Collection and production integrated automatic control method and system based on batch management

Abstract

The invention relates to the technical field of fuel intelligent management and control, and particularly discloses a sampling and processing integrated automatic control method and system based on batch management. The method comprises the following steps: firstly, creating a full-process dynamic data file with a unique identifier ID for each batch of materials; in the sampling link, a sample is bound with the intelligent container, and the multi-dimensional identity features are associated; in the sample processing process, operating parameters are collected in real time and compared with a preset standard, and the process confidence coefficient of the data file is dynamically calculated and updated; during sample circulation, bidirectional ID verification is carried out through a handshake request, decision making is carried out in combination with the process confidence coefficient, and subsequent equipment is authorized to be started only when verification is passed and the confidence coefficient is higher than a safety threshold value; when the sample is segmented, the data file is also split and completely inherits historical information. Through active inquiry and dynamic evaluation, forced consistency of physical samples and digital information in the whole process is realized, and the reliability of quality detection results is ensured.

Description

Technical Field

[0001] This invention relates to the field of intelligent fuel management and control technology, and in particular to an integrated automatic control method and system for sampling, preparation and analysis based on batch management. Background Technology

[0002] In industries such as coal-fired power generation and coal chemical engineering, intelligent fuel management is the cornerstone for achieving cost reduction, efficiency improvement, and safe production. As the source of an intelligent fuel management system, the importance of accurate and reliable sampling, processing, and chemical quality inspection of incoming coal is self-evident.

[0003] Currently, coal sample management in the industry primarily relies on technologies such as barcodes or RFID for process tracking. However, these systems generally suffer from a fundamental flaw: they are essentially passive, one-way information following mechanisms. The systems assume that the physical operations at the site are correct, and their function is limited to post-event recording of completed operations, lacking the ability to intervene during the process. In complex environments with multiple coal types, multiple batches, and fast-paced overlapping operations, if physical operational errors such as incorrect delivery, incorrect collection, or mislabeling of coal samples occur, existing systems not only fail to prevent them in time but also record erroneous information, resulting in a serious discrepancy between the physical coal sample and its digital identity, creating "dirty data." This information contamination can directly lead to economic losses in fuel pricing and settlement, and may mislead boiler blending due to erroneous coal quality data, potentially causing production safety accidents.

[0004] Furthermore, existing technologies have extremely limited utilization of sample preparation process data. Key process parameters such as particle size and drying temperature are typically recorded simply as historical information and are not effectively used to assess operational quality and sample reliability. If equipment failure or parameter drift causes sample preparation to deviate from the procedure, the system cannot provide early warnings of potential quality risks.

[0005] Therefore, the field of intelligent fuel management urgently needs a novel technological solution to change the passive information recording model and establish an intelligent closed-loop control system capable of proactive verification, early warning, and in-process intervention. This system must ensure that both the physical coal sample and its digital information undergo mandatory verification at every stage of the entire process, and deeply utilize process data to guarantee operational reliability, thereby eliminating batch contamination and process anomaly risks, and truly achieving end-to-end authenticity and data reliability in the sampling, preparation, and analysis stages. Summary of the Invention

[0006] To overcome the existing problems and shortcomings, this invention proposes an integrated automatic control method for sampling, preparation, and analysis based on batch management, characterized by the following steps:

[0007] Step S1: Obtain the coal batch information to create a preliminary full-process dynamic data file with a unique identifier ID, and match and activate the data file through a physical identity recognition device to bind it to the physical ID of the transport vehicle;

[0008] Step S2: Authorize sampling according to the activated data archive, and put the collected samples into a smart sample collection container with a unique identifier. At the same time, associate and bind the container's identifier, device code, and sampling time as a set of identity features with the identifier ID of the data archive.

[0009] Step S3: During sample processing, real-time operating parameters are appended to the corresponding data file, and the real-time operating parameters are compared with the preset standard process characteristic parameter library to dynamically calculate and update the process confidence of the data file;

[0010] Step S4: When the sample flows from the source stage to the target stage, the target stage device initiates a handshake request and performs bidirectional verification of the data file identifier ID involved. Only when the identifier ID verification is consistent and the process confidence of the data file is higher than the preset security threshold, the target stage device is authorized to start and update the data file status.

