Sample library full life cycle management system based on block chain

Abstract

The invention discloses a sample library full life cycle management system based on a block chain, and relates to the technical field of biological sample management, the sample library full life cycle management system comprises a sample management module, an inventory management module, a data analysis module, a knowledge graph module, a follow-up visit management module and an early warning module, the data cannot be tampered and traceability is ensured, and the knowledge graph module adopts a cold start and progressive construction method of a knowledge graph. According to the method, non-tampering recording of the whole process from warehousing to ex-warehousing of the sample is realized through a block chain technology, traceability of the whole life cycle of the sample is ensured, causal analysis and discovery, sample quality prediction, storage period quality prediction and optimal scheme recommendation are realized based on the knowledge graph and multi-dimensional data integration, and the method is suitable for large-scale popularization and application. The method provides data-driven decision support for sample management, and is suitable for medical institutions, scientific research institutions and other scenes requiring standardized and digital management of biological samples.

Description

Technical Field

[0001] This invention relates to the field of biological sample management technology, and in particular to a blockchain-based full lifecycle management system for sample banks. Background Technology

[0002] In the current era of rapid development in life sciences and precision medicine, the management level of sample banks, as the core carriers of biological samples and their associated data, directly impacts research quality, clinical decision-making, and public health safety. Traditional sample management models have long relied on manual recording and centralized information systems. While these methods have achieved some degree of process standardization, they have also revealed significant shortcomings in areas such as data authenticity, end-to-end traceability, cross-institutional collaboration, and privacy protection.

[0003] Traditional sample management systems struggle to trace the entire lifecycle of samples, resulting in incomplete historical information that affects data reliability. They also lack effective tracking of sample usage after leaving the database, posing security risks. Furthermore, traditional databases are susceptible to tampering, making it difficult to guarantee the authenticity of sample data and undermining the credibility of research data.

[0004] Therefore, the blockchain-based sample library lifecycle management system can effectively solve the above problems through the immutability and traceability characteristics of blockchain. Summary of the Invention

[0005] To address the aforementioned issues, this application proposes a blockchain-based full lifecycle management system for biological samples, applicable to scenarios such as medical institutions and research institutes that require standardized and digital management of biological samples.

[0006] A blockchain-based full lifecycle management system for sample libraries includes:

[0007] The sample management module is responsible for the management of the entire sample lifecycle, including sample entry management, sample exit management, location management, specification management, barcode management, and a blockchain-based sample traceability system.

[0008] The inventory management module is responsible for real-time monitoring, statistical analysis, early warning management and inventory counting of sample inventory, including real-time inventory monitoring, inventory early warning, inventory counting, storage condition recording, intelligent inventory optimization and resource scheduling engine;

[0009] The data analysis module provides advanced analysis functions for sample data, including sample quality analysis, sample usage analysis, sample correlation analysis, predictive models, and an experimental design recommendation engine.

[0010] The knowledge graph module includes a sample knowledge graph, a literature mining engine, association discovery, a visual query interface, and intelligent large model basic data construction;

[0011] The follow-up management module includes intelligent follow-up planning, multi-channel reminders, structured records, follow-up effectiveness evaluation, and personalized follow-up plans;

[0012] The early warning module is used to implement a multi-dimensional intelligent early warning mechanism, including a multi-level early warning system, abnormal storage condition early warning, intelligent inventory early warning, follow-up time node early warning, and intelligent early warning distribution.

[0013] Preferably, the sample entry management is used to realize sample information collection, sample code generation, quality inspection, allocation of storage location, barcode printing, entry confirmation and blockchain recording;

[0014] Sample outbound management is used to realize outbound application, approval process, sample location, outbound operation, usage record and blockchain record;

[0015] Location management is used to enable virtual modeling of storage locations, visualization and adjustment of location locations, and recording of location status;

[0016] Specification management is used to define sample types, manage container types, and configure storage conditions;

[0017] Barcode management is used to configure barcode generation rules, print barcodes, and implement barcode scanning interfaces.

[0018] Blockchain records are used to record a transaction on the blockchain for each sample status change, including key data such as operation type, operator, operation time, and sample information, to ensure that the data is tamper-proof and traceable;

[0019] The sample traceability system is a full-chain traceability system built on blockchain technology, which supports tracking the entire process from sample collection, processing, storage to use, and provides a visual traceability map.

[0020] Preferably, real-time inventory monitoring is used to monitor the status of sample inventory in real time and provide multi-dimensional inventory statistical analysis;

[0021] Inventory alerts are used to provide low inventory and expiration warnings, ensuring the safety of sample inventory.

