Fruit tree group phenotypic character survey data processing method, device, equipment and medium

Abstract

The invention provides a fruit tree group phenotypic character survey data processing method, device and equipment and a medium, and relates to the field of agricultural information technology and data processing.The method comprises the steps that a data processing request is received; when it is determined that the data processing request is a group data creation instruction, group field information in the data processing request is analyzed, and a fruit tree group data record is generated; when it is determined that the data processing request is a plant recording operation, in response to a plant recording instruction for a target plant, editing the content of a target plant data record associated with the fruit tree group data record, and generating a plant data record; when it is determined that the data processing request is a task processing instruction for the target investigation task, obtaining a task context bound with the target investigation task; and executing a data processing operation associated with the task context according to the type of the task processing instruction. Structured collection, forced association storage and traceable management of fruit tree group phenotypic character survey data are realized.

Description

Technical Field

[0001] This invention relates to the field of agricultural information technology and data processing technology, and in particular to a method, apparatus, equipment and medium for processing data from a survey of phenotypic traits of fruit tree populations. Background Technology

[0002] The fruit tree industry is a crucial pillar of modern agriculture and rural revitalization in my country. Its variety improvement, efficient cultivation, and resource conservation all heavily rely on precise and systematic phenotypic data. Fruit tree phenotypic data encompasses population information, individual tree traits, interannual phenological observations, and fruit quality indicators, characterized by multiple data dimensions, long observation periods (perennials), significant individual differences in individual trees, and complex data structures. This data serves as the core basis for breeders to select superior individual trees, researchers to explore the genetic laws of traits, and producers to conduct precision agronomic management.

[0003] Currently, the acquisition and management of fruit tree population phenotypic data generally follows the traditional "paper record-manual processing" model. Researchers need to manually record a large amount of information in the field and then digitize it into a computer afterward. This model has the following inherent drawbacks due to its deep coupling with the characteristics of fruit tree data: 1. Data quality and integrity are difficult to guarantee: manual recording is prone to errors, and the observation items for fruit trees are numerous and the cycle is as long as several years, which can easily lead to data omissions or inconsistencies, thus damaging the scientific value of the data.

[0004] 2. Complex data relationships are difficult to maintain: Fruit tree data naturally exhibits a multi-layered nested relationship of "population → individual tree → year → trait". Paper records or simple spreadsheets cannot enforce the maintenance of this traceable and structured association, resulting in isolated data, difficulty in tracing its origin, and an inability to support in-depth longitudinal analysis and genetic research.

[0005] 3. Low efficiency in collaboration and sharing: Paper-based or scattered electronic files make it difficult to achieve secure and convenient real-time data sharing and collaborative operations among breeding teams, resource nurseries, and experimental stations, forming "data silos" and hindering the integration and utilization of resources.

[0006] 4. Difficulty in multimodal data fusion: Modern fruit tree research increasingly relies on multi-source data such as images, videos, and environmental sensors. Traditional methods struggle to accurately correlate and manage these unstructured data with structured trait descriptions in a spatiotemporal manner.

[0007] 5. Lengthy and inefficient data processing: From field records to analyzable datasets, multiple manual steps such as copying, inputting, proofreading, and linking are required, which is time-consuming and labor-intensive and seriously delays breeding decisions and scientific research progress.

[0008] Therefore, given the specific needs of fruit tree population phenotypic data management, there is an urgent need for a dedicated technical solution that can achieve full-process digitalization from data collection, structured storage, correlation management to intelligent analysis, in order to overcome the aforementioned technical bottlenecks and improve the informatization and intelligentization level of fruit tree scientific research and industry. Summary of the Invention

[0009] This invention provides a method, apparatus, equipment, and medium for processing data from a survey of phenotypic traits of fruit tree populations, in order to solve the technical problems of low data collection efficiency, poor accuracy, fragmented management, and difficulty in sharing and in-depth analysis caused by reliance on manual paper records in the prior art.

[0010] This invention provides a method for processing fruit tree population phenotypic trait survey data, comprising: receiving a data processing request, the data processing request including at least one of a population data creation instruction, a plant creation operation, and a task processing instruction; when the data processing request is determined to be a population data creation instruction, parsing the population field information in the population data creation instruction, and generating a fruit tree population data record based on the population field information; when the data processing request is determined to be a plant creation operation, in response to a plant record instruction for a target plant, editing the content of the target plant data record associated with the fruit tree population data record to generate a plant data record; when the data processing request is determined to be a task processing instruction for a target survey task, obtaining a task context bound to the target survey task; wherein the task context includes at least a target population identifier and a target plant identifier; and executing a data processing operation associated with the task context according to the type of task processing instruction.

[0011] According to the present invention, a method for processing phenotypic trait survey data of fruit tree populations performs data processing operations associated with the task context based on the type of task processing instruction, including: when the task processing instruction is determined to be a data recording instruction, receiving survey data returned based on the target survey task, generating survey data records, and using the target population identifier and target plant identifier in the task context to associate and store the generated survey data records with the corresponding fruit tree population data records and plant data records; when the task processing instruction is determined to be a task recording instruction, generating task recording data in response to the task recording instruction based on the target survey task.

