Hierarchical data interaction industrial measurement and control method

Abstract

The application relates to the technical field of industrial automation, and particularly discloses a layered data interaction industrial measurement and control method, which binds field sensors and actuators through an embedded control module, collects equipment operation data, and executes local autonomous process control logic. When communication is abnormal or interrupted, the embedded control module stores event data and a consistency identifier in a local cache unit to maintain autonomous operation, thereby avoiding system shutdown caused by communication interruption; after communication is restored, breakpoint continuation and integrity check are completed based on the consistency identifier, so that data integrity and business continuity are guaranteed. The layered architecture of software and hardware decoupling provides a standardized data interface to one or more upper software platforms through a data interaction and distribution mechanism, the upper platforms are only used for data display, analysis, reporting or business integration, do not participate in process control, stability, expansibility and adaptability are improved, and the method is suitable for various industrial measurement and control scenes.

Description

Technical Field

[0001] This invention relates to the field of industrial automation technology, and in particular to a hierarchical data interaction industrial measurement and control system and method. Background Technology

[0002] In the field of industrial automation and intelligent measurement and control, various industrial scenarios such as weighing, batching, access control, liquid level monitoring, and emission monitoring place stringent demands on the stability, scalability, and compatibility of systems. Existing industrial measurement and control systems generally employ a tightly coupled architecture, resulting in a deep binding between the software layer and hardware control logic. Process control relies excessively on upper-level software commands, leading to insufficient redundancy and a complex interplay between data acquisition and execution logic. This architecture is particularly limiting in multi-site deployment scenarios, exhibiting a strong dependence on communication. Any communication interruption can cause system downtime, severely impacting the continuity of industrial production and resulting in high maintenance costs and extremely poor scalability.

[0003] While some existing solutions attempt to address these issues by incorporating modular or edge computing technologies, numerous limitations remain. Firstly, the upper-level software is not entirely detached from process control, failing to achieve true hardware-software decoupling. This results in insufficient system flexibility and difficulty adapting to diverse industrial scenarios. Secondly, multi-protocol compatibility is weak, supporting only 2-3 types of industrial protocols. Protocol expansion requires modification of core code, leading to inefficient cross-system integration and long adaptation cycles. Furthermore, in abnormal or network outage scenarios, the ability to guarantee data consistency and integrity is weak, easily resulting in data loss and sequence errors, failing to meet the requirements for traceability and auditability of critical data in industrial production.

[0004] The existing architecture struggles to cope with industry trends such as large-scale heterogeneous device access and business continuity assurance, and a single device failure can easily lead to a complete supply chain paralysis. Furthermore, in specific scenarios such as fault linkage, resource scheduling, predictive maintenance, self-learning optimization, and trajectory auditing, there is a lack of solutions deeply integrated with the core architecture, resulting in fragmented functionality and poor collaboration.

[0005] Therefore, there is an urgent need for an industrial measurement and control architecture that can simultaneously solve the two core pain points of software and hardware decoupling and multi-protocol compatibility, cover the collaborative needs of all scenarios, and improve stability, scalability and adaptability to meet the diversified needs of modern industrial production. This architecture is a layered data interaction industrial measurement and control system and method. Summary of the Invention

[0006] This invention provides a hierarchical data interaction industrial measurement and control system and method, which can improve stability, scalability and adaptability, and meet the diversified needs of modern industrial production.