[0011] Step S5: When a sample is divided into subsamples, create a subdata archive that inherits all historical information and current process confidence from the parent data archive.

[0012] Furthermore, in step S1, the physical identity recognition device includes a license plate recognition device, a ship automatic identification system signal receiver, or an RFID reader.

[0013] Furthermore, in step S2, the identity feature set also includes environmental sensor data from the sampling points.

[0014] Furthermore, in step S4, when the two-way verification of the identifier ID is inconsistent or the process confidence level is lower than the preset security threshold, the system immediately performs a locking operation, prohibits the target link device from starting, and triggers an alarm.

[0015] Furthermore, in step S3, the standard process characteristic parameter library is established based on material types and historical data, and the process confidence is calculated based on the degree of deviation between the real-time operating parameters and the corresponding parameters in the standard library.

[0016] Furthermore, the process confidence level is divided into at least three levels: high, medium, and low, corresponding to three different control strategies: automatic release, early warning prompt, and forced locking, respectively.

[0017] Furthermore, the handshake request in step S4 is initiated by the device controller of the target segment to the central data management and control platform after recognizing the identity of the smart sample collection container.

[0018] Furthermore, the two-way verification is performed by the central data management platform, and its verification content includes: confirming that the identity of the sample collection container is bound to a data file identifier ID, confirming that the status of the data file is pending processing, and confirming that the equipment in the target stage has been assigned the task of processing the data file identifier ID.

[0019] Furthermore, in step S5, while creating a sub-data file for the sub-sample, a unique identifier associated with the sub-data file is affixed to the sub-sample container using an automatic labeling machine.

[0020] An integrated automatic control system for sampling, preparation, and analysis based on batch management includes:

[0021] The central data management and control platform is used to create, store, and manage dynamic data archives throughout the entire process, and has a built-in standard process characteristic parameter library based on historical data.

[0022] The physical identity recognition and activation module is used to identify the physical ID of the means of transport and activate the corresponding data file;

[0023] The intelligent sampling and multidimensional binding module is used to perform sampling and bind a set of multidimensional identity features to the sample;

[0024] The process data analysis and confidence assessment module is used to receive real-time operating parameters during sample processing, compare them with the standard process characteristic parameter library, and dynamically calculate and update the process confidence of the data archive.

[0025] The process flow control module is used to perform a handshake protocol for bidirectional ID verification during the handover of processes, and, in conjunction with the confidence level output by the process confidence assessment module, ultimately decide whether to authorize passage, issue a warning, or execute a lock and alarm.

[0026] The beneficial effects of this invention are:

[0027] This invention transforms the traditional passive and error-prone information recording mode into an intelligent management and control mode that proactively inquires, provides early warnings, and intervenes in the process by constructing a closed-loop control mechanism based on two-way verification and dynamic evaluation. By establishing a mandatory handshake protocol of "verification first, operation later" at the handover of each link, the risk of sample batch confusion caused by physical operation errors is eliminated from the mechanism.

[0028] This invention introduces process confidence level as a key criterion for process authorization, establishing a dynamic assessment capability for the quality of the operation process. By comparing real-time operation parameters of sample preparation and other steps with a standard feature library, the system achieves a shift from "passive traceability" to "proactive early warning." When process parameters deviate from the standard, the system does not simply record them, but proactively quantifies the risk of the operation and adopts differentiated control strategies such as automatic release, early warning prompts, or forced locking based on the preset confidence level, effectively improving the quality assurance level of the entire sample life cycle.

[0029] This invention also constructs a dynamically evolving and inheritable full-lifecycle digital archive for each batch of coal samples. This archive not only integrates identity transfer information but also continuously overlays environmental data, equipment codes, and real-time process parameters from each stage. When samples are segmented, all sub-samples can completely inherit all historical information from their parent archives, achieving unprecedented information transparency and deep traceability. This provides a solid and reliable data foundation for enterprises to achieve refined quality management, accurate fault tracing, and in-depth big data-based analysis applications. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0032] The present application will be described below with reference to specific embodiments:

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0034] Example 1:

[0035] Step S1: Pre-create and activate the full-process dynamic data archive

[0036] The core of this step is to first obtain the coal batch information to create a preliminary full-process dynamic data file with a unique identifier ID, and then match and activate the file through a physical identity recognition device and bind it to the transportation vehicle carrying the material with a physical ID.

