Biological resource management method, system and medium based on RFID

Abstract

The present invention discloses a biological resource management method, system and medium based on RFID, which belongs to the field of biological resource preservation technology, and specifically includes: realizing dynamic prediction of the validity period of biological resource bags and intelligent monitoring of the storage environment by integrating RFID technology, deep learning prediction model, multi-source data fusion and neural network algorithm; the system uses RFID reader to read and analyze the label information of biological resource bags, predicts and compares the validity period to determine whether the biological resources are expired; enables sensor array to monitor the storage environment, combines advanced algorithms to evaluate the environmental suitability, and marks and warns once an abnormality is found; according to the warning level and the urgency of biological resources, the system automatically triggers the emergency processing process and performs predictive maintenance; adopts blockchain technology to ensure the encrypted storage and anonymization of process data, combines smart contracts to realize automatic data recording, review and report generation, and ensures the transparency, security and efficiency of the biological resource management process.

Description

Technical Field

[0001] The present invention belongs to the technical field of biological resource preservation, and in particular to a biological resource management method, system and medium based on RFID. Background Art

[0002] In the vast natural world, biological resources, with their endless diversity and complexity, provide countless precious treasures for human society. Among these resources, blood products are particularly special and vital. They not only maintain the continuation of life, but also play an immeasurable role in the field of modern medicine.

[0003] The management and preservation of biological resources involve complex business processes. The management and tracking of biological resources are crucial to ensuring the safety and effectiveness of biological resources. In traditional biological resource management systems, the main technology used is barcode standard technology, which was developed by the International Society of Blood Transfusion and is a global standard specifically for blood labeling. However, barcode labels have significant technical limitations, such as small information storage capacity, read-only information, and poor reliability. Especially in humid environments or under wear and tear, the readability of barcodes will be greatly reduced, and even misreading or misreading may occur. These defects make it difficult for traditional systems to maintain and track biological resource management on a daily basis, and they cannot effectively guarantee the safety and traceability of biological resources.

[0004] For example, the patent with publication number CN117775513A discloses a cold chain transportation and storage system for blood products, including: a storage box; an external temperature monitoring unit; an internal temperature monitoring unit; an adjustment unit: used to receive external temperature information and internal temperature information, and control the operation of air equipment according to the external temperature information and the internal temperature information; an external refrigeration unit; an internal refrigeration unit: used to receive external temperature information and internal temperature information, and reduce the temperature inside the storage box according to the external temperature information and the internal temperature information, which is used to solve the problem of the temperature sensor unit used in the prior art to a certain extent, and monitor the average temperature of the vehicle environment during the transportation of biological resources. In actual use, it is not convenient to monitor the temperature inside a single storage box for placing biological resources in the car, which may cause the temperature in individual storage boxes to exceed the set temperature, causing the internal biological resources to deteriorate.

[0005] The above existing technologies all have the following problems: although the system includes external and internal temperature monitoring units, and corresponding refrigeration units to adjust the temperature, it mainly focuses on the monitoring and control of the overall or average temperature, which is not enough to ensure that each storage box, especially when there are multiple layers or separated areas in the storage box, can achieve a uniform and appropriate temperature. For biopharmaceuticals that require highly precise temperature control, this limitation may cause the temperature of some areas to exceed the safe range, thereby affecting the quality of biological resources; the system is relatively basic in temperature control and does not combine advanced means such as RFID technology to achieve personalized management of individual biological resources or storage boxes; although the system includes a regulating unit to control the air equipment and refrigeration unit based on temperature information, it lacks a highly integrated control system to automatically respond to temperature anomalies or other potential problems. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention proposes a biological resource management method, system and medium based on RFID. The system uses an RFID reader to read and analyze the label information of the biological resource bag, predicts and compares the validity period to determine whether the biological resource is expired; enables the sensor array to monitor the storage environment, combines advanced algorithms to evaluate the environmental suitability, and marks and warns once an abnormality is found; according to the warning level and the urgency of the biological resource, the system automatically triggers the emergency processing process and performs predictive maintenance; uses blockchain technology to ensure the encrypted storage and anonymization of process data, and combines smart contracts to realize automatic data recording, review and report generation, ensuring the transparency, security and efficiency of the biological resource management process.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] RFID-based biological resource management methods include:

