Method and system for identifying and tracing full-life-cycle science and technology data

Abstract

The embodiment of the invention relates to and particularly relates to a full-life-cycle science and technology data identification and traceability method and a full-life-cycle science and technology data identification and traceability system. The invention discloses a full-life-cycle science and technology data identification and traceability method. The method comprises the following steps: setting an evidence storage server and an identification server; the evidence storage server receives data fingerprints, data identifiers and data descriptions of the scientific research data; the evidence storage server generates evidence storage information for each piece of scientific research data; packaging the multiple pieces of evidence storage information into an evidence storage packet according to a preset period, and extracting data fingerprints of the evidence storage packet and storing the data fingerprints; the identification server receives scientific research data and data description of the scientific research data, takes the data description as a label of the scientific research data, and generates sample data; inputting the sample data into a pre-accessed large language model, and obtaining a data recognition model according to the large language model; and according to the response of the data identification model to the to-be-identified scientific research data, obtaining data description for identifying the scientific research data.

Description

Technical Field

[0001] Multiple embodiments of this specification relate to, specifically, a method and system for identifying and tracing scientific and technological data throughout its life cycle. Background Art

[0002] Scientific and technological data refers to various types of information and materials generated in the process of scientific research and technological development. This information can be data records obtained through experiments, observations, surveys, simulations, etc., or it can be content obtained after preliminary processing and analysis of the data. It can also be data directly output by instruments and equipment. Scientific and technological data not only includes numerical data, such as measurements and statistical results, but also includes content in various forms such as text, images, audio, and video. Scientific and technological data is the basis of scientific research and an important basis for the establishment or completion of related scientific and technological projects. Therefore, it is very important to ensure the authenticity of scientific and technological data and to achieve traceability of scientific and technological data. There are many types of scientific and technological data, and it is still difficult to manually identify their types. At present, there is a lack of effective technology for tracing and identifying scientific and technological data throughout the life cycle. Summary of the invention

[0003] Multiple embodiments of this specification describe a method and system for identifying and tracing scientific and technological data throughout its life cycle.

[0004] In a first aspect, the embodiments of this specification provide a method for identifying and tracing scientific and technological data throughout the entire life cycle, including the steps of: Set up the evidence storage server and identification server; The evidence storage server receives data fingerprints, data identifiers and data descriptions of scientific research data, wherein the scientific research data includes output data of scientific research equipment and research data; The evidence storage server generates evidence information for each scientific research data, and the evidence information includes the data fingerprint and data identification; Packing the plurality of evidence information into an evidence package at a preset period, extracting the data fingerprint of the evidence package and storing the evidence through the blockchain, and storing the evidence package; The recognition server receives the scientific research data and a data description of the scientific research data, uses the data description as a label of the scientific research data, and generates sample data; Inputting the sample data into a pre-connected large language model, and obtaining a data recognition model according to the large language model; According to the response of the data identification model to the scientific research data to be identified, a data description for identifying the scientific research data is obtained.

[0005] In a second aspect, the embodiments of this specification provide a full life cycle technology data identification and traceability system, including: Evidence server and identification server, The evidence server receives data fingerprints, data identifiers and data descriptions of scientific research data, the scientific research data including output data of scientific research equipment and research data, generates evidence information for each scientific research data, the evidence information including the data fingerprint and data identifier, packages a plurality of the evidence information into an evidence package at a preset period, extracts the data fingerprint of the evidence package and performs evidence storage through blockchain, and stores the evidence package; The recognition server receives scientific research data and data descriptions of the scientific research data, uses the data descriptions as labels for the scientific research data, generates sample data, inputs the sample data into a pre-connected large language model, obtains a data recognition model based on the large language model, and obtains a data description for identifying the scientific research data based on the response of the data recognition model to the scientific research data to be identified.

[0006] In a third aspect, an embodiment of this specification provides an electronic device, including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method described in any one of the above aspects.

[0007] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the above aspects.

[0008] In a fifth aspect, an embodiment of this specification provides a computer program product, including a computer program, which implements the method described in any of the above aspects when executed by a processor.

[0009] The beneficial effects brought by the technical solutions provided by some embodiments of this specification include at least: In multiple embodiments of this specification, a method and system for identifying and tracing scientific and technological data throughout the life cycle is provided. The data concentrator is set to collect the output data of scientific research equipment and the research data manually input through the scientific research terminal. Then, the scientific research data is traced and identified through the evidence storage server and the identification server. The authenticity of the scientific research data is guaranteed. At the same time, for given scientific research data, the system can automatically identify its type and give a data description.