[0037] In practice, when a batch of fuel plans for "low-sulfur thermal coal" is generated, the central data management platform in this system will proactively or passively obtain the business information for that batch from upper-level management systems such as the Enterprise Resource Planning (ERP) system. This information includes not only the material type but may also cover key data such as the supplier, contract number, and planned arrival time. After receiving this information, the platform immediately generates a new record in the database, namely, a preparatory full-process dynamic data archive, and assigns it a globally unique identifier ID, such as "Twin-ID-001". At this time, the internal status of this data archive is marked as "pending activation" or "pending arrival," and it awaits events in the on-site operation process to trigger subsequent processes.

[0038] When the transport vehicle (such as a truck or ship) carrying the batch of coal arrives at the designated operating area at the plant entrance, the physical identification device deployed in that area automatically captures its Physical ID2. Depending on the type of transport vehicle, different physical identification devices may be used, such as license plate recognition devices or Automatic Identification System (AIS) signal receivers for ships. This Physical ID is reported to the central data management platform. The platform immediately executes a matching and verification procedure: it retrieves and compares the received Physical ID with the batch information in all pre-registered data archives in a "pending activation" state.

[0039] Once the physical ID is confirmed to match the vehicle information recorded in the "Twin-ID-001" data file, the platform immediately activates the data file. The activation process includes two key actions: first, updating the internal status of the data file from "pending activation" to "pending sampling," signifying that the batch of fuel has actually arrived at the plant and the process has officially begun; second, creating a strong binding relationship in the data file, associating the captured vehicle physical ID with "Twin-ID-001," ensuring that a one-to-one correspondence is established between the file in the central data management platform and the carrier entity in the on-site operation process at the source of the process.

[0040] Step S2: Authorization Sampling and Binding of Multidimensional Identity Features

[0041] The core of this step is that the system first authorizes sampling based on the activated data archive, and after sampling, the collected samples are put into a smart sample collection container with a unique identifier. At the same time, the container's identifier, device code, sampling time and other information are used as a set of identity features and associated with the identifier ID of the data archive.

[0042] In practice, once the status of data file "Twin-ID-001" changes to "Pending Sampling," the central data management platform not only issues a start command to the designated vehicle sampling machine controller but also simultaneously sends the operational formula parameters associated with that batch of material, "low-sulfur thermal coal." This formula may include standard sampling depth, quincunx sampling point layout strategy, and minimum sampling quality requirements. This dual-delivery mechanism of authorization and formula ensures that the sampling equipment is not only authorized to operate but also operates according to the correct procedures.

[0043] Upon receiving instructions and the formula, the vehicle sampling machine begins sampling. Once completed, the sample is automatically transferred and placed into a smart sampling container. This container possesses a globally unique identifier, such as "Container-ID-A," due to its built-in RFID chip or a surface-attached QR code. At this point, the system performs a crucial multi-dimensional identity binding operation, specifically:

[0044] Once the sample collection container is in place and confirmed to be fully loaded, the sampler controller automatically aggregates information from four dimensions, including the container identity "Container-ID-A", its own preset device code "Sampler-01", the current precise timestamp obtained by calling the system clock, and real-time environmental snapshots read from field sensors, such as temperature and humidity.

[0045] The controller packages this dataset, containing information about the container, device, time, and environment, and reports it along with the task ID "Twin-ID-001" to the central data management platform. Upon receiving the data, the platform performs an association binding, completely recording this initial identity data set in a dedicated field of the "Twin-ID-001" data file. This operation establishes an initial association information set for the physical sample, including container identification, device code, precise time, and optional environmental parameters. This information set is firmly bound to the unique ID of the data file, providing a highly reliable data foundation for process traceability and data verification in all subsequent stages.

