Scientific research activity management and application platform supporting multidisciplinary sharing

Abstract

The present application relates to a scientific research activity management and application platform supporting multidisciplinary sharing. The platform comprises a scientific research activity management module deployed in the cloud, and is configured to allow users to perform scientific research activity management by running the scientific research activity management module, without installing additional software. The scientific research activity management module comprises a scientific research unit design environment, and scientific research unit code packages of different disciplines designed and generated by users on the basis of a scientific research unit syntax defined within the scientific research unit design environment and shared by multiple disciplines, wherein each scientific research unit code package at least comprises a scientific research protocol for a corresponding discipline, and the scientific research protocol is used for defining the basic information, multimodal information, and data fields of scientific research experiments in the discipline. The present application can meet diverse and customized requirements for scientific research data recording and scientific research activity management across different disciplines, thereby achieving efficient accumulation of multidisciplinary expert knowledge, and providing rich resources for AI applications based on interdisciplinary expert knowledge.

Description

A scientific research activity management and application platform supporting multi-disciplinary sharing TECHNICAL FIELD

[0001] The present application relates to the technical field of scientific research project management, and in particular to a scientific research activity management and application platform supporting multi-disciplinary sharing. BACKGROUND

[0002] In recent years, artificial intelligence (AI) has shown increasing potential in supporting and promoting scientific innovation in the field of natural sciences. From protein structure prediction to drug discovery, from identification of new chemical reactions to synthesis of new materials, artificial intelligence has shown great potential to assist or even replace human scientists. Strategic use of artificial intelligence technology has the potential to accelerate the progress of natural science research and enhance the technological competitiveness of universities and enterprises.

[0003] Data is the foundation of science and the cornerstone of artificial intelligence. To widely realize AI-driven progress in natural science research, the first step is to promote the electronicization of multi-disciplinary scientific research data. This is because, first of all, existing AI models have strict requirements for high-quality and large amounts of training data. Therefore, current AI-assisted scientific research is usually focused on specific field problems with relatively rich public data, such as protein structure, compound structure, medical images, and scientific literature. In contrast, in those natural science fields that are not covered by public data sets, AI-enabled research is still limited due to the difficulty of obtaining relevant scientific research data (including experimental protocols, experimental data, etc.). Secondly, cross-disciplinary research is increasingly important in promoting scientific discovery. On the one hand, the number of cross-disciplinary research is growing; on the other hand, cross-disciplinary research is gradually becoming a source of major scientific breakthroughs. Therefore, creating a multi-disciplinary scientific research data electronicization platform is an important way to promote scientific research data electronicization and meet the future development of science. In order to promote the electronic recording of laboratory scientific research data, many functional electronic laboratory notebooks (ELNs) have been developed. However, these ELNs are usually designed for a single discipline, and lack the versatility required to achieve multi-disciplinary scientific research data electronicization, for example, due to financial and human costs, and significant professional barriers between different disciplines, existing platforms usually adopt a centralized design concept, providing users with predefined functions and data recording templates, and cannot support custom research protocols, so they cannot record different types of scientific research data required by different protocols.

[0004] Therefore, no existing technology has yet found a research management and application platform that can well meet the diverse and fine-grained customization needs of scientific research data recording in different disciplines. Consequently, it is also not yet possible to efficiently accumulate high-precision and high-quality expert knowledge from front-line researchers in multiple disciplines through such a platform, thereby providing a key resource available for training interdisciplinary AI research models. Summary of the Invention

[0005] This application addresses the aforementioned deficiencies in the prior art. There is a need for a research activity management and application platform that supports multidisciplinary collaboration, enabling users to manage research activities without installing additional software. This platform allows for the design and generation of research protocols across different disciplines using a unified research unit syntax, through a cloud-based research activity management module. The platform of this application enables the efficient accumulation of high-precision, high-quality expert knowledge by frontline researchers across multiple disciplines, thereby providing crucial and abundant resources for AI applications based on interdisciplinary expert knowledge.

[0006] According to the first aspect of this application, a research activity management and application platform supporting multidisciplinary use is provided. This platform includes a research activity management module deployed in the cloud, configured to allow users to manage research activities by running the module without requiring additional software installation. The research activity management module includes a research unit design environment and research unit code packages for different disciplines designed and generated by the user based on a multidisciplinary research unit syntax defined by the research unit design environment. Each research unit code package contains at least a research protocol for the corresponding discipline, which defines the basic information, multimodal information, and data fields of the research experiment in that discipline.

[0007] The various embodiments of this application provide a research activity management and application platform that supports multidisciplinary use. This platform offers users a cloud-based research unit design environment, enabling them to design and generate research unit code packages for different disciplines based on a shared multidisciplinary research unit syntax. This breaks down the barriers between different disciplines; users only need to design and describe the corresponding discipline's research protocol to define the basic information, multimodal information, and data fields of the research experiment. With low platform development and maintenance costs, it effectively meets the diverse and fine-grained customization needs of research data recording across different disciplines. Furthermore, users can easily manage research activities without installing additional software, maximizing convenience for researchers across various disciplines and facilitating widespread adoption. This enables the efficient accumulation of high-precision, high-quality expert knowledge by frontline researchers in multiple disciplines, providing crucial and abundant resources for AI applications based on interdisciplinary expert knowledge.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0009] It should be understood that the foregoing general description and the following detailed description are merely illustrative and explanatory, and are not intended to limit the scope of the claimed invention. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1(a) shows a schematic diagram of a research activity management and application platform supporting multidisciplinary use according to an embodiment of this application.

[0012] Figure 1(b) shows another schematic diagram of a research activity management and application platform supporting multidisciplinary use according to an embodiment of this application.

[0013] Figure 1(c) shows another schematic diagram of the composition of a scientific research activity management and application platform supporting multidisciplinary use according to an embodiment of this application.

[0014] Figure 1(d) shows another schematic diagram of a research activity management and application platform supporting multidisciplinary use according to an embodiment of this application.

[0015] Figure 1(e) shows another schematic diagram of a research activity management and application platform supporting multidisciplinary use according to an embodiment of this application.

[0016] Figure 2 shows a schematic diagram of the composition structure of the scientific research unit code package according to an embodiment of this application.

[0017] Figure 3(a) shows a schematic diagram of the composition structure of a scientific research activity management module according to an embodiment of this application.

[0018] Figure 3(b) shows another structural diagram of the scientific research activity management module according to an embodiment of this application.

[0019] Figure 4 illustrates the steps of recording data in a research unit according to an embodiment of this application.

[0020] Figure 5(a) shows a schematic diagram of the chat interface of the AI ​​system tool according to an embodiment of this application.

[0021] Figure 5(b) shows a schematic diagram of using AI system tools to perform custom scientific research unit syntax checking according to an embodiment of this application.

[0022] Figure 5(c) illustrates a schematic diagram of injecting relevant information of a research unit as a context into an AI system tool chat dialogue according to an embodiment of this application.

[0023] Figure 5(d) shows the answers given by GPT-4o to user questions in the embodiments of this application.

[0024] Figure 5(e) shows a schematic diagram of analyzing research records using AI system tools according to an embodiment of this application.

[0025] Figure 6(a) shows a schematic diagram of the research unit workflow according to an embodiment of this application.

[0026] Figure 6(b) shows a schematic diagram of the research path according to an embodiment of this application.

[0027] Figure 6(c) shows a schematic diagram of scientific research data generated by executing a research path according to an embodiment of this application.

[0028] Figure 7 illustrates a process of generating an automatically executable research unit workflow using AI system tools according to an embodiment of this application.

[0029] Figure 8(a) shows a schematic diagram of a form-style research unit recording interface according to an embodiment of this application.

[0030] Figure 8(b) shows a schematic diagram of a WYSIWYG style research unit recording interface according to an embodiment of this application.

[0031] Figure 9 shows a schematic diagram of a knowledge sharing module according to an embodiment of this application.

[0032] Figure 10 shows a schematic diagram of scientific research conclusions automatically generated by AI system tools according to an embodiment of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the described embodiments of this application without creative effort are within the scope of protection of this application.

[0034] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. Words such as "comprising" or "including" mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, without excluding other elements or objects.

[0035] The terms "first," "second," and similar words used in this application do not indicate any order, quantity, or importance, but are merely used for distinction. Words such as "including" or "comprising" mean that the element preceding the word encompasses the elements listed after it, and do not exclude the possibility of encompassing other elements as well. The execution order of the steps in the method described in conjunction with the accompanying drawings in this application is not intended to be limiting. As long as the logical relationship between the steps is not affected, several steps can be integrated into a single step, a single step can be decomposed into multiple steps, and the execution order of the steps can be changed according to specific needs.

