A melon breeding whole-process information task tracking management system

Abstract

The present application relates to the field of agricultural informatization technology, and discloses a kind of informationization task tracking management system of Hami melon breeding whole process.The event creation module in the system is used to create breeding event with space-time attribute;Task planning module generates pre-defined associated breeding task based on breeding event and specifies the person responsible;Task execution and data binding module binds the germplasm material identification generated in task execution with trait survey data as breeding entity data and corresponding task;Breeding map generation and display module dynamically generates and visualizes informationization breeding map based on event, task and binding data, and the map takes event as root node, clearly shows the logic and state association of whole process.The present application realizes the digital closed-loop tracking of breeding process, solves the data island and traceability problem, and significantly improves the efficiency of breeding management through visual map and intelligent verification.

Description

Technical Field

[0001] This application relates to the field of agricultural information technology, and in particular to an information-based task tracking and management system for the entire process of Hami melon breeding. Background Technology

[0002] Currently, Hami melon breeding work mainly relies on paper records and scattered spreadsheets for management, a traditional model with inherent technical shortcomings. First, the breeding process is fragmented; there is a lack of effective digital correlation between macro-level breeding events, specific field tasks, and observed physical data, making full-process tracking difficult. Second, data silos are formed and traceability is difficult; key germplasm resource identifiers, trait survey data, and other core information are stored separately, failing to establish strong binding relationships with the tasks, personnel, and environmental information that generated them, resulting in low data traceability efficiency and difficulty in locating errors. Finally, management lacks a global and interconnected view; project managers cannot intuitively and in real-time grasp the overall progress of the breeding project, the genetic lineage of materials, and the logical dependencies between tasks, leading to insufficient decision support and limited scientific rigor. Therefore, there is an urgent need for a new management system that can achieve full-process digital integration of breeding, automatic correlation between tasks and data, and intuitive presentation of the overall logical status. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, the embodiments of this application provide an information-based task tracking and management system for the entire process of Hami melon breeding to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, this application provides an information-based task tracking and management system for the entire process of Hami melon breeding, comprising: The event creation module is used to create breeding events with time period and location attributes in response to user operations; The task planning module is used to generate at least one predefined type of breeding task associated with the breeding event based on the breeding event, and to assign a responsible person and a planned time for each breeding task; The task execution and data binding module is used to associate and bind the generated breeding entity data with the corresponding breeding task during the execution of the breeding task; wherein, the breeding entity data includes germplasm material identification and trait survey data; The breeding atlas generation and display module is used to dynamically generate and visualize an information-based breeding atlas based on the breeding event, all associated breeding tasks and their bound breeding entity data. The information-based breeding atlas takes the breeding event as the root node, each breeding task and its bound breeding entity data as associated child nodes, and uses connecting lines to represent the generation, membership or data feedback relationships between them, thereby reflecting the logic and state associations of the entire breeding process.

[0005] Optionally, the step of creating a breeding event with time period and location attributes in response to a user operation specifically includes: Receive event configuration information input by the user, wherein the event configuration information at least defines the time period and execution location of the event; Based on the event configuration information, a unique breeding event identifier is generated, and an event data object indexed by the breeding event identifier is created; Obtain environmental information associated with the execution location and associate the environmental information with the event data object.

[0006] Optionally, the step of generating at least one predefined type of breeding task associated with the breeding event, and assigning a responsible person and planned time for each breeding task, specifically includes: Obtain the attribute information of the breeding event and determine at least one predefined type breeding task to be generated; A unique task object is generated for each identified breeding task, and a designated person in charge and a planned time are assigned to each task object; Each generated task object is associated with a breeding event and stored in the event data object corresponding to the breeding event.

[0007] Optionally, the predefined type of breeding task includes hybridization pollination task, field survey task, and seed harvesting task; wherein, when the hybridization pollination task is performed, the germplasm material identifiers associated with the hybridization pollination task include male parent identifier and female parent identifier.

[0008] Optionally, during the execution of the breeding task, the generated breeding entity data is associated and bound with the corresponding breeding task, specifically including: Obtain the task object corresponding to the breeding task to be executed and the person in charge configured in the task object; Respond to the execution feedback data submitted by the current person in charge regarding the breeding task; The germplasm material identification and trait survey data in the execution feedback data are respectively structured to generate standardized breeding entity data; The breeding entity data is associated and bound with the task object, and the execution status of the task object is updated.

[0009] Optionally, the step of performing structured processing on the germplasm material identification and trait survey data in the execution feedback data to generate standardized breeding entity data specifically includes: Identify the task type of the breeding task and determine the corresponding data processing rules based on the task type; Based on the data processing rules, the germplasm material identifiers are standardized to generate standardized material identifier objects; Based on the data processing rules, the trait survey data is formatted and units are standardized to generate standardized trait data objects. The material identifier object and the trait data object are encapsulated into standardized breeding entity data.

[0010] Optionally, the step of dynamically generating and visualizing an information-based breeding atlas based on the breeding events, all associated breeding tasks, and their bound breeding entity data specifically includes: Obtain the event data object corresponding to the breeding event, all associated task objects, and their bound breeding entity data; Based on the event data object, the task object, and the breeding entity data, the corresponding graph nodes and the connection relationships between the nodes are determined; wherein, the graph nodes include the root node representing the breeding event, the task node representing each breeding task, and the data node representing each breeding entity data; Generate graph model data based on the graph nodes and the connection relationships; Based on the map model data, the visualization engine is driven to render and display the information-based breeding map in the user interface.

[0011] Optionally, the system further includes a dynamic verification and early warning module for map anomalies, used for: Monitor update operations on the breeding entity data or the breeding task status; When the update operation is detected, the consistency of the associated nodes and connections in the information-based breeding graph is checked according to the preset domain rule set. The domain rule set includes at least: data consistency rules for checking the trait logic rationality between data nodes of germplasm materials with genetic association; and process logic rules for checking the state logic rationality between breeding task nodes with dependency relationships. If the verification result indicates an anomaly, the identified abnormal nodes and associated paths will be visually highlighted in the information-based breeding map, and an anomaly warning report will be generated.

