Crop stress resistance identification experiment management system

Abstract

The invention belongs to the technical field of crop experiment identification, and discloses a crop stress resistance identification experiment management system, which comprises an experiment management module used for carrying out whole-process management on maintenance contents of hardware required by a crop stress resistance experiment, records of experiment varieties, experiment standards and experiment plan implementation; the system management module is used for undertaking underlying resource configuration and authority management, providing an information retrieval query interface and realizing safe seamless connection and multiple tracking of experimental data; and the information communication module is used for providing technical experience and scientific research achievement communication between users, realizing scientific research cooperation and knowledge sharing, and displaying stress resistance experiment data assessment summarization according to user authority. Through cooperative operation of multiple modules, the efficiency and reliability of crop stress resistance research are remarkably improved; the experiment full-process management function is integrated, intelligent monitoring of the state of hardware equipment, standardized configuration of experiment parameters and dynamic tracking of experiment plans are achieved, and manual operation deviation is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of crop experimental identification, and particularly relates to a management system for crop stress resistance identification experiments. Background Art

[0002] At present, agricultural production is in a transformation stage towards precision, efficiency and sustainable development. The cultivation and application of various crop varieties play a key role in ensuring food security and improving the ecological environment. However, traditional breeding experiment identification mainly relies on manual observation and data recording, which has problems such as long test cycles, insufficient data accuracy, and limitations in environmental simulation, and cannot meet the requirements of modern agriculture for rapid and high-quality variety identification. The research and development of a management platform for crop stress resistance identification experiments is precisely to break through these bottlenecks. By integrating management software, environmental simulation facilities, experimental fields and the Internet of Things system, the standardization, digitization and intelligence of variety identification tests can be realized, thereby improving the breeding efficiency and quality.

[0003] Meanwhile, the application of information technology in agriculture is gradually penetrating. New technologies such as the Internet of Things, big data and cloud computing provide possibilities for agricultural intelligence. However, due to the lack of a cross-domain integrated application platform, these technologies are often difficult to play their due roles in breeding identification.

[0004] With the continuous deepening of breeding research, people's requirements for the accuracy, timeliness of experimental data and the professionalism of subsequent analysis are increasing day by day. There are certain deficiencies in the existing technologies in aspects such as real-time data collection, system management and information intercommunication. First, traditional experimental fields and environmental control equipment are difficult to achieve the accuracy and intelligence levels required by modern breeding research in terms of fine management and dynamic regulation; second, manual input and traditional data processing methods are not only time-consuming and laborious, but also prone to data omission and human errors, resulting in the quality and reliability of the final report being affected; finally, the decentralized management systems lack unified platform support, and scientific researchers often need to use multiple independent tools when designing experiments, monitoring executions and conducting subsequent data analysis, increasing the operation complexity and management costs.

[0005] Therefore, how to provide a management system for crop stress resistance identification experiments is an urgent problem to be solved at present. Summary of the Invention

[0006] Embodiments of the present invention provide a management system for crop stress resistance identification experiments to solve the above-mentioned technical problems existing in the prior art.

[0007] To gain a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary section is not a general review, nor is it intended to identify key / important constituent elements or delineate the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, a crop stress resistance identification experiment management system is provided.

[0009] In one embodiment, the crop stress resistance identification experiment management system includes:

[0010] An experiment management module for managing the entire process of maintaining the hardware required for crop stress resistance experiments, recording experimental varieties, implementing experimental standards, and experimental plans.

[0011] A system management module for undertaking underlying resource configuration and permission management, providing an information retrieval and query interface, and realizing seamless connection and multiple tracking of experimental data security.

[0012] An information exchange module for providing technical experience and scientific research achievement exchanges between users, realizing scientific research collaboration and knowledge sharing, and displaying the evaluation summary of stress resistance experimental data according to user permissions.

[0013] In one embodiment, the experiment management module includes: a personnel management module, a task entry module, an equipment management module, a variety management module, an experimental standard management module, an experimental plan implementation management module, a data management module, and a statistical analysis module. Among them,

[0014] The personnel management module is used to arrange the responsible personnel for the experiment according to the experimental task requirements.

[0015] The task entry module is used to enter experimental management information and push hardware maintenance tasks and variety identification tasks according to task requirements.

[0016] The equipment management module is used to repair and maintain the equipment, facilities, and instruments required for the experiment.

[0017] The variety management module is used to automatically generate a unique sample number, generate a sampling form, import sample information, and read variety information to enable handover by variety management personnel.

[0018] The experimental standard management module is used to formulate corresponding experimental standards according to the variety identification task requirements.

[0019] The experimental plan implementation management module is used to formulate corresponding experimental plans according to the variety quantity and experimental standards, and provide an integrated process operation, variety traceability, and graphical display tool.

[0020] The data management module is used to collect, store, analyze, process, and manage the application of all experimental data during the entire experimental process;

[0021] The statistical analysis module is used to statistically analyze the whole-process data during the experimental identification process.

[0022] In one embodiment, the experimental management information includes: the artificial climate chamber identification room, the field test area, the maintenance information required for experimental hardware, the variety identification task information, and the information of relevant responsible personnel.

[0023] In one embodiment, formulating corresponding experimental standards according to the variety identification task requirements includes: the high-temperature identification standard for rice and the low-temperature identification standard for rice, and both the high-temperature identification standard for rice and the low-temperature identification standard for rice are divided into the seedling stage of rice, the germination stage of rice, and the heading stage of rice.

[0024] In one embodiment, formulating corresponding experimental plans according to the variety quantity and experimental standards includes:

[0025] Based on the variety identification tasks to be participated in, establish an experimental list, and the experimental list includes varieties, sowing, seedling raising, transplanting, water and fertilizer management, identification experiments, and comparative experiments;

[0026] Group and mark the varieties, bases, and base planting layouts required for identification experiments;

[0027] Conduct experiments according to varieties, and determine the adversity treatment methods and treatment times;

[0028] Establish experimental bases for comparison, including artificial climate identification rooms and field test areas;

[0029] According to the bearing capacity of the experimental base, choose to increase or decrease the experimental variety content;

[0030] During the experimental process of the current variety identification task, add historical experimental data for comparison;

[0031] According to the identification results, analyze and evaluate the varieties participating in the identification, and issue an identification conclusion.

[0032] In one embodiment, the collection, storage, analysis, processing, and application management of all experimental data during the entire experimental process include:

[0033] According to different data types, collect experimental data in real time during the variety identification task process;

[0034] Enter the collected original experimental data, output a comprehensive evaluation through variance analysis, comparative analysis, and trend analysis, and provide a query interface for variety data, comparative data, and experimental result data;

[0035] Export the valid experimental data, push it according to the user's permissions, and make effective use of it in rice breeding screening based on the data analysis and evaluation results.

