A recording method and application platform based on plant whole cycle growth data chain

Abstract

The embodiment of the application provides a recording method and application platform based on plant whole-cycle growth data chain, which comprises the following steps: S1: generating a formula code based on tissue culture formula data, distributing the formula code to the corresponding tissue culture container, and continuously tracking the formula code when the tissue culture container is transferred; S2: establishing a plant growth whole-cycle tissue culture time sequence model, wherein the time sequence model defines the transfer rule of the formula code in each growth cycle of the plant; S3: the time sequence model verifies the transfer legality of the formula code based on the transfer rule; if the transfer legality of the formula code is verified, the tissue culture container enters the next growth cycle; and S4: recording the data when the formula code verification is passed and associating the data to a database. Through the establishment of the time sequence model and the definition of the transfer rule, the verification of the formula code transfer process and the logical consistency guarantee are realized, and the technical problems of data chain breakage or association error caused by the error of the formula code during the transfer between different systems are effectively overcome.

Description

Technical Field

[0001] This application relates to the field of plant tissue culture, and in particular to a recording method and application platform based on a plant full-cycle growth data chain. Background Technology

[0002] In the field of plant tissue culture research and production, accurate data recording of the entire growth cycle of plants, from tissue culture seedlings to mature plants, is a crucial foundation for optimizing tissue culture formulations, improving seedling quality, and accelerating the breeding process. In particular, systematically comparing the growth performance of plants under different tissue culture formulations allows for the scientific selection of the optimal formulation, which is of great significance to both research and industry.

[0003] Currently, to achieve this goal, a segmented data management strategy is typically adopted, with each segment collected and managed by an independent system. These systems generally rely on physical associations using identifiers such as formula codes to form a complete data chain. However, this approach has significant limitations: during the long process of the entire growth cycle, manual scanning is prone to omissions and errors; the identifier carrier itself may be damaged or malfunction due to harsh environments such as high temperature and humidity; and more importantly, there is a lack of an automated mechanism to verify the logic and rationality of formula codes flowing across modules and in chronological order. This leads to potential breaks or incorrect associations in the data chain at critical points, making it difficult to guarantee the integrity and reliability of the recorded data. Ultimately, formula analysis and optimization conclusions based on flawed data lose their accuracy and reference value, severely hindering industry development.

[0004] Therefore, there is an urgent need for a method that can ensure a continuous, accurate, and reliable recording of plant growth data throughout its entire life cycle, in order to solve the above problems. Summary of the Invention

[0005] This application provides a recording method and application platform based on a plant full-cycle growth data chain. The method establishes a plant growth full-cycle tissue culture time series model and defines flow rules including legal paths, state transition conditions, and time window constraints. It realizes the verification and logical consistency guarantee of the formula code flow process, ensuring the accuracy and completeness of data association at each stage from formula preparation, environmental culture to phenotypic collection. It effectively overcomes the technical problem of data chain breakage or association errors caused by errors when formula codes are transferred between different systems.

[0006] Firstly, a recording method based on a plant full-cycle growth data chain is provided, the method comprising:

[0007] S1: Generate a formula code based on tissue culture formula data, assign the formula code to the corresponding tissue culture container, and continuously track the formula code when the tissue culture container is transferred.

[0008] S2: Establish a time-series model of plant growth cycle tissue culture, wherein the time-series model defines the flow rules of the formula code in each growth cycle of the plant, and the flow rules include: legal path, state transition conditions and time window constraints.

[0009] S3: When the formula code is transferred to a new growth cycle along with the tissue culture container, the time series model verifies the legality of the transfer of the formula code based on the transfer rule; if the legality verification of the transfer of the formula code passes, the tissue culture container enters the next growth cycle;

[0010] S4: Record the data when the recipe code verification is successful and associate it with the database.

[0011] It should be understood that by establishing a time-series model of the entire plant growth cycle in tissue culture and defining flow rules that include legal paths, state transition conditions, and time window constraints, automated real-time verification and logical consistency assurance of the cross-cycle flow process of formula codes have been achieved. This ensures the accuracy and completeness of data association at each stage from formula preparation and environmental cultivation to phenotypic collection, significantly improving the reliability and credibility of the entire growth data chain and providing a solid data foundation for the accurate screening and optimization of tissue culture formulas.

