Figure multi-dimensional monitoring data processing and storage system based on block chain

Abstract

The invention provides a block chain-based character multi-dimensional monitoring data processing and storage system, and relates to the technical field of data processing and storage. According to the method, the multi-dimensional monitoring data, including the physiological features, the behavior features and the pathological medical records, of the figure is integrated, and deep processing is performed by using data correlation analysis, so that the integrity and correlation of the data are remarkably improved, and the security and credibility of the data are ensured by means of a block chain technology; according to the method, the data levels are divided by setting the multi-level association degree threshold, the data mapping relation layer is constructed, and retrieval guidance is optimized, so that high retrieval efficiency is ensured, the efficiency and accuracy of database construction of the character model are improved, and an effective data support guarantee is provided for commemorating characters.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and storage, and in particular to a multi-dimensional monitoring data processing and storage system for people based on blockchain. Background Art

[0002] In the digital age, with the rapid development of information technology, building a database of commemorative figures has become an important way of cultural inheritance and commemoration. However, in the process of building such a database, there are many technical problems and challenges.

[0003] Traditional character database construction methods often focus on single-dimensional data collection, such as focusing only on the physiological or behavioral characteristics of characters, while ignoring the correlation between data of different dimensions. This single-dimensional data collection method not only leads to insufficient data integrity, but also makes it difficult to fully reflect the true state of the character. At the same time, due to the lack of effective data association analysis methods, the intrinsic connection between data of different dimensions is often ignored, which limits the accuracy and reliability of character databases in subsequent applications.

[0004] As the scale of character databases continues to expand, the efficiency of data processing and retrieval has become increasingly prominent. Traditional data processing methods are often unable to cope with the processing needs of large-scale data, resulting in low data processing efficiency. At the same time, due to the lack of effective retrieval methods and optimization strategies, character databases often have problems such as slow response speed and inaccurate query results during the retrieval process.

[0005] In the process of building a character database, the security and credibility of data are crucial. However, traditional data storage methods often have security risks such as data leakage and tampering, which leads to doubts about the credibility of data. Especially in scenarios involving personal privacy and sensitive information, the security and credibility of data are more prominent. Therefore, how to ensure the security and credibility of data in the character database during storage, transmission and processing has become a technical problem that needs to be solved urgently.

[0006] Therefore, it is necessary to provide a blockchain-based multi-dimensional character monitoring data processing and storage system to solve the above technical problems. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a blockchain-based character multi-dimensional monitoring data processing and storage system for solving the problems of insufficient data integrity and correlation, insufficient security and low retrieval efficiency when processing and storing character multi-dimensional monitoring data.

[0008] The present invention provides a blockchain-based multi-dimensional monitoring data processing and storage system for people, the processing and storage system comprising:

[0009] A data acquisition module is used to acquire multi-dimensional monitoring data of a person, including physiological characteristic data, behavioral characteristic data, and electronic medical record data of a pathology;

[0010] The data analysis module is used to process the physiological characteristic data, behavioral characteristic data and electronic medical record data of the person through the data association analysis method based on the acquired multi-dimensional monitoring data of the person, and obtain the association analysis results;

[0011] The association classification module is used to set a multi-level association degree threshold based on the association analysis result, and classify the physiological characteristic data, behavioral characteristic data and specific characteristic data in the electronic medical record data of the pathology according to the set multi-level association degree threshold to obtain the association level classification result;

[0012] A data storage module, used to establish a data mapping relationship layer based on the association level classification result, and store the data mapping relationship layer in the blockchain;

[0013] The database construction module is used to construct a character database according to the data mapping relationship layer and generate a search guide, wherein the search guide constructs multiple nodes to jointly process the search request.

[0014] Preferably, the steps of obtaining multi-dimensional monitoring data of a person, including physiological characteristic data, behavioral characteristic data and electronic medical record data of a pathology, are as follows:

[0015] By consulting relevant documents and biometric technology, physiological characteristic data is obtained, including the person's height, weight, body shape, gender, age, skin color, hair color, eye color, face and fingerprint information;

[0016] Capture data on people’s behavior characteristics through video surveillance and sensors, including posture, expression, and walking style;

[0017] Obtain electronic medical record data of a person's pathology from the medical institution's detection system.