[0010] Other features and advantages of the various embodiments of the present specification will be further disclosed in the following detailed description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 Schematic diagram of the application scenario of the data identification and tracing method provided in the embodiments of this specification.

[0013] Figure 2 A flowchart of a data identification and tracing method provided in an embodiment of this specification.

[0014] Figure 3 A schematic diagram of data storage provided in accordance with an embodiment of this specification.

[0015] Figure 4 Schematic diagram of the flow of the method for receiving scientific research data by the evidence storage server.

[0016] Figure 5 Schematic diagram of the flow chart of the method for obtaining data identification model.

[0017] Figure 6 A schematic diagram of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0018] The following is an explanation and description of the technical solutions of the embodiments of this specification in conjunction with the drawings of the embodiments of this specification, but the following embodiments are only preferred embodiments of this specification, not all. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of this specification.

[0019] The terms "first", "second", "third", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0020] In the following description, terms such as "inside", "outside", "up", "down", "left", "right", etc. that indicate directions or positional relationships are only used to facilitate the description of the embodiments and simplify the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation of this specification.

[0021] The data involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection of relevant data complies with the relevant laws, regulations and standards of relevant countries and regions.

[0022] Before describing the technical solution in this specification, an introduction is given to the application scenarios and related technologies of the technical solution.

[0023] In the process of project review, the authenticity of scientific research data21 is of vital importance. Scientific research data21 is the cornerstone of scientific research and technological development, and it is directly related to the reliability of research conclusions and the validity of scientific discoveries. If scientific research data21 is distorted, it will not only lead to wrong conclusions, but may also mislead subsequent research directions, waste precious scientific research resources, and even affect the formulation of public policies and social development decisions. Therefore, during project review, ensuring the authenticity of data is one of the core elements of evaluating the quality of a project.

[0024] When review experts review projects, they judge whether the research methods are scientific and reasonable, whether the experimental results are convincing, and whether the theoretical assumptions are valid based on the submitted data. Only based on real data can accurate research conclusions be drawn. Researchers must be responsible for their work, honestly record and report experimental results, and avoid any form of data falsification or tampering. Once data falsification occurs, it will not only damage the career of the individual involved, but also have a negative impact on the reputation of the entire scientific research institution.

[0025] In research involving sensitive areas such as health and the environment, it is particularly important to ensure the authenticity of data. For example, in the process of drug development, the authenticity of clinical trial data directly affects the safety and effectiveness evaluation of new drugs, and ultimately determines whether they can be marketed to benefit patients. Therefore, strictly controlling the authenticity of data during the project review stage is a manifestation of social responsibility and a key step in protecting the public interest.

[0026] Scientific research data 21 includes output data of scientific research equipment 13, that is, raw data, which refers to the unprocessed initial observations or measurements. The output data of scientific research equipment 13 comes directly from experimental, observational and other equipment. This type of data retains the most original information. The data after preliminary processing refers to the results obtained after cleaning, converting, summarizing and other operations on the original data. Text data covers various written records, such as research reports and papers. Data in the form of images, audio, video and other forms are also becoming more and more common in modern scientific research. This type of unstructured data often contains rich visual or auditory information and can be used in multiple fields such as biometrics, speech processing, and natural scene understanding. These data are directly processed by scientific researchers or project personnel and input into the receiving device.

[0027] Scientific research data 21 not only needs to be authentic and traceable, but also needs to study the identification technology of scientific research data 21 due to the wide variety of scientific research data 21. To this end, this application proposes a method and system for identifying and tracing scientific and technological data throughout the life cycle. Please refer to the attached Figure 1 , the system collects the output data of the scientific research equipment 13 and the research data manually input through the scientific research terminal 12 by setting up a data concentrator 11. Then, the scientific research data 21 is traced and identified through the evidence server 20 and the identification server 30. The authenticity of the scientific research data 21 is guaranteed. At the same time, for a given scientific research data 21, the system can automatically identify its type and give a data description. Exemplarily, the data description is that the data is generated by the automatic monitoring equipment of the meteorological station, which is usually used for climate change research.