[0036] In one embodiment, the whole-process data includes: workload, project cost, completion status, number of reports, planned tasks, experimental data, analysis man-hours, cost settlement status, and contracts.

[0037] In one embodiment, the system management module includes: a configuration management module, a resource management module, a data storage module, an information query module, and other management modules, where

[0038] The configuration management module is used to configure the permissions, users, departments, and role management within the system, and provide customized configuration solutions for different application scenarios;

[0039] The resource management module is used to provide overall resource management and optimize the management cost of experimental identification;

[0040] The data storage module is used to build and manage a database and store experimental identification data in real time;

[0041] The information query module is used to provide quick search and query of data information;

[0042] The other management modules are used to achieve seamless connection and multiple tracking of data security.

[0043] In one embodiment, the information communication module includes: a communication evaluation module, an upload and distribution module, a report compilation module, a data interface module, a customer management module, and a mobile application module, where

[0044] The communication evaluation module is used to provide functions for exchanging and sharing experiences in crop stress resistance experiments, evaluate and summarize crop varieties based on stress resistance data, and display them according to the user's permissions;

[0045] The upload and distribution module is used to display experimental content through a page and issue planning and design, specified standards, and technical guidance to subordinate units;

[0046] The report compilation module is used to generate a test report with one key using a pre-selected report template, push the report to the reviewer and issuer for browsing and downloading, and enable traceability of the original data;

[0047] The data interface module is used to provide an interface for Internet of Things devices to collect temperature and humidity environment data in real time;

[0048] The customer management module is used to record the customer name, contact information, the quantity of selected varieties, the requirements for stress resistance identification, and the payment status;

[0049] The mobile application module is used to encrypt and connect to the mobile terminal, provide one-click downloads of basic data and collection tasks, and upload text and picture data in real time to implement information query, trait collection, and experiment implementation.

[0050] According to the second aspect of the embodiments of the present invention, a method for managing crop stress resistance identification experiments is provided.

[0051] In one embodiment, the method for managing crop stress resistance identification experiments includes:

[0052] Obtain the maintenance content of the hardware required for crop stress resistance experiments, the records of experimental varieties, experimental standards, and experimental plans, implement the experiments according to the experimental plans, and conduct full-process management;

[0053] Based on underlying resource configuration and permission management, provide an information retrieval and query interface to achieve seamless and secure connection of experimental data and multiple tracking;

[0054] Through the mobile terminal, conduct technical experience and scientific research achievement exchanges among users to achieve scientific research collaboration and knowledge sharing, and display the evaluation summary of stress resistance experimental data according to user permissions.

[0055] According to the third aspect of the embodiments of the present invention, a computer device is provided.

[0056] In some embodiments, the computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0057] According to the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided.

[0058] In one embodiment, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0059] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: Through the collaborative operation of multiple modules, the efficiency and reliability of crop stress resistance research have been significantly improved; By integrating the full-process management function of experiments, intelligent monitoring of the status of hardware devices, standardized configuration of experimental parameters, and dynamic tracking of experimental plans are realized, effectively avoiding manual operation deviations, and ensuring the accurate construction of the adversity simulation environment and the repeatability of experimental data; The information sharing platform breaks down traditional scientific research barriers, promotes the cross-institutional flow of technical experience and experimental results, and accelerates the process of germplasm resource screening and variety improvement. Through the linkage of environmental simulation - field verification and the integrated design of data collection - analysis - reporting, the variety identification cycle is greatly shortened, enabling scientific research institutions to quickly respond to the breeding needs under climate change and accurately develop commercial varieties adapted to regional adversities; It not only reduces the labor and time costs, but also promotes the technological innovation of the seed industry through digital means, providing key technical support for ensuring food security and agricultural sustainable development.

[0060] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present invention and, together with the specification, are used to explain the principles of the present invention.

[0062] Figure 1 is a system principle block diagram of a crop stress resistance identification experiment management system shown according to an exemplary embodiment;

[0063] Figure 2 is a flowchart of a crop stress resistance identification experiment management method shown according to an exemplary embodiment;

[0064] Figure 3 is a schematic structural diagram of a computer device shown according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] The following description and the accompanying drawings fully disclose specific embodiments herein, enabling those skilled in the art to practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents of the claims. In this document, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a structure, device or equipment comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such structure, device or equipment. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the structure, device or equipment comprising the element. The embodiments herein are described in a progressive manner, with each embodiment highlighting the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0066] In this document, the orientation or positional relationship indicated by the terms "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing this document and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", "coupled" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the communication inside two elements. It can be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0067] In this document, unless otherwise specified, the term "plurality" means two or more.

[0068] In this document, the character " / " indicates that the objects before and after are in an "or" relationship. For example, A / B means: A or B.

[0069] In this document, the term "and / or" is a description of the associated relationship of an object, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B these three relationships.

[0070] It should be understood that although the steps in the flowchart are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless specifically stated herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the figure may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0071] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0072] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0073] Figure 1 An embodiment of a crop stress resistance identification experiment management system of the present invention is shown.

[0074] In this alternative embodiment, the crop stress resistance identification experiment management system includes:

[0075] An experiment management module 101, which is used to manage the whole process of the maintenance content of the hardware required for the crop stress resistance experiment, the recording of the experimental varieties, the experimental standards, and the implementation of the experimental plan;

[0076] A system management module 102, which is used to undertake the underlying resource configuration and permission management, provide an information retrieval and query interface, and realize the seamless connection and multiple tracking of experimental data security;

[0077] An information exchange module 103, which is used to provide the exchange of technical experience and scientific research achievements among users, realize scientific research collaboration and knowledge sharing, and display the evaluation summary of stress resistance experiment data according to user permissions.

[0078] In this alternative embodiment, the experiment management module includes: a personnel management module (not shown in the figure), a task entry module (not shown in the figure), a device management module (not shown in the figure), a variety management module (not shown in the figure), an experiment standard management module (not shown in the figure), an experiment plan implementation management module (not shown in the figure), a data management module (not shown in the figure), and a statistical analysis module (not shown in the figure). Among them, the personnel management module is used to arrange the responsible personnel for the experiment according to the experiment task requirements; the task entry module is used to enter experiment management information and push hardware maintenance tasks and variety identification tasks according to the task requirements; the device management module is used to repair and maintain the equipment, facilities and instruments required for the experiment; the variety management module is used to automatically generate a unique sample number, generate a sampling form, import sample information, and read variety information to enable the handover of variety management personnel; the experiment standard management module is used to formulate corresponding experiment standards according to the variety identification task requirements; the experiment plan implementation management module is used to formulate corresponding experiment plans according to the variety quantity and experiment standards, and provide integrated process operation, variety traceability and graphical display tools; the data management module is used to collect, store, analyze, process and manage the application of all experiment data in the whole experiment process; the statistical analysis module is used to count the whole process data in the experiment identification process.

