory environment; V102. Placing the excited quantum sensor in a medical device inventory environment so that the excited quantum sensor interacts with the environment; V103, measure the quantum state and obtain the quantum state information after interaction with the environment; V104, transmits the measured information to the edge computing gateway module for real-time data processing; V105. Use the processed information to monitor small changes in the medical device inventory environment in real time.

3. The medical device inventory intelligent allocation and supervision system based on the Internet of Things according to claim 1 is characterized in that: The specific process of data transmission during communication through quantum entanglement is as follows: S101. At the initial stage of communication, two distant quantum entangled states are established; S102. Use quantum entanglement for key distribution to ensure that both parties have the same encryption key and any attempt to monitor the key will be detected immediately. S103. During the communication process, the established quantum entangled state is used to transmit information through the state of the quantum bit; S104. Correcting errors caused by environmental interference in quantum states through quantum error correction; S105. Use the established quantum communication channel to transmit medical device inventory data in real time; S106. Regularly refresh the quantum keys used to enhance the security of the system.

4. The medical device inventory intelligent allocation and supervision system based on the Internet of Things according to claim 1 is characterized in that: The real-time performance indicator information of the edge computing gateway during operation includes system response delay information. The operating status indicator information of the edge computing gateway during operation includes data queue length growth information and packet loss rate increase information. After collection, the system response delay information is processed by the monitoring module to generate a system response delay index, and the data queue length growth information and packet loss rate increase information are processed to generate a data queue length abnormal growth index and a packet loss rate variation index.

5. The medical device inventory intelligent allocation and supervision system based on the Internet of Things according to claim 4 is characterized in that: The logic for obtaining the system response delay index is as follows: A1. In time period G, for tasks during the runtime of the edge computing gateway, record the timestamp of the edge computing gateway task completion response, the timestamp of task submission, the timestamp of task completion, and the timestamp of task start execution to form a task time list; A2. Calculate the total time from task submission to response completion. The calculation expression is: , where Indicates the total time from task submission to response completion. Indicates the timestamp of the task completion response. Indicates the timestamp of task submission; A3. Calculate the actual execution time of the task. The calculation expression is: , where Indicates the actual execution time of the task. The timestamp indicating the task completion. Indicates the timestamp when the task starts executing; A4. Calculate the waiting time of the task in the queue. The calculation expression is: , where Indicates the time the task waits in the queue; A5. Calculate the system response delay index. The calculation expression is: , where Indicates the system response delay index.

6. The medical device inventory intelligent allocation and supervision system based on the Internet of Things according to claim 5 is characterized in that: The logic for obtaining the abnormal growth index of the data queue length is as follows: B1. In the time period G, collect n data points, each data point represents the length of the data queue at the corresponding moment, and calculate the mean length of the data queue. The calculation expression is: ,in, and Indicates the start and end time of time period G, ,function Indicates the length of the data queue at time t; B2. Calculate the abnormal growth index of the data queue length. The calculation expression is: , where represents the variance of the lengths of n data queues, , represents the cubic difference of the lengths of n data queues, , Indicates the abnormal growth index of the data queue length.

7. The medical device inventory intelligent allocation and supervision system based on the Internet of Things according to claim 6 is characterized in that: The logic for obtaining the packet loss rate variation index is as follows: C1. Collect the packet loss rate information within G time when the edge computing gateway processes data, sample it at the selected time interval, obtain a series of packet loss rate data nodes, and mark the packet loss rate data nodes as , w represents the number of the packet loss rate data node, w=1, 2, 3, 4, ..., m, m is a positive integer; C2. The packet loss rate data node calculates the average packet loss rate and the standard deviation of the packet loss rate in G time when the edge computing gateway processes data. The calculation expression is: , ; C3. Calculate the packet loss rate variation index. The calculation expression is: , where Indicates the packet loss rate variation index.

8. The medical device inventory intelligent allocation and supervision system based on the Internet of Things according to claim 7 is characterized in that: Obtain the system response delay index when processing data at the edge computing gateway , Data queue length abnormal growth index , Packet Loss Rate Variation Index Then, the system response delay index , Data queue length abnormal growth index , Packet Loss Rate Variation Index Generate performance anomaly evaluation coefficient , through the performance anomaly evaluation coefficient Monitor the performance of the edge computing gateway when processing data and generate abnormal signals or normal signals.

9. The medical device inventory intelligent allocation and supervision system based on the Internet of Things according to claim 1 is characterized in that: The specific steps of using DNA storage and combining quantum blockchain to store medical device information in the form of DNA sequences are as follows: S201, encoding medical device information into a DNA sequence; S202, actually synthesizing the encoded DNA sequence; S203, storing the synthesized DNA sequence in a specially designed medium; S204. When data needs to be retrieved, DNA extraction is performed to extract the DNA from the storage medium, and the DNA sequence is read using DNA sequencing to restore the original medical device information; S205. Generate a quantum hash value from the original data of the medical device information, and write the generated quantum hash value into the quantum blockchain; S206. Use smart contracts in blockchain to implement logic and operations; S207. By implementing access control mechanisms in smart contracts, ensure that only authorized users can retrieve, modify or add medical device information, and use encryption and privacy protection technologies to ensure patient privacy and data security.

Images