[0009] Step S1: Install and configure the RFID reader and antenna network, input the RFID tag information, and the control system reads the tag information of the biological resource bag through the RFID reader, and uses the date comparison algorithm combined with the deep learning model to build an expiration date prediction model, dynamically predict the expiration date in the RFID tag of the biological resource bag, and compare the expiration date information with the current time to determine whether the biological resource is expired;

[0010] Step S2: If the biological resource is within the validity period, the system starts the sensor array to monitor the storage environment in real time, and uses a multi-source data fusion algorithm and a neural network algorithm to determine whether the current storage environment is suitable for biological resource storage. If the environmental parameters exceed the preset safety range, it is marked as an abnormal state;

[0011] Step S3: Based on the abnormal detection results and the urgency of the biological resources, a hierarchical warning strategy is formulated. Once the warning condition is triggered, the control system automatically performs predictive maintenance operations according to the preset emergency processing process;

[0012] Step S4: The whole process data of handover, warehousing, outbound storage, inventory and environmental monitoring is encrypted and stored through blockchain technology, and sensitive information is anonymized. Smart contracts are written to automatically perform data recording, review and report generation tasks;

[0013] The specific steps of constructing the validity period prediction model in step S1 include:

[0014] S1.1: Attach RFID tags to biological resource bags and enter initial information;

[0015] S1.2: Configure an RFID reader. When the biological resource bag enters the reading range of the reader, the information on the tag is automatically read and the read RFID tag information is preprocessed;

[0016] S1.3: Use historical data and deep learning algorithms to build an expiration date prediction model, regularly use the built prediction model to predict the expiration date of biological resource bags in RFID tags, compare the predicted expiration date with the current time, and calculate the remaining validity period T i ;

[0017] S1.4: Set the biological resource limited period fluctuation threshold range to a. If T i -a≥0, it means that the biological resource has expired;

[0018] The specific steps of step S2 include:

[0019] S2.1: Install a sensor array in the storage area of ​​biological resources, read the temperature, humidity, light and gas concentration data from the sensor array in real time, and obtain the storage environment data X = {x1,...,x N}, and preprocess the storage environment dataset, where x N Indicates the Nth storage environment data, where N indicates the number of storage environment data;

[0020] S2.2: Use feature extraction algorithm to extract the features from X = {x1, ..., x N Extracting biological resource storage characteristics from and store biological resources Input into the pre-trained neural network model to obtain the probability value P of the current biological resource storage environment suitability i , i represents the current biological resource location index;

[0021] S2.3: Set the biological resource storage environment threshold h,

[0022] If P i <h, it means that the current storage environment is not suitable for biological resource storage and is marked as abnormal;

[0023] If P i ≥h, it means that the current biological resources can continue to be stored;

[0024] S2.4: Isolate biological resources marked as abnormal.

[0025] Specifically, the validity period prediction model in step S1 not only considers the fixed validity period in the RFID tag, but also performs dynamic prediction in combination with biological resource type, storage condition historical data, and transportation condition factors.

[0026] Specifically, storing environmental parameters in step S2 includes: storing temperature, humidity, light, and gas concentration information of the environment.

[0027] Specifically, in step S3, the urgency of biological resources is divided into three levels: emergency, sub-urgent and non-urgent according to clinical use, urgency of demand and availability of substitutes.

[0028] The biological resource management system based on RFID includes: prediction module, environmental assessment module, strategy formulation module, and recording module;

[0029] The prediction module is responsible for inputting RFID tag information and dynamically predicting the validity period of the biological resource bag to determine whether the biological resource is expired;

[0030] The environmental assessment module is used to monitor the storage environment in real time during the validity period of the biological resources and assess whether the environmental parameters are suitable for the storage of biological resources;

[0031] The strategy formulation module is used to formulate and implement early warning strategies based on the data provided by the prediction module and the environmental assessment module;

[0032] The recording module is responsible for the encrypted storage, anonymization processing, automatic recording, review and report generation tasks of process data.

[0033] Specifically, the environmental assessment module includes: a sensor array unit, a preprocessing unit, and an environmental assessment unit;

[0034] The sensor array unit is deployed in the storage area to continuously monitor temperature, humidity, and light environment parameters and provide comprehensive environmental data;

[0035] The pre-processing unit is used to perform preliminary cleaning and formatting on the raw sensor data to remove invalid or abnormal data;

[0036] The environment evaluation unit is used to analyze the preprocessed data and evaluate whether the current storage environment meets the requirements.