[0046] Step S3: Process parameter comparison and dynamic confidence assessment

[0047] This step aims to quantitatively assess process standardization. Its core objective is to ensure that the sample strictly adheres to pre-defined standard process procedures at every stage of its processing. To achieve this, the system continuously compares real-time collected operational parameters with a standard process characteristic parameter library at high frequency. Based on the comparison results, it dynamically calculates and updates the process confidence level of the data archive using a specific algorithm. This confidence level directly reflects the degree of consistency between the operations performed on the sample in the on-site workflow and the standard procedures specified in the central data management platform. A high confidence level means that it can be highly believed that the sample has been "correctly processed."

[0048] Taking the "Twin-ID-001" low-sulfur thermal coal sample as an example, once it moves from the sampling stage to the sample preparation stage, the data archive status is updated to "sample preparation in progress." The sample preparation machine begins performing a series of operations on the sample, including crushing, reducing, and drying. At this time, the process data analysis and confidence assessment module deployed on the sample preparation machine controller or edge computing node continuously collects a complete set of key performance indicators, such as the motor current of the crusher, the vibration frequency of the crushing chamber, the real-time temperature curve in the drying chamber, and the duration of the drying process.

[0049] At the same time, the module will retrieve a process parameter model specifically customized for the material category of "low-sulfur thermal coal" from the "Standard Process Characteristic Parameter Library" built into the central data management platform. This model not only includes simple upper and lower limit ranges, such as the drying temperature should be (105±2)℃, but may also include more complex process curve templates, such as the slope of the standard heating curve and the standard deviation of temperature fluctuation in the isothermal stage.

[0050] The calculation and updating of confidence levels is an iterative process. Initial confidence level. Set to 100%. In the... At each time point, the module collects a set of real-time parameters. and with standard parameter model Perform a comparison. Confidence level. The update can be performed using a weighted decay factor algorithm. First, the deviation of each parameter at the current time is calculated. For range-type parameters, the deviation can be calculated as follows:

[0051]

[0052] in, It is a parameter exist Real-time value at any given moment. and These are the midpoint and upper limit values ​​of its standard range, respectively.

[0053] Based on the importance weight of each parameter Calculate the overall deviation at the current moment. Finally, the confidence level of the update process:

[0054]

[0055] in, It is an adjustable attenuation coefficient used to control the impact of a single deviation on the overall confidence level. This algorithm ensures that any small process deviation will lead to a quantitative decrease in confidence level, and that continuous deviations will cause a cumulative decrease in confidence level.

[0056] To achieve more intelligent evaluation, this invention can introduce machine learning-based anomaly detection algorithms, such as Isolation Forest or Autoencoder. The system can train the model using historical "perfect" process data, enabling it to learn the inherent patterns of standard operation. In real-time monitoring, the collected multidimensional process parameters are input as a vector into the trained model. The model outputs an anomaly score, which more accurately and sensitively reflects the deviation of the current operating state from the ideal state and is directly used to adjust the process confidence. This method is particularly adept at capturing complex, non-linear correlations between multiple parameters, which is difficult to achieve with traditional threshold-based comparison methods.

[0057] Based on the calculated process confidence value, the system will classify it into at least three levels and trigger different control strategies. This embodiment uses the following settings:

[0058] High confidence level (95%-100%): The process fully complies with the standards, the system automatically records and releases the process, and no manual intervention is required.

[0059] Medium confidence level (80%-95%): When a slight or brief parameter deviation occurs, the system sends an early warning to the central monitoring room to alert management personnel, but the process continues.

[0060] Low confidence level (<80%): When a serious or persistent parameter deviation occurs, the system determines that the process is no longer trustworthy, immediately performs a forced lockout operation, suspends the current equipment operation, triggers the highest level of audible and visual alarm, and marks the data file as "abnormal" for manual review and intervention.

[0061] This step transforms the originally vague process management into a highly transparent, quantifiable, and proactively intervened closed-loop control system by combining algorithms, AI models, and a confidence assessment mechanism for hierarchical control strategies.

