Laboratory quality management data processing method and system based on artificial intelligence

Abstract

The invention relates to the technical field of laboratory quality management, in particular to a laboratory quality management data processing method and system based on artificial intelligence, and the method comprises the steps: firstly collecting laboratory data, preprocessing the data, generating a unique hash value, uploading the unique hash value to a block chain, and storing the original data in an IPFS distributed storage system; then laboratory data is analyzed through an AI algorithm, an analysis result is fed back to the smart contract to trigger a corresponding operation, the system can monitor the data in real time through the smart contract on the block chain and automatically execute a quality control operation according to a preset rule, in addition, a data sharing mechanism allows cross-mechanism data access, and the data sharing efficiency is improved. And the access permissions of different roles are controlled through the permission of the block chain, so that the privacy of the data is ensured. According to the invention, through intelligent data processing, transparent auditing of the block chain and decentralized management, the beneficial technical effects of high efficiency, safety and traceability in the laboratory quality management process are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of laboratory quality management, and in particular to a data processing method and system for laboratory quality management based on artificial intelligence. Background Art

[0002] With the rapid development of science and technology, laboratories, as important bases for scientific research and technological innovation, have a quality management level that is directly related to the accuracy and reliability of scientific research results. However, traditional laboratory quality management methods often have problems such as low efficiency, many human errors, and difficulty in dealing with large amounts of data. Therefore, the research on laboratory quality management data processing methods and systems based on artificial intelligence has emerged, aiming to improve the efficiency and accuracy of laboratory quality management by introducing artificial intelligence technology.

[0003] A search of the prior art revealed that a data processing method and system for medical laboratory quality management is disclosed in a Chinese invention patent with a publication number of CN109800591B. The invention stores laboratory-related data of each laboratory in a database of a server, and the terminals of each laboratory and the terminals of laboratory personnel can communicate with the server, thereby realizing the integrated storage of laboratory-related data of each laboratory, which is convenient for centralized management, and further, through centralized management of data, it is conducive to ensuring the improvement of data quality.

[0004] With the expansion of laboratory scale and the increase of scientific research activities, the amount of data generated has increased dramatically. Traditional quality management methods are difficult to effectively process and analyze these laboratory data, resulting in low management efficiency. In addition, due to the high security level of test data in the laboratory, there is a risk of leakage of test data during storage and transmission. Summary of the invention

[0005] The purpose of the present invention is to address the problems existing in the background technology and to propose a data processing method and system for laboratory quality management based on artificial intelligence.

[0006] The technical solution of the present invention is a data processing method for laboratory quality management based on artificial intelligence, comprising the following implementation steps:

[0007] S1. The laboratory management system collects laboratory data. For data that cannot be automatically obtained, the user manually enters the data through the laboratory management system interface;

[0008] S2. The laboratory management system aligns data from different sources through timestamps. The laboratory data is stored in a decentralized data storage system. The SHA-256 algorithm is used to generate a unique hash value for each piece of collected laboratory data. The hash value of the data and related metadata are stored on the laboratory data blockchain. The laboratory data blockchain automatically adds an accurate timestamp for each on-chain operation.

[0009] S3. The laboratory management system uses the Z-score standardization method to process laboratory data and transmits the laboratory data to the AI ​​analysis platform that has passed the data analysis qualification check. The AI ​​model in the AI ​​analysis platform extracts features, outputs analysis results, and inputs the analysis results into the smart contract in the laboratory data blockchain;

[0010] S4. Set up smart contracts on the laboratory data blockchain, define the quality control rules for laboratory data, and monitor laboratory data in real time. After receiving the analysis results from the AI ​​analysis platform, the smart contract automatically performs corresponding operations according to the rules, and automatically approves the test data that needs to be approved and verified according to the predefined approval rules, and then uploads each approval result to the laboratory data blockchain.

[0011] Preferably, the decentralized data storage system adopts the IPFS distributed storage system. After the data is stored in the IPFS distributed storage system, the system generates a unique IPFS hash.

[0012] Preferably, the laboratory data includes one or more of personnel data in the laboratory, various equipment data, sensor data, and manually recorded experimental conditions or experimental results.

[0013] Preferably, the specific implementation steps of S4 are as follows:

[0014] S4-1. Perform data verification in the smart contract to check whether the data meets the standards defined in the contract. The smart contract automatically executes the predetermined operation based on the verification results. When abnormal data is detected, the system automatically takes measures according to the smart contract rules and records all trigger conditions and notifications on the laboratory data blockchain.

