A method and system for managing an industrial manufacturing process based on a PLC

Abstract

The application relates to the field of industrial manufacturing process management, in particular to an industrial manufacturing process management method and system based on PLC. The method comprises the following steps: obtaining running parameters at an upstream processing station, identifying transient running deviation to obtain transient running deviation information; binding the transient running deviation information with the unique identity of a workpiece to generate binding deviation information; querying the binding deviation information at a downstream processing station and adjusting the processing parameters of the equipment of the downstream processing station; generating an event data packet containing the transient running deviation information, the adjusted processing parameters and the running state of the equipment of the downstream processing station and sending the event data packet to a central management system; and the central management system receives the event data packet and triggers a pre-maintenance alarm when a preset early warning condition is met. The method solves the problems of untimely equipment running state monitoring, slow process parameter adjustment and low production resource allocation efficiency in traditional industrial manufacturing management, and solves the technical problems that predictive maintenance and intervention cannot be performed.

Description

Technical Field

[0001] This application relates to the field of industrial manufacturing process management, and more specifically, to a PLC-based industrial manufacturing process management method and system. Background Technology

[0002] In modern industrial manufacturing, the pursuit of production efficiency and product quality is paramount. However, traditional production management methods often face challenges such as untimely monitoring of equipment operating status, slow response to process parameter adjustments, and inefficient allocation of production resources. For example, in automated production lines with multiple workstations connected in series, the central manufacturing execution system (MES) typically acquires equipment operating status data by periodically polling the programmable logic controller (PLC). This periodic acquisition method struggles to capture the extremely short-lived, instantaneous equipment malfunctions that occur between two acquisition cycles.

[0003] These unrecorded transient anomalies, while insufficient to trigger a single piece of equipment to shut down, can lead to minor, tolerable quality defects in the product that can affect subsequent processes. When semi-finished products with such defects flow to downstream workstations, they force downstream equipment to operate under suboptimal conditions, such as requiring greater force to complete assembly tasks, resulting in continuous, slight overload. Existing management systems, lacking the ability to detect high-frequency transient events and unable to effectively correlate a missed "cause" from an upstream workstation (e.g., transient motor overload) with a series of seemingly normal "results" in downstream workstations (e.g., long-term fluctuations in assembly torque within the upper limit of the normal range), cannot identify this process of equipment wear and tear that propagates from upstream problems downstream and gradually accumulates.

[0004] This deficiency prevents the system from tracing and pinpointing the true source of problems before irreversible physical damage and sudden downtime occur in downstream equipment, thus hindering predictive maintenance and intervention. For example, in an automated production line for automotive body structural components, a welding robot experiences a momentary overload due to early wear, resulting in a minor weld point misalignment, which goes undetected by the central system. This defective workpiece then flows to the assembly station, forcing the assembly robot to complete the assembly with greater torque. Over time, this accumulates, damaging the assembly robot's reducer and causing line downtime. The central system, lacking correlation analysis capabilities, cannot link the upstream anomaly to the downstream cumulative damage, thus failing to provide early warnings and root cause maintenance.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] This application discloses a PLC-based industrial manufacturing process management method and system, which aims to solve the problems of untimely monitoring of equipment operating status, slow response to process parameter adjustments, and low efficiency of production resource allocation in traditional industrial manufacturing management. In particular, it addresses the technical challenge of capturing instantaneous operational anomalies and conducting effective correlation analysis, which leads to the inability to perform predictive maintenance and intervention.

[0007] The technical solution of this application is as follows: In a first aspect, this application discloses a PLC-based industrial manufacturing process management method, applied to an industrial manufacturing production line consisting of upstream and downstream processing stations connected in series. The method includes: At the upstream processing station, operating parameters used to characterize the operating status of the equipment are acquired, and instantaneous operating deviations are identified based on preset identification rules to obtain instantaneous operating deviation information. The instantaneous operating deviation is used to characterize the transient deviation of the operating parameters relative to the normal operating range within a preset time window. Obtain the unique identifier of the workpiece currently being processed by the upstream processing station, bind the instantaneous running deviation information with the unique identifier of the workpiece, generate the binding deviation information corresponding to the workpiece, and store the binding deviation information; At the downstream processing station, the unique identifier of the workpiece arriving at the downstream processing station is obtained, and the binding deviation information of the workpiece is queried based on the unique identifier of the workpiece. When the binding deviation information of the workpiece indicates that there is an instantaneous running deviation of the workpiece from the upstream processing station, the processing parameters of the equipment at the downstream processing station are adjusted according to the binding deviation information so that the processing strategy of the equipment at the downstream processing station adapts to the actual state of the workpiece. At the downstream processing station, after the processing of the workpiece is completed, an event data packet containing the instantaneous running deviation information of the workpiece, the adjusted processing parameters, and the equipment operating status of the downstream processing station is generated and sent to the central management system. The central management system receives and stores event data packets, analyzes the correlation between the instantaneous operational deviation of the upstream processing station and the equipment operating status of the downstream processing station based on the event data packets, and triggers a pre-maintenance alarm when the correlation meets the preset early warning conditions.

[0008] Furthermore, when acquiring operating parameters used to characterize the operating status of the equipment, and identifying instantaneous operating deviations based on preset identification rules to obtain instantaneous operating deviation information, the specific steps include: Obtain operating parameters that characterize the operating status of equipment at upstream processing stations, and these operating parameters include operating parameters of at least two different dimensions; The operating parameters are synchronized in time, cleaned, and feature extracted to obtain an instantaneous feature vector that characterizes the transient operating state of the equipment. Based on preset recognition rules, instantaneous operational deviations are identified from instantaneous feature vectors, and instantaneous operational deviation information is generated, wherein the instantaneous operational deviation information includes at least one of deviation type and deviation degree; The instantaneous operational deviation information is bound to the unique identifier of the workpiece to generate and store the corresponding binding deviation information, which includes: The instantaneous operational deviation information is associated and encapsulated with the workpiece's unique identifier to form bound deviation information, which is then stored in a data storage location that is queried based on the unique identifier.

[0009] Furthermore, operating parameters characterizing the equipment's operating status are acquired, and instantaneous operating deviations are identified based on preset recognition rules to obtain instantaneous operating deviation information, specifically including: Real-time acquisition of current processing task type, workpiece material properties, and operating environment parameters; Based on the processing task type, obtain the reference range of equipment operating parameters and the initial deviation identification threshold corresponding to the processing task type from the preset processing task parameter set; Based on the material properties of the workpiece, the reference range of equipment operating parameters and the initial deviation identification threshold are corrected to obtain the reference range of equipment operating parameters and the initial deviation identification threshold after workpiece adaptability adjustment. Based on the operating environment parameters, the baseline range of equipment operating parameters and the initial deviation identification threshold after workpiece adaptability adjustment are further corrected to obtain the dynamic operating parameter range and deviation identification threshold under the current working conditions. The system compares the real-time collected operating parameters with the dynamic operating parameter range, and determines whether there is an instantaneous operating deviation that lasts for a shorter than the preset time threshold and does not reach the equipment shutdown threshold based on the deviation identification threshold, so as to generate instantaneous operating deviation information.

[0010] Furthermore, the method also includes: At the downstream processing station, the event data packet is encapsulated into a structured data packet and sent to the central management system. The structured data packet includes at least the unique identifier of the workpiece, the upstream station identifier, the downstream station identifier, the timestamp, the deviation type and degree in the instantaneous running deviation information, the parameter type and parameter value of the adjusted processing parameters, and the equipment operating status of the downstream processing station. Based on event data packet analysis, the correlation between instantaneous operational deviations of upstream processing stations and equipment operating status of downstream processing stations is established, and a pre-maintenance alarm is triggered when the correlation meets preset warning conditions. Specifically, this includes: The central management system receives structured data packets and matches and integrates them based on the workpiece's unique identifier and timestamp to establish the workpiece's processing history. Based on processing history, the central management system statistically analyzes the frequency of deviation events and the distribution of adjustment parameters for at least one device to obtain statistical results. The statistical results are compared with the preset equipment operating health benchmark. When the deviation of the statistical results from the equipment operating health benchmark reaches a preset level, a pre-maintenance alarm is triggered, and the alarm is associated with the equipment and the type of potential problem. Based on the prompt information, the system analyzes the correlation between the instantaneous operational deviation of the upstream processing station and the equipment operating status of the downstream processing station according to the structured data packet, and triggers a pre-maintenance alarm when the correlation meets the preset warning conditions.