[0018] Preferably, the method of processing the physiological characteristic data, behavioral characteristic data and electronic medical record data of the person based on the acquired multi-dimensional monitoring data of the person through a data association analysis method comprises the following specific steps:

[0019] Perform cleaning, deduplication, and standardization preprocessing operations on the acquired multi-dimensional monitoring data of people;

[0020] Through the data correlation analysis method, the similarity between the physiological characteristic data, behavioral characteristic data and the electronic medical record data of pathology is calculated, and the correlation analysis results are obtained using statistical analysis.

[0021] Preferably, the method of setting a multi-level correlation threshold based on the correlation analysis result, and classifying the physiological characteristic data, behavioral characteristic data and specific characteristic data in the electronic medical record data of the pathology into correlation levels according to the set multi-level correlation threshold, is specifically performed as follows:

[0022] Based on the results of the association analysis, multi-level association thresholds are set, including high association level, medium association level and low association level;

[0023] Physiological characteristic data are set as basic characteristics, and behavioral characteristic data and electronic medical record data of pathology are set as associated characteristics;

[0024] Based on the association analysis results, the association degree between the basic features and the associated features is calculated by the correlation system or mutual information method in the association calculation method;

[0025] Based on the obtained correlation degree of the basic features and the associated features, each basic feature and the associated feature is divided into an association level through a set multi-level association degree threshold to obtain an association level division result.

[0026] Preferably, the data mapping relationship layer is established based on the association level division result, and the data mapping relationship layer is stored in the blockchain, and the specific steps are:

[0027] According to the result of the association level division, the mapping rules are determined, specifically: a direct mapping relationship is established between the data with high association levels, while the data with low association levels are mapped through intermediate nodes;

[0028] According to the determined mapping rules, the basic features and the associated features are mapped into the relational layer using a graph structure or a data model of a relational database, so as to obtain a data mapping relational layer of the basic features and the associated features;

[0029] Using blockchain technology, the data mapping relationship layer of basic features and associated features is encoded into a format stored on the blockchain and then stored on the blockchain.

[0030] Preferably, the character database is constructed according to the data mapping relationship layer, and a search guide is generated, wherein the search guide constructs multiple nodes to jointly process the search request, and the specific steps are:

[0031] Determine a data table of a character database, wherein the data table is specifically a basic information table, a physiological characteristic table, a behavioral characteristic table or a pathological record table;

[0032] Define fields for each data table and establish associations between data tables based on the data mapping relationship layer stored in the blockchain;

[0033] According to the requirements of the database structure, the basic features and associated features are converted into a format that conforms to the database structure and imported into the corresponding data tables;

[0034] Construct multiple retrieval nodes, and configure parameters for each retrieval node, wherein the retrieval node configuration parameters are specifically a query timeout or a maximum result set size.

[0035] Compared with related technologies, the blockchain-based multi-dimensional monitoring data processing and storage system for people provided by the present invention has the following beneficial effects:

[0036] The present invention integrates multi-dimensional monitoring data of characters, including physiological characteristics, behavioral characteristics and pathological medical records, and uses data correlation analysis for in-depth processing, which not only significantly improves the integrity and correlation of data, but also ensures the security and credibility of data with the help of blockchain technology; by setting multi-level correlation thresholds to divide data levels, constructing a data mapping relationship layer, and optimizing retrieval guidelines, high retrieval efficiency is guaranteed, the efficiency and accuracy of database construction of character models are improved, and effective data support is provided for commemorating characters. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a system block diagram of the blockchain-based character multi-dimensional monitoring data processing and storage system provided by the present invention. DETAILED DESCRIPTION

[0038] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0039] Example

[0040] like Figure 1 As shown, the multi-dimensional monitoring data processing and storage system based on blockchain includes:

[0041] A data acquisition module is used to acquire multi-dimensional monitoring data of a person, including physiological characteristic data, behavioral characteristic data, and electronic medical record data of a pathology;