[0062] Step S4: Closed-loop flow control based on "handshake protocol" and confidence level

[0063] This step is the core mechanism to ensure the safe and correct transfer of samples between each stage. It combines identity verification with process quality assessment results to form a closed-loop decision-making and control process. When a smart sample collection container carrying a sample moves from the source stage equipment to the target stage equipment, such as from the sample preparation machine to the automated packaging machine, the system does not assume that the handover is valid by default. Instead, it executes a strict handshake protocol of "active challenge - two-way verification - confidence check".

[0064] Taking the "Twin-ID-001" data file generated in the previous steps as an example, after it completes the sample preparation process, the process confidence level is updated to 88%, and its status is marked as "pending packaging" by the system. The intelligent sample collection container "Container-ID-A" containing the sample arrives at the entrance of the automatic packaging machine via the conveyor line. At this time, the packaging machine controller, as the target device, scans the container identifier "Container-ID-A" with its integrated RFID reader, but does not start the operation immediately. Instead, it uses this identifier as evidence to initiate a "handshake request" to the central data management platform.

[0065] Upon receiving a request, the central data management platform immediately performs a two-way verification, which includes at least three levels of confirmation:

[0066] Identity verification: The platform verifies whether “Container-ID-A” is bound to a valid data archive identifier ID. In this example, it is confirmed to be “Twin-ID-001”.

[0067] Status Confirmation: The platform checks whether the current status of the "Twin-ID-001" data file is "Pending Packaging" to prevent process jumps or errors.

[0068] Task Confirmation: The platform confirms whether the automated packaging machine that initiated the request has been pre-assigned a task to process the "Twin-ID-001" file, thereby preventing the sample from being sent to the wrong device.

[0069] After all three ID verifications pass, the system does not directly authorize device startup but instead proceeds to process confidence assessment. The process flow control module reads the final process confidence value (88%) recorded in the "Twin-ID-001" data file and compares it with a preset safety threshold. Assuming the safety threshold is set to 80%, since 88% is higher than 80%, the system determines that while the sample processing has minor flaws, it is generally reliable and meets the minimum quality requirements for proceeding to the next stage. Therefore, the central data management platform sends an "authorize startup" command to the automatic packaging machine controller and updates the status of the "Twin-ID-001" data file to "packaging in progress." Simultaneously, because the confidence level is not 100%, the system will automatically generate a medium-level warning record in the log according to preset rules for subsequent review by quality management personnel.

[0070] Step S5: Sample Segmentation and Sub-Data Archive Information Inheritance

[0071] This step aims to address the question of how to ensure that each subsample has an independent, traceable identity file in the central data management platform, inheriting complete historical information, when a physical sample is divided into multiple parts.

[0072] Continuing with the "Twin-ID-001" data file as an example, after passing the closed-loop flow control in step S4, the sample is sent to the automated sample packaging equipment, with a process confidence level of 88% and a status of "packaging in progress". According to the preset testing procedures, this prepared sample needs to be precisely divided into three subsamples: one "test sample" for immediate submission, and two "reservation samples" for future review or arbitration.

[0073] After the automated sample packaging equipment completes the physical segmentation operation, its controller sends a "segmentation complete" signal to the central data management platform. This signal includes the number of segments; the aforementioned "Twin-ID-001" data is currently segmented into 3 parts. Upon receiving the signal, the central data management platform immediately triggers the data file splitting mechanism.

[0074] The platform first locks the status of the parent data file "Twin-ID-001" to "segmented and archived," marking the end of its physical entity's lifecycle. Next, the platform creates three entirely new child data files for this parent file and assigns each a unique child identifier ID with a discernible inheritance relationship, such as "Sub-ID-001-A," "Sub-ID-001-B," and "Sub-ID-001-C."

[0075] These three newly created sub-data archives will completely and losslessly copy all historical information of their parent archive "Twin-ID-001" since its creation, including but not limited to:

[0076] Original batch information: supplier, coal type "low-sulfur thermal coal", vehicle license plate number, etc.

[0077] The entire process identity feature set includes: the physical ID of the means of transport, the smart sampling container ID "Container-ID-A" bound during sampling, the sampler code "Sampler-01", the sampling timestamp, etc.