[0007] To solve the above-mentioned technical problems, this application provides the following technical solution: A hierarchical data interaction industrial measurement and control method includes: Step 1: Deploy at least one embedded control module. The embedded control module establishes a unique binding relationship with the sensors and actuators in the industrial field, independently collects equipment operation data, performs standardized processing on the equipment operation data to obtain standardized collected data, and then autonomously executes the process control logic according to a preset strategy. The preset strategy supports threshold adjustment, hysteresis setting and jitter suppression. Step 2: In the event of a communication failure, the embedded control module locally caches event data and records consistency identification information through a local block cache unit to maintain autonomous operation and complete process control. The consistency information is used to ensure the integrity and orderliness of data retransmission. Step 3: Deploy the data interaction and distribution engine. The data interaction and distribution engine receives standardized collection data reported by the embedded control module. First, it performs format conversion and integrity verification on the standardized collection data to obtain compliant data. Then, it performs cache scheduling and reliable distribution on the compliant data to generate distributable data. Finally, it distributes the distributable data to multiple upper-level software platforms across systems through a plug-in protocol adaptation unit. The plug-in protocol adaptation unit supports at least two industrial communication protocols. Step 4: After communication is restored, the data interaction and distribution engine performs monotonic merging and integrity verification on the cached event data based on the consistency information to obtain complete event data and complete the breakpoint resume transmission. Step 5: The upper-level software platform receives the distributable data from the data interaction and distribution engine through a standardized interface, and only displays / analyzes / reports / integrates the distributable data.

[0008] The basic principles and beneficial effects of this solution are as follows: This solution breaks the constraints of a tightly coupled model through a layered architecture of "autonomous execution of embedded control modules + data consumption by upper-level software." After establishing a unique binding relationship with sensors and actuators, the embedded control module independently completes data acquisition, standardized processing, and execution of process control logic, without relying on upper-level software commands, completely severing the control dependency link between hardware and software. The upper-level software platform only receives distributable data through standardized interfaces and only undertakes data consumption functions such as display and analysis; it does not have underlying process control permissions, achieving complete decoupling of hardware and software functions. Under this architecture, hardware-side control logic optimization or hardware device replacement does not require modification of the upper-level software, and upgrades and iterations of the upper-level software will not affect the underlying control execution, reducing system maintenance costs and providing a foundation for independent evolution of hardware and software.

[0009] The data interaction and distribution engine features a plug-in protocol adaptation unit that enables multi-protocol compatibility. This unit supports at least two industrial communication protocols, and protocol extensions are installed via plug-in modules, eliminating the need to modify the engine's core code. This overcomes the limitations of customized development and difficult expansion required for system protocol adaptation. Standardized data collected by the embedded control module, after being converted by the engine, can be quickly adapted to the communication protocol requirements of different upper-level software platforms through the adaptation unit, achieving efficient cross-system distribution. This plug-in design allows the system to flexibly interface with various industrial protocols, meeting the cross-system interface needs in scenarios such as weighing, liquid level monitoring, and emission monitoring, thus improving the system's protocol compatibility.

[0010] Meanwhile, in the event of communication failure, the embedded control module locally caches event data and records consistency identification information through a local block cache unit, maintaining autonomous operation and avoiding system downtime caused by network outages. After communication is restored, it completes breakpoint resumption based on consistency information, ensuring data integrity and business continuity, and improving stability. The hardware-software decoupled architecture supports batch addition of embedded control modules and flexible access to the upper-level software platform. The pluggable protocol adaptation unit reduces the difficulty of integrating new protocols and can quickly respond to the needs of multi-site deployment and heterogeneous device access, improving scalability. The preset strategies of the embedded control module support threshold adjustment, hysteresis setting, and jitter suppression, which can adapt to the operating characteristics of different devices. Multi-protocol compatibility and hierarchical data processing mechanisms can seamlessly adapt to various industrial measurement and control scenarios such as weighing, batching, and access control, improving universality and adaptability.

[0011] Furthermore, in step 1, the threshold adjustment in the preset strategy adopts an adaptive threshold algorithm, and the adaptive threshold can be calculated using the following formula: in, The adaptive threshold after the kth adjustment. The threshold after the (k-1)th adjustment. For learning rate, The characteristic value of the device operation data collected in the kth iteration is denoted as ; the jitter suppression is achieved through a cooling period and a hysteresis strategy.

[0012] The beneficial effects are as follows: the adaptive threshold algorithm enables the threshold to be dynamically adjusted according to the operating status of the equipment, thereby improving the accuracy of fault identification; the cooling period and hysteresis strategy effectively suppress false triggering caused by signal jitter, ensure the stable execution of control logic, and reduce the losses caused by frequent equipment switching.