[0042] In the specific implementation process, the specific steps for obtaining multi-dimensional monitoring data of a person, including physiological characteristic data, behavioral characteristic data and electronic medical record data of a pathology, are as follows:

[0043] By consulting relevant documents and biometric technology, physiological characteristic data including the person's height, weight, body shape, gender, age, skin color, hair color, eye color, face and fingerprint information are obtained;

[0044] Specifically, by examining official documents such as the person's ID card, passport, driver's license, etc., basic information such as the person's height, weight, body shape, gender, age, skin color, hair color, etc. is obtained, and eye color, facial and fingerprint information is obtained through data recorded by facial recognition or fingerprint recognition devices;

[0045] Capture data on people’s behavior characteristics through video surveillance and sensors, including posture, expression, and walking style;

[0046] Specifically, the camera is used to capture the person's posture and walking style, as well as their facial expressions in different situations. The pressure sensor is used to record the pressure distribution of the person when walking or standing. The acceleration sensor is used to measure the person's moving speed and acceleration to record the person's walking style.

[0047] Obtain electronic medical record data of a person's pathology from the medical institution's detection system;

[0048] Specifically, the pathological medical records of a person can be directly obtained by securely accessing the detection system or database of a medical institution.

[0049] The data analysis module is used to process the physiological characteristic data, behavioral characteristic data and electronic medical record data of the person based on the acquired multi-dimensional monitoring data of the person through the data correlation analysis method to obtain the correlation analysis result.

[0050] In the specific implementation process, based on the acquired multi-dimensional monitoring data of the person, the specific steps of processing the physiological characteristic data, behavioral characteristic data and electronic medical record data of the pathology through the data correlation analysis method are as follows:

[0051] Perform cleaning, deduplication, and standardization preprocessing operations on the acquired multi-dimensional monitoring data of people;

[0052] Specifically, data items irrelevant to the characteristic data of multi-dimensional monitoring data of people are deleted to reduce the complexity of subsequent processing, and data from different sources are converted into a unified format for subsequent processing and analysis; duplicate data records are found and deleted to avoid data redundancy and deviation in subsequent analysis; multi-dimensional monitoring data of people are standardized to meet certain specifications and standards, which is conducive to eliminating dimensional differences between different data items and improving data comparability;

[0053] Through the data correlation analysis method, the similarity between the physiological characteristics data, behavioral characteristics data and electronic medical records of pathology is calculated, and the correlation analysis results are obtained by statistical analysis;

[0054] Specifically, the similarity or correlation between different data items in the electronic data of the physiological characteristic data, behavioral characteristic data and pathological medical records is calculated through the Pearson correlation coefficient, Spearman rank correlation coefficient or mutual information in the data association analysis method; and the calculated similarity or correlation results are statistically collated and analyzed, specifically including the calculation of statistical quantities: mean and standard deviation.

[0055] Exemplarily, in this embodiment, the Pearson correlation coefficient is used to calculate the similarity between a person's heart rate and blood pressure. Through calculation, it is found that there is a significant positive correlation between heart rate and blood pressure (that is, when the heart rate increases, the blood pressure tends to increase as well). In addition, mutual information is used to evaluate the correlation between a person's behavioral characteristics and medical history. Through calculation, it is found that people with poor behavioral characteristics tend to have more serious medical histories.

[0056] The association classification module is used to set multi-level association thresholds based on the association analysis results, and classify the physiological characteristic data, behavioral characteristic data and specific characteristic data in the electronic data of pathological medical records of the person into association levels according to the set multi-level association thresholds to obtain the association level classification results.