[0011] Furthermore, based on event data packet analysis, the correlation between the instantaneous operational deviation of the upstream processing station and the equipment operating status of the downstream processing station is established, and a pre-maintenance alarm is triggered when the correlation meets preset early warning conditions, specifically including: The instantaneous operational deviation information, adjusted processing parameters, and equipment operating status of downstream processing stations in the event data packet are extracted into data points of multiple dimensions. Construct a composite data structure that includes upstream deviation features and downstream adjustment parameter features based on data points from multiple dimensions; Correlation analysis is performed on multi-dimensional features in composite data structures to determine the strength and direction of the correlation between different features, and the correlation analysis results are obtained. Based on the results of correlation analysis, combinations of deviation events and adjustment behaviors with nonlinear correlation characteristics are identified; A correlation report is generated based on the combination of deviation events and adjustment behaviors according to nonlinear correlation characteristics. The correlation report includes the upstream deviation type, downstream adjustment strategy, and potential equipment risk level. When the risk level of potential equipment in the associated report meets the preset warning conditions, a pre-maintenance alarm is triggered.

[0012] Furthermore, when the potential equipment risk level in the associated report meets the preset warning conditions, the pre-maintenance alarm will be triggered, and will also include: Based on the correlation report, the devices associated with the alarm, the types of potential problems, and the risk level of the potential devices are determined. Then, the advance maintenance alarms are classified according to the devices, types of potential problems, and risk levels of the potential devices. Based on the current workpiece flow status, equipment load, and maintenance resource availability on the production line, priority is assigned to different categories of pre-maintenance alarms; Priority-based sorting and filtering of pre-maintenance alerts allows for their display and notification according to priority.

[0013] Furthermore, based on the current workpiece flow status, equipment load, and maintenance resource availability on the production line, priorities are assigned to different categories of pre-maintenance alarms, specifically including: Obtain the current workpiece flow status on the production line, which includes the type of workpiece being processed, the number of workpieces, and the preset production priority and quality requirements for each workpiece; Obtain real-time equipment load and maintenance resource availability for each device on the current production line; Based on workpiece type, preset production priority, and quality requirements, assess the degree of primary impact of each pre-maintenance alarm on product quality and production schedule; Based on equipment load and maintenance resource availability, assess the secondary impact of each pre-maintenance alarm on equipment operational stability and maintenance response time; By combining the first level of impact, the second level of impact, and the potential equipment risk level, a priority value is dynamically generated to characterize the urgency of each pre-maintenance alarm, and priority is assigned to each pre-maintenance alarm based on the priority value.

[0014] Furthermore, the pre-maintenance alerts are sorted and filtered based on priority to display and notify users according to priority, specifically including: Based on priority and the devices associated with alarms that are pre-maintained, the maintenance objects associated with the alarms are identified, and the corresponding maintenance teams or personnel are identified accordingly. Obtain the permission scope and information requirements of the maintenance team or maintenance personnel, and based on the permission scope and information requirements, trim or supplement the content of the pre-maintenance alerts to generate customized alert content; Based on the terminal type and response capability of the maintenance team or maintenance personnel, select the message push method and push customized alarm content to the maintenance team's collaboration platform or the maintenance personnel's mobile terminal application; After receiving customized alarm content, the collaboration platform or maintenance terminal outputs a pre-maintenance alarm in at least one of the following ways, depending on the scope of permissions and the type of terminal: pop-up notification, voice notification, or vibration notification.

[0015] Furthermore, when comparing the statistical results with preset equipment operating health benchmarks, the specific steps include: Obtain the current running time, cumulative running cycle, and historical maintenance records of at least one device; Obtain the current processing task type corresponding to at least one device and the workpiece batch information of the current production line, wherein the workpiece batch information includes the material properties and processing requirements of the workpiece; Based on current operating time, cumulative operating cycle and historical maintenance records, assess the wear status and potential failure tendency of the corresponding equipment, and generate equipment aging impact parameters. Based on workpiece batch information and the current processing task type, assess the cumulative stress or wear caused by the workpiece batch to the corresponding equipment, and generate workpiece cumulative impact parameters. Based on the preset equipment operating health benchmark adjustment rules, and combined with the equipment aging influence parameters and workpiece cumulative influence parameters, the preset equipment operating health benchmark is modified to obtain the dynamic health benchmark under the current working conditions. The statistical results were compared with dynamic health benchmarks.

[0016] Secondly, this application also discloses a PLC-based industrial manufacturing process management system, which includes: The upstream workstation data acquisition module is used to acquire operating parameters that characterize the operating status of the equipment at the upstream processing workstation, and to identify instantaneous operating deviations based on preset identification rules to obtain instantaneous operating deviation information. The instantaneous operating deviation is used to characterize the transient deviation of the operating parameters relative to the normal operating range within a preset time window. The information binding and transfer module is used to obtain the unique identifier of the workpiece currently being processed by the upstream processing station, bind the instantaneous running deviation information with the unique identifier of the workpiece, generate the binding deviation information corresponding to the workpiece, and store the binding deviation information. The downstream workstation strategy adjustment module is used to obtain the unique identifier of the workpiece arriving at the downstream processing station, and query the binding deviation information of the workpiece based on the unique identifier of the workpiece; when the binding deviation information of the workpiece indicates that there is an instantaneous running deviation of the workpiece from the upstream processing station, the processing parameters of the equipment at the downstream processing station are adjusted according to the binding deviation information so that the processing strategy of the equipment at the downstream processing station adapts to the actual state of the workpiece. The event-driven data sending module is used to generate an event data packet containing the instantaneous running deviation information of the workpiece, the adjusted processing parameters, and the equipment operating status of the downstream processing station after the processing of the workpiece is completed, and send the event data packet to the central management system. The central management and early warning module is used to receive and store event data packets in the central management system, analyze the correlation between the instantaneous operating deviation of the upstream processing station and the equipment operating status of the downstream processing station based on the event data packets, and trigger a pre-maintenance alarm when the correlation meets the preset early warning conditions.

[0017] Beneficial Effects: This application overcomes the shortcomings of traditional MES systems, such as the inability to capture instantaneous anomalies due to periodic data polling and the lack of cross-workstation correlation analysis capabilities. Through real-time, high-frequency data acquisition and deviation information binding based on workpiece identification, this application can effectively perceive and record extremely short-duration instantaneous operational deviations, preventing these "overlooked causes" from leading to downstream equipment operating in a "seemingly normal" state for extended periods. This proactive management approach allows the system to trace and locate the true source of problems before irreversible physical damage and sudden downtime occur in downstream equipment, enabling predictive maintenance and intervention. For example, in automated production lines for automotive body structural components, this application can effectively correlate instantaneous overload caused by early wear of welding station robots with long-term accumulated reducer damage in assembly robots, issuing early warnings to prevent production line downtime and significantly improve production efficiency and equipment reliability. Attached Figure Description

[0018] Figure 1 A schematic diagram of a PLC-based industrial manufacturing process management method provided in this application.

[0019] Figure 2 A flowchart of a PLC-based industrial manufacturing process management system provided in this application.

[0020] In the diagram: 1. Upstream workstation data acquisition module; 2. Information binding and transfer module; 3. Downstream workstation strategy adjustment module; 4. Event-driven data sending module; 5. Central management and early warning module. Detailed Implementation

[0021] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] Reference Figure 1 This application proposes a PLC-based industrial manufacturing process management method, applied to an industrial manufacturing production line consisting of upstream and downstream processing stations connected in series. The method includes: S1000: At the upstream processing station, the operating parameters used to characterize the operating status of the equipment are acquired, and the instantaneous operating deviation is identified based on the preset identification rules to obtain the instantaneous operating deviation information. The instantaneous operating deviation is used to characterize the transient deviation of the operating parameters relative to the normal operating range within the preset time window. S2000: Obtain the unique identifier of the workpiece currently being processed by the upstream processing station, bind the instantaneous running deviation information with the unique identifier of the workpiece, generate the binding deviation information corresponding to the workpiece, and store the binding deviation information; S3000: At the downstream processing station, obtain the unique identification of the workpiece arriving at the downstream processing station, and query the binding deviation information of the workpiece based on the unique identification of the workpiece; when the binding deviation information of the workpiece indicates that there is an instantaneous running deviation of the workpiece from the upstream processing station, adjust the processing parameters of the equipment at the downstream processing station according to the binding deviation information so that the processing strategy of the equipment at the downstream processing station adapts to the actual state of the workpiece. S4000: At the downstream processing station, after completing the processing of the workpiece, an event data packet is generated containing the instantaneous running deviation information of the workpiece, the adjusted processing parameters, and the equipment operating status of the downstream processing station, and the event data packet is sent to the central management system. S5000: In the central management system, it receives and stores event data packets, analyzes the correlation between the instantaneous operating deviation of the upstream processing station and the equipment operating status of the downstream processing station based on the event data packets, and triggers a pre-maintenance alarm when the correlation meets the preset early warning conditions.

[0024] In this application, "PLC" refers to a programmable logic controller, which is widely used in industrial automation to control and monitor various equipment and sensors on the production line. The "PLC" in this application is used to acquire equipment operating parameters in real time and perform preliminary data processing and deviation identification.