[0013] Furthermore, in step 3, the priority scheduling of the data interaction and distribution engine adopts a weighted fair queue scheduling algorithm, and the scheduling weight of the distributable data can be calculated using the following formula: in, Let i be the scheduling weight of the i-th class of distributable data. Let i be the priority coefficient for the i-th data type. Let i be the packet loss rate of the link for the i-th type of data. Let be the urgency coefficient of the i-th type of data. , , Let be the weight coefficient, and satisfy... The back pressure control is achieved through queue water level monitoring. Back pressure is triggered when the queue water level is higher than the first preset threshold and released when it is lower than the second preset threshold.

[0014] The beneficial effects are as follows: the weighted fair scheduling algorithm balances the data transmission needs of different priorities, link quality and urgency, ensuring that critical data is delivered first; backpressure control effectively suppresses queue congestion in high-concurrency scenarios, avoids data loss, and improves high-concurrency resilience.

[0015] Furthermore, in step 1, after collecting the device's operating data, the embedded control module performs anomaly determination based on multi-sensor cross-validation and difference analysis, outputs the anomaly level, and then triggers linkage actions such as shutdown, redundancy switching, degradation / bypass, or local alarm according to the anomaly level, and records the action code and cause code.

[0016] The beneficial effects are as follows: multi-source data fusion anomaly judgment improves the accuracy and timeliness of fault identification and reduces the risk of misjudgment by a single sensor; hierarchical linkage handling enables rapid response and precise control of faults; action codes and cause codes facilitate subsequent auditing and traceability, and enhance system reliability and maintainability.

[0017] Furthermore, in step 1, the embedded control module periodically collects its own operating status and generates a resource status table, which includes processor load, remaining memory, interface occupancy rate, and communication quality indicators. When a task request is received, the module determines executability based on the resource status table and task constraints. If executable, it executes locally; otherwise, it generates a dispatch request. The data interaction and distribution engine aggregates the statuses of multiple modules for dynamic orchestration to determine the target module and execution sequence.

[0018] The beneficial effects are: resource status awareness enables dynamic adaptation and allocation of tasks, avoiding resource waste or overload caused by fixed allocation; the collaborative orchestration mechanism improves the efficiency of multi-module collaboration, and redundant assignment reduces the impact of single point of failure, enhancing system flexibility and scalability.

[0019] Furthermore, in step 1, historical operation logs of the equipment are collected, and after data governance, they are uploaded to the main control terminal / cloud to train the basic prediction model and version it; a lightweight model is deployed at the edge to analyze the real-time collected equipment operation data, output risk scores and uncertainties, and combine adaptive thresholds and hysteresis strategies to trigger early warning prompts, load reduction operation, redundancy switching or maintenance work order dispatch maintenance intervention actions.

[0020] The beneficial effects are as follows: the hierarchical predictive maintenance mechanism takes into account both real-time performance and global optimization, identifies equipment degradation trends in advance, and reduces unplanned downtime; the lightweight model adapts to edge resource constraints, and the multi-dimensional intervention strategy balances equipment maintenance and production continuity, thereby reducing operation and maintenance costs.

[0021] Furthermore, in step 1, task-side metrics and module-side metrics are collected to establish a historical behavior table. The task-side metrics include time consumption, completion quality, and energy consumption, while the module-side metrics include response latency, load rate, and failure rate. In step 3, based on the historical behavior table, optimization feedback is generated through rule-based learning and model-based learning to dynamically adjust the task allocation strategy and module response parameters. The strategy release adopts a shadow evaluation and gray release mechanism and sets a non-deterioration baseline.

[0022] The beneficial effects are as follows: the two-way self-learning mechanism enables the continuous evolution of control logic, adapts to field fluctuations and equipment status changes, and reduces reliance on manual intervention; shadow evaluation and gray release ensure the security of strategy updates, and non-degraded baselines ensure system stability and improve overall operational performance.