[0057] In the specific implementation process, based on the association analysis results, a multi-level association threshold is set, and the specific characteristic data in the electronic medical record data of the person's physiological characteristic data, behavioral characteristic data, and pathological medical records are divided into association levels according to the set multi-level association threshold. The specific steps are:

[0058] Based on the results of the association analysis, multi-level association thresholds are set, including high association level, medium association level and low association level;

[0059] Specifically, based on the association analysis results, the association thresholds of high association level, medium association level and low association level are set. It should be noted that the association threshold can be a specific value or a relative proportion or percentage. In this embodiment, high association level: correlation coefficient> 0.8 (indicating strong correlation), medium association level: 0.5 < correlation coefficient≤0.8 (indicating medium correlation), low association level: correlation coefficient≤ 0.5 (indicating weak correlation);

[0060] Physiological characteristic data are set as basic characteristics, and behavioral characteristic data and electronic medical record data of pathology are set as associated characteristics;

[0061] Based on the association analysis results, the association degree between the basic features and the associated features is calculated by the correlation system or mutual information method in the association calculation method;

[0062] For example, the linear correlation between the heart rate and blood pressure of the target person is calculated using the correlation system method, and the correlation coefficient is 0.9; at the same time, the nonlinear correlation between the heart rate and sleep quality of the target person is calculated using the mutual information method, and the mutual information value is 0.6;

[0063] Based on the obtained correlation degree of the basic features and the associated features, each basic feature and the associated feature is divided into an association level through a set multi-level association degree threshold to obtain an association level division result;

[0064] Specifically, the correlation degree of each basic feature and associated feature is compared with the set multi-level correlation degree threshold to obtain a comparison result. Then, according to the comparison result, the feature is divided into corresponding correlation levels, and the correlation level division result of each feature is recorded for subsequent analysis and processing;

[0065] For example, based on the calculated correlation coefficient between the target person's heart rate and blood pressure (0.9) and the correlation coefficient between the heart rate and sleep quality (0.6), and the set multi-level correlation thresholds (such as high correlation level: correlation coefficient greater than 0.8; medium correlation level: correlation coefficient between 0.5 and 0.8), heart rate and blood pressure are classified as high correlation levels, while heart rate and sleep quality are classified as medium correlation levels.

[0066] The data storage module is used to establish a data mapping relationship layer based on the association level division results, and store the data mapping relationship layer in the blockchain.

[0067] In the specific implementation process, based on the association level division results, the data mapping relationship layer is established, and the specific steps of storing the data mapping relationship layer in the blockchain are:

[0068] According to the result of the association level division, the mapping rules are determined, specifically: a direct mapping relationship is established between the data with high association levels, while the data with low association levels are mapped through intermediate nodes;

[0069] Specifically, according to the result of the association level division, the mapping rules between different features are determined. A direct mapping relationship will be established between the data with high association levels, which specifically means that they are highly associated and can be directly associated with each other; while the data with low association levels are mapped through intermediate nodes to reflect the weaker association between them;

[0070] According to the determined mapping rules, the basic features and the associated features are mapped into the relational layer using a graph structure or a data model of a relational database, so as to obtain a data mapping relational layer of the basic features and the associated features;

[0071] Specifically, in this embodiment, physiological characteristic data (height, weight, body shape, gender, age, skin color, hair color, eye color, face and fingerprint information) are defined as basic characteristics; behavioral characteristic data (posture, expression and walking style) and electronic medical record data of pathology are regarded as associated characteristics; then, in the relationship layer, a node is created for each characteristic, specifically, nodes are created for physiological characteristics such as height, weight, and body shape, and nodes are created for behavioral characteristics such as posture, expression, and walking style, as well as pathological data such as medical history and diagnosis records; and each basic characteristic and associated characteristic is mapped to a corresponding node in the relationship layer, and edges are established between the created nodes. For example, if there is an association between height and weight (such as BMI index), an edge is established between them. Similarly, medical history is associated with certain physiological characteristics (such as age and gender), so edges can also be established between them.

[0072] Using blockchain technology, the data mapping relationship layer of basic features and associated features is encoded into a format stored on the blockchain and then stored on the blockchain;

[0073] Specifically, a blockchain platform is selected, specifically a public blockchain, a private blockchain or a consortium blockchain, the data mapping relationship layer is encoded into a format stored on the blockchain, and the data is converted into a data structure suitable for blockchain storage, such as JSON, XML and other formats; the encoded data mapping relationship layer is stored on the blockchain, specifically sending the data to the nodes in the blockchain network, and waiting for the nodes to confirm and record the transaction.

