Laboratory whole-process intelligent management system and method

Abstract

The invention provides a laboratory full-process intelligent management system and method, and the system comprises a configuration module which is used for carrying out the project configuration of a plurality of experiment projects, and obtaining a basic business process; the management module is used for processing the basic business process according to the user information and a target experiment item when the user inputs the target experiment item to obtain a target business process; and the supervision module is used for monitoring the target business process in real time, obtaining the execution condition, generating a dynamic management strategy for the target business process according to the execution condition, and optimizing the target business process according to the dynamic management strategy until the experiment project is completed. Reliable whole-process intelligent management of experiment items is ensured, and the whole-process intelligent management effect of a laboratory is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a laboratory full-process intelligent management system and method. Background Art

[0002] At present, laboratories play a vital role in many fields such as modern scientific research, teaching, and testing. Therefore, laboratory management is particularly important.

[0003] However, traditional laboratory management methods often focus on managing each step accordingly. Once an abnormality occurs in one step, the entire experimental data or experimental results will be abnormal. At the same time, the management of laboratory equipment is inefficient. Equipment reservations, usage records, maintenance and other operations often rely on manual records, and it is impossible to effectively understand the full process of laboratory equipment use, which greatly reduces the effectiveness of laboratory management standards and safety assurance.

[0004] Therefore, in order to overcome the above-mentioned defects, the present invention provides a laboratory full-process intelligent management system and method. Summary of the Invention

[0005] The present invention provides a laboratory full-process intelligent management system and method, which is used to accurately and effectively determine the basic business process by configuring the experimental project. At the same time, when a user inputs a target experimental project, the basic business process is processed according to the user information to accurately and effectively determine the target business process corresponding to the target experimental project. Finally, the target business process is monitored in real time, and dynamic management and optimization of the target business process according to the monitoring results are achieved, thereby ensuring reliable full-process intelligent management of the experimental project and guaranteeing the full-process intelligent management effect of the laboratory.

[0006] A full-process intelligent management system for laboratories, including:

[0007] Configuration module, used to configure several experimental projects and obtain basic business processes;

[0008] The management module is used to process the basic business process according to the user information and the target experimental project when the user enters the target experimental project, and obtain the target business process;

[0009] The supervision module is used to monitor the target business process in real time, obtain the execution status, and generate a dynamic management strategy for the target business process based on the execution status. The target business process is optimized according to the dynamic management strategy until the experimental project is completed.

[0010] Preferably, a laboratory full-process intelligent management system configuration module includes:

[0011] A reading unit is used to determine a number of experimental projects in the laboratory and read the multi-dimensional experimental correlation factors of each experimental project; wherein the multi-dimensional experimental correlation factors include: experimental operation procedures associated with the experimental project, experimental equipment associated with the experimental project, and personnel information associated with the experimental project;

[0012] The data configuration unit is used to collect data based on the experimental correlation factors of each dimension. At the same time, it obtains the management requirements corresponding to the experimental correlation factors of each dimension, and configures the data sets corresponding to the experimental correlation factors of each dimension according to the management requirements; and determines the basic business processes corresponding to each experimental project based on the data configuration results.

[0013] Preferably, a laboratory full-process intelligent management system, a data configuration unit, includes:

[0014] A data processing subunit, configured to:

[0015] Collect all experimental data of the historical experimental project, read the project label of each experimental project, and classify all experimental data according to the project label to obtain the experimental data set corresponding to each historical experimental project;

[0016] According to the multi-dimensional experimental correlation factors of the experimental projects, the experimental data sets corresponding to the historical experimental projects are subjected to a second classification to obtain the sub-experimental data sets corresponding to the experimental correlation factors of each dimension;

[0017] Data configuration subunit, used for:

[0018] Obtain the management requirements corresponding to the experimental correlation factors of each dimension, and extract the demand keywords of the management requirements and the correlation logic between the demand keywords;

[0019] According to the demand keywords of management needs and the association logic between demand keywords, the corresponding sub-experimental data sets will be configured. At the same time, the data configuration results of each sub-experimental data set will be integrated to generate the basic business process corresponding to the experimental project.

