A medical sample visualized traceability and permission management integrated system and method

Abstract

This invention discloses an integrated system and method for visual traceability and access control of medical samples, belonging to the field of intelligent medical sample management technology. It achieves real-time data integrity verification and tamper prevention by separating the on-chain edge hash digest with the off-chain encrypted original data. A dynamic access control mechanism with six levels of binding—identity, terminal, location, time, sensitivity level, and environmental state—is employed, automatically enforcing access control in case of environmental anomalies. Based on a nonlinear coupling model of sample appearance defect characteristics and cumulative environmental deviations, automatic defect classification and closed-loop handling without manual intervention are achieved. This invention solves problems such as easy data tampering, coarse access control, subjective defect judgment, lack of consistency between on-chain and off-chain data, and high deployment costs. It is suitable for medical institutions and third-party testing institutions, can be deployed in a lightweight manner, and meets the needs of medical supervision and judicial evidence preservation.

Description

Technical Field

[0001] This invention relates to the fields of intelligent management of medical samples, Internet of Things, edge computing, consortium blockchain, data security, and machine vision and environment coupled modeling technology, specifically to an integrated system and method for visual traceability and access control of medical samples. Background Technology

[0002] Current medical sample management suffers from several problems: data is easily tampered with, there is a lack of on-chain and off-chain consistency verification mechanisms, and traceability results lack judicial credibility. Access control relies solely on identity verification, failing to consider the security status of the sample environment, and cannot proactively block operations when samples are abnormal, posing a risk of risk spread. Sample defect determination depends on human experience, resulting in inconsistent standards, low efficiency, and a lack of objective quantitative models. The blockchain and storage architecture is inadequate; directly uploading raw data to the chain leads to high costs and low efficiency. The system suffers from hardware redundancy and complex deployment, making it difficult to implement in lightweight solutions in small and medium-sized medical institutions. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an integrated system and method for visual traceability and access control of medical samples. Through hash consistency verification, environment-driven mandatory access control, and image-environment nonlinear coupling hierarchy, it realizes reliable traceability of medical samples throughout their entire life cycle, refined access control, and automated defect handling. The solution has outstanding substantive features and significant progress.

[0004] This invention employs an on-chain digest, off-chain storage, and real-time consistency verification structure. Automatic hash comparison is used during data access to block tampering and address data trust issues. A mandatory access control mechanism prioritizes environmental security over identity permissions, breaking through traditional identity permission logic. Unconditional access control is applied when the sample environment is abnormal, preventing risk propagation and accountability confusion. A non-linear coupling model of image defect features and cumulative environmental deviation is used to perform an exponential non-linear mapping of cumulative environmental deviation, improving sensitivity to minor, persistent anomalies and achieving objective, automatic classification. The system adopts a lightweight architecture, requiring only on-chain digests, edge computing, and general-purpose hardware for deployment.

[0005] Technical solution

[0006] 1. System Composition

[0007] The data processing module (1) is used for hash calculation, coupled calculation, and risk assessment. The encrypted storage module (2) is used for storing keys, permission policies, and defect thresholds. The external collaboration interface (3) is used to connect with the hospital information system and human resources system. The data acquisition module (4) includes RFID, image acquisition components, temperature and humidity sensing components, and cleanliness sensing units (41). The blockchain interaction module (61) is used for evidence storage, verification, and multi-node consensus. The permission control module (62) is used for six-level verification and automatic forced access control. The closed-loop processing module (63) is used for command output and physical isolation control. The visualization module (7) is used for information display and traceability query. The off-chain encrypted storage system (8) is used for encrypted storage of raw data.

[0008] 2. Method and Flow

[0009] Medical samples are uniquely identified and bound, and multi-source data collection is completed. Hash digests are generated through edge computing, and the digests and storage indexes are uploaded to the blockchain. The original data is encrypted and stored in an off-chain encrypted storage system. During data access, on-chain and off-chain hash consistency checks are performed; access is blocked if an anomaly is detected. Six-level permission checks are implemented, and the system automatically locks permissions in case of environmental anomalies. Image defect features are extracted, and cumulative environmental deviation values ​​are calculated. A non-linear coupling model is used to output a comprehensive risk value and defect level. The system automatically executes closed-loop processing and stores the processing results on the blockchain for evidence. The entire process information is displayed through a visualization module.