[0022] Inventory counting is used to support the inventory counting process, including generating inventory counting plans, assigning inventory counting tasks, executing inventory counting, recording inventory counting results, analyzing discrepancies, adjusting inventory, and generating inventory counting reports;

[0023] The storage condition log is used to record the ideal and actual conditions for sample storage, and supports software alerts for abnormal conditions;

[0024] Intelligent inventory optimization is an inventory optimization system based on knowledge graph evolutionary algorithms. It provides optimal inventory strategy recommendations based on historical usage data, sample value, and storage costs.

[0025] The resource scheduling engine is used to achieve intelligent scheduling of sample resources across laboratories and institutions, thereby improving the overall resource utilization rate.

[0026] Preferably, sample quality analysis is used to analyze the factors affecting sample quality and their changing trends, providing data support for sample quality management;

[0027] Sample usage analysis is used to analyze sample usage and effectiveness, and to optimize sample resource allocation.

[0028] Sample association analysis is used to analyze the relationships between samples, supporting scientific research findings.

[0029] Predictive models are used to achieve causal analysis findings, sample quality prediction, and storage period quality prediction.

[0030] The experimental design recommendation engine is used to recommend the optimal experimental design and sample combination based on sample characteristics and research objectives, thereby improving research efficiency.

[0031] Preferably, the sample knowledge graph is used to construct a knowledge graph containing entities such as samples, diseases, genes, proteins, and drugs, revealing the complex relationships between them;

[0032] Literature mining engines are used to automatically extract research findings and knowledge related to samples from scientific literature, enriching the content of knowledge graphs.

[0033] Association discovery is used for reasoning capabilities based on knowledge graphs to discover potential associations and research value between samples;

[0034] The visual query interface is used to provide an intuitive graphical interface that supports complex knowledge queries and exploration;

[0035] The foundational data for building intelligent large models is used to generate the data required for deep learning from knowledge graphs. The foundational data is accumulated, and when the data accumulates to a certain level, it is added to the deep learning module, introducing the AI ​​engine.

[0036] Preferably, the intelligent follow-up plan automatically generates the optimal follow-up plan based on sample type, research purpose and historical data, and sets the follow-up period and reminder method;

[0037] Multi-channel reminders can be sent via email, SMS, WeChat, APP push, and other channels, and custom reminder templates and strategies are supported;

[0038] Structured records use a structured approach to document follow-up results, including sample quality assessment, status descriptions, and image data, and support standardized data collection.

[0039] Follow-up effectiveness evaluation is used to comprehensively analyze follow-up completion rate, sample quality change trends, and research impact, and to optimize follow-up strategies and resource allocation.

[0040] Personalized follow-up protocols are used to generate individualized follow-up plans based on sample characteristics and research needs, thereby improving follow-up efficiency and quality.

[0041] Preferably, a multi-level early warning system is used to realize a multi-level early warning system from the sample level, batch level to the system level, covering comprehensive risk monitoring;

[0042] Anomaly warning for storage conditions is used to monitor parameters such as temperature and humidity in the sample storage environment. When an anomaly is detected, an alert is triggered, and risk assessment and handling suggestions are provided.

[0043] The intelligent inventory warning uses a dynamic threshold algorithm to intelligently adjust the inventory warning threshold based on historical usage patterns and future demand forecasts, providing early warnings of potential inventory risks.

[0044] The follow-up time node early warning is used to monitor the implementation of the follow-up plan. When the follow-up time node is approaching, a tiered reminder is triggered to ensure that key follow-ups are not missed.

[0045] The intelligent early warning distribution system is used to intelligently distribute early warning information based on the warning type, severity, and responsible person settings, ensuring that early warning information is delivered to relevant personnel in a timely manner.

[0046] Preferably, the knowledge graph module employs a cold start and incremental construction method for knowledge graphs, specifically including the following steps:

[0047] S1. Use UMLS knowledge graph as an external knowledge reference, as the knowledge graph basis ontology and knowledge framework of the sample library full life cycle management system, and after trimming and expansion, form a localized domain ontology.

[0048] S2. After domain ontology modeling, entities such as sample type, experiment type, operation method, operation process, equipment and their relationships are structured into a knowledge graph, and semantic associations are established between different methods, steps and related data elements.

[0049] S3. Based on the existing knowledge graph and combined with existing sample types, experiment types, and other experimental conditions, retrieve and recommend suitable methods, steps, or operating procedures. Users can then personalize the recommended content according to their actual experimental needs.

[0050] S4. Record the user's adjustments as a new localization scheme and associate it with the existing knowledge graph. Through modifications and adjustments in each experiment, gradually improve the local knowledge graph.

[0051] S5. Structured / unstructured data generated by business systems, along with user adjustments, feedback, and evaluations, are written into a knowledge graph to form a knowledge loop for continuous optimization.