[0020] Preferably, in a laboratory full-process intelligent management system, in the data configuration unit, the management requirements corresponding to the experimental correlation factors of each dimension include:

[0021] When the experimental correlation factor is the experimental operation process associated with the experimental project, the corresponding management requirements are the standardization degree of each process step in the experimental operation process and the execution order of each process step;

[0022] When the experimental correlation factor is the experimental equipment associated with the experimental project, the corresponding management requirements are: the experimental equipment corresponding to each process step in the experimental operation process, the linkage management of the experimental equipment, the working status of the experimental equipment, the name label of the experimental equipment, and the usage of the experimental equipment;

[0023] When the experimental association factor is the personnel information associated with the experimental project, it includes personnel role management and personnel experimental authority management.

[0024] Preferably, a laboratory full-process intelligent management system, the management module includes:

[0025] A user information identification unit is used to identify user information and determine user permissions when the user enters a target experimental project;

[0026] The matching unit is used to read the target project tag of the target experimental project, match the experimental data in the preset intelligent project management library according to the target project tag to determine the target basic business process, and extract the target basic business process according to user permissions to generate the target business process of the target user based on the target experimental project.

[0027] Preferably, a laboratory full-process intelligent management system, a supervision module, includes:

[0028] Monitoring unit for:

[0029] Read the process structure of the target business process and determine the business links included in the target business process based on the process structure;

[0030] Based on the business links, a parallel monitoring mechanism is configured for the target business process. At the same time, the monitoring focus and monitoring parameters of each business link are obtained, and the parallel monitoring mechanism is configured based on the monitoring focus and monitoring parameters of the monitoring focus;

[0031] Based on the configuration results, the parallel monitoring mechanism is controlled to independently monitor each business link in the target business process;

[0032] The result determination unit is used to obtain multi-dimensional monitoring results of each monitoring focus in each business link based on independent monitoring results, and logically associate the multi-dimensional monitoring results of each business link based on the business logic between each business link to obtain the final execution status.

[0033] Preferably, a laboratory full-process intelligent management system, a supervision module, includes:

[0034] Policy development unit, used to:

[0035] Determine the execution parameters of the target business process for the target experimental project based on the execution status, and quantify the execution parameters based on the project composition of the target experimental project to obtain the quantitative parameters of each project body;

[0036] Obtain the expected execution results of the target experimental project, and compare the quantitative parameters of each project subject with the corresponding expected execution results;

[0037] Determine the current execution offset of each project entity based on the difference comparison result, and determine the dynamic optimization index for each project entity based on the execution offset;

[0038] Summarize the dynamic optimization indicators of each project entity to obtain the dynamic management strategy at each moment;

[0039] The optimization management unit is used to adjust the local parameters of the target business process based on the dynamic management strategy, and continuously process the target experimental project based on the local parameter adjustment results until the experimental project is completed.

[0040] Preferably, a laboratory full-process intelligent management system, in the supervision module, also includes: a report generation unit, which is used to collect the full-process data of the target experimental project when the experimental project is completed, and generate a project report based on the full-process data.

[0041] A laboratory full-process intelligent management method, including:

[0042] Step 1: Configure several experimental projects to obtain basic business processes;

[0043] Step 2: When the user enters the target experimental project, the basic business process is processed according to the user information and the target experimental project to obtain the target business process;

[0044] Step 3: Monitor the target business process in real time, obtain the execution status, and generate a dynamic management strategy for the target business process based on the execution status. Optimize the target business process according to the dynamic management strategy until the experimental project is completed.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] By configuring the experimental projects, the basic business processes can be accurately and effectively determined. At the same time, when the user inputs the target experimental project, the basic business process is processed according to the user information to accurately and effectively determine the target business process corresponding to the target experimental project. Finally, the target business process is monitored in real time, and dynamic management and optimization of the target business process is achieved based on the monitoring results, ensuring reliable full-process intelligent management of the experimental projects and guaranteeing the full-process intelligent management effect of the laboratory.