[0025] "Instantaneous operational deviation" refers to the transient deviation of equipment operating parameters from the normal operating range within a preset time window. This deviation is usually short-lived but can have a cumulative impact on workpiece quality or equipment lifespan. Examples include instantaneous fluctuations in motor current and brief anomalies in sensor readings.

[0026] A "unique identifier" refers to a unique identification code assigned to each workpiece, such as a QR code, RFID tag, or serial number, used to track the status and historical information of the workpiece throughout the production process.

[0027] "Binding deviation information" refers to a data packet that associates instantaneous operational deviation information with the unique identifier of the workpiece, ensuring that each workpiece carries a record of any instantaneous deviations that may have occurred during its processing at the upstream processing station.

[0028] An "event data packet" refers to a set of data generated after processing is completed at a downstream processing station. It contains information on the instantaneous deviation of the workpiece, the adjusted processing parameters, and the operating status of the equipment at the downstream processing station, which is used by the central management system for further analysis.

[0029] The "central management system" refers to the upper-level system responsible for receiving, storing, and analyzing event data packets from various workstations, and performing correlation analysis and early warning management, such as an MES system or a SCADA system.

[0030] The method of this application is applied to an industrial manufacturing production line consisting of upstream and downstream processing stations connected in series. The workpiece goes through multiple processing steps in sequence, and each step is completed by a processing station.

[0031] At the upstream processing station, operating parameters characterizing the equipment's operating status are acquired, and instantaneous operational deviations are identified based on preset recognition rules to obtain instantaneous operational deviation information. For example, the PLC can connect to sensors at the upstream processing station to collect operating parameters in real time, such as motor current, voltage, temperature, vibration frequency, pressure, and speed, and identify anomalies through threshold comparison, moving average, or statistical methods. For instance, when the motor current instantaneously exceeds 10% of the normal operating range within 100 milliseconds, but does not reach the equipment shutdown threshold, it can be identified as an instantaneous operational deviation.

[0032] The system acquires the unique identifier of the workpiece currently being processed by the upstream machining station, binds the instantaneous operational deviation information to the workpiece's unique identifier, generates corresponding bound deviation information for the workpiece, and stores the bound deviation information. For example, when a workpiece enters the upstream machining station, its unique identifier can be obtained through a barcode scanner, RFID reader, or vision recognition system; once an instantaneous operational deviation is detected, the "PLC" or edge computing unit associates the deviation information (such as deviation type, deviation degree, occurrence time, etc.) with the unique identifier, and can write it to an RFID tag, store it in a local database, or send it to a temporary workpiece information server for storage via the network.

[0033] At the downstream processing station, a unique identifier for each workpiece arriving at the station is acquired, and its binding deviation information is queried based on this identifier. When the binding deviation information indicates a momentary operational deviation from the upstream processing station, the processing parameters of the downstream processing station's equipment are adjusted accordingly to adapt the downstream processing station's processing strategy to the actual state of the workpiece. For example, if a momentary current fluctuation at the upstream welding station may slightly reduce the weld strength, the downstream processing station's (e.g., assembly station's) PLC can appropriately increase the assembly torque, adjust the processing speed, or change the tool feed rate to compensate for any potential defects upstream.

[0034] At downstream processing stations, after completing the processing of the workpiece, an event data packet is generated containing instantaneous operational deviation information of the workpiece, adjusted processing parameters, and the operating status of the equipment at the downstream processing station. This event data packet is then sent to the central management system. For example, the inherited instantaneous operational deviation information, the adjusted processing parameters (which parameters were adjusted and by how much), and the operating status of the downstream processing station equipment (motor load, temperature, vibration, etc.) are integrated and encapsulated into a structured event data packet, which is then sent to the central management system via industrial Ethernet or other communication protocols.

[0035] The central management system receives and stores event data packets. Based on these packets, it analyzes the correlation between instantaneous operational deviations of upstream processing stations and the operating status of downstream processing stations. When the correlation meets preset warning conditions, a pre-maintenance alarm is triggered. For example, the central management system can use correlation analysis, pattern recognition, or machine learning to identify potential correlations between "instantaneous overload of upstream motors" and "long-term high-load operation of downstream assembly robots" from historical event data packets. When a certain upstream deviation causes downstream equipment to operate at high loads more frequently than a threshold or for an excessively long period, a pre-maintenance alarm is triggered, guiding maintenance personnel to inspect the relevant equipment and preventing sudden failures and production line downtime.

[0036] Specifically, compared to traditional periodic data acquisition methods, this application can capture short-term but critical anomalies. For example, a motor current overload lasting 50 milliseconds might be overlooked between two acquisition cycles, but this application can identify and record it. Simultaneously, by using a unique identifier to bind deviation information as it flows with the workpiece, downstream processing stations can "know" the upstream situation and adjust their processing strategies. Finally, the central management system establishes a cross-station causal chain based on event data packets, triggering pre-maintenance alarms before irreversible damage occurs to downstream equipment. For example, if a frequent upstream instantaneous operational deviation is detected, causing downstream equipment to be under high load for extended periods, the system prompts maintenance personnel to inspect the upstream equipment, thereby reducing the risk of unexpected downstream downtime.

[0037] In summary, this application constructs an industrial manufacturing process management system capable of real-time perception, intelligent adjustment, and predictive maintenance, solving problems such as difficulty in capturing instantaneous operational deviations, disconnection of upstream and downstream information, and insufficient pre-maintenance, and improving production efficiency, product quality, and equipment operational stability.

[0038] In another embodiment of this application, it is further proposed to obtain operating parameters used to characterize the operating state of the device, and to identify instantaneous operating deviations based on preset identification rules to obtain instantaneous operating deviation information, including: S1100: Obtain operating parameters to characterize the operating status of the equipment at the upstream processing station, and the operating parameters include operating parameters of at least two different dimensions; S1200: Performs time synchronization, data cleaning, and feature extraction on operating parameters to obtain an instantaneous feature vector that characterizes the transient operating state of the equipment; S1300: Based on preset recognition rules, instantaneous operational deviations are identified from instantaneous feature vectors, and instantaneous operational deviation information is generated, wherein the instantaneous operational deviation information includes at least one of deviation type and deviation degree; The instantaneous operational deviation information is bound to the unique identifier of the workpiece to generate binding deviation information corresponding to the workpiece, and the binding deviation information is stored, including: S2100: The instantaneous operational deviation information is associated and encapsulated with the unique identifier of the workpiece to form bound deviation information, and the bound deviation information is stored in the data storage location that is queried based on the unique identifier.

[0039] The operating parameters include at least two different dimensions, specifically referring to equipment operating data collected from multiple physical or operational dimensions. These can include, for example, the equipment's vibration frequency, temperature, current, voltage, pressure, rotational speed, and power consumption. Comprehensive analysis of multi-dimensional data provides a more complete picture of the equipment's transient operating status, reducing the risk of misjudgment or information omission caused by data from a single dimension.

[0040] Furthermore, the operating parameters undergo time synchronization, data cleaning, and feature extraction to obtain an instantaneous feature vector characterizing the transient operating state of the equipment. Time synchronization refers to aligning operating parameters from different sensors or data sources according to a unified time reference to ensure consistency in the time dimension. Data cleaning refers to preprocessing the raw collected operating parameters, such as removing noise, filling in missing values, and correcting outliers. Feature extraction refers to extracting key features from the cleaned operating parameters, such as statistical features (mean, variance, kurtosis, skewness), frequency domain features (spectral energy after Fourier transform, dominant frequency), and time domain features (waveform features, autocorrelation coefficient), and combining them into an instantaneous feature vector to facilitate subsequent deviation identification.

[0041] Specifically, instantaneous operational deviations are identified from instantaneous feature vectors based on preset identification rules, and instantaneous operational deviation information is generated. The preset identification rules can be a combination of one or more algorithms, such as methods based on statistical thresholds (e.g., the 3σ criterion), machine learning models (e.g., Support Vector Machine (SVM), Isolation Forest, Neural Network (NN)), or expert system rules. Instantaneous operational deviation information includes at least one of the following: deviation type and deviation degree. Deviation types include, for example, "overheating," "abnormal vibration," or "current fluctuation," while deviation degrees are expressed as percentages, levels (slight, moderate, severe), or specific numerical values, to achieve the classification and quantification of instantaneous operational deviations.

[0042] Specifically, instantaneous operational deviation information is associated and encapsulated with the unique identifier of the workpiece to form bound deviation information. Association encapsulation refers to logically binding instantaneous operational deviation information (including deviation type, deviation degree, etc.) with the unique identifier of the currently processed workpiece (such as RFID tag, QR code, serial number, etc.) into a data packet or record to ensure workpiece-level traceability and to enable each workpiece to carry its instantaneous operational deviation history experienced at the upstream processing station.