[0074] The database construction module is used to construct a character database according to the data mapping relationship layer and generate a search guide, wherein the search guide constructs multiple nodes to jointly process the search request.

[0075] In the specific implementation process, the specific steps of building a character database based on the data mapping relationship layer and generating search guidelines are as follows:

[0076] Determine a data table of a character database, wherein the data table is specifically a basic information table, a physiological characteristic table, a behavioral characteristic table or a pathological record table;

[0077] Specifically, determine the basic information table, physiological characteristics table, behavioral characteristics table or pathological record table of the character database; each data table should contain a set of related fields for storing specific types of information, for example, the basic information table contains fields such as name, age or gender; the physiological characteristics table contains fields such as height, weight or blood type; the behavioral characteristics table may contain fields such as gait, expression or habits; the pathological record table contains fields such as medical history, diagnosis results or treatment conditions; name each data table and ensure that the naming meets certain specifications for subsequent management and maintenance. It should be noted that the naming should be as concise and clear as possible to clearly reflect the content of the data table;

[0078] Define fields for each data table and establish associations between data tables based on the data mapping relationship layer stored in the blockchain;

[0079] Specifically, by setting a foreign key, establishing an index or creating a joint table, an association is established between the data tables according to the association relationship in the data mapping relationship layer; in this embodiment, an association is established between the basic information table and the physiological characteristic table, so that the corresponding physiological characteristic information can be quickly found through a certain field (such as name or ID number) in the basic information table;

[0080] For example, if there is a unique identification field ID (as the primary key) in the PersonInfo table, then in the PhysiologicalFeatures table, PersonID can be set as a foreign key and specified to reference the ID field in the PersonInfo table. In this way, an association can be established between the basic information table and the physiological feature table through the PersonID field.

[0081] According to the requirements of the database structure, the basic features and associated features are converted into a format that conforms to the database structure and imported into the corresponding data tables;

[0082] Specifically, use the tools or scripts provided by the database management system to import the converted basic features and associated features into the corresponding data tables according to the requirements of the database structure. The process can be implemented by batch insertion, data loading tools or ETL (Extract, Transform, Load) process. Finally, after the data is imported, the data is verified and checked.

[0083] Construct multiple retrieval nodes and configure parameters for each retrieval node, wherein the retrieval node configuration parameters are specifically a query timeout or a maximum result set size;

[0084] Specifically, according to the structure of the database, multiple search nodes are constructed, wherein each search node corresponds to one or more data tables and contains a set of related fields and conditions. Then, parameters are configured for each search node, including query timeout and maximum result set size. The configured parameters are helpful to control query performance and resource consumption.

[0085] Exemplarily, in this embodiment, the basic information retrieval node is used to query the basic information of a person, such as name, age, gender, etc. The node is configured with parameters such as a query timeout of 5 seconds and a maximum result set size of 100 items;

[0086] Physiological characteristics retrieval node: used to query the physiological characteristics of people, such as height, weight, blood type, etc.; this node is configured with parameters such as query timeout of 5 seconds and maximum result set size of 100;

[0087] Comprehensive search node: used to perform complex queries based on multiple conditions, such as querying for information about people who meet a specific age range and height range at the same time;

[0088] It should be noted that after the retrieval nodes are built and configured, testing and optimization work is carried out to ensure that these nodes can meet the needs and performance requirements of the system. For example, the correctness and performance of the retrieval nodes are verified by simulating query operations; and the query effect is optimized by adjusting parameters.

[0089] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0091] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. The multi-dimensional monitoring data processing and storage system based on blockchain is characterized by: The processing and storage system comprises: A data acquisition module is used to acquire multi-dimensional monitoring data of a person, including physiological characteristic data, behavioral characteristic data, and electronic medical record data of a pathology; The data analysis module is used to process the physiological characteristic data, behavioral characteristic data and electronic medical record data of the person through the data association analysis method based on the acquired multi-dimensional monitoring data of the person, and obtain the association analysis results; The association classification module is used to set a multi-level association degree threshold based on the association analysis result, and classify the physiological characteristic data, behavioral characteristic data and specific characteristic data in the electronic medical record data of the pathology according to the set multi-level association degree threshold to obtain the association level classification result; A data storage module, used to establish a data mapping relationship layer based on the association level classification result, and store the data mapping relationship layer in the blockchain; The database construction module is used to construct a character database according to the data mapping relationship layer and generate a search guide, wherein the search guide constructs multiple nodes to jointly process the search request.