[0047] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0048] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 This is a structural diagram of a laboratory full-process intelligent management system in an embodiment of the present invention;

[0051] Figure 2 This is a structural diagram of a configuration module in a laboratory full-process intelligent management system according to an embodiment of the present invention;

[0052] Figure 3 The present invention provides a flow chart of a method for intelligent management of the entire laboratory process. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0054] Example 1:

[0055] This embodiment provides a laboratory full-process intelligent management system, such as Figure 1 Shown, including:

[0056] Configuration module, used to configure several experimental projects and obtain basic business processes;

[0057] The management module is used to process the basic business process according to the user information and the target experimental project when the user enters the target experimental project, and obtain the target business process;

[0058] The supervision module is used to monitor the target business process in real time, obtain the execution status, and generate a dynamic management strategy for the target business process based on the execution status. The target business process is optimized according to the dynamic management strategy until the experimental project is completed.

[0059] In this embodiment, project configuration refers to parameter configuration based on personnel and processes required by the experimental project.

[0060] In this embodiment, the basic business process refers to: the experimental personnel corresponding to each experimental project (including the identity information of each experimental personnel: such as experimenter, administrator, etc.), the experimental process of each experimental project and the experimental equipment corresponding to the experimental project (the equipment and the experimental process correspond to each other).

[0061] In this embodiment, the target experimental project refers to a specific experimental project that needs to be performed currently.

[0062] In this embodiment, the target business process refers to a specific business process that is obtained by adjusting the basic business process according to user information and the specific conditions of the current experimental project and is capable of processing the target experimental project.

[0063] In this embodiment, the execution status refers to parameters such as the current execution progress and specific execution degree of the target business process.

[0064] In this embodiment, the dynamic management strategy is generated based on the execution situation, and is used to dynamically adjust the execution process when the target business process executes the target experimental project. For example, it can be a specific method or plan for adjusting the target business process when an exception occurs during the execution process.

[0065] The beneficial effects of the above technical solution are: by configuring the experimental projects, the basic business processes can be accurately and effectively determined. At the same time, when the user inputs the target experimental project, the basic business process is processed according to the user information to achieve accurate and effective determination of the target business process corresponding to the target experimental project. Finally, the target business process is monitored in real time, and dynamic management and optimization of the target business process is achieved based on the monitoring results, ensuring reliable full-process intelligent management of the experimental projects and guaranteeing the full-process intelligent management effect of the laboratory.

[0066] Example 2:

[0067] Based on Example 1, this example provides a laboratory full-process intelligent management system, such as Figure 2 As shown, the configuration module includes:

[0068] A reading unit is used to determine a number of experimental projects in the laboratory and read the multi-dimensional experimental correlation factors of each experimental project; wherein the multi-dimensional experimental correlation factors include: experimental operation procedures associated with the experimental project, experimental equipment associated with the experimental project, and personnel information associated with the experimental project;

[0069] Among them, the experimental operation process includes sample pretreatment, instrument parameter setting, and the steps that need to be performed during the experiment; the experimental equipment includes the equipment required to complete the experimental project and its model, calibration status, maintenance records, etc.; the personnel information includes the role information of the personnel involved in the experiment, such as: person in charge, operator, reviewer, etc.

[0070] The data configuration unit is used to collect data based on the experimental correlation factors of each dimension. At the same time, it obtains the management requirements corresponding to the experimental correlation factors of each dimension, and configures the data sets corresponding to the experimental correlation factors of each dimension according to the management requirements; and determines the basic business processes corresponding to each experimental project based on the data configuration results.

[0071] In this embodiment, management requirements refer to laboratory management specifications, which clarify the management requirements for data in each dimension. When the experimental correlation factor is the experimental operation process associated with the experimental project, the corresponding management requirements are the degree of standardization of each process step in the experimental operation process and the execution order of each process step; when the experimental correlation factor is the experimental equipment associated with the experimental project, the corresponding management requirements are: the experimental equipment corresponding to each process step in the experimental operation process, the linkage management of the experimental equipment, the working status of the experimental equipment, the name label of the experimental equipment, and the usage of the experimental equipment; when the experimental correlation factor is the personnel information associated with the experimental project, it includes personnel role management and personnel experimental authority management.

[0072] In this embodiment, data configuration is to perform structured processing on data according to management requirements, and the basic business process corresponding to each experimental project is determined through the data configuration result.