[0043] The binding deviation information is stored in a data storage location that allows for querying based on a unique identifier. This data storage location can be a distributed database, local PLC memory, a database on an edge computing node, or a cloud database. This ensures that downstream processing stations can quickly and accurately query the binding deviation information using the workpiece's unique identifier, thus providing a basis for adjusting processing parameters.

[0044] In another embodiment of this application, it is further proposed to obtain operating parameters used to characterize the operating state of the device, and to identify instantaneous operating deviations based on preset identification rules to obtain instantaneous operating deviation information, specifically including: S1400: Real-time acquisition of current processing task type, workpiece material properties, and operating environment parameters; S1500: Based on the processing task type, obtain the reference range of equipment operating parameters corresponding to the processing task type and the initial deviation identification threshold from the preset processing task parameter set; S1600: Based on the material properties of the workpiece, the reference range of the equipment operating parameters and the initial deviation identification threshold are corrected to obtain the reference range of the equipment operating parameters and the initial deviation identification threshold after the workpiece adaptability adjustment. S1700: Based on the operating environment parameters, the baseline range of equipment operating parameters and the initial deviation identification threshold after the workpiece adaptability adjustment are further corrected to obtain the dynamic operating parameter range and deviation identification threshold under the current working conditions. S1800: Compares the real-time collected operating parameters with the dynamic operating parameter range, and determines whether there is an instantaneous operating deviation with a duration shorter than the preset time threshold and not reaching the equipment shutdown threshold based on the deviation identification threshold, so as to generate instantaneous operating deviation information.

[0045] Specifically, real-time acquisition of the current processing task type, workpiece material properties, and operating environment parameters refers to the real-time collection of key information related to the current processing process through sensors, production management systems, or manual input. The processing task type can be understood as the specific processing operation currently being performed by the equipment, such as drilling, milling, welding, and polishing. Different task types have different requirements and standards for the equipment's operating status. The workpiece material properties refer to the physical and chemical characteristics of the workpiece being processed, such as hardness, density, toughness, and thermal conductivity. These properties directly affect the load and stress that the equipment bears during processing. Operating environment parameters include, but are not limited to, external environmental factors such as workshop temperature, humidity, vibration, and power supply voltage fluctuations. These factors may affect the equipment's sensor readings and mechanical performance.

[0046] Furthermore, based on the machining task type, the baseline range of equipment operating parameters and the initial deviation identification threshold corresponding to the machining task type are obtained from a preset machining task parameter set. The preset machining task parameter set can be a database or lookup table stored in the PLC or central management system, containing predefined normal operating parameter ranges for various known machining task types and thresholds for initial deviation judgment. For example, for a "drilling" task, the normal range and initial deviation identification threshold for parameters such as spindle speed, feed rate, and motor current will be significantly different from those for a "milling" task.

[0047] Based on this, and according to the material properties of the workpiece, the baseline range of equipment operating parameters and the initial deviation identification threshold are corrected to obtain the workpiece-adapted baseline range of equipment operating parameters and the initial deviation identification threshold. For example, when machining high-hardness materials, the equipment may require greater torque or lower speed. In this case, the original baseline range and threshold need to be adjusted accordingly to avoid misjudging normal heavy-load operation as a deviation or treating minor anomalies as normal. This correction aims to make the identification rules better adapt to the differences in equipment operation caused by different material properties.

[0048] Simultaneously, based on the operating environment parameters, the baseline range of equipment operating parameters and the initial deviation identification threshold, after workpiece adaptability adjustments, are further revised to obtain the dynamic operating parameter range and deviation identification threshold under the current operating conditions. For example, when the ambient temperature is high, the motor temperature of the equipment may generally be high. If the threshold at normal temperature is still used, false alarms may be frequently triggered. By considering environmental parameters for correction, the identification rules can be made closer to the current actual operating conditions, improving the accuracy of identification. Therefore, the dynamic operating parameter range and deviation identification threshold are the final judgment criteria after comprehensively considering the processing task, workpiece material, and operating environment.

[0049] Finally, the real-time collected operating parameters are compared with the dynamic operating parameter range, and based on the deviation identification threshold, it is determined whether there is an instantaneous operating deviation that lasts for a shorter than a preset time threshold and does not reach the equipment shutdown threshold, thus generating instantaneous operating deviation information. Instantaneous operating deviation refers to a situation where the equipment operating parameters exceed the normal range for a short period of time, but are not serious enough to require immediate shutdown. This deviation may indicate potential wear, loosening, or minor faults, and can be mitigated by timely detection and adjustment to reduce further damage or quality risks.

[0050] In some preferred embodiments, the following specific example illustrates the situation: Imagine a CNC machining production line for automotive parts, where the upstream machining station is responsible for rough machining the engine block.

[0051] First, the system obtains in real time the current machining task type as "cylinder block rough milling", the material property of the workpiece as "cast iron", and the operating environment parameters, such as the workshop temperature as 28°C.

[0052] Based on the machining task type of "cylinder block rough milling", the system obtains the corresponding equipment operating parameter reference range from the preset machining task parameter set. For example, the spindle speed should be between 800-1200 RPM, the spindle motor current should be between 15-25A, and the initial deviation recognition threshold.

[0053] Next, considering the workpiece's material properties as "cast iron," which has high hardness, the system corrects the reference range and initial deviation recognition threshold. For example, the normal range of the spindle motor current may be corrected to 18-28 A to accommodate the greater torque required for cast iron machining, while the current deviation threshold is slightly relaxed to avoid misjudging normal heavy load fluctuations as deviations.

[0054] The system then further considers the operating environment parameters, namely a workshop temperature of 28°C. If historical data shows that the motor typically exhibits slightly higher normal operating temperature and current at higher ambient temperatures, the system will again revise the baseline range and thresholds after workpiece adaptability adjustments. For example, the dynamic operating parameter range of the spindle motor current might be ultimately determined to be 19-29 A, with corresponding adjustments made to the deviation identification threshold.

[0055] During machining, if the spindle motor current is detected to reach 30A for a short period (e.g., for 2 seconds) but does not reach the equipment shutdown threshold (e.g., 35A), the system will compare it with the dynamic operating parameter range (19-29A) under the current operating conditions and determine that a momentary operating deviation has occurred based on the adjusted deviation identification threshold. At this time, the system will generate momentary operating deviation information including the deviation type (e.g., "spindle overload") and the deviation degree (e.g., "minor"), and bind it to the unique identifier of the currently machining cylinder for subsequent querying and processing at downstream workstations. This dynamically adjusted identification method effectively distinguishes between normal operating condition fluctuations and true momentary deviations, improving the accuracy and reliability of identification.

[0056] In another embodiment of this application, the method further includes: S5100: At the downstream processing station, the event data packet is encapsulated into a structured data packet and sent to the central management system. The structured data packet includes at least the unique identifier of the workpiece, the upstream station identifier, the downstream station identifier, the timestamp, the deviation type and degree in the instantaneous running deviation information, the parameter type and parameter value of the adjusted processing parameters, and the equipment operating status of the downstream processing station. Based on event data packet analysis, the correlation between instantaneous operational deviations of upstream processing stations and equipment operating status of downstream processing stations is established, and a pre-maintenance alarm is triggered when the correlation meets preset early warning conditions, including: S5200: The central management system receives structured data packets and matches and integrates them based on the workpiece's unique identifier and timestamp to establish the workpiece's processing history. S5300: The central management system statistically analyzes the frequency of deviation events and the distribution of adjustment parameters for at least one device based on the processing history, and obtains the statistical results. S5400: Compares the statistical results with the preset equipment operating health benchmark. When the deviation of the statistical results from the equipment operating health benchmark reaches a preset level, a pre-maintenance alarm is triggered, and the alarm message indicates the equipment associated with the alarm and the type of potential problem. S5500: Based on the prompt information, analyze the correlation between the instantaneous operating deviation of the upstream processing station and the equipment operating status of the downstream processing station according to the structured data packet, and trigger the pre-maintenance alarm when the correlation meets the preset warning conditions.

[0057] Specifically, the structured data packet is designed to contain several key fields, such as a unique identifier for each workpiece to be processed; upstream and downstream station identifiers to specify the specific station where the event occurred; a timestamp to record the chronological order of the event; the type and degree of deviation in the instantaneous operational deviation information to quantify the abnormal conditions of the upstream station; the parameter type and value of the adjusted processing parameters to record the specific adjustments made by the downstream station to adapt to the workpiece state; and the equipment operating status of the downstream processing station to reflect the actual performance of the downstream equipment during processing. This standardized encapsulation of fields ensures the integrity and consistency of the data during transmission and processing.