[0023] Furthermore, in step 1, the embedded control module captures the source of the instruction, the issuance time, the execution action, and the response status, and caches them locally in a block format after adding consistency information; in step 2, a chain of events is constructed through the data interaction and distribution engine, which associates commands and responses from multiple devices; in step 4, the host system displays the event chain in time sequence, supports multi-dimensional timeline playback, and generates a sequence fingerprint during playback to compare with the original trajectory to verify the determinism of the playback.

[0024] The beneficial effects are as follows: full-chain event logging enables traceable operations and delineable responsibilities, meeting compliance audit requirements; chained events and replay deterministic verification ensure data authenticity; multi-dimensional replay improves the efficiency of fault review and shortens the time for problem localization.

[0025] Furthermore, in step 3, the plug-in protocol adaptation unit supports at least two of the industrial communication protocols including Profinet, EtherNet / IP, and CC-LinkIE. Protocol extensions are implemented through plug-in installation, without requiring modification of the core code of the data interaction and distribution engine. In step 4, the integrity verification adopts a hash chain verification method, optionally calculating the data hash value using the following formula: in, Let k be the hash value of the kth data item. The hash value of the (k-1)th data item. This is a string concatenation operation. This is the original content of the k-th data item. It is the SHA-256 hash function.

[0026] The benefits are as follows: pluggable protocol extensions reduce system upgrade costs, adapt to the protocol requirements of different industrial scenarios, and improve compatibility; hash chain verification ensures the integrity and tamper-proofness of data transmission and storage, enhances data trustworthiness, and meets security and compliance requirements. Attached Figure Description

[0027] Figure 1 This is a flowchart of steps 1-2 of an embodiment of a hierarchical data interaction industrial measurement and control method.

[0028] Figure 2 This is a flowchart of steps 3-5 of an embodiment of a hierarchical data interaction industrial measurement and control method. Detailed Implementation

[0029] The following detailed description illustrates the specific implementation method: This invention discloses a hierarchical data interaction industrial measurement and control method, as shown in the appendix. Figure 1 Appendix Figure 2 As shown, this application is used in the scenario of emission monitoring and pump station linkage control in chemical industrial parks. It involves emission sensors, pump station actuators, data interaction and distribution engine, and upper-level monitoring platform. The specific implementation process is as follows: Step 1: Deployment of embedded control modules, data processing, and process execution.

[0030] Three embedded control modules, numbered M1, M2, and M3, are deployed and connected to sensors (SO2 sensor, NO sensor, etc.) at three emission monitoring points in the chemical industrial park, respectively. x Sensors (including flow sensors) and their corresponding pump station actuators (regulating valves, start / stop pumps) establish a unique binding relationship through a challenge-response authentication mechanism, with a binding lease set for 90 days. The sensors collect real-time equipment operating data: SO2 concentration (unit: mg / m³), NO... xConcentration (unit: mg / m³), emission flow rate (unit: m³ / h). The embedded control module standardizes the collected data: first, it performs noise reduction, using a median filter algorithm to remove outliers caused by sudden sensor interference, such as removing 120 mg / m³ (far exceeding the normal fluctuation range) from the SO2 concentration data, retaining a reasonable data range; then, it performs temperature drift compensation, based on the ambient temperature collected by the sensor's built-in temperature sensor, using a linear compensation formula. Correcting the effect of temperature drift, among which, For the compensated data, This is the original data. For compensation coefficient, For ambient temperature, The reference temperature was used; finally, feature extraction was performed, and the maximum concentration, average concentration, and fluctuation variance within 5 minutes were extracted as feature values ​​to obtain standardized collected data.

[0031] The autonomous execution flow control logic based on the preset strategy is as follows: Threshold adjustment adopts an adaptive threshold algorithm, with a learning rate set as follows. Initial threshold (SO2 concentration threshold); Feature value from the first acquisition The threshold after the first adjustment The feature values ​​collected in the second sampling. Then the threshold after the second adjustment This process is repeated to adapt the device's operating state; jitter suppression is achieved through a cooling period and hysteresis strategy. The cooling period is set to 500ms, and the hysteresis amplitude is 10% of the threshold, i.e., when the SO2 concentration is higher than... The alarm logic is triggered when the time is lower than the threshold. Clear alarms promptly to avoid frequent switching.

