Full-life-cycle tracing and intelligent scheduling management and control method for scientific research materials

Abstract

The invention discloses a scientific research material full life cycle tracing and intelligent scheduling management and control method, and belongs to the technical field of scientific research management informatization. The method comprises the following steps: endowing each independent scientific research material with a unique tracing identifier, and creating a digital file bound with each independent scientific research material for continuously recording full life cycle data of the material; state information and operation events of the scientific research materials are automatically collected in all links of the life cycle of warehousing, storage, receiving, experimental use and final disposal of the scientific research materials, and are updated to corresponding digital archives in real time; and experimental operation data and experimental output data generated by the scientific research materials in the experimental process are acquired. According to the invention, a dynamic digital identifier along with the full life cycle of each independent material is created for each independent material, and the operation data and the result data of each independent material in the experiment process are automatically associated, so that a material entity-circulation state-experiment behavior-scientific research output full link and a structured data chain are systematically constructed in the field of scientific research management for the first time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of scientific research management information technology, and in particular to a scientific research material full life cycle tracing and intelligent scheduling management and control method. BACKGROUND

[0002] In scientific research and production activities such as shipbuilding, special equipment development and maintenance support, the management of key materials such as precision parts, special materials, electronic components, and oil is a basic work to ensure the smooth progress of the task. At present, equipment manufacturing units (such as shipbuilding units, etc.) generally use various information means for auxiliary management. Common technical solutions include: using a material management system to electronically register the entry and exit of materials, and linking with the procurement and inventory modules; using barcodes or two-dimensional codes to identify material packaging to achieve rapid inventory and basic information query; using radio frequency identification (RFID) technology to scan and locate important assets in batches; and using intelligent storage equipment with temperature and humidity monitoring function to continuously monitor and alarm the specific storage environment. These technologies have to some extent recorded the static information and part of the dynamic flow information of the materials, replaced the traditional pure paper account book, and improved the standardization of management and the efficiency of some links.

[0003] However, the above-mentioned existing technical solutions still have systematic deficiencies when dealing with complex, dynamic and highly reliable and traceable ship and special tasks. First, the existing solutions focus on the management of a single link or a single attribute, such as inventory quantity or location. The data generated by them are isolated from each other and cannot be deeply linked with core activity data such as equipment testing, task execution, and maintenance, forming a data island that cannot be analyzed based on a complete data chain. Second, the management functions generally remain at the level of recording and querying, lacking deep mining and intelligent analysis capabilities for accumulated data, making it difficult to actively predict resource demand to optimize scheduling or quickly and objectively trace the root cause to a specific batch of materials or historical state when equipment fails or tasks fail. Third, the scheduling decision of the material is highly dependent on human experience and cannot comprehensively consider the expiration date, unsealing state, historical use efficiency and other multi-dimensional attributes of the material for optimized matching, which is prone to resource mismatch and waste. Finally, valuable equipment use experience and material matching knowledge are difficult to effectively deposit and reuse, and there is still a lack of efficient and irrefutable technical support means for the control and audit process of confidential and high-risk materials.

[0004] Therefore, there is an urgent need for a systematic method that can realize the full-chain connection of ship and special material data, has intelligent analysis and decision-making capabilities, and can drive closed-loop precise control. SUMMARY

[0005] The application provides a scientific research material full life cycle tracing and intelligent scheduling management and control method to solve the problem that scientific research material data and scientific research experiment process cannot be intelligently fused and closed-loop controlled in the prior art.

[0006] To achieve the above object, the application provides the following technical scheme. A scientific research material full life cycle tracing and intelligent scheduling management and control method comprises the following steps. S1: A unique tracing identifier is given to each independent scientific research material, and a digital archive bound thereto is created to continuously record the full life cycle data of the material; S2: In each link of the life cycle of the scientific research material, such as storage, storage, use, experiment use and final disposal, the state information and operation events thereof are automatically collected and updated to the corresponding digital archive in real time; S3: The experiment operation data and experiment output data generated in the experiment process of the scientific research material are recorded in association with the digital archive of the material; S4: Based on the full life cycle data, experiment operation data and experiment output data recorded in association in the digital archive, an analysis model is constructed through a rule engine or a machine learning model to evaluate the material state, analyze the performance correlation and identify risk factors; S5: Based on the output result of the analysis model, a management and control instruction for the material inventory, scheduling or use is generated and executed through a scheduling algorithm or rule matching.

