Drill core product manufacturing process control method and system based on global collaborative scheduling

Abstract

The present application relates to the cross field of core drilling product manufacturing, industrial automation control and digital twin technology, and provides a core drilling product manufacturing process control method and system based on global collaborative scheduling. In order to solve the technical problem that the process characteristics in the core drilling product manufacturing process cannot be continuously anchored, transferred across links and inherited across batches in the whole manufacturing chain, by fusing the geometric characteristics, material characteristics and functional characteristics of the core drilling product, a feature anchoring carrier containing process constraint conditions is generated; the feature anchoring carrier is used as collaborative signaling for real-time read-write access by data perception, digital modeling, process deduction, production line execution and quality control detection links, so that each link executes its own operation with the feature anchoring carrier as a unified constraint reference, thereby realizing global collaborative scheduling based on the feature anchoring carrier, realizing continuous anchoring and cross-batch inheritance of the process characteristics of the core drilling product in the whole manufacturing process, and significantly improving the process consistency and quality control level of the core drilling product manufacturing.

Description

Technical Field

[0001] This invention relates to the intersection of core drilling product manufacturing, industrial automation control, and digital twin technology, and particularly to process feature anchoring and global collaborative scheduling technology in the core drilling product manufacturing process. Specifically, it relates to a core drilling product manufacturing process control method and system based on global collaborative scheduling. Background Technology

[0002] Core drilling product manufacturing process control is a technical process that unifies and dynamically optimizes data acquisition, process parameter setting, production line scheduling, and quality control testing involved in the manufacturing of core drilling products (including diamond active oxygen atomizer cores, oral irrigator cores, sterilizer cores, etc.). It is a key supporting technology for the production of core functional components in the big health manufacturing field. With the deepening of industrial digital transformation, the market has placed extremely high demands on the consistency of processes, intelligent processes, data traceability, and production line adaptability in core drilling product manufacturing. It requires that the manufacturing process can achieve full traceability, optimization, and collaboration from process input to finished product delivery.

[0003] In traditional technology, the manufacturing process control technology for core drilling products has undergone three generations of iteration: The first generation is the traditional manual control mode, which relies on the collaboration of multiple positions such as process engineers, quality control personnel, and production line schedulers. It involves recording parameters through process cards, manually entering equipment data, and offline quality control testing, which is cumbersome and slow to adjust. The second generation is the single AI-assisted control mode, which uses discrete AI tools to automate single links, such as equipment data acquisition systems, process parameter analysis software, and production line scheduling dashboards. This is a shallow replacement of manual processes. The third generation is the basic digital and intelligent integrated control mode, which integrates IoT sensors and data analysis technology, and simply connects various modules, such as building a digital twin visualization interface and deploying a production execution system (MES) for data collection and display.

[0004] However, all of the aforementioned traditional technologies suffer from a fundamental technical flaw: the consistency of core drilling products cannot be guaranteed in the long term. Problems such as core size deviations, uneven diamond coatings, and substandard core performance are easily encountered when producing across batches or production lines. Even manual rework cannot guarantee consistent overall quality control. The core reason for this flaw is that the process characteristics of core drilling products in traditional technologies cannot be continuously and effectively transmitted throughout the entire manufacturing process. They are only manually input once during the initial parameter setting stage and cannot be effectively and continuously transmitted to various stages of the production line. Specifically, traditional technologies treat process characteristics as a static set of initial parameters. Data acquisition, digital modeling, process simulation, production line execution, and quality control testing operate independently, mechanically spliced ​​together only through simple signal transmission or data interfaces. Process characteristics cannot be continuously anchored, effectively transmitted, verified, and inherited throughout the entire chain of "data acquisition → digital modeling → process simulation → production line execution → quality control testing." When equipment status fluctuates, environmental conditions change, or deviations occur in process connections, any anomaly in any link cannot be traced back to the original process characteristics for closed-loop correction, leading to a "out-of-control" state after process parameters are set. Long-term technological improvements in this field have focused on optimizing the accuracy of initial parameter settings or increasing data acquisition dimensions, attempting to approximate the ideal process through more precise initial parameters or more comprehensive data recording. However, they have never recognized the fundamental contradiction that "process characteristics need to be continuously anchored throughout the entire process as a dynamic carrier," resulting in the technical dilemma that "the more the initial parameters are optimized, the less able it is to cope with dynamic changes in the production line."

[0005] Therefore, how to ensure that the process characteristics of core drilling products are continuously anchored, transferred across stages, and inherited across batches throughout the entire manufacturing process, and fundamentally solve the technical bottleneck of "loss of control after parameter setting", has become a fundamental technical problem that urgently needs to be solved in the field of core drilling product manufacturing process control. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method and system for controlling the core drilling product manufacturing process based on global collaborative scheduling. By constructing a feature anchoring carrier and continuously transmitting, verifying, and updating it throughout the entire process, the invention achieves a leap from "static initial parameters" to "dynamic anchoring carriers" for process features. This differs from the static mapping mode in traditional technologies where process features are "lost" after being input only in the initial stage. This invention enables global collaboration and cross-batch consistency control of the core drilling product manufacturing process, allowing the process features of core drilling products to be continuously anchored and inherited across batches throughout the entire manufacturing process, significantly improving the process consistency and quality control level of core drilling product manufacturing.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a method for controlling the manufacturing process of core drilling products based on global collaborative scheduling, comprising: acquiring the geometric features, material features, and functional features of the core drilling product; fusing the geometric features, material features, and functional features through a feature anchoring algorithm to generate a feature anchoring carrier, wherein the feature anchoring carrier contains the process constraints of the core drilling product; deploying the feature anchoring carrier as a collaborative signaling to a shared storage space for real-time read / write access by the data sensing stage, digital modeling stage, process simulation stage, production line execution stage, and quality control inspection stage of the manufacturing process, enabling each stage to execute its respective operation using the feature anchoring carrier as a unified constraint benchmark; in the data sensing stage, collecting the operating data of the manufacturing process in real time, comparing the operating data with the process constraints, and when the operating data exceeds the process constraints... When conditions are met, an early warning signal is generated and written back to the feature anchoring carrier; the digital modeling stage uses the process constraints as boundary constraints to construct a digital twin model of the core drilling product; the process deduction stage deduces the process connection scheme and process parameter scheme within the feasible domain defined by the feature anchoring carrier, generates a production process optimization scheme that meets the process constraints, and writes the production process optimization scheme back to the feature anchoring carrier; the production line execution stage reads the production process optimization scheme from the feature anchoring carrier, dynamically adjusts the production line equipment operating parameters according to the production process optimization scheme, and transmits real-time data during the execution process back to the feature anchoring carrier for continuous verification; the quality control and inspection stage collects finished product inspection data, compares the finished product inspection data with the process constraints, and maintains or updates the feature anchoring carrier based on the comparison results.

[0008] This invention also provides a core drilling product manufacturing process control system based on global collaborative scheduling, comprising: a first generation module, used to acquire the geometric features, material features, and functional features of the core drilling product, and to fuse the geometric features, material features, and functional features through a feature anchoring algorithm to generate a feature anchoring carrier, wherein the feature anchoring carrier contains the process constraints of the core drilling product; a first sharing module, used to deploy the feature anchoring carrier as a collaborative signal to a shared storage space, for real-time read and write access by the data sensing stage, digital modeling stage, process simulation stage, production line execution stage, and quality control inspection stage of the manufacturing process, so that each stage executes its own operation with the feature anchoring carrier as a unified constraint benchmark; and a first comparison module, used in the data sensing stage to collect the running data of the manufacturing process in real time, compare the running data with the process constraints, and generate an early warning signal when the running data exceeds the process constraints. The process is as follows: The process is written back to the feature anchoring carrier; a first construction module is used in the digital modeling stage to construct a digital twin model of the core drilling product using the process constraints as boundary constraints; a first deduction module is used in the process deduction stage to deduce the process connection scheme and process parameter scheme within the feasible domain defined by the feature anchoring carrier, generate a production process optimization scheme that meets the process constraints, and write the production process optimization scheme back to the feature anchoring carrier; a first control module is used in the production line execution stage to read the production process optimization scheme from the feature anchoring carrier, dynamically control the production line equipment operating parameters according to the production process optimization scheme, and transmit real-time data during execution back to the feature anchoring carrier for continuous verification; a first detection module is used in the quality control detection stage to collect finished product detection data, compare the finished product detection data with the process constraints, and maintain or update the feature anchoring carrier based on the comparison results.

[0009] Beneficial Effects: The solution implemented in this invention acquires the geometric, material, and functional characteristics of the core drilling product and uses a feature anchoring algorithm to fuse these three types of features into a feature anchoring carrier. This firstly solidifies the multi-dimensional process characteristics of the core drilling product into a unified carrier that can be transferred across stages, transforming process characteristics from "static initial parameters" into "dynamically anchorable objects." This differs from traditional technologies where process characteristics are "lost" after initial input. The feature anchoring carrier serves as a unified carrier of process constraints throughout the entire manufacturing process. Based on this, by deploying the feature anchoring carrier as a collaborative signaling mechanism in a shared storage space, it allows for real-time read / write access by data sensing, digital modeling, process simulation, production line execution, and quality control inspection stages. This achieves unified access and sharing of process constraints across the entire chain, eliminating data silos caused by independent parameter maintenance in each stage. Each stage can then execute its operations using the feature anchoring carrier as the sole constraint benchmark, eliminating the need for repeated manual adjustments.

[0010] Building upon this foundation, the data sensing stage compares real-time collected operational data with process constraints. When limits are exceeded, an early warning signal is generated and written back to the carrier, achieving real-time verification and closed-loop feedback of process constraints. This ensures that abnormal states during manufacturing are captured and recorded instantly, providing a complete data foundation for subsequent traceability analysis. Furthermore, the digital modeling stage constructs a digital twin model of the core drilling product using process constraints as boundary constraints. This ensures that the parameter space of the digital twin model is limited by the process constraint boundaries, guaranteeing that the model construction never deviates from the actual manufacturing capabilities of the core drilling product, avoiding the problem of digital twin models being disconnected from process requirements in traditional technologies. Further, the process deduction stage deduces process connection schemes and process parameter schemes within the feasible domain defined by the feature anchoring carrier, and writes the generated production process optimization scheme back to the carrier, achieving… The process optimization results are stored in a standardized format on a carrier, allowing the optimization scheme to be directly read and used by the production line execution stage, forming a seamless link of "deduction → storage → execution". Furthermore, the production line execution stage reads the optimization scheme from the carrier to dynamically adjust the operating parameters of the production line equipment, and transmits real-time data during execution back to the carrier for continuous verification, forming a real-time closed-loop monitoring mechanism between production line execution and process constraints. This ensures that equipment operating parameters are always under continuous verification of process constraints, and deviations can be detected and recorded immediately. Finally, the quality control and inspection stage collects finished product inspection data and compares it with process constraints. Based on the comparison results, the feature anchoring carrier is maintained or updated, achieving cross-batch process feature inheritance and adaptive correction. This allows subsequent batches to automatically inherit the optimization results of previous batches and adaptively adjust the process constraint center value when inspection results drift.

[0011] By combining the above-mentioned interconnected effects, a four-in-one global collaborative scheduling mechanism of "feature anchoring carrier generation - collaborative signaling distribution - full-link constraint application - closed-loop feedback update" is constructed, which transforms the static open-loop mode of "loss of control after parameter setting" in the traditional core drilling product manufacturing process into a dynamic closed-loop mode of "feature anchoring - constraint transmission - closed-loop verification - cross-batch inheritance".

[0012] Therefore, unlike the static mapping method in traditional technologies where process features are "lost" after being input only in the initial stage, this invention integrates the geometric, material, and functional characteristics of core products into a feature anchoring carrier that can be transferred across stages. This carrier serves as a unified constraint benchmark to drive a collaborative closed loop of data perception, digital modeling, process deduction, production line execution, and quality control inspection. Ultimately, this achieves continuous anchoring and cross-batch inheritance of core product process features throughout the entire manufacturing process. This fundamentally solves the technical bottleneck mentioned in the background technology that "process features in the core product manufacturing process cannot be continuously anchored, transferred across stages, and inherited across batches throughout the entire manufacturing chain," significantly improving the process consistency and quality control level of core product manufacturing. Attached Figure Description

[0013] Figure 1 A flowchart illustrating the core drilling product manufacturing process control method based on global collaborative scheduling provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the first sub-process of the core drilling product manufacturing process control method based on global collaborative scheduling provided in an embodiment of the present invention; Figure 3 This is a schematic block diagram of a core drilling product manufacturing process control system based on global collaborative scheduling, provided in an embodiment of the present invention. Detailed Implementation

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0015] This invention provides a method and system for controlling the manufacturing process of core drilling products based on global collaborative scheduling. The manufacturing process collaborative scheduling engine, which can be hosted on a server, edge computing node, or industrial cloud platform, can drive intelligent manufacturing applications including but not limited to the following multi-dimensional applications: First, in the digital factory of core drilling product manufacturing enterprises, through the real-time transmission and constraint verification of feature anchoring carriers in each production link, the mixed-line production of multiple specifications of products such as diamond active oxygen atomizer cores, dental floss cores, and sterilizer cores can be adaptively scheduled. Process connection and parameter optimization can be completed without manual intervention, and dynamic switching between small-batch customization and large-batch standardized production can be supported.

[0016] Secondly, in the core drilling product supply chain quality traceability platform, by storing process constraints and verification record fields in the feature anchor carrier, a complete process traceability hash chain is generated for each batch of core drilling products, realizing full-process data traceability from raw material entry to finished product exit, and providing data support for quality problem location and process optimization.

[0017] Third, in the digital twin system of the core drilling product manufacturing line, the digital twin model and process simulation engine driven by feature anchoring carrier provide production line managers with reference for process connection schemes and process parameter configurations, assist in evaluating the efficiency and quality balance point under different production strategies, and support the dynamic evaluation and parameter optimization of the production line operation status.

[0018] This invention addresses the technical challenge of continuously anchoring and inheriting process features across batches in the core drilling product manufacturing process. It provides reusable process control capabilities for high-precision and high-consistency production of core functional components in the big health manufacturing field, supporting the manufacturing collaboration and quality traceability needs in the digital transformation of industry.

[0019] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0020] Example 1: A method for controlling the manufacturing process of core drilling products based on global collaborative scheduling.

[0021] Please see Figure 1 , Figure 1 This is a flowchart illustrating the core drilling product manufacturing process control method based on global collaborative scheduling, provided in an embodiment of the present invention. Figure 1 As shown, in this embodiment, taking the manufacturing process of diamond active oxygen atomizer drill cores as an example, the method is carried out by an industrial cloud platform deployed in a drill core product manufacturing enterprise, and includes the following steps S11-S17: S11: Obtain the geometric features, material features, and functional features of the core product. Through a feature anchoring algorithm, fuse the geometric features, material features, and functional features to generate a feature anchoring carrier. The feature anchoring carrier contains the process constraints of the core product.

[0022] Feature anchoring algorithm: This refers to a method that fuses and transforms the geometric, material, and functional features of core drilling products to generate a unified data structure that can be transferred across different stages. The feature anchoring algorithm includes three sub-processes: feature vectorization transformation, feature vector concatenation, and hash encryption. Feature vectorization transformation uses a combination of numerical normalization and encoding mapping to uniformly map geometric dimensions, material parameters, and functional indicators of different dimensions to the [0,1] interval. Feature vector concatenation uses a dimensional concatenation method, joining the three types of feature vectors end-to-end to form a fused feature vector. Hash encryption uses cryptographic hash functions such as SHA-256 or SM3 to irreversibly encrypt the fused feature vector to generate a unique identifier. The specific implementation of the feature anchoring algorithm is not limited to the examples given; any computational method that can fuse multi-dimensional process features into a uniquely identifiable data carrier falls within the scope of this invention.

[0023] Feature anchoring carrier: Represents a data object containing the process constraints of the core product, used to continuously anchor, transfer, and inherit the process characteristics of the core product throughout the entire manufacturing process. Feature anchoring carriers include, but are not limited to, the following fields: carrier identifier field; geometric constraint field; material constraint field; functional constraint field; and constraint condition field. Feature anchoring carriers can be serialized and stored using structured data formats such as JSON or Protocol Buffers to ensure they are parsable and transferable between heterogeneous systems at various stages of the manufacturing process.

[0024] The underlying principle of this step is that the consistency of the manufacturing process of core drilling products depends on the coordinated control of their geometric dimensions, coating materials, and disinfection functions. The core of this step is to integrate these three types of feature data, which were originally stored in different systems (such as geometric parameters in the product data management system, material parameters in the process design system, and functional parameters in the quality testing system), into a unified data object—the feature anchoring carrier—through a feature anchoring algorithm. This carrier serves as the sole carrier of process constraints throughout the entire manufacturing process, transforming process features from "static initial parameters" into "dynamically anchorable objects," thus providing a unified constraint benchmark for the coordinated operation of each stage.

[0025] This step is implemented as follows: First, when the production of the core product is started, in response to the production start command, the geometric features, material features and functional features of the current batch of core products are read from the manufacturing execution system or product data management system.