[0037] Specifically, the strategy formulation module includes: an anomaly detection unit, an emergency level classification unit, an early warning strategy unit, and an emergency processing unit;

[0038] The anomaly detection unit is used to monitor the data stream through an algorithm and identify abnormal situations such as environmental exceeding standards and biological resources approaching expiration;

[0039] The urgency classification unit is used to classify the problems into different levels according to the severity and urgency of the abnormalities;

[0040] The warning strategy unit is used to formulate corresponding warning measures according to different emergency levels;

[0041] The emergency processing unit is used to formulate and execute emergency response plans for specific abnormal situations and respond to warnings in a timely manner.

[0042] Specifically, a computer-readable storage medium stores computer instructions, which, when executed, execute the steps of the RFID-based biological resource management method.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The present invention proposes a biological resource management system based on RFID, and optimizes and improves the architecture, operation steps and processes. The system has the advantages of simple process, low investment and operation costs, and low production work costs.

[0045] 2. The present invention proposes an RFID-based biological resource management method, which automatically reads the label information of the biological resource bag through RFID technology, and uses advanced date comparison algorithms and deep learning models to predict the validity period, thereby improving the accuracy and efficiency of biological resource management, reducing the errors that may be caused by manual operations, and can achieve real-time and dynamic predictions; the system monitors the storage environment in real time, uses sensor arrays and multi-source data fusion algorithms, combined with neural network algorithms, to comprehensively and accurately evaluate whether the storage environment is suitable for biological resource storage. Once it is found that the environmental parameters exceed the preset safety range, it is immediately marked as an abnormal state and corresponding measures are taken, thereby effectively preventing the deterioration or contamination of biological resources caused by environmental factors and ensuring the quality and safety of biological resources.

[0046] 3. The present invention proposes a biological resource management method based on RFID. Based on the abnormal detection results and the urgency of biological resources, the system automatically formulates a graded early warning strategy, and when the early warning condition is triggered, it automatically performs predictive maintenance operations according to the preset emergency processing process. This intelligent early warning and emergency processing mechanism can quickly respond to potential problems, reduce delays and errors in human intervention, and improve the efficiency and effectiveness of emergency processing; the process data is encrypted, stored and anonymized through blockchain technology to ensure data security and privacy protection. At the same time, smart contracts are written to automatically execute data recording, review and report generation tasks, which improves the automation and transparency of data processing, facilitates supervision and auditing, and enhances the traceability of biological resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the biological resource management method based on RFID of the present invention;

[0048] Figure 2 This is a flow chart of the working principle of the biological resource management method based on RFID of the present invention;

[0049] Figure 3 This is the architecture diagram of the RFID-based biological resource management system of the present invention. DETAILED DESCRIPTION

[0050] Example 1

[0051] See also Figure 1-Figure 2 The present invention provides an embodiment: a biological resource management method based on RFID, comprising the following steps:

[0052] Step S1: Install and configure the RFID reader and antenna network, input the RFID tag information, and the control system reads the tag information of the biological resource bag through the RFID reader, and uses the date comparison algorithm combined with the deep learning model to build an expiration date prediction model, dynamically predict the expiration date in the RFID tag of the biological resource bag, and compare the expiration date information with the current time to determine whether the biological resource is expired;

[0053] In the present invention, before handover and storage, the group reading characteristics of RFID are utilized, and the RFID tag information on the entire batch of biological resource bags is read at one time through the optimized arrangement of readers and antennas.

[0054] Among them, biological resource storage and labeling include:

[0055] (1) Preparation and attachment of RFID tags: First, prepare RFID tags with unique identification codes and ensure that these tags are suitable for attachment to biological resource bags. The tags are designed to be passive and can be activated and exchange data under the radio frequency energy emitted by the RFID reader;

[0056] (2) Information collection and entry: When biological resources are collected or received into the warehouse, the label on the biological resource bag is immediately scanned using an RFID reader, and the basic information of the biological resource, such as blood type, collection time, expected validity period, and source hospital, is entered into the system, and the corresponding data in the RFID tag is updated at the same time.