[0007] Preferably, in step S2, the state information includes environmental sensor data and visual recognition data, and the operation events are automatically captured through a scanning device or an information system interface.

[0008] Preferably, in step S3, the experiment operation data includes specific experiment steps, duration, amount of the material used and the tracing identifiers of other materials used in cooperation.

[0009] Preferably, in step S3, the experiment output data includes qualitative evaluation of experiment results, quantitative measurement data and success / failure marking of experiment results.

[0010] Preferably, in step S4, the risk factors are identified through a rule engine or a machine learning model, comprising the following steps: The digital archive of the scientific research material associated with the experiment marked as failed is extracted; The state information, operation events and experiment operation data recorded in the archive are analyzed; Through statistical correlation analysis or decision tree algorithm, the influence factor combination with an occurrence frequency exceeding a preset threshold is identified.

[0011] Preferably, the analysis model in step S4 is also used to predict the stage-by-stage material demand list of the scientific research project or the researcher according to historical consumption patterns, project plan data and experimental scheme similarity matching.

[0012] Preferably, the generation and execution of the control instruction in step S5 include: according to the predicted material demand list, through real-time inventory comparison and state checking algorithm, automatically generating cross-project adjustment scheme, procurement application or production reservation instruction.

[0013] Preferably, the control instruction generated in step S5 also includes triggering an automatic lock, a priority use mark or a scrap reminder process for the material approaching the expiration date, the inventory being lower than the safety threshold or the material being identified by the analysis model as having a known risk.

[0014] Preferably, the method further comprises: S6: structurally processing the historically accumulated digital archives and associated data, extracting material-experiment-result association rules, and generating an experimental auxiliary knowledge base; the knowledge base is used to respond to queries and provide knowledge feedback including material recommendations, historical operation cases and associated risks based on the association rules.

[0015] Preferably, the key operation event and state data of the digital archives are stored in the blockchain through a hash value to ensure the non-tamperability and auditability of the full life cycle data.

[0016] The present application provides a scientific research material full life cycle tracing and intelligent scheduling and control method, compared with the prior art, has the following remarkable beneficial effects: 1、The present application creates a dynamic digital identifier for each independent material throughout its full life cycle, and automatically associates its operation data and result data in the experimental process. The present application first systematically constructs a full-link, structured data chain of material entity-flow state-experimental behavior-research output in the field of scientific research management. This completely changes the situation of scattered, fragmented and weakly associated data under the traditional mode, and provides a reliable data cornerstone for realizing high-value intelligent analysis.

[0017] 2、The present application constructs an analysis model through a rule engine, a machine learning model and a special algorithm, and drives the generation of automatic scheduling and control instructions. This enables the management system to have thinking and decision-making capabilities, and can actively assess risks, predict demands and optimize scheduling, changing the traditional mode of people looking for materials and people managing things to a mode of people connecting with things and intelligently managing things, significantly improving the forward-looking and scientific nature of management.

[0018] 3、The invention can quickly and objectively locate the root cause of the problem by analyzing the model of the failure experiment associated data and pattern recognition, such as identifying specific batch reagents or non-conventional storage conditions as risk factors. This not only greatly improves the problem troubleshooting efficiency, but also provides direct data support for optimizing procurement decisions and reducing hidden costs caused by repeated experiments due to material quality problems.

[0019] 4、The invention can realize intelligent allocation and priority planning of materials across projects and teams by combining demand forecasting with real-time inventory status verification (such as expiration date and whether the bottle is opened). It ensures that qualified materials are matched to urgent experiments at the right time, while prioritizing the use of near-expiration materials, thereby ensuring the continuity of scientific research activities while minimizing waste and achieving optimal resource allocation.