[0036] It should also be understood that the term "and / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Furthermore, the character " / " in this application generally indicates that the preceding and following related objects have an "or" relationship.

[0037] To keep the following description of the embodiments of this application clear and concise, detailed descriptions of known functions and known components are omitted.

[0038] Figure 1(a) shows a schematic diagram of a research activity management and application platform supporting multidisciplinary use according to an embodiment of this application.

[0039] As shown in Figure 1(a), the research activity management and application platform 10 supporting multidisciplinary use according to the embodiments of this application includes at least a research activity management module 11, and the research activity management module 11 further includes a research unit design environment 111, and research unit code packages 121, 122, etc. corresponding to different disciplines.

[0040] According to an embodiment of this application, a new research unit syntax is defined in the research unit design environment 111, which can be used by multiple disciplines. Thus, one or more users 12 from different disciplines can design their corresponding research protocols (hereinafter also referred to as research unit protocols, without distinction) based on the research unit syntax defined by the research unit design environment 111. This includes defining the basic information, multimodal information, and data fields of the research experiments in that discipline. The files defining this information are packaged in a folder, which generates the research unit code package for that discipline. In other words, a brand-new research unit (hereinafter also referred to as Research Unit, or abbreviated as RU, without distinction) is established. For example, the research unit code package 121 in Figure 1(a) corresponds to the research unit related to protein purification in the life science discipline, while the research unit code package 122 corresponds to the research unit related to carbon nanotube self-dispersion research in the materials science discipline. These research unit code packages can be parsed and run by the research activity management and application platform 10. Thus, researchers from different disciplines can conduct research experiments in that discipline by establishing research units, provided that they follow the research protocols of the corresponding disciplines.

[0041] As an example, in a research protocol, basic information about a research experiment may include, for instance, detailed descriptions of the research protocol related to the research activity (such as an experiment) by the user, including introduction, methods, and steps; multimodal information may include user-inserted multimodal files, such as images, videos, audio, and documents (such as PDFs), to provide a more intuitive and detailed description of the research activity; data fields may include various data fields required in the user-defined research protocol, such as research variables, steps, and checkpoints related to the research activity. For example, if a user needs to add a data field for solvent volume in the research protocol to record the amount of solvent used in the experiment, they can define a data field with the ID solvent_volume to record the solvent volume in that data field.

[0042] The research unit syntax (hereinafter also referred to as research unit Markdown, the two are not distinguished) in this application embodiment is a newly defined lightweight markup language that can be used by multiple disciplines. Users can use the research unit syntax, for example, by creating formatted text through a simple text editor, to customize basic information, multimodal information, and data fields in research agreements. Similar to the standard Markdown language, it has a clear syntax, is compatible with the standard Markdown specification (CommonMark) syntax, and has been extended to meet the syntax requirements of research agreements, models, assigners, etc., in the research units of this application. Its unique feature compared to the standard Markdown syntax is that research unit Markdown provides users with template syntax. Using template syntax, users can insert and customize data fields (hereinafter also referred to as research unit data fields, the two are not distinguished). Users can generate research unit variables, research unit steps, or research unit checkpoints with unique IDs based on the template syntax of data fields. In research unit Markdown, {{...}} is used to represent templates. Commonly used templates for defining data fields such as research unit variables, research unit steps, and research unit checkpoints are shown in Table 1.

[0043] Table 1 provides a template example for defining data fields in a research agreement.

[0044] The scientific research activity management and application platform 10 and the scientific research activity management module 11 according to the embodiments of this application can be deployed locally or in the cloud. From the perspective of facilitating multi-terminal sharing, it is generally preferred to deploy in the cloud. Thus, users do not need to install additional software locally, but can manage scientific research activities simply by running the scientific research activity management module 11 in the cloud.

[0045] Figure 2 shows a schematic diagram of the composition structure of the scientific research unit code package according to an embodiment of this application.

[0046] Figure 2 shows that, taking research unit code package 121 as an example, when a user designs a research unit code package for a corresponding discipline based on the research unit design environment, in addition to designing research protocols, they can also design and generate a research unit model. The model is used to define the type constraints and / or numerical verification relationships of the data fields, as well as the combination verification relationships of the types and / or values ​​between the various data fields.

[0047] More specifically, the type constraint includes constraining the corresponding data field to use predefined multimodal information during data entry, wherein the predefined multimodal information includes one or more of text, images, videos, audio, and files. In some embodiments, users can further define the data type (e.g., various numeric types, time types, etc.) of the data field previously defined in the research protocol. For example, if the `solvent_volume` data field is defined as a floating-point number, then when the user enters non-floating-point data (e.g., a string of letters) in this field, the system will indicate a type error.

[0048] The numerical verification relationship includes constraints that require the corresponding data fields to follow a specified pattern and / or not exceed a preset value range during data entry. Users can further define verification rules for the data fields defined in the research agreement. For example, they can add verification rules to the `solvent_volume` data field and its type constraint (floating-point number) to ensure that the floating-point number entered into this data field must be greater than zero. In this case, if the user enters a negative floating-point number, the system will display a numerical verification error.

[0049] The combined validation relationships include constraining the types and / or values ​​of each data field to meet predetermined constraints. As an example, following a specified pattern can be, for instance, using a RegExp regular expression (RE) to constrain the composition and pattern of strings in a field. For example, constraining a field related to email addresses to record values ​​containing exactly one "@" symbol, etc. In other embodiments, other patterns and value range constraints can be set, which are not listed here. This allows for the rapid identification of abnormal research data. If any validation relationship fails, the reason for the failure will be displayed, and the user can correct the values ​​of invalid data fields one by one according to the error message until a valid record is obtained. Through the above validation process, the platform can ensure that the data entered by the user meets the requirements.

[0050] As shown in Figure 2, in some embodiments, if a user defines multiple data fields with dependency and assignment relationships in a research agreement, these relationships can be further customized using an assigner. Specifically, when a user designs a research unit code package for a corresponding discipline based on a research unit design environment, it can also include an assigner for the data fields. This assigner is used by the user to assign values ​​to the data fields based on the data field dependency graph and assignment rules. In some embodiments, the data field dependency graph is a single-level or multi-level directed acyclic graph, the assignment relationships between the defined data fields are single or multiple dependencies, and each data field is assigned a value by at most one assigner. An assigner can have one or more upstream data fields as dependencies. Simultaneously, a data field can actually be a dependency of one or more data fields, but for a specific data field, the method for determining its field value should be unique. As an example, if two data fields, solvent_volume and solvent_volume_2, are defined in a research protocol, and the user uses an assigner to ensure that solvent_volume_2 is always twice the value of solvent_volume, then whenever a new value is entered for solvent_volume, the system will automatically set solvent_volume_2 to twice the solvent volume. For example, if the value of solvent_volume is 5, the system will automatically assign the value 10 to solvent_volume_2. This can greatly improve the efficiency and accuracy of research data recording.

[0051] By defining models of research units, user input can be dynamically verified to ensure the accuracy of data entry. By defining assigners, multi-level and multi-dependent field dependencies can be automatically calculated based on the value of a certain input field, thereby ensuring that even if there are complex dependencies between multiple data fields, they can be efficiently entered and run correctly. This can significantly promote the electronic management of laboratory research data, including data from outsourced experiments and orders, as well as the efficient retrieval of research plans, research data, and other related content.

[0052] Figure 3(a) shows a schematic diagram of the composition structure of a scientific research activity management module according to an embodiment of the present application. As shown in Figure 3(a), in some embodiments, the scientific research activity management module 11 may include a scientific research unit recording environment 112 in addition to a scientific research unit design environment 111, which provides users with an environment for recording and storing data based on a pre-designed scientific research unit.

[0053] Figure 4 illustrates the steps of recording research units in a research unit recording environment according to an embodiment of this application. As mentioned above, when the user-designed research unit code package includes a research protocol for the corresponding discipline, a model of the research unit, and an assigner for data fields, the research unit recording environment 112 can perform the operation steps shown in Figure 4 when recording the relevant content of the research unit.

[0054] First, in step 401, the model of the research unit can be converted into a data field JSON Schema.