[0012] According to a method for processing fruit tree population phenotypic trait survey data provided by the present invention, the task recording instruction includes a phenological period survey instruction; when the task processing instruction is determined to be a task recording instruction, the method receives survey data returned based on the target survey task and generates a survey data record, including: when the task recording instruction is determined to be a phenological period survey instruction, displaying a corresponding survey page in response to the phenological period survey instruction; selecting a first target population from the population list on the survey page in response to a first population selection instruction; and displaying a phenological period trait selection page in response to a trait selection instruction; wherein the phenological period trait selection page includes At least one selectable phenological trait; in response to the selection operation for the target phenological trait, a trait data recording interface is displayed, and survey data associated with the target phenological trait is received through the trait data recording interface; wherein, the survey data includes thumbnails of time data and image data, the image data is acquired based on the target survey task and stored in a preset background album folder, and the thumbnails are generated based on the image data and displayed in the trait data recording interface; based on the time data and image data, survey data records associated with the target population and the target phenological trait are generated.

[0013] According to a method for processing fruit tree population phenotypic trait survey data provided by the present invention, the task recording instruction includes a morphological survey instruction; when the task processing instruction is determined to be a task recording instruction, receiving survey data returned based on the target survey task and generating a survey data record, the method further includes: when the task recording instruction is determined to be a morphological survey instruction, displaying a corresponding survey page in response to the morphological survey instruction; selecting a second target population from the population list on the survey page in response to a second population selection instruction; selecting a survey date from the survey date list on the survey page in response to a date selection instruction; and loading and displaying a list of morphological survey traits associated with the target population in response to a second trait selection instruction; displaying a morphological data recording interface corresponding to the target morphological survey trait in response to a morphological selection operation for the target morphological survey trait in the morphological survey trait list; and editing the content of the target morphological survey trait in the morphological data recording interface in response to a morphological recording operation for the target morphological survey trait to generate a survey data record.

[0014] According to the present invention, a method for processing fruit tree population phenotypic trait survey data includes, before loading and displaying a list of morphological survey traits associated with the target population based on the target population and the survey date, verifying the completeness of the population name, survey date and morphological survey traits, and loading and displaying a list of morphological survey traits associated with the target population based on the successful verification. After generating survey data records, the process includes: responding to data management instructions by displaying a data management page, which shows multiple survey data records in a list format, with each record including the group name, survey characteristics, survey date, and progress status; responding to evaluation instructions for at least one target survey data record by invoking a preset evaluation strategy to evaluate the morphological survey characteristics of the corresponding target survey data record and obtain corresponding characteristic evaluation results; wherein, the preset evaluation strategy is configured in advance for different morphological survey characteristics and their corresponding preset evaluation rules and preset result value rules; and responding to statistical instructions for at least one target survey data record by summarizing the corresponding characteristic evaluation results and obtaining corresponding summary results.

[0015] According to the present invention, a method for processing phenotypic trait survey data of fruit tree populations includes: responding to a data management instruction by displaying a data management page, wherein the data management page displays multiple survey records in a list format, and each survey record includes a population name, survey trait, survey date, and progress status; responding to a record trigger operation for a target record by redirecting to the corresponding survey page to continue or modify the survey data record; responding to a statistical request on the data management page by receiving filtering conditions, wherein the filtering conditions include the target population name and the target date; and based on the filtering conditions, querying and aggregating corresponding data from the database to generate and display a statistical report.

[0016] According to the present invention, a method for processing fruit tree population phenotypic trait survey data includes: storing fruit tree population data records, plant data records, survey data records and / or task record data in key-value pairs to the client's local storage space; clearing the records in the cloud data in response to the record synchronization upload command; dividing the stored data in the client's local storage space into blocks according to a preset size limit; dividing the upload batches based on each block; and uploading the data to the cloud database in batches by calling a preset cloud function.

[0017] This invention also provides a data processing device for a survey of phenotypic traits of fruit tree populations, comprising: a request receiving module for receiving data processing requests, the data processing requests including at least one of a population data creation instruction, a plant creation operation, and a task processing instruction; a population processing module for, when determining that the data processing request is a population data creation instruction, parsing the population field information in the population data creation instruction, and generating fruit tree population data records based on the population field information; a plant editing module for, when determining that the data processing request is a plant creation operation, responding to a plant record instruction for a target plant, editing the content of the target plant data record associated with the population data record, and generating a plant data record; a task processing module for, when determining that the data processing request is a task processing instruction for a target survey task, obtaining a task context bound to the target survey task; wherein the task context includes at least a target population identifier and a target plant identifier; and a task execution module for executing data processing operations associated with the task context according to the type of task processing instruction.

[0018] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the fruit tree population phenotypic trait survey data processing method as described above.

[0019] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the fruit tree population phenotypic trait survey data processing method as described above.

[0020] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the data processing method for investigating the phenotypic traits of fruit tree populations as described above. The beneficial effects of this invention are as follows: The method, apparatus, equipment, and medium for processing fruit tree population phenotypic trait survey data provided by this invention, through the construction of a three-level structured data model of "population-plant-survey task" and a context-driven task execution mechanism, specifically addresses the pain points of fruit tree data management and achieves the following technical effects: 1. Standardized and digitalized data collection at the source: Data is entered through a mobile-guided interface, integrating multimodal data such as images and time, eliminating manual errors and ensuring the accuracy and consistency of long-term observation of perennial fruit tree data.

[0021] 2. A traceable complex data association system was forcibly established: Through logical binding, the inherent connection between the group, individual tree, and survey data was forcibly established, forming a clear and stable tree-like data structure, which perfectly meets the management needs of multi-layered nesting and long-term tracking of fruit tree data, and ensures the complete traceability of all data.