[0032] Anomaly detection and linkage are as follows: The embedded control module performs anomaly detection based on multi-sensor cross-validation and difference analysis. For example, when the SO2 sensor detects a concentration exceeding the threshold, and NO... x If the sensor concentration increases by more than 30% synchronously while the flow sensor shows no abnormalities, it is judged as a Level 1 anomaly, triggering the pump station to reduce its load. If only a single sensor's data exceeds the threshold, it is judged as a Level 3 anomaly, triggering a local audible and visual alarm. Resource status acquisition and task execution judgment are as follows: The embedded control module collects its own operating status every 30 seconds and generates a resource status table, including processor load, remaining memory, interface occupancy rate, and communication quality indicators. When it receives a task request to "increase the sampling frequency to 1 second / time", it determines, based on the resource status table, that the processor load does not exceed the 80% threshold and that memory is sufficient, thus meeting the task constraints, and executes the task locally. If the M2 module's processor load reaches 90%, it is determined that the task cannot be executed, and a dispatch request is generated and sent to the data interaction and distribution engine.

[0033] Step 2: Local caching and autonomous operation in case of communication failure.

[0034] When communication in the chemical industrial park is interrupted due to fiber optic failure, the embedded control module M1 detects a communication connection timeout (timeout threshold of 3 seconds), i.e., a communication anomaly. It then locally caches event data and records consistency identification information through the local block cache unit: the event data includes a globally unique event identifier, timestamp, anomaly level, and executed action; the consistency information includes the event sequence number and data verification summary to ensure the integrity and orderliness of data retransmission; the module continues to autonomously execute process control according to preset strategies, such as continuously monitoring emission concentrations, and automatically triggering the pump station to resume normal operation when the concentration drops below the threshold, ensuring that emission control is not interrupted.

[0035] Step 3: Deployment of the data interaction and distribution engine and data processing and distribution.

[0036] One data interaction and distribution engine is deployed in a distributed architecture to receive standardized collected data reported by three embedded control modules. The engine converts the JSON data reported by the modules into the industry-standard OPCUA format. A hash verification method is used to calculate the hash value of the data and compare it with the verification value reported by the modules; if they match, the data is considered compliant. A weighted fair queue scheduling algorithm is employed, with weighted coefficients set. , , Priority coefficient for emission exceedance alarm data (i=1) Packet loss rate Urgency level coefficient Then the scheduling weight Priority coefficient for routine monitoring data (i=2) Packet loss rate Urgency level coefficient Then the scheduling weight The engine prioritizes distributing alarm data. A first preset threshold (high queue level) is set at 80%, and a second preset threshold (low queue level) is set at 50%. When the peak data received by the engine causes the queue level to reach 85%, a backpressure mechanism is triggered, sending a flow-limiting command to the embedded control module, which temporarily reduces its reporting frequency. When the queue level drops to 45%, the backpressure is released, and the normal reporting frequency is restored. The engine sends a data reception confirmation to the module; data packets without confirmation are retransmitted after 3 seconds, up to a maximum of 5 retransmissions. The event_id is used as an idempotent key to avoid duplicate processing of the same data. The pluggable protocol adaptation unit pre-integrates Profinet and EtherNet / IP industrial communication protocols, distributing distributable data to the HMI monitoring screen (Profinet protocol), the MES production management system (EtherNet / IP protocol), and the environmental audit system (EtherNet / IP protocol), respectively. When a new CC-LinkIE protocol needs to be added for system integration, it is achieved by installing a protocol plugin without modifying the engine's core code.

[0037] Step 4: Resume transmission after communication is restored.