[0028] An interactive interface is provided on the scientific research terminal 12. The interactive interface is used to upload research data. Or request the identification or tracing of given scientific research data 21. Among them, the interactive interface can be run on the terminal device in the form of a browser, or it can be run on the terminal device in the form of an independent application, etc. The specific presentation form of the client is not limited here. The server involved in this application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms. The scientific research terminal 12 can be a smart phone, a tablet computer, a laptop, a PDA, a personal computer, a smart speaker, a smart TV, a smart watch, a car-mounted device, a wearable device, etc., but is not limited thereto. The scientific research terminal 12 and the server can be directly or indirectly connected by wired or wireless communication, and this application is not limited here. The number of servers and scientific research terminals 12 is also not limited.

[0029] This manual first provides a method for identifying and tracing scientific and technological data throughout the life cycle. Figure 2 , including the steps of: Step S101) Setting up the evidence storage server 20 and the identification server 30. Setting up the evidence storage server 20 and the identification server 30 to complete different tasks respectively, realizing business separation, so that the system can bear more loads, and the mutual influence between businesses is low, and has higher security.

[0030] Step S102) The evidence server 20 receives the data fingerprint 24, data identifier 25 and data description of the scientific research data 21, and the scientific research data 21 includes the output data of the scientific research equipment 13 and the research data. The data fingerprint 24 can be used to store the scientific research data 21 and provide authenticity proof for the scientific research data 21. Data fingerprint 24 (Data Fingerprint), sometimes also called data hash or content fingerprint, refers to a fixed-length string generated after a piece of data is processed by a specific algorithm. This string is a unique identifier of the original data and can be used to quickly identify, verify data integrity or detect duplicate data. Data fingerprint 24 is widely used in many fields such as file systems, database management, network security and software development. Data fingerprint 24 can also be used as an index key to speed up the search process, especially in distributed file systems. The method of generating data fingerprint 24 usually relies on hash functions, such as MD5, SHA-1, SHA-256, etc. These functions receive data input of arbitrary length and output a hash value of fixed length.

[0031] Step S103 ) The evidence server 20 generates evidence information for each scientific research data 21 , and the evidence information includes the data fingerprint 24 and the data identifier 25 .

[0032] The scientific research data 21 can be stored through the data fingerprint 24 and the data identifier 25. The method of verifying the data fingerprint 24 is to use the agreed extraction function to extract the data fingerprint 24 of the scientific research data 21, and compare it with the data fingerprint 24 stored on the blockchain. If they match, it means that the scientific research data 21 has not been tampered with and is authentic. On the contrary, if they do not match, it means that the scientific research data 21 has been tampered with.

[0033] On the other hand, as an improved implementation, the evidence server 20 and the identification server 30 continuously exchange evidence codes 22 in pairs, the evidence information includes the data fingerprint 24, the data identifier 25, a pair of evidence codes 22 and a supplementary code 23, and the method for the evidence server 20 to generate the supplementary code 23 includes: continuously trying to generate the supplementary code 23 until the supplementary code 23 satisfies the end preset position of the hash value of the evidence information of the scientific research data 21, which is numerically continuous. For space reasons, it is assumed that the end preset position is the last 3 positions, and the hash value is expressed in hexadecimal. Assuming that the hash value of the first scientific research data 21 is: 846E8C38ADF75840D6FB72E2B35A624CB1625D49F6CFB8D65A931D0029884354, the pair of evidence codes 22 are: 4ED12B, 64C87A, and the supplementary code 23 is: 82D5AC, then after splicing the hash value of the scientific research data 21, the pair of evidence codes 22, and the supplementary code 23, extract " The hash value of "846E8C38ADF75840D6FB72E2B35A624CB1625D49F6CFB8D65A931D00298843544ED12B64C87A82D5AC" is 92CDB42B62F6A62034EBB14E83BBC987F00B6E7D02596B38274C1C5D7D5254C0. That is, the last 3 digits are "4C0", which means that the last 3 digits of the hash value of the next scientific research data 21, a pair of certificate codes 22 and the supplementary code 23 are required to be "4C1". Since the hash function is almost random, the probability of obtaining a hash value with the last 3 digits of "4C1" is 1 / (16^3), that is, 1 / 4096. That is to say, the supplementary code 23 is randomly generated, and each time the supplementary code 23 is generated, there is a 1 / 4096 probability that the last 3 digits of the hash value are continuous. That is, on average, the supplementary code 23 is randomly generated 4096 times for each scientific research data 21. When more digits are required at the end, the difficulty will be higher.