[0022] 3. Overcame the challenge of collaborative management of fruit tree data: Supported a mode combining local caching and cloud synchronization, enabling secure sharing and real-time collaborative investigation of the same batch of fruit tree resource data by multiple users across regions, effectively breaking down "data silos".

[0023] 4. Enables in-depth analysis and intelligent decision-making: The highly structured data provides a directly usable data foundation for rapid querying, multidimensional statistics, visualization analysis, and integration with advanced breeding models (such as genetic evaluation and phenotypic prediction), greatly improving the efficiency of data value transformation and accelerating the process of fruit tree breeding and scientific research.

[0024] This invention closely integrates with the actual needs of the fruit tree industry, effectively solving its unique big data management challenges, and has significant practicality, innovation, and promotional value. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating the method for processing fruit tree population phenotypic trait survey data provided by the present invention; Figure 2 This is a schematic diagram of the architecture of the fruit tree population phenotypic trait survey data processing method provided by the present invention; Figure 3 This is a schematic diagram of the survey page provided by the present invention; Figure 4 This is a schematic diagram of the data management page provided by the present invention; Figure 5 This is a schematic diagram of the structure of the fruit tree population phenotypic trait survey data processing device provided by the present invention; Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0028] It should be noted that the core of the method, apparatus, and system provided by this invention lies in constructing a universal digital phenotypic data management model applicable to fruit tree populations. This model achieves structured data collection, associated storage, and efficient management through a three-level structure of "population-plant-task" and a context-driven mechanism. To facilitate a thorough understanding by those skilled in the art, a grape population will be used as an example to illustrate a preferred embodiment of this invention in detail below. It is understood that this invention is also applicable to other fruit tree species such as apples, pears, citrus, and peaches, and the specific trait database can be flexibly configured according to the agronomic standards of different fruit trees.

[0029] Figure 1 This is a flowchart illustrating the data processing method for fruit tree population phenotypic trait surveys provided by the present invention, as shown below. Figure 1 As shown, the method includes: S11, Receive a data processing request, the data processing request including at least one of a population data creation instruction, a plant creation operation and a task processing instruction; S12, when it is determined that the data processing request is a group data creation instruction, the group field information in the group data creation instruction is parsed, and a result tree group data record is generated based on the group field information; S13, when it is determined that the data processing request is a plant creation operation, in response to the plant record instruction for the target plant, the content of the target plant data record associated with the fruit tree group data record is edited to generate the plant data record. S14, when it is determined that the data processing request is a task processing instruction for the target survey task, the task context bound to the target survey task is obtained; wherein, the task context includes at least the target population identifier and the target plant identifier; S15, based on the type of task processing instruction, execute the data processing operation associated with the task context.

[0030] The following is a detailed combination Figures 2-5 This invention describes a method for processing data from a survey of phenotypic traits in fruit tree populations.

[0031] Step S11: Receive a data processing request, which includes at least one of a population data creation instruction, a plant creation operation, and a task processing instruction. Step S12: When it is determined that the data processing request is a group data creation instruction, the group field information in the group data creation instruction is parsed, and a result tree group data record is generated based on the group field information.

[0032] It should be added that the group field information includes the group name, creation time, number of individuals in the group, geographical location, parent name, and mother name. Before generating the succulent tree group data record based on the group field information, the process includes: verifying the integrity of the parsed group field information, determining whether the target specified field exists, and if the verification passes based on the existence of the target specified field, generating the succulent tree group data record based on the group field information.

[0033] It should be noted that the target specified fields can be set based on actual design requirements, such as group name, number of individuals in the group, and geographical location, etc., without further restrictions here. Additionally, if validation fails, creation will fail.

[0034] Furthermore, after generating the fruit tree group data record, it also includes: in response to the group editing command, editing or deleting the content of the corresponding fruit tree group data record.

[0035] Step S13: When the data processing request is determined to be a plant creation operation, in response to the plant record instruction for the target plant, the content of the target plant data record associated with the fruit tree group data record is edited to generate the plant data record.

[0036] In this embodiment, the plant data record is used to store information about individual plants in the population, including fields such as individual plant ID, individual plant code, individual plant name, and population name, and is associated with the fruit tree population data through the population name.

[0037] Furthermore, after generating plant data records, the process includes: in response to plant editing instructions, editing or deleting the content of the corresponding plant data records.

[0038] In addition, inserting or deleting individual tree data records will cause changes in the population size, thus affecting population changes. Therefore, the fruit tree population data records are automatically updated by associating them with the fruit tree population data records.

[0039] Step S14: When it is determined that the data processing request is a task processing instruction for the target survey task, the task context bound to the target survey task is obtained; wherein, the task context includes at least the target population identifier and the target plant identifier.

[0040] Step S15: Execute data processing operations associated with the task context according to the type of task processing instruction.

[0041] In this embodiment, based on the type of task processing instruction, data processing operations associated with the task context are performed, including: when the task processing instruction is determined to be a data recording instruction, receiving survey data returned based on the target survey task, generating survey data records, and using the target group identifier and target plant identifier in the task context to associate and store the generated survey data records with the corresponding fruit tree group data records and plant data records; when the task processing instruction is determined to be a task recording instruction, responding to the task recording instruction, generating task recording data based on the target survey task.

[0042] It should be added that the method for synchronizing to the cloud database can be referred to below, and will not be repeated here. In addition, the survey data record is used to store survey result records, including fields such as record ID, population name, individual plant name, survey date, trait type, trait value, and completion status. It is associated with population data and plant data through the population name and individual plant name. The target survey task record is used to store survey task records, including fields such as task ID, record ID, trait type, survey date, total population, and number of completed tasks. It is associated with the survey data through the record ID.