[0015] S4-2. Smart contracts define approval process rules. When the set conditions are triggered, approval tasks are automatically generated and assigned to management personnel. The approval tasks include but are not limited to checking abnormal situations and confirming data accuracy.

[0016] S4-3. Notify the assigned personnel of the generated approval task and ask them to approve it. The approval request is generated through a smart contract, including the data of the triggering conditions and the recommended operations. The approver reviews the approval request and makes a decision. The approval result is recorded and uploaded to the chain through the smart contract.

[0017] S4-4. Record the approval operations including the approval personnel, approval time, and approval results on the laboratory data blockchain to generate approval records to ensure the transparency and compliance of the approval process.

[0018] Preferably, the data analysis qualification verification process is as follows:

[0019] S5-1, the AI ​​analysis platform sends a data analysis request Query to the laboratory management system;

[0020] S5-2. When the laboratory management system receives the data analysis request Query, it generates a random number Rt and sends it to the AI ​​analysis platform;

[0021] S5-3, the AI ​​analysis platform generates a random number Rr, and calculates the verification information I = H(ID||Rr||Rt), and then transmits the random number Rr and the verification information I to the laboratory management system;

[0022] Among them, ID is the ID of the AI ​​analysis platform, || is the string connection symbol, and H is the hash function;

[0023] S5-4. The laboratory management system performs operations on the IDs of the authorized AI analysis platforms in the laboratory internal database in a traversal manner to determine whether a certain ID exists. i (1≦i≦n), so that H(ID i The equation ||Rr||Rt)=I holds true. If so, it means that the AI ​​analysis platform has been authorized to analyze the laboratory data. The laboratory management system then outputs the laboratory data to the AI ​​model in the AI ​​analysis platform.

[0024] Preferably, the AI ​​model in the AI ​​analysis platform performs feature extraction, and the extracted features include: utilizing the constructed time series feature extraction model to extract time series features from equipment operation data through an LSTM network, utilizing Fourier transform to convert laboratory data from the time domain to the frequency domain to extract potential periodic features, utilizing the constructed convolutional neural network to extract image features from image data generated in the experiment, utilizing the constructed text feature extraction model to perform keyword extraction and topic model analysis on unstructured text data through natural language processing technology using a BERT+BiLSTM+self attention+CRF model.

[0025] Preferably, the method for accessing laboratory data is as follows:

[0026] S7-1. The laboratory manager classifies the collected laboratory data through the laboratory management system and establishes the relationship between data types and authority levels: and

[0027] S7-2. Attach attributes to the classified laboratory data to construct the attribute set Ω:

[0028]

[0029] Among them, attribute Att1 represents public data, attribute Att2 represents restricted data, and attribute Att3 represents sensitive data;

[0030] S7-3. Correlation between construction attributes and laboratory data hash values

[0031] S7-4. When a user or organization needs to submit an access request, indicating the type of data required for access, the smart contract on the laboratory data blockchain will review and approve the data access request based on pre-defined authorization rules, check the identity, role authority, and data attributes of the accessing party, and then Confirm whether the type of data being accessed meets the authorization conditions;

[0032] S7-5, if the data access request of the accessing party is approved, the node on the laboratory data blockchain queries the record on the laboratory data blockchain according to the request, obtains the IPFS hash of the laboratory data corresponding to the laboratory data hash value, and uses the IPFS hash found on the laboratory data blockchain to access the actual laboratory data in the IPFS distributed storage system.

[0033] The technical solution proposed by the present invention is: a data processing system for laboratory quality management based on artificial intelligence, which is applicable to the data processing method for laboratory quality management based on artificial intelligence proposed above, and is characterized by comprising:

[0034] Laboratory management system for collecting and preprocessing laboratory data;

[0035] AI analysis platform, the running carrier is an AI algorithm box, which has a series of built-in AI models to analyze various types of laboratory data;

[0036] The laboratory data blockchain generates a unique hash value for the laboratory data through a hash algorithm, stores it in the laboratory data blockchain, generates an accurate timestamp for each data, and records all operation processes to form a traceable audit record. The smart contracts in the laboratory data blockchain can automatically perform operations according to predefined rules, and can automatically trigger alarms or start automatic adjustment and maintenance processes for equipment;

[0037] IPFS distributed storage system, used to store laboratory data.