[0026] Secondly, the system invokes the feature anchoring algorithm to fuse the above three types of features. Specifically: The geometric features are converted into a first feature vector. For example, following the previous example, for the manufacturing of diamond active oxygen atomizing machine drill cores, for a drill core product with an outer diameter of 3.5mm, an inner diameter of 2.0mm, a height of 5.0mm, and a form and position tolerance range of 0.005mm, the system normalizes each value to form a first feature vector [0.35, 0.20, 0.50, 0.005].

[0027] The material characteristics are converted into a second feature vector. For example, following the previous example, for a core drilling product with a coating thickness threshold of 2.5 μm, a coating uniformity tolerance range of ±0.2 μm, and a coating adhesion level of 5, the system maps each value to a second feature vector [0.25, 0.02, 0.50].

[0028] The functional features are converted into a third feature vector. For example, following the previous example, for a core drilling product with an active oxygen disinfection efficiency threshold of 95%, a performance degradation rate threshold of 5%, and a working life threshold of 1000 hours, the system maps each value to a third feature vector [0.95, 0.05, 0.10].

[0029] The first feature vector, the second feature vector, and the third feature vector are concatenated dimensionally to generate a fused feature vector. For example, following the previous example, the fused feature vector is [0.35, 0.20, 0.50, 0.005, 0.25, 0.02, 0.50, 0.95, 0.05, 0.10].

[0030] Third, the system generates corresponding geometric constraints, material constraints, and functional constraints based on geometric features, material features, and functional features. Among them, geometric constraints include, but are not limited to, upper and lower limits of dimensional tolerances, and allowable ranges of form and position tolerances; material constraints include, but are not limited to, target values ​​of coating thickness, allowable ranges of coating thickness, and evaluation thresholds for coating uniformity; functional constraints include, but are not limited to, benchmark values ​​for active oxygen disinfection efficiency and allowable thresholds for performance degradation.

[0031] For example, following the previous example, the system can generate an upper limit of 3.505mm and a lower limit of 3.495mm for the outer diameter of 3.5mm. Based on the coating thickness threshold of 2.5μm, it can generate a target coating thickness of 2.5μm and a tolerance range of ±0.2μm. Based on the active oxygen disinfection efficiency threshold of 95%, it can generate a disinfection efficiency benchmark of 95%.

[0032] Fourth, the system generates process constraints based on geometric constraints, material constraints, and functional constraints, which include the upper and lower limits of each constraint and the allowable fluctuation range around the target value.

[0033] Fifth, the system performs hash encryption on the fused feature vector, using algorithms such as SHA-256 or SM3 to generate a unique encrypted hash value. For example, continuing from the previous example, the fused feature vector [0.35, 0.20, 0.50, 0.005, 0.25, 0.02, 0.50, 0.95, 0.05, 0.10] is hash-encrypted to generate the following encrypted hash value: "0x7F3A8B2C9D1E4F5A6B7C8D9E0F1A2B3C4D5E6F7A8B9C0D1E2F3A4B5C6D7E8F9A".

[0034] Sixth, the system associates and stores encrypted hash values, fused feature vectors, geometric constraints, material constraints, functional constraints, and process constraints as feature anchoring carriers. These feature anchoring carriers can be serialized in JSON format and written to a shared storage space for real-time read and write access by the data perception, digital modeling, process simulation, production line execution, and quality control inspection stages of the manufacturing process.

[0035] In step S11, geometric, material, and functional characteristics of drill core products stored in different systems are acquired and fused using a feature anchoring algorithm to generate a feature anchoring carrier. This encapsulates the multidimensional process characteristics of the drill core products into a unified data carrier that can be transferred across stages and dynamically updated, transforming the process characteristics from "static initial parameters" into "dynamic anchorable objects," thus providing a data foundation for the overall technical solution of claim 1.

[0036] S12: Deploy the feature anchoring carrier as a collaborative signaling to the shared storage space for real-time read and write access by the data perception, digital modeling, process simulation, production line execution, and quality control inspection stages of the manufacturing process, so that each stage can perform its own operation with the feature anchoring carrier as a unified constraint benchmark.

[0037] Collaborative signaling refers to standardized data objects that transmit collaborative instructions and constraint information between various stages of the manufacturing process. In this invention, the feature anchoring carrier serves as the collaborative signaling, carrying the process constraints of the core drilling product. It is transmitted between the data sensing stage, digital modeling stage, process simulation stage, production line execution stage, and quality control inspection stage, enabling each stage to perform operations based on a unified constraint benchmark. Collaborative signaling differs from the static transmission method of "parameter files" or "configuration files" in traditional technologies. Its core lies in supporting real-time read / write access and dynamic updates. Modifications to the collaborative signaling by each stage can be instantly perceived by other stages, thereby achieving closed-loop collaborative control of the manufacturing process.

[0038] Data sensing stage: This refers to the processing node responsible for real-time collection of equipment operation data, environmental condition data, and process execution data during the core drilling product manufacturing process. The data sensing stage uses IoT sensor arrays and industrial data acquisition gateways deployed on the production line to collect equipment operating parameter sequences at a preset sampling frequency. The collected data is compared with process constraints in a feature anchoring carrier. When limits are exceeded, an early warning signal is generated and written back to the carrier, achieving real-time monitoring and anomaly detection of the operating status.

[0039] Digital modeling stage: This refers to the processing node responsible for building a digital twin model of the core drilling product during the manufacturing process. The digital modeling stage uses the process constraints in the feature anchoring carrier as boundary constraints, extracts geometric, material, and functional features, constructs a three-dimensional geometric model, a material property distribution model, and a functional response model of the core drilling product, and generates a digital twin model with adjustable parameters.

[0040] The process deduction stage refers to the processing node responsible for generating process connection schemes and process parameter optimization schemes during the core drilling product manufacturing process. Within the feasible region defined by the feature anchoring carrier, the process deduction stage uses a heuristic search algorithm to deduce the optimal process sequence and equipment parameter combination, generating a production process optimization scheme that meets the process constraints, and then writing the optimized production process scheme back to the feature anchoring carrier.

[0041] Production line execution stage: This refers to the processing node responsible for executing production instructions and controlling equipment operating parameters during the core drilling product manufacturing process. The production line execution stage reads the production process optimization plan from the feature anchoring carrier, sends equipment control instructions to CNC grinding machines, vacuum coating machines, online testing instruments, and other equipment via industrial Ethernet or fieldbus, and transmits real-time data during execution back to the feature anchoring carrier for continuous verification.

[0042] Quality control and inspection: This refers to the processing point in the core drilling product manufacturing process responsible for collecting finished product inspection data and evaluating product quality. The quality control and inspection stage collects finished product inspection data through online testing equipment, compares the inspection data with the process constraints in the feature anchoring carrier, and maintains or updates the feature anchoring carrier based on the comparison results, achieving cross-batch inheritance and adaptive correction of process features.

[0043] The underlying principle of this step is that the feature anchoring carrier carries all the process constraint information of the core product, but if it is only generated and not accessed by various manufacturing stages, it cannot play its collaborative role. The core of this step is to deploy the feature anchoring carrier to a shared storage space and give each stage real-time read / write access capabilities, making the feature anchoring carrier a "collaborative signaling" throughout the entire manufacturing process. Unlike the static transmission method in traditional technologies where process parameters are "lost" after being input only in the initial stage, this step ensures that any modification to the feature anchoring carrier by any stage can be instantly perceived by other stages, thereby achieving closed-loop collaborative control of the manufacturing process.

[0044] This step is implemented as follows: First, after generating the feature anchoring carrier, the system deploys the feature anchoring carrier as a collaborative signaling to a shared storage space. The shared storage space can be implemented using a distributed cache (such as Redis) or a centralized database, and supports high-concurrency read / write operations and data consistency guarantees. Specifically, the system can serialize the feature anchoring carrier in a format such as JSON or Protocol Buffers, write it to the shared storage space, and set the carrier identifier field as a unique index.

[0045] Secondly, the system broadcasts a notification of feature anchor carrier deployment completion to the data perception, digital modeling, process simulation, production line execution, and quality control inspection stages of the manufacturing process. Each stage can then establish a connection with the shared storage space and obtain the access address and read / write permissions of the feature anchor carrier in real time.

[0046] Third, each stage reads the feature anchoring carrier from the shared storage space upon startup and parses the process constraints stored therein. Taking the data sensing stage as an example, it reads the dimensional tolerance range from the geometric constraint field, the coating thickness tolerance range from the material constraint field, and the disinfection efficiency threshold from the functional constraint field, and loads these constraints into local memory as a benchmark for subsequent data comparison.

[0047] Fourth, each step, in the process of performing its own operation, uses the feature anchoring carrier as a unified constraint benchmark and performs the subsequent steps S13 to S17 accordingly.

[0048] Fifth, when any stage updates the feature anchoring carrier, the shared storage space ensures data consistency through a version number mechanism. Specifically, during each update, the system reads the current carrier version number, increments the updated carrier version number, and writes it. If a version number conflict is detected during the writing process, the latest version is read again and the update is merged to ensure that concurrent read and write operations on the feature anchoring carrier by each stage do not lead to data overwriting or loss.

[0049] It should be noted that the specific implementation methods of the aforementioned shared storage space are not limited to the examples given. In one example, a Redis distributed cache can be used, suitable for high-concurrency read / write scenarios; in another example, a relational database such as MySQL can be used, suitable for scenarios requiring persistent storage. Any data storage solution that can support real-time read / write access across multiple stages falls within the scope of protection of this invention.

[0050] In step S12, by deploying the feature anchoring carrier as collaborative signaling to the shared storage space, a unified access point for process constraints at each stage of the manufacturing process is first established, eliminating data silos caused by independent parameter maintenance at each stage in traditional technologies. Based on this, by providing real-time read and write access for the data sensing, digital modeling, process simulation, production line execution, and quality control inspection stages, dynamic transmission and real-time sharing of process constraints between stages are achieved. Unlike the static transmission method in traditional technologies where process parameters are "lost" after being input only in the initial stage, this invention enables any stage to... Modifications to the feature anchoring carrier can be immediately perceived by other stages. Based on this, by enabling each stage to perform its operations with the feature anchoring carrier as a unified constraint benchmark, deep collaboration between data perception, digital modeling, process deduction, production line execution, and quality control inspection is achieved, forming a complete closed loop of "perception-modeling-deduction-execution-quality control". This provides infrastructure support for the "global collaborative scheduling" of claim 1, enabling each stage to operate collaboratively with a unified constraint benchmark, fundamentally solving the problem of consistency loss of control caused by the independent operation of each stage and the inability to transfer process features across stages in traditional technologies.

[0051] S13: In the data sensing stage, the operation data of the manufacturing process is collected in real time, and the operation data is compared with the process constraints. When the operation data exceeds the process constraints, an early warning signal is generated and written back to the feature anchoring carrier.

[0052] The underlying principle of this step is that the data sensing link is responsible for capturing the equipment operating status in real time during the core product manufacturing process. The core of this step is to compare the collected operating data with the process constraints in the feature anchoring carrier in real time. When the parameters exceed the limits, an early warning signal is generated and written back to the carrier. Thus, the data sensing link is no longer just a data collector, but becomes a real-time feedback entry point for the closed-loop control of the manufacturing process. Abnormal states are captured and recorded in the global collaborative signaling, providing timely and accurate feedback input for the collaborative response of subsequent links.

[0053] This step is implemented as follows: First, the data sensing stage reads the feature anchoring carrier from the shared storage space, parses the constraint field, obtains the process constraints, and determines the allowable upper limit, allowable lower limit, and allowable fluctuation range for each equipment parameter based on the process constraints. For example, following the previous example, the system maps the outer diameter constraint to the constraint of the measurement value of the detection equipment, maps the coating thickness constraint to the joint constraint of the deposition rate and coating time of the coating machine, and maps the grinding machine spindle speed constraint to an allowable range of 8000-12000 rpm and an allowable fluctuation range of ±500 rpm, etc.

[0054] Secondly, the system uses an IoT sensor array deployed on the core drilling production line to collect equipment operating parameter sequences at a preset sampling frequency. The industrial data acquisition gateway receives the raw equipment operating parameter sequences and performs time-series alignment and standardization. Based on the sampling timestamps of each sensor, the gateway interpolates data sequences from different sampling frequencies to the same time base, generating a timestamped standardized operating dataset. Time-series alignment can be achieved using known techniques such as linear interpolation or spline interpolation, which will not be elaborated upon here. Each data point in the standardized operating dataset includes at least the equipment identifier, parameter name, timestamp, and measured value.

[0055] Third, the system compares each data point in the standardized operating dataset with the device parameters, including boundary comparison (to determine whether it exceeds the upper or lower limit of the allowable range) and stability comparison (to determine whether the fluctuation range exceeds the allowable fluctuation range).

[0056] Fourth, when any data point exceeds the upper limit or falls below the lower limit, the system generates an early warning signal containing the over-limit parameter identifier, over-limit value, over-limit type, and over-limit timestamp. The early warning signal is then associated with the standardized operating dataset and stored in the verification record field of the feature anchoring carrier, enabling real-time monitoring and anomaly detection of the core drilling product manufacturing process.

[0057] In step S13, by collecting operational data in real time and comparing it with process constraints in multiple dimensions, real-time monitoring and anomaly detection of equipment operating status are first realized. Based on this, by generating early warning signals and writing them back to the feature anchoring carrier, the abnormal status is persistently recorded in the global collaborative signaling, providing a data foundation for the closed-loop feedback of claim 1 and solving the technical defect that abnormal status cannot be captured and transmitted in real time in traditional technology.

[0058] S14: The digital modeling step uses the process constraints as boundary constraints to construct a digital twin model of the core drilling product.

[0059] A digital twin model is a high-fidelity mapping model of the physical entity of a core drilling product in digital space, used to simulate, predict, and optimize the product's manufacturing process and performance. In this invention, the digital twin model comprises at least three levels: a three-dimensional geometric model, a material property distribution model, and a functional response model, and is parametrically constructed using process constraints in a feature-anchored carrier as boundary constraints. The digital twin model differs from traditional static three-dimensional models used only for visualization; its core lies in the real-time dynamic correlation between model parameters and process constraints. When process constraints are updated, the model parameters can automatically adapt and adjust.

[0060] The underlying principle of this step is that the digital twin model is a high-fidelity mapping of the physical drill core in digital space. The core of this step lies in using the process constraints within the feature-anchored carrier as the modeling boundary, ensuring that the parameter space of the digital twin model is always limited by process requirements. Unlike traditional techniques where digital twin models are only used for visualization and are detached from process requirements, this step enables the digital twin model to dynamically and automatically adapt to changes in process constraints, providing an accurate simulation and verification platform for subsequent process derivations.

[0061] This step is implemented as follows: First, the digital modeling process reads the feature anchoring carrier from the shared storage space and obtains the process constraints stored in the geometric constraint field, material constraint field, and functional constraint field.

[0062] Secondly, the system constructs an initial three-dimensional geometric model of the core drilling product based on geometric constraints. Parametric modeling can be implemented using CAD software secondary development interfaces or open-source geometric kernels (such as Open CASCADE). Existing technologies can be referenced, and will not be elaborated here.

[0063] Third, the system generates a material property distribution model based on material constraints. For example, following the previous example, the system obtains the target value and allowable tolerance range for the coating thickness, constructs a coating region on the surface layer of the initial three-dimensional geometric model, and parameterizes the coating thickness as a variable thickness field along the surface normal. Based on the coating uniformity evaluation threshold, the system sets thickness uniformity constraints within the coating region to ensure that the coating thickness fluctuates within the allowable tolerance range. It also parameterizes the physical properties of the coating material, such as the elastic modulus, coefficient of thermal expansion, and hardness, and stores them in association with the coating thickness field.

[0064] Fourth, the system generates a functional response model based on functional constraints. For example, following the previous example, the system obtains the baseline value of active oxygen disinfection efficiency and the allowable threshold for performance degradation, and establishes a correlation function between coating parameters (thickness, uniformity, adhesion) and disinfection efficiency. This correlation function can be constructed using a response surface model or a surrogate model. For instance, disinfection efficiency data under different combinations of coating parameters can be obtained through finite element simulation or experimental design, and a coating thickness-uniformity-efficiency response surface can be generated. The functional response model takes the coating parameters as input, outputs the predicted disinfection efficiency value, and uses the allowable threshold for performance degradation as the constraint boundary of the model output.

[0065] Fifth, the system uses the dimensional tolerance range and coating thickness tolerance range in the process constraints as boundary constraints to parametrically fuse the initial 3D geometric model, material property distribution model, and functional response model, generating a parameter-adjustable digital twin model. Furthermore, it establishes real-time synchronization between the digital twin model and the feature anchoring carrier to achieve dynamic automatic adaptation of the digital twin model, providing accurate simulation for subsequent process derivation. Specifically, the system unifies the dimensional parameters of the geometric model, the coating thickness parameters of the material model, and the efficiency prediction parameters of the functional model into the same parameter space, establishing a parameter mapping table. When any process constraint changes, the system automatically adjusts the corresponding parameters of the associated model according to the mapping table, achieving dynamic adaptation of the digital twin model.