[0055] Then, in step 402, a structured storage scheme is automatically generated for the research unit based on the JSON Schema of the data fields. This ensures that, when a user submits research records based on the research unit and all data fields conform to the constraints and validation relationships of the model, the research records are stored as JSON conforming to the structured storage scheme corresponding to that research unit. In the embodiments of this application, regardless of whether the user-defined data fields are simple text or more complex multimodal data, the research unit recording environment 112 can automatically generate the corresponding data structure and ensure the consistency and integrity of the data during the input and storage process. Since the automatically generated data structure is based on standardized protocols and definitions, research data can be easily shared globally. This automation of structured storage not only simplifies the work of researchers but also improves the reproducibility of research data and the ability to collaborate across laboratories. Furthermore, with the automatically generated data structure, researchers can focus on experiments and data recording without worrying about underlying data management issues. This feature greatly reduces the data management burden on researchers and improves the efficiency of research work.

[0056] In other embodiments, the research unit recording environment 112 can be further configured to automatically generate a corresponding research unit recording interface based on the research protocol and the data field JSON Schema. The research unit recording interface has a stylized style obtained based on the custom research unit syntax parsing and interactive controls corresponding to the data types of each data field obtained based on the data field JSON Schema parsing. For example, font settings associated with the heading level can be provided according to the basic style of the research unit Markdown. The recording interface of multimodal information with different data types can also be set to form style, embedded WYSIWYG style, etc. as needed. Thus, it can adapt to the diverse needs of research data and effectively improve the convenience and accuracy of researchers in the recording process.

[0057] Figures 8(a) and 8(b) respectively illustrate the form style and WYSIWYG style research unit record interface according to embodiments of this application. Since the research unit model has been converted into a data field JSON Schema in step 401 of Figure 4, for example, the user has defined different data types (such as strings, integers, floating-point numbers, booleans, dates, enumeration values, etc.) for different fields in the model, in this case, appropriate interactive controls can be generated for each data field in the research unit record interface 800 shown in Figures 8(a) and 8(b). More specifically, the research unit record environment 112 can parse the data types of each field in the data field JSON Schema that are specially annotated, and thereby generate the corresponding interactive controls. As an example, in Figures 8(a) and 8(b), interactive controls 801 and 801' are generated based on data fields with a data type annotated as positive integers. Therefore, it can be seen that the automatically generated interactive controls 801 and 801' in the research unit recording interface 800 have buttons for increasing and decreasing values; while interactive controls 802 and 802' are generated based on data fields with a data type of time, and thus use a clock face as a prompt icon. The research unit recording interface 800 helps scientists focus their time and energy on defining and developing the substantive content of research plans, such as research protocols, models, and data fields, without needing to worry about the research recording interface, research data storage structure, and methods. This allows scientists to efficiently design high-quality research plans that meet actual research needs in a user-friendly manner during their daily research activities and use them for research data recording.

[0058] Figure 3(b) illustrates another structural diagram of the research activity management module according to an embodiment of this application. As shown in Figure 3(b), the research activity management module 11 may include a research unit design environment 111, a research unit recording environment 112, and a research report generator and reader 113. The research report generator and reader 113 is configured to, at least in response to a user's first operation on a research unit, run locally and automatically generate a formatted research report for the research unit based on the historical research records of the research unit, enabling the user to read the generated formatted research report locally. Furthermore, the research report generator and reader 113 can support formatted output of report content, such as PDF format, etc., which are not listed here. Users can directly use these reports for literature publication, internal discussion, or printing, greatly simplifying the process of generating research records and reports. It's worth noting that when rendering research reports, only static text files related to the research unit code package, such as research protocols, data field JSON schemas, and JSON records of the research unit, are needed. Furthermore, the data field JSON schema is automatically generated and saved when the platform first loads the user-defined research unit code package. Subsequent times when generating research reports, users do not need to run the Python file of the research unit model or execute Python code again; they only need to call the already generated data field JSON schema file. This eliminates the security risks associated with users reading and generating research reports locally. Therefore, research units can serve as a standard format for research data exchange in the future.

[0059] Figure 1(b) illustrates another component of a research activity management and application platform supporting multidisciplinary sharing according to an embodiment of this application. As shown in Figure 1(b), in addition to the research activity management module 11 and research unit code packages for various disciplines, the research activity management and application platform 10 may also include a research unit sharing and application tool 13. The research unit sharing and application tool 13 may be configured, for example, to: generate a unique ID number for each research record of a research unit and associate the unique ID number with the record time, so that each research record is tamper-proof; when a user wants to modify a submitted research record, a copy of the research record is generated for the user, and a new unique ID number is generated for the copy of the research record and associated with the modification time, so that the user can make modifications on the copy with the new unique ID number. Due to the above-described method of ensuring data tamper-proofing, the research activity management and application platform according to an embodiment of this application can actually serve as an evidence platform for research activity history / records, for example, as corroboration when research evidence is needed for certain matters. For example, in the field of scientific research, there are often disputes about who first discovered a phenomenon. If the relevant researchers recorded the phenomenon on the platform of this application and the relevant scientific research data was left on the platform, then these non-tamperable scientific research records can serve as evidence of their scientific research discovery.

[0060] In other embodiments, the research unit sharing and application tool 13 is further configured to: in response to a second operation of a research unit by a user with corresponding permissions, mark the research unit with a hierarchy, the hierarchy being divided into at least a laboratory level and a project level from high to low, and provide a user access control mechanism corresponding to the public level for each level and / or each research unit; and set a hierarchy mark for each user corresponding to the hierarchical division of the research unit.

[0061] In some embodiments, as examples only, providing corresponding user access control mechanisms for each level and / or each research unit may specifically include the following: setting access permissions for each laboratory level that are open to all users; setting access permissions for each project level that are marked for users at a specific level; and when a specific user has access permissions for a specific project, that specific user has access permissions for all research units in that specific project.

[0062] As an example only, setting access permissions for users tagged with a specific level for each project level can specifically include: setting all users to have access permissions for that project level; or, only users with the same lab level tag to have access permissions for that project level; or, only users with the same project level tag to have access permissions for that project level.

[0063] As an example only, providing corresponding user access control mechanisms for each level and / or each research unit may also include: in response to a third operation by a user with the corresponding permissions, setting a group tag for users with the same laboratory level tag, and setting access permissions for specific project levels for users with each group tag accordingly.

[0064] Other user access control mechanisms can be set up according to the specific characteristics and requirements of the discipline and institution. They will not be listed here. The principle of maximizing sharing under the premise of ensuring the security of experimental plans and experimental data and privacy management requirements is taken as reference.

[0065] Figure 1(c) shows another schematic diagram of the composition of a scientific research activity management and application platform supporting multidisciplinary use according to an embodiment of this application.

[0066] As shown in Figure 1(c), based on the structure of the scientific research activity management and application platform 10 shown in Figure 1(a), it can further include knowledge sharing modules corresponding to each scientific research unit. Figure 9 shows a schematic diagram of the knowledge sharing module according to an embodiment of this application. The knowledge sharing module 900 for the protein purification scientific research unit shown in Figure 9 can support real-time discussions by multiple users, provide a rich text editor, support comment and reply functions, and provide a voting mechanism, etc., which will not be elaborated here. Different scientific research units usually correspond to different disciplines. Therefore, setting up separate knowledge sharing modules can make it more convenient for the maintainers of the scientific research unit or other interested users to provide or obtain more professional knowledge related to the scientific research activities of the scientific research unit in a question-and-answer manner in the knowledge sharing module.

[0067] In this embodiment of the application, the content in the research protocol and the knowledge sharing section can both come from the expert experience and knowledge of community users, and the content in the knowledge sharing section can be dynamically updated in real time on the research activity management and application platform without modifying the research unit code package, which gives the knowledge sharing section a more flexible update feature.

[0068] In other embodiments, the scientific research activity management and application platform 10 can be further configured to use the content in the knowledge sharing section for updating the scientific research protocol of the corresponding scientific research unit. As an example only, for instance, the update of the aforementioned scientific research protocol can be triggered periodically or manually by the user. This allows the latest domain knowledge from the knowledge sharing section to be incorporated into the scientific research unit code package, so that newly generated scientific research units using the same code package have more advanced features within the domain.

[0069] In the case that the scientific research activity management and application platform includes knowledge sharing modules corresponding to each scientific research unit, the scientific research unit sharing and application tool 13 shown in Figure 1(b) can be further configured to support the sharing and application of relevant information and data of scientific research units across laboratories or projects. The relevant information and data include the code package, scientific research records and knowledge sharing modules of the scientific research unit, specifically including the following steps:

[0070] In response to the user's fourth action (not shown), a copy of the code package of the upstream research unit is created and applied to downstream research units with the same or lower public access level. If the user chooses to synchronize research records, the historical research records of the upstream research unit are also linked to the downstream research units. In this way, even if the upstream project is deleted for some reason, the corresponding research units in the downstream project can continue to operate unaffected.