[0012] Optionally, the process logic rules are used to verify the rationality of the state logic between breeding task nodes with dependencies, specifically including: When the status of a breeding task node is updated to "completed", retrieve the status of all its upstream dependent breeding task nodes. Determine whether the status of all upstream dependent breeding task nodes is "completed"; If not, then it is determined to be an abnormal process logic.

[0013] Optionally, the data consistency rule is used to verify the logical rationality of traits among data nodes of genetically related germplasm materials, specifically including: When an update operation on the trait survey data of offspring germplasm materials is detected, the trait data of the corresponding parent germplasm materials are obtained. Based on the preset genetic laws, the theoretical range of traits of the offspring germplasm materials is calculated; Determine whether the trait survey data in the update operation falls within the scope of the trait theory; If not, it is determined to be a data inconsistency anomaly.

[0014] The information-based task tracking and management system for the entire Hami melon breeding process provided in this application achieves a fundamental transformation in breeding management from discrete to structured and visualized by constructing a digital closed loop between events, tasks, data, and graphs. Its technical effectiveness is mainly reflected in two logically progressive levels: First, the system, through event creation and task planning modules, decomposes macro-level breeding activities into standardized, assignable, and traceable micro-level task units, establishing a precise connection between top-level planning and specific operations; second, through task execution and data binding modules, it forcibly associates and binds breeding entity data such as germplasm material identification and trait survey data generated in the field with their source tasks, forming a complete and traceable data chain, fundamentally solving the problems of information gaps and traceability.

[0015] Secondly, based on the aforementioned data integration, the breeding atlas generation and display module can dynamically aggregate all related information and generate a visual atlas with events as root nodes and tasks and data as child nodes, clearly presenting complex process logic, material lineages, and data pedigrees. Furthermore, through an integrated dynamic anomaly verification mechanism, the system can perform real-time auditing and consistency checks on the data in the atlas based on genetic rules and process logic, proactively identifying potential data contradictions and process anomalies. This series of technical measures works together to ultimately achieve visualized monitoring of the entire breeding process, intelligent assurance of data quality, and strong support for management decisions. Attached Figure Description

[0016] Figure 1A flowchart illustrating an information-based task tracking management system for the entire process of cantaloupe breeding provided in an embodiment of this application; The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in the embodiments of this invention are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0020] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0021] Furthermore, the timing of the steps in the following embodiments is merely an example and not a strict limitation.

[0022] In practice, the server-side equipment deployed in the information-based task tracking and management system for the entire Hami melon breeding process may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node, providing information-based task tracking and management for the entire Hami melon breeding process to various user terminals. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various user terminals. Or, it can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide information-based task tracking and management for the entire Hami melon breeding process to various user terminals.

[0023] In terms of implementation, the information-based task tracking and management system for the entire Hami melon breeding process and the user terminal are mutually compatible. That is, if the information-based task tracking and management system for the entire Hami melon breeding process is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the information-based task tracking and management system for the entire Hami melon breeding process is implemented as a website, then the user terminal is implemented as a webpage; or if the information-based task tracking and management system for the entire Hami melon breeding process is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0024] like Figure 1 The figure shown is a system architecture diagram of an information-based task tracking and management system for the entire process of Hami melon breeding provided in an embodiment of the present invention.

[0025] The information-based task tracking and management system 100 for the entire Hami melon breeding process described in this invention can be set up in a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the information-based task tracking and management system 100 for the entire Hami melon breeding process may include an event creation module 101, a task planning module 102, a task execution and data binding module 103, and a breeding atlas generation and display module 104. The modules described in this invention can also be called units, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0026] In this embodiment of the invention, the information-based task tracking and management system for the entire Hami melon breeding process can be implemented independently and can be called by other modules. This "calling" can be understood as a module connecting to multiple modules of another type and providing corresponding services to those connected modules. For example, the sharing and evaluation module can call the same information collection module to obtain the information collected by that module. Based on the above characteristics, the information-based task tracking and management system for the entire Hami melon breeding process provided in this embodiment of the invention can adjust its applicable scope by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the information-based task tracking and management system for the entire Hami melon breeding process. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in a cloud server.

[0027] The following describes, with reference to specific embodiments, each component and specific workflow of the information-based task tracking and management system for the entire Hami melon breeding process: Event creation module 101 is used to create breeding events with time period and location attributes in response to user operations.

[0028] In this embodiment, the event creation module 101 is the system's entry point and core data processing unit for creating breeding events with time period and location attributes in response to user operations. This module aims to initialize a macroscopic, unstructured breeding activity into a standardized digital object with a unique identifier, clear spatiotemporal boundaries, and associated environmental background. This provides unified and traceable foundational data for subsequent full-process information tracking and management, directly solving the initial problem of information gaps caused by arbitrary information recording and a lack of standardized anchor points in traditional breeding management.

[0029] In some embodiments, the creation of a breeding event with time period and location attributes in response to a user operation specifically includes: Receive event configuration information input by the user, wherein the event configuration information at least defines the time period and execution location of the event; Based on the event configuration information, a unique breeding event identifier is generated, and an event data object indexed by the breeding event identifier is created; Obtain environmental information associated with the execution location and associate the environmental information with the event data object.

[0030] In the embodiments of this application, a breeding event refers to a top-level management unit of a breeding activity with a clear start and end time and a fixed geographical scope, such as an annual hybrid combination trial or a round of variety comparison trials.

[0031] The system creates breeding events and initializes data through the following specific steps. These steps are executed sequentially, with the output of the previous step constituting the input of the next step, forming a complete data creation and encapsulation process.