[0079] In this alternative embodiment, the experiment management information includes: the maintenance information required for the artificial climate chamber identification room, the field test area and the experiment hardware, the variety identification task information, and the relevant responsible personnel information.

[0080] In this alternative embodiment, formulating corresponding experiment standards according to the variety identification task requirements includes: the rice high-temperature identification standard and the rice low-temperature identification standard, and both the rice high-temperature identification standard and the rice low-temperature identification standard are divided into the rice seedling stage, the rice bud stage and the rice heading stage.

[0081] It should be noted that the experimental standard management module uses technologies such as natural language processing, knowledge graph, rule engine, and blockchain to solve the three core problems of information fragmentation, logical rigidity, and uncontrollable execution existing in traditional standard management. The unstructured characteristics of standard documents make manual parsing inefficient and error-prone. Through entity recognition and relationship extraction, natural language processing technology converts text into machine-readable structured triples, breaking through the dependence on manual experience and realizing the accurate decomposition and digital expression of standard elements. Knowledge graph technology constructs a hierarchical semantic network through ontology modeling, establishing multi-dimensional associations between discrete standard parameters (such as temperature, humidity, and observation indicators) and elements such as crop growth stages and stress types, making implicit logics such as "monitoring pollen activity is required for low-temperature identification during the heading stage" explicit and providing a knowledge base for dynamic reasoning. Based on this, the rule engine realizes the automation of logical reasoning. For example, when the task requirement involves compound stress (high temperature + drought), the system can automatically integrate the parameters of two independent standards to generate a new standard that conforms to the multi-factor interaction, while traditional manual formulation requires repeated trial and error. The device adaptation algorithm finds a balance between the scientificity of the standard and the device capabilities through a multi-objective optimization model. For example, when the temperature control accuracy of the environmental simulation room is insufficient, it automatically suggests extending the stress time or increasing the sample size to compensate for data reliability and avoid experiment interruption caused by device limitations.

[0082] The core effects brought by the technology combination are reflected in three aspects: First, the efficiency of standard formulation has leaped. Through semantic parsing and knowledge reasoning, the time-consuming for generating new standards has been shortened from weeks required by manual work to the minute level, especially adapting to the new stress identification requirements brought about by climate change. Second, the execution process is precisely controllable. The constraint rules of the knowledge graph are compared with the Internet of Things data in real time, and parameter deviations can be dynamically corrected (such as triggering an alarm when the temperature fluctuation exceeds the limit), increasing the experimental success rate by more than 40%. Finally, the standard system continues to evolve. Blockchain evidence storage ensures that the revision process is auditable, and the closed-loop feedback formed by the experimental data feeding back to the knowledge graph drives the standard parameters to shift from static provisions to dynamic optimization. For example, automatically adjusting the temperature gradient interval according to historical data improves the discrimination of variety stress resistance by 30%.

[0083] The underlying logic of the technology is to upgrade standard management from a "document library" to a "knowledge - decision system", realizing the intelligent control of the entire life cycle of standards through the integration of data-driven and rule-based reasoning, providing a technical base with normativity, flexibility, and traceability for crop stress resistance identification.

[0084] The implementation process of the experimental standard management module is divided into the following steps:

[0085] S11. Extract entities and parse relationships from the input standard documents through natural language processing technology, identify core elements such as adversity types, growth stages, and experimental parameters, convert unstructured text into machine-readable triple data, and establish a preliminary knowledge base.

[0086] S12. Define standard classes, attributes, and constraint rules based on ontology modeling language, and construct a hierarchical knowledge framework. For example, divide the "rice high-temperature identification standard" into subclasses such as seedling stage, germination stage, and heading stage, and each subclass is associated with attributes such as temperature range, duration, and observation indicators to ensure the rigor of logical relationships.

[0087] For example, construct an ontology model based on OWL language, define the class of "experimental standard" and its subclasses (such as rice high-temperature identification standard), and the associated attributes include temperature threshold, humidity range, observation frequency, etc. Convert triple entities into nodes through the Neo4j graph database, and establish relationship edges such as "rice heading stage low-temperature identification - need to monitor - pollen activity" to achieve complex association query functions. For example, when the user queries "seedling stage high-temperature related parameters", the system returns the accurate numerical range of 38 - 42°C along the path of "seedling stage high-temperature identification → control parameters → temperature range", and at the same time displays the associated equipment requirements (such as light intensity ≥ 10000 Lux).

[0088] S13. Store the knowledge graph using a graph database. The nodes represent experimental standard entities, and the edges represent parameter association relationships, supporting multi-hop queries and dynamic expansion. For example, quickly retrieve the associated temperature control requirements and measurement methods through the "heading stage low-temperature identification" node.

[0089] For example, after the user inputs the task description of "cold resistance identification of a certain plateau rice", the semantic parsing engine extracts key entities through the BiLSTM-CRF model: "a certain plateau" (geographical feature), "rice" (crop type), "cold resistance" (adversity type). The intention classification module determines that the task objective is "stress-resistant variety screening" rather than "mechanism research", and triggers the standard generation rule chain. The system expands the synonyms of "cold resistance" to "low-temperature tolerance", matches the main class of "rice low-temperature identification standard" in the knowledge graph, and associates the standard templates of the two subclasses of "heading stage" and "seedling stage".

[0090] S14. Integrate a rule engine to achieve semantic reasoning. According to the variety identification task description input by the user, match the standard template in the knowledge graph, and dynamically adjust parameters in combination with predefined logical rules. For example, automatically append light intensity constraints when the task involves high-altitude geographical features.

[0091] Based on the matching standard template, the rule engine loads the basic parameters: for example, the temperature gradient for low temperature identification during the heading stage is set to three groups of 15°C, 18°C, and 21°C, with a duration of 5 days. The observation indicators include pollen viability and seed setting rate. Since the task includes the characteristics of a certain plateau region, the system automatically adds a constraint condition of light intensity ≤ 8000 Lux through the association rule of "high altitude area → need to superimpose light control" in the knowledge graph to form a composite experimental standard. At this time, the device adaptation module detects the performance parameters of the environmental simulation chamber. If the maximum output of the light control module is 10000 Lux, this parameter is marked as an executable state.

[0092] S15. Introduce the device capability adaptation algorithm, perform compatibility verification on the generated standard parameters and the hardware performance data. If the device cannot meet the specific parameter requirements, an alternative solution is generated based on the constraint satisfaction algorithm to prioritize ensuring the effectiveness of the core indicators.

[0093] When the user attempts to adjust the standard parameters (such as setting the low temperature group during the heading stage to 12°C), the system detects the feasibility through the constraint satisfaction algorithm: the attribute value of "lower limit of low temperature tolerance during the rice heading stage" in the knowledge graph is 15°C, and the lowest temperature control capability of the device is 10°C. At this time, a three-level response mechanism is triggered - first, a warning of "exceeding the recommended threshold may cause plant death" is popped up. If the user insists on execution, the data compensation plan is started, requiring the sample repetition number to be increased to 8 groups to ensure statistical power, and at the same time, the recovery period observation time is automatically extended to 10 days to capture delayed damage.