[0073] The beneficial effect of the above technical solution is to transform the people, objects and processes in the laboratory into manageable, traceable and optimizable digital assets. In essence, it is to solve the problems of unclear processes, unclear responsibilities and low resource utilization in traditional laboratory management through standardization and informatization, and ultimately achieve the goals of improving experimental quality, controlling risks and optimizing efficiency.

[0074] Example 3:

[0075] Based on Example 2, this embodiment provides a laboratory full-process intelligent management system, a data configuration unit, including:

[0076] A data processing subunit, configured to:

[0077] Collect all experimental data of the historical experimental project, read the project label of each experimental project, and classify all experimental data according to the project label to obtain the experimental data set corresponding to each historical experimental project;

[0078] According to the multi-dimensional experimental correlation factors of the experimental projects, the experimental data sets corresponding to the historical experimental projects are subjected to a second classification to obtain the sub-experimental data sets corresponding to the experimental correlation factors of each dimension;

[0079] Data configuration subunit, used for:

[0080] Obtain the management requirements corresponding to the experimental correlation factors of each dimension, and extract the demand keywords of the management requirements and the correlation logic between the demand keywords;

[0081] According to the demand keywords of management needs and the association logic between demand keywords, the corresponding sub-experimental data sets will be configured. At the same time, the data configuration results of each sub-experimental data set will be integrated to generate the basic business process corresponding to the experimental project.

[0082] In this embodiment, the project label is used as a representation identifier to distinguish different experimental projects.

[0083] In this embodiment, the first classification refers to classifying all experimental data of the collected historical experimental projects by project labels to obtain experimental data sets corresponding to different historical experimental projects.

[0084] In this embodiment, the second classification refers to the result of classifying the experimental data according to the experimental correlation factors of different dimensions, that is, obtaining a set of experimental data corresponding to each dimensional experimental correlation factor, which is called a sub-experimental data set.

[0085] In this embodiment, the association logic refers to the execution logic between management requirement keywords, wherein the management requirement keywords are used to represent the data segments of the core content of the management requirement. For example, when the experimental association factor is: experimental equipment associated with the experimental project, then when the management requirement is to perform experimental equipment status monitoring and fault warning, the management requirement keywords are: operating status, monitoring, and warning, then the association logic between the management requirement keywords is: collecting operating parameters, setting warning thresholds to trigger alarms, and identifying equipment abnormalities.

[0086] The working principle and beneficial effects of the above technical solution are: by collecting all experimental data of historical experimental projects, and effectively classifying all experimental data into the first category according to the project label of each experimental project, thereby obtaining the experimental data set corresponding to each historical experimental project; through multi-dimensional experimental correlation factors, the experimental data set corresponding to the historical experimental project is effectively classified into the second category, thereby effectively obtaining the sub-experimental data set, and effectively realizing data configuration through the association logic between management requirements and the requirement keywords of management requirements, thereby ensuring the comprehensiveness, orderliness and accuracy of the obtained basic business processes.

[0087] Example 4:

[0088] Based on Example 2, this example provides a laboratory full-process intelligent management system. In the data configuration unit, the management requirements corresponding to the experimental correlation factors of each dimension include:

[0089] When the experimental correlation factor is the experimental operation process associated with the experimental project, the corresponding management requirements are the standardization degree of each process step in the experimental operation process and the execution order of each process step;

[0090] When the experimental correlation factor is the experimental equipment associated with the experimental project, the corresponding management requirements are: the experimental equipment corresponding to each process step in the experimental operation process, the linkage management of the experimental equipment, the working status of the experimental equipment, the name label of the experimental equipment, and the usage of the experimental equipment;

[0091] When the experimental association factor is the personnel information associated with the experimental project, it includes personnel role management and personnel experimental authority management.

[0092] Example 5:

[0093] Based on Example 1, this embodiment provides a laboratory full-process intelligent management system, the management module includes:

[0094] A user information identification unit is used to identify user information and determine user permissions when the user enters a target experimental project;

[0095] The matching unit is used to read the target project tag of the target experimental project, match the experimental data in the preset intelligent project management library according to the target project tag to determine the target basic business process, and extract the target basic business process according to user permissions to generate the target business process of the target user based on the target experimental project.