[0038] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0039] The present invention designs a data processing method and system for laboratory quality management based on artificial intelligence. The method combines artificial intelligence and blockchain technology. First, laboratory data is collected in real time, pre-processed, and a unique hash value is generated and uploaded to the blockchain. The original data is stored in the IPFS distributed storage system to ensure the integrity and non-tamperability of the data. Then, the laboratory data is analyzed and predicted by the AI ​​algorithm, and the analysis results are fed back to the smart contract to trigger the corresponding operation. Through the smart contract on the blockchain, the system can monitor the data in real time and automatically perform quality control operations according to preset rules. In addition, the data sharing mechanism allows cross-institutional data access, and the access rights of different roles are controlled through the permissions of the blockchain to ensure the security and privacy of the data.

[0040] The present invention achieves the beneficial technical effects of high efficiency, safety and traceability in the laboratory quality management process through intelligent data processing, transparent auditing and decentralized management of blockchain. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is an architecture diagram of a data processing system for laboratory quality management based on artificial intelligence proposed by the present invention;

[0042] Figure 2 This is a block structure diagram of the laboratory data blockchain;

[0043] Figure 3 The network structure diagram of the time series feature extraction model;

[0044] Figure 4 This is the network structure diagram of the text feature extraction model. DETAILED DESCRIPTION

[0045] Embodiment 1, as Figure 1 As shown, the data processing system for laboratory quality management based on artificial intelligence proposed in the present invention includes: a laboratory management system, an AI analysis platform, a laboratory data blockchain and an IPFS distributed storage system;

[0046] The laboratory management system is used to collect various types of data in the laboratory, including but not limited to various equipment data, sensor data, and text data;

[0047] The AI ​​analysis platform is operated by an AI algorithm box, which is an intelligent edge application server with a built-in domestic high-performance AI computing chip and high-performance decoding and reasoning capabilities. Its core is to use deep learning technology to customize and build a series of security models to analyze various types of laboratory data.

[0048] On the one hand, the laboratory data blockchain generates a unique hash value for laboratory data, processing results, analysis reports, etc. through a hash algorithm and stores it in the laboratory data blockchain. Since the laboratory data blockchain is tamper-proof, any change to the laboratory data will result in a change in the hash value, ensuring that the data cannot be tampered with or forged throughout its life cycle. On the other hand, it generates an accurate timestamp for each data and records all operation processes to form a traceable audit record. All laboratory data collection, processing, analysis and verification operations will be accurately recorded on the chain to ensure that the specific time and responsible person of each operation can be traced later. On the third hand, the smart contract in the laboratory data blockchain can automatically perform operations according to predefined rules, and can automatically trigger alarms or start the automatic adjustment and maintenance process of equipment;

[0049] The IPFS distributed storage system is used to store laboratory data.

[0050] Embodiment 2, a data processing method for laboratory quality management based on artificial intelligence proposed in the present invention, which is applicable to a data processing system for laboratory quality management based on artificial intelligence proposed in Embodiment 1, specifically includes the following implementation steps:

[0051] S1. The laboratory management system (LIMS) collects personnel data, various equipment data, and sensor data (such as temperature, humidity, and pressure sensors) in the laboratory. For data that cannot be automatically obtained (such as manually recorded experimental conditions or results), users manually enter data through the laboratory management system interface;

[0052] It should be noted that the data types include but are not limited to equipment parameters (temperature, pressure, operating status), environmental data, experimental result data, personnel data, etc., and each data contains detailed metadata (timestamp, equipment ID, operator ID, etc.);

[0053] S2. The laboratory management system aligns data from different sources through timestamps to ensure that all data can be analyzed and processed within the same time frame, stores laboratory data on multiple nodes, and generates a unique hash value (the hash value length is 256 bits) for each collected laboratory data using the SHA-256 algorithm. The hash value of the data and related metadata (such as timestamp, data source equipment, operator, etc.) are stored on the laboratory data blockchain. The laboratory data blockchain automatically adds an accurate timestamp to each on-chain operation to ensure that the time of data collection can be accurately tracked in the future. The specific contents include:

[0054] S2-1. The laboratory management system uses timestamps to synchronize data from different sources and merges data from different sources into a unified data structure to ensure that all data fields are correctly aligned;