[0078] Complete process parameter records: All real-time operating parameters recorded during the sample preparation stage, such as crusher motor current and drying temperature curve.

[0079] Final process confidence level: Inheriting the final evaluation result of the parent file, i.e., 88%.

[0080] After the inheritance is completed, the automatic labeling machine, according to the instructions issued by the central data management platform, prints out a QR code or RFID tag containing the corresponding sub-data file identifier ID (such as "Sub-ID-001-A"), and accurately affixes it to the three newly generated sub-sample containers. At this point, the three sub-samples in the field operation process correspond one-to-one with the three sub-data files in the central data management platform. Any sub-sample, whether on the analyzer in the laboratory or on the shelf in the storage warehouse, can instantly trace its complete "past and present" by scanning its identifier, revealing which vehicle it came from, when it was collected, what processing procedures it underwent, and the results of the compliance assessment of that process.

[0081] Example 2:

[0082] This embodiment describes an integrated automatic control system for sampling, preparation, and analysis based on batch management. This system is deployed within the fuel management system of a smart coal-fired power plant to implement the automatic control method described in Embodiment 1. The specific structure and collaborative operation of the system are as follows:

[0083] This system mainly includes a central data management and control platform, a physical identity recognition and activation module, an intelligent sampling and multi-dimensional binding module, a process data analysis and confidence assessment module, and a process flow control module.

[0084] Central Data Management and Control Platform: This platform serves as the central hub of the entire system and is deployed as a server cluster or cloud platform. It is responsible for creating, storing, and managing dynamic data archives for all batches throughout the entire process. The platform pre-builds a standard process characteristic parameter library based on historical big data analysis. This library defines standard parameter ranges or curve templates for different coal types under different processes such as crushing and drying.

[0085] Physical Identity Recognition and Activation Module: This module consists of hardware devices deployed at the factory entrance and corresponding software interfaces. In this embodiment, it is an automatic license plate recognition system. When a coal truck enters, the module's camera captures the license plate image, identifies the physical ID, such as license plate information, and transmits it to the central data management platform to activate the pre-created data file "Twin-ID-001," achieving the initial binding between the data file and the transport entity.

[0086] Intelligent Sampling and Multidimensional Binding Module: This module consists of a fully automated vehicle sampling machine and an intelligent sample collection container with a built-in RFID chip. Upon receiving authorization from the central data management platform, the sampling machine samples the designated vehicle. After sampling, the sample is placed into an intelligent sample collection container with an RFID tag "Container-ID-A". At this point, the module's controller collects the sampling machine's own device code "Sampler-01", the current timestamp, and the container ID "Container-ID-A", packages this set of identification features, and uploads it to the central data management platform, where it is associated and bound with the data file "Twin-ID-001".

[0087] Process data analysis and confidence assessment module: This module is typically deployed as embedded software within the edge computing gateway or its host computer of each stage of the processing equipment (such as the sample preparation machine). During the sample preparation stage, sensors on the sample preparation machine continuously send real-time operating parameters to this module. This module immediately calls upon the standard process characteristic parameter library in the central data management platform for high-frequency comparison and dynamically calculates the process confidence level based on a preset algorithm. For example, when a deviation of 3°C from the standard drying temperature is detected, this module updates the confidence level of the "Twin-ID-001" file from 98% to 88%.

[0088] Process Flow Control Module: This module is the decision-making and execution unit that ensures the correct flow of the process. When the container containing the sample, "Container-ID-A," reaches the next stage, such as when the sample is completed and enters the automatic packaging machine for packaging, the module is triggered after the RFID reader at the packaging machine's entrance identifies the ID. It first initiates a "handshake request" to the central data management platform, which performs strict two-way ID verification. After successful verification, the module further checks the latest confidence level calculated by the process data analysis and confidence level assessment module. If this value is higher than the preset 80% safety threshold, the process flow control module finally issues an "authorized start" command to the packaging machine. If the confidence level is lower than the threshold, the module will perform a locking operation, preventing the equipment from starting and triggering an alarm.