[0032] The first step is to receive the event configuration information input by the user. This step relies on the system's graphical user interface and backend data receiving service. Users access the event creation form page provided by the system through a browser or dedicated client software on a personal computer or mobile terminal. This form page serves as the hardware interface for information interaction between the user and the system. Users fill in configuration information in the form, including at least the event name, planned start date, planned end date, and the execution location number selected from a predefined list. When the user clicks the submit button, the data in the form is encapsulated into a structured data packet via HTTP or HTTPS protocol and transmitted to the backend application interface deployed on a cloud or local server.

[0033] This step generates a standardized, unprocessed set of event configuration information data, which transforms user intent into a machine-readable initial data stream. For example, the event name entered by the user could be "Spring Disease-Resistant Hybridization Experiment of a Certain Year," and the selected execution location number could be "Field A01."

[0034] The second step is to generate a unique breeding event identifier based on the event configuration information and create an event data object indexed by this identifier. The core of this step lies in the unique identifier generation algorithm and database operations of the backend server. The system calls a specific algorithm, such as the snowflake algorithm or a composite algorithm based on timestamps and random numbers, to generate a unique string code for this breeding event across the entire system. This code is the breeding event identifier. The breeding event identifier is a unique, lifelong digital identity card assigned to each breeding event by the system, used to accurately locate and associate the event in all subsequent processes.

[0035] Subsequently, the system uses the breeding event identifier as the primary key or core index to create a new record in a relational database or document database. This record is the event data object, which is a data entity stored in the database with a specific structure and contains all the attribute information of the breeding event.

[0036] The system will write the information received in the first step, such as the event name, time period, and execution location number, along with the generated breeding event identifier, into the corresponding field of the newly created event data object, and mark the initial state of the object as "created". For example, the system may generate a breeding event identifier in the form of "EVT_number" and create a corresponding event data object.

[0037] The third step is to obtain environmental information associated with the execution location and link this environmental information to the event data object. The implementation of this step depends on the pre-built environmental information database and data association technology of the system.

[0038] The system extracts the value of the execution location number field from the event data object created in the second step. Next, using this location number as the query key, the system accesses an independent environmental monitoring information database. This database continuously receives and stores data from sensors deployed in various breeding fields (i.e., execution locations) via IoT technology. These sensors include, but are not limited to, air temperature and humidity sensors, soil temperature and humidity sensors, and light intensity sensors. The system performs a query to obtain typical environmental parameters related to the location number over a recent period or historically, such as average temperature, average humidity, and soil pH. These parameters constitute the environmental information. Finally, the system establishes a logical link between the retrieved environmental information data records and the event data object created in the second step using techniques such as foreign key associations in the database, document embedding, or establishing relational mapping tables. For example, the system queries the environmental database based on the execution location number to obtain a set of environmental data and associates the identifier of this environmental data record with the event data object for storage.

[0039] The task planning module 102 is used to generate at least one predefined type of breeding task associated with the breeding event, and to assign a responsible person and a planned time for each breeding task.

[0040] In this embodiment, the task planning module is a core functional component of the system used to automatically decompose and plan specific execution work units based on the created breeding events. The core function of this module is to transform a macro-level breeding event instance into a series of orderly, assignable, and traceable micro-level breeding tasks, thereby accurately connecting the top-level plan with specific field operations and solving the problems of process fragmentation and execution chaos caused by vague task assignment and unclear dependency relationships in traditional breeding.

[0041] In some embodiments, the predefined type of breeding task includes a hybridization pollination task, a field survey task, and a seed harvesting task; wherein, when the hybridization pollination task is performed, the germplasm material identifiers associated with the hybridization pollination task include a male parent identifier and a female parent identifier.

[0042] In this embodiment, the predefined type of breeding task is a standard operation type that the system predefines based on knowledge in the field of breeding. These task types have clear operating specifications and expected data outputs. For example, the core operation of the hybridization pollination task is to complete the pollination between designated parents, and its expected output data is the identification of hybrid offspring germplasm materials; the core operation of the field survey task is to observe and record the material traits at a specific time; and the core operation of the seed harvesting task is to harvest and mark mature seeds.

[0043] In this embodiment of the application, the person in charge of each breeding task is a specific user authorized and assigned by the system to execute the task, and the planned time is the time period during which the task is scheduled to be executed.

[0044] In some embodiments, generating at least one predefined type of breeding task associated with the breeding event, and assigning a responsible person and planned time for each breeding task, specifically includes: Obtain the attribute information of the breeding event and determine at least one predefined type breeding task to be generated; A unique task object is generated for each identified breeding task, and a designated person in charge and a planned time are assigned to each task object; Each generated task object is associated with a breeding event and stored in the event data object corresponding to the breeding event.

[0045] The first step is to obtain the attribute information of the breeding event and determine at least one predefined type of breeding task to be generated. The implementation of this step relies on the system's rule matching engine and event attribute parsing technology. The system reads the specific attribute information of the breeding event from the event data object generated and stored by the preceding event creation module. This attribute information includes at least the event name and event type.

[0046] The system takes the read event attribute information as input and submits it to a pre-configured rule matching engine for processing. This rule matching engine stores configuration rules defined by domain experts. These configuration rules establish a mapping relationship from specific event attributes to a series of predefined breeding tasks. The engine compares the input event attribute information with the condition parts of all configuration rules. When a rule is completely matched, it is triggered, and the output is a list of predefined breeding tasks. This process realizes rule-based automated task derivation.

[0047] For example, the event creation module creates an event data object whose event type attribute is marked as "hybridization combination experiment". After the system obtains this attribute, the rule matching engine determines the four specific breeding tasks to be generated based on the preset rule "If the event type is 'hybridization combination experiment', then generate a task sequence: [hybridization pollination task, flowering period field survey task, fruiting period field survey task, seed harvesting task]". This step ensures the scientific nature and consistency of task planning by transforming domain knowledge into executable machine rules, replacing human experience judgment.

[0048] The second step is to generate a unique task object for each determined breeding task and to assign a responsible person and a planned time for each task object. The core of this step lies in the system's unique identifier generator, access to the organizational role database, and the planned time calculation algorithm.