[0034] Since blockchain evidence storage requires initiating a transfer transaction on the blockchain, the information that needs to be stored is added to the transaction notes. Transactions require payment of handling fees. When the number of times of storage is large, more funds are consumed. In order to save this part of funds, the frequency of storage needs to be reduced. In the time between two storages, the scientific research data 21 can be tampered with without being discovered. Therefore, the frequency of storage cannot be too low. For this reason, this embodiment uses the requirements of the supplementary code 23 to perform special processing between two storages, so that after the scientific research data 21 is tampered with, a new supplementary code 23 needs to be generated immediately. When the number of consecutive tail digits is required to be sufficient, the time required to try to generate the supplementary code 23 in theory exceeds the time interval between two storages, and the authenticity of each scientific research data 21 can be guaranteed. The total number of digits of the pair of storage codes 22 and the supplementary code 23 is a preset length. The supplementary code 23 set is stored continuously. Since a pair of storage codes 22 are stored on the storage server 20 and the identification server 30 respectively, when the scientific research data 21 is tampered with, not only the storage server 20 needs to be modified, but also the identification server 30 needs to be modified. When the management or modification permissions of two servers are controlled by different entities, it is more difficult to tamper with scientific research data21.

[0035] Step S104) Pack the multiple evidence information into an evidence package at a preset period, extract the data fingerprint 24 of the evidence package, and store the evidence through the blockchain. When the supplementary code 23 is used and the end preset bits of the hash value of the evidence information of the scientific research data 21 are required to be numerically continuous, the period can be increased to reduce the number of times the blockchain is used for evidence storage, but the same data authenticity can still be guaranteed.

[0036] On the other hand, please see the attached Figure 4 The method for the evidence storage server 20 to receive the data fingerprint 24, data identifier 25 and data description of the scientific research data 21 includes: Step S201) Setting a data concentrator 11 for a plurality of scientific research equipment 13, wherein the data concentrator 11 receives output data of the scientific research equipment 13; Step S202) The data concentrator 11 packages the received output data of the scientific research equipment 13 into scientific research data 21 at a preset time period, and extracts the data fingerprint 24 of the scientific research data 21; Step S203) generating a data identifier 25 for the scientific research data 21 according to a preset identifier generation rule; Step S204) Generate a data description according to the manually preset description of the scientific research equipment 13 and the current state setting of the scientific research equipment 13; Step S205) The data concentrator 11 receives the research data and data description submitted manually, extracts the data fingerprint 24 of the research data, and generates a data identifier 25 for the research data; Step S206) The data concentrator 11 periodically reports the data fingerprint 24, data identifier 25 and data description of the scientific research data 21 to the evidence server 20. By setting the data concentrator 11 to collect the output data of the scientific research equipment 13 and perform evidence storage operations on the data concentrator 11, the security of the data concentrator 11 is controlled, thereby having higher security.

[0037] As a recommended approach, the data concentrator 11 is a trusted device. Trusted devices refer to hardware and software systems that are recognized as safe, reliable and capable of ensuring data integrity and privacy protection. These devices play a vital role in the entire scientific research data 21 life cycle. From data collection, transmission, storage to final analysis and sharing, they ensure data quality and security at every link. The design and implementation of trusted devices rely on a series of technical and management measures. First, at the hardware level, such devices are usually equipped with dedicated security chips or modules for encryption and decryption, identity authentication, and integrity protection of key operations.

[0038] Step S105 ) The recognition server 30 receives the scientific research data 21 and the data description of the scientific research data 21 , uses the data description as a label of the scientific research data 21 , and generates sample data.

[0039] Step S106) Input the sample data into a pre-connected large language model, and obtain a data recognition model based on the large language model. The method of inputting the sample data into the pre-connected large language model includes: Constructing, according to the sample data, a natural language task instructing the large language model to learn the sample data; The natural language task is submitted to the large language model.

[0040] Step S107) According to the response of the data identification model to the scientific research data 21 to be identified, a data description for identifying the scientific research data 21 is obtained.