[0052] Preferred methods for constructing a blockchain include:

[0053] Define the roles of participating blockchain nodes: sample receiver, sample packer, sample storer, sample experimenter, external institution experimenter, sample quality control officer, and sample disposer;

[0054] Determine the blockchain nodes based on the business process;

[0055] The business processes involved in the node include sample receiving, sample packaging, sample storage, sample experimentation, sample quality control process, and sample disposal.

[0056] In summary, the blockchain-based sample bank lifecycle management system of this invention, compared with traditional technologies, achieves immutable recording of samples from entry to exit through blockchain technology, records sample status changes, ensures data integrity and authenticity, and ensures traceability throughout the sample lifecycle. Based on knowledge graphs and multi-dimensional data integration, it enables causal analysis discovery, sample quality prediction, storage period quality prediction, and optimal solution recommendation, providing data-driven decision support for sample management. It supports various sample entry methods such as batch import, single entry, and template data adaptation, and can automatically allocate storage locations, significantly improving sample processing speed. Based on rule engines and algorithms, it achieves inventory warnings, follow-up time node warnings, and storage condition warnings, ensuring sample security and research progress.

[0057] The technical method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0058] Figure 1 This is a block diagram of the blockchain-based sample library lifecycle management system of the present invention;

[0059] Figure 2 This is a schematic diagram of the blockchain model of the present invention;

[0060] Figure 3 This is a schematic diagram illustrating the steps involved in constructing the knowledge graph module of this invention. Detailed Implementation

[0061] The technical method of the present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0062] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.

[0063] Techniques, systems, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the instruction manual.

[0064] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0065] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0066] This invention provides a blockchain-based full lifecycle management system for a sample library, such as... Figure 1 As shown, it includes:

[0067] The sample management module is responsible for the management of the entire sample lifecycle, including sample entry management, sample exit management, location management, specification management, barcode management, and a blockchain-based sample traceability system.

[0068] Preferably, the sample entry management is used to realize sample information collection, sample code generation, quality inspection, allocation of storage location, barcode printing, entry confirmation and blockchain recording;

[0069] The warehousing process is as follows: sample information collection (interfacing with the HIS system), sample code generation, quality inspection, allocation of storage location, barcode printing, warehousing confirmation, and blockchain recording.

[0070] Sample outbound management is used to realize outbound application, approval process, sample location, outbound operation, usage record and blockchain record;

[0071] The outbound process is as follows: outbound application, approval process, approval, sample location, outbound operation, usage record, and blockchain record.

[0072] Location management is used to enable virtual modeling of storage locations, visualization and adjustment of location locations, and recording of location status;

[0073] Grid management is used to define sample types, manage container types, and configure storage conditions.

[0074] Barcode management is used to configure barcode generation rules, print barcodes, and implement barcode scanning interfaces.

[0075] Blockchain records are used to document each sample status change, recording a transaction on the blockchain. This includes key data such as operation type, operator, operation time, and sample information, ensuring data immutability and traceability. Figure 2 As shown;

[0076] Node A: The master server participates in the blockchain consensus, decides which transactions are written to the blockchain, is responsible for the main transaction processing, and stores the complete ledger data.

[0077] Node B: Backup server, participates in blockchain consensus, improves fault tolerance, stores complete ledger data, can serve as a backup server for the primary server, disaster recovery and backup, and can switch over when the primary server fails.

[0078] Node C: Primarily serves as a supervisory server for observation, viewing information and performing only readable operations on the ledger. It does not participate in consensus or write operations, and is mainly used for querying information during auditing and inspection.

[0079] Node D: Business system server, mainly interacts with the blockchain through API, initiates transactions and queries blockchain data, and does not participate in consensus operations or ledger storage.

[0080] The sample traceability system is a full-chain traceability system built on blockchain technology, which supports tracking the entire process from sample collection, processing, storage to use, and provides a visual traceability map.

[0081] Preferred methods for constructing a blockchain include:

[0082] Define the roles of participating blockchain nodes: sample receiver, sample packer, sample storer, sample experimenter, external institution experimenter, sample quality control officer, and sample disposer;

[0083] Determine the blockchain nodes based on the business process;

[0084] The business processes involved in the node include sample receiving, sample packaging, sample storage, sample experimentation, sample quality control process, and sample disposal.

[0085] Upon receiving a new transaction, each blockchain node first verifies the identities and permissions of both parties by determining the transaction type, such as Create, Transfer, Process, or Store, and checks the consistency between the data hash and the original data. Once multiple nodes reach a consensus, the transaction is written into the blockchain ledger.

[0086] The inventory management module is responsible for real-time monitoring, statistical analysis, early warning management and inventory counting of sample inventory, including real-time inventory monitoring, inventory early warning, inventory counting, storage condition recording, intelligent inventory optimization and resource scheduling engine;

[0087] Preferably, real-time inventory monitoring is used to monitor the status of sample inventory in real time and provide multi-dimensional inventory statistical analysis;

[0088] Inventory alerts are used to provide low inventory and expiration warnings, ensuring the safety of sample inventory.