[0058] After receiving the structured data packet, the central management system uses the unique identifier and timestamp of the workpiece as key indexes to match and integrate relevant data from different workstations and time points, thereby establishing a processing history record for each workpiece. This processing history record covers key events and status changes in the processing process from upstream to downstream.

[0059] In practical applications, based on processing history records, the central management system performs statistical analysis on the frequency of deviation events and the distribution of adjustment parameters for specific equipment: the frequency of deviation events is used to characterize the number or density of instantaneous operational deviations that occur in the equipment within a certain period of time; the distribution of adjustment parameters is used to characterize the range and trend of parameter adjustments made by downstream equipment in response to upstream deviations; and the statistical results are used to form quantitative indicators of the health status of equipment operation.

[0060] Furthermore, the central management system compares the statistical results with the preset equipment operating health benchmarks. When the statistical results deviate from the preset equipment operating health benchmarks to a preset level, such as when the frequency of deviation events is significantly higher than the preset equipment operating health benchmarks, or when the fluctuation range of the adjustment parameter distribution exceeds the normal threshold, the system triggers a pre-maintenance alarm message. The message indicates the equipment associated with the alarm and the type of potential problem, providing maintenance personnel with actionable clues for locating and handling the issue.

[0061] Based on this, the central management system, using the prompt information and combining the fine-grained fields in the structured data packet, further analyzes the correlation between the instantaneous operational deviation of the upstream processing station and the equipment operating status of the downstream processing station. For example, it analyzes whether a specific upstream deviation corresponds to a specific downstream parameter adjustment, or whether it has a strong correlation with the abnormal operating status of the downstream equipment (such as increased vibration or increased energy consumption). When the correlation meets the preset early warning conditions, such as a high probability correlation between upstream deviation and downstream equipment performance degradation, the system triggers a pre-maintenance alarm.

[0062] In some preferred embodiments, the following specific example illustrates the situation: In a precision machining production line for automotive parts, the upstream machining station is responsible for rough machining and heat treatment of metal blanks, while the downstream machining station performs precision cutting and surface treatment. When the temperature of the upstream heat treatment furnace deviates momentarily within a preset time window (e.g., temperature fluctuation exceeds ±2℃, duration is less than 5 seconds, but does not reach the shutdown threshold), the upstream machining station obtains the instantaneous operational deviation information of "instantaneous temperature deviation, deviation degree: +3℃". At the same time, the workpiece is assigned a unique identifier "Part_XYZ_001", and the two are bound together as "Part_XYZ_001_TempDev_Plus3C" and stored.

[0063] When workpiece “Part_XYZ_001” arrives at the downstream precision cutting station, the downstream station queries its binding deviation information. Due to the upstream temperature deviation, the PLC system of the downstream station adjusts the machining parameters of the precision cutting equipment according to the binding deviation information, such as reducing the cutting speed by 5% and the feed rate by 2%, to adapt to the changes in material hardness or internal stress that may be caused by the workpiece's heat treatment deviation. After machining is completed, the downstream station generates a structured data package, which includes: the workpiece's unique identifier “Part_XYZ_001”, the upstream station identifier “HeatTreat_01”, the downstream station identifier “CNC_Mill_02”, the timestamp “2023-10-27_10:35:12”, the deviation type “temperature deviation” and deviation degree “+3℃” in the instantaneous running deviation information, the parameter type “cutting speed” and parameter value “95%” of the adjusted machining parameters, and the equipment operating status of the downstream machining station (e.g., spindle speed, tool wear index, vibration level, etc.).

[0064] The structured data packets are sent to the central management system. After receiving them, the central management system matches and integrates “Part_XYZ_001” and “2023-10-27_10:35:12” and adds them to the processing history of “Part_XYZ_001”. The system continuously collects a large number of structured data packets and performs statistics on the frequency of instantaneous temperature deviation events of the “HeatTreat_01” heat treatment furnace based on the processing history. When it is found that the frequency of instantaneous temperature deviation events in the past 24 hours is significantly higher than the preset equipment operating health benchmark (e.g., more than 5 times per hour), the system triggers a pre-maintenance alarm message, indicating “Alarm associated equipment: HeatTreat_01 heat treatment furnace, potential problem type: heating element aging or temperature sensor drift”.

[0065] Furthermore, the central management system analyzes the correlation between the instantaneous temperature deviation of the "HeatTreat_01" heat treatment furnace and the spindle vibration level of the "CNC_Mill_02" precision cutting equipment based on the prompt information. When a specific type of temperature deviation is detected in the heat treatment furnace, the spindle vibration level of the precision cutting equipment generally increases in subsequent processing, and the correlation strength meets the preset warning conditions. The system triggers the final pre-maintenance alarm and recommends that the "HeatTreat_01" heat treatment furnace be inspected and calibrated to reduce long-term damage to downstream equipment and product quality risks.

[0066] In another embodiment of this application, it is further proposed to analyze the correlation between the instantaneous operational deviation of the upstream processing station and the equipment operating status of the downstream processing station based on event data packets, and to trigger a pre-maintenance alarm when the correlation meets preset early warning conditions, specifically including the following steps: S5600: Extracts instantaneous operational deviation information, adjusted processing parameters, and equipment operating status of downstream processing stations from the event data packet into data points of multiple dimensions; S5610: Construct a composite data structure containing upstream deviation features and downstream adjustment parameter features based on data points from multiple dimensions; S5620: Perform correlation analysis on multi-dimensional features in composite data structures to determine the strength and direction of the correlation between different features and obtain the correlation analysis results; S5630: Identify combinations of deviation events and adjustment behaviors with nonlinear correlation characteristics based on correlation analysis results; S5640: Generate a correlation report based on the combination of deviation events and adjustment behaviors according to the nonlinear correlation characteristics. The correlation report includes the upstream deviation type, downstream adjustment strategy, and potential equipment risk level. S5650: When the risk level of potential equipment in the associated report meets the preset warning conditions, a pre-maintenance alarm is triggered.

[0067] Specifically, the event data packet is generated by the downstream machining station after the workpiece is processed. It contains instantaneous deviation information of the workpiece, adjusted machining parameters, and the operating status of the equipment at the downstream machining station. The instantaneous deviation information can include the type of deviation (e.g., abnormal temperature, excessive vibration, pressure fluctuation, etc.) and the degree of deviation; the adjusted machining parameters can include the type of parameter adjusted (e.g., feed rate, depth of cut, coolant flow rate, etc.) and the parameter value; the operating status of the equipment at the downstream machining station can include the real-time temperature, vibration frequency, energy consumption, and output rate of the equipment. This information is used as independent data points for subsequent analysis.

[0068] Among them, the composite data structure is used to organize and logically integrate data points such as the instantaneous operation deviation information of the upstream, the adjusted processing parameters of the downstream, and the equipment operation status of the downstream processing station. For example, it can be a multidimensional array, table, graph structure or specific data object to form a data entity that can reflect the linkage effect of "upstream-downstream" and facilitate overall analysis.

[0069] In practical applications, correlation analysis of multi-dimensional features in composite data structures can be performed using Pearson correlation coefficient, Spearman rank correlation coefficient, mutual information, decision tree, support vector machine or neural network, etc., to quantify the direction and strength of the correlation between upstream deviation features (such as deviation type, deviation degree) and downstream adjustment parameter features (such as adjustment magnitude, adjustment frequency) and the equipment operating status of downstream processing stations (such as performance indicators, health status).

[0070] Furthermore, nonlinear correlation features are used to describe relationships that cannot be characterized by linear models. For example, a deviation from upstream may cause a sharp deterioration in the operating status of downstream processing station equipment under certain conditions, while having little impact under other conditions, or the combined effect of multiple upstream deviations may be significantly greater than simple superposition. Identifying nonlinear correlation features can be done using deep learning, nonlinear regression models, or complex event processing (CEP) techniques to capture hidden patterns.

[0071] The correlation report is used to summarize the identified nonlinear correlation characteristics in a structured manner, including at least the upstream deviation type, downstream adjustment strategy, and potential equipment risk level. The potential equipment risk level can be "low", "medium", "high" or a risk score. The preset early warning conditions can be set according to production needs. For example, an alarm can be triggered when the potential equipment risk level reaches "medium" or "high" to ensure timely response to high-risk events while controlling excessive alarms.

[0072] In another embodiment of this application, it is further proposed that after S5650, the following is included: S5651: Based on the association report, determine the devices associated with the alarm, the types of potential problems, and the risk level of the potential devices, and then classify the advance maintenance alarms according to the devices, types of potential problems, and risk levels of the potential devices; S5652: Assign priority to different categories of pre-maintenance alarms based on the current workpiece flow status, equipment load, and maintenance resource availability of the production line; S5653: Prioritize and filter pre-maintenance alerts to display and notify them according to priority.