[0066] Sixth, the system associates and stores the completed digital twin model with the feature anchoring carrier. Specifically, the system generates a unique model identifier for the digital twin model, writes the model identifier and the storage path of the model file into the verification record field of the feature anchoring carrier, and establishes a real-time synchronization mechanism between the key parameters of the digital twin model (such as geometric dimensions and coating thickness) and the corresponding process constraints in the feature anchoring carrier. When the process constraints are updated, the digital twin model automatically triggers model reconstruction or parameter remapping through the synchronization mechanism.

[0067] In step S14, a digital twin model is constructed using process constraints as boundary constraints. This unifies the model parameter space with process requirements, ensuring that the model does not deviate from actual manufacturing. Based on this, through parameterized fusion and real-time synchronization, the digital twin model achieves immediate response and dynamic automatic adaptation to changes in process constraints. This provides accurate input for subsequent process simulations and solves the technical defects of traditional technologies, such as the digital twin model being disconnected from the dynamic changes in the process in real time and unable to support process optimization verification.

[0068] S15: The process deduction step deduces the process connection scheme and process parameter scheme within the feasible domain defined by the feature anchoring carrier, generates a production process optimization scheme that meets the process constraints, and writes the production process optimization scheme back to the feature anchoring carrier.

[0069] Feasible region: refers to the parameter space defined by process constraints, within which any combination of parameters satisfies all process constraints. In this invention, the feasible region is jointly defined by geometric, material, and functional constraints in the feature anchoring carrier, and generated after pruning by inter-process coupling constraint rules. Specifically, it is a multi-dimensional parameter space formed by the allowable value ranges and combinations of various process parameters (such as grinding speed, feed rate, coating temperature, deposition rate, etc.). The feasible region differs from the independent value range of a single parameter in traditional technologies. Its core lies in considering the coupling constraint relationships between parameters; only parameter combinations that simultaneously satisfy all constraints belong to feasible solutions within the feasible region.

[0070] Production process optimization plan: This refers to a complete manufacturing plan that includes the sequence of processes and the configuration of equipment parameters for each process, used to guide dynamic adjustment of the production line execution. A production process optimization plan should include at least three parts: process sequence identifiers, equipment parameter sets, and projection timestamps. The process sequence identifiers determine the execution order and dependencies of each process; the equipment parameter sets contain the operating parameters (such as speed, temperature, pressure, etc.) and target values ​​of the equipment corresponding to each process; and the projection timestamps record the generation time of the optimization plan for subsequent traceability and version management.

[0071] The underlying principle of this step is as follows: the process deduction stage is responsible for finding the optimal production plan within the feasible region defined by the process constraints. The core of this step lies in introducing the concept of feasible region into the process deduction—the feasible region is defined by the process constraints in the feature anchoring carrier and generated after pruning the coupling constraints between processes. This ensures that the deduced optimized plan not only satisfies single-parameter constraints but also the coupling relationships between processes. Unlike traditional optimization methods that only consider the independent value range of single parameters, the optimized plan generated in this step has higher feasibility and robustness.

[0072] This step is implemented as follows: First, the process simulation stage reads the feature anchoring carrier from the shared storage space, parses the geometric constraint field, material constraint field and functional constraint field, and extracts the allowable upper limit value, allowable lower limit value and allowable fluctuation range of each process parameter.

[0073] Secondly, the system maps the allowed upper limit, allowed lower limit, and allowed fluctuation range to the parameter space coordinate system, generating an initial parameter space spanned by feasible intervals of each parameter dimension.

[0074] Third, the system performs constraint pruning on the initial parameter space according to preset inter-process coupling constraint rules, deleting parameter combinations that do not satisfy the inter-process coupling relationship, and generating a feasible region defined by the feature anchoring carrier. These preset inter-process coupling constraint rules include, but are not limited to: the correlation between surface roughness in the grinding process and coating adhesion in the coating process (excessive roughness leads to decreased adhesion), the matching relationship between coating temperature and deposition rate (the deposition rate needs to be reduced when the temperature is too low to ensure coating quality), and the matching relationship between grinding accuracy and measurement resolution in the inspection process. The specific content of the preset inter-process coupling constraint rules can also be customized according to the actual manufacturing process of the core drilling product.

[0075] Fourth, the system acquires the current equipment status data and material inventory data of the core drilling production line to construct the current production line status vector. The equipment status data includes the current operating status (idle / operating / faulty), current load, and remaining lifespan of each piece of equipment; the material inventory data includes the inventory of core drilling blanks and the remaining amount of diamond coating material. The system encodes the above data into a status vector, which serves as the initial state for optimization search. For example, continuing from the previous example, the current production line status vector is: [Grinding machine idle, Coating machine running (current temperature 238℃), Testing equipment idle, Core drilling blank inventory 500 pieces, Coating material remaining 2000g].

[0076] Fifth, the system constructs a multi-objective optimization function with the primary optimization objective of minimizing process waiting time and equipment idle time, and the secondary optimization objective of maximizing the matching degree between process parameters and the center values ​​of constraint boundaries.

[0077] Specifically, the first optimization objective expression can be: Minimize F1 = Σ(T_wait_i + T_idle_i); Where T_wait_i is the waiting time of process i, and T_idle_i is the idle time of device i, which are calculated based on the current production line state vector.

[0078] The second optimization objective expression can be: Maximize F2 = Σ(1 - |P_i - C_i| / R_i); Where P_i is the candidate process parameter value for process i, C_i is the constraint boundary center value of the parameter, and R_i is the allowable fluctuation range of the parameter.

[0079] The system combines the two optimization objectives into a weighted comprehensive optimization function: F = w1 * F1_normalized + w2 * (1 - F2_normalized); Among them, w1 and w2 are weight coefficients that can be dynamically adjusted according to the production scenario (e.g., increase w1 to focus on efficiency during mass production, and increase w2 to focus on quality during high-precision products). F1_normalized and F2_normalized are the normalized optimization target values, respectively.

[0080] Sixth, the system employs a heuristic search algorithm to iteratively search within the feasible region. The current production line state vector is used as the initial search state, and a multi-objective optimization function is used as the fitness function to evaluate the merits of each candidate solution, generating a set of candidate process connection schemes and a set of candidate process parameter schemes. The heuristic search algorithm includes, but is not limited to, genetic algorithms, particle swarm optimization, or simulated annealing. Taking the genetic algorithm as an example, the system encodes the process sequence and process parameters into chromosomes, and the initial population is randomly generated within the feasible region. It iterative evolution is achieved through selection, crossover, and mutation operations, with a population size of 100 per generation and 500 generations. A multi-objective optimization function is used as the fitness function to evaluate the merits of each individual; individuals with higher fitness values ​​have a greater probability of being selected. After iterative evolution, the system outputs the top 10 individuals with the best fitness values ​​as the candidate scheme set.

[0081] Seventh, the system cross-validates the candidate process connection schemes and candidate process parameter schemes, filters feasible solutions that satisfy all process constraints, and selects the solution with the optimal comprehensive optimization objective value from the feasible solutions as the production process optimization scheme. Cross-validation includes: using a digital twin model to simulate and verify the candidate schemes, checking for process conflicts, equipment overload, or process parameter exceeding limits; for schemes that pass simulation verification, their comprehensive optimization objective value is further calculated. The system selects the solution with the optimal comprehensive optimization objective value from all feasible solutions as the production process optimization scheme.

[0082] Eighth, the system writes the process sequence identifier and equipment parameter set in the production process optimization scheme into the optimization scheme field of the feature anchoring carrier, and writes the deduction timestamp into the verification record field.

[0083] In step S15, the feasible region definition, multi-objective optimization, and heuristic search are used to accurately define the optimization parameter space, avoiding the problem of infeasibility caused by parameter mismatch. Based on this, by constructing a multi-objective optimization function and heuristic search, the coordinated optimization of manufacturing efficiency and process accuracy is achieved. Finally, by writing the optimized solution back to the carrier, a directly readable instruction set is provided for the production line execution stage, and a complete collaborative implementation link is provided for the process deduction stage of claim 1. This enables the process deduction stage to generate an optimized solution that simultaneously satisfies efficiency, accuracy, and feasibility, and seamlessly transfers the optimized solution to the production line execution stage. This fundamentally solves the technical defects of traditional technology, such as the disconnect between process optimization and production line execution and the inability to automatically import optimized solutions into the execution equipment.

[0084] S16: The production line execution process reads the production process optimization plan from the feature anchoring carrier, dynamically adjusts the production line equipment operating parameters according to the production process optimization plan, and transmits real-time data during the execution process back to the feature anchoring carrier for continuous verification.

[0085] The underlying principle of this step is that the production line execution phase is responsible for translating the optimized plan into executable instructions for the equipment, and continuously monitoring and correcting them during execution. The core of this step lies in forming a closed-loop regulation of "instruction issuance - real-time data acquisition - deviation calculation - instruction correction," ensuring that equipment operating parameters remain within the allowable range of process constraints. Unlike the open-loop control mode in traditional technologies where equipment parameters are set and then not adjusted, this step achieves real-time perception and dynamic correction of equipment operating deviations.

[0086] This step is implemented as follows: First, the production line execution process reads the optimization scheme field of the feature anchor carrier from the shared storage space, obtains the production process optimization scheme, and parses it to obtain the process sequence identifier and its corresponding equipment parameter set.

[0087] Secondly, the system retrieves the corresponding process control instruction template from the preset process control instruction library based on the process sequence identifier. The preset process control instruction library refers to a database that stores standard control instruction templates for each process in the core drilling product manufacturing process. The preset process control instruction library can be deployed in a local industrial control system or a cloud-based instruction library. It includes standardized control instruction templates for each process, such as grinding, cleaning, coating, and testing. The instruction templates can be written using G-code, OPC UA instruction sets, or equipment manufacturer-defined instruction formats. The instruction templates can be written in a standardized format (such as G-code or OPC UA instruction sets) to ensure that controllers from different equipment manufacturers can parse and execute them.

[0088] The templates contain parameter placeholders (such as $SPINDLE_SPEED and $FEED_RATE) for the system to fill in specific parameter values ​​during runtime. For example, the grinding process instruction template can use G-code format, including the spindle start instruction "M03 S$SPINDLE_SPEED", the feed axis movement instruction "G01 X$POSITION F$FEED_RATE", the coolant switch instruction "M08", and the machining end instruction "M05", etc. The coating process instruction template can use the OPC UA instruction set, including the chamber vacuum instruction "SetVacuum(pressure value)", the heating and temperature rise instruction "SetTemperature($TEMPERATURE)", the coating source start instruction "StartDeposition($RATE)", the deposition monitoring instruction "MonitorThickness()", and the cooling instruction "CoolDown()", etc. The inspection process instruction template includes the equipment start instruction "StartMeasurement()", the data acquisition instruction "ReadData()", and the result output instruction "OutputResult()", etc.

[0089] Third, the system fills the parameter values ​​from the equipment parameter set into the process control instruction template, generating an executable equipment control instruction sequence. Furthermore, the system distributes the equipment control instruction sequence to the corresponding production line equipment via industrial Ethernet or fieldbus. This production line equipment includes one or more of the following: CNC grinding machines, vacuum coating machines, and online testing instruments. During the distribution process, the system records the instruction distribution timestamp and the equipment response status (e.g., "received," "executing," "completed") for subsequent verification and traceability.

[0090] Fourth, during the execution of the equipment control command sequence, the system collects the actual operating parameters of the equipment in real time and calculates the deviation between the actual operating parameters and the target parameters in the equipment parameter set. For example, continuing the previous example, the system uses an IoT sensor array deployed on the equipment to collect actual operating parameters at a preset sampling frequency (e.g., 100Hz for grinding processes and 10Hz for coating processes), including the actual spindle speed of the grinding machine, the actual position of the feed axis, the actual temperature of the coating machine chamber, and the real-time monitoring value of the deposition rate. The system compares the actual operating parameters with the target parameters item by item and calculates the deviation value Δ = |actual value - target value|.

[0091] Fifth, when the calculated deviation exceeds a preset deviation threshold, the system generates a parameter correction command. This preset deviation threshold can be set according to the process accuracy requirements; for example, the spindle speed deviation threshold for grinding is ±50 rpm, and the temperature deviation threshold for coating is ±2℃. For instance, if the actual spindle speed of the grinding machine is 9580 rpm, and the target value is 9500 rpm, the deviation of 80 rpm exceeds the preset threshold of 50 rpm. The system then generates a speed correction command to adjust the target speed to 9530 rpm (a slight adjustment towards the actual value within the allowable range).

[0092] Sixth, the system associates and stores the actual operating parameters, the results of deviation calculation, and the generation timestamp of parameter correction instructions, and sends them back to the verification record field of the feature anchoring carrier.

[0093] In step S16, by reading the production process optimization plan and generating equipment control instructions, the seamless connection between the production process optimization plan and the execution link is first realized. Based on this, through the closed-loop adjustment of deviation calculation and parameter correction instructions, the real-time monitoring and dynamic correction of the equipment operating status are realized, providing a complete implementation link for the production line execution link of claim 1.

[0094] S17: The quality control and inspection process collects finished product inspection data, compares the finished product inspection data with the process constraints, and maintains or updates the feature anchoring carrier based on the comparison results.

[0095] The underlying principle of this step is as follows: In the quality control and inspection phase, the finished product is inspected, and the results are fed back to the process constraints. The core of this step lies in the three-state branching process of the feature anchoring carrier based on the inspection results: the carrier remains unchanged when qualified; the carrier is locked and the line is stopped when exceeding limits; and the carrier's center value is fine-tuned when systematic drift occurs. Unlike traditional technologies that only perform a binary judgment of qualified / unqualified, this step introduces a "drift fine-tuning" mechanism, enabling process constraints to adaptively evolve with the slow changes in production line status, achieving the inheritance and optimization of process features across batches.

[0096] This step is implemented as follows: First, after the core drilling product completes production line execution, the quality control and inspection process collects finished product inspection data through online inspection equipment. This online inspection equipment includes, but is not limited to, laser diameter gauges, optical microscopes, spectral thickness gauges, and active oxygen disinfection efficiency testers. Taking a diamond active oxygen atomizing machine core drilling product as an example, the laser diameter gauge collects the actual outer and inner diameter dimensions of the core drilling product with a measurement accuracy of ±0.0005mm; the optical microscope, in conjunction with image analysis software, collects the surface morphology of the coating and calculates the coating uniformity index; the spectral thickness gauge collects the actual thickness of the diamond coating with a measurement accuracy of ±0.01μm; and the active oxygen disinfection efficiency tester collects the amount of active oxygen generated by the core drilling product under operating conditions and calculates the actual disinfection efficiency. The system encapsulates the above inspection data into a finished product inspection dataset, which includes at least one of the following: actual outer diameter of the core drilling product, actual inner diameter of the core drilling product, actual coating thickness of the core drilling product, actual coating uniformity index, and actual active oxygen disinfection efficiency.

[0097] Secondly, the system reads the process constraints from the feature anchoring carrier. Specifically, the system analyzes the geometric constraint fields in the feature anchoring carrier to extract the upper limit value of dimensional tolerance, the lower limit value of dimensional tolerance, and the allowable range of form and position tolerance; analyzes the material constraint fields to extract the target value of coating thickness, the allowable range of coating thickness, and the evaluation threshold of coating uniformity; and analyzes the functional constraint fields to extract the benchmark value of active oxygen disinfection efficiency and the allowable threshold of performance degradation. The above process constraints serve as the judgment criteria for quality control inspection.

[0098] Third, the system compares the finished product inspection data with the process constraints item by item and generates comparison results. The comparison results include, but are not limited to, the judgment conclusion (qualified, exceeding the upper limit, exceeding the lower limit), the offset direction (positive offset, negative offset), and the offset amount (the difference between the measured value and the target value).

[0099] Fourth, the system performs branch processing based on the comparison results: Branch 1, when all comparison results are qualified, the system keeps the current version of the feature anchoring carrier unchanged; Branch 2, when any comparison result exceeds the upper or lower limit, the system locks the feature anchoring carrier and generates a batch stop command; Branch 3, when the comparison result is qualified but there is an offset direction and the cumulative offset reaches the preset drift threshold, the system fine-tunes the corresponding process constraints in the feature anchoring carrier according to the offset direction and offset.

[0100] In step S17, by collecting finished product inspection data and comparing it with process constraints, the quality control and inspection process not only undertakes the function of quality judgment, but also serves as a driving node for the accumulation of process knowledge and adaptive evolution.

[0101] In this embodiment, through sequential processing of S11-S17, and through a series of interconnected technical means such as feature anchoring carrier generation, collaborative signaling distribution, data perception verification, digital twin modeling, process deduction and optimization, production line closed-loop execution, and quality control adaptive update, the drilling core product manufacturing process control method based on global collaborative scheduling of claim 1 is fully realized, providing a complete technical implementation solution for high-precision and high-consistency production of drilling core products.

[0102] Example 2: Feature anchoring carrier generation mechanism.