[0089] Inventory counting is used to support the inventory counting process, including generating inventory counting plans, assigning inventory counting tasks, executing inventory counting, recording inventory counting results, analyzing discrepancies, adjusting inventory, and generating inventory counting reports;

[0090] The storage condition log is used to record the ideal and actual conditions for sample storage, and supports software alerts for abnormal conditions;

[0091] Intelligent inventory optimization is an inventory optimization system based on knowledge graph evolutionary algorithms. It provides optimal inventory strategy recommendations based on historical usage data, sample value, and storage costs.

[0092] The resource scheduling engine is used to achieve intelligent scheduling of sample resources across laboratories and institutions, thereby improving the overall resource utilization rate.

[0093] The data analysis module provides advanced analysis functions for sample data, including sample quality analysis, sample usage analysis, sample correlation analysis, predictive models, and an experimental design recommendation engine.

[0094] Preferably, sample quality analysis is used to analyze the factors affecting sample quality and their changing trends, providing data support for sample quality management;

[0095] Sample usage analysis is used to analyze sample usage and effectiveness, and to optimize sample resource allocation.

[0096] Sample association analysis is used to analyze the relationships between samples, supporting scientific research findings.

[0097] Predictive models are used to achieve causal analysis findings, sample quality prediction, and storage period quality prediction.

[0098] The experimental design recommendation engine is used to recommend the optimal experimental design and sample combination based on sample characteristics and research objectives, thereby improving research efficiency.

[0099] like Figure 3 As shown, the knowledge graph module includes a sample knowledge graph, a literature mining engine, association discovery, a visual query interface, and intelligent large model basic data construction.

[0100] Preferably, the sample knowledge graph is used to construct a knowledge graph containing entities such as samples, diseases, genes, proteins, and drugs, revealing the complex relationships between them;

[0101] Literature mining engines are used to automatically extract research findings and knowledge related to samples from scientific literature, enriching the content of knowledge graphs.

[0102] Association discovery is used for reasoning capabilities based on knowledge graphs to discover potential associations and research value between samples;

[0103] The visual query interface is used to provide an intuitive graphical interface that supports complex knowledge queries and exploration;

[0104] The foundational data for building intelligent large models is used to generate the data required for deep learning from knowledge graphs. The foundational data is accumulated, and when the data accumulates to a certain level, it is added to the deep learning module, introducing the AI ​​engine.

[0105] Preferably, the knowledge graph module employs a cold start and incremental construction method for knowledge graphs, specifically including the following steps, such as... Figure 2 As shown:

[0106] S1. Use UMLS knowledge graph as an external knowledge reference, as the knowledge graph basis ontology and knowledge framework of the sample library full life cycle management system, and after trimming and expansion, form a localized domain ontology.

[0107] The core entity types that should be included are: sample, packaging event, storage unit, experiment, quality control, disposal, personnel, equipment, standards, and process.

[0108] S2. After domain ontology modeling, entities such as sample type, experiment type, operation method, operation process, equipment and their relationships are structured into a knowledge graph, and semantic associations are established between different methods, steps and related data elements.

[0109] Design the semantic relationships of entities:

[0110] Sample - Packaging event - Packaging personnel - Equipment.

[0111] Sample - Storage unit - Storage conditions - Device.

[0112] Sample - Experiment - Experimenters - Experimental Equipment - Experimental Methods - Experimental Results.

[0113] Sample - Quality Control - Quality Control Plan - Quality Control Results - Quality Control Personnel.

[0114] Sample - Disposal - Disposal Method - Disposal Personnel.

[0115] Sample-Standard-Experimental Procedure.

[0116] S3. Based on the existing knowledge graph and combined with existing sample types, experiment types, and other experimental conditions, retrieve and recommend suitable methods, steps, or operating procedures. Users can then personalize the recommended content according to their actual experimental needs.

[0117] S4. Record the user's adjustments as a new localization scheme and associate it with the existing knowledge graph. Through modifications and adjustments in each experiment, gradually improve the local knowledge graph.

[0118] S5. Structured / unstructured data generated by business systems, along with user adjustments, feedback, and evaluations, are written into a knowledge graph to form a knowledge loop for continuous optimization.

[0119] To progressively refine the knowledge graph, at the start of each business process—such as packaging, storage, experimentation, quality control, and disposal—the system retrieves and recommends suitable methods, steps, or operational procedures based on the existing knowledge graph and experimental conditions, including sample types and experiment types. Users then personalize the recommendations according to their actual experimental needs, such as modifying parameters or adding / deleting steps. The system records these adjustments as new localized solutions and associates them with the existing knowledge graph. Through modifications and adjustments made with each experiment, the local knowledge graph is gradually improved.