[0043] In one alternative embodiment, reference Figure 2 and Figure 3 The task recording instruction includes a phenological period survey instruction. When the task processing instruction is determined to be a task recording instruction, the system receives survey data returned based on the target survey task and generates a survey data record, including: when the task recording instruction is determined to be a phenological period survey instruction, displaying the corresponding survey page in response to the phenological period survey instruction; selecting the first target group from the group list on the survey page in response to the first group selection instruction; and displaying the phenological period trait selection page in response to the first trait selection instruction; wherein the phenological period trait selection page includes at least one selectable phenological period survey trait; displaying the trait data recording interface in response to the selection operation for the target phenological period survey trait, and receiving survey data associated with the target phenological period survey trait through the trait data recording interface; wherein the survey data includes thumbnails of time data and image data, the image data is obtained based on the target survey task and stored in a preset background album folder, and the thumbnails are generated based on the image data and displayed in the trait data recording interface; and generating a survey data record associated with the target group and the target phenological period survey trait based on the time data and image data.

[0044] It should be added that the phenological characteristics surveyed include the pompom stage, leaf expansion stage, initial flowering stage, full bloom stage, late flowering stage, fruit setting stage, color change stage, and maturity stage. Additionally, the front-end phenomenological data recording interface provides a full-screen preview of the thumbnail in response to thumbnail clicks. Furthermore, when collecting images, a mini-program based on the target application app can be used, allowing data recording directly in the field, improving work convenience.

[0045] In another optional embodiment, the task recording instruction includes a morphological survey instruction; when the task processing instruction is determined to be a task recording instruction, receiving survey data returned based on the target survey task and generating a survey data record, further including: when the task recording instruction is determined to be a morphological survey instruction, displaying a corresponding survey page in response to the morphological survey instruction; selecting a second target group from the group list on the survey page in response to a second group selection instruction, selecting a survey date from the survey date list on the survey page in response to a date selection instruction, and loading and displaying a list of morphological survey traits associated with the target group in response to a second trait selection instruction; displaying a morphological data recording interface corresponding to the target morphological survey trait in response to a morphological selection operation for the target morphological survey trait in the morphological survey trait list; and editing the content of the target morphological survey trait in the morphological data recording interface in response to a morphological recording operation for the target morphological survey trait to generate a survey data record.

[0046] It should be noted that the group list on the survey page is affected by the creation, editing, or deletion of the corresponding fruit tree group data records. Therefore, by determining the corresponding target group identifier and target plant identifier, the generated survey data records are associated with the corresponding fruit tree group data records and plant data records, thereby enabling the automatic updating of the group list on the survey page based on the updates of the groups.

[0047] Furthermore, the list of morphological survey traits includes at least one optional morphological survey trait, which includes single seed weight, longitudinal and transverse diameters, seed quantity and quality, sugar-acid ratio, soluble solids, titratable acid, and sensory evaluation. Accordingly, in response to the morphological recording operation for the target morphological survey trait, the content of the target morphological survey trait in the morphological data recording interface is edited to generate survey data, including: in response to the morphological recording operation for the target morphological survey trait, loading and displaying multiple fields corresponding to the target morphological survey trait; when a field is determined to be a non-target field, displaying a list of field text entries, and in response to a field selection operation, using the selected value as the content of the corresponding field; when a field is determined to be a target field, displaying a text input box, and in response to text input, using the text input as the content of the corresponding field.

[0048] It should be noted that the single-grain weight includes two fields: total weight (g) and number of grains; the longitudinal and transverse diameters include two fields: longitudinal diameter (mm) and transverse diameter (mm); the seed quantity and quality include three fields: number of grains, total number of grains, and total weight (g); the sugar-acid ratio includes soluble solids (°Brix), initial value (ml), and final value (ml), where soluble solids are measured by a refractometer (saccharimeter), and the initial value (ml) and final value (ml) correspond to the readings of sodium hydroxide solution in the burette before and after titration of titrantable acid; sensory evaluation includes fields such as grain color, grain shape, fruit taste, aroma, seeds, texture, defects, and advantages. Except for defects and advantages, which are text input, the other fields are text selections.

[0049] Furthermore, for standardization and normalization, the above fields can be pre-configured with corresponding text entries based on actual design requirements and prior experience. For example, fruit color includes blue-black, purplish-black, purplish-red, dark red, red, pink, yellow, yellowish-green, and green; fruit shape includes cylindrical, oblong, oval, round, flattened, ovate, blunt ovate, obovate, curved, and waisted; fruit taste includes sour, sweet-sour, sweet-sour, sweet, and bland; aroma includes strong aroma, light aroma, and no aroma; seeds include normal, sterility type II, sterility type I, and none; texture includes soft, brittle, and hard, without further limitations here.

[0050] In one optional embodiment, before loading and displaying the list of morphological survey traits associated with the target group based on the target group and the survey date, the process includes: verifying the completeness of the group name, survey date, and morphological survey traits; and, based on successful verification, loading and displaying the list of morphological survey traits associated with the target group. It should be added that if verification fails, a notification should pop up.