[0103] In this embodiment, a feature anchoring algorithm is used to fuse the geometric features, the material features, and the functional features to generate a feature anchoring carrier, including: The geometric features are converted into a first feature vector, and geometric constraints are generated based on the geometric features. The geometric features include at least one of the core outer diameter, inner diameter, height, and geometric tolerance range. The geometric constraints include an upper limit value and a lower limit value for dimensional tolerance. The geometric constraints further include an allowable range for geometric tolerance. The material features are converted into a second feature vector, and material constraints are generated based on the material features. The material features include at least one of diamond coating thickness threshold, coating uniformity tolerance range, and coating adhesion level. The material constraints include a coating thickness target value and a coating thickness tolerance range. The material constraints further include a coating uniformity evaluation threshold. The functional features are converted into a third feature vector, and functional constraints are generated based on the functional features. The functional features include at least one of active oxygen disinfection efficiency threshold, performance degradation rate threshold, and working life threshold. The functional constraints include a benchmark value for active oxygen disinfection efficiency, and the functional constraints further include a performance degradation allowable threshold. Based on the geometric constraints, material constraints, and functional constraints, process constraints are generated, which include the upper limit value, lower limit value, and allowable fluctuation range around the target value for each constraint. The first feature vector, the second feature vector, and the third feature vector are concatenated to generate a fused feature vector; The fused feature vector is hashed and encrypted to generate a unique encrypted hash value; The encrypted hash value, the fused feature vector, the geometric constraints, the material constraints, the functional constraints, and the process constraints are associated and stored as the feature anchoring carrier.

[0104] The underlying principle of this embodiment is that the feature anchoring carrier serves as the collaborative signaling throughout the entire manufacturing process, and the accuracy of its generation directly impacts the collaborative effect of all subsequent stages. The core of this embodiment lies in converting the geometric, material, and functional features of the core product into feature vectors, generating corresponding constraints based on each feature, and then concatenating the three feature vectors into a fused feature vector before hash encryption to ensure the unique identification and tamper-proof capability of the carrier content. This progressive processing chain of "feature → vector → constraint → fusion → encryption" enables the feature anchoring carrier to not only carry complete process constraint information but also possess verifiable data integrity protection, further enhancing the uniqueness, tamper-proof capability, and traceability of the feature anchoring carrier.

[0105] This embodiment is implemented as follows: First, the system reads the specification parameters of the current batch of core products from the manufacturing execution system or product data management system, and extracts geometric features, material features and functional features.

[0106] In terms of geometric feature processing, the system extracts at least one of the following: outer diameter, inner diameter, height, and geometric tolerance range of the drill core. For example, taking a diamond active oxygen atomizer drill core as an example, the system reads an outer diameter of 3.5 mm, an inner diameter of 2.0 mm, a height of 5.0 mm, and a geometric tolerance range of 0.005 mm.

[0107] In terms of material feature processing, the system extracts at least one of the following: diamond coating thickness threshold, coating uniformity tolerance range, and coating adhesion level. For example, following the previous example, the system reads a coating thickness threshold of 2.5 μm, a coating uniformity tolerance range of ±0.2 μm, and a coating adhesion level of 5.

[0108] In terms of functional feature processing, the system extracts at least one of the following: active oxygen disinfection efficiency threshold, performance degradation rate threshold, and working life threshold. For example, following the previous example, the system reads an active oxygen disinfection efficiency threshold of 95%, a performance degradation rate threshold of 5%, and a working life threshold of 1000 hours.

[0109] Secondly, the system converts the aforementioned geometric features into a first feature vector and generates geometric constraints based on these features. The system can use the Min-Max normalization method to map each value of the geometric features to the [0,1] interval: the outer diameter is normalized to 0.35 (assuming a historical maximum outer diameter of 10mm), the inner diameter to 0.20, the height to 0.50, and the geometric tolerances to 0.005, generating the first feature vector [0.35, 0.20, 0.50, 0.005]. Furthermore, the system generates geometric constraints based on the geometric features, including an upper limit of 3.505mm for dimensional tolerance, a lower limit of 3.495mm for dimensional tolerance, and an allowable range of 0.005mm for geometric tolerance.

[0110] Third, the system converts the aforementioned material characteristics into a second feature vector and generates material constraints based on these characteristics. The system can normalize the coating thickness threshold to 0.25 (assuming a historical maximum thickness of 10 μm), the coating uniformity tolerance to 0.02, and the coating adhesion level to 0.50, generating a second feature vector [0.25, 0.02, 0.50]. Furthermore, the system generates material constraints based on these characteristics, including a target coating thickness of 2.5 μm, a coating thickness tolerance range of ±0.2 μm, and a coating uniformity evaluation threshold of 0.2 μm.

[0111] Fourth, the system converts the aforementioned functional characteristics into a third feature vector and generates functional constraints based on these characteristics. Specifically, the system normalizes the active oxygen disinfection efficiency threshold to 0.95, the performance degradation rate threshold to 0.05, and the working life threshold to 0.10 (assuming a historical maximum lifespan of 10,000 hours), generating a third feature vector [0.95, 0.05, 0.10]. Furthermore, the system generates functional constraints based on these characteristics, including a baseline active oxygen disinfection efficiency of 95% and an allowable performance degradation threshold of 5%.

[0112] Fifth, based on geometric constraints, material constraints, and functional constraints, the system generates process constraints that include the upper and lower allowable limits for each constraint, as well as the allowable fluctuation range around the target value. For example, continuing from the previous example, the process constraints include: For geometric constraints: the upper limit of the outer diameter is 3.505 mm, the lower limit is 3.495 mm, and the allowable fluctuation range is ±0.005 mm; For material constraints: the upper limit of the coating thickness is 2.7 μm, the lower limit is 2.3 μm, and the allowable fluctuation range is ±0.2 μm; Functional constraints: The lower limit of active oxygen disinfection efficiency is 95%, and the allowable fluctuation range is ±2%.

[0113] Sixth, the system concatenates the first, second, and third feature vectors according to their dimensions to generate a fused feature vector. Continuing the previous example, the fused feature vector can be: [0.35,0.20,0.50,0.005,0.25,0.02,0.50,0.95,0.05,0.10]; This fused feature vector integrates 10 dimensions of information from three types of features.

[0114] Seventh, the system performs hash encryption on the fused feature vector, using the SHA-256 algorithm to generate a unique encrypted hash value. Furthermore, the system associates and stores the encrypted hash value, fused feature vector, geometric constraints, material constraints, functional constraints, and process constraints as a feature anchoring carrier. This feature anchoring carrier can be serialized in JSON format. The carrier identifier field stores the encrypted hash value, the geometric constraint field stores the geometric constraint object, the material constraint field stores the material constraint object, the functional constraint field stores the functional constraint object, the constraint field stores the process constraint object, and the optimization scheme field and verification record field are initialized to empty. The serialized JSON data is written to a shared storage space for subsequent reading and access.

[0115] This embodiment achieves refined generation of feature anchoring carriers through a series of technical means, including geometric feature vectorization and constraint generation, material feature vectorization and constraint generation, functional feature vectorization and constraint generation, fusion of three types of constraints to generate process constraints, feature vector splicing, hash encryption, and associated storage. Thus, based on the feature anchoring carrier generation achieved in the above embodiment, it further realizes the unique identification and anti-tampering capability of the feature anchoring carrier, solving the technical defects of traditional technology where process features cannot be uniquely identified and the carrier is easily tampered with, and significantly improving the data security and traceability of the feature anchoring carrier.

[0116] Example 3: Definition of feature anchoring carrier data structure.

[0117] In this embodiment, the feature anchoring carrier is stored using a preset data structure, which includes: The carrier identifier field is used to store the encrypted hash value; The carrier version field is used to store the version number of the feature anchor carrier. The version number is initialized to a first preset value and incremented each time the feature anchor carrier is updated. The geometric constraint field is used to store the geometric constraint conditions; The material constraint field is used to store the material constraint conditions; The functional constraint field is used to store the functional constraints. The constraint field is used to store the process constraints; The optimization scheme field is used to store the production process optimization scheme generated in the process simulation stage. The production process optimization scheme includes process sequence identifier, equipment parameter set, and simulation timestamp, and is initialized to empty. The verification record field is used to store the real-time data and verification results returned from each stage. The verification results include the data source stage identifier, data collection timestamp, verification deviation value, and verification result identifier, which are initialized to empty.

[0118] The conceptual principle of this embodiment is that the feature anchoring carrier needs to be transferred between various stages of the entire manufacturing process and read and updated by heterogeneous systems in different stages. The core of this embodiment is to design a data structure that includes carrier identification field, carrier version field, geometric constraint field, material constraint field, functional constraint field, constraint condition field, optimization scheme field, and verification record field, so that the storage, access, update and traceability of feature carrier data have clear technical specifications, and provide a unified data foundation for global collaborative scheduling.

[0119] This embodiment is implemented as follows: First, after generating the feature anchoring carrier, the system serializes and stores the feature anchoring carrier according to a preset data structure. The system creates a carrier identifier field and writes the generated encrypted hash value into this field, which serves as the unique index key for the feature anchoring carrier in the shared storage space.

[0120] Secondly, the system creates a carrier version field, initialized to a first preset value (e.g., value 1), to support subsequent concurrent update control. When the feature anchor carrier is updated in the data perception, process simulation, or quality control inspection stages, the system reads the current version number and increments it during the update write.

[0121] Third, the system creates a geometric constraint field and writes the geometric constraint conditions generated in the above embodiments into this field; The system creates a material constraint field and writes the material constraint conditions generated in the above embodiments into this field. The system creates a functional constraint field and writes the functional constraints generated in the above embodiments into this field. The system creates a constraint field and writes the process constraints generated in the above embodiments into this field. Fourth, the system creates an optimization scheme field, which is initialized as an empty array []. This field is written into the generated production process optimization scheme by the process simulation stage during the execution of the process simulation stage. It includes the process sequence identifier (such as "grinding process" → "cleaning process" → "coating process" → "inspection process"), equipment parameter set (such as grinding machine spindle speed 9500rpm, coating machine chamber temperature 245℃) and simulation timestamp.

[0122] Fifth, the system creates a verification record field, which is initialized as an empty array []. When the data perception stage is executed, the data perception stage writes an over-limit warning signal; when the production line execution stage is executed, the production line execution stage writes the actual operating parameters, deviation calculation results and correction instruction records; when the quality control and inspection stage is executed, the quality control and inspection stage writes the finished product inspection data, comparison results and fine-tuning records.

[0123] Sixth, the system serializes all the above fields into JSON format data and writes it to the shared storage space. After successful writing, the system broadcasts a notification of carrier deployment completion to each stage of the manufacturing process, allowing each stage to establish a connection and obtain access permissions.

[0124] In this embodiment, an eight-field structured design, including carrier identifier, carrier version, geometric constraint, material constraint, functional constraint, constraint condition, optimization scheme, and verification record, is implemented to achieve a standardized storage and access mechanism for feature-anchored carriers. This further realizes the structured organization and version management of carrier data based on the feature-anchored carrier generation achieved in the above embodiment, and further solves the technical defects of inconsistent process data formats and inability to trace version changes in traditional technologies, significantly improving the data consistency and maintainability of collaborative scheduling.

[0125] Example 4: IoT data sensing and constraint verification mechanism.

[0126] In this embodiment, during the data sensing stage, real-time operational data of the manufacturing process is collected, and the operational data is compared with the process constraints. When the operational data exceeds the process constraints, an early warning signal is generated and written back to the feature anchoring carrier, including: The process constraints are read from the feature anchoring carrier, and the process constraints include the upper limit value, the lower limit value, and the allowable fluctuation range corresponding to each constraint. Based on the process constraints, the allowable upper limit, allowable lower limit, and allowable fluctuation range of each equipment parameter are determined. The equipment parameters include at least one of the following: grinding machine spindle speed, grinding machine feed speed, coating machine chamber temperature, coating machine deposition rate, and measurement values ​​from testing equipment. The IoT sensor array deployed on the core drilling product production line collects the equipment operating parameter sequence according to a preset sampling frequency. The equipment operating parameter sequence includes at least one of the following: grinding machine spindle speed, grinding machine feed speed, coating machine chamber temperature, coating machine deposition rate, and measurement values ​​from the detection equipment. The system receives the sequence of operating parameters of the equipment through an industrial data acquisition gateway, performs time-series alignment on the sequence of operating parameters, and generates a standardized operating dataset with timestamps. Compare the values ​​of each data point in the standardized operational dataset with the device parameters; When any data point exceeds the upper limit or falls below the lower limit, an early warning signal is generated, which includes an over-limit parameter identifier, an over-limit value, and an over-limit timestamp. The early warning signal is then associated with the standardized running dataset and stored in the verification record field of the feature anchoring carrier.

[0127] Process constraints refer to the set of manufacturing process constraint parameters generated by converting geometric, material, and functional constraints. They include the upper and lower allowable limits for each constraint, as well as the allowable fluctuation range around the target value. Process constraints are stored in a structured data format within the constraint field of the feature anchoring carrier and serve as the benchmark for comparing operational data during the data sensing phase.

[0128] The underlying principle of this embodiment is that the data sensing stage is the entry point for real-time monitoring of the manufacturing process, and the accuracy and timeliness of the collected data directly affect the reliability of anomaly detection. The core of this embodiment lies in collecting operating parameters from an IoT sensor array deployed on the production line. After time-series alignment by an industrial data acquisition gateway, a standardized operating dataset is generated. This dataset is then compared with equipment parameter constraints for boundary and stability checks, enabling immediate detection and precise location of operational anomalies. This complete chain of "collection → alignment → standardization → comparison → early warning" transforms the traditional passive "timed retrieval" collection mode into an "event-driven" proactive sensing mode, further improving the real-time performance of operating data collection and the accuracy of anomaly detection.

[0129] This embodiment is implemented as follows: First, the data sensing stage reads the feature anchoring carrier from the shared storage space, parses the constraint field, and obtains the process constraints.

[0130] Secondly, the data sensing stage determines the allowable upper limit, allowable lower limit, and allowable fluctuation range for each equipment parameter based on process constraints. Specifically, the system maps the outer diameter constraint to the constraints of the measurement value of the detection equipment (allowable upper limit 3.505mm, allowable lower limit 3.495mm); maps the coating thickness constraint to the joint constraint of the deposition rate and coating time of the coating machine (the product of deposition rate and time should fall within the range of 2.3μm to 2.7μm); maps the grinding machine spindle speed constraint to the allowable range of 8000rpm to 12000rpm and the allowable fluctuation range of ±500rpm during the grinding process; maps the grinding machine feed rate constraint to the allowable range of 0.5mm / s to 1.5mm / s and the allowable fluctuation range of ±0.2mm / s; and maps the coating machine chamber temperature constraint to the allowable range of 220℃ to 260℃ and the allowable fluctuation range of ±5℃.

[0131] Third, the system uses an IoT sensor array deployed on the core drilling production line to collect equipment operating parameter sequences at a preset sampling frequency. Taking the grinding process as an example, the system deploys speed and displacement sensors on the CNC grinding machine spindle and feed axis, continuously collecting spindle speed and feed rate data at a sampling frequency of 100Hz to form a raw data stream. Taking the coating process as an example, the system deploys a temperature sensor inside the vacuum coating machine chamber, collecting chamber temperature data at a sampling frequency of 10Hz; and a deposition rate monitor is deployed on the coating source side, collecting deposition rate data at a sampling frequency of 1Hz. Taking the inspection process as an example, the system deploys a data interface on the online inspection instrument, collecting the measured values ​​for each inspection at a sampling frequency of 0.5Hz. The raw signals collected by the above sensors are converted from analog to digital and then aggregated through an industrial data acquisition gateway to generate a raw equipment operating parameter sequence containing equipment identification, parameter type, sampling timestamp, and sampled values.

[0132] Fourth, the industrial data acquisition gateway receives the raw device operating parameter sequences uploaded by the IoT sensor array and performs time-series alignment and standardization processing on the data. Specifically, the gateway interpolates data sequences with different sampling frequencies to the same time base (e.g., using 100Hz as the base time axis) based on the sampling timestamps of each sensor. Time-series alignment can be performed using linear interpolation: for missing time points, linear interpolation is used to fill in the missing time points. The specific implementation of time-series alignment can use well-known techniques in the field, such as linear interpolation or spline interpolation, which will not be elaborated here.

[0133] Fifth, the system compares each data point in the standardized operational dataset with the device parameters. The comparison process consists of two dimensions: boundary comparison, which determines whether the measured value exceeds the upper limit or falls below the lower limit; and stability comparison, which calculates the fluctuation range of the measured value relative to the target value and determines whether it exceeds the allowable fluctuation range.

[0134] Sixth, when any data point is determined to be out of limit during boundary comparison or stability comparison, the system generates an early warning signal to achieve real-time monitoring and anomaly detection of the core drilling product manufacturing process. The early warning signal can be encapsulated in JSON format, including the out-of-limit parameter identifier, out-of-limit value, out-of-limit type, and out-of-limit timestamp.

[0135] Seventh, the system associates the generated warning signal with the corresponding standardized operational dataset and writes it back to the verification record field of the feature anchoring carrier. Specifically, the system reads the current feature anchoring carrier from the shared storage space, obtains the current content of the verification record field (stored as a JSON array), and appends the current warning record to the end of the array. Each warning record includes at least the data source stage identifier (e.g., "DATA_SENSING"), data collection timestamp, verification deviation value (the difference between the measured value and the target value), and verification result identifier (e.g., "OVER_UPPER" or "FLUCTUATION_EXCEED"). The system writes the updated verification record field back to the carrier and increments the carrier version number. If a version number conflict is detected during the writing process (i.e., other stages have updated the carrier simultaneously), the system rereads the latest version, merges the updates, and attempts to write again until successful.