[0010] 3. Core Coupling Algorithm

[0011] The nonlinear coupling model uses the following formula: Where F a D is the first risk coefficient for image defect feature mapping. e The cumulative deviation value obtained by integrating the time series of environmental parameters is 1−e⁻ᵞ・ᴰ e This is an exponential nonlinear mapping that significantly amplifies small, persistent deviations. α, β, and γ are pre-defined weighting coefficients based on sample type, and R is the comprehensive risk value, used for automatic three-level defect classification. Compared to linear weighting, this nonlinear model is more sensitive to low-intensity, long-term environmental shifts, enabling earlier identification of potential risks and avoiding the neglect of small cumulative deviations by linear weighting, thus achieving more accurate early warning.

[0012] 4. Environment-driven forced access control

[0013] The safety of medical samples takes precedence over operator access rights. When environmental parameters such as temperature, humidity, and cleanliness exceed limits, or when the terminal is outside the preset safe zone, the system automatically, unavoidably, and unconditionally locks access rights to prevent samples from being transferred, tested, or disposed of without authorization, thus avoiding risk spread and unclear liability. This mechanism addresses the specific problems in medical sample management scenarios and is not a simple transplantation of conventional access control.

[0014] Beneficial effects

[0015] Data integrity can be verified in real time, and tampering can be automatically identified and blocked, meeting regulatory and judicial evidence preservation requirements. A mandatory access control mechanism prioritizing environmental security enhances the security of samples throughout the entire process and reduces management risks. Automatic quantification and grading of defects are achieved based on a nonlinear coupling model, resulting in objective and consistent results without manual intervention. Only digests are stored on-chain, reducing storage and computing costs; general-purpose hardware allows for rapid deployment, adaptable to small and medium-sized institutions. Independent claims remove unnecessary hardware features, resulting in a more reasonable scope of protection and reducing the possibility of design circumvention. All operation records are recorded on-chain, making them auditable and traceable, complying with medical data compliance requirements. Attached Figure Description

[0016] Figure 1. System overall architecture block diagram;

[0017] Figure 2. Hardware deployment diagram;

[0018] Figure 3. Defect closed-loop handling flowchart.

[0019] The module consists of: 1-Data processing module; 2-Encrypted storage module; 3-External collaboration interface; 4-Data acquisition module; 41-Cleanliness sensing unit; 61-Blockchain interaction module; 62-Access control module; 63-Closed-loop disposal module; 7-Visualization module; 8-Off-chain encrypted storage system. Detailed Implementation

[0020] Data Acquisition Module (4) collects sample unique identifiers, temperature and humidity, cleanliness (41), and appearance images. Data Processing Module (1) generates hash digests, and Blockchain Interaction Module (61) uploads the digests and indexes to the blockchain. Raw data is encrypted and stored in an off-chain encrypted storage system (8). Access Control Module (62) verifies six-dimensional information in real time, and automatically locks access in case of environmental anomalies. Data Processing Module (1) performs nonlinear coupling calculations and outputs a comprehensive risk value and defect level. Closed-Loop Disposal Module (63) automatically performs isolation, re-inspection, or scrapping operations and controls the physical locking of storage devices. Disposal results are uploaded to the blockchain for evidence storage, and Visualization Module (7) displays the entire process information.

[0021] Example

[0022] A secondary hospital's laboratory department has implemented a lightweight deployment of this system. The hardware utilizes general-purpose RFID tags, cameras, temperature and humidity sensors, and an edge gateway, connecting to a regional medical consortium blockchain, eliminating the need for self-built nodes. If the temperature continuously exceeds the limit during sample transport, the system automatically locks access; it is only unlocked after the environment in the laboratory returns to normal. A defect analysis engine uses a non-linear coupling model to identify serious defects, automatically triggering re-testing. Data throughout the process is tamper-proof, and can be verified in real-time by the regulatory end. Deployment costs have been reduced by approximately 65%, and daily sample processing efficiency has increased by approximately 40%.