[0051] In one alternative embodiment, reference Figure 4 After generating survey data records, the process includes: responding to data management instructions by displaying a data management page, which shows multiple survey data records in a list format, with each record including the group name, survey characteristics, survey date, and progress status; responding to evaluation instructions for at least one target survey data record by invoking a preset evaluation strategy to evaluate the morphological survey characteristics of the corresponding target survey data record and obtain corresponding characteristic evaluation results; wherein, the preset evaluation strategy is configured in advance for different morphological survey characteristics and their corresponding preset evaluation rules and preset result value rules; and responding to statistical instructions for at least one target survey data record by summarizing the corresponding characteristic evaluation results and obtaining corresponding summary results.

[0052] It should be noted that due to the inconsistency and uncertainty of the maturity period of individual plants in different populations, it is necessary to conduct multiple measurements on the same individual plant. That is, the measurement result of a single plant is not unique, but there will be multiple measurements on different survey dates. Therefore, it is necessary to summarize the results. Digital recording avoids the errors of manual recording, and automated data verification and calculation improve work efficiency.

[0053] Specifically, the pre-defined evaluation strategy can be configured based on different morphological survey traits and prior experience. For example, for morphological survey traits, the pre-defined evaluation rule for single-grain weight is the total weight (g) divided by the number of berries, with the result rounded to two decimal places; the pre-defined evaluation rule for seed quantity is the total number of seeds divided by the number of berries, with the result rounded to one decimal place; the pre-defined evaluation rule for seed quality is the total weight (g) divided by the number of berries, with the result rounded to three decimal places; the pre-defined evaluation rule for aspect ratio is the longitudinal diameter (mm) divided by the transverse diameter (mm), with the result rounded to two decimal places; and the pre-defined evaluation rule for titratable acid content is (terminal value (ml) - initial value (ml)). 0.1 0.075 The preset result grading rule is to round the result to two decimal places. The preset evaluation rule for the sugar-acid ratio is to divide the titratable acid content by the soluble solids content, and the preset result grading rule is to round the result to two decimal places. For multiple measurements of the same plant, the system can automatically summarize the results according to preset rules (such as taking the maximum value or the average value) to obtain the final evaluation result for that plant.

[0054] In an optional embodiment, the method further includes: in response to a data management instruction, displaying a data management page, the data management page displaying multiple survey records in a list format, and each survey record including a group name, survey trait survey date, and progress status; and in response to a record triggering operation for a target record, jumping to the corresponding survey page to continue or modify the survey data record.

[0055] It should be added that the progress status is displayed in the form of completed number / total number, and the progress on the data management page will be automatically updated as the corresponding record is added.

[0056] In an optional embodiment, the method further includes: receiving filtering conditions in response to a statistical request on a data management page; wherein the filtering conditions include the target group name and the target date; and querying and aggregating corresponding data from the database based on the filtering conditions to generate and display a statistical report.

[0057] It should be added that the method also includes: in response to a data export request on the data management page, selecting the corresponding survey data record and exporting it in a preset export format. The preset export format can be configured according to actual design requirements, such as JSON and Excel, etc., and is not further limited here.

[0058] In an optional embodiment, the method further includes: storing the fruit tree group data records, plant data records, survey data records, and / or task record data in key-value pairs to the client's local storage space; in response to the record synchronization upload command, clearing the records in the cloud data, and dividing the stored data in the client's local storage space into blocks according to a preset size limit, and dividing the upload batches based on each block, thereby calling a preset cloud function to upload the data to the cloud database in batches, thereby accelerating the data upload speed and solving the limitation of system data interaction response time.

[0059] It should be added that when performing local storage, the Storage API can be used for local data persistence, and local saving should be performed every time a corresponding record is edited or generated. Additionally, when saving locally, plant data records need to be associated with the corresponding fruit tree group data, survey data records need to be associated with the corresponding fruit tree group and plant data records, and task record data needs to be associated with the corresponding survey data record using the record ID.

[0060] When performing cloud synchronization, the cloud development capabilities of the corresponding application (APP) can be used to synchronize and back up cloud data. By combining local storage and cloud storage, both real-time data availability and data security are ensured, achieving data synchronization and backup. Furthermore, cloud synchronization can be triggered upon APP launch or by clicking the synchronize data button. The preset size limit can be designed according to actual design requirements, such as less than or equal to 64KB; no further limitation is made here.

[0061] In one optional embodiment, to verify the effectiveness of the above method, a six-month practical application test was conducted at a grape breeding research institute. Eight researchers used the system to record grape breeding data. During the test, data from 15 grape populations, trait data from 1200 individual vines, over 5000 survey records, and more than 800 field photographs were recorded. Compared to traditional paper-based recording methods, the digital recording method avoids errors from manual copying, the data verification function ensures data integrity, and improves data accuracy; the automated data calculation and statistical functions save significant manual processing time, increasing work efficiency by approximately 40%; electronic data facilitates querying, filtering, and statistical analysis, allowing researchers to quickly obtain the information they need, and data management is convenient; the cloud synchronization function enables different researchers to share data, promoting team collaboration; and the use of paper materials is reduced, lowering material costs and storage space requirements.

[0062] In summary, this embodiment of the invention receives data processing requests and executes corresponding operations based on the type of the request. When a group data creation instruction is received, field information is parsed to generate a fruit tree group data record. When a request is for a plant creation operation, the newly generated plant data record is associated with the group data record to forcibly establish a group-plant hierarchical relationship, forming a clear and orderly tree-like data structure. This ensures data integrity and traceability, avoids data chaos, and provides the possibility for subsequent precise statistical analysis based on the group. When a request is for a task processing instruction, recording does not begin directly. Instead, the task context bound to the task is first obtained, which includes at least the target group identifier and the target plant identifier. This ensures that the target survey task is accurately applied to the predetermined group and plant, achieving context-driven precise execution. Digital recording avoids errors from manual recording, improves the accuracy and efficiency of data recording, facilitates data querying, statistics, and analysis, and realizes the digital and systematic management of fruit tree group phenotypic trait survey data.