[0136] This embodiment achieves real-time perception and anomaly detection mechanism for operational data through a series of technical means, including reading process constraints and mapping equipment parameters, collecting data from IoT sensor arrays, aligning industrial data acquisition gateway timing, generating standardized operational datasets, comparing multi-dimensional values, and generating and writing back early warning signals. This further enables the immediate detection and precise location of operational anomalies, building upon the data perception achieved in the previous embodiment. It also addresses the technical shortcomings of traditional technologies, such as delayed data acquisition and reliance on manual inspection for anomaly detection, significantly improving the real-time monitoring and anomaly response speed of the core drilling product manufacturing process.

[0137] Example 5: Adjustable parameter digital twin modeling mechanism.

[0138] In this embodiment, please refer to Figure 2 , Figure 2 This is a schematic diagram of the first sub-process of the core drilling product manufacturing process control method based on global collaborative scheduling provided in an embodiment of the present invention. Figure 2 As shown, in this embodiment, the digital modeling step uses the process constraints as boundary constraints to construct a digital twin model of the core drilling product, including: S21: Obtain the geometric constraint field in the feature anchoring carrier, extract the three-dimensional geometric parameters of the core product, and construct the initial three-dimensional geometric model of the core product; S22: Obtain the material constraint field in the feature anchoring carrier, extract the material property parameters of the diamond coating, map the material property parameters to the surface layer of the initial three-dimensional geometric model, and generate a material property distribution model; S23: Obtain the functional constraint field in the feature anchoring carrier, extract the correlation function between the active oxygen disinfection function and the coating parameters, embed the correlation function into the material property distribution model, and generate a functional response model; S24: Using the dimensional tolerance range and coating thickness tolerance range in the process constraints as boundary constraints, perform parametric modeling on the initial three-dimensional geometric model, the material property distribution model and the functional response model to generate a parameter-adjustable digital twin model. S25: Associate the digital twin model with the feature anchoring carrier and store them together, and write the model identifier of the digital twin model into the verification record field of the feature anchoring carrier.

[0139] The underlying principle of this embodiment is that the accuracy of the digital twin model directly affects the reliability of process deduction and optimization decisions. The core of this embodiment lies in employing a layered modeling method, embedding the geometric, material, and functional characteristics of the core drilling product layer by layer into the digital twin model: first, an initial 3D geometric model is constructed based on geometric constraints; then, a material property distribution model is generated based on material constraints; next, a functional response model is embedded based on functional constraints; and finally, parameterized fusion is performed using process constraints as boundaries. This four-layer progressive modeling architecture of "geometry → material → function → parameterization" enables the digital twin model to automatically adapt and adjust to changes in process constraints, further improving the fit between the digital twin model and process constraints and its parameter self-adaptation capability.

[0140] This embodiment is implemented as follows: First, the digital modeling stage reads the feature anchoring carrier from the shared storage space and parses the geometric constraint fields. For example, following the previous example, the system extracts the three-dimensional geometric parameters of the core product, including an outer diameter of 3.500mm, an inner diameter of 2.000mm, a height of 5.000mm, and a geometric tolerance range of 0.005mm. The system uses a parametric modeling method to construct an initial three-dimensional geometric model: with the core axis as the Z-axis, an outer cylindrical surface is constructed with the outer diameter as the radius, an inner cylindrical surface is constructed with the inner diameter as the radius, and the upper and lower end faces are constructed with the height as the height, generating a complete hollow cylindrical geometric model.

[0141] The system uses the dimensional tolerance range as a constraint on modeling accuracy, embeds tolerance zone information into the geometric model, and generates a parameterizable geometric model whose outer diameter can be dynamically adjusted within the range of 3.495mm to 3.505mm.

[0142] Secondly, the system analyzes the material constraint fields in the feature anchoring carrier and extracts the material property parameters of the diamond coating, including the target coating thickness of 2.5 μm, the coating thickness tolerance range of ±0.2 μm, and the coating uniformity evaluation threshold of 0.2 μm. The system maps the material property parameters to the surface layer of the initial 3D geometric model to generate a material property distribution model. Specifically, the system identifies the outer cylindrical surface, inner cylindrical surface, and upper and lower end faces of the geometric model as the coating area and constructs a variable thickness field along the surface normal on the surface layer. The coating thickness field is expressed parametrically: the target thickness is 2.5 μm, and the thickness distribution along the circumferential and axial directions of the surface meets the uniformity evaluation threshold constraint, that is, the deviation of the coating thickness from the target value at any position does not exceed 0.2 μm. The system also incorporates the material physical properties of the diamond coating (such as an elastic modulus of approximately 1050 GPa and a coefficient of thermal expansion of approximately 1.0 × 10⁻⁶). -6 The material properties (K, hardness approximately 8000 HV) are associated with the thickness field and stored to generate a complete material property distribution model.

[0143] Third, the system analyzes the functional constraint fields in the feature anchoring carrier and extracts the correlation function between the active oxygen disinfection function and the coating parameters. The correlation function is constructed using a response surface model, and disinfection efficiency data under different combinations of coating parameters are obtained through finite element simulation. The resulting coating thickness-uniformity-efficiency response surface is then fitted and generated.

[0144] For example, following the previous example, the correlation function can be expressed as η = f(t, u), where η is the active oxygen elimination efficiency, t is the coating thickness, and u is the coating uniformity index. When the coating thickness is 2.5 μm and the uniformity deviation is ±0.1 μm, the elimination efficiency is approximately 96%; when the coating thickness is 2.3 μm, the elimination efficiency drops to 93%. The system embeds the correlation function into the material property distribution model to generate a functional response model. This model takes the coating parameters as input, outputs the predicted elimination efficiency value, and uses a performance degradation allowable threshold of 5% as the constraint boundary of the model output. That is, it identifies parameter combinations in the model output that may lead to efficiency degradation exceeding 5%.

[0145] Fourth, the system uses the dimensional tolerance range and coating thickness tolerance range in the process constraints as boundary constraints to parametrically fuse the initial three-dimensional geometric model, material property distribution model and functional response model, generating a digital twin model with adjustable parameters.

[0146] Furthermore, the system establishes a parameter mapping table, unifying the dimensional parameters (outer diameter, inner diameter, height) of the geometric model, the coating thickness parameters of the material model, and the efficiency prediction parameters of the functional model into the same parameter space. When any process constraint changes, the system automatically adjusts the corresponding parameters of the associated models according to the mapping table. For example, if the quality control inspection finds that the coating thickness is consistently biased towards the lower limit, the system updates the target value of the coating thickness in the feature anchoring carrier to 2.48 μm. The digital twin model automatically reads the updated target value and regenerates the material property distribution model (with the coating thickness field adjusted to the 2.48 μm target value) and the functional response model (recalculating the predicted disinfection efficiency value under the corresponding thickness), achieving dynamic model adaptation.

[0147] Fifth, the system associates and stores the completed digital twin model with the feature anchoring carrier. The system generates a unique model identifier for the digital twin model and writes the model identifier and the storage path of the model file into the verification record field of the feature anchoring carrier. Furthermore, the system establishes a real-time synchronization mechanism between the digital twin model and the feature anchoring carrier: when the process constraints in the feature anchoring carrier are updated, the digital twin model automatically triggers model reconstruction or parameter remapping through the synchronization mechanism, ensuring that the digital twin model always reflects the latest process requirements.

[0148] It should be noted that the construction method of the above-mentioned functional response model is not limited to the response surface methodology. In one example, a Physical Information Neural Network (PINN) can be used to construct an end-to-end mapping model between coating parameters and disinfection efficiency, using physical equations (such as mass transfer equations and reaction kinetic equations) as loss function constraints to improve the model's prediction accuracy and generalization ability. In another example, Gaussian process regression can be used to construct a surrogate model, providing a quantitative assessment of the uncertainty of the prediction results. The implementation of parametric modeling is not limited to the examples given; feature-based parametric modeling or history-based parametric modeling can be used. Any method that can establish the correlation between process parameters and functional performance and achieve parametric modeling falls within the protection scope of this invention.

[0149] In this embodiment, a series of technical means, including geometric constraint extraction and 3D geometric model construction, material property extraction and material property distribution model generation, functional correlation function extraction and functional response model embedding, parametric fusion and digital twin model generation, and model association storage and synchronization mechanism, are used to realize a parameter-adjustable digital twin modeling mechanism. Based on the digital modeling achieved in the above embodiment, this further realizes the real-time synchronization and automatic adaptation of model parameters and process constraints, which solves the technical defects of traditional technology where digital twin models are disconnected from process requirements and cannot support process optimization verification. This significantly improves the responsiveness of digital twin models to process changes and the accuracy of simulation.

[0150] Example 6: Multi-objective optimization deduction mechanism.

[0151] In this embodiment, the process deduction step deduces the process connection scheme and process parameter scheme within the feasible domain defined by the feature anchoring carrier, and generates a production process optimization scheme that satisfies the process constraints, including: Read the geometric constraint field, material constraint field, and functional constraint field from the feature anchoring carrier, and extract the allowable upper limit value, allowable lower limit value, and allowable fluctuation range of each process parameter; The allowed upper limit, the allowed lower limit, and the allowed fluctuation range are mapped to the parameter space coordinate system to generate an initial parameter space spanned by feasible intervals of each parameter dimension; The initial parameter space is constrained and pruned according to the preset inter-process coupling constraint rules, and parameter combinations that do not satisfy the inter-process coupling relationship are deleted to generate the feasible domain defined by the feature anchoring carrier. Obtain the current equipment status data and material inventory data of the core drilling product production line, and construct the current production line status vector; The first optimization objective is to minimize process waiting time and equipment idle time, and the second optimization objective is to maximize the matching degree between process parameters and constraint boundary center values. The process waiting time and equipment idle time are calculated based on the current production line state vector to construct a multi-objective optimization function. A heuristic search algorithm is used to iteratively search within the feasible region, with the current production line state vector as the initial search state and the multi-objective optimization function as the fitness function to evaluate the merits of each candidate solution, thereby generating a set of candidate process connection schemes and a set of candidate process parameter schemes. Cross-validate the candidate process connection scheme set and the candidate process parameter scheme set, screen feasible solutions that satisfy all process constraints, and select the solution with the best comprehensive optimization objective value from the feasible solutions as the production process optimization scheme; Write the process sequence identifier and equipment parameter set in the production process optimization scheme into the optimization scheme field of the feature anchoring carrier, and write the deduction timestamp into the verification record field.

[0152] Preset inter-process coupling constraint rules: These refer to the set of rules governing parameter dependencies between different processes in the core drilling product manufacturing process. These include, but are not limited to: the correlation between surface roughness in the grinding process and coating adhesion in the coating process (excessive roughness leads to decreased adhesion), the matching relationship between coating temperature and deposition rate (the deposition rate needs to be reduced when the temperature is too low to ensure coating quality), and the matching relationship between grinding accuracy and measurement resolution in the inspection process. These coupling constraint rules are stored in the system knowledge base in the form of logical expressions or numerical thresholds, and are used to prune the initial parameter space during the feasible domain generation process.

[0153] The underlying principle of this embodiment is that the process deduction stage needs to simultaneously optimize both manufacturing efficiency and process accuracy while satisfying process constraints. The core of this embodiment lies in generating a feasible region by pruning the parameter space defined by process constraints using coupling rules. Then, a multi-objective optimization function is constructed with the first optimization objective being minimizing process waiting time and equipment idle time, and the second optimization objective being maximizing the matching degree between process parameters and the central values ​​of constraint boundaries. A heuristic search algorithm is then used to efficiently explore the optimal solution within the feasible region. This optimization chain of "feasible region definition → multi-objective modeling → heuristic search → cross-validation" transforms the traditional "single-objective optimization" mode into a "multi-objective collaborative optimization" mode, further improving the overall quality and search efficiency of the production process optimization scheme.

[0154] This embodiment is implemented as follows: First, the process deduction stage reads the feature anchoring carrier from the shared storage space, parses the geometric constraint field, material constraint field and functional constraint field, and extracts the allowable upper limit value, allowable lower limit value and allowable fluctuation range of each process parameter.

[0155] For example, following the previous example, the system extracts the following allowable values ​​for the grinding machine spindle speed in the grinding process: upper limit 12000 rpm, lower limit 8000 rpm, and allowable fluctuation range ±500 rpm; upper limit 1.5 mm / s, lower limit 0.5 mm / s, and allowable fluctuation range ±0.2 mm / s; upper limit 260℃, lower limit 220℃, and allowable fluctuation range ±5℃; and upper limit 0.5 μm / min, lower limit 0.3 μm / min, and allowable fluctuation range ±0.05 μm / min.

[0156] Secondly, the system maps the aforementioned upper limit, lower limit, and fluctuation range to the parameter space coordinate system, generating an initial parameter space spanned by feasible intervals of each parameter dimension. Specifically, the system constructs an initial multidimensional parameter space using each process parameter as a coordinate axis and the allowed value range of that parameter as the feasible range on the coordinate axis. For example, following the previous example, the initial parameter space includes the grinding machine speed dimension [8000, 12000], the feed rate dimension [0.5, 1.5], the coating temperature dimension [220, 260], and the deposition rate dimension [0.3, 0.5].

[0157] Third, the system performs constraint pruning on the initial parameter space according to preset inter-process coupling constraint rules, deleting parameter combinations that do not satisfy the inter-process coupling relationship, and generating the feasible region defined by the feature anchoring carrier. The preset inter-process coupling constraint rules include, but are not limited to: Rule 1 (Grinding-Coating Coupling): When the surface roughness of the ground surface exceeds Ra0.4μm, the coating adhesion level cannot reach level 5. Based on the mapping relationship between grinding parameters (rotation speed, feed rate) and surface roughness, the system will remove rotation speed-feed combinations that result in a roughness exceeding Ra0.4μm from the parameter space.

[0158] Rule 2 (Temperature-rate coupling): When the coating temperature is below 240℃, the deposition rate must not exceed 0.35μm / min, otherwise the coating uniformity will decrease. The system removes the intersection region of the temperature dimension [220,240) and the deposition rate dimension (0.35,0.5] from the parameter space.

[0159] Rule 3 (Accuracy-Measurement Coupling): The accuracy of grinding dimensions must match the measurement resolution of the inspection equipment. When the dimensional tolerance is less than 0.002 mm, a high-precision inspection equipment must be selected. The system will mark tolerance combinations that exceed the measurement capabilities of the current inspection equipment as infeasible.

[0160] The remaining parameter space after constraint pruning is the feasible region. Any combination of parameters within this region satisfies both single-parameter constraints and inter-process coupling constraints.

[0161] Fourth, the system acquires the current equipment status data and material inventory data of the core drilling product production line, and constructs the current production line status vector.

[0162] Fifth, the system constructs a multi-objective optimization function with the primary optimization objective of minimizing process waiting time and equipment idle time, and the secondary optimization objective of maximizing the matching degree between process parameters and the center values ​​of constraint boundaries.

[0163] Sixth, the system uses a heuristic search algorithm to iteratively search within the feasible region, taking the current production line state vector as the initial search state and using a multi-objective optimization function as the fitness function to evaluate the merits of each candidate solution, generating a set of candidate process connection schemes and a set of candidate process parameter schemes.

[0164] Taking the genetic algorithm as an example, the main processes include: 1) Encoding: Encoding the process sequence and process parameters into chromosomes. The process sequence uses permutation encoding, such as [grinding, cleaning, coating, inspection]; the process parameters use real number encoding, such as [9500, 1.0, 245, 0.42], which correspond to grinding speed, feed rate, coating temperature, and deposition rate, respectively. 2) Initialization: The initial population is randomly generated within the feasible region, and the population size is set to 100. 3) Fitness evaluation: Using the multi-objective optimization function F as the fitness function, the fitness value of each individual is calculated. Individuals with higher fitness values ​​indicate a better balance between efficiency and accuracy. 4) Selection: Using the roulette wheel selection method, individuals with higher fitness values ​​have a greater probability of being selected. 5) Crossover: For process sequences, partial mapping crossover is used; for process parameters, simulated binary crossover is used, with a crossover probability of 0.8. 6) Mutation: For process sequences, exchange mutation is used; for process parameters, polynomial mutation is used, with a mutation probability of 0.1. 7) Iteration: The selection, crossover, and mutation operations are repeated for 500 iterations. 8) Output: After evolution, the top 10 individuals with the best fitness values ​​are output as the candidate solution set.

[0165] Seventh, the system performs cross-validation on the candidate process connection scheme set and the candidate process parameter scheme set, and screens feasible solutions that satisfy all process constraints. Cross-validation includes: 1) Simulation verification: The candidate scheme is simulated using a digital twin model to check for problems such as process conflicts (e.g., the same equipment is scheduled at the same time), equipment overload (e.g., exceeding the rated power of the equipment) or process parameters exceeding the limit (e.g., temperature fluctuations exceed the allowable range).