[0055] S2-2, the laboratory management system stores the laboratory data in a decentralized data storage system. The present invention adopts the IPFS (InterPlanetary File System) distributed storage system. After the data is stored in the IPFS distributed storage system, the system generates a unique IPFS hash;

[0056] S2-3. The laboratory management system calculates the hash value of the laboratory data, and packages the data hash value and metadata (timestamp, IPFS hash) into the block through the smart contract Register(), such as Figure 2 As shown, each block contains the following:

[0057] Block header: Generates a block header, including version number, parent block, timestamp, random number (Nonce), and target hash;

[0058] Block body: Pack the generated data hash value and IPFS hash into a block body;

[0059] Merkle root: Build a Merkle tree, organize the data hash values ​​into a tree structure, and calculate the root hash value;

[0060] S2-4. Submit the block through the laboratory data blockchain network. The laboratory data blockchain network nodes use the consensus mechanism Proof of Work to verify the validity of the block. Once the block is verified, it is added to the end of the laboratory data blockchain and becomes part of the laboratory data blockchain. All laboratory data blockchain nodes update their own laboratory data blockchain copies to ensure data synchronization;

[0061] S2-5. Use UTC time format to record the timestamps of data and blocks to ensure the consistency of all time records, and generate accurate timestamps through the system clock or network time protocol (NTP) and store them in the laboratory data blockchain;

[0062] S2-6. The laboratory's internal database stores the hash value of the data, and its structure includes: the hash value of the test data and other metadata (including but not limited to timestamp and data type);

[0063] It should be noted that when a user needs to access laboratory data, the user sends an access request to the laboratory data blockchain network based on the test data hash value in the laboratory's internal database. The node on the laboratory data blockchain queries the record on the laboratory data blockchain based on the request, obtains the IPFS hash of the laboratory data and the corresponding laboratory data hash value, and uses the IPFS hash found on the laboratory data blockchain to access the actual laboratory data in the IPFS distributed storage system.

[0064] The user can choose whether to recalculate the hash value of the retrieved data and compare it with the hash value stored on the laboratory data blockchain to confirm whether the laboratory data has been tampered with;

[0065] S3. The laboratory management system uses the Z-score standardization method to process laboratory data and transmits the laboratory data to the AI ​​analysis platform. The AI ​​model in the AI ​​analysis platform performs the following feature extraction, outputs the analysis results, and inputs the analysis results into the smart contract in the laboratory data blockchain;

[0066] Time series feature extraction: Figure 3 As shown, a time series feature extraction model is constructed to extract time series features such as trends, seasonal changes, and periodic fluctuations from equipment operation data through the LSTM (Long Short-Term Memory) network;

[0067] In the figure, (1) Input layer: Time series data is used as the input sequence of the model. Assume that there are N sets of input information, and the dimension of each set of information is D, then the input sequence is recorded as X = {x 1 , x 2 , ..., x N}∈R D×N ;

[0068] (2) Attention layer: weights are allocated based on the attention scores of the input sequence and the output elements. First, the attention distribution of the input sequence is calculated as w = (w 1 , w 2 , ..., w N), and then combine the attention distribution with the input vector to get a new vector as the input of the LSTM layer:

[0069] The attention calculation formula is as follows:

[0070]

[0071] In the formula, w i (i=1,2,...,N) is the attention distribution, α is the attention variable, and α∈[1,N], i is the vector index, X is the input sequence, s(x n ,q) is the attention scoring function: s(x n ,q)=(x n ) T q, and n=1, 2, ..., N;

[0072] (3) LSTM hidden layer: The LSTM neural network is used to mine the time series information of the output of the Attention layer, establish the mapping relationship between the multi-dimensional input sequence and the prediction target, and use the historical sequence as input to predict the output;

[0073] (4) Fully connected layer: transform the dimension of the result of the LSTM hidden layer to obtain a one-dimensional vector;

[0074] (5) Output layer: Output the prediction results after dimension reduction by the fully connected layer;

[0075] Frequency domain feature extraction: Using Fourier transform, laboratory data is converted from the time domain to the frequency domain to extract potential periodic features, such as frequency patterns in the vibration data of the detection equipment;

[0076] Image feature extraction: For the image data generated in the experiment (such as microscope images, material defect detection images), a convolutional neural network (CNN) is used to extract key features such as edges, shapes, textures, etc. The present invention uses VGG16 as the network structure of CNN;

[0077] Text feature extraction: such as Figure 4 As shown in the figure, a text feature extraction model is constructed. Through natural language processing (NLP) technology, BERT (preprocessing model) + BiLSTM (bidirectional long short-term memory network) + self attention + CRF (conditional random field) model is used to extract keywords or analyze topic models for unstructured text data (such as experimental reports) to automatically identify important information.