[0063] The following describes the fruit tree population phenotypic trait survey data processing device provided by the present invention. The fruit tree population phenotypic trait survey data processing device described below and the fruit tree population phenotypic trait survey data processing method described above can be referred to in correspondence with each other.

[0064] Figure 5 A schematic diagram of a data processing device for a fruit tree population phenotypic trait survey is shown. The device includes: The request receiving module 51 receives a data processing request, which includes at least one of a population data creation instruction, a plant creation operation, and a task processing instruction. When the group processing module 52 determines that the data processing request is a group data creation instruction, it parses the group field information in the group data creation instruction and generates a result tree group data record based on the group field information. When the data processing request is determined to be a plant creation operation, the plant editing module 53 responds to the plant record instruction for the target plant, edits the content of the target plant data record associated with the fruit tree group data record, and generates the plant data record. When the task processing module 54 determines that the data processing request is a task processing instruction for the target survey task, it obtains the task context bound to the target survey task; wherein, the task context includes at least the target population identifier and the target plant identifier. The task execution module 55 performs data processing operations associated with the task context based on the type of task processing instruction.

[0065] In this embodiment, the group processing module 52 is further configured to: after generating the fruit tree group data record, in response to the group editing instruction, edit or delete the content of the corresponding fruit tree group data record.

[0066] In addition, the plant editing module 53 is also used to: after generating plant data records, in response to plant editing instructions, edit or delete the content of the corresponding plant data records.

[0067] The task execution module 55 includes: a survey data recording unit, which, when the task processing instruction is determined to be a data recording instruction, receives survey data returned based on the target survey task, generates survey data records, and associates and stores the generated survey data records with the corresponding fruit tree group data records and plant data records using the target group identifier and target plant identifier in the task context; and a task data recording unit, which, when the task processing instruction is determined to be a task recording instruction, generates task record data in response to the task recording instruction based on the target survey task.

[0068] In an optional embodiment, the task recording instruction includes a phenological period survey instruction; the survey data recording unit is configured to: when the task recording instruction is determined to be a phenological period survey instruction, display a corresponding survey page in response to the phenological period survey instruction; select a first target group from the group list on the survey page in response to a first group selection instruction, and display a phenological period trait selection page in response to a trait selection instruction; wherein the phenological period trait selection page includes at least one selectable phenological period survey trait; display a trait data recording interface in response to a selection operation for a target phenological period survey trait, and receive survey data associated with the target phenological period survey trait through the trait data recording interface; wherein the survey data includes thumbnails of time data and image data, the image data is acquired based on the target survey task and stored in a preset background album folder, and the thumbnails are generated based on the image data and displayed in the trait data recording interface; and generate survey data records associated with the target group and the target phenological period survey trait based on the time data and image data.

[0069] In another optional embodiment, the task recording instruction includes a morphological survey instruction; the survey data recording unit is further configured to: when the task recording instruction is determined to be a morphological survey instruction, display the corresponding survey page in response to the morphological survey instruction; in response to the second group selection instruction, select a second target group from the group list on the survey page; in response to the date selection instruction, select a survey date from the survey date list on the survey page; and in response to the second trait selection instruction, load and display a list of morphological survey traits associated with the target group; in response to a morphological selection operation for a target morphological survey trait in the list of morphological survey traits, display a morphological data recording interface corresponding to the target morphological survey trait; and in response to a morphological recording operation for the target morphological survey trait, edit the content of the target morphological survey trait in the morphological data recording interface to generate a survey data record.

[0070] Furthermore, the list of morphological survey traits includes at least one optional morphological survey trait, which includes single seed weight, longitudinal and transverse diameters, seed quantity and quality, sugar-acid ratio, soluble solids, titratable acid, and sensory evaluation. Correspondingly, the survey data recording unit is also used to: in response to a morphological recording operation for a target morphological survey trait, load and display multiple fields corresponding to the target morphological survey trait; when a field is determined to be a non-target field, display a list of field text entries, and in response to a field selection operation, use the selected value as the content of the corresponding field; when a field is determined to be a target field, display a text input box, and in response to text input, use the text input as the content of the corresponding field.

[0071] In an optional embodiment, the device further includes a verification module configured to: verify the completeness of the group name, survey date, and morphological survey traits before loading and displaying the list of morphological survey traits associated with the target group based on the target group and survey date; and load and display the list of morphological survey traits associated with the target group if the verification passes. It should be added that if the verification fails, a notification should be displayed.

[0072] In an optional embodiment, the device further includes a data management module, configured to: after generating survey data records, in response to a data management instruction, display a data management page, the data management page displaying multiple survey data records in a list format, each survey data record including a group name, survey date of survey characteristics, and progress status; in response to an evaluation instruction for at least one target survey data record, invoke a preset evaluation strategy to evaluate the morphological survey characteristics of the corresponding target survey data record, and obtain the corresponding characteristic evaluation result; wherein, the preset evaluation strategy is configured in advance for different morphological survey characteristics and their corresponding preset evaluation rules and preset result value rules; in response to a statistical instruction for at least one target survey data record, summarize the corresponding characteristic evaluation results, and obtain the corresponding summary result.