[0096] Among them, when the user enters the target experimental project, the user information is identified and the user authority is determined; including: reading the first target project tag of the target experimental project, and performing a first match in the preset intelligent experimental project data management library according to the first target project tag; when the target project tag matches the historical experimental project in the preset intelligent experimental project data management library, the first historical experimental data corresponding to the target experimental project is retrieved according to the matching result; the first target basic business process is determined according to the first historical experimental data; reading the user authority, and matching the operable business process in the first target basic business process according to the user authority, and performing process extraction on the first target basic business process according to the matching result to generate the first target business process of the target user based on the target experimental project; when the target project tag cannot be matched to the historical experimental project in the preset intelligent experimental project data management library, the target experimental project is read, the target experimental project is split into projects, and multiple sub-experimental projects that complete the target experimental project are obtained; obtain A second target project label is assigned to each sub-experimental project, and the second target project label of each sub-experimental project is matched for the second time in the preset intelligent experimental project data management library; based on the second matching result, the matching first sub-experimental project and the unmatched second sub-experimental project in the preset intelligent experimental project data management library are determined; the second historical experimental data corresponding to the first sub-experimental project is retrieved from the preset intelligent experimental project data management library, and at the same time, the second target basic business process is determined based on the second historical experimental data; the operable business process is matched in the second target basic business process based on user permissions, and the process is extracted for the second target basic business process based on the matching result to generate the second target business process corresponding to the target user based on the first sub-experimental project; the second sub-experimental project is set according to user permissions to generate the third target business process corresponding to the second sub-experimental project; the second target business process is integrated with the third target business process to obtain the fourth target business process of the target user based on the target experimental project.

[0097] The benefits of the above are: strict user permission identification, eliminating unauthorized operations, ensuring the security of experimental data and processes, and reducing the risk of data leakage and misoperation. Quickly matching preset processes based on target project tags avoids duplicate planning, significantly shortening experimental preparation time, and improving project progress efficiency. Customized processes based on user permissions meet the needs of different roles (such as operators and administrators), ensuring that users obtain information and operate steps accurately and effectively.

[0098] Example 6:

[0099] Based on Example 1, this embodiment provides a laboratory full-process intelligent management system, a supervision module, including:

[0100] Monitoring unit for:

[0101] Read the process structure of the target business process and determine the business links included in the target business process based on the process structure;

[0102] Based on the business links, a parallel monitoring mechanism is configured for the target business process. At the same time, the monitoring focus and monitoring parameters of each business link are obtained, and the parallel monitoring mechanism is configured based on the monitoring focus and monitoring parameters of the monitoring focus;

[0103] Based on the configuration results, the parallel monitoring mechanism is controlled to independently monitor each business link in the target business process;

[0104] The result determination unit is used to obtain multi-dimensional monitoring results of each monitoring focus in each business link based on independent monitoring results, and logically associate the multi-dimensional monitoring results of each business link based on the business logic between each business link to obtain the final execution status.

[0105] Example 7:

[0106] Based on Example 1, this embodiment provides a laboratory full-process intelligent management system, a supervision module, including:

[0107] Policy development unit, used to:

[0108] Determine the execution parameters of the target business process for the target experimental project based on the execution status, and quantify the execution parameters based on the project composition of the target experimental project to obtain the quantitative parameters of each project body;

[0109] Obtain the expected execution results of the target experimental project, and compare the quantitative parameters of each project subject with the corresponding expected execution results;

[0110] Determine the current execution offset of each project entity based on the difference comparison result, and determine the dynamic optimization index for each project entity based on the execution offset;

[0111] Summarize the dynamic optimization indicators of each project entity to obtain the dynamic management strategy at each moment;

[0112] The optimization management unit is used to adjust the local parameters of the target business process based on the dynamic management strategy, and continuously process the target experimental project based on the local parameter adjustment results until the experimental project is completed.

[0113] In this embodiment, the execution parameter refers to the specific degree of execution of the operation on the target experimental item, for example, the amount of material collected.

[0114] In this embodiment, project subject quantification refers to the process of determining the limited scope of the execution object of each step in each target experimental project.

[0115] In this embodiment, the execution offset refers to the degree of difference between the quantitative parameter of each project entity and the expected execution result.