[0094] S16. Through the closed-loop feedback mechanism, the experimental execution data is input back into the knowledge graph in reverse, and the difference analysis is used to identify the deviation between the standard parameters and the actual effect, triggering the standard revision process, such as optimizing the temperature gradient setting or adjusting the observation frequency.

[0095] During the experiment, the Internet of Things devices upload environmental data in real time (such as temperature fluctuation ±0.3°C), and the computer vision system collects phenotypic data (the detection accuracy of pollen viability reaches 95%). The quality assessment module compares the actual execution parameters with the standard requirements. If the detected temperature control deviation exceeds the preset threshold of ±0.5°C, the data of this batch is automatically marked as requiring re-experiment. At the same time, after analyzing the historical data, the reinforcement learning model finds that the cold resistance discrimination between the 18°C and 15°C treatments is insufficient, and it is recommended to adjust the temperature gradient to 14°C, 17°C, and 20°C to improve the identification efficiency. After the optimization plan is confirmed by experts, the standard parameters in the knowledge graph are updated in reverse.

[0096] S17. Use blockchain technology to record the change history of the standard version to ensure that the revision process is traceable. Each parameter adjustment is anchored to the experimental verification data to meet the audit requirements of the variety approval agency for the standard basis.

[0097] Each standard revision records the operation traces through blockchain technology, and uses the Hyperledger Fabric framework to store the revised content, the modifying personnel, and the timestamp information. When the variety approval agency requests to trace the standard basis of a certain experiment, the system can quickly locate the 2023 V2.1 version of the rice low-temperature identification standard adopted in this experiment, and display the parameter constraint rule set in effect at that time to ensure the legal effect of the experimental data. The version evolution function of the knowledge graph supports the historical comparison of standard parameters. For example, it visually displays the basis documents and experimental verification data for the shortening of the low-temperature duration at the heading stage from 7 days to 5 days during the period from 2022 to 2023.

[0098] In this alternative embodiment, the formulation of the corresponding experimental plan according to the variety quantity and the experimental standard includes:

[0099] S21. Based on the variety identification tasks to be participated in, establish an experiment list, where the experiment list includes varieties, sowing, seedling raising, transplanting, water and fertilizer management, identification experiments, and comparative experiments.

[0100] It should be noted that based on the requirements of variety identification tasks, create an experiment main table in a relational database (such as PostgreSQL), and the fields include metadata such as variety ID, genotype, and source institution. The associated sub-tables record the sowing date, seedling raising conditions (temperature and humidity thresholds), transplanting specifications (row and plant spacing), and water and fertilizer management plans (EC value, fertilization cycle).

[0101] Define the experimental stage task chain through a workflow engine (such as Apache Airflow): sowing → seedling raising monitoring → transplanting → stress treatment → data collection. Set the completion conditions (such as the need to reach the state of three leaves and one heart in the seedling raising stage) and dependency relationships (transplanting needs to be executed within 48 hours after the completion of seedling raising) for each node.

[0102] S22. Group and mark the varieties, bases, and base planting layouts required for identification experiments.

[0103] It should be noted that use the GIS spatial database to store the base geographical information (soil type, slope), facility capacity (artificial climate chamber area, number of large field partitions), and group the varieties through the greedy algorithm: allocate the varieties with the same stress treatment type (such as all requiring high-temperature stress) to the same climate chamber to reduce duplicate equipment configuration.

[0104] Generate a planting layout map based on the two-dimensional bin-packing algorithm (First-Fit Decreasing), and automatically calculate the optimal row and plant spacing arrangement of the large field experimental area to ensure that the spatial distribution of the comparative experimental group and the control group meets the requirements of the statistical randomized block design.

[0105] S23. Conduct experiments according to the varieties to determine the stress treatment method and treatment time.

[0106] It should be noted that the API of the experimental standard management module is called to obtain the stress parameters of the current variety (for example, for high-temperature treatment, it is necessary to maintain 38°C ± 0.5°C at the heading stage for 72 hours), and the heading time is predicted by combining the crop growth model (such as WOFOST), and the treatment window is dynamically set (for example, the stress is started on the 60th day after sowing);

[0107] The Internet of Things control instructions are batch-issued to the environmental simulation chamber, and the sampling frequencies of the sensor networks (temperature, humidity, CO2 concentration) are synchronously configured (for example, recorded once every 15 minutes).

[0108] S24. Establish experimental bases for comparison, including artificial climate appraisal chambers and field test areas.

[0109] S25. According to the bearing capacity of the experimental base, choose to increase or decrease the experimental variety content.

[0110] It should be noted that the resource status of the base is monitored in real time (such as the occupancy rate of the artificial climate chamber and the load of the field irrigation system), and a mixed-integer programming model is used for capacity prediction: if the utilization rate of the climate chamber exceeds 85% due to the current task, an alarm is triggered and a solution is recommended (such as postponing some non-urgent experiments or enabling standby equipment); when new varieties are added, the experimental groups are merged according to genotype similarity through the K-means clustering algorithm to reduce the number of repeated treatments. For example, three rice varieties with similar drought resistance are merged into a group to share the same stress treatment process.

[0111] S26. During the experimental process of the current variety identification task, historical experimental data is added for comparison.

[0112] It should be noted that a data lake (Delta Lake) is constructed to store the historical experimental data sets (stress response curves, yield correlation coefficients). When the current experiment is started, comparable data subsets are automatically extracted through feature matching (such as the same genotype, similar treatment conditions);

[0113] The contrastive learning framework (such as SimCLR) is used to extract the latent representations of phenotypic features (leaf area index, biomass), the similarity matrix between the current experimental group and the historical data is calculated, and the key difference points are visually displayed (such as the photosynthetic rate increases by 15% under the new treatment plan).

[0114] S27. According to the identification results, the varieties participating in the identification are analyzed and evaluated, and an identification conclusion is issued.

[0115] It should be noted that the integrated statistical analysis library (Python SciPy) is used to perform analysis of variance (ANOVA) and multiple comparison tests (Tukey HSD) to determine the stress resistance level of the variety (for example, the drought resistance index ≥ 0.8 is highly resistant);

[0116] The conclusion report automatically fills data through a template engine (Jinja2), and key conclusions (such as "the thousand-grain weight decline rate of variety A is significantly lower than that of the control group under drought stress") are accompanied by a data traceability chain, and clicking on it can penetrate to query the original sensor data and processing logs.

[0117] In this alternative embodiment, the acquisition, storage, analysis, processing, and application management of all experimental data throughout the entire experimental process include:

[0118] S31. According to different data types (environmental data, phenotypic data, variety data, video data, and other data, etc.), collect experimental data during the variety identification task in real time.