[0166] 2) Feasibility Screening: For solutions that pass simulation verification, their comprehensive optimization objective value is further calculated, and solutions that do not meet the constraints are eliminated. The system selects the solution with the optimal comprehensive optimization objective value from all feasible solutions as the production process optimization solution. For example, following the previous example, the optimal solution is: process sequence [grinding, cleaning, coating, inspection], equipment parameter set [grinding speed 9520rpm, feed rate 1.02mm / s, coating temperature 244℃, deposition rate 0.41μm / min], and comprehensive optimization objective value F=0.87.

[0167] Eighth, the system writes the process sequence identifier and equipment parameter set in the production process optimization scheme into the optimization scheme field of the feature anchoring carrier, and writes the deduction timestamp into the verification record field.

[0168] It should be noted that the specific selection and parameter settings of the heuristic search algorithms mentioned above are not limited to the examples given. In one example, a particle swarm optimization algorithm can be used, which converges quickly to the optimal solution through iterative updates of particle positions and velocities. This algorithm is suitable for optimization problems in continuous parameter spaces, and its inertia weight can be set to 0.7, with a learning factor of 1.5. In another example, a simulated annealing algorithm can be used, where the probability of accepting suboptimal solutions is controlled by temperature parameters. The initial temperature can be set to 100°C, and the annealing rate to 0.95, avoiding getting trapped in local optima. The specific content of the inter-process coupling constraint rules is also not limited to the examples given and can be customized according to the actual manufacturing process of the core drilling products. Any algorithm capable of searching for optimal solutions that satisfy multiple constraints in a high-dimensional parameter space falls within the protection scope of this invention.

[0169] This embodiment achieves a multi-objective optimization deduction mechanism through a series of technical means, including process parameter constraint extraction, initial parameter space generation, coupling constraint pruning and feasible region definition, production line state vector construction, multi-objective optimization function construction, heuristic search and fitness evaluation, cross-validation and optimal solution selection, and optimization scheme writing. Based on the process deduction achieved in the above embodiment, it further realizes the synergistic optimization of efficiency and accuracy, solves the technical defects of traditional single-objective optimization that cannot take into account both efficiency and quality and has low optimization scheme search efficiency, and significantly improves the overall quality and solution efficiency of production process optimization schemes.

[0170] Example 7: Production line closed-loop execution and dynamic control mechanism.

[0171] In this embodiment, the production line execution stage reads the production process optimization plan from the feature anchoring carrier, dynamically adjusts the production line equipment operating parameters according to the production process optimization plan, and transmits real-time data during the execution process back to the feature anchoring carrier, including: The production process optimization scheme is read from the optimization scheme field of the feature anchoring carrier, and the process sequence identifier and its corresponding equipment parameter set are obtained by parsing. Based on the process sequence identifier, the corresponding process control instruction template is retrieved from the preset process control instruction library; The parameter values ​​from the equipment parameter set are filled into the process control instruction template to generate an executable equipment control instruction sequence; The equipment control command sequence is sent to the corresponding production line equipment via industrial Ethernet or fieldbus, and the production line equipment includes one of CNC grinding machine, vacuum coating machine, and online testing instrument. During the execution of the equipment control command sequence, the actual operating parameters of the equipment are collected in real time, and the deviation between the actual operating parameters and the target parameters in the equipment parameter set is calculated. When the result of the deviation calculation exceeds the preset deviation threshold, a parameter correction instruction is generated and sent to the corresponding device to bring the device operating parameters back to the allowable range of the target parameters. The actual operating parameters, the results of the deviation calculation, and the generation timestamp of the parameter correction instruction are associated and stored, and then sent back to the verification record field of the feature anchoring carrier.

[0172] The underlying principle of this embodiment is that the production line execution stage is the crucial link in translating optimized solutions into physical actions, and its execution accuracy directly affects product quality. The core of this embodiment lies in forming a closed-loop regulation system: "instruction generation → equipment execution → real-time data acquisition → deviation calculation → correction instruction." Unlike the open-loop control mode in traditional technologies where equipment parameters are set and then not adjusted, this embodiment acquires actual operating parameters in real time and calculates the deviation with the target parameters. When the deviation exceeds a threshold, a correction instruction is automatically generated, ensuring that the equipment operating parameters remain within the allowable range of process constraints. This continuous verification and dynamic correction during execution further improves the stability of equipment operation and its compliance with process constraints.

[0173] This embodiment is implemented as follows: First, the production line execution process reads the feature anchoring carrier from the shared storage space, parses the optimization scheme field, obtains the production process optimization scheme, and parses it to obtain the process sequence identifier and its corresponding equipment parameter set. Continuing the previous example, the system reads the JSON-formatted production process optimization scheme from the optimization scheme field, parses it to obtain the process sequence identifier ["grinding process" → "cleaning process" → "coating process" → "inspection process"], and the corresponding equipment parameter set for each process: grinding machine spindle speed 9520 rpm, grinding machine feed rate 1.02 mm / s, coating machine chamber temperature 244℃, deposition rate 0.41 μm / min.

[0174] Secondly, based on the process sequence identifier, the system retrieves the corresponding process control instruction template from the preset process control instruction library. Furthermore, the system fills the process control instruction template with parameter values ​​from the equipment parameter set, generating an executable equipment control instruction sequence. Specifically, the system parses the parameter placeholders in the instruction template, replaces the placeholders with the corresponding target parameter values ​​from the production process optimization plan, and generates complete equipment control instructions.

[0175] Third, the system sends equipment control command sequences to the corresponding production line equipment via industrial Ethernet or fieldbus. During the execution of the equipment control command sequences, the system collects the actual operating parameters of the equipment in real time through IoT sensor arrays deployed on the equipment, and calculates the deviation between the actual operating parameters and the target parameters in the equipment parameter set.

[0176] Fourth, when the calculated deviation exceeds the preset deviation threshold, the system generates a parameter correction command. If the deviation persists, the system can use a proportional-integral-derivative (PID) control algorithm to iteratively generate correction commands and send them to the corresponding equipment, gradually bringing the equipment operating parameters back to the allowable range of the target parameters. The PID control algorithm can refer to relevant existing technologies, and its proportional coefficient, integral time, and derivative time can be preset according to the equipment response characteristics, which will not be elaborated here.

[0177] For example, continuing from the previous example, a spindle speed deviation of 15 rpm in the grinding machine does not exceed the threshold and requires no correction; however, a temperature deviation of 2°C in the coating machine chamber reaches the threshold, triggering a system correction. The system can use a proportional-integral-derivative (PID) control algorithm to generate parameter correction commands and calculate the correction step size: Correction step size = Kp × Δ + Ki × ∫Δ dt + Kd × dΔ / dt; Where Kp is the proportional coefficient (set to 0.8), Ki is the integral coefficient (set to 0.1), and Kd is the derivative coefficient (set to 0.05). For a temperature deviation of 2℃, the system calculates a correction step size of 1.6℃, generates a temperature correction command, and adjusts the target temperature to 245.6℃ (original target 244℃ + correction step size 1.6℃, but not exceeding the allowable upper limit of 260℃). The system sends the correction command to the coating machine via industrial Ethernet, so that the equipment operating parameters gradually return to the allowable range of the target parameters.

[0178] Seventh, the system associates and stores the actual operating parameters, the results of deviation calculation, and the generation timestamp of parameter correction instructions, and sends them back to the verification record field of the feature anchoring carrier.

[0179] This embodiment achieves a closed-loop execution and dynamic control mechanism for the production line through a series of technical means, including optimized scheme reading and parsing, process control instruction template calling, parameter value filling and instruction sequence generation, industrial Ethernet / fieldbus instruction issuance, real-time acquisition and deviation calculation, PID correction instruction generation and issuance, and data feedback and verification records. Based on the production line execution achieved in the above embodiment, it further realizes continuous verification and dynamic correction of the execution process, further solving the technical defects of traditional technologies where equipment parameters cannot be automatically corrected after setting, and execution results cannot be fed back to global collaborative signaling. This significantly improves the process compliance and stability of core drilling product manufacturing.

[0180] Example 8: Quality control three-state branch and drift triggering mechanism.

[0181] In this embodiment, the quality control and inspection process collects finished product inspection data, compares the finished product inspection data with the process constraints, and maintains or updates the feature anchoring carrier based on the comparison results, including: After the core drilling product completes production line execution, finished product testing data is collected through online testing equipment. The finished product testing data includes at least one of the following: actual outer diameter of the core drilling product, actual inner diameter of the core drilling product, actual coating thickness, actual coating uniformity index, and actual active oxygen disinfection efficiency. Read the corresponding process constraints from the geometric constraint field, material constraint field, and functional constraint field of the feature anchoring carrier, including at least one of the following: dimensional tolerance range, coating thickness tolerance range, coating uniformity tolerance range, and active oxygen disinfection efficiency threshold. The finished product inspection data is compared with the process constraints item by item to generate comparison results for each item. The comparison results include qualified, exceeding the upper limit, exceeding the lower limit, offset direction, and offset amount. When all comparison results are qualified, the current version of the feature anchoring carrier remains unchanged; When any comparison result exceeds the upper or lower limit, the feature anchoring carrier is locked, a batch stop command is generated, and the comparison result is written as a quality anomaly record into the verification record field of the feature anchoring carrier. When the comparison result is qualified but the offset direction exists and the cumulative offset reaches the preset drift threshold, the corresponding process constraints in the feature anchoring carrier are fine-tuned according to the offset direction and the offset, the version number of the feature anchoring carrier is updated, and the updated constraints are written into the corresponding constraint field.

[0182] The underlying principle of this embodiment is that the quality control and inspection stage is a crucial quality checkpoint in the manufacturing process, and its processing logic directly impacts the process inheritance and optimization of subsequent batches. The core of this embodiment lies in executing a three-state branching process based on the comparison results between finished product inspection data and process constraints. This three-state branching system of "qualified maintenance → over-limit locking → drift triggering" enables the quality control and inspection stage not only to perform quality judgment functions but also to serve as a driving node for process knowledge accumulation and adaptive evolution, further enhancing its ability to respond to graded product quality status and its adaptive optimization capabilities.

[0183] This embodiment achieves the following: First, after the core drilling product completes production line execution, the quality control and inspection process collects finished product inspection data through online inspection equipment. Furthermore, the system reads the corresponding process constraints from the geometric constraint field, material constraint field, and functional constraint field of the feature anchoring carrier.

[0184] Secondly, the system compares the finished product inspection data with the process constraints item by item, generates comparison results for each item, and performs branch processing based on the comparison results: Branch 1 (All Pass): When all comparison results are pass, the system maintains the current version of the feature anchoring carrier unchanged. The system writes the pass record of this test into the verification record field, including the test timestamp, the measured values ​​of each parameter, and the comparison judgment result, but does not make any modifications to the carrier's constraint condition field.

[0185] Branch Two (Over-Limit Locking): When any comparison result exceeds the upper or lower limit, the system locks the feature anchor carrier and generates a batch stop command. The system marks the status field of the feature anchor carrier as "locked," prohibiting subsequent write operations on that feature anchor carrier. The system generates a batch stop command containing the stop reason, abnormal batch identifier, and stop timestamp, and sends it to the production line controller via industrial Ethernet, triggering a production line shutdown. Simultaneously, the system writes the comparison result as a quality anomaly record to the verification record field of the feature anchor carrier for subsequent quality traceability analysis.

[0186] Branch 3 (Drift Trigger): When the comparison result is qualified but there is a deviation direction and the cumulative deviation reaches the preset drift threshold, the system fine-tunes the corresponding process constraints in the feature anchoring carrier according to the deviation direction and deviation amount. The preset drift threshold is a threshold parameter used to determine whether the process parameter deviation exceeds the normal fluctuation range. The preset drift threshold is used to identify the systematic drift trend of process parameters. When the cumulative deviation of multiple consecutive batches of test data exceeds this threshold, the fine-tuning of the process constraints is triggered. For example, the cumulative threshold for coating thickness deviation can be set to 0.05μm, and the cumulative threshold for disinfection efficiency deviation can be set to 1%.

[0187] For example, continuing from the previous example, if the coating thickness detection result is 2.48 μm (within the tolerance range), but the cumulative offset of 10 consecutive batches reaches -0.06 μm, exceeding the preset drift threshold of 0.05 μm, the system triggers a fine-tuning mechanism. The system determines the fine-tuning direction (adjusting in the positive direction) based on the offset direction (negative offset), calculates the fine-tuning step size based on the filtered offset trend value (fine-tuning step size = filtered offset trend value × adjustment coefficient 0.5), and adjusts the target coating thickness value downwards by 0.03 μm from 2.5 μm (actual offset trend -0.06 μm × 0.5), resulting in an updated target value of 2.47 μm. The system maintains the allowable range width of the process constraints unchanged (still ±0.2 μm), only adjusting the center value position, i.e., the updated allowable range of coating thickness is 2.27 μm to 2.67 μm. The system writes the updated target coating thickness value into the material constraint field of the feature anchoring carrier and increments the carrier version number. At the same time, the system writes the fine-tuning record into the verification record field, which includes the fine-tuning timestamp, the constraints before fine-tuning (target value 2.5μm), the constraints after fine-tuning (target value 2.47μm), and the fine-tuning trigger reason ("10 consecutive batches of coating thickness deviated from the lower limit, with a cumulative offset of -0.06μm").

[0188] It should be noted that the specific values ​​of the preset drift threshold and the fine-tuning step size adjustment coefficient mentioned above are not limited to the examples given. In one example, an exponentially weighted moving average can be used instead of a simple cumulative offset, giving more weight to recent data and improving the sensitivity to trend changes; in another example, an adaptive adjustment coefficient can be used, increasing the adjustment step size when the drift trend accelerates. Any technical means that can achieve adaptive fine-tuning of the process constraint center value is within the protection scope of this invention.

[0189] In this embodiment, a series of technical means, including finished product inspection data acquisition, process constraint reading, item-by-item numerical comparison and comparison result generation, qualified retention processing, over-limit locking and line stop command generation, and drift trigger condition judgment, realize the three-state branch of quality control and drift trigger mechanism. Thus, based on the quality control inspection realized in the above embodiment, it further realizes the graded response of product quality status, and further solves the technical defects of traditional technology that only performs qualified / unqualified binary judgment and cannot identify systematic drift trends. It significantly improves the intelligent decision-making ability and process self-adaptation ability of the quality control inspection link.

[0190] Example 9: Process constraint drift fine-tuning mechanism.

[0191] In this embodiment, when the comparison result is qualified but the offset direction exists and the cumulative offset amount reaches a preset drift threshold, the corresponding process constraints in the feature anchoring carrier are fine-tuned according to the offset direction and the offset amount, including: The comparison result data of historical batches is read from the verification record field of the feature anchoring carrier. The comparison result data of historical batches includes the offset direction and offset sequence of N consecutive batches. The offset sequence is filtered by moving average to eliminate random fluctuations in a single batch and generate a filtered offset trend value. When the absolute value of the filtered offset trend value exceeds the preset drift threshold, the fine-tuning condition is triggered; The fine-tuning direction is determined based on the offset direction, and the fine-tuning step size is calculated based on the filtered offset trend value. The fine-tuning step size is positively correlated with the filtered offset trend value. The fine-tuning step size is adjusted in the opposite direction of the offset direction to the center value of the corresponding process constraint in the feature anchoring carrier to generate the updated process constraint. Keep the allowable range of the process constraints unchanged, and only adjust the position of the center value; Write the updated process constraints into the constraint field corresponding to the feature anchoring carrier, and increment the version number; The fine-tuning record is written into the verification record field of the feature anchoring carrier. The fine-tuning record includes the fine-tuning timestamp, the constraints before fine-tuning, the constraints after fine-tuning, and the fine-tuning trigger reason.

[0192] The underlying principle of this embodiment is that, in long-term continuous production, the process parameters of core drilling products may systematically drift due to factors such as equipment wear and environmental changes. If the process constraint center value remains fixed, even if all test data are within the tolerance range, it may gradually deviate from the optimal process range. The core of this embodiment lies in adaptively adjusting the process constraint center value based on the deviation trend of historical batch test data, so that subsequent batches can automatically inherit the optimization results of previous batches, forming a closed-loop optimization link of "detection → filtering → trend identification → fine-tuning → inheritance", which can bring cross-batch process consistency assurance and accuracy progression capability to the system.

[0193] This embodiment is implemented as follows: First, when the comparison result in the above embodiment is qualified but there is a deviation direction and the cumulative deviation reaches the preset drift threshold, the system triggers the drift fine-tuning process. The system reads the comparison result data of historical batches from the verification record field of the feature anchoring carrier and constructs a sequence of deviation directions and deviation amounts for N consecutive batches. The value of N can be preset according to the production stability requirements, for example, N=10.

[0194] For example, following the previous example, the system reads the coating thickness detection data of the most recent 10 batches and extracts the offset direction and offset amount of each batch: batch 1 offset -0.01μm, batch 2 offset -0.02μm, batch 3 offset -0.01μm, batch 4 offset -0.03μm, batch 5 offset -0.02μm, batch 6 offset -0.04μm, batch 7 offset -0.03μm, batch 8 offset -0.05μm, batch 9 offset -0.04μm, and batch 10 offset -0.06μm.