[0116] In this embodiment, the dynamic optimization index refers to the specific parameters and parameter ranges that need to be optimized in each project entity.

[0117] In this embodiment, local parameter adjustment refers to adjusting only the local range that does not meet the expected execution result, rather than adjusting the entire process.

[0118] The working principle and beneficial effects of the above technical solution are: by analyzing the target business process, the execution effect of the target business process on the target experimental project can be determined, so as to realize dynamic adjustment of the local area that needs to be optimized in the target business process according to the execution effect, thereby ensuring the reliability and accuracy of the processing of the target experimental project.

[0119] Example 8:

[0120] Based on Example 1, this embodiment provides a laboratory full-process intelligent management system, a supervision module, including:

[0121] The encapsulation unit is used to encapsulate the execution status data to obtain a target monitoring package when obtaining the execution status based on the real-time monitoring process of the monitoring terminal;

[0122] Shared transmission unit for:

[0123] Obtaining a data sharing terminal and determining a first data communication address of the data sharing terminal, and at the same time, obtaining a second data communication address of the monitoring terminal;

[0124] A data communication link is established according to the first data communication address and the second data communication address, and the target monitoring packet is transmitted to the shared terminal based on the data communication link.

[0125] In this embodiment, the target monitoring package refers to the result of encapsulating data corresponding to the execution status obtained when monitoring the monitoring process.

[0126] The working principle and beneficial effects of the above technical solution are: by encapsulating the execution status data to obtain the target monitoring package, the effective integration of the execution status data can be effectively guaranteed, the integrity of the data transmission can be guaranteed, and the data transmission sharing of the target monitoring package can be effectively realized by building a data communication link, thereby ensuring the accuracy of data transmission.

[0127] Example 9:

[0128] Based on Example 1, this embodiment provides a laboratory full-process intelligent management system. The supervision module also includes: a report generation unit, which is used to collect the full-process data of the target experimental project when the experimental project is completed, and generate a project report based on the full-process data.

[0129] Example 10:

[0130] This embodiment provides a laboratory full-process intelligent management method, such as Figure 3 Shown, including:

[0131] Step 1: Configure several experimental projects to obtain basic business processes;

[0132] Step 2: When the user enters the target experimental project, the basic business process is processed according to the user information and the target experimental project to obtain the target business process;

[0133] Step 3: Monitor the target business process in real time, obtain the execution status, and generate a dynamic management strategy for the target business process based on the execution status. Optimize the target business process according to the dynamic management strategy until the experimental project is completed.

[0134] The beneficial effects of the above technical solution are: by configuring the experimental projects, the basic business processes can be accurately and effectively determined. At the same time, when the user inputs the target experimental project, the basic business process is processed according to the user information to achieve accurate and effective determination of the target business process corresponding to the target experimental project. Finally, the target business process is monitored in real time, and dynamic management and optimization of the target business process is achieved based on the monitoring results, ensuring reliable full-process intelligent management of the experimental projects and guaranteeing the full-process intelligent management effect of the laboratory.

[0135] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A laboratory full-process intelligent management system, characterized in that: include: Configuration module, used to configure several experimental projects and obtain basic business processes; The management module is used to process the basic business process according to the user information and the target experimental project when the user enters the target experimental project, and obtain the target business process; The supervision module is used to monitor the target business process in real time, obtain the execution status, and generate a dynamic management strategy for the target business process based on the execution status. The target business process is optimized according to the dynamic management strategy until the experimental project is completed.

2. A laboratory full-process intelligent management system according to claim 1, characterized in that: Configuration modules, including: A reading unit is used to determine a number of experimental projects in the laboratory and read the multi-dimensional experimental correlation factors of each experimental project; wherein the multi-dimensional experimental correlation factors include: experimental operation procedures associated with the experimental project, experimental equipment associated with the experimental project, and personnel information associated with the experimental project; The data configuration unit is used to collect data based on the experimental correlation factors of each dimension. At the same time, it obtains the management requirements corresponding to the experimental correlation factors of each dimension, and configures the data sets corresponding to the experimental correlation factors of each dimension according to the management requirements; and determines the basic business processes corresponding to each experimental project based on the data configuration results.