[0120] The business processes involved are as follows:

[0121] Sample dispensing:

[0122] Based on the selected sample type and the packaged data, the system automatically filters packaging methods, equipment, and environmental standards from the existing knowledge graph. Users can modify and adjust the recommended content, such as packaging parameters, required equipment, and environmental standard parameters, according to the actual situation. If the adjusted parameters will generate undesirable results in the historical records, an alert will be issued when adjusting the parameters. After adjustment, the system will record the adjusted packaging parameters, environmental parameters, equipment used, personnel, etc., and establish semantic associations with entities such as samples, subsamples, personnel, and equipment. The new method will be used as the localized packaging method, and subsequent similar operations can be recommended according to the new scheme.

[0123] Sample storage:

[0124] The system automatically selects storage location allocation schemes and operation procedures from the existing knowledge graph based on the selected sample type and storage conditions, such as temperature and humidity requirements, required equipment, and storage locations. Users can adjust the existing scheme, including temperature and humidity requirements, equipment, and storage locations. If the adjusted parameters generate undesirable results in the historical records, an alert will be issued when adjusting the parameters. After adjustment, the system records detailed elements such as storage units, storage conditions, equipment, time, and personnel, and establishes semantic associations with entities such as samples, storage units, equipment, and personnel. Subsequent similar operations will be performed according to the optimized scheme.

[0125] Sample experiment:

[0126] Based on the selected sample type, experiment type, and experimental procedure, the system automatically filters experimental methods, consumables, equipment, and experimental steps from the existing knowledge graph. Users can adjust experimental steps, parameters, and equipment according to the existing plan. If the adjusted parameters generate undesirable results in the history, an alert will be issued when adjusting the parameters. After adjustment, the system records detailed experimental methods, equipment, consumables, personnel, and experimental results, and establishes semantic associations with samples, equipment, experimental personnel, and results. Subsequent similar operations will be recommended based on the optimized plan.

[0127] Sample quality control:

[0128] The system automatically filters information such as quality control plans, operating procedures, and key points from the existing knowledge graph based on sample type and existing experimental methods. Users can adjust the existing plan accordingly. After adjustment, the system records information such as quality control methods, equipment, personnel, and results, and establishes semantic associations with samples, quality control plans, equipment, and personnel. Subsequent similar operations will be performed according to the optimized plan.

[0129] Sample handling:

[0130] Based on experimental results and sample status, the system automatically filters treatment methods and operation steps from the existing knowledge graph. Users can adjust the existing plan according to the plan. After adjustment, the system records the treatment method, equipment, personnel, time, conclusion, etc., and establishes semantic association with the sample, treatment method, equipment, and personnel. Subsequent similar operations will be performed, that is, the system will recommend according to the optimized plan and generate intelligent conclusions based on the experimental results.

[0131] Through the above-mentioned recommendation -> adjustment -> recording -> knowledge accumulation, the knowledge graph system of this invention will continuously absorb localized experience and gradually form a knowledge system that meets the actual needs of the system, thereby realizing the adaptive evolution of the knowledge graph, improving the intelligence level of the system, and increasing efficiency.

[0132] Data Analysis:

[0133] The experimental results and related procedures, parameters, etc., are statistically analyzed over a period of time. Evaluations can then be selected at relevant knowledge nodes, and a recommended order can be formed based on the evaluation system.

[0134] The driving methods and steps of knowledge graphs:

[0135] Knowledge modeling and semantic association:

[0136] UMLS knowledge graphs are used as external knowledge references, serving as the foundational ontology and knowledge framework for the sample library lifecycle management system. Based on the sample library lifecycle management methodology, the core entities should include the following types: samples, packaging events, storage units, experiments, quality control, disposal, personnel, equipment, standards, and processes. Through tailoring, expanding, and localizing the UMLS core entities, a domain ontology adapted to the sample library lifecycle management system is generated. After modeling the domain ontology, entities such as sample types, experiment types, operation methods, operation processes, and equipment, along with their relationships, are structured into a knowledge graph. Semantic associations are established between different methods, steps, and related data elements.

[0137] Intelligent Recommendation:

[0138] Once a user enters a business process, the system automatically filters relevant methods, processes, and historical local solutions from the knowledge graph based on existing conditions.

[0139] User localization optimization:

[0140] Users can adjust, supplement, and optimize the recommended methods, processes, or steps. The system records these adjustments and links them to the original methods, processes, and steps, forming localized knowledge nodes. After each experiment, users can score the current solution, gradually forming a scoring mechanism for subsequent recommended solutions, allowing for the selection of the best solution based on the scores.