[0057] Step S2: If the biological resource is within the validity period, the system starts the sensor array to monitor the storage environment in real time, and uses a multi-source data fusion algorithm and a neural network algorithm to determine whether the current storage environment is suitable for biological resource storage. If the environmental parameters exceed the preset safety range, it is marked as an abnormal state;

[0058] Step S3: Based on the abnormal detection results and the urgency of biological resources, a hierarchical early warning strategy is formulated. Once the early warning condition is triggered, the control system automatically performs predictive maintenance operations according to the preset emergency processing process. The early warning strategy includes: clarifying the early warning level, early warning scope, early warning information release method and emergency response measures;

[0059] Furthermore, the specific steps of step S3 include:

[0060] (1) Anomaly detection and assessment: Collect storage environment parameters in real time, apply anomaly detection algorithms to analyze them, and assess the severity and urgency of anomalies based on the anomaly scores or labels output by the algorithms;

[0061] (2) Classification of biological resource urgency: Classify biological resources according to their type, shelf life, and clinical demand factors, and determine the priority and handling requirements of biological resources of different urgency levels;

[0062] (3) Formulate a graded warning strategy: Based on the anomaly detection results and the urgency of biological resources, formulate a graded warning strategy, set warning conditions, such as anomaly score thresholds, urgency levels, and determine the recipients of warning information, notification methods, and content;

[0063] (4) Early warning system configuration: Configure the early warning system in the control system, including the setting of early warning conditions and the mechanism for sending early warning information, to ensure that the early warning system can receive abnormal detection results in real time and trigger early warnings according to preset conditions;

[0064] (5) Emergency response process design: Design an emergency response process to clarify the measures to be taken when the warning conditions are triggered. The process should include but is not limited to: notifying relevant personnel, assessing the scope of impact, adjusting storage environment parameters, and transferring or protecting biological resources;

[0065] (6) Predictive maintenance operations: According to the emergency response process, the control system automatically performs predictive maintenance operations, where predictive maintenance operations include adjusting temperature and humidity environmental parameters, starting backup storage devices, or notifying maintenance personnel to conduct inspections and repairs;

[0066] (7) Recording and feedback: Record warning events, emergency response processes and results, analyze the causes and treatment effects of warning events, and provide feedback for optimizing warning strategies and emergency response processes.

[0067] Step S4: The data of the entire process of handover, warehousing, outbound storage, inventory and environmental monitoring are encrypted and stored through blockchain technology, and sensitive information is anonymized. Smart contracts are written to automatically execute data recording, review and report generation tasks.

[0068] Specifically, the specific steps of constructing the validity period prediction model in step S1 include:

[0069] S1.1: Attach RFID tags to biological resource bags and enter initial information;

[0070] S1.2: Configure an RFID reader. When the biological resource bag enters the reading range of the reader, the information on the tag is automatically read and the read RFID tag information is preprocessed;

[0071] S1.3: Use historical data and deep learning algorithms to build an expiration date prediction model, regularly use the built prediction model to predict the expiration date of biological resource bags in RFID tags, compare the predicted expiration date with the current time, and calculate the remaining validity period T i ;

[0072] S1.4: Set the biological resource limited period fluctuation threshold range to a. If T i -a≥0, it means that the biological resource has expired.

[0073] Specifically, the specific steps of step S2 include:

[0074] S2.1: Install a sensor array in the storage area of ​​biological resources, read the temperature, humidity, light and gas concentration data from the sensor array in real time, and obtain the storage environment data X = {x1,...,x N}, and preprocess the storage environment dataset, where x N Indicates the Nth storage environment data, where N indicates the number of storage environment data;

[0075] S2.2: Use feature extraction algorithm to extract the features from X = {x1, ..., x N Extracting biological resource storage characteristics from and store biological resources Input into the pre-trained neural network model to obtain the probability value P of the current biological resource storage environment suitability i , i represents the current biological resource location index;

[0076] S2.3: Set the biological resource storage environment threshold h,

[0077] If P i <h, it means that the current storage environment is not suitable for biological resource storage and is marked as abnormal;

[0078] If P i ≥h, it means that the current biological resources can continue to be stored;

[0079] S2.4: Isolate biological resources marked as abnormal.

[0080] The validity period prediction model in step S1 not only considers the fixed validity period in the RFID tag, but also performs dynamic prediction based on the type of biological resources, historical data of storage conditions, and transportation conditions.

[0081] The environmental parameters stored in step S2 include: storing the temperature, humidity, light, and gas concentration information of the environment.

[0082] In step S3, the urgency of biological resources is divided into three levels: emergency, sub-urgent, and non-urgent according to clinical use, urgency of demand, and availability of substitutes.