[0049] For each breeding task identified in the first step, the system first calls an algorithm similar to that used to generate breeding event identifiers to generate a unique task identifier for the task across the entire system. Then, the system creates a new task object in the database. This is a data entity indexed by the task identifier and used to store all attribute information of the task. The system populates basic information into this task object, including the task identifier, the task type inherited from the first step, and the task name. Next, the responsible person is configured. Based on the preset mapping relationship between task types and personnel roles, or based on a load balancing scheduling algorithm, the system automatically selects one or more users from the system's organizational role database, or the administrator manually designates one or more users as the designated responsible person for the task, and fills in the user identifiers of these responsible persons into the corresponding fields of the task object. Finally, the planned time is configured. The system reads the time period attribute of the breeding event and, based on the predefined relative time offset model of each task type within the event period, automatically calculates the theoretical start and end dates of the task and fills them into the task object as the planned time.

[0050] For example, for a given "hybrid pollination task", the system generates a task identifier "TASK_number1", creates a corresponding task object, and assigns user "Zhang San" as the person in charge from the role library according to the rule that "hybrid pollination tasks are usually performed by technicians". At the same time, based on the model that "hybrid pollination is carried out on a specific number of days after the start of the event", combined with the start date of the event "March 15, 2023", the system calculates the planned time for the task as "from April 10, 2023 to April 20, 2023".

[0051] The third step is to associate the generated task objects with breeding events and store them in the event data objects corresponding to the breeding events. The implementation of this step depends on the relational modeling and data update operations of the database.

[0052] The system will persistently store all task objects created in batches in the second step through database operations. The key technical action is to establish and store the subordinate association between these task objects and their source—the breeding event. The system sets a field in the data structure of each task object to store the unique identifier of its breeding event, i.e., the breeding event identifier. At the same time, in the event data object of the breeding event, a special field (such as a list or through a foreign key relationship) is set up to reference or contain the task identifiers of all its sub-tasks. Through this bidirectional indexed data structure design, the system completes the logical binding between task objects and event data objects.

[0053] For example, the system sets the value of the "Event Identifier" field of the task object "TASK_ID1" to "BREED_EVT_ID"; at the same time, the system adds the task identifier "TASK_ID1" to the "Task List" field of the event data object identified as "BREED_EVT_ID".

[0054] This module automatically decomposes events through a rules engine, solidifies task details through objectification technology, and constructs a management topology through data association, thereby transforming the vague, volatile, and memory-dependent work arrangements of the traditional model into a clear, stable, and globally traceable digital workflow.

[0055] The task execution and data binding module 103 is used to associate and bind the generated breeding entity data with the corresponding breeding task during the execution of the breeding task; wherein, the breeding entity data includes germplasm material identification and trait survey data.

[0056] In this embodiment of the application, the task execution and data binding module 103 is a key functional unit used to capture, standardize and strongly correlate core breeding data generated in the field with the source task when the breeding task is actually executed. This module directly addresses and solves the most prominent problems of "data silos" and "difficulty in traceability" in traditional breeding, that is, field observation data exists in unstructured form (such as paper records), which is loosely associated with specific tasks, operators and breeding materials or even lost, resulting in the inability to effectively trace back, verify and analyze the data.

[0057] In the embodiments of this application, breeding entity data is a set of core data that is generated or confirmed during the execution of specific breeding tasks and characterizes the essential features of breeding materials; germplasm material identifier is a standardized code that uniquely identifies a specific breeding material (such as a parent, a single hybrid offspring plant or a line); trait survey data is information obtained through observation or measurement that describes the numerical value or state of specific traits (such as plant height, fruit weight, sugar content) of breeding materials.

[0058] In some embodiments, associating and binding the generated breeding entity data with the corresponding breeding task during the execution of the breeding task specifically includes: Obtain the task object corresponding to the breeding task to be executed and the person in charge configured in the task object; Respond to the execution feedback data submitted by the current person in charge regarding the breeding task; The germplasm material identification and trait survey data in the execution feedback data are respectively structured to generate standardized breeding entity data; The breeding entity data is associated and bound with the task object, and the execution status of the task object is updated.

[0059] The first step is to obtain the task object corresponding to the breeding task to be executed and the person in charge configured in the task object. The implementation of this step depends on the task scanning of the mobile terminal application and the authentication and data retrieval mechanism of the backend service.

[0060] The assigned user (i.e., the person in charge) initiates task execution using a dedicated application on their mobile device (such as a smartphone or tablet) by scanning a task QR code posted in the field or on a task list, or by directly selecting a task from the application's task list. This QR code or task selection action embeds a unique identifier for the task. The mobile application then sends a request data packet containing this task identifier and the current user's identity information to the backend server via the network. Upon receiving the request, the backend service first verifies whether the current user's identity matches the person in charge information configured in the task object, thus verifying execution permissions. If verification is successful, the backend service uses the received task identifier as a query key to retrieve and obtain complete task object data from the system's task database.

[0061] For example, user Zhang San scans a QR code using the app. This code corresponds to the task identifier "TASK_ID1". The app sends this identifier along with Zhang San's user ID to the server. The server verifies that Zhang San is indeed the person in charge configured in the task object "TASK_ID1". Then, it loads all the contents of the task object (including task type, scheduled time, etc.) from the database into memory to provide a complete execution context for subsequent steps.

[0062] The second step is to respond to the execution feedback data submitted by the person currently responsible for the breeding task. This step is implemented based on dynamic form rendering technology and data submission interface.

[0063] After the initial permission and data verification is successful, the backend service determines the data fields to be collected based on the task type in the obtained task object and sends a dynamically generated electronic form template to the mobile terminal application. The field structure of this form template is strongly correlated with the task type, guiding the user to enter data correctly. After the person in charge actually completes the task in the field, they fill in or select the corresponding content in the form on the mobile terminal application according to the on-site situation. This content constitutes the execution feedback data, which is a set of raw, unprocessed data submitted by the user. It may contain non-standardized material numbers, manually entered morphological values, and units. After the user clicks submit, the mobile terminal application sends this request packet containing the task identifier and execution feedback data to the backend data receiving interface.