[0141] Knowledge Evolution and Continuous Optimization:

[0142] Each user's adjustment, supplement, and evaluation will gradually be added to the knowledge graph as new knowledge points, forming an adaptive evolution of knowledge. By combining the relationship between the scheme and the results, such as experimental results and storage results, the knowledge graph is continuously cleaned, optimized, and expanded, improving efficiency and the practicality of the knowledge graph.

[0143] Data flow and closed loop:

[0144] Sample reception:

[0145] Laboratory personnel receive samples. If the sample meets the specifications (e.g., quality inspection), a transaction is created, and sample information (sample ID, patient ID, collection time, method, etc.) is obtained through the LIS or HIS system. This information is stored in the system, and a hash value is generated from the relevant data to create the create transaction. The receiving node signs the transaction and broadcasts it to the consortium blockchain. Blockchain nodes reach consensus and record the transaction. Upon completion, a receipt (hash value) is generated, triggering subsequent processes. If the quality inspection fails, a checkout transaction is executed.

[0146] Sample dispensing:

[0147] Laboratory packaging personnel initiate a Process transaction, recording packaging information such as time, environment, and equipment at that time, generating subsamples and establishing a chain link with the original sample. All packaging information and subsamples are automatically uploaded to the chain through smart contracts.

[0148] Sample storage:

[0149] Laboratory storage personnel initiate Store transactions, allocate storage locations for samples, and record relevant information (storage time, storage conditions, equipment, location, etc.). Storage status is recorded on the blockchain at time intervals. If an abnormal change in storage status is detected based on storage conditions during storage, it is automatically recorded on the blockchain in an abnormal Alert transaction. Abnormal transactions are processed until the samples are discarded or storage conditions change back to normal, ensuring that the storage status and storage history of each sample are fully traceable.

[0150] Sample experiment:

[0151] Sample experiments are divided into in-house experiments and external experiments:

[0152] Our organization's independent experiments:

[0153] Researchers initiate Laboratory transactions, recording experimental consumables, equipment, and time according to the experimental methods. Experimental samples and their sub-samples are linked in a chain, and all experimental samples and information are automatically uploaded to the blockchain via smart contracts. During the experiment, the results and sample information status at each step are stored according to the experimental plan. Experimental results are discarded or appropriately processed based on the results or abnormal experimental conditions, ensuring full traceability of sample information and experimental conditions at every step.

[0154] External testing:

[0155] Researchers initiate a Laboratory transaction. The sending and receiving organizations and the organization reach a consensus on the blockchain nodes through sending and receiving data, record the results, and publish them to the consortium blockchain. Once the experimental results are generated, the sending organization records the results, reaches a consensus with the organization on the blockchain nodes, and publishes them to the consortium blockchain. The entire process ensures traceability at every step: sending samples, receiving samples, returning experimental results, and receiving experimental results.

[0156] Sample quality control process:

[0157] Laboratory quality control personnel initiate Qc transactions based on the quality inspection plan, specifying the quality control plan. The quality control process generates Qc_exec transactions, recording relevant parameters. All data is updated and stored on the blockchain.

[0158] Sample handling:

[0159] After the experiment, the researchers determine the sample disposal method based on the sample status and initiate disposal transactions, including re-experimentation, remaining storage, or checkout transactions. They record sample information and disposal methods. For example, when initiating a checkout transaction, authorization is verified with the sample disposal personnel via digital signatures, and the transaction is published to the consortium blockchain based on blockchain node consensus.

[0160] Through the above process of receiving, packaging, storing, experimenting, quality control, and handling, a complete lifecycle record of the sample is formed, ensuring that the sample status, causes, and handling methods at each step can be traced throughout the entire process, forming a complete lifecycle record chain for the sample library.

[0161] The follow-up management module includes intelligent follow-up planning, multi-channel reminders, structured records, follow-up effectiveness evaluation, and personalized follow-up plans;

[0162] Preferably, the intelligent follow-up plan automatically generates the optimal follow-up plan based on sample type, research purpose and historical data, and sets the follow-up period and reminder method;

[0163] Multi-channel reminders can be sent via email, SMS, WeChat, APP push, and other channels, and custom reminder templates and strategies are supported;

[0164] Structured records use a structured approach to document follow-up results, including sample quality assessment, status descriptions, and image data, and support standardized data collection.

[0165] Follow-up effectiveness evaluation is used to comprehensively analyze follow-up completion rate, sample quality change trends, and research impact, and to optimize follow-up strategies and resource allocation.

[0166] Personalized follow-up protocols are used to generate individualized follow-up plans based on sample characteristics and research needs, thereby improving follow-up efficiency and quality.

[0167] The early warning module is used to implement a multi-dimensional intelligent early warning mechanism, including a multi-level early warning system, abnormal storage condition early warning, intelligent inventory early warning, follow-up time node early warning, and intelligent early warning distribution.