[0073] Specifically, classifying pre-maintenance alarms involves grouping alarms that are similar in nature, have a similar scope of impact, or are of comparable urgency based on information obtained from related reports, such as the equipment associated with the alarm, the type of potential problem, and the risk level of the potential equipment. For example, all alarms related to "bearing wear" can be grouped into one category, or all "high-risk" alarms can be grouped into one category, so that maintenance personnel can quickly grasp the commonalities and potential impact of the alarms.

[0074] Among them, prioritizing different categories of pre-maintenance alarms by combining the current workpiece flow status, equipment load, and maintenance resource availability of the production line refers to further incorporating real-time production line conditions and maintenance execution conditions on the basis of alarm classification, quantitatively assessing the importance or urgency of various alarms and generating dynamic priority values; among them, workpiece flow status reflects the criticality and quality requirements of production tasks, equipment load reflects the current workload and risk exposure level of equipment, and maintenance resource availability reflects the feasibility and response time of maintenance tasks.

[0075] In practical applications, prioritizing and filtering pre-maintenance alarms to display and notify them according to priority means that the system sorts alarms to be processed from highest to lowest priority value, and only displays or notifies alarms within a specific priority range based on user needs or preset rules. For example, the system prioritizes pushing the highest priority alarms, while temporarily hiding lower priority alarms or providing non-urgent notifications to avoid information overload and ensure focused handling.

[0076] The solution proposed in this application reduces the risk of information overload caused by concurrent multi-source alarms in complex industrial scenarios and improves the resource matching efficiency of maintenance response by introducing alarm classification, priority allocation, and sorting and filtering mechanisms: classification is used to aggregate similar problems and form a macro view; priority allocation is used to determine the order of handling under real-time operating conditions and resource constraints; sorting and filtering are used to present key information in a structured manner, reducing the screening cost for maintenance personnel among a large number of alarms, thereby improving the efficiency and accuracy of maintenance decisions.

[0077] In another embodiment of this application, S5652 further includes: S5652-1: Obtain the current workpiece flow status on the production line. The workpiece flow status includes the type of workpiece being processed, the number of workpieces, and the preset production priority and quality requirements for each workpiece. S5652-2: Obtain the real-time equipment load and maintenance resource availability of each device on the current production line; S5652-3: Based on workpiece type, preset production priority, and quality requirements, assess the degree of primary impact of each pre-maintenance alarm on product quality and production schedule; S5652-4: Based on equipment load and maintenance resource availability, assess the secondary impact of each pre-maintenance alarm on equipment operational stability and maintenance response time; S5652-5: By combining the first degree of impact, the second degree of impact, and the potential equipment risk level, a priority value is dynamically generated to characterize the urgency of each pre-maintenance alarm, and a priority is assigned to each pre-maintenance alarm based on the priority value.

[0078] Specifically, obtaining the current workpiece flow status on the production line refers to the system's real-time monitoring and collection of detailed information about the workpieces being processed on the production line. Workpiece type can refer to different product models or specifications, such as type A workpiece, type B workpiece, etc.; workpiece quantity refers to the total number of various types of workpieces currently at different processing stages on the production line; preset production priority refers to the pre-set processing priority order in the production plan for specific workpieces or orders, such as urgent orders having higher priority than regular orders; quality requirements refer to the various technical standards and quality indicators that the workpieces must meet during processing. This information collectively describes the immediate production tasks and product characteristics of the production line.

[0079] Obtaining real-time equipment load and maintenance resource availability for each device on the production line can be understood as the system continuously monitoring the operating status and resource allocation of all equipment on the production line. Equipment load refers to the current workload or utilization rate of the equipment, such as CPU utilization, spindle speed, and processing time expressed as a percentage. Maintenance resource availability refers to the real-time status of resources such as manpower, spare parts, and tools currently available to handle maintenance alarms, such as the availability of maintenance personnel and the inventory of critical spare parts. This data provides a foundation for assessing maintenance response capabilities.

[0080] In practical applications, based on workpiece type, preset production priority, and quality requirements, the degree of primary impact of each pre-maintenance alarm on product quality and production schedule is assessed. The purpose is to quantify the direct impact of alarm events on production targets. For example, if an alarm-related equipment failure may lead to the scrapping of high-priority, high-value workpieces or severely delay the delivery of critical orders, its primary impact will be assessed as high.

[0081] Furthermore, based on equipment load and maintenance resource availability, the secondary impact of each pre-maintenance alarm on equipment operational stability and maintenance response time is assessed. The aim is to measure the indirect impact of alarm events on the overall stability and maintenance efficiency of the production system. For example, if the equipment associated with an alarm is currently under extremely high load and maintenance resources are scarce, the secondary impact of that alarm will be high because it may lead to prolonged equipment downtime, thereby affecting the operation of the entire production line.

[0082] Therefore, by comprehensively considering the first and second levels of impact and the potential equipment risk level, a priority value is dynamically generated to characterize the urgency of each pre-maintenance alarm, and priorities are assigned to each pre-maintenance alarm based on the priority value. The potential equipment risk level is determined based on related reports and reflects the inherent risk of equipment failure or performance degradation. A comprehensive priority value is obtained by weighting the evaluation results of these three dimensions or by fusing them through a multi-factor decision model; a higher value indicates a more urgent alarm that requires priority handling.

[0083] In another embodiment of this application, S5643 further includes: S5653-1: Based on priority and the devices associated with alarms in advance maintenance alarms, determine the maintenance objects associated with the alarms and identify the corresponding maintenance teams or maintenance personnel accordingly; S5653-2: Obtain the permission scope and information requirements of the maintenance team or maintenance personnel, and based on the permission scope and information requirements, trim or supplement the content of the pre-maintenance alerts to generate customized alert content; S5653-3: Based on the terminal type and response capability of the maintenance team or maintenance personnel, select the message push method and push customized alarm content to the maintenance team's collaboration platform or the maintenance personnel's mobile terminal application; S5653-4: After receiving customized alarm content, the collaborative platform or maintenance terminal shall output a pre-maintenance alarm in at least one of the following ways, namely pop-up prompt, voice prompt or vibration prompt, according to the scope of permissions and terminal type.

[0084] Specifically, "determining the maintenance object associated with the alarm and identifying the corresponding maintenance team or personnel" means that the system automatically identifies the maintenance team or personnel responsible for maintaining the equipment associated with the pre-defined maintenance alarm (e.g., specific equipment in an upstream or downstream processing station), combined with the equipment's type, location, potential problem type, and preset maintenance responsibility allocation rules. For example, alarms for mechanical faults are sent to the mechanical maintenance team, and alarms for electrical faults are sent to the electrical maintenance team.

[0085] "Obtaining the permission scope and information requirements corresponding to the maintenance team or personnel, and tailoring or supplementing the content of pre-maintenance alerts based on the permission scope and information requirements to generate customized alert content" refers to the system identifying the target maintenance team or personnel, querying their permission configuration in the central management system to determine the scope of accessible data, and determining information requirements based on their roles (e.g., junior technician, senior engineer, team leader, etc.); tailoring the content of information that exceeds the permissions or is irrelevant to their responsibilities based on the permission scope, and supplementing the content of information required for maintenance but not included in the original alert based on the information requirements (e.g., detailed historical operating data of relevant equipment, links to standard operating procedures, preliminary diagnostic suggestions, etc.) to form customized alert content.

[0086] "Selecting a message push method and pushing customized alarm content to the maintenance team's collaboration platform or the maintenance personnel's mobile terminal application" means that the system selects a message push method based on the maintenance team's or maintenance personnel's terminal type (e.g., PC, mobile phone, tablet, etc.) and their response capabilities (e.g., whether immediate response is required, whether it is during working hours, etc.): alarms that require urgent handling are prioritized to be pushed to the mobile terminal application, and alarms that require team collaboration are prioritized to be pushed to the collaboration platform.

[0087] "After receiving customized alarm content, the collaboration platform or maintenance terminal outputs a pre-maintenance alarm in at least one of the following ways, namely pop-up prompts, voice prompts, or vibration prompts, according to the scope of permissions and terminal type." This means that after receiving customized alarm content, the terminal selects the prompting method based on the terminal type, scope of permissions, and alarm priority: for high-priority alarms, both vibration and voice prompts can be used and a pop-up window can be displayed; for general-priority alarms, pop-up prompts or message list displays can be used.

[0088] This application's solution achieves targeted outreach by "identifying the maintenance object associated with the alarm and accordingly identifying the corresponding maintenance team or personnel," and achieves information redundancy removal and completion by "obtaining the corresponding permission scope and information requirements of the maintenance team or personnel, and trimming or supplementing the content of the pre-maintenance alarm based on the permission scope and information requirements to generate customized alarm content." It also achieves channel and presentation method adaptation by "selecting a message push method and pushing the customized alarm content to the maintenance team's collaboration platform or the maintenance personnel's mobile terminal application" and "after receiving the customized alarm content, the collaboration platform or maintenance terminal outputs the pre-maintenance alarm in at least one of the following ways: pop-up notification, voice notification, or vibration notification, according to the permission scope and terminal type," thereby reducing information overload and shortening the response chain.