[0071] Next, when the knowledge sharing levels of downstream and upstream research units are the same, the knowledge sharing sections of the upstream and downstream research units are synchronized bidirectionally. When the knowledge sharing level of the downstream research unit is lower than that of the upstream research unit, the knowledge sharing section of the upstream research unit is synchronized unidirectionally to the knowledge sharing section of the downstream research unit. Since an upstream research unit can be applied by multiple downstream projects, a radial network of research units can be formed around a single research unit on the platform, with the upstream research unit as the central node. Through bidirectional synchronization of the knowledge sharing sections, the application of research units gradually evolves from individual behavior to community collaboration. This research unit network naturally evolves into a theme-based community (the theme being the content of the research unit). With continuous application from upstream and downstream, problems related to the research unit can be gradually discovered, and continuous feedback and optimization from the community can continuously improve the research unit in practical application. This community-driven model effectively transforms the accumulation of individual experience into collective wisdom, laying a solid foundation for the long-term development of research units.

[0072] Through the steps described above, the research units designed by users can be applied to different laboratories or projects with a single click on the platform of this application embodiment. In this way, platform users can conveniently use research units designed and provided by others without any prior experience in writing research units, promoting the global sharing of research units and research plans. On the other hand, researchers can share their research plans and data records through the platform, enabling other laboratories to easily reproduce these experiments, improving the reproducibility of research plans and results. Furthermore, it promotes global scientific collaboration by sharing scientific experience across laboratories, projects, and research units.

[0073] Figure 1(d) shows another schematic diagram of a research activity management and application platform supporting multidisciplinary use according to an embodiment of this application. Based on the structure shown in Figure 1(a), the research activity management and application platform 10 may further include an AI system tool 14 centered on a large language model. Figure 5(a) shows a schematic diagram of the chat interface of the AI ​​system tool according to an embodiment of this application.

[0074] As shown in Figure 5(a), the AI ​​system tool 14 is configured to include a chat interface 141. For example, the chat interface 141 is named "AI Masterbrain". The chat interface 141 may automatically generate a research unit code package for the user upon receiving content and instructions from the user's chat conversation, and may also automatically generate such a package upon receiving relevant information introduced through "Add context". Furthermore, the research unit code package can be revised through the user's interaction with the AI ​​system tool 14 in the dialog box of the chat interface 141.

[0075] 5(b) shows a schematic diagram of using AI system tools to perform custom scientific research unit syntax checking according to an embodiment of this application.

[0076] As shown in Figure 5(b), the AI ​​system tool 14 also includes a research unit syntax checker 142, used to check whether the code conforms to the custom research unit syntax. Thus, automatically generating research unit code packages for the user and revising the research unit code packages through user interaction with the AI ​​system tool 14 in the chat interface 141 further includes: when the user provides the AI ​​system tool 14 with a protocol document to be converted into a research protocol in the chat interface 141, injecting the custom research unit syntax and examples of converting reference documents into research protocols as context into the chat dialogue, and causing the large language model to perform the following operations: generating research protocols in the research unit code package based on the chat dialogue with context; generating models of research units in the research unit code package based on the generated research protocols and the chat dialogue with context; and generating assigners for data fields in the research unit code package based on the generated research protocols, models, and the chat dialogue with context. In this way, providing the chat dialogue with contextual information to the AI ​​system tool 14 can help it more accurately determine the data type of the data field, especially for certain data types that require direct reading of the original document to be converted provided by the user for accurate determination. Based on this, the syntax parts corresponding to the model and assigner in the research unit syntax of this application are used to generate the model and data field assigner of the research unit in the research unit code package in a progressive and more accurate manner.

[0077] Based on this, the research unit syntax checker 142 is used to perform a research unit syntax check on the research unit code package generated by the large language model, and the syntax check result is fed back to the large language model so that the large language model can regenerate the research unit code package based on the syntax check result until the syntax in the generated research unit code package is completely correct.

[0078] It is worth noting that, since the platform according to the embodiments of this application is an actively developing framework, and the research unit grammar shared by multiple disciplines in the platform is constantly being updated, in order to ensure that the AI ​​system tools can continuously be compatible with the latest research unit grammar, the fine-tuning model strategy is not adopted in the embodiments of this application. Instead, as mentioned above, the feature of significantly enhanced context length of the large language model (e.g., qwen-long supports 10,000,000 tokens of context, gpt-4o supports 128,000 tokens of context) is utilized to inject the currently used version of the research unit grammar as the context into the dialogue. This method greatly improves the generation efficiency of research units and ensures that the content generated by the large language model conforms to the latest research unit grammar based on a comprehensive understanding of it.

[0079] In other embodiments, when a user asks questions or provides suggestions for modification regarding a specific part of the research unit code package in the chat interface 141, the large language model provides an explanation of the user's question or a revision scheme that meets the user's suggested modification, thereby achieving targeted optimization of the research plan. For example, a user can ask questions about the syntax of the research unit, and the system tools can provide detailed answers.

[0080] With AI-driven automatic generation of research protocols, researchers no longer need to manually write complex protocols, lowering the learning curve for research unit frameworks and simplifying the project initiation process, significantly reducing the difficulty of designing research units. Furthermore, AI can understand the research needs of different disciplines and generate appropriate research protocols for interdisciplinary projects, which is particularly important for projects involving multidisciplinary collaboration, improving the coordination and implementation efficiency of interdisciplinary projects.

[0081] In some embodiments, when a user opens a chat interface 141 under a specific research unit and asks a question, the relevant information of the specific research unit is injected into the chat dialogue as a context, so that the AI ​​system tool 14 generates an answer associated with the specific research unit in the chat 141. The relevant information of the research unit includes at least the research protocol in the code package of the research unit, and the content in the knowledge sharing sections set up corresponding to the research unit that can be obtained according to the public level of the research unit.

[0082] Unlike general chatbots (such as ChatGPT), the AI-assisted QA function provided by the AI ​​system tool has two unique characteristics: First, it can fully utilize research unit information relevant to the dialogue (such as research agreements); second, it can utilize content from knowledge-sharing sections related to the research unit, as well as knowledge resources across the entire platform. Therefore, the deeply integrated AI system tool can more accurately answer user questions about specific research scenarios, enhancing the intelligent support for research data recording and analysis.

[0083] Figure 5(c) illustrates a schematic diagram of injecting relevant information of a research unit as context into an AI system tool chat dialogue according to an embodiment of this application. As shown in Figure 5(c), a user raises the following question in a research unit related to protein purification (Purification of Lipoylated H Protein):

[0084] How can I improve the yield?

[0085] Since the AI ​​system tools inject relevant information about the research unit into the dialogue—in this embodiment, the relevant information being the research unit's research protocol—the following response will be given based on this specific scenario:

[0086] To improve the yield in the protein purification experiment, consider the following strategies:

[0087] 1. Optimize Lysis Conditions: Adjust buffer composition, temperature, and duration to maximize protein release.

[0088] 2. Minimize Protein Loss: Use gentle techniques, keep the process cold, and reduce the number of purification steps.

[0089] 3. Optimize Affinity Chromatography: Fine-tune binding and elution conditions specific to your protein's affinity tag.

[0090] 4. Prevent Proteolysis: Add protease inhibitors that work quickly at cold temperatures to prevent protein degradation.

[0091] 5. Carefully Concentrate and Refold Protein: Use appropriate methods to avoid loss and aggregation during concentration and refolding.

[0092] This demonstrates that the above response is more specific, targeted, and in-depth than the generic ChatGPT response shown in Figure 5(d). This is because the research protocol of the research unit into which the AI ​​system tool injects the dialogue provides clear experimental background information, enabling it to understand that the dialogue takes place in a protein purification experimental scenario and to infer that the user's mention of "the yield" refers to "the yield in the protein purification experiment," thus providing a more professional and targeted response. Therefore, even if the user has no background knowledge of AI, they can automatically provide a response tailored to the specific research unit scenario through simple research unit customization, without any secondary AI development. This greatly improves the practicality and convenience of AI in scientific research applications.

[0093] In some embodiments, such as when a user asks the same question again, the AI ​​system tool can also dynamically retrieve content from the available knowledge-sharing sections in the research unit through a search service and apply it to the answer. This not only enables efficient retrieval of research agreements, research plans, and research data, but also facilitates interdisciplinary research Q&A, improves the shareability of research plans, data, and expert experience within and between laboratories, and even globally, weaving the research knowledge carried by the platform into an organic whole, enabling efficient dissemination and application of knowledge in a broad research community, and promoting cross-project and cross-laboratory research collaboration and knowledge sharing.