[0119] It should be noted that by docking devices such as environmental sensors and phenotypic imagers through the Internet of Things protocol (MQTT / Modbus), dynamic data such as temperature, humidity, and chlorophyll content are collected in real time. For example, field cameras capture crop images at a rate of 5 frames per second, and edge computing nodes run a lightweight YOLOv5 model to extract structured features such as plant height and leaf area. At the same time, a standardized form interface is developed for experimenters to enter manual data such as pest and disease levels and operation records.

[0120] The original data is uniformly converted through JSON Schema (such as mapping the temperature values of sensors of different brands to "value: float, unit: °C"), and the streaming processing engine (Apache Flink) performs real-time anomaly detection (marking data as invalid when the temperature > 50 °C). The cleaned data is classified and buffered into the Kafka message queue according to the variety ID and experimental batch label to ensure the efficiency and consistency of subsequent processing.

[0121] S32. Enter the collected original experimental data, output a comprehensive evaluation through analysis of variance, comparative analysis, and trend analysis, and provide a query interface for variety data, comparative data, and experimental result data.

[0122] It should be noted that analysis of variance is used to determine the significance of the effects of different experimental treatments on the stress resistance of varieties. First, the experimental data are grouped according to the treatment conditions. For example, the growth data of the same variety under three stress conditions of high temperature, low temperature, and drought are divided into three groups. The differences between the mean values of the data within each group and the overall mean value (between-group variance) are calculated, and at the same time, the degree of fluctuation of the data within each group (within-group variance) is calculated. By calculating the F value (the ratio of the between-group variance to the within-group variance), if the F value exceeds the preset threshold (such as the critical value obtained by looking up the table) and the corresponding P value is less than 0.05, it is determined that the differences between different treatments are statistically significant. For example, if the plant height of the high-temperature treatment group is significantly lower than that of the control group, it indicates that the variety is sensitive to high temperature. The analysis results are presented in tabular form, showing the mean values, standard deviations, and significance markers of each group (such as using "*" to indicate P < 0.05 and "**" to indicate P < 0.01), and a conclusion description is automatically generated, such as "The high-temperature treatment has a highly significant effect on the plant height of variety A."

[0123] Comparative analysis screens candidate varieties with excellent stress resistance by horizontally comparing the performances of different varieties or the same variety under different treatments. The system classifies the experimental data according to the variety or treatment type and extracts key indicators (such as survival rate, chlorophyll content) for comparison. For example, under drought stress, the relative water content of the leaves of variety B and variety C is compared. If the value of variety B is continuously higher than that of variety C and the fluctuation is smaller, it is determined that its drought resistance is better. The analysis results are presented in visual charts. For example, a bar chart shows the indicator differences of each variety under the same treatment, and a line chart shows the response curve of the same variety under different stresses, and key inflection points are marked (such as a significant decrease on the 5th day of stress treatment). At the same time, a comparative conclusion is generated, such as "The biomass decline rate of variety B under drought conditions is 30% lower than that of variety C."

[0124] Trend analysis predicts the long-term change rules of variety stress resistance indicators based on time series data. The system performs smoothing processing (such as the moving average method) on the continuously collected data (such as daily photosynthetic rate, weekly plant height increment) and fits a linear or non-linear trend model. For example, a polynomial regression model is used to analyze the change trend of the transpiration rate of variety D under high-temperature treatment over time. If the model shows that the transpiration rate continuously decreases with the prolongation of the treatment time and the goodness of fit (R 2 ) is higher than 0.9, it is determined that the variety has weak high-temperature tolerance. The analysis results are presented in the form of a trend line superimposed on the original data points, and a prediction conclusion is output, such as "If high temperature persists for 15 days, the transpiration rate of variety D is expected to drop to 40% of the initial value."

[0125] The comprehensive evaluation integrates the results of three types of analyses to form a multi-dimensional evaluation of the variety's stress resistance. The system screens key influencing factors based on the significance results of the variance analysis, determines the relative advantages by combining the variety rankings of the comparative analysis, and then evaluates the long-term resistance potential according to the prediction results of the trend analysis. For example, if a certain variety shows a significant response to high temperature in the variance analysis (P<0.01), ranks in the top 10% in terms of survival rate in the comparative analysis, and the trend analysis predicts that its yield will only decrease by 5% under continuous high temperature, then the comprehensive evaluation is "highly resistant to high temperature variety, recommended for priority promotion".

[0126] The final report is presented in combination with text summaries and charts, including the classification of resistance levels (such as high resistance, medium resistance, sensitive), the types of dominant stress environments, and suggestions for potential application areas, providing a direct basis for variety approval and breeding decisions.

[0127] S33. Export the valid experimental data, push it according to the user permissions, and make effective use of it in rice breeding screening based on the data analysis and data evaluation results.

[0128] In this alternative embodiment, the whole-process data includes: workload, project cost, completion status, number of reports, planned tasks, experimental data, analysis man-hours, cost settlement status, and contracts.

[0129] In this alternative embodiment, the system management module includes: a configuration management module (not shown in the figure), a resource management module (not shown in the figure), a data storage module (not shown in the figure), an information query module (not shown in the figure), and other management modules (not shown in the figure). Among them, the configuration management module is used to configure the internal permissions, users, departments, and role management of the system, and provide customized configuration solutions for different application scenarios; the resource management module is used to provide overall resource management and optimize the management cost of experimental identification; the data storage module is used to build and manage the database and store experimental identification data in real time; the information query module is used to provide quick search and query of data information; the other management modules are used to achieve seamless connection of data security and multiple tracking.

[0130] It should be noted that the configuration management module constructs a dynamic permission system based on the Attribute-Based Access Control (ABAC) model, associates the user departments, roles (such as experimentalists, auditors, administrators) with the experimental scenarios (such as transgenic variety identification), and calculates the permission scope in real time through the policy engine. For example, transgenic experimental data is only open to users with a biosafety level III qualification. The microservices architecture is adopted to provide customized configuration interfaces, supporting the creation of scenario-based permission templates on demand (such as the "drought resistance identification project team" template automatically associates the operation permissions of temperature control equipment and the query permissions of drought resistance data).

[0131] The resource management module integrates resource data such as equipment (environmental simulation chamber, phenotyping detector), consumables (seeds, reagents), and human resources (experimental staff working hours) by constructing a resource topology map, and optimizes the resource allocation plan through a genetic algorithm. For example, when multiple projects are in parallel, it automatically calculates equipment usage conflicts and generates an optimal schedule. The cost control module interfaces with the financial system, real-time statistics the consumption rate of experimental consumables (e.g., unit variety identification cost = total consumable cost / number of identified varieties), and generates a heat map of resource utilization rate.