[0073] In an optional embodiment, the data management module is further configured to: in response to a data management instruction, display a data management page, the data management page displaying multiple survey records in a list format, and each survey record including a group name, survey characteristics, survey date, and progress status; and in response to a record triggering operation for a target record, jump to the corresponding survey page to continue or modify the survey data record.

[0074] In an optional embodiment, the device further includes a report generation module for: receiving filtering conditions in response to a statistical request on a data management page; wherein the filtering conditions include the target group name and the target date; and querying and aggregating corresponding data from the database based on the filtering conditions to generate and display a statistical report.

[0075] It should be added that the device also includes a data export module, which is used to: respond to a data export request on the data management page, select the corresponding survey data record, and export it in a preset export format. The preset export format can be configured according to actual design requirements, such as JSON and Excel, etc., without further limitation here.

[0076] In an optional embodiment, the device further includes a synchronization backup module, used to: store the fruit tree group data records, plant data records, survey data records and / or task record data in key-value pairs to the client's local storage space; in response to a record synchronization upload command, clear the records in the cloud data, and divide the stored data in the client's local storage space into blocks according to a preset size limit, and divide the upload batches based on each block, so as to call a preset cloud function to upload the data to the cloud database in batches, thereby accelerating the data upload speed and solving the limitation of system data interaction response time.

[0077] In summary, this embodiment of the invention receives data processing requests through a request receiving module and performs corresponding operations based on the type of the data processing request. When the request is for creating group data, the group processing module parses the field information to generate fruit tree group data records. When the request is for creating a plant, the plant editing module associates the newly generated plant data records with the group data records, thus forcibly establishing a group-plant hierarchical relationship and forming a clear and orderly tree-like data structure. This ensures data integrity and traceability, avoids data chaos, and provides the possibility for subsequent precise statistical analysis based on the group. When the request is for a task processing instruction, recording does not start directly. Instead, the task processing module first obtains the task context bound to the task, which includes at least the target group identifier and the target plant identifier. This ensures that the target survey task of the task execution module accurately acts on the predetermined group and plant, achieving context-driven precise execution. Digital recording avoids errors from manual recording, improves the accuracy and efficiency of data recording, facilitates data querying, statistics, and analysis, and realizes the digital and systematic management of fruit tree group phenotypic trait survey data.

[0078] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communications bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other through the communications bus 640. The processor 610 can invoke logical instructions in the memory 630 to execute a method for processing fruit tree population phenotypic trait survey data. This method includes: receiving a data processing request, which includes at least one of a population data creation instruction, a plant creation operation, and a task processing instruction; when the data processing request is determined to be a population data creation instruction, parsing the population field information in the instruction and generating a fruit tree population data record based on the population field information; when the data processing request is determined to be a plant creation operation, in response to a plant record instruction for a target plant, editing the content of the target plant data record associated with the fruit tree population data record to generate a plant data record; when the data processing request is determined to be a task processing instruction for a target survey task, obtaining a task context bound to the target survey task, wherein the task context includes at least a target population identifier and a target plant identifier; and executing a data processing operation associated with the task context according to the type of the task processing instruction.

[0079] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0080] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the fruit tree population phenotypic trait survey data processing method provided by the above methods. The method includes: receiving a data processing request, the data processing request including at least one of a population data creation instruction, a plant creation operation, and a task processing instruction; when the data processing request is determined to be a population data creation instruction, parsing the population field information in the population data creation instruction, and generating a fruit tree population data record based on the population field information; when the data processing request is determined to be a plant creation operation, in response to a plant record instruction for a target plant, editing the content of the target plant data record associated with the fruit tree population data record to generate a plant data record; when the data processing request is determined to be a task processing instruction for a target survey task, obtaining a task context bound to the target survey task; wherein the task context includes at least a target population identifier and a target plant identifier; and executing a data processing operation associated with the task context according to the type of task processing instruction.

[0081] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a method for processing fruit tree population phenotypic trait survey data provided by the methods described above. The method includes: receiving a data processing request, the data processing request including at least one of a population data creation instruction, a plant creation operation, and a task processing instruction; when the data processing request is determined to be a population data creation instruction, parsing the population field information in the population data creation instruction and generating a fruit tree population data record based on the population field information; when the data processing request is determined to be a plant creation operation, in response to a plant record instruction for a target plant, editing the content of the target plant data record associated with the fruit tree population data record to generate a plant data record; when the data processing request is determined to be a task processing instruction for a target survey task, obtaining a task context bound to the target survey task; wherein the task context includes at least a target population identifier and a target plant identifier; and executing a data processing operation associated with the task context according to the type of the task processing instruction.

[0082] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units 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. Those skilled in the art can understand and implement this without any creative effort.

[0083] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing data of a phenotypic trait survey of a fruit tree population, characterized by, The method comprises: receiving a data processing request, the data processing request comprising at least one of a group data creation instruction, a plant creation operation, and a task processing instruction; when the data processing request is determined to be the group data creation instruction, parsing group field information in the group data creation instruction, and generating fruit tree group data records based on the group field information; when the data processing request is determined to be the plant creation operation, in response to a plant record instruction for a target plant, editing the content of a target plant data record associated with the fruit tree group data record to generate a plant data record; when the data processing request is determined to be a task processing instruction for a target investigation task, obtaining a task context bound to the target investigation task; wherein the task context at least comprises a target group identifier and a target plant identifier; performing a data processing operation associated with the task context according to the type of the task processing instruction.