[0094] In other embodiments, the AI ​​system tool allows users to manually select the background knowledge that can be applied when the AI ​​system tool answers, using buttons such as "Add context." For example, when the AI ​​system tool is opened in the research agreement interface, it may default to injecting all agreement information related to the relevant research agreement. In other cases, when the user opens the AI ​​system tool in the knowledge sharing section interface, it defaults to injecting the research agreement information and knowledge from the knowledge sharing section contained in that research unit. Of course, users can also configure the scope of the context themselves, such as whether to use knowledge from all knowledge sharing sections under that project, or knowledge from publicly accessible knowledge sharing sections within that laboratory, or even the entire platform, etc. Furthermore, users can also inject historical records as context into the AI ​​system tool, in which case the AI ​​system tool will be able to take historical records into account when intelligently analyzing questions.

[0095] Figure 5(e) illustrates a schematic diagram of analyzing research records using an AI system tool according to an embodiment of this application. As shown in Figure 5(e), when a user opens a chat interface 141 under a specific research unit and requests analysis of research record 143 (the numbers 219, 218, 217, etc. shown in the figure are simplified versions of research record IDs, corresponding to 5 research records with different unique IDs, which respectively correspond to research records #5, #4, and #3 shown in 144), the research protocol and JSON Schema of the research unit's data field are used as the context, so that the AI ​​system tool 14 can generate an analysis report of the research records of the specific research unit based on the historical research records 144 of the specific research unit and the context. Because the embodiments according to this application separate the research unit code package from the research record, meaning that to understand any research record generated based on the same research unit code package, only the research unit code package and its corresponding data field JSON schema need to be provided, even if multiple research records need to be analyzed, as long as they correspond to the same research unit code package, only the research protocol and the research unit data field JSON schema need to be provided once as context, without the need to provide them repeatedly. This saves resources and is more efficient without losing any useful information. Of course, when a deeper analysis and understanding of each research record is required, the model and assigner parts of the research record can be selectively injected into the AI ​​system tool 14 as a context, and this application does not impose any restrictions on this.

[0096] In other embodiments, users may also open the chat interface 141 within a specific research unit and request analysis of research records with a specified analytical intent. In this case, the research agreement corresponding to the research unit, the JSON schema of the research unit's data fields, and the specified analytical intent are used as contextual information. This allows the AI ​​system tool 14 to generate a response or analysis report on the specified analytical intent of the research records of the specific research unit based on the historical research records of that specific research unit and the contextual information. Furthermore, embodiments of this application also support multi-turn dialogues. When a user raises further or completely different analytical intents in different rounds of dialogue, the analysis results of the previous round can be provided to the AI ​​system tool 14 as contextual information, enabling the AI ​​system tool 14 to perform a more in-depth and comprehensive analysis.

[0097] The AI ​​system tool according to embodiments of this application can bridge different fields by understanding research protocols and data from various disciplines. Researchers can use this tool to quickly understand and apply experimental data and methods from other disciplines, greatly reducing knowledge barriers between disciplines. Furthermore, the AI ​​system tool can not only answer protocol-related questions but also provide suggestions based on existing data to help researchers optimize experimental results. It is a powerful assistant / AI mentor for scientists to improve research efficiency and can also facilitate one-click analysis of research data, generation of AI-based heuristic suggestions, and generation of research reports.

[0098] Figure 1(e) shows another schematic diagram of a research activity management and application platform supporting multidisciplinary use according to an embodiment of this application. As shown in Figure 1(e), based on Figure 1(d), the research activity management and application platform 10 may further include a research process integration tool 15.

[0099] In some embodiments, the research process integration tool 15 may be configured, for example, to generate an automatically executable research unit workflow (hereinafter referred to as RUW) based on a user-given research unit workflow diagram (hereinafter also referred to as RUWG) or, based on a user-given research unit workflow diagram and workflow diagram logic (hereinafter also referred to as RUWGL), using the AI ​​system tool 14. The research unit workflow diagram is a directed graph and contains multiple research units, wherein the disciplines corresponding to each research unit are the same or different.

[0100] It is worth noting that the research activity management and application platform according to the embodiments of this application may include research units from any same or different fields. Therefore, the research unit workflow diagram in the embodiments of this application can be any combination of research units from the same or different fields / disciplines in any way. In some other embodiments, it is not even necessary to pre-design a fully-formed workflow diagram. Instead, multiple research units of interest can be selected as nodes in the same research unit workflow diagram as needed, based on the specific goals to be achieved. Furthermore, constraints and limitations can be imposed only when necessary through workflow diagram logic, making the form flexible. As an example only, the above workflow method can be applied to multiple fields such as carbon nanotube material self-dispersion research, new drug discovery, protein engineering, bioengineering fermentation research, chemical synthesis of gold nanoparticles, and single-cell sequencing research. For example, each research unit can represent a step in a research activity of the same or related disciplines. Multiple research units can be combined according to experimental needs to construct complex experimental workflows, thereby helping users to achieve automated management of multiple stages of experimental steps or research projects. As an example, the regular maintenance of a complete set of scientific instruments can be designed as a workflow, with each scientific unit in the workflow representing the maintenance work of different scientific instruments. By automatically designing and executing the workflow, the electronic and intelligent maintenance of scientific instruments can be achieved.

[0101] In other embodiments, each research unit can represent experimental steps from different disciplines. This flexible collaborative approach among research units can also empower interdisciplinary research and greatly promote the efficient generation of multidisciplinary / integrated innovative results. Furthermore, this workflow approach can provide methodological and implementation support for industrialized scientific research that relies on automated research equipment.

[0102] Taking the ultrasonic dispersion study of carbon nanotubes as an example, Figure 6(a) shows a schematic diagram of the research unit workflow according to an embodiment of this application, and Figure 6(b) shows a schematic diagram of the research path (also referred to as the research unit path, hereinafter also denoted as RUP) according to an embodiment of this application.

[0103] In Figures 6(a) and 6(b), RU1-RU4 represent four research units. RU1 represents the preparation of a dispersion from carbon nanotube powder, RU2 represents ultrasonic dispersion, RU3 represents the preparation of a low-concentration dispersion from a high-concentration carbon nanotube dispersion, and RU4 represents the characterization of the dispersion. In actual research on the ultrasonic dispersion of carbon nanotubes, the above research units can be carried out sequentially to achieve specific research objectives. As shown in Figure 6(b), in research path 1, a high-concentration dispersion is first prepared using carbon nanotube powder (RU1). Next, the dispersion is dispersed using ultrasonic technology (RU2). Then, the dispersion result is characterized to verify the effect of this dispersion (RU4). The characterization result shows that the current dispersion structure has not yet reached the expected level. Therefore, the high-concentration dispersion is prepared into a low-concentration dispersion (RU3), and then ultrasonically dispersed again (RU2). Finally, the characterization is performed (RU4). The characterization result reaches the expected level, and the process can be terminated at this point (End).

[0104] However, in real scientific research, other research paths similar to research path 1 are usually impossible to exhaustively list. For example, based on the dispersion characterization results, it can be determined whether re-ultrasonic dispersion and characterization are needed (meaning the RU2→RU4 process needs to be repeated), or whether the dispersion should be further diluted and ultrasonicated before characterization (meaning the RU3→RU2→RU4 process needs to be repeated). The above process can be repeated until a satisfactory dispersion result is obtained. Research paths 2-6 show some possible research paths, but all possible paths are often impossible to exhaustively list. Therefore, Figure 6(a) shows a workflow diagram composed of four research units: RU1, RU2, RU3, and RU4. It can be seen that this is a directed graph that can form multiple research paths. Users can describe a research process by defining such a directed graph, where each directed edge represents the logical relationship between each RU. Such a directed graph can handle logical relationships with cyclic topological structures well. The workflow diagram can be regarded as a "path set" composed of all reasonable research paths. It can be seen that research paths 1-6 in Figure 6(b) all conform to the topological structure of the workflow diagram in Figure 6(a).

[0105] To ensure that the research path generated based on the workflow diagram conforms to the logic of scientific research in this field, corresponding workflow diagram logic can be added to the workflow diagram. In the above embodiment, the workflow diagram logic may include the following four items:

[0106] 1. The entire dispersion process must be carried out in a solution system; preparing a dispersion from a solid powder is only the first step in the experiment: RU1 must be the starting point of the research path.