[0041] Please refer to the attached Figure 5 The method for obtaining a data recognition model according to the large language model includes: Step S301) Construct a natural language task for generating extended samples based on the sample data, and submit it to the large language model to obtain extended samples. First, create natural language tasks based on the existing sample data. These tasks are designed to generate more samples, i.e., extended samples. These natural language tasks can be designed to fill in blanks, generate question-answer pairs, and summarize text. The purpose is to use the capabilities of the large language model to create new samples that are similar to but different from the original samples. After submitting these natural language tasks to the pre-trained large language model, the model will generate a series of new samples according to the task requirements, thereby enriching the training set.

[0042] Step S302) Establish a neural network model as a student model and use the large language model as a teacher model. The student model is usually a model with a simpler structure and fewer parameters, and its goal is to learn by imitating the behavior of the teacher model. The student model may reduce accuracy, but has higher efficiency and takes up less resources.

[0043] Step S303) enables the student model to learn the teacher model on the sample data and the extended sample. This can be achieved in a variety of ways, such as directly copying the prediction results of the teacher model as labels, or allowing the student model to try to imitate the activation state of the middle layer of the teacher model.

[0044] Step S304) A data recognition model is obtained according to the learned neural network model. The data recognition model maintains a low computational cost while approaching the performance of the teacher model. It is used in actual application environments and can quickly respond to user queries, conduct large-scale data analysis, and other scenarios without affecting user experience or business efficiency due to resource limitations.

[0045] On the other hand, this specification provides a full life cycle scientific and technological data identification and traceability system, including: The evidence storage server 20 and the identification server 30, The evidence server 20 receives the data fingerprint 24, data identifier 25 and data description of the scientific research data 21, the scientific research data 21 includes the output data of the scientific research equipment 13 and the research data, generates evidence information for each scientific research data 21, the evidence information includes the data fingerprint 24 and the data identifier 25, packages a plurality of the evidence information into an evidence package at a preset period, extracts the data fingerprint 24 of the evidence package and performs evidence storage through the blockchain, and stores the evidence package; The recognition server 30 receives the scientific research data 21 and the data description of the scientific research data 21, uses the data description as a label of the scientific research data 21, generates sample data, inputs the sample data into a pre-connected large language model, obtains a data recognition model based on the large language model, and obtains a data description for identifying the scientific research data 21 based on the response of the data recognition model to the scientific research data 21 to be identified.

[0046] See also Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification is shown.

[0047] like Figure 6 As shown, the electronic device 1100 may include: at least one processor 1101, at least one network interface 1104, a user interface 1103, a memory 1105 and at least one communication bus 1102. Among them, the communication bus 1102 can be used to realize the connection and communication of the above-mentioned components. Among them, the user interface 1103 may include a button, and the optional user interface may also include a standard wired interface and a wireless interface. Among them, the network interface 1104 may include but is not limited to a Bluetooth module, an NFC module, a Wi-Fi module, etc. Among them, the processor 1101 may include one or more processing cores. The processor 1101 uses various interfaces and lines to connect various parts in the entire electronic device 1100, and executes various functions and processes data of the routing device 1100 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1105, and calling data stored in the memory 1105. Optionally, the processor 1101 can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor 1101 may integrate one or a combination of CPU, GPU and modem, etc. Among them, the CPU mainly processes the operating system, user interface and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication.

[0048] It is understandable that the above-mentioned modem may not be integrated into the processor 1101, but may be implemented by a separate chip.

[0049] Among them, the memory 1105 may include RAM or ROM. Optionally, the memory 1105 includes a non-transitory computer-readable medium. The memory 1105 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1105 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1105 may also be at least one storage device located away from the aforementioned processor 1101. The memory 1105 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application. The processor 1101 may be used to call the application stored in the memory 1105 and execute the methods in the above-mentioned multiple embodiments.

[0050] The embodiments of this specification also provide a computer-readable storage medium, which stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes the multiple steps in the above embodiments. If the components of the above electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0051] The embodiments of this specification also provide a computer program product, including a computer program, which implements multiple steps in the above embodiments when executed by a processor.

[0052] In the absence of conflict, the technical features in this embodiment and implementation scheme can be combined arbitrarily.

[0053] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes a plurality of computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of this specification is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes multiple available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

[0054] When implemented by hardware or firmware, the aforementioned method flow is programmed into the hardware circuit to obtain the corresponding hardware circuit structure and realize the corresponding function. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit, and its logic function is determined by the user programming the device. The designer programs by himself to "integrate" a digital system on a PLD, without asking a chip manufacturer to design and make a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when writing program development, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL), and HDL is not just one, but many. Those skilled in the art should also be aware that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit to easily obtain the hardware circuit that implements the logic method flow.