[0020] 5、The invention can automatically generate a knowledge base to support decision-making by processing and extracting rules from massive amounts of historical associated data. This converts individual, implicit experimental experience (such as the effect of a specific brand combination being better) into institutional, explicit, and queryable knowledge assets, providing intelligent assistance for experimental design, material selection, and new employee training, and effectively supporting the continuous accumulation and improvement of laboratory research capabilities.

[0021] 6、The invention provides an unalterable, traceable, and efficient verification method for the management of high-risk chemical materials through blockchain technology, greatly enhancing the transparency, credibility, and audit efficiency of the management process, and providing a solid guarantee for responding to strict compliance audits. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 is a flowchart of the method of the invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the invention. Obviously, the described embodiments are only a part of the embodiments of the invention, not all embodiments. Based on the embodiments in the invention, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the invention.

[0024] It should be noted that, in this document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions, and the terms including, including, or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that includes a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article, or apparatus. Without more limitations, the elements defined by the statement including… are not excluded from the process, method, article, or apparatus including the elements.

[0025] Referring to FIG. 1, the present application provides a scientific research material full life cycle tracking and intelligent scheduling management method, aiming to solve the problems of fragmented material management data, difficult tracking, inefficient scheduling and lack of intelligent decision support in the prior art. The method creates a unique digital archive for each independent scientific research material to establish its digital identity; automatically collects state and operation data during the entire process of material warehousing, storage, use, use, and disposal, and updates the archive in real time; then the operation steps, environmental parameters and experimental success or failure results during the experiment are deeply associated with the corresponding material archive. Based on this complete and associated data basis, an analysis model is constructed using a rule engine or a machine learning model to evaluate the material state, perform performance correlation analysis and identify risk factors. Finally, according to the model analysis results, precise management and control instructions for inventory optimization, cross-project scheduling or risk warning are automatically generated and executed through scheduling algorithms or rule matching, thereby forming a closed-loop intelligent management and control system from data perception, intelligent analysis to automatic execution System architecture overview: The system architecture implemented by the method mainly includes the following four layers: Perception and collection layer: composed of identification codes (such as two-dimensional codes, RFID) attached to the material, environmental sensors, intelligent storage device terminals and user operation terminals, responsible for automatically obtaining material identity, state and flow event.

[0026] Data layer: constructs and maintains dynamic digital archives for core material objects, and integrates experimental process and result data through interfaces to form a correlated data set.

[0027] Analysis and decision-making layer: built-in rule engine, machine learning model and various algorithms analyze, evaluate and predict the information in the data layer, and generate decision-making instructions.

[0028] Execution feedback layer: receives decision-making instructions, drives the inventory system and scheduling process to perform actual operations, and sends notifications and feedback to relevant personnel, forming a management and control loop.

[0029] Embodiment 1 This embodiment provides a scientific research material full life cycle traceability and intelligent scheduling management and control method for full process traceability and near-term management and control of ship oil, and the specific implementation content includes: Implementation purpose: demonstrate the basic traceability process of fuel oil used by the ship power system from storage to filling, as well as rule-based state early warning and disposal.

[0030] Implementation steps: S1: The oil depot manager receives a batch of marine diesel oil (batch number: F-2023-0501). Use the handheld terminal to scan the oil tank identification code, and the system automatically generates a unique internal traceability code FUEL-F-0501-001 and creates its core digital archive, recording oil type, specification, supplier, batch, flash point, freezing point, and expiration date.

[0031] S2: The batch of oil is stored in a dedicated tank. The temperature sensor and liquid level sensor in the tank area continuously monitor the data, and the system automatically records the environmental data (such as temperature: 25℃) and inventory data to the digital archive of the material every minute. During the filling operation, the operator confirms by scanning the code, and the archive records the filling time, receiving ship / device number, and associated task number.