[0107] 2. Each distributed system must go through the preparation, ultrasonic treatment and characterization stages: a study path must include at least one (RU1→RU2→RU4) instance, and this order is irreversible.

[0108] 3. Based on the characterization results, determine whether: 1) the sample needs to be sonicated again (RU4→RU2), or 2) the dispersion solution needs to be further diluted before sonication (RU4→RU3→RU2). After repeating either of these two paths, characterization (RU4) must be performed again to confirm the subsequent results. Based on the results of RU4, these two paths can be followed alternately.

[0109] 4. The characterization process (RU4), as the only quality control step in the experiment, can appear in the middle of the steps, but must always be the last step in the research path.

[0110] 5. The research path can be terminated when the characterization results (RU4) meet the research objectives.

[0111] In the process of conducting scientific research according to a research path generated based on a workflow diagram, the obtained scientific research data can be considered as the scientific research records generated by the scientific research units on that path conducting research in a connected order. Figure 6(c) shows a schematic diagram of the scientific research data generated by executing the research path according to an embodiment of this application. Figure 6(c) shows the scientific research records generated sequentially by each scientific research unit when executing research path 1 in Figure 6(b), wherein RUR i This represents the research record generated in step i, with the superscript RU. j This indicates that the research record was created by RU. j The above research data can also be represented as a list of research records as shown in Figure 6(c).

[0112] Figure 7 illustrates a schematic diagram of the process of generating an automatically executable research unit workflow using AI system tools according to an embodiment of this application. As shown in Figure 7, the specific process of generating an automatically executable research unit workflow using AI system tools is as follows.

[0113] In step 701, based on the research unit workflow diagram given by the user and the current research intention (hereinafter also referred to as RP) automatically generated by the user / AI system tool, and combined with the research progress of the previous research units in the research unit workflow, the AI ​​system tool can be used to determine whether there is a feasible research strategy for the current research intention. If the determination is "yes" in step 701, proceed to step 702.

[0114] Suppose the user provides any research unit workflow (RUW) defined by the research unit workflow diagram (RUWG) and workflow diagram logic (RUWGL), the RUW can be expressed as follows (1)-(3): RUW=(RUWG,RUWGL) (1) RUWG=(RUs,RUEs) (2) RUs={RU1,RU2,...,RU m} (3)

[0115] In equation (3), RUs represents the set of research units that make up RUWG, and RUEs in equation (2) represents the set of edges in RUWG.

[0116] The work to be accomplished by AI system tools can be expressed as follows (4): (RUW,RP) → Automation process? →RC (4)

[0117] That is, given a RUW and RP, can we use the RUs in the RUW to generate a RUP, obtain the RURs corresponding to each RU in this RUP, and finally obtain the RC (research conclusion) corresponding to the RP? The above problem can be described by the following equations (5)-(8): RC=AI1(RUW,RP,RURs) 1→n (5)

[0118] Where n represents the total number of RUs traversed on the research path RUP. This represents the i-th RU visited on the path. Note that i only represents the location number, not the RU number. Represents the i-th RU (i.e., ...) on the path. The generated RURs. Thus, a sequence of RURs is obtained. 1→n This includes the RURs generated by the corresponding RUs in steps 1 to n of the RUP. Therefore, as shown in equation (5), through comprehensive analysis of RUWs and RURs... 1→n The characteristics of (containing RUP information) can be automatically analyzed by AI1 to obtain the RC corresponding to RP. Here, AI1 refers to a certain artificial intelligence algorithm that has been trained to solve the above problems. It can be a single deep learning network or it can be composed of multiple sub-networks with different functions. This application does not limit it in this regard.

[0119] The above process can be further decomposed into the following equation (9):

[0120] In the above process, when a research proposal (RP) is given, we can have the AI ​​system tool design a feasible research strategy (RS) for that RP, combining various information from the research resource (RUW). The core purpose of this step is to allow the AI ​​system tool to comprehensively consider the RUW and the corresponding RP, and to carry out the following two key steps: 1. Determine whether the RP can be properly solved through a reasonable application of the RUW; 2a. If the determination is no, the user should be directly informed that the RP is not suitable for solving with the RUW; 2b. If the determination is yes, then what specific RS should be applied to achieve the RP, and provide the specific RS, thereby guiding how to select a suitable RU through automated methods to provide guidance for scientific research. As an example, the RUW related to the self-dispersion research of carbon nanotubes shown in Figures 6(a) and 6(b) is obviously not applicable to the RP of "studying how cells undergo mitosis". Therefore, in the embodiments of this application, the rationality of the RP can be determined first. If the RP is determined to be unreasonable, the conclusion will be that the RUW cannot support the current RP, that is, there is no feasible research strategy for the current scientific research intention. In other embodiments, different RPs actually require different research strategies. For example, for the RUWs related to the self-dispersion research of carbon nanotubes shown in Figures 6(a) and 6(b), the two different RPs, "investigating how to disperse carbon nanotubes to an average diameter of 20-30 nm using m-cresol with as little sonication as possible" and "investigating how to disperse carbon nanotubes to an average diameter of 20-30 nm using m-cresol with as little dilution as possible", require different RSs. The generation of the above RSs can be achieved by the method represented by the following equation (10): RS∨End=AI2(RUW,RP) (10)

[0121] Equation (10) above indicates that AI method 2 (AI2) automatically determines whether a given RUW can support the research of RP. If so, RS is given; otherwise, the RUP is terminated (End). Here, AI2 can be an AI method under the same framework as AI1, or it can be an independent AI method specifically for RS generation. This application does not limit this.

[0122] Next, if the AI ​​system tool determines that there is a feasible research strategy for the current research objective, in step 702, it automatically selects the next research unit to conduct research in the research unit workflow, or terminates the current research path based on the research progress of each research unit. It is understood that the current research path will only be terminated under the following two circumstances: 1. Each research unit has completed its own research, achieved the required research objective, and obtained the corresponding research conclusions; 2. Despite multiple attempts to apply the RUs involved in RUW, the target RP still cannot be achieved.

[0123] After obtaining RS, the first one can be selected automatically according to the following formula (11).

[0124] Similarly, AI3 can be an AI method within the same framework as AI1 or AI2, or it can be a separate AI method specifically for RU selection. This application does not impose any restrictions on this.

[0125] Then, through application and execution That is, when After completing the scientific research, one can obtain its corresponding... Based on this, the AI ​​system tools can be used to analyze... To obtain a preliminary conclusion (RC1). This preliminary conclusion can include a rich dimension of meaning beyond the simple literal meaning of "summary", such as: 1) what preliminary conclusions the current RUR can provide for the RP; 2) whether it is sufficient to respond to the RP; 3) if it is insufficient to respond, what further plans are there for subsequent scientific research; 4) due to the great unknown and uncertainty of scientific research, the AI ​​system tool can also reflect on the rationality of the RS at this time, and if it is unreasonable, how to make appropriate adjustments; 5) whether any noteworthy anomalies / special phenomena and / or anomalous / special data were found in the research process, etc., which will not be listed here. This process can be expressed as the following formula (12):

[0126] Similarly, AI4 can be an AI method within the same framework as AI1, AI2, or AI3, or it can be a separate AI method specifically for generating RC. This application does not impose any restrictions on this.

[0127] In subsequent steps, equations (11)-(12) can be repeated to select each RU on the RUP sequentially until the end. The general representation of this process is shown in equations (13)-(15): RCs 1→i-1 =[RC1,RC2,...,RC i-1 (13)

[0128] RC i =AI4(RUW,RP,RS,RURs) 1→i ,RCs 1→i-1 (15)

[0129] In equations (13)-(15), when i = 1, RCs 1→0 =[], RURs 1→0= [], then equation (14) and equation (11) have the same form, and equation (15) and equation (12) have the same form.

[0130] In this embodiment of the application, when a feasible research strategy exists, after each research unit in the research path completes its research, the AI ​​system tool can be used to automatically generate interim conclusions based on the historical research records of the research path. Considering the interim conclusions, the system can automatically select the research unit to carry out the next research in the workflow of the research unit.

[0131] In some embodiments, the interim conclusions may include, for example, whether historical research record data can meet the current research intent, whether the current research strategy is effective, whether it needs to be corrected or optimized based on actual research data, and suggestions for the next step, etc. They may also be other results that evaluate previous research processes or research results based on certain evaluation criteria; this application does not specifically limit these. In other words, the research unit to be conducted in the next step is not selected based on a static strategy, but is related to the actual research records of the current research unit, and even the interim conclusions formed by previous research processes. Thus, not only can a workflow diagram for any user-designed or given research unit be generated in a general way to automatically execute research paths, but the research process can also be dynamically adjusted to make the entire research process approach optimal, promoting the efficient execution of iterative optimization research.