[0089] In another embodiment of this application, the step of comparing the statistical results with a preset equipment operating health benchmark is further proposed, including: S5410: Obtain the current running time, cumulative running cycle, and historical maintenance records of at least one device; S5420: Obtain the current processing task type corresponding to at least one device and the workpiece batch information of the current production line, wherein the workpiece batch information includes the material properties and processing requirements of the workpiece; S5430: Based on current operating time, cumulative operating cycle and historical maintenance records, assess the wear status and potential failure tendency of the corresponding equipment, and generate equipment aging impact parameters; S5440: Based on workpiece batch information and the current processing task type, assess the cumulative stress or wear caused by the workpiece batch to the corresponding equipment, and generate workpiece cumulative impact parameters; S5450: Based on the preset equipment operating health benchmark adjustment rules, combined with the equipment aging influence parameters and workpiece cumulative influence parameters, the preset equipment operating health benchmark is modified to obtain the dynamic health benchmark under the current working conditions. S5460: Compare statistical results with dynamic health benchmarks.

[0090] Specifically, obtaining the current operating time, cumulative operating cycle, and historical maintenance records of at least one device refers to collecting equipment operating time data, cumulative operating cycle data since the last major overhaul or installation, and historical maintenance records such as maintenance, upkeep, and component replacements in real time or periodically by interacting with the equipment controller or historical database, as a basis for assessing the long-term health status and wear and tear of the equipment.

[0091] The system acquires the current processing task type corresponding to at least one device and the workpiece batch information of the current production line. The workpiece batch information includes the material properties and processing requirements of the workpiece. This means that the system obtains the processing task type (e.g., drilling, milling, welding, etc.) currently being performed by the device from the production planning system or manufacturing execution system (MES), and obtains the material (e.g., steel, aluminum alloy, composite material, etc.) and process requirements (e.g., accuracy grade, surface roughness, etc.) of the current workpiece batch to support the assessment of the short-term and cumulative impact on the workpiece.

[0092] In practical applications, based on the current running time, cumulative running cycle, and historical maintenance records, the wear status and potential failure tendency of the corresponding equipment are assessed, and equipment aging impact parameters are generated. This refers to using a preset equipment life model, wear curve, or expert experience rules, combined with operation and maintenance data, to calculate quantitative parameters to characterize the degree of performance degradation or increased failure risk caused by long-term operation.

[0093] Based on workpiece batch information and the current processing task type, the cumulative stress or wear caused by the workpiece batch to the corresponding equipment is evaluated, and workpiece cumulative impact parameters are generated. This means taking into account the different effects of different materials and tasks on equipment wear. For example, high-hardness materials or high-intensity tasks lead to higher cumulative stress, thereby forming quantitative parameters.

[0094] Therefore, based on the preset equipment operating health benchmark adjustment rules, and combined with the equipment aging influence parameters and workpiece cumulative influence parameters, the preset equipment operating health benchmark is corrected to obtain the dynamic health benchmark under the current operating conditions. This refers to the weighted, offset, or nonlinear adjustment of the static benchmark through mathematical models, lookup tables, or machine learning algorithms to make it reflect the true health level under the current operating conditions.

[0095] Finally, comparing the statistical results with the dynamic health benchmark means comparing the statistical results obtained from the central management system, such as the frequency of equipment deviation events and the distribution of adjustment parameters, with the dynamic health benchmark in order to more accurately identify anomalies and potential risks.

[0096] The proposed solution quantifies long-term wear and failure tendency by measuring the aging effect parameters of equipment, quantifies the differential stress caused by materials and tasks by measuring the cumulative effect parameters of workpieces, and then forms a dynamic health benchmark based on the equipment operating health benchmark adjustment rules. This overcomes the limitation of static benchmarks not adapting to changes in operating conditions, makes the comparison results more timely and reduces false alarms and false alarms, thereby optimizing maintenance plans, extending equipment life and improving production line stability.

[0097] Reference Figure 2 The specific implementation of this application also discloses a PLC-based industrial manufacturing process management system, including: Upstream workstation data acquisition module 1 is used to acquire operating parameters that characterize the operating status of the equipment at the upstream processing workstation, and to identify instantaneous operating deviations based on preset identification rules to obtain instantaneous operating deviation information. The instantaneous operating deviation is used to characterize the transient deviation of the operating parameters relative to the normal operating range within a preset time window. The information binding and transfer module 2 is used to obtain the unique identifier of the workpiece currently being processed by the upstream processing station, bind the instantaneous running deviation information with the unique identifier of the workpiece, generate the binding deviation information corresponding to the workpiece, and store the binding deviation information. Downstream station strategy adjustment module 3 is used to obtain the unique identification of the workpiece arriving at the downstream processing station, and query the binding deviation information of the workpiece based on the unique identification of the workpiece; when the binding deviation information of the workpiece indicates that the workpiece has an instantaneous running deviation from the upstream processing station, the processing parameters of the equipment at the downstream processing station are adjusted according to the binding deviation information so that the processing strategy of the equipment at the downstream processing station adapts to the actual state of the workpiece. The event-driven data sending module 4 is used to generate an event data packet containing the instantaneous running deviation information of the workpiece, the adjusted processing parameters, and the equipment operating status of the downstream processing station after the processing of the workpiece is completed at the downstream processing station, and send the event data packet to the central management system. The central management and early warning module 5 is used to receive and store event data packets in the central management system, analyze the correlation between the instantaneous operating deviation of the upstream processing station and the equipment operating status of the downstream processing station based on the event data packets, and trigger a pre-maintenance alarm when the correlation meets the preset early warning conditions.

[0098] The industrial manufacturing process management system based on PLC proposed in this application aims to solve the problems of untimely monitoring of equipment operating status, slow response to process parameter adjustments, and low efficiency of production resource allocation in traditional industrial manufacturing processes.

[0099] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A PLC-based industrial manufacturing process management method, characterized in that, Applied to an industrial manufacturing production line consisting of upstream and downstream processing stations connected in series, the method includes: At the upstream processing station, operating parameters used to characterize the operating status of the equipment are acquired, and instantaneous operating deviations are identified based on preset identification rules to obtain instantaneous operating deviation information. The instantaneous operating deviation is used to characterize the transient deviation of the operating parameters relative to the normal operating range within a preset time window. Obtain the unique identifier of the workpiece currently being processed by the upstream processing station, bind the instantaneous running deviation information to the unique identifier of the workpiece, generate binding deviation information corresponding to the workpiece, and store the binding deviation information; At the downstream processing station, a unique identifier of the workpiece arriving at the downstream processing station is obtained, and the binding deviation information of the workpiece is queried based on the unique identifier of the workpiece; when the binding deviation information of the workpiece indicates that the workpiece has an instantaneous running deviation from the upstream processing station, the processing parameters of the equipment at the downstream processing station are adjusted according to the binding deviation information so that the processing strategy of the equipment at the downstream processing station adapts to the actual state of the workpiece. At the downstream processing station, after the processing of the workpiece is completed, an event data packet containing the instantaneous running deviation information of the workpiece, the adjusted processing parameters, and the equipment operating status of the downstream processing station is generated, and the event data packet is sent to the central management system. The central management system receives and stores the event data packets, analyzes the correlation between the instantaneous operational deviation of the upstream processing station and the equipment operating status of the downstream processing station based on the event data packets, and triggers a pre-maintenance alarm when the correlation meets the preset early warning conditions.

2. The PLC-based industrial manufacturing process management method according to claim 1, characterized in that, Obtain operating parameters that characterize the device's operating status, and identify instantaneous operating deviations based on preset recognition rules to obtain instantaneous operating deviation information, including: The operating parameters used to characterize the operating status of the equipment at the upstream processing station are obtained, and the operating parameters include operating parameters of at least two different dimensions; The operating parameters are time-synchronized, data-cleaned, and feature-extracted to obtain an instantaneous feature vector characterizing the transient operating state of the equipment; The instantaneous operational deviation is identified by the instantaneous feature vector based on the preset identification rules, and the instantaneous operational deviation information is generated, wherein the instantaneous operational deviation information includes at least one of deviation type and deviation degree; The step of binding the instantaneous operational deviation information with the unique identifier of the workpiece to generate binding deviation information corresponding to the workpiece and storing the binding deviation information includes: The instantaneous operational deviation information is associated and encapsulated with the unique identifier of the workpiece to form the binding deviation information, and the binding deviation information is stored in a data storage location that is queried based on the unique identifier.