[0032] S3: The oil is used for power system testing of a certain type of ship. During the test, the power system monitoring equipment records the oil consumption rate, engine emission data, etc. under different working conditions. After the test is completed, the system automatically binds these operation data and test results with the digital archive of traceability code FUEL-F-0501-001.

[0033] S4: The system's built-in rule engine scans all digital archives daily. When it detects that the expiration date of this batch of oil is less than 60 days, the rule is triggered.

[0034] S5: According to the output of the rule engine, the system automatically executes two control instructions: one is to mark the status of this oil as "near-term priority use" in the inventory management interface; the second is to send a priority use reminder notice to the oil management department and the commanding officer of the ship with a recent sea plan.

[0035] Implementation effect: Real-time visualization and automatic early warning of oil status are achieved, effectively reducing waste due to expiration of oil, and ensuring the safety and economy of ship power oil.

[0036] Embodiment 2 This embodiment provides a scientific research material full life cycle traceability and intelligent scheduling management and control method for root cause traceability analysis of a certain type of complex equipment system test failure case, and the specific implementation content includes: Implementation purpose: Through the correlation of failure test data, locate the specific electronic component batch that causes multiple tests to fail, and realize accurate problem tracing.

[0037] Implementation steps: S1-S3: Establish a digital archive for all core chips, capacitors, connectors, and other electronic components used by the guidance system of the system, and continuously record their storage environment (such as the temperature and humidity of the anti-static cabinet, access records) and the test task (test task ID) they are assembled for each time.

[0038] S4: The analysis model identifies that the dynamic performance test of the same type of product seeker has been marked as "unqualified" for three consecutive times, and the phenomenon is that the terminal accuracy is out of tolerance. The model automatically extracts the digital archive set of all electronic components used in the three tests. Through decision tree algorithm feature analysis, the model finds a key commonality: the same production batch (IC-B202312) of a certain type of gyroscope signal processing chip is assembled in the seeker of the three tests. The digital archive of this batch of chips shows that although a certain noise index of the retest is within the qualified range, the discreteness is significantly higher than the historical data of other successful test batches.

[0039] S5: The system generates and executes control instructions: immediately lock all chips of this batch in the inventory, suspend for subsequent product assembly; at the same time, push detailed root cause analysis report to the quality department, general assembly workshop and design unit, clearly pointing out that the performance consistency of IC-B202312 batch chips is suspected to be insufficient, which may affect the guidance accuracy, and suggests that the products assembled with this batch be checked specially.

[0040] Implementation effect: The troubleshooting time of a certain type of complex equipment system test failure is shortened from weeks of manual fault tree analysis to minutes of automatic correlation analysis of the system, which quickly blocks the continued installation of problem components, avoids greater quality risks and task delays, and forms a traceable supplier quality feedback chain.

[0041] Example 3 The embodiment provides a scientific research material full life cycle tracing and intelligent scheduling control method, which is used for material demand prediction and active scheduling of a ship annual overhaul project, and the specific implementation content includes: Implementation purpose: Based on maintenance engineering plan and historical consumption data, predict the materials needed during the overhaul, and automatically generate an optimized scheduling scheme by considering the inventory status.

[0042] Implementation steps: S1-S3: Establish a digital archive for special steel, anticorrosive paint, and shipborne equipment spare parts required for ship maintenance, and record the information of each time of use, use, and associated maintenance work order.

[0043] S4: The analysis model identified that a certain destroyer is about to enter a three-month overhaul period. Based on the historical overhaul material consumption list of this type of ship, the contents of the current overhaul engineering package, and maintenance history data of similar ships, the model predicts the list of key materials required during the overhaul, including 50 square meters of composite material for a certain type of radar antenna radome and 12 spare cylinder liners for a certain type of main engine.