[0038] Communication was restored after a two-hour interruption. The data interaction and distribution engine processed the 120 event data cached in the M1 module based on consistency information (event sequence number, event_id): merging data in timestamp order and removing three duplicate reports; a hash chain verification method was used, through a formula... Calculate the data hash value, where Use the SHA-256 hash function; assume the hash value of the first data item is... The hash value of the second data item After sequential calculation, the data is compared with the hash chain reported by the module to confirm that the data has not been tampered with or missing, thus obtaining complete event data and completing the breakpoint resume transmission to each upper-level platform.

[0039] Step 5: Data processing on the host software platform.

[0040] After receiving distributable data, the HMI monitoring screen displays the emission concentration curves of each monitoring point and the operating status of the pumping station in real time; the MES production management system generates emission statistics reports based on the data and optimizes production scheduling; the environmental audit system archives the data for compliance review, and none of the upper-level platforms have the authority to trigger the start-up and shutdown of the underlying pumping station, threshold adjustment and other process controls.

[0041] Step 6: Self-learning optimization and trajectory auditing and playback Data collection includes task-side metrics and module-side metrics: Task-side metrics include data collection time, completion quality, and energy consumption; module-side metrics include response latency, load rate, and failure rate, all uniformly written into the historical behavior table; Module response parameters are adjusted through rule-based learning, such as adjusting the sampling frequency during high-load periods from 1 second / time to 2 seconds / time; Task allocation strategies are optimized through model-based learning, prioritizing the allocation of data archiving tasks from the environmental audit system to the lower-load M3 module; A shadow evaluation mechanism is adopted, where the new strategy is first run in the background to compare the effects, and if there is no degradation after 1 day, it is gradually released to 1 module, and after 24 hours of normal operation, it is fully deployed, setting the non-degradation baseline to 95% of the original operating metrics, and allowing for rapid rollback in case of anomalies.

[0042] The embedded control module M1 captures the source of the instruction, the issuance time, the execution action, and the response status, and caches it locally in a block after adding consistency information. The data interaction and distribution engine constructs a chain of events, linking the complete chain of "sensor detection of exceeding the standard → module judgment of abnormality → triggering load reduction → concentration recovery → restoration of normal operation", and annotates information such as control source and triggering conditions. The upper-level environmental protection audit system displays the event chain in time sequence, supports multi-dimensional timeline playback, generates a sequence fingerprint during playback and compares it with the original trajectory to verify that the playback result is consistent with the original execution, ensuring audit traceability.

[0043] Example 2 This embodiment also discloses a hierarchical data interaction industrial measurement and control system for executing the method of Embodiment 1 above. The system architecture is as follows: The embedded control module cluster includes three embedded control modules (M1, M2, M3). Each module is configured with a binding unit, a data acquisition and processing unit, a strategy execution unit, a resource status acquisition unit, a local cache unit, and a communication unit. Among them, the binding unit supports challenge-response authentication to achieve unique binding and lease management with sensors and actuators. The data acquisition and processing unit integrates median filtering and temperature drift compensation algorithms, has feature extraction function, and outputs standardized acquisition data. The strategy execution unit has built-in adaptive threshold algorithm, cool-down period and hysteresis strategy, supports anomaly level judgment and linkage action triggering (shutdown, redundancy switching, etc.), and records action code and cause code. The resource status acquisition unit periodically collects indicators such as processor load and remaining memory, generates a resource status table, and has the function of task executability judgment and dispatch request generation. The local cache unit supports block caching of event data and consistency information and has power failure protection function. The communication unit supports bidirectional communication with the data interaction and distribution engine to realize data reporting and instruction reception.

[0044] The data interaction and distribution engine comprises a data receiving unit, a data processing unit, a protocol adaptation unit, a breakpoint resumption unit, a collaborative orchestration unit, and an optimization engine unit. The data receiving unit receives standardized collected data reported by the embedded control module and supports parallel access from multiple modules. The data processing unit includes a format conversion module, an integrity verification module, a priority scheduling module, a backpressure control module, and a retransmission and idempotency processing module. The protocol adaptation unit integrates Profinet and EtherNet / IP protocol plugins, supporting protocol extensions. The breakpoint resumption unit performs monotonic merging and integrity verification (hash chain verification) of event data based on consistency information, enabling breakpoint resumption. The collaborative orchestration unit aggregates resource status tables from multiple modules, dynamically orchestrates dispatch requests, and determines the target module and execution sequence. The optimization engine unit generates optimization feedback based on historical behavior tables through rule-based and model-based learning, supporting shadow evaluation, canary releases, and policy rollback.