[0055] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Without departing from the design spirit of this specification, various modifications and improvements made to the technical solutions of this specification by ordinary technicians in this field should fall within the scope of protection determined by the claims of this specification.

Claims

1. A method for identifying and tracing scientific and technological data throughout the entire life cycle, characterized in that: Includes steps: Set up the evidence server and identification server; The evidence storage server receives data fingerprints, data identifiers and data descriptions of scientific research data, wherein the scientific research data includes output data of scientific research equipment and research data; The evidence storage server generates evidence information for each scientific research data, and the evidence information includes the data fingerprint and data identification; Packing the plurality of evidence information into an evidence package at a preset period, extracting the data fingerprint of the evidence package and storing the evidence through the blockchain, and storing the evidence package; The recognition server receives the scientific research data and a data description of the scientific research data, uses the data description as a label of the scientific research data, and generates sample data; Inputting the sample data into a pre-connected large language model, and obtaining a data recognition model according to the large language model; According to the response of the data identification model to the scientific research data to be identified, a data description for identifying the scientific research data is obtained.

2. A method for identifying and tracing scientific and technological data throughout the life cycle according to claim 1, characterized in that: The evidence server and the identification server continuously exchange evidence codes in pairs, and the evidence information includes the data fingerprint, the data identifier, a pair of evidence codes and a supplementary code. The method for generating the supplementary code by the evidence storage server includes: Continue to try to generate supplementary codes until the supplementary codes satisfy the preset end position of the hash value of the evidence information of the scientific research data, which is numerically continuous.

3. A method for identifying and tracing scientific and technological data throughout the life cycle according to claim 1 or 2, characterized in that: The method for the evidence storage server to receive the data fingerprint, data identification and data description of scientific research data includes: Setting a data concentrator for a plurality of scientific research equipment, wherein the data concentrator receives output data of the scientific research equipment; The data concentrator packages the received output data of the scientific research equipment into scientific research data at a preset time period, and extracts the data fingerprint of the scientific research data; Generate a data identifier for the scientific research data according to a preset identifier generation rule; Generate data description based on the manually preset scientific research equipment description and the current scientific research equipment status setting; The data concentrator receives manually submitted research data and data description, extracts data fingerprints of the research data, and generates data identifiers for the research data; The data concentrator periodically reports the data fingerprint, data identification and data description of the scientific research data to the evidence storage server.

4. A method for identifying and tracing scientific and technological data throughout the life cycle according to claim 3, characterized in that: The data concentrator is a trusted device.

5. A method for identifying and tracing scientific and technological data throughout the life cycle according to claim 1 or 2, characterized in that: The method of inputting the sample data into the pre-connected large language model includes: Constructing, according to the sample data, a natural language task instructing the large language model to learn the sample data; The natural language task is submitted to the large language model.

6. A method for identifying and tracing scientific and technological data throughout the life cycle according to claim 1 or 2, characterized in that: The method for obtaining a data recognition model according to the large language model includes: Constructing a natural language task for generating an extended sample according to the sample data, and submitting the task to the large language model to obtain the extended sample; Establishing a neural network model as a student model and using the large language model as a teacher model; Based on the sample data and the extended samples, the student model is enabled to learn the teacher model; A data recognition model is obtained according to the learned neural network model.

7. A full life cycle scientific and technological data identification and traceability system, characterized in that: include: Evidence server and identification server, The evidence server receives data fingerprints, data identifiers and data descriptions of scientific research data, the scientific research data including output data of scientific research equipment and research data, generates evidence information for each scientific research data, the evidence information including the data fingerprint and data identifier, packages a plurality of the evidence information into an evidence package at a preset period, extracts the data fingerprint of the evidence package and performs evidence storage through blockchain, and stores the evidence package; The recognition server receives scientific research data and data descriptions of the scientific research data, uses the data descriptions as labels for the scientific research data, generates sample data, inputs the sample data into a pre-connected large language model, obtains a data recognition model based on the large language model, and obtains a data description for identifying the scientific research data based on the response of the data recognition model to the scientific research data to be identified.

8. An electronic device, characterized in that including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. Computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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