[0064] For example, for the "hybrid pollination task", the issued form contains fields such as "father parent identifier", "mother parent identifier", and "pollination date". After Zhang San completes the pollination in the field, he fills in "P001" as the father parent and "P002" as the mother parent in the form and then submits it. This original record is the execution feedback data.

[0065] The third step is to perform structured processing on the germplasm material identification and trait survey data in the execution feedback data to generate standardized breeding entity data. This step is the core of achieving data standardization and value enhancement, and its implementation depends on a task type recognition engine, a predefined data processing rule base, and a series of data transformation algorithms.

[0066] The fourth step is to associate and bind standardized breeding entity data with task objects and update the execution status of task objects. This step is implemented based on database transaction operations and state machine management.

[0067] Within a database transaction, the system performs two core operations. First, it persistently stores the standardized breeding entity data generated in the third step into the database. During this process, it explicitly records which task (through a task identifier) ​​generated the entity data, thus establishing a "generated from" association between the breeding entity data and the task object. Simultaneously, it adds a field to the task object's data structure that references the breeding entity data record it generated. Second, based on the data submission and processing success signals, the system updates the task object's execution status from "pending execution" or "in execution" to "completed." The status update follows predefined state machine transition rules.

[0068] For example, the system stores the material identifier object "H001" and its related property data objects in the database, and records "source_task_id:TASK_number1" in the metadata of these objects; at the same time, it updates the status field value of task "TASK_number1" to "completed" and adds a reference to "H001" to its "output data" list.

[0069] In some embodiments, the step of performing structured processing on the germplasm material identification and trait survey data in the execution feedback data to generate standardized breeding entity data specifically includes: Identify the task type of the breeding task and determine the corresponding data processing rules based on the task type; Based on the data processing rules, the germplasm material identifiers are standardized to generate standardized material identifier objects; Based on the data processing rules, the trait survey data is formatted and units are standardized to generate standardized trait data objects. The material identifier object and the trait data object are encapsulated into standardized breeding entity data.

[0070] First, the system identifies the task type of the breeding task and determines the corresponding data processing rules based on the task type. The backend service parses the task identifier in the submitted data packet and associates it with the loaded task object to clarify its task type (such as "hybrid pollination"). Based on this task type, the system calls a set of rules designed specifically for this type from a pre-configured data processing rule library. These rule sets are defined by domain experts and clearly specify how to process the data unique to this task.

[0071] Secondly, based on data processing rules, the germplasm material identifiers are standardized to generate standardized material identifier objects. The system extracts the original material identifier strings from the execution feedback data; according to the rules, the system may perform a series of operations, such as checking whether the identifier conforms to the naming convention, verifying the existence of parental identifiers, and automatically generating new and unique offspring material identifiers for hybridization tasks and establishing associations with the parents. After processing, the system creates a material identifier object, which is a structured data entity containing standardized identifiers and their metadata (such as parental relationships, creation tasks, and creation times).

[0072] For example, for the hybridization pollination task, the rules require that a unique new offspring identifier be generated for each successful hybridization combination. After verifying the validity of “P001” and “P002”, the system automatically generates a new identifier “H001” and creates a material identifier object, the content of which records that the parent of “H001” is “P001” and the parent is “P002”.

[0073] Furthermore, based on data processing rules, the trait survey data undergoes format conversion and unit standardization to generate standardized trait data objects. The system extracts the original trait values ​​and units from the execution feedback data. According to the rules, the system calls a unit conversion algorithm to convert the data into the standard units specified within the system (e.g., converting "jin" to "gram"), and performs format conversion and range verification according to preset data types (e.g., integers, floating-point numbers, dates). After processing, the system creates a trait data object, which is a structured data entity containing the trait name, standard value, standard unit, observation time, and associated material identifier.

[0074] For example, for the trait of "fruit weight", the rule specifies that the standard unit is "gram". If the user enters "1.5 jin", the system will convert it to "750 grams" and create a trait data object to record this result.

[0075] Finally, the system encapsulates the generated standardized material identifier objects and standardized trait data objects into a logical whole, namely, standardized breeding entity data.

[0076] The breeding atlas generation and display module 104 is used to dynamically generate and visualize an information-based breeding atlas based on the breeding event, all associated breeding tasks and their bound breeding entity data. The information-based breeding atlas takes the breeding event as the root node, each breeding task and its bound breeding entity data as associated child nodes, and uses connecting lines to represent the generation, membership or data feedback relationships between them, thereby reflecting the logic and state association of the entire breeding process.

[0077] In this embodiment of the application, the breeding map generation and display module is a core functional unit used to integrate, organize and intuitively present the various elements and their complex relationships in the entire breeding process. This module addresses the core problem of the discrete information in each link of traditional breeding management and the inability to intuitively grasp the overall progress and internal logical relationship. It automatically synthesizes scattered digital objects such as events, tasks and data into a dynamic and interactive global map according to their internal relationships.

[0078] In this embodiment, the information-based breeding atlas is a visualization model that dynamically displays all entity objects and their logical relationships in the entire breeding process using a graphical topological structure; the root node is the top-level node in the atlas representing the entire breeding event; the task node is an atlas element representing a specific breeding task; the data node is an atlas element representing a set of specific breeding entity data; and the connecting line is a vector line segment in the atlas used to connect two nodes and characterize their specific relationship.

[0079] In some embodiments, the dynamic generation and visualization of an information-based breeding atlas based on the breeding events, all associated breeding tasks, and their bound breeding entity data specifically includes: Obtain the event data object corresponding to the breeding event, all associated task objects, and their bound breeding entity data; Based on the event data object, the task object, and the breeding entity data, the corresponding graph nodes and the connection relationships between the nodes are determined; wherein, the graph nodes include the root node representing the breeding event, the task node representing each breeding task, and the data node representing each breeding entity data; Generate graph model data based on the graph nodes and the connection relationships; Based on the map model data, the visualization engine is driven to render and display the information-based breeding map in the user interface.