[0083] Example 2

[0084] See also Figure 3 Another embodiment provided by the present invention is a biological resource management system based on RFID, comprising:

[0085] Prediction module, environmental assessment module, strategy formulation module, and recording module;

[0086] The prediction module is responsible for inputting RFID tag information and dynamically predicting the validity period of the biological resource bag to determine whether the biological resource has expired;

[0087] Environmental assessment module, used to monitor the storage environment in real time during the validity period of biological resources and assess whether environmental parameters are suitable for biological resource storage;

[0088] A strategy formulation module is used to formulate and implement early warning strategies based on the data provided by the prediction module and the environmental assessment module;

[0089] The recording module is responsible for the encrypted storage, anonymization, automatic recording, review, and report generation of process data. The process data includes but is not limited to the source of biological resources, storage time, storage location, environmental monitoring data, outbound destination, and usage.

[0090] The prediction module includes: RFID reading and writing unit, validity period prediction unit, and expiration judgment unit;

[0091] RFID reading and writing unit: reads the RFID tag information on the biological resource bag through the RFID reader, including the blood type, blood volume and expiration date of the biological resource, realizes non-contact information reading, and improves the efficiency and accuracy of data entry;

[0092] An expiration date prediction unit is used to use the read information, combined with the current date, to predict the remaining time of the expiration date of each biological resource to help plan usage and priority;

[0093] The expiration judgment unit is used to judge which biological resources are about to expire or have expired based on the prediction results, so as to timely discover expired biological resources, prevent them from being misused, and ensure the safety of clinical blood use.

[0094] The environmental assessment module includes: a sensor array unit, a preprocessing unit, and an environmental assessment unit;

[0095] Sensor array units, deployed in storage areas, are used to continuously monitor temperature, humidity, and light environmental parameters, providing comprehensive environmental data to ensure compliance with standards for biological resource preservation;

[0096] The preprocessing unit is used to perform preliminary cleaning and formatting of the raw sensor data, remove invalid or abnormal data, and ensure the accuracy and consistency of the data;

[0097] The environmental assessment unit is used to analyze the preprocessed data and evaluate whether the current storage environment meets the requirements.

[0098] The strategy formulation module includes: anomaly detection unit, urgency classification unit, early warning strategy unit, and emergency handling unit;

[0099] Anomaly detection unit, which is used to monitor data streams through algorithms and identify abnormal situations such as environmental exceeding standards and biological resources approaching expiration;

[0100] The urgency classification unit is used to classify problems into different levels according to the severity and urgency of the exceptions, so as to facilitate priority processing;

[0101] The early warning strategy unit is used to formulate corresponding early warning measures according to different levels of urgency, such as sending emails, text messages or activating on-site alarms;

[0102] The emergency response unit is used to develop and implement emergency response plans for specific abnormal situations, such as quickly transferring biological resources that are about to expire to high-demand areas, responding to early warnings in a timely manner, preventing problems from expanding, and ensuring the safety and quality of biological resources.

[0103] The recording module includes: data encryption unit, data anonymization unit, and smart contract unit;

[0104] Data encryption unit, used to encrypt sensitive information, such as the circulation records of biological resources and patient information, to prevent data leakage and tampering and protect data security;

[0105] Data anonymization unit is used to remove personal identification information and protect personal privacy without affecting the value of data utilization, and to achieve data sharing and utilization while protecting user privacy;

[0106] Smart contract units are used to use blockchain technology to automatically execute the terms of the agreement on the biological resource supply chain, such as automatically confirming the receipt of biological resources, ensuring that transactions are transparent and cannot be tampered with, improving trust and efficiency, and reducing human intervention and errors.

[0107] Example 3

[0108] A computer-readable storage medium stores computer instructions, which execute the steps of a biological resource management method based on RFID when the computer instructions are executed, wherein the storage medium can be a volatile or non-volatile computer-readable storage medium.

[0109] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0111] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in the field can also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, which are all within the protection of the present invention.