[0195] Secondly, the system applies a moving average filter to the offset sequence to eliminate random fluctuations in single batches and generate a filtered offset trend value. The moving average filter can use a fixed window size (e.g., window size set to 3 or 5) and performs an arithmetic average of the offsets within the window. Moving average filtering refers to a signal processing method that performs weighted averaging on the offset sequences of consecutive batches to eliminate random fluctuations in single batches. The moving average filter uses a fixed window size (e.g., window size set to 5) and performs an arithmetic average of the offsets within the window to generate a filtered offset trend value. Moving average filtering can effectively suppress random fluctuations caused by factors such as sensor noise and instantaneous environmental disturbances, extracting the true drift trend of process parameters.

[0196] For example, continuing from the previous example, taking a window size of 5 as an example, the system calculates: The average value of batches 1-5 = (-0.01-0.02-0.01-0.03-0.02) / 5 = -0.018μm; The average value of batches 2-6 = (-0.02-0.01-0.03-0.02-0.04) / 5 = -0.024μm; The average value of batches 3-7 = (-0.01-0.03-0.02-0.04-0.03) / 5 = -0.026μm; The average value of batches 4-8 = (-0.03-0.02-0.04-0.03-0.05) / 5 = -0.034μm; The average value of batches 5-9 = (-0.02-0.04-0.03-0.05-0.04) / 5 = -0.036μm; The average value of batches 6-10 = (-0.04-0.03-0.05-0.04-0.06) / 5 = -0.044μm; The latest filtered offset trend value is taken as the mean of the most recent window, -0.044μm.

[0197] The filtered offset trend value reflects the direction and magnitude of the systematic change in process parameters over a period of time. A positive value indicates that the process parameters are continuously biased towards the upper limit, while a negative value indicates that they are continuously biased towards the lower limit. The absolute value of the filtered offset trend value is compared with a preset drift threshold to determine whether a fine-tuning condition is triggered.

[0198] Third, the system compares the absolute value of the filtered offset trend value with a preset drift threshold. When the absolute value of the filtered offset trend value exceeds the preset drift threshold, a fine-tuning condition is triggered. The preset drift threshold is a threshold value used to determine whether the drift of process parameters exceeds the normal fluctuation range. The preset drift threshold is set according to the process stability requirements of the core drilling product; for example, the cumulative threshold for coating thickness offset is set to 0.05 μm, and the cumulative threshold for disinfection efficiency offset is set to 1%. When the absolute value of the filtered offset trend value exceeds this threshold, the system determines that there is a systematic drift and triggers the fine-tuning mechanism.

[0199] Fourth, the system determines the fine-tuning direction based on the offset direction: the system adjusts the fine-tuning step size of the center value of the corresponding process constraint in the feature anchoring carrier in the opposite direction of the offset direction. If the offset direction is positive (the measured value is consistently higher than the target value), the constraint center value is adjusted in the negative direction; if the offset direction is negative, it is adjusted in the positive direction.

[0200] The fine-tuning step size is calculated based on the filtered offset trend value. The fine-tuning step size refers to the magnitude of adjustment to the center value of the process constraint. The fine-tuning step size is positively correlated with the filtered offset trend value, and the calculation formula is: Δ_adj = k × |Δ_trend|, where Δ_trend is the filtered offset trend value, and k is the adjustment coefficient (preset to 0.5) to avoid sudden changes in the process due to excessively large single adjustment.

[0201] Fifth, the system adjusts the fine-tuning step size of the center value of the corresponding process constraint in the feature anchoring carrier in the opposite direction of the offset direction to generate updated process constraints. The system keeps the allowable range width of the process constraints unchanged, only adjusting the position of the center value.

[0202] For example, continuing from the previous example, if the measured coating thickness values ​​of 10 consecutive batches consistently deviate from the lower limit, the filtered offset trend value is -0.03 μm, and the preset drift threshold is 0.02 μm, the system triggers a fine-tuning condition, adjusting the target coating thickness value downwards by 0.015 μm from 2.50 μm (fine-tuning step size = 0.03 μm × 0.5), resulting in an updated target value of 2.485 μm. The system maintains the allowable range width of the process constraints unchanged (still ±0.20 μm), only adjusting the center value position, i.e., the updated allowable range of coating thickness is 2.285 μm to 2.685 μm.

[0203] Sixth, the system writes the updated process constraints into the constraint field corresponding to the feature anchor carrier and increments the version number. Furthermore, the system writes the fine-tuning record into the verification record field of the feature anchor carrier. The fine-tuning record includes the fine-tuning timestamp, the constraints before fine-tuning, the constraints after fine-tuning, and the reason for fine-tuning. For example, following the previous example, the fine-tuning record is encapsulated in JSON format and includes, but is not limited to, the following: Fine-tune the timestamp: e.g., "2024-01-15T10:45:00.123Z"; Before fine-tuning, the constraints are: {"parameter":"coating thickness", "target value":2.50, "tolerance":0.2}; Fine-tuned constraints: {"parameter":"coating thickness", "target value":2.526, "tolerance":0.2}; Fine-tuning trigger reason: {"Offset trend":-0.052, "Drift threshold":0.05, "Cumulative batches":10}.

[0204] In this embodiment, through a series of technical means such as historical batch data reading, moving average filtering, drift trend identification, fine-tuning step size calculation, center value adjustment, version update and record writing, an adaptive fine-tuning mechanism for the center value of process constraints is realized. Thus, based on the real-time responsive replenishment decision-making achieved in the above embodiment, the inheritance and optimization of cross-batch process characteristics are further realized. This further solves the technical defects of traditional technology, such as the inability to guarantee cross-batch process consistency and the inability to inherit process optimization results across batches. It significantly improves the continuous optimization and process precision advancement capabilities of core drilling product manufacturing.

[0205] The core drilling product manufacturing process control method based on global collaborative scheduling described in the above embodiments can be recombined with the technical features included in different embodiments as needed to obtain a combined implementation scheme, but all are within the protection scope claimed by this invention.

[0206] Example 10: Control system for core drilling product manufacturing process based on global collaborative scheduling.

[0207] This embodiment provides a core drilling product manufacturing process control system based on global collaborative scheduling. This system corresponds one-to-one with the core drilling product manufacturing process control method based on global collaborative scheduling described above. Please refer to [link / reference]. Figure 3 , Figure 3 This is a schematic block diagram of a core drilling product manufacturing process control system based on global collaborative scheduling, provided as an embodiment of the present invention. Figure 3 As shown, the core drilling product manufacturing process control system 30 based on global collaborative scheduling can be applied to a server. The core drilling product manufacturing process control system 30 based on global collaborative scheduling includes: a first generation module 31, a first sharing module 32, a first comparison module 33, a first construction module 34, a first deduction module 35, a first control module 36, and a first detection module 37. The above functional modules are described in detail below: The first generation module 31 is used to acquire the geometric features, material features and functional features of the core product, and to fuse the geometric features, material features and functional features through a feature anchoring algorithm to generate a feature anchoring carrier. The feature anchoring carrier contains the process constraints of the core product. The first shared module 32 is used to deploy the feature anchoring carrier as a collaborative signaling to the shared storage space, so that the data perception link, digital modeling link, process simulation link, production line execution link and quality control inspection link of the manufacturing process can read and write in real time, so that each link can perform its own operation with the feature anchoring carrier as a unified constraint benchmark. The first comparison module 33 is used to collect the operating data of the manufacturing process in real time in the data perception stage, compare the operating data with the process constraints, and generate an early warning signal when the operating data exceeds the process constraints and write it back to the feature anchoring carrier. The first construction module 34 is used in the digital modeling stage to construct a digital twin model of the core drilling product using the process constraints as boundary constraints. The first deduction module 35 is used to deduce the process connection scheme and process parameter scheme within the feasible domain defined by the feature anchor carrier in the process deduction stage, generate a production process optimization scheme that meets the process constraints, and write the production process optimization scheme back to the feature anchor carrier. The first control module 36 is used to read the production process optimization scheme from the feature anchoring carrier during the production line execution process, dynamically control the operating parameters of the production line equipment according to the production process optimization scheme, and send the real-time data during the execution process back to the feature anchoring carrier for continuous verification. The first detection module 37 is used to collect finished product detection data in the quality control detection process, compare the finished product detection data with the process constraints, and maintain or update the feature anchoring carrier based on the comparison results.

[0208] In one embodiment, the first generation module 31 includes: The first generation submodule is used to convert the geometric features into a first feature vector and generate geometric constraints based on the geometric features. The geometric features include at least one of the core outer diameter, inner diameter, height, and geometric tolerance range. The geometric constraints include an upper limit value and a lower limit value of the dimensional tolerance. The geometric constraints further include the allowable range of the geometric tolerance. The second generation submodule is used to convert the material features into a second feature vector and generate material constraints based on the material features. The material features include at least one of diamond coating thickness threshold, coating uniformity tolerance range, and coating adhesion level. The material constraints include a coating thickness target value and a coating thickness tolerance range. The material constraints further include a coating uniformity evaluation threshold. The third generation submodule is used to convert the functional features into a third feature vector and generate functional constraints based on the functional features. The functional features include at least one of active oxygen disinfection efficiency threshold, performance decay rate threshold, and working life threshold. The functional constraints include an active oxygen disinfection efficiency benchmark value and further include a performance decay allowable threshold. The fourth generation submodule is used to generate process constraints based on the geometric constraints, the material constraints, and the functional constraints, including the allowable upper limit value, the allowable lower limit value, and the allowable fluctuation range around the target value for each constraint. The fifth generation submodule is used to concatenate the first feature vector, the second feature vector, and the third feature vector to generate a fused feature vector; The sixth generation submodule is used to perform hash encryption on the fused feature vector to generate an encrypted hash value with a unique identifier; The first association submodule is used to associate and store the encrypted hash value, the fused feature vector, the geometric constraints, the material constraints, the functional constraints, and the process constraints as the feature anchoring carrier.

[0209] In one embodiment, the feature anchoring carrier is stored using a preset data structure, the preset data structure including: The carrier identifier field is used to store the encrypted hash value; The carrier version field is used to store the version number of the feature anchor carrier. The version number is initialized to a first preset value and incremented each time the feature anchor carrier is updated. The geometric constraint field is used to store the geometric constraint conditions; The material constraint field is used to store the material constraint conditions; The functional constraint field is used to store the functional constraints. The constraint field is used to store the process constraints; The optimization scheme field is used to store the production process optimization scheme generated in the process simulation stage. The production process optimization scheme includes process sequence identifier, equipment parameter set, and simulation timestamp, and is initialized to empty. The verification record field is used to store the real-time data and verification results returned from each stage. The verification results include the data source stage identifier, data collection timestamp, verification deviation value, and verification result identifier, which are initialized to empty.

[0210] In one embodiment, the first comparison module 33 includes: The first reading submodule is used to read the process constraints from the feature anchoring carrier. The process constraints include the upper limit value, the lower limit value, and the allowable fluctuation range corresponding to each constraint. The first determining submodule is used to determine the allowable upper limit, allowable lower limit, and allowable fluctuation range of each equipment parameter according to the process constraints. The equipment parameters include at least one of the following: grinding machine spindle speed, grinding machine feed speed, coating machine chamber temperature, coating machine deposition rate, and measurement values ​​from the detection equipment. The first acquisition submodule is used to acquire a sequence of equipment operating parameters according to a preset sampling frequency through an IoT sensor array deployed on the core drilling product production line. The sequence of equipment operating parameters includes at least one of the following: grinding machine spindle speed, grinding machine feed speed, coating machine chamber temperature, coating machine deposition rate, and measurement values ​​from the detection equipment. The seventh generation submodule is used to receive the equipment operating parameter sequence through the industrial data acquisition gateway, perform time-series alignment on the equipment operating parameter sequence, and generate a standardized operating dataset with timestamps. The first comparison submodule is used to compare each data point in the standardized operational dataset with the device parameters. The first early warning submodule is used to generate an early warning signal containing an over-limit parameter identifier, an over-limit value, and an over-limit timestamp when any data point exceeds the upper limit or falls below the lower limit, and to associate the early warning signal with the standardized running dataset and store it in the verification record field of the feature anchoring carrier.

[0211] In one embodiment, the first building module 34 includes: The first extraction submodule is used to obtain the geometric constraint field in the feature anchoring carrier, extract the three-dimensional geometric parameters of the core product, and construct the initial three-dimensional geometric model of the core product. The second extraction submodule is used to obtain the material constraint field in the feature anchoring carrier, extract the material property parameters of the diamond coating, map the material property parameters to the surface layer of the initial three-dimensional geometric model, and generate a material property distribution model. The third extraction submodule is used to obtain the functional constraint field in the feature anchoring carrier, extract the correlation function between the active oxygen disinfection function and the coating parameters, embed the correlation function into the material property distribution model, and generate a functional response model. The first modeling submodule is used to parametrically model the initial three-dimensional geometric model, the material property distribution model, and the functional response model using the dimensional tolerance range and coating thickness tolerance range in the process constraints as boundary constraints, and generate a digital twin model with adjustable parameters. The first storage submodule is used to associate and store the digital twin model with the feature anchoring carrier, and to write the model identifier of the digital twin model into the verification record field of the feature anchoring carrier.

[0212] In one embodiment, the first inference module 35 includes: The second reading submodule is used to read the geometric constraint field, material constraint field and functional constraint field from the feature anchoring carrier, and extract the allowable upper limit value, allowable lower limit value and allowable fluctuation range of each process parameter; The first mapping submodule is used to map the allowed upper limit value, the allowed lower limit value, and the allowed fluctuation range to the parameter space coordinate system to generate an initial parameter space spanned by feasible intervals of each parameter dimension. The first deletion submodule is used to perform constraint pruning on the initial parameter space according to the preset inter-process coupling constraint rules, delete parameter combinations that do not satisfy the inter-process coupling relationship, and generate the feasible domain defined by the feature anchoring carrier. The first construction submodule is used to obtain the current equipment status data and material inventory data of the core drilling product production line and construct the current production line status vector. The second construction submodule is used to construct a multi-objective optimization function with the first optimization objective being to minimize process waiting time and equipment idle time, and the second optimization objective being to maximize the matching degree between process parameters and constraint boundary center values. The process waiting time and equipment idle time are calculated based on the current production line state vector. The first search submodule is used to iteratively search within the feasible region using a heuristic search algorithm, taking the current production line state vector as the initial search state, and using the multi-objective optimization function as the fitness function to evaluate the merits of each candidate solution, thereby generating a set of candidate process connection schemes and a set of candidate process parameter schemes. The first selection submodule is used to cross-validate the candidate process connection scheme set and the candidate process parameter scheme set, screen feasible solutions that satisfy all process constraints, and select the solution with the best comprehensive optimization objective value from the feasible solutions as the production process optimization scheme. The first writing submodule is used to write the process sequence identifier and equipment parameter set in the production process optimization scheme into the optimization scheme field of the feature anchoring carrier, and write the deduction timestamp into the verification record field.

[0213] In one embodiment, the first control module 36 includes: The first parsing submodule is used to read the production process optimization scheme from the optimization scheme field of the feature anchoring carrier, and parse it to obtain the process sequence identifier and its corresponding equipment parameter set; The first calling submodule is used to call the corresponding process control instruction template from the preset process control instruction library according to the process sequence identifier; The first input submodule is used to input the parameter values ​​from the equipment parameter set into the process control instruction template to generate an executable equipment control instruction sequence. The first sending submodule is used to send the sequence of equipment control commands to the corresponding production line equipment via industrial Ethernet or fieldbus. The production line equipment includes one of CNC grinding machine, vacuum coating machine, and online testing instrument. The first calculation submodule is used to collect the actual operating parameters of the device in real time during the execution of the device control command sequence, and to calculate the deviation between the actual operating parameters and the target parameters in the device parameter set. The first correction submodule is used to generate a parameter correction instruction when the result of the deviation calculation exceeds a preset deviation threshold, and send the parameter correction instruction to the corresponding device so that the device operating parameters return to the range allowed by the target parameters. The first feedback submodule is used to associate and store the actual operating parameters, the result of the deviation calculation, and the generation timestamp of the parameter correction instruction, and then send them back to the verification record field of the feature anchoring carrier.

[0214] In one embodiment, the first detection module 37 includes: The second data acquisition submodule is used to collect finished product testing data through online testing equipment after the core drilling product has completed production line execution. The finished product testing data includes at least one of the following: actual outer diameter of the core drilling product, actual inner diameter of the core drilling product, actual coating thickness, actual coating uniformity index, and actual active oxygen disinfection efficiency. The third reading submodule is used to read the corresponding process constraints from the geometric constraint field, material constraint field, and functional constraint field of the feature anchoring carrier, including at least one of the following: dimensional tolerance range, coating thickness tolerance range, coating uniformity tolerance range, and active oxygen disinfection efficiency threshold. The second comparison submodule is used to compare the finished product test data with the process constraints item by item, and generate the comparison results for each item. The comparison results include qualified, exceeding the upper limit, exceeding the lower limit, offset direction, and offset amount. The first retention submodule is used to keep the current version of the feature anchoring carrier unchanged when all comparison results are qualified; The eighth generation submodule is used to lock the feature anchoring carrier, generate a batch stop command, and write the comparison result as a quality anomaly record into the verification record field of the feature anchoring carrier when any comparison result exceeds the upper or lower limit. The first update submodule is used to fine-tune the corresponding process constraints in the feature anchoring carrier according to the offset direction and the offset amount when the comparison result is qualified but the offset direction exists and the offset amount reaches the preset drift threshold, update the version number of the feature anchoring carrier, and write the updated constraints into the corresponding constraint field.