[0132] Next, in step 703, after the current research path is completed, the AI ​​system tools are used to automatically generate a research conclusion for the research unit workflow and the current research intent given by the user, based on the historical research records of the current research path.

[0133] Figure 10 illustrates a schematic diagram of research conclusions automatically generated by an AI system tool according to an embodiment of this application. As shown in Figure 10, research conclusions can include multiple aspects, such as whether the research conducted at each node in the workflow diagram has achieved the research intent given by the user, or whether any special phenomena, data, or process discoveries worthy of further research have been observed throughout the entire research path history, or suggestions for potential new research directions and strategy optimizations discovered in the future, etc. This application does not impose any limitations on these aspects.

[0134] In other words, during the generation and execution of research pathways, sequences of RCs are actually generated. 1→n (n represents the total number of times RU was applied in this RUP). These intermediate conclusions can also help generate the final scientific research conclusions. Therefore, equation (5) can be extended to the following equation (16): RC=AI1(RUW,RP,RS,RURs)1→n ,RCs 1→n (16)

[0135] In some embodiments, since an RU is essentially defined as a research scheme with adjustable space, this adjustable space actually comes from the data fields defined in the research protocol of the RU. For example, in the ultrasonic dispersion RU in Figures 6(a) and 6(b), a data field related to ultrasonic time is defined in its research protocol. That is, if you want to apply the RU, you need to know the value of the data field first, and then you can obtain the corresponding research record. Usually, this part of the data field can be called parameter data field (while others can be called feedback data field). Therefore, while automatically selecting the research unit to carry out the next research research in the research unit workflow diagram, it is also necessary to further use AI system tools to automatically design research parameters that meet the current research intention for the research unit that should carry out the next research research. The predicted research parameters will be assigned to the various parameter data fields defined in the research protocol of the research unit, so that the research unit can carry out research when the parameters are determined. The research parameters that meet the current research intention can be automatically derived using AI system tools as follows (17):

[0136] in, Representative research record The collection of all data fields in In the context of AI5, it refers to a collection of parameter-type data fields. AI5 can be an AI method within the same framework as AI1, AI2, AI3, or AI4, or it can be an independent AI method specifically designed to derive the values ​​of parameter-type data fields. This application does not impose any restrictions on this.

[0137] Therefore, in the embodiments according to this application, for the first time, a series of AI automation methods such as AI1-AI5 in the AI ​​system tools are creatively and universally implemented to coordinate the application and development of each RU, and to achieve AI research automation under any given workflow diagram (or workflow diagram + workflow diagram logic) and research intention.

[0138] In other embodiments, the research workflow integration tool can be further configured to integrate the generated, automatically executable research unit workflows into a single research unit code package. Specifically, for example, the various research units to be applied in the research unit workflow can be defined first in any research project. It is worth noting that these research units should be defined under the same research project in the same laboratory. As mentioned above, since a research unit workflow is essentially to obtain a list of research records related to the research unit workflow, users can define the research unit workflow in a workflow-type research unit variable with an ID such as cnt_dispersion in the research protocol. In this way, a research unit workflow can be integrated into a single research unit code package, thereby enabling it to be shared with all users worldwide in the same way as the research unit code package.

[0139] As more institutions and laboratories utilize the multidisciplinary research activity management and application platform supported by the embodiments of this application, the most extensive research activities occurring in each laboratory will be efficiently digitized and electronically processed. This will provide firsthand data nourishment from the real front lines of research for future AI research models. Whether these research data are used for AI applications by the laboratories themselves or through data collaboration with AI researchers / laboratories, this data will become nourishment for the next generation of artificial intelligence. Consequently, it will play a more important role in promoting the spiral progress of data and intelligence, fostering the formation of a unified global interdisciplinary scientific community and social network, promoting global research equality, facilitating the rapid release and sharing of research data, promoting the circulation of research data as an asset, promoting global research cooperation and division of labor, and promoting research automation.

[0140] Furthermore, the research activity management and application platform according to the embodiments of this application is also applicable to various scenarios with potential customized recording needs. As an example only, it can be directly applied to electronic medical record scenarios. Similar to the scientific research described in other embodiments of this application, in medical / hospital / clinical scenarios, various institutions / departments have a need for medical data recording (such as electronic medical records). However, the content, type, and specifications of the data to be recorded vary between different departments, different diseases, and different medical scenarios. It is conceivable that if each department records data in its own customized way, it will not only be inefficient but also hinder sharing and experience accumulation. With the help of the research activity management and application platform in the embodiments of this application, users from different professional fields can customize the protocols, models, and assigners of medical records according to the research unit syntax. This allows for tailored medical recording methods for specific needs, and the customized research unit code package can be widely shared across different departments. This enables a single design for hospital-wide / cross-hospital application, achieving unification and standardization for specific medical records. Therefore, this application has enormous application potential and significant economic benefits in multiple industries.

[0141] Furthermore, although exemplary embodiments have been described herein, their scope includes any and all embodiments based on this application that have equivalent elements, modifications, omissions, combinations (e.g., schemes involving intersections of various embodiments), adaptations, or alterations. Elements in the claims will be interpreted broadly based on the language used in the claims and are not limited to the examples described in this specification or during the implementation of this application, which will be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered illustrative only, and the true scope and spirit are indicated by the full scope of the claims and their equivalents.

[0142] The above description is intended to be illustrative and not restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. Other embodiments can be used by those skilled in the art when reading the above description. Furthermore, in the above detailed description, various features may be grouped together to simplify the application. This should not be construed as an intention that a disclosed feature not claimed is necessary for any claim. Rather, the subject matter of the application may be less than all the features of a particular disclosed embodiment. Thus, the claims are incorporated herein by reference as examples or embodiments, wherein each claim is an independent, separate embodiment, and these embodiments are contemplated as being able to be combined with each other in various combinations or arrangements. The scope of this application should be determined by reference to the claims and the full scope of their equivalents.

[0143] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A research activity management and application platform supporting multidisciplinary collaboration, characterized in that, It includes a research activity management module deployed in the cloud, and is configured to enable users to manage research activities by running the research activity management module without installing additional software; The research activity management module includes a research unit design environment and research unit code packages for different disciplines designed and generated by the user based on a multidisciplinary research unit syntax defined by the research unit design environment. Each research unit code package contains at least a research protocol for the corresponding discipline. The research protocol is used to define the basic information, multimodal information and data fields of the research experiment of that discipline.

2. The scientific research activity management and application platform according to claim 1, characterized in that, The research unit design environment is further configured such that the research unit code package designed by the user for the corresponding discipline contains a model of the research unit. This model is used to define the type constraints and / or numerical validation relationships of the data fields, as well as the combination validation relationships of the types and / or values ​​between the various data fields; wherein... The type constraint includes constraining the corresponding data field to use predefined multimodal information during data entry, wherein the predefined multimodal information includes one or more of text, images, videos, audio, and files; The numerical verification relationship includes constraining the corresponding data fields to follow a specified pattern and / or not exceed a preset value range when entering data; The combined verification relationship includes constraining the types and / or values ​​of each data field to meet predetermined constraints.

3. The scientific research activity management and application platform according to claim 2, characterized in that, The research unit design environment is further configured such that the research unit code package designed by the user for the corresponding discipline includes an assigner for data fields. This assigner is used by the user to assign values ​​to data fields based on a data field dependency graph and assignment rules. The data field dependency graph is a single-level or multi-level directed acyclic graph. The assignment relationships between the defined data fields are single or multiple dependencies, and each data field is assigned a value by at most one assigner.

4. The scientific research activity management and application platform according to any one of claims 1-3, characterized in that, The research unit syntax provides users with template syntax for data fields, enabling users to generate research unit variables, research unit steps, or research unit checkpoints with unique IDs based on the template syntax of the data fields.

5. The scientific research activity management and application platform according to claim 3, characterized in that, The scientific research activity management module also includes a scientific research unit recording environment, which is configured as follows: Convert the research unit model into a JSON schema for data fields; Based on the JSON Schema of the data fields, a structured storage scheme is automatically generated for the research unit. This allows the research records to be stored as JSON that conforms to the structured storage scheme corresponding to the research unit, provided that the data fields conform to the constraints and validation relationships of the model when the user submits the storage of research records based on the research unit.