[0168] Preferably, a multi-level early warning system is used to realize a multi-level early warning system from the sample level, batch level to the system level, covering comprehensive risk monitoring;

[0169] Anomaly warning for storage conditions is used to monitor parameters such as temperature and humidity in the sample storage environment. When an anomaly is detected, an alert is triggered, and risk assessment and handling suggestions are provided.

[0170] The intelligent inventory warning uses a dynamic threshold algorithm to intelligently adjust the inventory warning threshold based on historical usage patterns and future demand forecasts, providing early warnings of potential inventory risks.

[0171] The follow-up time node early warning is used to monitor the implementation of the follow-up plan. When the follow-up time node is approaching, a tiered reminder is triggered to ensure that key follow-ups are not missed.

[0172] The intelligent early warning distribution system is used to intelligently distribute early warning information based on the warning type, severity, and responsible person settings, ensuring that early warning information is delivered to relevant personnel in a timely manner.

[0173] In addition, the blockchain-based sample library lifecycle management system also includes a client layer, a gateway layer, a service layer (core business service cluster, blockchain multi-chain service, distributed message queue, scheduled task scheduling, data encryption service), a storage layer (relational database cluster MySQL, distributed caching system Redis, object storage service MinIO, multi-chain blockchain storage, time-series database, graph database, distributed log system), a user permission module, and a mobile application.

[0174] The user permissions module includes:

[0175] Multi-dimensional permission model: Implements multi-dimensional permission control based on roles, departments, projects, and data levels to meet complex authorization needs.

[0176] Fine-grained data permissions: Implement field-level data access control to ensure the security of sensitive data.

[0177] Permission Inheritance and Delegation: Supports permission inheritance and temporary delegation mechanisms to simplify permission management processes.

[0178] Zero Trust Security Architecture: Employing a zero trust security model, it implements continuous verification and the principle of least privilege to improve system security.

[0179] Comprehensive audit trail: Records detailed user operation logs, supports multi-dimensional query and analysis, and meets compliance requirements and security audit needs.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical methods of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical methods of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical methods to deviate from the spirit and scope of the technical methods of the present invention.

Claims

1. A blockchain-based sample library lifecycle management system, characterized in that, include: The sample management module is responsible for the management of the entire sample lifecycle, including sample entry management, sample exit management, location management, specification management, barcode management, and a blockchain-based sample traceability system. The inventory management module is responsible for real-time monitoring, statistical analysis, early warning management and inventory counting of sample inventory, including real-time inventory monitoring, inventory early warning, inventory counting, storage condition recording, intelligent inventory optimization and resource scheduling engine; The data analysis module provides advanced analysis functions for sample data, including sample quality analysis, sample usage analysis, sample correlation analysis, predictive models, and an experimental design recommendation engine. The knowledge graph module includes a sample knowledge graph, a literature mining engine, association discovery, a visual query interface, and intelligent large model basic data construction; The follow-up management module includes intelligent follow-up planning, multi-channel reminders, structured records, follow-up effectiveness evaluation, and personalized follow-up plans; The early warning module is used to implement a multi-dimensional intelligent early warning mechanism, including a multi-level early warning system, abnormal storage condition early warning, intelligent inventory early warning, follow-up time node early warning, and intelligent early warning distribution.

2. The blockchain-based sample library lifecycle management system according to claim 1, characterized in that: Sample entry management is used to realize sample information collection, sample code generation, quality inspection, storage location allocation, barcode printing, entry confirmation and blockchain recording; Sample outbound management is used to realize outbound application, approval process, sample location, outbound operation, usage record and blockchain record; Location management is used to enable virtual modeling of storage locations, visualization and adjustment of location locations, and recording of location status; Specification management is used to define sample types, manage container types, and configure storage conditions; Barcode management is used to configure barcode generation rules, print barcodes, and implement barcode scanning interfaces. Blockchain records are used to record a transaction on the blockchain for each change in sample status, including key data such as operation type, operator, operation time, and sample information, to ensure that the data is tamper-proof and traceable; The sample traceability system is a full-chain traceability system built on blockchain technology, which supports tracking the entire process from sample collection, processing, storage to use, and provides a visual traceability map.

3. The blockchain-based sample library lifecycle management system according to claim 2, characterized in that: Real-time inventory monitoring is used to monitor the status of sample inventory in real time and provide multi-dimensional inventory statistical analysis; Inventory alerts are used to provide low inventory and expiration warnings, ensuring the safety of sample inventory. Inventory counting is used to support the inventory counting process, including generating inventory counting plans, assigning inventory counting tasks, executing inventory counting, recording inventory counting results, analyzing discrepancies, adjusting inventory, and generating inventory counting reports; The storage condition log is used to record the ideal and actual conditions for sample storage, and supports software alerts for abnormal conditions; Intelligent inventory optimization is an inventory optimization system based on knowledge graph evolutionary algorithms that provides target inventory strategy suggestions based on historical usage data, sample value, and storage costs. The resource scheduling engine is used to achieve intelligent scheduling of sample resources across laboratories and institutions, thereby improving the overall resource utilization rate.