[0045] The upper-level software platform includes an HMI monitoring screen, a MES production management system, and an environmental audit system, each equipped with a data receiving unit, a data processing unit, and an audit and playback unit. The data receiving unit receives distributable data from the data interaction and distribution engine via a standardized interface. The data processing unit provides real-time display through the HMI, generates reports and integrates with production through the MES, and the environmental audit system performs data archiving and compliance review. The audit and playback unit features event chain display, multi-dimensional timeline playback, and playback deterministic verification. Auxiliary units include a time synchronization unit and a security protection unit. The time synchronization unit uses PTP time synchronization technology to unify the time of the embedded control module, the data interaction and distribution engine, and the upper-level software platform. The security protection unit supports encrypted data transmission and storage, and has access control and permission management functions. This system achieves edge autonomy through an embedded control module cluster, facilitates data flow through the data interaction and distribution engine, and focuses on data consumption through the upper-level software platform. The collaborative work of these units ensures the stability, scalability, and traceability of industrial measurement and control.

[0046] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A hierarchical data interaction industrial measurement and control method, characterized in that, include: Step 1: Deploy at least one embedded control module. The embedded control module establishes a unique binding relationship with the sensors and actuators in the industrial field, independently collects equipment operation data, performs standardized processing on the equipment operation data to obtain standardized collected data, and then autonomously executes the process control logic according to a preset strategy. The preset strategy supports threshold adjustment, hysteresis setting and jitter suppression. Step 2: In the event of a communication failure, the embedded control module locally caches event data and records consistency identification information through a local block cache unit to maintain autonomous operation and complete process control. The consistency information is used to ensure the integrity and orderliness of data retransmission. Step 3: Deploy the data interaction and distribution engine. The data interaction and distribution engine receives standardized collection data reported by the embedded control module. First, it performs format conversion and integrity verification on the standardized collection data to obtain compliant data. Then, it performs cache scheduling and reliable distribution on the compliant data to generate distributable data. Finally, it distributes the distributable data to multiple upper-level software platforms across systems through a plug-in protocol adaptation unit. The plug-in protocol adaptation unit supports at least two industrial communication protocols. Step 4: After communication is restored, the data interaction and distribution engine performs monotonic merging and integrity verification on the cached event data based on the consistency information to obtain complete event data and complete the breakpoint resume transmission. Step 5: The upper-level software platform receives distributable data from the data interaction and distribution engine through a standardized interface, and only displays / analyzes / reports / integrates the distributable data; it does not participate in the execution of process control logic and does not have the authority to trigger process control logic.

2. The hierarchical data interaction industrial measurement and control method according to claim 1, characterized in that, In step 3, the scheduling of the data interaction and distribution engine determines the distribution order or distribution weight based on data priority, link status and / or urgency; the reliable distribution processing includes at least one of priority queue, rate limiting, back pressure control, retransmission and idempotent deduplication; wherein back pressure control is achieved through queue level monitoring, back pressure is triggered when the queue level is higher than a first preset threshold, and back pressure is released when it is lower than a second preset threshold.

3. The hierarchical data interaction industrial measurement and control method according to claim 2, characterized in that, In step 1, the embedded control module periodically collects its own operating status and generates a resource status table, which includes processor load, remaining memory, interface occupancy rate and / or communication quality indicators. Based on the resource status table and task constraints, the embedded control module determines the executability of task requests and executes the task locally when it is executable, or generates a dispatch request when it is not executable.