[0080] The first step is to obtain the event data object corresponding to the breeding event, all associated task objects, and their bound breeding entity data. This step relies on the system database's relational query and data aggregation services. When a user requests to view the graph of a specific breeding event, the system uses the unique identifier of that breeding event as the global query key. First, the system uses this identifier to retrieve the complete event data object from the event data table. Next, the system queries the task data table for all records whose "belonging event identifier" field equals the breeding event identifier, thus obtaining all task objects associated with that event. Then, the system iterates through each obtained task object, and based on its task identifier, queries the breeding entity data table for all records whose "source task identifier" field matches it, thus obtaining all breeding entity data bound to each task. This data includes standardized material identification objects and trait data objects.

[0081] For example, if a user requests to view the graph of the event with the identifier "BREED_EVT_202403150001", the system will load all tasks (such as hybridization and survey tasks) belonging to the event, as well as all materials (such as H001) and trait data (if repeated) into memory through one query of the event table and two cascading queries of the task table and entity data table, forming a structured data set to be visualized.

[0082] The second step is to determine the corresponding graph nodes and the connections between nodes based on the acquired event data objects, task objects, and breeding entity data. This step is the key to realizing the mapping from relational data to the graph model, and its core is the definition of node types and relational semantic rules.

[0083] The system first classifies and maps data into different categories of graph nodes based on their type and role. Event data objects representing breeding events are mapped to a root node, each task object representing each breeding task is mapped to a task node, and each material identifier object and trait data object representing each breeding entity data is mapped to a data node. Material identifier objects are usually used as parent data nodes, and their associated trait data objects are attached to them as child data nodes.

[0084] While determining the nodes, the system determines the connection relationships between these nodes based on predefined business logic rules. These connection relationships are directional and are used to represent specific semantics.

[0085] The main rules include: connections from the root node (event) to the task node represent "membership" or "containment" relationships; connections from the task node to the data nodes (materials or traits) it generates represent "generation" relationships; connections from the material identifier data node to its specific trait data node represent "ownership" or "data feedback" relationships; and if the material is a hybrid offspring, connections from its nodes to the parent and parent material nodes represent "inheritance" relationships. This step transforms static, tabular data records into a dynamic graph data model that reveals the business logic network by assigning graph node identities and establishing semantic connections.

[0086] The third step is to generate graph model data based on the determined graph nodes and connections. This step relies on graph data serialization technology.

[0087] The system organizes all the nodes and connections identified in the second step into a specific data structure. This data structure is usually a JSON object containing an array of nodes and an array of edges, or a dataset that conforms to the query result format of a graph database. Each element in the node array contains a unique identifier for the node, a type label (such as "Event", "Hybridization Task", "Material H001", "Truth-Fruit Weight"), a status attribute (such as the task status "Completed"), and other metadata that needs to be displayed. Each element in the edge array contains the starting node identifier, the ending node identifier, and the relationship type attribute of the edge.

[0088] The process of generating graph model data is the process of serializing the business graph model into a standardized data format that the front-end visualization engine can recognize and process.

[0089] The fourth step is to drive the visualization engine to render and display the information-based breeding map in the user interface based on the generated map model data. The implementation of this step depends on the front-end visualization graphics library and layout algorithm.

[0090] The system backend sends the graph model data generated in the third step to the user's frontend browser via an API interface. The frontend application loads a specific visualization engine. After receiving the graph model data, the engine first calls algorithms such as force-directed layout, tree layout, or hierarchical layout to automatically calculate the reasonable position of each node on the two-dimensional or three-dimensional canvas to ensure that the graph is clear and readable. Subsequently, the engine draws the graph using different shapes (such as rectangles and circles), colors (such as yellow for tasks in progress and green for completed tasks) and sizes according to the node type and status attributes. At the same time, it draws connecting lines using different styles (such as solid lines, dashed lines, and arrows) according to the relationship type of the edges. Finally, an interactive information-based breeding graph is rendered on the user interface. Users can interact with the graph by zooming, dragging, and clicking on nodes to view details, and dynamically explore the progress and relationships of the entire breeding process.

[0091] This module breaks down the barriers between different stages and tables by aggregating and elevating scattered events, tasks, and data entities into a unified graph model view, making complex breeding processes, material lineages, and data pedigrees readily apparent.

[0092] In some embodiments, the system further includes a dynamic verification and early warning module for map anomalies, used for: Monitor update operations on the breeding entity data or the breeding task status; When the update operation is detected, the consistency of the associated nodes and connections in the information-based breeding graph is checked according to the preset domain rule set. The domain rule set includes at least: data consistency rules for checking the trait logic rationality between data nodes of germplasm materials with genetic association; and process logic rules for checking the state logic rationality between breeding task nodes with dependency relationships. If the verification result indicates an anomaly, the identified abnormal nodes and associated paths will be visually highlighted in the information-based breeding map, and an anomaly warning report will be generated.

[0093] In this embodiment, the dynamic verification and early warning module for breeding map anomalies is an intelligent subsystem that performs real-time, automatic logical monitoring and auditing of information-based breeding maps. By listening to data and status changes and applying domain knowledge rules for verification, it aims to proactively and promptly detect potential logical contradictions and data anomalies throughout the entire breeding process, thereby transforming the management approach from reactive post-event inspection to proactive in-process intervention.

[0094] The first step is to listen for updates to breeding entity data or breeding task status. This step relies on the coordinated operation of database triggers and application event publish-subscribe mechanisms.

[0095] The system sets triggers at the database level for breeding entity data tables and breeding task status fields, or embeds event publication points in critical data update methods in the application code. When any user or system process performs an operation to insert or modify breeding entity data or change the breeding task status, the corresponding trigger is activated or an event is published; the listener captures this operation and immediately encapsulates an update operation event data packet, which at least contains the unique identifier of the operated object, the operation type, and the new value after the change.