2. The method for processing data on phenotypic traits of fruit tree populations according to claim 1, characterized in that, According to the type of the task processing instruction, performing a data processing operation associated with the task context, comprising: when the task processing instruction is a data record instruction, receiving investigation data returned based on the target investigation task, generating investigation data records, and storing the generated investigation data records in association with corresponding fruit tree group data records and plant data records by using the target group identifier and the target plant identifier in the task context; when the task processing instruction is a task record instruction, in response to the task record instruction, generating task record data according to the target investigation task.

3. The method for processing data on phenotypic traits of fruit tree populations according to claim 2, characterized in that, The task record instruction comprises a phenological phase investigation instruction; when the task processing instruction is the task record instruction, receiving investigation data returned based on the target investigation task to generate investigation data records, comprising: when the task record instruction is the phenological phase investigation instruction, in response to the phenological phase investigation instruction, displaying a corresponding investigation page; in response to a first group selection instruction, selecting a first target group from a group list of the investigation page, and in response to a trait selection instruction, displaying a phenological phase trait selection page; wherein the phenological phase trait selection page comprises at least one selectable phenological phase investigation trait; in response to a selection operation for a target phenological phase investigation trait, displaying a trait data record interface, and receiving investigation data associated with the target phenological phase investigation trait through the trait data record interface; wherein the investigation data comprises time data and a thumbnail of image data, the image data is obtained based on the target investigation task and stored in a preset background album folder, the thumbnail is generated based on the image data and displayed in the trait data record interface; generating investigation data records associated with the target group and the target phenological phase investigation trait according to the time data and the image data.

4. The method for processing data on phenotypic traits of fruit tree populations according to claim 2, characterized in that, The task record instruction comprises a morphological investigation instruction; when the task processing instruction is the task record instruction, receiving investigation data returned based on the target investigation task to generate investigation data records, further comprising: When the task record instruction is determined to be a morphology investigation instruction, a corresponding investigation page is displayed in response to the morphology investigation instruction; In response to a second group selection instruction, a second target group is selected from a group list of the investigation page, in response to a date selection instruction, an investigation date is selected from an investigation date list of the investigation page, and in response to a second trait selection instruction, a morphology investigation trait list associated with the target group is loaded and displayed; In response to a morphology selection operation for a target morphology investigation trait in the morphology investigation trait list, a morphology data record interface corresponding to the target morphology investigation trait is displayed; In response to a morphology record operation for the target morphology investigation trait, the content of the target morphology investigation trait in the morphology data record interface is edited to generate an investigation data record.

5. The method for processing data on phenotypic traits of fruit tree populations according to claim 4, characterized in that, Before loading and displaying the morphology investigation trait list associated with the target group according to the target group and the investigation date, the method comprises: Verifying the integrity of the group name, the investigation date, and the morphology investigation trait, and based on the verification passing, loading and displaying the morphology investigation trait list associated with the target group; After generating the investigation data record, the method comprises: In response to a data management instruction, a data management page is displayed, the data management page displays a plurality of investigation data records in a list form, and each investigation data record comprises a group name, an investigation trait, an investigation date, and a progress state; In response to an evaluation instruction for at least one target investigation data record, a preset evaluation strategy is called to evaluate the morphology investigation trait of the corresponding target investigation data record to obtain a corresponding trait evaluation result; wherein the preset evaluation strategy is configured in advance for different morphology investigation traits and their corresponding preset evaluation rules and preset result value rules; In response to a statistical instruction for at least one target investigation data record, the corresponding trait evaluation results are summarized to obtain a corresponding summary result.

6. The method for processing data on phenotypic traits of fruit tree populations according to claim 3 or 4, characterized in that, The method further comprises: In response to a data management instruction, a data management page is displayed, the data management page displays a plurality of investigation records in a list form, and each investigation record comprises a group name, an investigation trait, an investigation date, and a progress state; In response to a record triggering operation for a target record, the corresponding investigation page is jumped to to continue or modify the investigation data record; In response to a statistical request on the data management page, a filtering condition is received; wherein the filtering condition comprises a target group name and a target date; Based on the filtering condition, corresponding data is queried and aggregated from the database to generate and display a statistical report.

7. The method for processing data on phenotypic traits of fruit tree populations according to claim 2, characterized in that, The method further comprises: The fruit tree group data record, the plant data record, the investigation data record, and / or the task record data are respectively stored in a key-value pair form to a client local storage space; In response to a record synchronization upload instruction, the records of the cloud end data are emptied, and the storage data in the client local storage space are divided into blocks according to a preset size limit, and based on the division of each block, an upload batch is divided to call a preset cloud function to upload to the cloud end database in batches.

8. A data processing device for investigating phenotypic traits of fruit tree populations, characterized in that, It comprises: The request receiving module receives a data processing request, and the data processing request comprises at least one of a group data creation instruction, a plant creation operation and a task processing instruction; The group processing module, when determining that the data processing request is the group data creation instruction, parses group field information in the group data creation instruction, and generates fruit tree group data records based on the group field information; The plant editing module, when determining that the data processing request is the plant creation operation, responds to a plant record instruction for a target plant, edits content of target plant data records associated with the fruit tree group data records, and generates plant data records; The task processing module, when determining that the data processing request is a task processing instruction for a target investigation task, acquires a task context bound with the target investigation task; wherein the task context at least comprises a target group identifier and a target plant identifier; The task execution module executes a data processing operation associated with the task context according to a type of the task processing instruction.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, The processor executes the computer program to implement the fruit tree group phenotype trait investigation data processing method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the fruit tree group phenotype trait investigation data processing method according to any one of claims 1 to 7.

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