4. The blockchain-based sample library lifecycle management system according to claim 3, characterized in that: Sample quality analysis is used to analyze the factors and trends that affect sample quality, providing data support for sample quality management. Sample usage analysis is used to analyze sample usage and effectiveness, and to optimize sample resource allocation. Sample association analysis is used to analyze the relationships between samples, supporting scientific research findings; Predictive models are used to achieve causal analysis findings, sample quality prediction, and storage period quality prediction. The experimental design recommendation engine is used to recommend the optimal experimental design and sample combination based on sample characteristics and research objectives, thereby improving research efficiency.

5. The blockchain-based sample library lifecycle management system according to claim 4, characterized in that: Sample knowledge graphs are used to construct knowledge graphs containing entities such as samples, diseases, genes, proteins, and drugs, revealing the complex relationships between them; Literature mining engines are used to automatically extract research findings and knowledge related to samples from scientific literature, enriching the content of knowledge graphs. Association discovery is used for reasoning capabilities based on knowledge graphs to discover potential associations and research value between samples; The visual query interface is used to provide an intuitive graphical interface that supports complex knowledge queries and exploration; The foundational data for building intelligent large models is used to generate the data required for deep learning from knowledge graphs. The foundational data is accumulated, and when the data accumulates to a certain level, it is added to the deep learning module, introducing the AI ​​engine.

6. The blockchain-based sample library lifecycle management system according to claim 5, characterized in that: The intelligent follow-up plan automatically generates the optimal follow-up plan based on sample type, research purpose, and historical data, and sets the follow-up period and reminder method; Multi-channel reminders can be sent via email, SMS, WeChat, APP push, and other channels, and custom reminder templates and strategies are supported; Structured records use a structured approach to record follow-up results, including sample quality assessment, status description, image data, etc., and support standardized data collection. Follow-up effectiveness evaluation is used to comprehensively analyze follow-up completion rate, sample quality change trend and research impact, and to optimize follow-up strategies and resource allocation; Personalized follow-up protocols are used to generate individualized follow-up plans based on sample characteristics and research needs, thereby improving follow-up efficiency and quality.

7. The blockchain-based sample library lifecycle management system according to claim 6, characterized in that: The multi-level early warning system is used to realize a multi-level early warning system from the sample level, batch level to the system level, covering comprehensive risk monitoring; Anomaly warning for storage conditions is used to monitor parameters such as temperature and humidity in the sample storage environment. When an anomaly is detected, an alert is triggered, and risk assessment and handling suggestions are provided. The intelligent inventory warning uses a dynamic threshold algorithm to intelligently adjust the inventory warning threshold based on historical usage patterns and future demand forecasts, providing early warnings of potential inventory risks. The follow-up time node early warning is used to monitor the implementation of the follow-up plan. When the follow-up time node is approaching, a tiered reminder is triggered to ensure that key follow-ups are not missed. Intelligent early warning distribution is used to intelligently distribute early warning information based on the warning type, severity, and responsible person settings, ensuring that early warning information is delivered in a timely manner.

8. The blockchain-based sample library lifecycle management system according to claim 6, characterized in that, The knowledge graph module employs a cold start and incremental construction method for knowledge graphs, specifically including the following steps: S1. Use UMLS knowledge graph as an external knowledge reference, as the knowledge graph basis ontology and knowledge framework of the sample library full life cycle management system, and after trimming and expansion, form a localized domain ontology. S2. After domain ontology modeling, entities such as sample type, experiment type, operation method, operation process, equipment and their relationships are structured into a knowledge graph, and semantic associations are established between different methods, steps and related data elements. S3. Based on the existing knowledge graph and combined with the existing sample types, experiment types and other experimental conditions, retrieve and recommend suitable methods, steps or operation procedures. Users can then personalize the recommended content according to their actual experimental needs. S4. Record the user's adjustments as a new localization scheme and associate it with the existing knowledge graph. Through modifications and adjustments in each experiment, gradually improve the local knowledge graph. S5. Structured / unstructured data generated by business systems, along with user adjustments, feedback, and evaluations, are written into a knowledge graph to form a knowledge loop for continuous optimization.

9. The blockchain-based sample library lifecycle management system according to claim 6, characterized in that, The ways to build a blockchain include: Define the roles of participating blockchain nodes: sample receiver, sample packer, sample storer, sample experimenter, external institution experimenter, sample quality control officer, and sample disposer; Determine the blockchain nodes based on the business process; The business processes involved in the node include sample receiving, sample packaging, sample storage, sample experimentation, sample quality control process, and sample disposal.

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