[0044] S5: After receiving the forecast list, the scheduling algorithm initiates an inventory-status check. The check reveals 60 square meters of composite material for radar radomes in stock, but 20 square meters of this material has been stored for nearly its specified lifespan according to the material's manufacturing process, and its status is marked as "needs priority use." The available safety stock is 40 square meters, lower than the forecasted demand. The algorithm then generates a cross-project transfer instruction, queries other maintenance projects for temporarily stored, unopened materials of the same model, and generates a transfer request. Simultaneously, for main engine cylinder liner spare parts, while inventory is sufficient, a batch is approaching its mandatory re-inspection deadline in 90 days; the system generates an instruction to "prioritize the release of this batch for overhaul."

[0045] Implementation Results: The system shifted from a reactive to a proactive approach, issuing early warnings and initiating cross-project resource allocation before material shortages actually occurred, ensuring the continuity of critical overhaul tasks. Simultaneously, by prioritizing the use of near-expiration or re-inspection materials through status verification, the inventory structure was optimized, reducing overall maintenance costs and the risk of obsolescence.

[0046] Example 4 This embodiment provides a method for full lifecycle traceability and intelligent scheduling and management of scientific research materials, used for constructing an equipment maintenance knowledge base based on historical data mining. Specific implementation details include: Objective: To accumulate and extract valuable experience from historical equipment maintenance and material usage data, forming searchable and reusable structured knowledge to assist in maintenance plan development and spare parts selection.

[0047] Implementation steps: S1-S3: The system has been running for a long time and has accumulated a large number of spare parts replacement records, detailed maintenance operation steps and corresponding post-maintenance performance test data (such as bench test power, fuel consumption and emissions) for a certain type of diesel engine. All of these data are linked to the digital files of each replaced spare parts.

[0048] S4: The system background performs offline mining on the above-mentioned associated data, discovers effective rules such as when replacing the fuel injection nozzle of brand A, if the high-pressure oil pump adjusting gasket of brand B is used synchronously, and the valve clearance is adjusted to the upper limit of the specification value, the success rate of engine power recovery is increased by 90%, etc. through the association rule learning algorithm. These rules are stored in a structured manner to form a maintenance optimization knowledge base of the diesel engine. S5: When a maintenance engineer submits a query about the "insufficient power recovery" of the engine in the system, the knowledge base engine actively provides auxiliary suggestions based on rule matching: recommends the use of a specific combination of fuel injection nozzles and gaskets in stock, and prompts to avoid using a certain brand of cylinder gasket that has been stored for more than the specified period, as historical data shows that it has a high risk of seal failure.

[0049] Implementation effect: The implicit maintenance experience of the master is converted into explicit organizational knowledge assets, reducing the skill threshold and trial and error cost of maintenance personnel, improving the success rate of equipment maintenance and the consistency of maintenance quality, and promoting the accumulation and inheritance of maintenance best practices.

[0050] Embodiment 5 The embodiment provides a scientific research material full life cycle tracing and intelligent scheduling management and control method for block chain notarization of controlled material full life cycle data, and the specific implementation content includes: Implementation purpose: For strictly controlled materials such as detonators and secret electronic components, ensure the non-tamperability and audit credibility of the key data in the full life cycle.

[0051] Implementation steps: S1: Create a digital file for each controlled material and assign a unique trace code.

[0052] S2-S3: At any key link (such as inspection at the warehouse entry point, use of the warehouse exit, assembly, use, and destruction and monitoring), two people are required to authorize the scanning operation, and the system will record the operation events (time, personnel, operation type, quantity, associated tasks) in the digital file in real time.

[0053] S4: In this embodiment, the analysis model focuses on compliance verification. After each operation, the system uploads the hash value of the event data to a pre-set internal blockchain node that meets the security requirements.

[0054] S5: The system executes management instructions based on blockchain storage. For example, when attempting to perform an explosive material taking operation, the system checks whether this operation will cause the temporary storage amount at the work site to exceed the safety limit. The limit is determined based on historical taking and consumption data from the chain, which is tamper-proof, thereby ensuring absolute reliability of safety and compliance control. During auditing, representatives or internal audit departments can quickly and reliably verify the authenticity and integrity of all controlled material flow records by verifying the chain hash sequence, without the need to check paper documents one by one. Implementation effect: Provides high data credibility and security for the management of high-risk and confidential materials, greatly simplifies the process of security checks and safety audits, enhances the legal and regulatory evidence effectiveness of the management process, and effectively prevents management loopholes and security risks.