[0215] In one embodiment, the first update submodule includes: The fourth reading submodule is used to read the comparison result data of historical batches from the verification record field of the feature anchoring carrier. The comparison result data of historical batches includes the offset direction and offset sequence of N consecutive batches. The ninth generation submodule is used to perform moving average filtering on the offset sequence to eliminate random fluctuations in a single batch and generate a filtered offset trend value. The first trigger submodule is used to trigger the fine-tuning condition when the absolute value of the filtered offset trend value exceeds the preset drift threshold. The second calculation submodule is used to determine the fine-tuning direction based on the offset direction and to calculate the fine-tuning step size based on the filtered offset trend value. The fine-tuning step size is positively correlated with the filtered offset trend value. The tenth generation submodule is used to adjust the fine-tuning step size of the center value of the corresponding process constraint in the feature anchoring carrier in the opposite direction of the offset direction to generate the updated process constraint. The second holding submodule is used to keep the allowable range width of the process constraint unchanged, and only adjust the position of the center value; The second writing submodule is used to write the updated process constraints into the constraint field corresponding to the feature anchoring carrier and increment the version number. The third writing submodule is used to write the fine-tuning record into the verification record field of the feature anchoring carrier. The fine-tuning record includes a fine-tuning timestamp, constraints before fine-tuning, constraints after fine-tuning, and fine-tuning trigger reason.

[0216] Specific limitations regarding the core drilling product manufacturing process control system based on global collaborative scheduling can be found in the limitations of the core drilling product manufacturing process control method based on global collaborative scheduling mentioned above, and will not be repeated here. Each module in the aforementioned core drilling product manufacturing process control system based on global collaborative scheduling can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0217] Those skilled in the art will understand that the methods and systems provided in the embodiments of the present invention can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. The methods and systems can also be implemented as computer program products stored in one or more computer-readable storage media, including but not limited to: disks, optical disks, read-only memory (ROM), random access memory (RAM), flash memory, etc. When the computer program product is executed by one or more data processing devices (such as computers), the devices perform the steps as described in any of the preceding method embodiments.

[0218] The software tools, components, or models not belonging to our company that appear in the embodiments of this invention are merely illustrative examples and do not represent actual use.

[0219] The data collection in this embodiment of the invention complies with the requirements of relevant laws and regulations, such as the Data Security Law of the People's Republic of China, the Personal Information Protection Law of the People's Republic of China, GDPR (General Data Protection Regulation of the European Union), or information security standards of other countries and regions.

[0220] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling the manufacturing process of core drilling products based on global collaborative scheduling, characterized in that, include: The geometric, material, and functional features of the core drilling product are obtained. The geometric, material, and functional features are then fused using a feature anchoring algorithm to generate a feature anchoring carrier. The feature anchoring carrier includes the process constraints of the core drilling product. The feature anchoring carrier is deployed as a collaborative signaling to a shared storage space, allowing real-time read and write access for the data perception, digital modeling, process simulation, production line execution, and quality control inspection stages of the manufacturing process, so that each stage can perform its own operation with the feature anchoring carrier as a unified constraint benchmark. In the data sensing stage, the operation data of the manufacturing process is collected in real time, and the operation data is compared with the process constraints. When the operation data exceeds the process constraints, an early warning signal is generated and written back to the feature anchoring carrier. The digital modeling process uses the process constraints as boundary constraints to construct a digital twin model of the core drilling product. The process deduction step deduces the process connection scheme and process parameter scheme within the feasible domain defined by the feature anchoring carrier, generates a production process optimization scheme that meets the process constraints, and writes the production process optimization scheme back to the feature anchoring carrier. The production line execution process reads the production process optimization plan from the feature anchoring carrier, dynamically adjusts the production line equipment operating parameters according to the production process optimization plan, and transmits real-time data during the execution process back to the feature anchoring carrier for continuous verification. The quality control and testing process collects finished product testing data, compares the finished product testing data with the process constraints, and maintains or updates the feature anchoring carrier based on the comparison results.

2. The core drilling product manufacturing process control method based on global collaborative scheduling as described in claim 1, characterized in that, The geometric features, material features, and functional features are fused using a feature anchoring algorithm to generate a feature anchoring carrier, including: The geometric features are converted into a first feature vector, and geometric constraints are generated based on the geometric features. The geometric features include at least one of the core outer diameter, inner diameter, height, and geometric tolerance range. The geometric constraints include an upper limit value and a lower limit value for dimensional tolerance. The geometric constraints further include an allowable range for geometric tolerance. The material features are converted into a second feature vector, and material constraints are generated based on the material features. The material features include at least one of diamond coating thickness threshold, coating uniformity tolerance range, and coating adhesion level. The material constraints include a coating thickness target value and a coating thickness tolerance range. The material constraints further include a coating uniformity evaluation threshold. The functional features are converted into a third feature vector, and functional constraints are generated based on the functional features. The functional features include at least one of active oxygen disinfection efficiency threshold, performance degradation rate threshold, and working life threshold. The functional constraints include a benchmark value for active oxygen disinfection efficiency, and the functional constraints further include a performance degradation allowable threshold. Based on the geometric constraints, material constraints, and functional constraints, process constraints are generated, which include the upper limit value, lower limit value, and allowable fluctuation range around the target value for each constraint. The first feature vector, the second feature vector, and the third feature vector are concatenated to generate a fused feature vector; The fused feature vector is hashed and encrypted to generate a unique encrypted hash value; The encrypted hash value, the fused feature vector, the geometric constraints, the material constraints, the functional constraints, and the process constraints are associated and stored as the feature anchoring carrier.

3. The core drilling product manufacturing process control method based on global collaborative scheduling as described in claim 2, characterized in that, The feature anchoring carrier is stored using a preset data structure, which includes: The carrier identifier field is used to store the encrypted hash value; The carrier version field is used to store the version number of the feature anchor carrier. The version number is initialized to a first preset value and incremented each time the feature anchor carrier is updated. The geometric constraint field is used to store the geometric constraint conditions; The material constraint field is used to store the material constraint conditions; The functional constraint field is used to store the functional constraints. The constraint field is used to store the process constraints; The optimization scheme field is used to store the production process optimization scheme generated in the process simulation stage. The production process optimization scheme includes process sequence identifier, equipment parameter set, and simulation timestamp, and is initialized to empty. The verification record field is used to store the real-time data and verification results returned from each stage. The verification results include the data source stage identifier, data collection timestamp, verification deviation value, and verification result identifier, which are initialized to empty.

4. The core drilling product manufacturing process control method based on global collaborative scheduling as described in any one of claims 1-3, wherein in the data sensing stage, real-time acquisition of manufacturing process operation data is performed, the operation data is compared with the process constraints, and when the operation data exceeds the process constraints, an early warning signal is generated and written back to the feature anchoring carrier, comprising: The process constraints are read from the feature anchoring carrier, and the process constraints include the upper limit value, the lower limit value, and the allowable fluctuation range corresponding to each constraint. Based on the process constraints, the allowable upper limit, allowable lower limit, and allowable fluctuation range of each equipment parameter are determined. The equipment parameters include at least one of the following: grinding machine spindle speed, grinding machine feed speed, coating machine chamber temperature, coating machine deposition rate, and measurement values ​​from testing equipment. The IoT sensor array deployed on the core drilling product production line collects the equipment operating parameter sequence according to a preset sampling frequency. The equipment operating parameter sequence includes at least one of the following: grinding machine spindle speed, grinding machine feed speed, coating machine chamber temperature, coating machine deposition rate, and measurement values ​​from the detection equipment. The system receives the sequence of operating parameters of the equipment through an industrial data acquisition gateway, performs time-series alignment on the sequence of operating parameters, and generates a standardized operating dataset with timestamps. Compare the values ​​of each data point in the standardized operational dataset with the device parameters; When any data point exceeds the upper limit or falls below the lower limit, an early warning signal is generated, which includes an over-limit parameter identifier, an over-limit value, and an over-limit timestamp. The early warning signal is then associated with the standardized running dataset and stored in the verification record field of the feature anchoring carrier.

5. The core drilling product manufacturing process control method based on global collaborative scheduling as described in any one of claims 1-3, characterized in that, The digital modeling step uses the process constraints as boundary constraints to construct a digital twin model of the core drilling product, including: Obtain the geometric constraint field in the feature anchoring carrier, extract the three-dimensional geometric parameters of the core product, and construct the initial three-dimensional geometric model of the core product. Obtain the material constraint field in the feature anchoring carrier, extract the material property parameters of the diamond coating, map the material property parameters to the surface layer of the initial three-dimensional geometric model, and generate a material property distribution model; Obtain the functional constraint field in the feature anchoring carrier, extract the correlation function between the active oxygen disinfection function and the coating parameters, embed the correlation function into the material property distribution model, and generate a functional response model; Using the dimensional tolerance range and coating thickness tolerance range in the process constraints as boundary constraints, the initial three-dimensional geometric model, the material property distribution model and the functional response model are parametrically modeled to generate a digital twin model with adjustable parameters. The digital twin model is associated with and stored with the feature anchoring carrier, and the model identifier of the digital twin model is written into the verification record field of the feature anchoring carrier.

6. The core drilling product manufacturing process control method based on global collaborative scheduling as described in any one of claims 1-3, characterized in that, The process deduction step deduces the process connection scheme and process parameter scheme within the feasible domain defined by the feature anchoring carrier, and generates a production process optimization scheme that satisfies the process constraints, including: Read the geometric constraint field, material constraint field, and functional constraint field from the feature anchoring carrier, and extract the allowable upper limit value, allowable lower limit value, and allowable fluctuation range of each process parameter; The allowed upper limit, the allowed lower limit, and the allowed fluctuation range are mapped to the parameter space coordinate system to generate an initial parameter space spanned by feasible intervals of each parameter dimension; The initial parameter space is constrained and pruned according to the preset inter-process coupling constraint rules, and parameter combinations that do not satisfy the inter-process coupling relationship are deleted to generate the feasible domain defined by the feature anchoring carrier. Obtain the current equipment status data and material inventory data of the core drilling product production line, and construct the current production line status vector; The first optimization objective is to minimize the process waiting time and equipment idle time, and the second optimization objective is to maximize the matching degree between process parameters and the center value of constraint boundary. The process waiting time and equipment idle time are calculated based on the current production line state vector to construct a multi-objective optimization function. A heuristic search algorithm is used to iteratively search within the feasible region, with the current production line state vector as the initial search state and the multi-objective optimization function as the fitness function to evaluate the merits of each candidate solution, thereby generating a set of candidate process connection schemes and a set of candidate process parameter schemes. Cross-validate the candidate process connection scheme set and the candidate process parameter scheme set, screen feasible solutions that satisfy all process constraints, and select the solution with the best comprehensive optimization objective value from the feasible solutions as the production process optimization scheme; Write the process sequence identifier and equipment parameter set in the production process optimization scheme into the optimization scheme field of the feature anchoring carrier, and write the deduction timestamp into the verification record field.

7. The core drilling product manufacturing process control method based on global collaborative scheduling as described in any one of claims 1-3, characterized in that, The production line execution process reads the production process optimization plan from the feature anchoring carrier, dynamically adjusts the production line equipment operating parameters according to the production process optimization plan, and transmits real-time data during the execution process back to the feature anchoring carrier, including: The production process optimization scheme is read from the optimization scheme field of the feature anchoring carrier, and the process sequence identifier and its corresponding equipment parameter set are obtained by parsing. Based on the process sequence identifier, the corresponding process control instruction template is retrieved from the preset process control instruction library; The parameter values ​​from the equipment parameter set are filled into the process control instruction template to generate an executable equipment control instruction sequence; The equipment control command sequence is sent to the corresponding production line equipment via industrial Ethernet or fieldbus, and the production line equipment includes one of CNC grinding machine, vacuum coating machine, and online testing instrument. During the execution of the equipment control command sequence, the actual operating parameters of the equipment are collected in real time, and the deviation between the actual operating parameters and the target parameters in the equipment parameter set is calculated. When the result of the deviation calculation exceeds the preset deviation threshold, a parameter correction instruction is generated and sent to the corresponding device to bring the device operating parameters back to the allowable range of the target parameters. The actual operating parameters, the results of the deviation calculation, and the generation timestamp of the parameter correction instruction are associated and stored, and then sent back to the verification record field of the feature anchoring carrier.

8. The core drilling product manufacturing process control method based on global collaborative scheduling as described in any one of claims 1-3, characterized in that, The quality control and inspection process collects finished product inspection data, compares the finished product inspection data with the process constraints, and maintains or updates the feature anchoring carrier based on the comparison results, including: After the core drilling product completes production line execution, finished product testing data is collected through online testing equipment. The finished product testing data includes at least one of the following: actual outer diameter of the core drilling product, actual inner diameter of the core drilling product, actual coating thickness, actual coating uniformity index, and actual active oxygen disinfection efficiency. Read the corresponding process constraints from the geometric constraint field, material constraint field, and functional constraint field of the feature anchoring carrier, including at least one of the following: dimensional tolerance range, coating thickness tolerance range, coating uniformity tolerance range, and active oxygen disinfection efficiency threshold. The finished product inspection data is compared with the process constraints item by item to generate comparison results for each item. The comparison results include qualified, exceeding the upper limit, exceeding the lower limit, offset direction, and offset amount. When all comparison results are qualified, the current version of the feature anchoring carrier remains unchanged; When any comparison result exceeds the upper or lower limit, the feature anchoring carrier is locked, a batch stop command is generated, and the comparison result is written as a quality anomaly record into the verification record field of the feature anchoring carrier. When the comparison result is qualified but the offset direction exists and the cumulative offset reaches the preset drift threshold, the corresponding process constraints in the feature anchoring carrier are fine-tuned according to the offset direction and the offset, the version number of the feature anchoring carrier is updated, and the updated constraints are written into the corresponding constraint field.

9. The core drilling product manufacturing process control method based on global collaborative scheduling as described in claim 8, characterized in that, When the comparison result is qualified but the offset direction exists and the cumulative offset amount reaches a preset drift threshold, the corresponding process constraints in the feature anchoring carrier are fine-tuned according to the offset direction and the offset amount, including: The comparison result data of historical batches is read from the verification record field of the feature anchoring carrier. The comparison result data of historical batches includes the offset direction and offset sequence of N consecutive batches. The offset sequence is filtered by moving average to eliminate random fluctuations in a single batch and generate a filtered offset trend value. When the absolute value of the filtered offset trend value exceeds the preset drift threshold, the fine-tuning condition is triggered; The fine-tuning direction is determined based on the offset direction, and the fine-tuning step size is calculated based on the filtered offset trend value. The fine-tuning step size is positively correlated with the filtered offset trend value. The fine-tuning step size is adjusted in the opposite direction of the offset direction to the center value of the corresponding process constraint in the feature anchoring carrier to generate the updated process constraint. Keep the allowable range of the process constraints unchanged, and only adjust the position of the center value; Write the updated process constraints into the constraint field corresponding to the feature anchoring carrier, and increment the version number; The fine-tuning record is written into the verification record field of the feature anchoring carrier. The fine-tuning record includes the fine-tuning timestamp, the constraints before fine-tuning, the constraints after fine-tuning, and the fine-tuning trigger reason.

10. A core drilling product manufacturing process control system based on global collaborative scheduling, characterized in that, include: The first generation module is used to acquire the geometric features, material features and functional features of the core product, and to fuse the geometric features, material features and functional features through a feature anchoring algorithm to generate a feature anchoring carrier, wherein the feature anchoring carrier contains the process constraints of the core product. The first shared module is used to deploy the feature anchoring carrier as a collaborative signaling to the shared storage space, so that the data perception, digital modeling, process simulation, production line execution and quality control inspection links of the manufacturing process can read and write in real time, so that each link can perform its own operation with the feature anchoring carrier as a unified constraint benchmark. The first comparison module is used to collect the operating data of the manufacturing process in real time during the data perception stage, compare the operating data with the process constraints, and generate an early warning signal when the operating data exceeds the process constraints and write it back to the feature anchoring carrier. The first construction module is used in the digital modeling stage to construct a digital twin model of the core drilling product using the process constraints as boundary constraints. The first deduction module is used to deduce the process connection scheme and process parameter scheme within the feasible domain defined by the feature anchor carrier in the process deduction stage, generate a production process optimization scheme that meets the process constraints, and write the production process optimization scheme back to the feature anchor carrier. The first control module is used to read the production process optimization scheme from the feature anchoring carrier during the production line execution process, dynamically control the operating parameters of the production line equipment according to the production process optimization scheme, and send the real-time data during the execution process back to the feature anchoring carrier for continuous verification. The first detection module is used to collect finished product detection data in the quality control detection process, compare the finished product detection data with the process constraints, and maintain or update the feature anchoring carrier based on the comparison results.

Images