[0089] Through the close integration and collaborative work of the above modules, this system connects independent devices into an intelligent whole, and achieves proactive and closed-loop control of the entire sampling, preparation and processing process through identity verification based on data archives and confidence assessment based on process data.

[0090] It should be noted that the above embodiments can be combined and adjusted as needed. Improvements and modifications can be made without departing from the principles of the present invention, and all such improvements and modifications are considered to be within the scope of protection of the present invention. The embodiments are described in a progressive manner; for any identical or similar parts not covered herein, please refer to each other.

[0091] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

Claims

1. An integrated automatic control method for sampling, preparation, and analysis based on batch management, characterized in that, Includes the following steps: Step S1: Obtain the coal batch information to create a preliminary full-process dynamic data file with a unique identifier ID, and match and activate the data file through a physical identity recognition device to bind it to the physical ID of the transport vehicle; Step S2: Authorize sampling according to the activated data archive, and put the collected samples into a smart sample collection container with a unique identifier. At the same time, associate and bind the container's identifier, device code, and sampling time as a set of identity features with the identifier ID of the data archive. Step S3: During sample processing, real-time operating parameters are appended to the corresponding data file, and the real-time operating parameters are compared with the preset standard process characteristic parameter library to dynamically calculate and update the process confidence of the data file; Step S4: When the sample flows from the source stage to the target stage, the target stage device initiates a handshake request and performs bidirectional verification of the data file identifier ID involved. Only when the identifier ID verification is consistent and the process confidence of the data file is higher than the preset security threshold, the target stage device is authorized to start and update the data file status. Step S5: When a sample is divided into subsamples, create a subdata archive that inherits all historical information and current process confidence from the parent data archive.

2. The method according to claim 1, characterized in that, In step S1, the physical identity recognition device includes a license plate recognition device, a ship automatic identification system signal receiver, or an RFID reader.

3. The method according to claim 1, characterized in that, In step S2, the identity feature set also includes environmental sensor data from sampling points.

4. The method according to claim 1, characterized in that, In step S4, when the two-way verification of the identifier ID is inconsistent or the process confidence level is lower than the preset security threshold, the system immediately performs a locking operation, prohibits the target link device from starting, and triggers an alarm.

5. The method according to claim 1, characterized in that, In step S3, the standard process characteristic parameter library is established based on material types and historical data, and the process confidence is calculated based on the degree of deviation between the real-time operating parameters and the corresponding parameters in the standard library.

6. The method according to claim 5, characterized in that, The process confidence level is divided into at least three levels: high, medium, and low, corresponding to three different control strategies: automatic release, early warning prompt, and forced locking, respectively.

7. The method according to claim 1, characterized in that, The handshake request in step S4 is initiated by the device controller of the target segment after recognizing the identity of the smart sample collection container and sending it to the central data management and control platform.

8. The method according to claim 7, characterized in that, The two-way verification is performed by the central data management platform. The verification content includes: confirming that the identity of the sample collection container is bound to a data file identifier ID, confirming that the status of the data file is pending processing, and confirming that the equipment in the target process has been assigned the task of processing the data file identifier ID.

9. The method according to claim 1, characterized in that, In step S5, while creating a sub-data file for the sub-sample, a unique identifier associated with the sub-data file is affixed to the sub-sample container using an automatic labeling machine.

10. An integrated automatic control system for sampling, preparation, and analysis based on batch management, characterized in that, include: The central data management and control platform is used to create, store, and manage dynamic data archives throughout the entire process, and has a built-in standard process characteristic parameter library based on historical data. The physical identity recognition and activation module is used to identify the physical ID of the means of transport and activate the corresponding data file; The intelligent sampling and multidimensional binding module is used to perform sampling and bind a set of multidimensional identity features to the sample; The process data analysis and confidence assessment module is used to receive real-time operating parameters during sample processing, compare them with the standard process characteristic parameter library, and dynamically calculate and update the process confidence of the data archive. The process flow control module is used to perform a handshake protocol for bidirectional ID verification during the handover of processes, and, in conjunction with the confidence level output by the process confidence assessment module, ultimately decide whether to authorize passage, issue a warning, or execute a lock and alarm.

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