Claims

1. A biological resource management method based on RFID, characterized in that: include: Step S1: Install and configure the RFID reader and antenna network, input the RFID tag information, and the control system reads the tag information of the biological resource bag through the RFID reader, and uses the date comparison algorithm combined with the deep learning model to build an expiration date prediction model, dynamically predict the expiration date in the RFID tag of the biological resource bag, and compare the expiration date information with the current time to determine whether the biological resource is expired; Step S2: If the biological resource is within the validity period, the system starts the sensor array to monitor the storage environment in real time, and uses a multi-source data fusion algorithm and a neural network algorithm to determine whether the current storage environment is suitable for biological resource storage. If the environmental parameters exceed the preset safety range, it is marked as an abnormal state; Step S3: Based on the abnormal detection results and the urgency of the biological resources, a hierarchical warning strategy is formulated. Once the warning condition is triggered, the control system automatically performs predictive maintenance operations according to the preset emergency processing process; Step S4: The whole process data of handover, warehousing, outbound storage, inventory and environmental monitoring is encrypted and stored through blockchain technology, and sensitive information is anonymized. Smart contracts are written to automatically perform data recording, review and report generation tasks; The specific steps of constructing the validity period prediction model in step S1 include: S1.1: Attach RFID tags to biological resource bags and enter initial information; S1.2: Configure an RFID reader. When the biological resource bag enters the reading range of the reader, the information on the tag is automatically read and the read RFID tag information is preprocessed; S1.3: Use historical data and deep learning algorithms to build an expiration date prediction model, regularly use the built prediction model to predict the expiration date of biological resource bags in RFID tags, compare the predicted expiration date with the current time, and calculate the remaining validity period T i ; S1.4: Set the biological resource limited period fluctuation threshold range to a. If T i -a≥0, it means that the biological resource has expired; The specific steps of step S2 include: S2.1: Install a sensor array in the storage area of ​​biological resources, read the temperature, humidity, light and gas concentration data from the sensor array in real time, and obtain the storage environment data X = {x1,...,x N }, and preprocess the storage environment dataset, where x N Indicates the Nth storage environment data, where N indicates the number of storage environment data; S2.2: Use feature extraction algorithm to extract the features from X = {x1, ..., x N Extracting biological resource storage characteristics from and store biological resources Input into the pre-trained neural network model to obtain the probability value P of the current biological resource storage environment suitability i , i represents the current biological resource location index; S2.3: Set the biological resource storage environment threshold h, If P i <h, it means that the current storage environment is not suitable for biological resource storage and is marked as abnormal; If P i ≥h, it means that the current biological resources can continue to be stored; S2.4: Isolate biological resources marked as abnormal.

2. The RFID-based biological resource management method according to claim 1, characterized in that: The validity period prediction model in step S1 not only considers the fixed validity period in the RFID tag, but also performs dynamic prediction in combination with the biological resource type, storage condition historical data, and transportation condition factors.

3. The RFID-based biological resource management method according to claim 2, characterized in that: The environmental parameters stored in step S2 include: storing the temperature, humidity, light, and gas concentration information of the environment.

4. The RFID-based biological resource management method according to claim 3, characterized in that: In step S3, the urgency of biological resources is divided into three levels: emergency, sub-urgent and non-urgent according to clinical use, urgency of demand and availability of substitutes.

5. A biological resource management system based on RFID, which is used to implement the biological resource management method based on RFID according to any one of claims 1 to 4, characterized in that: include: Prediction module, environmental assessment module, strategy formulation module, and recording module; The prediction module is responsible for inputting RFID tag information and dynamically predicting the validity period of the biological resource bag to determine whether the biological resource is expired; The environmental assessment module is used to monitor the storage environment in real time during the validity period of the biological resources and assess whether the environmental parameters are suitable for the storage of biological resources; The strategy formulation module is used to formulate and implement early warning strategies based on the data provided by the prediction module and the environmental assessment module; The recording module is responsible for the encrypted storage, anonymization processing, automatic recording, review and report generation tasks of process data.

6. The RFID-based biological resource management system according to claim 5, characterized in that: The environmental assessment module includes: a sensor array unit, a preprocessing unit, and an environmental assessment unit; The sensor array unit is deployed in the storage area to continuously monitor temperature, humidity, and light environment parameters and provide comprehensive environmental data; The pre-processing unit is used to perform preliminary cleaning and formatting on the raw sensor data to remove invalid or abnormal data; The environment evaluation unit is used to analyze the preprocessed data and evaluate whether the current storage environment meets the requirements.

7. The RFID-based biological resource management system according to claim 6, characterized in that: The strategy formulation module includes: an anomaly detection unit, an emergency level classification unit, an early warning strategy unit, and an emergency processing unit; The anomaly detection unit is used to monitor the data stream through an algorithm and identify abnormal situations such as environmental exceeding standards and biological resources approaching expiration; The urgency classification unit is used to classify the problems into different levels according to the severity and urgency of the abnormalities; The warning strategy unit is used to formulate corresponding warning measures according to different emergency levels; The emergency processing unit is used to formulate and execute emergency response plans for specific abnormal situations and respond to warnings in a timely manner.

8. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the steps of the RFID-based biological resource management method according to any one of claims 1 to 4 are executed.

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