[0132] Specifically, the resource management module uses a genetic algorithm to achieve resource optimization scheduling. The specific steps are as follows:

[0133] S41. Resource modeling and data integration: Construct a multi-dimensional resource pool model, and integrate data such as experimental equipment (such as the schedule of the environmental simulation chamber), consumables (seed and reagent inventory), and human resources (experimental staff skills and working hours). Define resource attributes: Equipment resources include capacity (maximum concurrent experiments) and usage cost (yuan / hour); consumable resources record real-time inventory and replenishment cycle; human resources are associated with skill tags (such as molecular marker detection qualification). Model the dependency relationship between resources through a topology network (such as an experiment that needs to occupy climate chamber A and detector B simultaneously), and generate an initial resource state matrix, including available time windows, real-time load rates, and unit cost coefficients.

[0134] S42. Genetic algorithm initialization and population generation: Encode the resource allocation plan into chromosomes, and gene segments represent equipment usage periods (such as the climate chamber from 09:00 to 12:00), consumable allocation amounts (50 ml of reagent X usage), and human resource scheduling (experimental staff P is responsible for task 1).

[0135] Randomly generate N feasible solutions (such as 200 solutions) for the initial population. Each solution needs to meet the basic constraints: equipment capacity is not exceeded, consumable demand ≤ inventory, and experimental staff skills match task requirements. Eliminate invalid solutions through a conflict detection algorithm (such as repeated occupation of the same equipment period), and retain compliant individuals to form the first-generation population.

[0136] S43. Fitness function design and evaluation: Define a fitness function to quantify the quality of the solution: Equipment utilization rate = total usage duration / (number of equipment × maximum available duration), consumable cost = Σ (single consumable usage × unit price), and human resource saturation = effective working hours / total working hours.

[0137] Introduce penalty terms to handle soft constraints: too high equipment switching frequency (deduct 5% of the score for each excess), and delay of urgent tasks (deduct 10% for each hour of overtime). Calculate the fitness value F of each individual = 0.4 × equipment utilization rate + 0.3 × (1 - standardized consumable cost) + 0.3 × human resource saturation - penalty term, and sort the scores to select the top 30% of high-quality individuals into the mating pool.

[0138] S44. Evolutionary Iteration and Solution Optimization: The individuals in the mating pool generate offspring through crossover operations: randomly select two parent solutions, and intercept and recombine the equipment scheduling gene segment and the consumable allocation gene segment (such as the climate chamber time period of Parent 1 + the reagent dosage of Parent 2).

[0139] Perform mutation operations on the offspring: randomly adjust the time period of a certain device (such as changing from 09:00 - 12:00 to 13:00 - 16:00) or replace the experimenter (under the premise of skill matching) with a 5% probability. After the new generation population undergoes conflict detection and fitness evaluation, retain the top 50 solutions to enter the next round of iteration.

[0140] Continue to evolve until the convergence condition is reached (such as the change in the highest fitness < 1% for 10 consecutive generations) or the maximum number of iterations (100 times), and output the globally optimal resource allocation solution.

[0141] S45. Dynamic Adjustment and Feedback Learning: Load the optimal solution into the actual scheduling system and monitor the changes in resource status in real time (such as sudden equipment failures, emergency replenishment of consumables). When the deviation exceeds the threshold (the actual - planned difference > 15%), trigger dynamic rescheduling: restart the genetic algorithm based on the current resource status matrix, and inject historical high - quality solutions (accounting for 50%) into the initial population to accelerate convergence.

[0142] At the same time, collect execution data (actual equipment utilization rate, consumable waste rate) to feed back the algorithm parameter library, and gradually optimize the fitness function weights (such as increasing the weight of equipment utilization rate to 0.5) to achieve self - evolution of the algorithm.

[0143] The data storage module adopts a hybrid storage architecture: time - series data (environmental sensor readings) are stored in InfluxDB, unstructured data (crop images, experimental videos) are stored in Ceph distributed object storage, and structured data (variety information, experimental records) are stored in a PostgreSQL relational database. Integrate multi - source data through a data lake (Delta Lake), ensure data consistency through ACID transactions, and support stream - batch integrated processing (such as writing sensor data in real time while performing historical data analysis).

[0144] The information query module constructs a multi - level index system: Elasticsearch full - text index supports fuzzy search (such as "salinity - tolerant rice varieties"), and the graph database (Neo4j) realizes associated retrieval (such as querying the parental pedigree and historical identification results of a certain variety). In addition, convert the user input (such as "2023 wheat drought - resistance data in East China") into SQL / SPARQL statements through a natural language query (NLQ) engine, and select the optimal execution path through a query optimizer.

[0145] Other management modules (data security and tracking) use Change Data Capture (CDC) technology to capture database change logs in real time and synchronize them to downstream systems (such as the breeding decision-making platform) through Kafka pipelines, with an end-to-end latency of less than 1 second. Moreover, the blockchain (Hyperledger Fabric) is used to record the data operation chain (such as "User A modified the drought resistance index of Variety B on August 20, 2023"), and zero-knowledge proof (ZKP) is combined to achieve operation traceability without disclosing sensitive information.

[0146] In this alternative embodiment, the information exchange module includes: an exchange evaluation module (not shown in the figure), an upload and distribution module (not shown in the figure), a report compilation module (not shown in the figure), a data interface module (not shown in the figure), a customer management module (not shown in the figure), and a mobile application module (not shown in the figure). Among them, the exchange evaluation module is used to provide functions for the exchange and sharing of crop stress resistance experiment experience, evaluate and summarize crop varieties based on stress resistance data, and display them according to user permissions; the upload and distribution module is used to display experiment content through a page and issue planning and design, specified standards, and technical guidance to subordinate units; the report compilation module is used to generate a test report with one click using a pre-selected report template, push the report to reviewers and issuers for viewing and downloading, and enable traceability of original data; the data interface module is used to provide interfaces for Internet of Things devices to collect temperature and humidity environment data in real time; the customer management module is used to record customer names, contact information, the number of selected varieties, stress resistance identification requirements, and payment status; the mobile application module is used to encrypt the connection to mobile terminals, provide one-click downloads of basic data and collection tasks, and upload text and picture data in real time to achieve information query, trait collection, and experiment implementation.

[0147] Figure 2 An embodiment of a method for managing crop stress resistance identification experiments of the present invention is shown.

[0148] In this alternative embodiment, the method for managing crop stress resistance identification experiments includes:

[0149] S201. Obtain the maintenance content of the hardware required for crop stress resistance experiments, records of experimental varieties, experimental standards, and experimental plans, implement the experiments according to the experimental plans, and conduct full-process management;

[0150] S202. Based on underlying resource configuration and permission management, provide an information retrieval and query interface to achieve seamless connection and multiple tracking of experimental data security;

[0151] S203. Conduct technical experience and scientific research achievement exchanges among users through mobile terminals to achieve scientific research collaboration and knowledge sharing, and display the evaluation summary of stress resistance experimental data according to user permissions.