[0078] It should be noted that before the laboratory management system transmits laboratory data to the AI ​​analysis platform, the laboratory management system and the AI ​​analysis platform need to interact initially. The laboratory management system checks the qualifications of the AI ​​analysis platform to analyze data. The interaction process is as follows:

[0079] (1) The AI ​​analysis platform sends a data analysis request Query to the laboratory management system;

[0080] (2) When the laboratory management system receives the query request, it generates a random number Rt and sends it to the AI ​​analysis platform;

[0081] (3) The AI ​​analysis platform generates a random number Rr and calculates the verification information I = H(ID||Rr||Rt), and then transmits the random number Rr and the verification information I to the laboratory management system;

[0082] Among them, ID is the ID of the AI ​​analysis platform, || is the string connection symbol, and H is the hash function;

[0083] (4) The laboratory management system performs operations on the IDs of the authorized AI analysis platforms in the laboratory internal database in a traversal manner to determine whether a certain ID exists. i (1≦i≦n, n is the number of authorized AI analysis platforms in the laboratory internal database), so that H(ID i ||Rr||Rt)=I equation holds true. If it does, it means that the AI ​​analysis platform has been authorized to analyze the laboratory data. After that, the laboratory management system outputs the laboratory data to the AI ​​model in the AI ​​analysis platform;

[0084] S4. As shown in Table 1, a smart contract is set up on the laboratory data blockchain to define the quality control rules of laboratory data and monitor laboratory data in real time. After receiving the analysis results of the AI ​​analysis platform, the smart contract automatically performs the corresponding operations according to the rules, and automatically approves the test data that needs to be approved and verified according to the predefined approval rules. After that, each approval result is uploaded to the laboratory data blockchain. The specific contents include:

[0085] S4-1. Perform data verification in the smart contract Monitor() to check whether the data meets the standards defined in the contract. The smart contract Execute() automatically executes the predetermined operation based on the verification results. When abnormal data is detected, the system automatically takes measures according to the smart contract rules and records all trigger conditions and notifications on the laboratory data blockchain for subsequent traceability and auditing.

[0086] Table 1 Main description of smart contract

[0087]

[0088] For example, the analyzed temperature trend is transmitted to the smart contract function. When the temperature exceeds the set threshold, the laboratory management system is notified through the laboratory data blockchain event (Event), and the laboratory management system sends an alarm notification to the laboratory personnel's device terminal (such as mobile phone, computer);

[0089] S4-2. The smart contract Examine() defines the approval process rules. When the set conditions are triggered (such as equipment failure), the system automatically generates an approval task and assigns the approval task to relevant personnel (such as laboratory administrators, quality control experts). The approval task includes but is not limited to checking abnormal conditions and confirming data accuracy.

[0090] S4-3. Notify the assigned personnel of the generated approval task and ask them to approve it. The approval request is generated through a smart contract, including the data of the triggering conditions and the recommended operations. The approver reviews the approval request and makes a decision (such as confirming the fault, approving the repair), and the approval result is recorded and uploaded to the chain through the smart contract;

[0091] S4-4. Record the approval operations including the approval personnel, approval time, and approval results on the laboratory data blockchain to generate approval records to ensure the transparency and compliance of the approval process.