[0055] Comparative Example 1 This comparative example provides a traditional manual account combined with a decentralized system management method, which includes the following specific contents: Implementation purpose: Through comparison, the technical advantages of the method of the present application compared with the traditional management method are highlighted.

[0056] Implementation scenario: A ship repair factory uses paper material taking sheets, multiple independent electronic spreadsheets (such as inventory tables, procurement tables), and financial systems to manage maintenance materials.

[0057] Implementation process: Material information is scattered in the warehouse's Excel inventory table, the procurement department's procurement records, and the workshop's material taking book, and the data is inconsistent and updated with a lag.

[0058] Spare parts taking requires filling out paper material taking sheets and being signed by multiple people, but the association with specific maintenance work orders relies on manual entry in another system, which is prone to errors and omissions.

[0059] Maintenance process and information of old parts replaced are recorded in the maintenance personnel's paper record book, which is completely decoupled from the new part inventory information and procurement information.

[0060] When a maintenance after-failure recurrence or a certain batch of spare parts needs to be traced, it is necessary to coordinate warehouse, workshop, and procurement personnel to check different records, which is extremely inefficient and difficult to clarify responsibilities.

[0061] Material procurement relies on periodic or irregular submission of applications by each workshop, lacks overall demand prediction based on the entire factory maintenance plan, and often results in emergency air transport procurement or long-term accumulation of certain materials.

[0062] When facing superior equipment inspection or audit, a large number of manpower is needed to organize the mountains of material taking sheets, acceptance sheets, and warehouse entry and exit records accumulated over the years, which is a heavy workload and difficult to prove the compliance and continuity of all operations.

[0063] Comparative effect: compared with the traditional method, the method shown in examples 1-5 realizes fundamental improvement in management efficiency, data consistency, problem tracing ability, resource scheduling foresight, security compliance guarantee, and solves the inherent disadvantages of information island, dependence on manual work, delayed response, lack of objective data support, etc.

[0064] Compared with examples 1-5 and comparative example 1, the scientific research material whole life cycle tracing and intelligent scheduling management method provided by the application forms a fundamental contrast with the traditional manual account management method, realizes the paradigm shift from relying on human fragmented management to relying on systematized intelligent management. The traditional mode relies on scattered paper and electronic records, resulting in the separation of material information, experimental data and use history from each other, forming an information island, low management efficiency and easy to make mistakes. The application creates a unified and dynamic digital file (S1-S3) for each material, automatically collects its whole life cycle state and experimental context data, and builds a complete and correlated digital mirror. On this basis, the built-in rule engine and machine learning model are analyzed and decided (S4), and the automatic scheduling and management instruction execution (S5) is driven, forming a closed-loop intelligent system of perception, analysis, decision and execution, which provides a new technical foundation for solving the core pain points of traditional scientific research material management.

[0065] Specifically, this change reflects a comprehensive innovation in five dimensions: at the data level, from chaotic and isolated records to unified and correlated digital files, laying the foundation for intelligent analysis; in problem tracing, from time-consuming and labor-intensive manual experience speculation to minute-level accurate root cause positioning based on data models (such as example 2 locking the problem serum batch through decision tree); in resource scheduling, from passive response to application to proactive and optimized scheduling based on demand prediction and real-time inventory status verification (such as example 3 cross-project adjustment and preferential use of near-expiry materials); in knowledge inheritance, implicit personal experience is converted into explicit structured knowledge base that can be queried and reused (such as example 4 mining experimental best practices); in compliance audit, from cumbersome paper verification to efficient and tamper-proof trusted verification based on blockchain storage (such as example 5 ensuring absolute trust in the circulation of controlled chemicals). In summary, the application systematically solves the five major problems inherent in traditional methods, such as low efficiency, difficult tracing, blind decision-making, knowledge loss and high compliance risk, and realizes a qualitative leap in precision, depth and reliability of scientific research material management.