[0096] For example, when a technician submits new fruit weight survey data on a mobile terminal, the system will trigger a "trait data update" event while storing the trait data object in the database. The event data includes the corresponding material identifier "H001", the trait name "fruit weight", and the newly measured value.

[0097] The second step is to perform consistency verification on the related graph nodes and connections in the information-based breeding graph when an update operation is detected, based on the preset domain rule set. This step is the core of intelligent verification, and its technical means include rule engine invocation, real-time traversal of graph data, and logical calculation.

[0098] After receiving an update operation event, the system first parses the event data to locate the specific affected node in the information-based breeding map. Next, based on the type and content of the operation object, the system matches and loads the required verification rules from a pre-defined set of domain rules. This set of domain rules is a set of logical judgment conditions pre-defined and coded by breeding experts, and includes at least process logic rules and data consistency rules. Then, starting from the currently updated node, the system iterates in real-time through and retrieves all logically related other nodes and their data according to the connection relationships stored in the map. Finally, the system substitutes the retrieved relevant node data into the matched rules for logical calculation to complete the consistency verification.

[0099] This step creatively transforms static domain knowledge into activity rules that can be dynamically triggered and computed based on the global graph context.

[0100] In some embodiments, the process logic rules are used to verify the rationality of the state logic between breeding task nodes with dependencies, specifically including: When the status of a breeding task node is updated to "completed", retrieve the status of all its upstream dependent breeding task nodes. Determine whether the status of all upstream dependent breeding task nodes is "completed"; If not, then it is determined to be an abnormal process logic.

[0101] The process logic rules are used to verify the rationality of the state logic between breeding task nodes with dependencies. The specific verification process is a condition judgment process.

[0102] When an operation event updating the status of a breeding task node to "completed" is detected, the system retrieves all upstream dependent task nodes from the graph model. Upstream dependencies are predefined during task planning and explicitly represented by connecting lines in the graph. The system then checks the status attributes of each upstream task node, verifying that all upstream dependent task nodes are in the "completed" state. If any upstream task node is not in the "completed" state, the system determines that the status update violates the process logic, constituting a process logic exception.

[0103] For example, the seed harvesting task node in the graph is updated to be completed, but according to the graph connection relationship, the status of its upstream hybridization pollination task node is still in progress. Applying this rule, the system immediately determines that this is a process logic anomaly, because it is impossible to complete harvesting without completing pollination.

[0104] In some embodiments, the data consistency rule is used to verify the logical rationality of traits among data nodes of genetically related germplasm materials, specifically including: When an update operation on the trait survey data of offspring germplasm materials is detected, the trait data of the corresponding parent germplasm materials are obtained. Based on the preset genetic laws, the theoretical range of traits of the offspring germplasm materials is calculated; Determine whether the trait survey data in the update operation falls within the scope of the trait theory; If not, it is determined to be a data inconsistency anomaly.

[0105] Data consistency rules are used to verify the logical rationality of traits among data nodes of genetically related germplasm materials. The specific verification process involves genetic theory calculations and numerical range judgments.

[0106] When an update operation event for the trait survey data of offspring germplasm materials is detected, the system first locates and obtains the corresponding paternal and maternal germplasm material data nodes from the graph based on the "inherited from" connection relationship; then, it extracts the historical survey data of the same trait from the paternal and maternal nodes; and then, based on the preset genetic law model, it calculates the theoretical range of the trait of the offspring germplasm material.

[0107] The formula for calculating genetic laws can be expressed as follows: ,in, This indicates the range of calculated theoretical values ​​for offspring traits. This represents the numerical value of that trait from the parent obtained from the graph. This represents the numerical value of that trait in the parent plant obtained from the graph. It is an allowable deviation threshold, which is determined by multiplying the standard deviation of segregation of the trait in historical breeding data by a configurable confidence coefficient, for example, a confidence coefficient of 2, to cover most normal genetic variations.

[0108] Finally, the system determines whether the new trait survey data in the update operation falls within the range of the calculated trait theory. If the new data falls completely outside this range, the system determines that the data is inconsistent and abnormal. The technical significance of this step is that it transforms genetic expertise into automatically executable numerical verification, which can effectively screen out abnormal data points that may be caused by input errors or extreme variations.

[0109] The third step is to visually highlight the identified abnormal nodes and related paths in the information-based breeding atlas if the verification result indicates an anomaly, and generate an anomaly warning report. The implementation of this step depends on the instruction interface and report generation template of the front-end visualization engine.

[0110] When the verification logic in the second step determines that there is an anomaly, the system will generate an anomaly alarm command. This command is sent to the user's front-end interface that is displaying the graph in real time via WebSocket or polling API. After receiving the command, the front-end visualization engine applies a predefined visual highlighting style to these nodes and their directly connected paths (connecting lines) on the rendered graph canvas according to the anomaly node identifier specified in the command. For example, the node borders and fill colors are changed to flashing red, and the connecting lines are thickened and changed to red.

[0111] Meanwhile, the system backend automatically fills in a structured anomaly warning report template based on information such as the anomaly type, involved nodes, and rule judgment criteria. It generates a warning report containing detailed time, location, problem description, and suggested handling measures, and stores it in the database or sends it to relevant personnel.

[0112] For example, when the system determines that there is an abnormal process logic in the seed harvesting task, the task node on the front-end graph and the connection line between it and the upstream hybridization pollination task node will immediately turn red and be highlighted. At the same time, the system generates a report record: "Warning: The task 'Seed Harvesting' (ID: TXXX) was marked as completed when the dependent task 'Hybridization Pollination' (ID: TYYY) was not completed. Please check." This module, through real-time monitoring and graph-based rule verification, enables automated inspection of the consistency between complex breeding process logic and genetic data. It can instantly detect logical paradoxes and data contradictions caused by human negligence or system errors. This not only greatly improves data quality and process compliance but also frees managers from tedious daily checks, allowing them to focus on handling genuine anomalies and critical decisions. This fundamentally solves the major shortcomings of traditional methods, such as severely delayed anomaly detection and reliance on accidental manual review, significantly improving the reliability and efficiency of the breeding research and development process.