[0092] Embodiment 3, a data processing method for laboratory quality management based on artificial intelligence proposed by the present invention, compared with Embodiment 2, also includes a laboratory data sharing method, and its specific implementation steps are as follows:

[0093] S1. The laboratory manager classifies the collected laboratory data through the laboratory management system and establishes an information table of laboratory access levels, as shown in Table 2:

[0094] Table 2 Laboratory access level information

[0095]

[0096] S2. Attach attributes to the classified laboratory data to construct the attribute set Ω:

[0097]

[0098] Among them, attribute Att1 represents public data, attribute Att2 represents restricted data, and attribute Att3 represents sensitive data;

[0099] S3. Correlation between construction attributes and laboratory data hash values As shown in Table 3, a laboratory data information table stored on the laboratory data blockchain is established;

[0100] Table 3 Laboratory data information stored on the laboratory data blockchain

[0101]

[0102] S4. When a user or organization needs to submit an access request, indicating the type of data required for access, the smart contract Access() on the laboratory data blockchain approves the data access request according to the pre-defined authorization rules, checks the identity, role permissions, and data attributes of the accessing party, and confirms whether the accessed data type meets the authorization conditions:

[0103] (1) When the access party applies to view the attribute of the laboratory data Att1, the smart contract Access() directly approves it. The node on the laboratory data blockchain queries the record on the laboratory data blockchain according to the request, obtains the IPFS hash of the laboratory data corresponding to the laboratory data hash value, and uses the IPFS hash found on the laboratory data blockchain to access the actual laboratory data in the IPFS distributed storage system;

[0104] (2) When the access party applies to view the attribute of the laboratory data Att2, if the access party's permission level is II, the smart contract Access() is approved and the access party is allowed to view the data. The node on the laboratory data blockchain queries the record on the laboratory data blockchain according to the request, obtains the IPFS hash of the laboratory data corresponding to the laboratory data hash value, and uses the IPFS hash found on the laboratory data blockchain to access the actual laboratory data in the IPFS distributed storage system;

[0105] On the contrary, if the accessing party does not have the permission to access the data, the smart contract Access() will fail to approve it and deny its access.

[0106] (3) When the attribute of the laboratory data that the accessor applies to view is Att3, if the accessor's permission level is III, the smart contract Access() is approved, allowing the accessor to view the data. The node on the laboratory data blockchain views the record on the laboratory data blockchain according to the request, obtains the IPFS hash of the laboratory data that the accessor applies to access, and uses the IPFS hash obtained from the laboratory data blockchain to access the laboratory data that the accessor wants to access in the IPFS distributed storage system;

[0107] On the contrary, if the accessing party does not have the permission to access the data it wants to access, the smart contract Access() will fail to approve it and deny the access to the accessing party.

[0108] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A data processing method for laboratory quality management based on artificial intelligence, characterized in that: The implementation steps include: S1. The laboratory management system collects laboratory data. For data that cannot be automatically obtained, the user manually enters the data through the laboratory management system interface; S2. The laboratory management system aligns data from different sources through timestamps. The laboratory data is stored in a decentralized data storage system. The SHA-256 algorithm is used to generate a unique hash value for each piece of collected laboratory data. The hash value of the data and related metadata are stored on the laboratory data blockchain. The laboratory data blockchain automatically adds an accurate timestamp for each on-chain operation. S3. The laboratory management system uses the Z-score standardization method to process laboratory data and transmits the laboratory data to the AI ​​analysis platform that has passed the data analysis qualification check. The AI ​​model in the AI ​​analysis platform extracts features, outputs analysis results, and inputs the analysis results into the smart contract in the laboratory data blockchain; S4. Set up smart contracts on the laboratory data blockchain, define the quality control rules for laboratory data, and monitor laboratory data in real time. After receiving the analysis results from the AI ​​analysis platform, the smart contract automatically performs corresponding operations according to the rules, and automatically approves the test data that needs to be approved and verified according to the predefined approval rules, and then uploads each approval result to the laboratory data blockchain.

2. The data processing method for laboratory quality management based on artificial intelligence according to claim 1, characterized in that: The decentralized data storage system uses the IPFS distributed storage system. After the data is stored in the IPFS distributed storage system, the system generates a unique IPFS hash.

3. The data processing method for laboratory quality management based on artificial intelligence according to claim 1 is characterized in that: Laboratory data includes one or more of the following: personnel data, various equipment data, sensor data, and manually recorded experimental conditions or experimental results in the laboratory.