3. A laboratory full-process intelligent management system according to claim 2, characterized in that: Data configuration unit, including: A data processing subunit, configured to: Collect all experimental data of the historical experimental project, read the project label of each experimental project, and classify all experimental data according to the project label to obtain the experimental data set corresponding to each historical experimental project; According to the multi-dimensional experimental correlation factors of the experimental projects, the experimental data sets corresponding to the historical experimental projects are subjected to a second classification to obtain the sub-experimental data sets corresponding to the experimental correlation factors of each dimension; Data configuration subunit, used for: Obtain the management requirements corresponding to the experimental correlation factors of each dimension, and extract the demand keywords of the management requirements and the correlation logic between the demand keywords; According to the demand keywords of management needs and the association logic between demand keywords, the corresponding sub-experimental data sets will be configured. At the same time, the data configuration results of each sub-experimental data set will be integrated to generate the basic business process corresponding to the experimental project.

4. A laboratory full-process intelligent management system according to claim 2, characterized in that: In the data configuration unit, the management requirements corresponding to the experimental correlation factors of each dimension include: When the experimental correlation factor is the experimental operation process associated with the experimental project, the corresponding management requirements are the standardization degree of each process step in the experimental operation process and the execution order of each process step; When the experimental correlation factor is the experimental equipment associated with the experimental project, the corresponding management requirements are: the experimental equipment corresponding to each process step in the experimental operation process, the linkage management of the experimental equipment, the working status of the experimental equipment, the name label of the experimental equipment, and the usage of the experimental equipment; When the experimental association factor is the personnel information associated with the experimental project, it includes personnel role management and personnel experimental authority management.

5. A laboratory full-process intelligent management system according to claim 1, characterized in that: Management modules, including: A user information identification unit is used to identify user information and determine user permissions when the user enters a target experimental project; The matching unit is used to read the target project tag of the target experimental project, match the experimental data in the preset intelligent project management library according to the target project tag to determine the target basic business process, and extract the target basic business process according to user permissions to generate the target business process of the target user based on the target experimental project.

6. A laboratory full-process intelligent management system according to claim 1, characterized in that: Regulatory modules, including: Monitoring unit for: Read the process structure of the target business process and determine the business links included in the target business process based on the process structure; Based on the business links, a parallel monitoring mechanism is configured for the target business process. At the same time, the monitoring focus and monitoring parameters of each business link are obtained, and the parallel monitoring mechanism is configured based on the monitoring focus and monitoring parameters of the monitoring focus; Based on the configuration results, the parallel monitoring mechanism is controlled to independently monitor each business link in the target business process; The result determination unit is used to obtain multi-dimensional monitoring results of each monitoring focus in each business link based on independent monitoring results, and logically associate the multi-dimensional monitoring results of each business link based on the business logic between each business link to obtain the final execution status.

7. A laboratory full-process intelligent management system according to claim 1, characterized in that: Regulatory modules, including: Policy development unit, used to: Determine the execution parameters of the target business process for the target experimental project based on the execution status, and quantify the execution parameters based on the project composition of the target experimental project to obtain the quantitative parameters of each project body; Obtain the expected execution results of the target experimental project, and compare the quantitative parameters of each project subject with the corresponding expected execution results; Determine the current execution offset of each project entity based on the difference comparison result, and determine the dynamic optimization index for each project entity based on the execution offset; Summarize the dynamic optimization indicators of each project entity to obtain the dynamic management strategy at each moment; The optimization management unit is used to adjust the local parameters of the target business process based on the dynamic management strategy, and continuously process the target experimental project based on the local parameter adjustment results until the experimental project is completed.

8. A laboratory full-process intelligent management system according to claim 1, characterized in that: The supervision module also includes: a report generation unit, which is used to collect the full-process data of the target experimental project when the experimental project is completed, and generate a project report based on the full-process data.

9. A laboratory full-process intelligent management method, characterized in that: include: Step 1: Configure several experimental projects to obtain basic business processes; Step 2: When the user enters the target experimental project, the basic business process is processed according to the user information and the target experimental project to obtain the target business process; Step 3: Monitor the target business process in real time, obtain the execution status, and generate a dynamic management strategy for the target business process based on the execution status. Optimize the target business process according to the dynamic management strategy until the experimental project is completed.

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