[0066] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, and all of them should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0067] Finally, the above merely provides the preferred embodiments of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for full lifecycle traceability and intelligent scheduling and control of scientific research materials, characterized in that, Includes the following steps: S1: Assign a unique traceability identifier to each individual scientific research material and create a digital file linked to it to continuously record the material's entire life cycle data; S2: Automatically collect status information and operation events at each stage of the life cycle of the scientific research materials, including warehousing, storage, requisition, experimental use and final disposal, and update them to the corresponding digital archives in real time; S3: Associate and record the experimental operation data and experimental output data generated by the scientific research materials during the experiment with the digital file of the materials. S4: Based on the full lifecycle data, experimental operation data and experimental output data of the associated records in the digital archive, an analysis model is built through a rule engine or machine learning model to assess the status of materials, analyze the correlation of effectiveness and identify risk factors. S5: Based on the output of the analysis model, generate and execute control instructions for material inventory, scheduling, or use through scheduling algorithms or rule matching.

2. The method for full lifecycle traceability and intelligent scheduling and control of scientific research materials as described in claim 1, characterized in that, In step S2, the status information includes environmental sensor data and visual recognition data, and the operation event is automatically captured through a scanning device or information system interface.

3. The method for full lifecycle traceability and intelligent scheduling and control of scientific research materials as described in claim 1, characterized in that, In step S3, the experimental operation data includes the specific experimental steps, duration, and quantity of materials used, as well as the traceability identifiers of other materials used in conjunction with the materials.

4. The method for full life-cycle traceability and intelligent scheduling and control of scientific research materials as described in claim 3, characterized in that, In step S3, the experimental output data includes qualitative evaluation of the experimental results, quantitative measurement data, and success or failure markers for the experimental results.

5. The method for full lifecycle traceability and intelligent scheduling and control of scientific research materials as described in claim 1, characterized in that, Step S4, which identifies risk factors using a rule engine or machine learning model, includes the following steps: Extract digital archives of research materials associated with experiments marked as failed; Analyze the status information, operation events, and experimental operation data recorded in the archives; By using statistical correlation analysis or decision tree algorithms, combinations of influencing factors that occur more frequently than a preset threshold are identified.

6. The method for full lifecycle traceability and intelligent scheduling and control of scientific research materials as described in claim 1, characterized in that, The analytical model in step S4 is also used to predict the phased material requirements list of scientific research projects or researchers based on historical consumption patterns, project plan data, and experimental scheme similarity matching.

7. The method for full lifecycle traceability and intelligent scheduling and control of scientific research materials as described in claim 1, characterized in that, Step S5 involves generating and executing control instructions, including: automatically generating cross-project allocation plans, procurement requests, or production reservation instructions based on the predicted material demand list and through real-time inventory comparison and status verification algorithms.

8. The method for full lifecycle traceability and intelligent scheduling and control of scientific research materials as described in claim 1, characterized in that, The control instructions generated in step S5 also include triggering automatic locking, priority use marking, or scrapping reminder processes for materials that are nearing their expiration date, have inventory levels below the safety threshold, or have known risks identified by the analysis model.

9. The method for full lifecycle traceability and intelligent scheduling and control of scientific research materials as described in claim 1, characterized in that, The method further includes: S6: The historically accumulated digital archives and related data are structured to extract the association rules of materials-experiments-results, and generate an experimental auxiliary knowledge base; the knowledge base is used to respond to queries and provides knowledge feedback including material recommendations, historical operation cases and related risks based on the association rules.

10. The method for full lifecycle traceability and intelligent scheduling and control of scientific research materials as described in claim 1, characterized in that, The key operational events and status data of the digital archives are stored on the blockchain using hash values ​​to ensure the immutability and auditability of the data throughout its entire lifecycle.

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