4. The data processing method for laboratory quality management based on artificial intelligence according to claim 1, characterized in that: The specific implementation steps of S4 are as follows: S4-1. Perform data verification in the smart contract to check whether the data meets the standards defined in the contract. The smart contract automatically executes the predetermined operation based on the verification results. When abnormal data is detected, the system automatically takes measures according to the smart contract rules and records all trigger conditions and notifications on the laboratory data blockchain. S4-2. Smart contracts define approval process rules. When the set conditions are triggered, approval tasks are automatically generated and assigned to management personnel. The approval tasks include but are not limited to checking abnormal situations and confirming data accuracy. S4-3. Notify the assigned personnel of the generated approval task and ask them to approve it. The approval request is generated through a smart contract, including the data of the triggering conditions and the recommended operations. The approver reviews the approval request and makes a decision. The approval result is recorded and uploaded to the chain through the smart contract. S4-4. Record the approval operations including the approval personnel, approval time, and approval results on the laboratory data blockchain to generate approval records to ensure the transparency and compliance of the approval process.

5. The data processing method for laboratory quality management based on artificial intelligence according to claim 1 is characterized in that: The data analysis qualification verification process is as follows: S5-1, the AI ​​analysis platform sends a data analysis request Query to the laboratory management system; S5-2. When the laboratory management system receives the data analysis request Query, it generates a random number Rt and sends it to the AI ​​analysis platform; S5-3, the AI ​​analysis platform generates a random number Rr, and calculates the verification information I = H(ID||Rr||Rt), and then transmits the random number Rr and the verification information I to the laboratory management system; Among them, ID is the ID of the AI ​​analysis platform, || is the string connection symbol, and H is the hash function; S5-4. The laboratory management system performs operations on the IDs of the authorized AI analysis platforms in the laboratory internal database in a traversal manner to determine whether a certain ID exists. i , so that H(ID i ||Rr||Rt)=I equation holds true. If it does, it means that the AI ​​analysis platform has been authorized to analyze the laboratory data. After that, the laboratory management system outputs the laboratory data to the AI ​​model in the AI ​​analysis platform; Among them, i is the serial number of the AI ​​analysis platform ID, 1≦i≦n, and n is the number of authorized AI analysis platforms in the laboratory's internal database.

6. The data processing method for laboratory quality management based on artificial intelligence according to claim 1, characterized in that: The AI ​​model in the AI ​​analysis platform performs feature extraction. The extracted features include: using the constructed time series feature extraction model to extract time series features from equipment operation data through the LSTM network, using Fourier transform to convert laboratory data from the time domain to the frequency domain to extract potential periodic features, using the constructed convolutional neural network to extract image features from image data generated in the experiment, and using the constructed text feature extraction model to perform keyword extraction and topic model analysis on unstructured text data through natural language processing technology using the BERT+BiLSTM+selfattention+CRF model.

7. A data processing method for laboratory quality management based on artificial intelligence according to any one of claims 1 to 6, characterized in that: The lab data can be accessed as follows: S7-1. The laboratory manager classifies the collected laboratory data through the laboratory management system and establishes the relationship between data types and authority levels: S7-2. Attach attributes to the classified laboratory data to construct the attribute set Ω: Among them, attribute Att1 represents public data, attribute Att2 represents restricted data, and attribute Att3 represents sensitive data; S7-3. Correlation between construction attributes and laboratory data hash values S7-4. When a user or organization needs to submit an access request, indicating the type of data required for access, the smart contract on the laboratory data blockchain will review and approve the data access request based on pre-defined authorization rules, check the identity, role authority, and data attributes of the accessing party, and then Confirm whether the type of data being accessed meets the authorization conditions; S7-5, if the data access request of the accessing party is approved, the node on the laboratory data blockchain queries the record on the laboratory data blockchain according to the request, obtains the IPFS hash of the laboratory data corresponding to the laboratory data hash value, and uses the IPFS hash found on the laboratory data blockchain to access the actual laboratory data in the IPFS distributed storage system.

8. A data processing system for laboratory quality management based on artificial intelligence, which is applicable to a data processing method for laboratory quality management based on artificial intelligence according to any one of claims 1 to 7, characterized in that: include: Laboratory management system for collecting and preprocessing laboratory data; AI analysis platform, the running carrier is an AI algorithm box, which has a series of built-in AI models to analyze various types of laboratory data; The laboratory data blockchain generates a unique hash value for the laboratory data through a hash algorithm, stores it in the laboratory data blockchain, generates an accurate timestamp for each data, and records all operation processes to form a traceable audit record. The smart contracts in the laboratory data blockchain can automatically perform operations according to predefined rules, and can automatically trigger alarms or start automatic adjustment and maintenance processes for equipment; IPFS distributed storage system, used to store laboratory data.

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