[0152] The present invention will be further described in detail below in conjunction with specific embodiments.

[0153] To improve the breeding efficiency and quality, the present invention has developed a crop stress resistance identification experiment management system (platform), which consists of crop stress resistance experiment identification management software, a crop stress resistance experiment identification environment simulation chamber, experimental fields, an Internet of Things system, etc.

[0154] The variety identification test meets the national requirements for variety approval, is carefully designed, facilitates variety identification testing, design and management, data collection, provides one-key report generation and professional data analysis functions. Among them, the software includes various functional modules of multiple categories, realizing the digitization of experiment management, system management, and information exchange, and can be customized and developed according to the different needs of scientific research institutions and enterprises.

[0155] 1. Design concept

[0156] Data storage is the foundation, user experience is the center, and data value mining is the goal.

[0157] High efficiency: Reduce the management cost of identification experiments, simplify the management process, and improve the management level.

[0158] Intelligence: Configure identification and detection tasks, calculate and judge identification and detection data, and generate inspection reports with one key, etc.

[0159] Simplification: The product performance is stable and mature, the functions are simple; the product is easy to operate.

[0160] 2. Product features

[0161] The product has strong compatibility and can be seamlessly connected to Windows, Linux, Android mobile devices, and WeChat mini-programs, allowing you to view and manage data anytime and anywhere.

[0162] 3. Diversified needs:

[0163] According to the actual needs of customers, free versions, cloud platform versions, standard enterprise versions, and advanced customized enterprise versions are tailored.

[0164] 4. Product functions

[0165] Functions such as excellent data statistics, data analysis, access performance, process control, and graphical display bring you a different user experience.

[0166] 5. Product support:

[0167] On-site requirement docking, on-site training, online Q&A, and version sequence upgrade. The system platform built by the present invention can support the information system for the whole process of modern crop stress resistance experiment and identification. It includes three major systems: an experiment management platform (experiment management module), a system management platform (system management module), and an information exchange platform (information exchange module), which can provide different versions and function combinations to meet the different needs of enterprises and research institutions.

[0168] I. Experiment Management Platform

[0169] The experiment management platform includes the whole-process management of the maintenance of the hardware required for the experiment, the recording of experimental varieties, experimental standards, the implementation of experimental plans, etc.

[0170] Main management contents: personnel management, equipment and facility management, variety management, experimental standard management, experimental plan implementation management, variety sample retention management, document management, message push management, consumable management, performance management.

[0171] 1. Personnel management: Arrange relevant responsible personnel according to task requirements.

[0172] 2. Task entry:

[0173] Enter the maintenance information required for the hardware such as artificial climate chamber appraisal rooms and field test areas;

[0174] Enter the information related to variety appraisal tasks;

[0175] Enter the information of relevant responsible personnel;

[0176] Push hardware maintenance tasks and variety appraisal tasks.

[0177] 3. Equipment management: Relevant personnel carry out inspection and maintenance on the equipment, facilities, and instruments required for the experiment.

[0178] 4. Variety management: Automatically generate a unique sample number, generate a sampling form, and can import sample information; read variety information and conduct handover by variety management personnel.

[0179] 5. Experimental standard management: Formulate corresponding experimental standards according to the experimental needs of variety appraisal.

[0180] High-temperature identification standards for rice (temperature, humidity, time): rice seedling stage, rice bud stage, rice heading stage.

[0181] Low-temperature identification standards for rice (temperature, humidity, time): rice seedling stage, rice bud stage, rice heading stage.

[0182] 6. Experimental calculation and implementation management: According to the number of varieties and experimental standards, formulate relevant experimental plans, and provide an integrated process operation, variety traceability, and graphical display tool.

[0183] According to the task of identifying varieties, determine personnel, varieties to be identified, identification groups, identification bases, etc.

[0184] 6.1. Experimental list: varieties, sowing, seedling raising, transplanting, water and fertilizer management, identification experiments, comparative experiments, etc.

[0185] 6.2. Experimental groups: Group and label the varieties, bases, and planting layouts in the bases that need to be identified in the experiments.

[0186] 6.3. Experimental design: Design experiments according to the varieties, and determine the adversity treatment methods and time.

[0187] 6.4. Experimental bases: Artificial climate identification rooms: A1, A2, A3...; Field test areas B1, B2, B3....

[0188] 6.5. Experimental addition: Increase or decrease the content of experimental varieties.

[0189] 6.6. Historical addition: Compare past experiments.

[0190] 6.7. Variety evaluation: Analyze and evaluate the identified varieties according to the identification results, and issue identification conclusions.

[0191] 7. Data management: Collection, storage, analysis, processing, application, etc. of the entire experimental data.

[0192] The processing of experimental data includes the following aspects:

[0193] 7.1. Data types: environmental data, phenotypic data, variety data, video data, other data.

[0194] 7.2. Data collection: Collection of experimental data, collection of field layout data, image collection, collection of artificial simulation indoor environment data, collection of outdoor field data, collection of phenotypic data, etc.

[0195] Collection, presentation, and analysis of identification data (how identification data enters the APP for collection, import, page filling, etc.).

[0196] 7.3. Data detection. Detection of variety data analysis, input the original data into the system for statistical analysis; review of the original data, three-level review of the original data, with high accuracy.

[0197] 7.4. Data analysis: variance analysis, comparative analysis, analysis record, import and export, trend analysis, comprehensive evaluation.

[0198] 7.5. Data query: Query of variety data, query of comparative data, query of experimental result data, import and export.

[0199] 7.6. Data Push: Import and export valid experimental data and push it according to permissions.

[0200] 7.7. Data Application: Promote the effective utilization of these data in rice breeding screening based on data analysis, data evaluation results, etc.

[0201] 8. Statistical Analysis: Workload statistics, project cost statistics, completion status statistics, report quantity statistics, planned task statistics, experimental data statistics, analysis man-hour statistics, cost settlement status statistics, contract statistics.

[0202] II. System Management Platform

[0203] The system management platform mainly includes permission management, customer management, database management, convenient retrieval capabilities, standardized process management, etc.

[0204] 1. System Management: Permission management, user management, department management, role management; Provide customized configuration solutions for different application scenarios.

[0205] 2. Resource Management: Overall resource management (software and hardware, facilities and equipment, customers, etc.), reduce management costs, and improve management levels.

[0206] 3. Data Storage: Database design and management.

[0207] 4. Information Query: Quick search and query.

[0208] 5. Other Management: Achieve seamless connection of data security, multiple tracking, and improve work efficiency.

[0209] III. Information Exchange Platform

[0210] The information exchange platform mainly includes technical experience exchange among peers, unit collaboration exchange, customer exchange, scientific research achievement exchange, etc.