Claims

1. A method for reliable management of medical samples, characterized in that, include: Each medical sample is uniquely identified, and images of the sample's appearance and environmental parameters throughout its entire lifecycle are collected. A hash operation is performed on the original data to generate a unique data digest. The data digest and storage index are stored on the blockchain for notarization, while the original data is encrypted and stored off-chain. When accessing data, the hash value of the off-chain data is recalculated and compared with the on-chain digest. If they do not match, the data is determined to have been tampered with and access is blocked. Multi-dimensional permission verification is performed based on personnel identity, operating terminal, geographical location, access time, sample sensitivity level, and environmental security status. If the environmental parameters exceed the limit or the terminal is not in the preset security area, the operating permission will be forcibly locked regardless of whether the identity is legitimate. Temporary permissions will be automatically revoked upon expiration. The comprehensive risk value is obtained by nonlinearly coupling the defect features of the appearance image with the cumulative deviation of environmental parameters. The defect level is automatically classified according to the comprehensive risk value, triggering closed-loop disposal of isolation, re-inspection or scrapping. The disposal results are stored on the blockchain for evidence, and the information of the whole process is visualized.

2. The method according to claim 1, characterized in that, The nonlinear coupling calculation includes: extracting image defect feature values ​​and mapping them to a first risk coefficient F. a The cumulative environmental deviation value D is obtained by integrating the time series of the difference between the environmental parameters and the safety threshold. e The comprehensive risk value is calculated using a pre-set nonlinear model. Where α, β, and γ are weight coefficients preset according to the sample type, α+β≤1, and γ>

0.

3. The method according to claim 2, characterized in that, The comprehensive risk value R corresponds to three levels of defects: R < the first threshold is a general defect, the first threshold ≤ R < the second threshold is a serious defect, and R ≥ the second threshold is a major defect. The classification result is automatically output by the coupled model and does not rely on manual judgment.

4. The method according to claim 1, characterized in that, The blockchain uses a consortium blockchain, which only stores summaries, indexes, timestamps, and operation records. Multi-node consensus is used to complete the notarization, and the original data files are not stored.

5. A trusted management system for medical samples, characterized in that, include: The data acquisition module communicates with external sensing devices to acquire sample identification, environmental parameters, and appearance images; The data processing module, connected to the data acquisition module, is used to perform hash operations, nonlinear coupling calculations, and comprehensive risk assessment. The blockchain interaction module is used to store summaries, indexes, and operation records on the blockchain for evidence, and to perform on-chain and off-chain data consistency verification. The access control module is used to perform six-level multi-dimensional access verification and automatically lock access when the environment is abnormal; the closed-loop handling module is used to output isolation, re-inspection or scrap instructions according to the defect level; the visualization module is used to display source tracing information, access status, defect level and handling progress. The encrypted storage module is used to encrypt and store raw data, keys, permission policies, and defect thresholds.

6. The system according to claim 5, characterized in that, The data acquisition module includes an RFID identification unit, an image acquisition unit, a temperature and humidity sensing unit, and a cleanliness sensing unit (41).

7. The system according to claim 5, characterized in that, The access control module is configured to: directly lock operation permissions when environmental parameters exceed the sample safety threshold or the terminal's geographical location is not in the preset clean area, and record permission changes on the blockchain in real time.

8. The system according to claim 5, characterized in that, The closed-loop processing module is configured to automatically control the sample storage device to perform physical locking and isolation, and synchronously upload the processing results to the blockchain for evidence storage.

9. The system according to claim 5, characterized in that, The blockchain interaction module is configured to: recalculate the hash value of the off-chain data when reading data and compare it with the on-chain digest; if they are inconsistent, an alarm is triggered and access is blocked.

10. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by a processor, it implements the method of any one of claims 1-4.

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