[0012] It should be understood that the time-series model, as a rule engine, has built-in flow rules that define the compliant flow path of recipe codes. These flow rules, through three major elements—legal path, state transition conditions, and time window constraints—form a normative framework that recipe codes must follow during cross-system flow. As the traceable entity identifier, the recipe code must undergo real-time verification by the time-series model based on the flow rules throughout its entire flow process. The three form a closed-loop relationship of "rule formulation - carrier identifier - rule execution," jointly ensuring the consistency and logical correctness of data when it flows between multiple independent systems.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, the flow rule includes:

[0014] Define the state sequence of the recipe code, which includes at least: recipe preparation state, environmental culture state, and phenotypic collection state;

[0015] Define the legal transition paths between each growth cycle in the state sequence;

[0016] A time window constraint is set for the transition of the state sequence between each of the growth cycles.

[0017] It should be understood that by clearly defining the state sequence, legal transfer path, and time window constraints that the formula code must follow, a standardized framework for cross-system data flow is constructed. This provides a logical basis for the automatic verification of the formula code flow process in each growth cycle, thereby effectively preventing data association errors caused by human error or inter-system coordination deviations. This ensures the integrity and reliability of the entire lifecycle data chain and provides a solid guarantee for subsequent data analysis and formula optimization.

[0018] In conjunction with the first aspect, in some implementations of the first aspect, step S3 includes:

[0019] S301: Verify whether the time interval between the transfer of the recipe code from the previous growth cycle to the current growth cycle meets the time window constraint;

[0020] S302: Verify whether the recipe code meets the state transition conditions from the previous growth cycle to the current growth cycle;

[0021] S303: Verify whether the transfer path of the recipe code is one of the valid transfer paths.

[0022] It should be understood that by using a triple verification mechanism of time window constraints, state transition conditions, and legal transition paths, the cross-cycle flow process of recipe codes is verified in real time from multiple dimensions. This effectively prevents data association failures caused by time deviations, logical sequence errors, or path errors, and ensures the integrity and consistency of the data chain in terms of time sequence, logic, and space.

[0023] In conjunction with the first aspect, in some implementations of the first aspect, the method for tracking the recipe code includes: configuring an RFID tag for the tissue culture container, wherein the recipe code is stored in the RFID tag;

[0024] RFID readers and spatial beacons are deployed at key nodes in the tissue culture container circulation path to automatically acquire the formula code and its spatial location information.

[0025] In conjunction with the first aspect, in some implementations of the first aspect, step S4 includes:

[0026] S401: Associate the recipe code with the environmental parameter data collected during the corresponding growth cycle;

[0027] S402: Associate the formula code with the phenotypic data collected during the corresponding growth cycle;

[0028] S403: Establish a full-cycle data chain with the recipe code as the unique index.

[0029] In conjunction with the first aspect, in some implementations of the first aspect, the method includes an exception handling mechanism: when the validity verification of the recipe code fails, the exception handling mechanism is triggered;

[0030] The anomaly handling mechanism includes: issuing an alarm, recording the abnormal event, suspending the flow of the tissue culture container, and providing corrective suggestions.

[0031] In conjunction with the first aspect, in some implementations of the first aspect, the method includes:

[0032] A digital twin visualization interface is constructed based on the aforementioned time series model;

[0033] The interface displays the current status, flow path, and historical trajectory of all tissue culture containers in real time; it also highlights and warns of abnormal events that deviate from the time series model.

[0034] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes:

[0035] Collect historical circulation data of the recipe code, and optimize and adjust the circulation rules in the time series model based on the historical circulation data;

[0036] The parameters of the time-series model are dynamically updated to adapt it to changes in the actual production environment. Secondly, a data chain recording application platform based on the entire plant growth cycle is provided, which enables any implementation method described in the first aspect to be executed. Attached Figure Description

[0037] Figure 1 A flowchart illustrating a recording method based on a plant full-cycle growth data chain, provided in this application embodiment. Detailed Implementation

[0038] The terminology used in the following embodiments is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, “at least one” and “one or more” refer to one, two, or more than two. The term “and / or” is used to describe the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can indicate: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character “ / ” generally indicates that the preceding and following related objects are in an “or” relationship.