[0113] In the embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0114] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0116] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application.

[0117] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, system, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. An information-based task tracking and management system for the entire process of Hami melon breeding, characterized in that, The system includes: The event creation module is used to create breeding events with time period and location attributes in response to user operations; The task planning module is used to generate at least one predefined type of breeding task associated with the breeding event based on the breeding event, and to assign a responsible person and a planned time for each breeding task; The task execution and data binding module is used to associate and bind the generated breeding entity data with the corresponding breeding task during the execution of the breeding task; wherein, the breeding entity data includes germplasm material identification and trait survey data; The breeding atlas generation and display module is used to dynamically generate and visualize an information-based breeding atlas based on the breeding event, all associated breeding tasks and their bound breeding entity data. The information-based breeding atlas takes the breeding event as the root node, each breeding task and its bound breeding entity data as associated child nodes, and uses connecting lines to represent the generation, membership or data feedback relationships between them, thereby reflecting the logic and state associations of the entire breeding process.

2. The information-based task tracking and management system for the entire process of Hami melon breeding as described in claim 1, characterized in that, The creation of breeding events with time period and location attributes in response to user operations specifically includes: Receive event configuration information input by the user, wherein the event configuration information at least defines the time period and execution location of the event; Based on the event configuration information, a unique breeding event identifier is generated, and an event data object indexed by the breeding event identifier is created; Obtain environmental information associated with the execution location and associate the environmental information with the event data object.

3. The information-based task tracking and management system for the entire process of Hami melon breeding as described in claim 1, characterized in that, Based on the breeding event, at least one predefined type of breeding task is generated that is associated with the breeding event, and a responsible person and planned time are assigned to each breeding task, specifically including: Obtain the attribute information of the breeding event and determine at least one predefined type breeding task to be generated; A unique task object is generated for each identified breeding task, and a designated person in charge and a planned time are assigned to each task object; Each generated task object is associated with a breeding event and stored in the event data object corresponding to the breeding event.

4. The information-based task tracking and management system for the entire process of Hami melon breeding as described in claim 1, characterized in that, The predefined breeding tasks include hybridization pollination tasks, field survey tasks, and seed harvesting tasks; wherein, when performing the hybridization pollination task, the germplasm material identifiers associated with the hybridization pollination task include male parent identifiers and female parent identifiers.

5. The information-based task tracking and management system for the entire process of Hami melon breeding as described in claim 1, characterized in that, The process of associating and binding the generated breeding entity data with the corresponding breeding task during the execution of the breeding task specifically includes: Obtain the task object corresponding to the breeding task to be executed and the person in charge configured in the task object; Respond to the execution feedback data submitted by the current person in charge regarding the breeding task; The germplasm material identification and trait survey data in the execution feedback data are respectively structured to generate standardized breeding entity data; The breeding entity data is associated and bound with the task object, and the execution status of the task object is updated.

6. The information-based task tracking and management system for the entire process of Hami melon breeding as described in claim 5, characterized in that, The step of performing structured processing on the germplasm material identification and trait survey data in the execution feedback data to generate standardized breeding entity data specifically includes: Identify the task type of the breeding task and determine the corresponding data processing rules based on the task type; Based on the data processing rules, the germplasm material identifiers are standardized to generate standardized material identifier objects; Based on the data processing rules, the trait survey data is formatted and units are standardized to generate standardized trait data objects. The material identifier object and the trait data object are encapsulated into standardized breeding entity data.

7. The information-based task tracking and management system for the entire process of Hami melon breeding as described in claim 1, characterized in that, The process of dynamically generating and visually displaying an information-based breeding atlas based on the breeding events, all associated breeding tasks, and their bound breeding entity data specifically includes: Obtain the event data object corresponding to the breeding event, all associated task objects, and their bound breeding entity data; Based on the event data object, the task object, and the breeding entity data, the corresponding graph nodes and the connection relationships between the nodes are determined; wherein, the graph nodes include the root node representing the breeding event, the task node representing each breeding task, and the data node representing each breeding entity data; Generate graph model data based on the graph nodes and the connection relationships; Based on the map model data, the visualization engine is driven to render and display the information-based breeding map in the user interface.

8. The information-based task tracking and management system for the entire process of Hami melon breeding as described in claim 7, characterized in that, The system also includes a dynamic verification and early warning module for map anomalies, used for: Monitor update operations on the breeding entity data or the breeding task status; When the update operation is detected, the consistency of the associated graph nodes and connection relationships in the information-based breeding graph is checked according to the preset domain rule set. The domain rule set includes at least: data consistency rules for verifying the logical rationality of traits among data nodes of germplasm materials with genetic associations; and process logic rules for verifying the logical rationality of states among breeding task nodes with dependencies. If the verification result indicates an anomaly, the identified abnormal nodes and associated paths will be visually highlighted in the information-based breeding map, and an anomaly warning report will be generated.

9. The information-based task tracking and management system for the entire process of Hami melon breeding as described in claim 8, characterized in that, The process logic rules are used to verify the rationality of the state logic between breeding task nodes with dependencies, specifically including: When the status of a breeding task node is updated to "completed", retrieve the status of all its upstream dependent breeding task nodes. Determine whether the status of all upstream dependent breeding task nodes is "completed"; If not, then it is determined to be an abnormal process logic.

10. The information-based task tracking and management system for the entire process of Hami melon breeding as described in claim 8, characterized in that, The data consistency rules are used to verify the logical rationality of traits among data nodes of genetically related germplasm materials, specifically including: When an update operation on the trait survey data of offspring germplasm materials is detected, the trait data of the corresponding parent germplasm materials are obtained. Based on the preset genetic laws, the theoretical range of traits of the offspring germplasm materials is calculated; Determine whether the trait survey data in the update operation falls within the scope of the trait theory; If not, it is determined to be a data inconsistency anomaly.

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