2. According to the blockchain-based character multi-dimensional monitoring data processing and storage system of claim 1, it is characterized in that: The steps of obtaining multi-dimensional monitoring data of a person, including physiological characteristic data, behavioral characteristic data and electronic medical record data of a pathology, are as follows: By consulting relevant documents and biometric technology, physiological characteristic data is obtained, including the person's height, weight, body shape, gender, age, skin color, hair color, eye color, face and fingerprint information; Capture data on people’s behavior characteristics through video surveillance and sensors, including posture, expression, and walking style; Obtain electronic medical record data of a person's pathology from the medical institution's detection system.

3. According to the blockchain-based character multi-dimensional monitoring data processing and storage system of claim 1, it is characterized in that: Based on the acquired multi-dimensional monitoring data of the person, the physiological characteristic data, behavioral characteristic data and electronic medical record data of the pathology of the person are processed by a data association analysis method, and the specific steps are as follows: Perform cleaning, deduplication, and standardization preprocessing operations on the acquired multi-dimensional monitoring data of people; Through the data correlation analysis method, the similarity between the physiological characteristic data, behavioral characteristic data and the electronic medical record data of pathology is calculated, and the correlation analysis results are obtained using statistical analysis.

4. According to the blockchain-based character multi-dimensional monitoring data processing and storage system of claim 1, it is characterized in that: The method of setting a multi-level correlation threshold based on the correlation analysis result and classifying the physiological characteristic data, behavioral characteristic data and specific characteristic data in the electronic medical record data of the pathology into correlation levels according to the set multi-level correlation threshold is as follows: Based on the results of the association analysis, multi-level association thresholds are set, including high association level, medium association level and low association level; Physiological characteristic data are set as basic characteristics, and behavioral characteristic data and electronic medical record data of pathology are set as associated characteristics; Based on the association analysis results, the association degree between the basic features and the associated features is calculated by the correlation system or mutual information method in the association calculation method; Based on the obtained correlation degree of the basic features and the associated features, each basic feature and the associated feature is divided into an association level through a set multi-level association degree threshold to obtain an association level division result.

5. According to the blockchain-based character multi-dimensional monitoring data processing and storage system of claim 1, it is characterized in that: The specific steps of establishing a data mapping relationship layer based on the association level division result and storing the data mapping relationship layer in the blockchain are as follows: According to the result of the association level division, the mapping rules are determined, specifically: a direct mapping relationship is established between the data with high association levels, while the data with low association levels are mapped through intermediate nodes; According to the determined mapping rules, the basic features and the associated features are mapped into the relational layer using a graph structure or a data model of a relational database, so as to obtain a data mapping relational layer of the basic features and the associated features; Using blockchain technology, the data mapping relationship layer of basic features and associated features is encoded into a format stored on the blockchain and then stored on the blockchain.

6. The multi-dimensional monitoring data processing and storage system based on blockchain according to claim 1 is characterized in that: The character database is constructed according to the data mapping relationship layer, and a search guide is generated, wherein the search guide constructs multiple nodes to jointly process the search request, and the specific steps are as follows: Determine a data table of a character database, wherein the data table is specifically a basic information table, a physiological characteristic table, a behavioral characteristic table or a pathological record table; Define fields for each data table and establish associations between data tables based on the data mapping relationship layer stored in the blockchain; According to the requirements of the database structure, the basic features and associated features are converted into a format that conforms to the database structure and imported into the corresponding data tables; Construct multiple retrieval nodes, and configure parameters for each retrieval node, wherein the retrieval node configuration parameters are specifically a query timeout or a maximum result set size.

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