6. The scientific research activity management and application platform according to claim 5, characterized in that, The research unit's recording environment is further configured as follows: Based on the research protocol and the JSON Schema of the data fields, a corresponding research unit record interface is automatically generated for each research unit. The research unit record interface has a stylized style obtained based on the parsing of the custom research unit syntax and interactive controls corresponding to the data types of each data field obtained based on the parsing of the JSON Schema of the data fields.

7. The scientific research activity management and application platform according to claim 1, characterized in that, The scientific research activity management module further includes a scientific research report generator and reader, which is configured to: respond to the user's first operation on the scientific research unit, based on the historical scientific research records of the scientific research unit, run locally and automatically generate a formatted scientific research report of the scientific research unit, and enable the user to read the generated formatted scientific research report locally.

8. The scientific research activity management and application platform according to any one of claims 1-3, characterized in that, The scientific research activity management and application platform further includes scientific research unit sharing and application tools, which are configured as follows: A unique ID number is generated for each research record in the research unit, and the unique ID number is associated with the record time to ensure that each research record is tamper-proof; When a user wants to modify a submitted research record, a copy of the research record is generated for the user, and a new unique ID number is generated for the copy of the research record and associated with the modification time, so that the user can make modifications on the copy with the new unique ID number.

9. The scientific research activity management and application platform according to claim 8, characterized in that, The research unit sharing and application tools are further configured as follows: In response to a second operation by a user with appropriate permissions on a research unit, the research unit is assigned a hierarchy, which is divided into at least a laboratory level and a project level from high to low. A user access control mechanism corresponding to the public level is provided for each level and / or each research unit. Set hierarchical labels for each user that correspond to the hierarchical division of scientific research units.

10. The scientific research activity management and application platform according to claim 9, characterized in that, Providing corresponding user access control mechanisms for each level and / or each research unit specifically includes: Configure access permissions for each laboratory level that are open to all users; Set access permissions for users at specific project levels; If a specific user has access to a specific project, that specific user has access to all research units within that specific project.

11. The scientific research activity management and application platform according to claim 10, characterized in that, Setting access permissions for specific user levels for each project layer includes: Configure all users to have access to this project layer; or... Only users with the same lab layer tag have access to this project layer; or... Only users with the same project layer tag have access to that project layer.

12. The scientific research activity management and application platform according to claim 9, characterized in that, Providing corresponding user access control mechanisms for each level and / or each research unit also includes: In response to a third action by a user with the appropriate permissions, a group tag is set for users with the same lab level tag, and access permissions to specific project levels are set for users with each group tag accordingly.

13. The scientific research activity management and application platform according to any one of claims 1-3, characterized in that, The research activity management and application platform further includes a knowledge sharing section corresponding to each research unit, so that the maintainer of the research unit or other interested users can provide or obtain knowledge related to the research activities of the research unit in a question-and-answer manner in the knowledge sharing section.

14. The scientific research activity management and application platform according to claim 13, characterized in that, The research activity management and application platform is further configured to use the content in the knowledge sharing section for updating the research agreements of the corresponding research units.

15. The scientific research activity management and application platform according to claim 9, characterized in that, The research activity management and application platform further includes a knowledge sharing section corresponding to each research unit, so that the maintainer of the research unit or other interested users can provide or obtain knowledge related to the research activities of the research unit in the form of questions and answers in the knowledge sharing section. The research unit sharing and application tool is further configured to support the sharing and application of relevant information and data of research units across laboratories or projects. This relevant information and data includes the research unit's code package, research records, and knowledge sharing modules, specifically including: In response to the user's fourth action, a copy of the code package of the upstream research unit is created and applied to the downstream research unit with the same or lower public level. If the user chooses to synchronize research records, the historical research records of the upstream research unit are also linked to the downstream research unit. When the knowledge sharing levels of downstream and upstream research units are the same, the knowledge sharing sections of the upstream and downstream research units are synchronized bidirectionally; when the knowledge sharing level of the downstream research unit is lower than that of the upstream research unit, the knowledge sharing section of the upstream research unit is synchronized unidirectionally to the knowledge sharing section of the downstream research unit.

16. The scientific research activity management and application platform according to any one of claims 1-3, characterized in that, The scientific research activity management and application platform further includes an AI system tool with a large language model as its core. The AI ​​system tool is configured to include a chat interface and can automatically generate scientific research unit code packages for users. Users can revise the scientific research unit code packages by interacting with the AI ​​system tool in the chat interface.

17. The scientific research activity management and application platform according to claim 16, characterized in that, The AI ​​system tools are further configured to include a research unit syntax checker, used to check whether the code conforms to the custom research unit syntax; The system automatically generates research unit code packages for users and revises these packages through user interaction with the AI ​​system tools in a chat interface. This further includes: When a user provides a protocol document to be converted into a research protocol to the AI ​​system tool in the chat interface, the custom research unit syntax and examples of converting reference documents into research protocols are injected into the chat dialogue as context. This enables the large language model to: generate research protocols in the research unit code package based on the contextualized chat dialogue; generate models of research units in the research unit code package based on the generated research protocols and the contextualized chat dialogue; and generate assigners for data fields in the research unit code package based on the generated research protocols, models, and the contextualized chat dialogue. The research unit syntax checker is used to check the research unit code package generated by the large language model, and the syntax check result is fed back to the large language model so that the large language model can regenerate the research unit code package based on the syntax check result until the syntax of the generated research unit code package is completely correct. When a user asks a question or provides suggestions for modification regarding a specific part of the research unit code package in the chat interface, the large language model provides an explanation of the user's question or a revision plan for the specified part.

18. The scientific research activity management and application platform according to claim 16, characterized in that, The AI ​​system tools are further configured as follows: When a user opens a chat interface and asks a question under a specific research unit, the relevant information of that specific research unit is injected into the chat dialogue as a context, so that the AI ​​system tool can generate an answer related to that specific research unit in the chat interface. The relevant information of the research unit includes at least the research protocol in the research unit's code package, as well as the content in the knowledge sharing sections set up corresponding to the research unit that can be obtained according to the public level of the research unit.

19. The scientific research activity management and application platform according to claim 16, characterized in that, The AI ​​system tools are further configured as follows: When a user opens a chat interface under a specific research unit and requests analysis of research records, the research agreement and JSON Schema of the research unit's data fields are used as the context, so that the AI ​​system tool can generate an analysis report of the research records of the specific research unit based on the historical research records of the specific research unit and the context. or, When a user opens a chat interface under a specific research unit and requests analysis of research records with a specified analytical intent, the research agreement corresponding to the research unit, the JSON Schema of the research unit's data fields, and the specified analytical intent are used as the context. This enables the AI ​​system tool to generate a response or analysis report on the specified analytical intent of the research records of the specific research unit based on the historical research records of the specific research unit and the context.

20. The scientific research activity management and application platform according to claim 16, characterized in that, The scientific research activity management and application platform further includes a scientific research process integration tool, which is configured as follows: Based on a user-provided research unit workflow diagram, or a user-provided research unit workflow diagram and workflow diagram logic, an AI system tool is used to generate an automatically executable research unit workflow. The research unit workflow diagram is a directed graph containing multiple research units, and the disciplines corresponding to each research unit may be the same or different.

21. The scientific research activity management and application platform according to claim 20, characterized in that, The specific methods for generating automated research unit workflows using AI system tools include: Based on the research unit workflow diagram and the current research intention given by the user, and combined with the research progress of the preceding research units in the research unit workflow, the AI ​​system tools are used to determine whether there is a feasible research strategy for the current research intention. If a feasible research strategy exists: automatically select the next research unit to conduct research in the research unit workflow, or terminate the current research path based on the research progress of each research unit. After the current research path is completed, AI system tools are used to automatically generate a workflow diagram for the research unit and research conclusions based on the historical research records of the current research path and the current research intent given by the user.

22. The scientific research activity management and application platform according to claim 21, characterized in that, The automatic selection of the next research unit to conduct research in the research unit workflow diagram further includes: automatically designing research parameters that meet the current research intentions for the research unit that should conduct research in the next step.

23. The scientific research activity management and application platform according to claim 21, characterized in that, When a feasible research strategy exists: The research unit that should be the next step in the research unit workflow diagram is automatically selected, further including: When a feasible research strategy exists: After the research is completed in the current research unit, the AI ​​system tool is used to automatically generate interim conclusions based on the historical research records of the research path. Taking into account the interim conclusions, the research unit to carry out the next research is automatically selected in the workflow of the research unit.

24. The scientific research activity management and application platform according to claim 20, characterized in that, The research workflow integration tool is further configured as follows: The generated, automatically executable research unit workflows are integrated into a single research unit code package.

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