[0039] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0040] In the field of plant tissue culture, accurate data recording throughout the entire growth cycle is crucial for optimizing formulations, improving seedling quality, and accelerating breeding. Currently, segmented data management is commonly used, relying on formulation codes for physical association. However, this method has significant drawbacks: manual scanning is prone to errors, labels are easily damaged, and there is a lack of automated verification mechanisms for cross-module transfer processes, leading to broken data chains or incorrect associations, severely impacting data reliability and the effectiveness of formulation optimization conclusions. Therefore, there is an urgent need for a recording method that can ensure a continuous, accurate, and reliable data chain throughout the entire growth cycle. This application provides a recording method and application platform based on a plant full-cycle growth data chain, which can effectively overcome the aforementioned problems.

[0041] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings.

[0042] Figure 1 A flowchart illustrating a recording method based on a plant full-cycle growth data chain, provided in this application embodiment. (Reference) Figure 1 In some examples, the method includes:

[0043] S1: Generate a formula code based on tissue culture formula data, assign the formula code to the corresponding tissue culture container, and continuously track the formula code when the tissue culture container is transferred.

[0044] S2: Establish a time-series model of plant growth cycle tissue culture, wherein the time-series model defines the flow rules of the formula code in each growth cycle of the plant, and the flow rules include: legal path, state transition conditions and time window constraints.

[0045] S3: When the formula code is transferred to a new growth cycle along with the tissue culture container, the time series model verifies the legality of the transfer of the formula code based on the transfer rule; if the legality verification of the transfer of the formula code passes, the tissue culture container enters the next growth cycle;

[0046] S4: Record the data when the recipe code verification is successful and associate it with the database.

[0047] In one possible implementation, the plant growth cycle tissue culture time-series model is established through formal modeling based on the theoretical framework of Finite-State Machine (FSM) and combined with knowledge of the tissue culture domain. This process first abstracts the entire cycle into a directed graph model consisting of finite state nodes and their interrelationships, based on standard operating procedures for tissue culture production and plant growth patterns. Each state node represents a growth stage with a clear start and end point. Then, all allowed transition paths between state nodes in the graph are explicitly defined through configurable rules, and each path is bound to a corresponding state transition triggering condition function and a time window threshold parameter. Finally, the directed graph model and its associated rules and constraints are instantiated and encapsulated using a programmatic data structure (such as a state transition table or rule configuration file), thereby constructing a computable time-series model that can be automatically loaded, interpreted, and executed by computing devices for real-time verification of the logical compliance and temporal consistency of recipe code flow.

[0048] In some examples, the flow rules include:

[0049] Define the state sequence of the recipe code, which includes at least: recipe preparation state, environmental culture state, and phenotypic collection state;

[0050] Define the legal transition paths between each growth cycle in the state sequence;

[0051] A time window constraint is set for the transition of the state sequence between each of the growth cycles.

[0052] In some examples, step S3 includes:

[0053] S301: Verify whether the time interval between the transfer of the recipe code from the previous growth cycle to the current growth cycle meets the time window constraint;

[0054] S302: Verify whether the recipe code meets the state transition conditions from the previous growth cycle to the current growth cycle;

[0055] S303: Verify whether the transfer path of the recipe code is one of the valid transfer paths.

[0056] In some examples, the recipe code tracking method includes:

[0057] The tissue culture container is equipped with an RFID tag, and the recipe code is stored in the RFID tag;

[0058] RFID readers and spatial beacons are deployed at key nodes in the tissue culture container circulation path to automatically acquire the formula code and its spatial location information.

[0059] In some examples, step S4 includes:

[0060] S401: Associate the recipe code with the environmental parameter data collected during the corresponding growth cycle;

[0061] S402: Associate the formula code with the phenotypic data collected during the corresponding growth cycle;

[0062] S403: Establish a full-cycle data chain with the recipe code as the unique index.

[0063] In one possible implementation, a distributed data association architecture with the recipe code as the primary key is constructed. After successful verification, this architecture automatically triggers a data retrieval command to capture multimodal data streams generated by environmental sensors and phenotypic acquisition devices in real time during the current growth cycle. This data, along with the recipe code, verification timestamp, and spatial location information, is encapsulated into an indivisible logical data unit. This unit is then atomically written to a hybrid storage system composed of a time-series database and a relational database through transactional operations. Simultaneously, the association record is updated within the full-cycle data chain index corresponding to the recipe code, ensuring that all verified data possesses complete traceability and consistency.