4. The hierarchical data interaction industrial measurement and control method according to claim 3, characterized in that, In step 1, after collecting the device's operating data, the embedded control module performs anomaly determination based on multi-sensor cross-validation and difference analysis, outputs the anomaly level, and then triggers linkage actions such as shutdown, redundancy switching, degradation / bypass, or local alarm according to the anomaly level, and records the action code and cause code.

5. The hierarchical data interaction industrial measurement and control method according to claim 4, characterized in that, In step 1, the embedded control module periodically collects its own operating status and generates a resource status table, which includes processor load, remaining memory, interface occupancy rate, and communication quality indicators. When a task request is received, the module determines its executability based on the resource status table and task constraints. If the task is executable, it executes locally; otherwise, it generates a dispatch request. The data interaction and distribution engine aggregates the statuses of multiple modules and dynamically arranges them to determine the target module and execution sequence.

6. The hierarchical data interaction industrial measurement and control method according to claim 5, characterized in that, In step 1, historical operation logs of the equipment are collected, and after data governance, they are uploaded to the main control terminal / cloud to train the basic prediction model and version it. Deploy lightweight models at the edge to analyze real-time collected equipment operation data, output risk scores and uncertainties, and trigger maintenance intervention actions such as early warning prompts, load reduction operation, redundancy switching, or maintenance work order dispatch by combining adaptive thresholds and hysteresis strategies.

7. The hierarchical data interaction industrial measurement and control method according to claim 6, characterized in that, In step 1, task-side metrics and module-side metrics are collected to establish a historical behavior table. The task-side metrics include time consumption, completion quality, and energy consumption, while the module-side metrics include response latency, load rate, and failure rate. In step 3, based on the historical behavior table, optimization feedback is generated through rule-based learning and model-based learning to dynamically adjust the task allocation strategy and module response parameters. The strategy release adopts a shadow evaluation and gray release mechanism and sets a non-deterioration baseline.

8. The hierarchical data interaction industrial measurement and control method according to claim 7, characterized in that, In step 1, the embedded control module captures the command source, issuance time, execution action, and response status, and caches them locally in a block format after adding consistency information. In step 2, a chain of events is constructed through the data interaction and distribution engine, which associates commands and responses from multiple devices. In step 4, the host system displays the event chain in time sequence, supports multi-dimensional timeline playback, and generates a sequence fingerprint during playback to compare with the original trajectory, verifying the determinism of the playback.

9. A hierarchical data interaction industrial measurement and control method according to claim 8, characterized in that, In step 3, the plug-in protocol adaptation unit supports at least two of the industrial communication protocols, including Profinet, EtherNet / IP, and CC-LinkIE. Protocol extensions are implemented through plug-in installation, without requiring modification of the core code of the data interaction and distribution engine. In step 4, the integrity verification adopts a hash chain verification method, optionally calculating the data hash value using the following formula: in, Let k be the hash value of the kth data item. The hash value of the (k-1)th data item. This is a string concatenation operation. This is the original content of the k-th data item. It is the SHA-256 hash function.

10. A hierarchical data interaction industrial measurement and control system, characterized in that, This includes an embedded control module, a data interaction and distribution engine, and an interface layer for the upper-level software platform; The embedded control module is used to establish a unique binding relationship with industrial field sensors and actuators, independently collect equipment operation data and execute process control logic autonomously locally based on preset strategies, and perform local block caching and maintain autonomous operation when communication is abnormal or interrupted. The data interaction and distribution engine is used to perform format conversion, integrity verification, caching, scheduling and reliable distribution processing on the data reported by the embedded control module, and distribute the distributable data to one or more upper-level software platforms through a plug-in protocol adaptation unit; the data interaction and distribution engine completes breakpoint resume transmission based on consistency identification information after communication is restored; and performs unified numbering or mapping on data from multiple embedded control modules to output distributable data with unified field caliber. The upper-level software platform interface layer is used to receive the distributable data through a standardized interface, and only performs display, analysis, report generation and business integration processing. It does not participate in the execution of process control logic and does not have the authority to trigger process control logic.

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