3. The PLC-based industrial manufacturing process management method according to claim 1, characterized in that, Obtain operating parameters that characterize the device's operating status, and identify instantaneous operating deviations based on preset recognition rules to obtain instantaneous operating deviation information, including: Real-time acquisition of the current processing task type, the material properties of the workpiece, and the operating environment parameters; Based on the processing task type, obtain the equipment operating parameter benchmark range and initial deviation identification threshold corresponding to the processing task type from the preset processing task parameter set; Based on the material properties of the workpiece, the reference range of the equipment operating parameters and the initial deviation identification threshold are corrected to obtain the reference range of the equipment operating parameters and the initial deviation identification threshold after workpiece adaptive adjustment. Based on the operating environment parameters, the baseline range of equipment operating parameters and the initial deviation identification threshold after workpiece adaptability adjustment are further corrected to obtain the dynamic operating parameter range and deviation identification threshold under the current working conditions. The real-time collected operating parameters are compared with the dynamic operating parameter range, and based on the deviation identification threshold, it is determined whether an instantaneous operating deviation occurs with a duration shorter than a preset time threshold and without reaching the equipment shutdown threshold, so as to generate the instantaneous operating deviation information.

4. The PLC-based industrial manufacturing process management method according to claim 1, characterized in that, Also includes: At the downstream processing station, the event data packet is encapsulated into a structured data packet and sent to the central management system. The structured data packet includes at least the unique identifier of the workpiece, the upstream station identifier, the downstream station identifier, the timestamp, the deviation type and degree in the instantaneous running deviation information, the parameter type and parameter value of the adjusted processing parameters, and the equipment operating status of the downstream processing station. The step of analyzing the correlation between the instantaneous operational deviation of the upstream processing station and the equipment operating status of the downstream processing station based on the event data packet, and triggering a pre-maintenance alarm when the correlation meets preset early warning conditions, includes: The central management system receives the structured data packet and matches and integrates the structured data packet based on the unique identifier of the workpiece and the timestamp to establish the processing history of the workpiece. Based on the processing history, the central management system statistically analyzes the frequency of deviation events and the distribution of adjustment parameters for at least one device to obtain statistical results. The statistical results are compared with a preset equipment operating health benchmark. When the deviation of the statistical results from the equipment operating health benchmark reaches a preset level, the pre-maintenance alarm is triggered, and the alarm information indicates the equipment associated with the alarm and the type of potential problem. Based on the prompt information, the system analyzes the correlation between the instantaneous operational deviation of the upstream processing station and the equipment operating status of the downstream processing station according to the structured data packet, and triggers a pre-maintenance alarm when the correlation meets the preset early warning conditions.

5. The PLC-based industrial manufacturing process management method according to claim 1, characterized in that, Based on the analysis of the event data packets, the correlation between the instantaneous operational deviation of the upstream processing station and the equipment operating status of the downstream processing station is analyzed, and a pre-maintenance alarm is triggered when the correlation meets preset early warning conditions, including: The instantaneous operational deviation information, adjusted processing parameters, and equipment operating status of the downstream processing station in the event data packet are extracted into data points of multiple dimensions. Construct a composite data structure that includes upstream deviation features and downstream adjustment parameter features based on data points from multiple dimensions; Correlation analysis is performed on the multi-dimensional features in the composite data structure to determine the correlation strength and direction between different features, and the correlation analysis results are obtained. Based on the correlation analysis results, combinations of deviation events and adjustment behaviors with nonlinear correlation characteristics are identified; A correlation report is generated based on the combination of deviation events and adjustment behaviors according to the nonlinear correlation characteristics. The correlation report includes the upstream deviation type, downstream adjustment strategy, and potential equipment risk level. When the risk level of a potential device in the associated report meets the preset warning conditions, the pre-maintenance alarm is triggered.

6. The PLC-based industrial manufacturing process management method according to claim 5, characterized in that, When the potential equipment risk level in the associated report meets the preset early warning conditions, the pre-maintenance alarm is triggered, including: Based on the associated report, the device associated with the alarm, the type of potential problem, and the risk level of the potential device are determined, and then the pre-maintenance alarm is classified according to the device, the type of potential problem, and the risk level of the potential device; Based on the current workpiece flow status, equipment load, and maintenance resource availability on the production line, priorities are assigned to different categories of pre-maintenance alarms; The pre-maintenance alerts are sorted and filtered based on the priority, so that the pre-maintenance alerts are displayed and notified according to the priority.

7. The PLC-based industrial manufacturing process management method according to claim 6, characterized in that, Based on the current workpiece flow status, equipment load, and maintenance resource availability on the production line, priorities are assigned to different categories of pre-maintenance alarms, including: Obtain the current workpiece flow status on the production line, which includes the type of workpiece being processed, the number of workpieces, and the preset production priority and quality requirements for each workpiece. Obtain real-time equipment load and maintenance resource availability for each device on the current production line; Based on the workpiece type, the preset production priority, and the quality requirements, assess the degree of primary impact of each pre-maintenance alarm on product quality and production schedule; Based on the equipment load and maintenance resource availability, assess the second impact of each pre-maintenance alarm on equipment operational stability and maintenance response time; By combining the first degree of impact, the second degree of impact, and the potential equipment risk level, a priority value is dynamically generated to characterize the urgency of each pre-maintenance alarm, and a priority is assigned to each pre-maintenance alarm based on the priority value.

8. The PLC-based industrial manufacturing process management method according to claim 6, characterized in that, Sort and filter the pre-maintenance alerts based on the priority, so as to display and notify the pre-maintenance alerts according to the priority, including: Based on the priority and the devices associated with the pre-maintenance alarm, the maintenance objects associated with the alarm are determined, and the corresponding maintenance teams or personnel are identified accordingly. Obtain the permission scope and information requirements corresponding to the maintenance team or maintenance personnel, and based on the permission scope and information requirements, trim or supplement the content of the pre-maintenance alert to generate customized alert content; Based on the terminal type and response capability of the maintenance team or maintenance personnel, select the message push method and push the customized alarm content to the maintenance team's collaboration platform or the maintenance personnel's mobile terminal application; After receiving the customized alarm content, the collaboration platform or maintenance terminal outputs a pre-maintenance alarm in at least one of the following ways, based on the scope of permissions and the terminal type: pop-up prompt, voice prompt, or vibration prompt.

9. The PLC-based industrial manufacturing process management method according to claim 4, characterized in that, The statistical results are compared with a preset equipment operating health benchmark, including: Obtain the current running time, cumulative running cycle, and historical maintenance records of at least one device; Obtain the current processing task type corresponding to at least one device and the workpiece batch information of the current production line, wherein the workpiece batch information includes the material properties and processing requirements of the workpiece; Based on the current running time, the cumulative running cycle, and the historical maintenance records, assess the wear status and potential failure tendency of the corresponding equipment, and generate equipment aging impact parameters. Based on the workpiece batch information and the current processing task type, assess the cumulative stress or wear caused by the workpiece batch to the corresponding equipment, and generate workpiece cumulative impact parameters. Based on the preset equipment operating health benchmark adjustment rules, and combined with the equipment aging influence parameters and the workpiece cumulative influence parameters, the preset equipment operating health benchmark is modified to obtain the dynamic health benchmark under the current working conditions. The statistical results are compared with the dynamic health benchmark.

10. A PLC-based industrial manufacturing process management system, characterized in that, include: The upstream workstation data acquisition module is used to acquire operating parameters that characterize the operating status of the equipment at the upstream processing workstation, and to identify instantaneous operating deviations based on preset identification rules to obtain instantaneous operating deviation information. The instantaneous operating deviation is used to characterize the transient deviation of the operating parameters relative to the normal operating range within a preset time window. The information binding and transfer module is used to obtain the unique identifier of the workpiece currently being processed by the upstream processing station, bind the instantaneous running deviation information with the unique identifier of the workpiece, generate binding deviation information corresponding to the workpiece, and store the binding deviation information; The downstream workstation strategy adjustment module is used to obtain the unique identifier of the workpiece arriving at the downstream processing station, and query the binding deviation information of the workpiece based on the unique identifier of the workpiece; when the binding deviation information of the workpiece indicates that the workpiece has an instantaneous running deviation from the upstream processing station, the processing parameters of the equipment at the downstream processing station are adjusted according to the binding deviation information so that the processing strategy of the equipment at the downstream processing station adapts to the actual state of the workpiece. The event-driven data transmission module is used to generate an event data packet containing instantaneous running deviation information of the workpiece, adjusted processing parameters, and equipment operating status of the downstream processing station after the downstream processing station has completed the processing of the workpiece, and to send the event data packet to the central management system. The central management and early warning module is used to receive and store the event data packets in the central management system, analyze the correlation between the instantaneous operating deviation of the upstream processing station and the equipment operating status of the downstream processing station based on the event data packets, and trigger a pre-maintenance alarm when the correlation meets the preset early warning conditions.

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