[0064] In some examples, the method includes an exception handling mechanism:

[0065] When the validity verification of the recipe code fails, an exception handling mechanism is triggered.

[0066] The anomaly handling mechanism includes: issuing an alarm, recording the abnormal event, suspending the flow of the tissue culture container, and providing corrective suggestions.

[0067] In some examples, the method includes:

[0068] A digital twin visualization interface is constructed based on the aforementioned time series model;

[0069] The interface displays the current status, flow path, and historical trajectory of all tissue culture containers in real time; it also highlights and warns of abnormal events that deviate from the time series model.

[0070] In some examples, the method further includes:

[0071] Collect historical circulation data of the recipe code, and optimize and adjust the circulation rules in the time series model based on the historical circulation data;

[0072] The parameters of the time-series model are dynamically updated to adapt it to changes in the actual production environment. This application also provides an application platform based on a plant full-cycle growth data chain recording system, which enables the methods described in any of the foregoing examples to be executed.

[0073] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. Any equivalent modifications or variations made by those skilled in the art based on the content disclosed in the present invention should be included within the scope of protection set forth in the claims.

Claims

1. A recording method based on a plant whole-cycle growth data chain, characterized in that, The method includes: S1: Generate a formula code based on tissue culture formula data, assign the formula code to the corresponding tissue culture container, and continuously track the formula code when the tissue culture container is being circulated. S2: Establish a time-series model of plant growth cycle tissue culture, wherein the time-series model defines the flow rules of the formula code in each growth cycle of the plant, and the flow rules include: legal path, state transition conditions and time window constraints. S3: When the formula code is transferred to a new growth cycle along with the tissue culture container, the time series model verifies the legality of the transfer of the formula code based on the transfer rule; if the legality verification of the transfer of the formula code passes, the tissue culture container enters the next growth cycle; S4: Record the data when the recipe code verification is successful and associate it with the database.

2. The method according to claim 1, characterized in that, The circulation rules include: Define the state sequence of the recipe code, which includes at least: recipe preparation state, environmental culture state, and phenotypic collection state; Define the legal transition paths between each growth cycle in the state sequence; A time window constraint is set for the transition of the state sequence between each of the growth cycles.

3. The method according to claim 2, characterized in that, Step S3 includes: S301: Verify whether the time interval between the transfer of the recipe code from the previous growth cycle to the current growth cycle meets the time window constraint; S302: Verify whether the recipe code meets the state transition conditions from the previous growth cycle to the current growth cycle; S303: Verify whether the transfer path of the recipe code is one of the valid transfer paths.

4. The method according to claim 3, characterized in that, The method for tracking the recipe code includes: The tissue culture container is equipped with an RFID tag, and the recipe code is stored in the RFID tag; RFID readers and spatial beacons are deployed at key nodes in the tissue culture container circulation path to automatically acquire the formula code and its spatial location information.

5. The method according to claim 4, characterized in that, Step S4 includes: S401: Associate the recipe code with the environmental parameter data collected during the corresponding growth cycle; S402: Associate the formula code with the phenotypic data collected during the corresponding growth cycle; S403: Establish a full-cycle data chain with the recipe code as the unique index.

6. The method according to claim 5, characterized in that, The method includes an exception handling mechanism: When the validity verification of the recipe code fails, an exception handling mechanism is triggered. The anomaly handling mechanism includes: issuing an alarm, recording the abnormal event, suspending the flow of the tissue culture container, and providing corrective suggestions.

7. The method according to claim 6, characterized in that, The method includes: A digital twin visualization interface is constructed based on the aforementioned time series model; The interface displays the current status, flow path, and historical trajectory of all tissue culture containers in real time. Anomalies that deviate from the time series model are highlighted and alerted.

8. The method according to claim 7, characterized in that, The method further includes: Collect historical circulation data of the recipe code, and optimize and adjust the circulation rules in the time series model based on the historical circulation data; The parameters of the time series model are dynamically updated to adapt the time series model to changes in the actual production environment.

9. A data chain recording application platform based on the entire plant growth cycle, characterized in that, The platform enables the method as described in any one of claims 1 to 8 to be performed.

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