[0211] 1. Exchange Evaluation: Exchange of rice stress resistance experiment experience, provide suggestions, share achievements, evaluate and summarize varieties based on stress resistance data, and present according to permissions.

[0212] 2. Upload and Distribution: Display content on the page for easy viewing, plan design, formulate standards, and provide technical guidance to subordinate units.

[0213] 3. Report Preparation: Select a report template, generate a test report with one key, review and sign the report. Push it to the reviewers and signers for report viewing and downloading. Archive and save the file report, and the original data can be traced.

[0214] 4. Data Interface: Environmental data such as temperature and humidity collected by the Internet of Things control system, phenotypic data, and platform interfaces of relevant units.

[0215] 5. Customer Management: customer name, contact information (phone number, email, WeChat), number of selected varieties, quantity per variety, requirements for stress resistance identification, and payment status. <9000442>6. Mobile Application: encrypted connection, one - click download of basic data and collection tasks, real - time upload of data such as text and pictures, and convenient functions for information query, trait collection, experiment implementation, etc. (mobile phone APP).

[0217] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non - volatile storage medium and an internal memory. The non - volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non - volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements the steps in the above - mentioned method embodiment.

[0218] Those skilled in the art can understand that Figure 3 the structure shown in

[0219] is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0220] In addition, the present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the steps in the above - mentioned method embodiment.

[0221] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided by the present invention can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0222] The present invention is not limited to the structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A management system for crop stress resistance identification experiments, characterized in that, Including: An experiment management module for the whole-process management of the maintenance content of the hardware required for crop stress resistance experiments, the recording of experimental varieties, experimental standards, and the implementation of experimental plans; A system management module for undertaking underlying resource configuration and permission management, providing an information retrieval and query interface, and realizing seamless connection and multiple tracking of experimental data security; An information communication module for providing exchanges of technical experience and scientific research achievements among users, realizing scientific research collaboration and knowledge sharing, and displaying the evaluation summary of stress resistance experimental data according to user permissions.

2. The crop stress resistance identification experiment management system according to claim 1, characterized in that, The said experiment management module includes: a personnel management module, a task entry module, a device management module, a variety management module, an experimental standard management module, an experimental plan implementation management module, a data management module, and a statistical analysis module. Among them, The said personnel management module is used to arrange the responsible personnel for the experiment according to the experimental task requirements; The said task entry module is used to enter experimental management information, and push hardware maintenance tasks and variety identification tasks according to task requirements; The said device management module is used to repair and maintain the equipment, facilities, and instruments required for the experiment; The said variety management module is used to automatically generate a unique sample number, generate a sampling form, import sample information, and read variety information, so that variety management personnel can conduct handovers; The said experimental standard management module is used to formulate corresponding experimental standards according to the requirements of variety identification tasks; The said experimental plan implementation management module is used to formulate corresponding experimental plans according to the variety quantity and experimental standards, and provide integrated process operations, variety traceability, and graphical display tools; The said data management module is used to collect, store, analyze, process, and manage the application of all experimental data in the whole experimental process; The said statistical analysis module is used to statistically analyze the whole-process data in the experimental identification process.

3. The crop stress resistance identification experiment management system according to claim 2, characterized in that: The said experimental management information includes: the maintenance information required for artificial climate chamber identification rooms, field test areas, and experimental hardware, variety identification task information, and relevant responsible personnel information.

4. The crop stress resistance identification experiment management system according to claim 2, wherein The said formulating corresponding experimental standards according to the requirements of variety identification tasks includes: rice high-temperature identification standards and rice low-temperature identification standards, and both the rice high-temperature identification standards and rice low-temperature identification standards are divided into rice seedling stage, rice bud stage, and rice heading stage.

5. The crop stress resistance identification experiment management system according to claim 2, wherein The said formulating corresponding experimental plans according to the variety quantity and experimental standards includes: Based on the variety identification tasks to be participated in, establishing an experimental list; Grouping and marking the experimental varieties, bases, and base planting layouts to be identified; Conducting experiments according to varieties, and determining stress treatment methods and treatment times; Establishing experimental bases for comparison, including artificial climate identification rooms and field test areas; According to the bearing capacity of the experimental base, choosing to increase or decrease the experimental variety content; During the experimental process of the current variety identification task, adding historical experimental data for comparison; According to the identification results, analyzing and evaluating the varieties participating in the identification, and issuing identification conclusions.

6. The crop stress resistance identification experiment management system according to claim 5, characterized in that: The said experimental list includes varieties, sowing, seedling raising, transplanting, water and fertilizer management, identification experiments, and comparative experiments.

7. The crop stress resistance identification experiment management system according to claim 2, wherein The said collecting, storing, analyzing, processing, and managing the application of all experimental data in the whole experimental process includes: According to different data types, real-time collection of experimental data during the variety identification task; Input the collected original experimental data, output comprehensive evaluation through variance analysis, comparative analysis and trend analysis, and provide a query interface for variety data, comparative data and experimental result data; The effective experimental data will be exported and pushed according to user permissions, and will be effectively utilized in rice breeding screening based on data analysis and evaluation results.

8. The crop stress resistance identification experiment management system according to claim 2, wherein The full process data includes: workload, project costs, completion status, number of reports, planned tasks, experimental data, analysis hours, cost settlement status and contracts.

9. The crop stress resistance identification experiment management system according to claim 1, characterized in that: The system management module includes: configuration management module, resource management module, data storage module, information query module and other management modules, among which, The configuration management module is used to configure the permissions, users, departments and role management within the system, and provide customized configuration solutions for different application scenarios; The resource management module is used to provide overall resource management and optimize the management cost of experimental identification; The data storage module is used to build and manage the database and store experimental identification data in real time; The information query module is used to provide fast search and query of data information; The other management modules are used to achieve seamless data connection and multiple tracking.

10. The crop stress resistance identification experiment management system according to claim 1, characterized in that, The information exchange module includes: communication and evaluation module, upload and download module, report preparation module, data interface module, customer management module and mobile application module, among which, The communication and evaluation module is used to provide a function for exchanging and sharing crop stress resistance experimental experience, evaluate and summarize crop varieties based on stress resistance data, and display them according to user permissions; The upload and download module is used to display the experimental content through the page and issue planning and design, specified standards and technical guidance to subordinate units; The report preparation module is used to generate a test report with one click using a pre-selected report template, push the report to the reviewer and issuer for browsing and downloading, and realize the traceability of the original data; The data interface module is used to provide an IoT device interface and collect temperature and humidity environmental data in real time; The customer management module is used to record the customer's name, contact information, number of varieties sent, stress resistance identification requirements, and payment status; The mobile application module is used to encrypt the connection to the mobile terminal, provide one-click downloading of basic data and collection tasks, and upload text and image data in real time to realize information query, trait collection and experiment implementation.

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