A production area visual inspection data intelligent analysis and early warning method

Abstract

The application relates to the technical field of industrial automation and intelligent manufacturing, and discloses a production area visual detection data intelligent analysis and early warning method, which comprises the following steps: analyzing a service message issued by a manufacturing system to obtain work order state characteristics, comparing real-time sensing data of a production line with target working condition parameters, and performing smooth switching on an early warning evaluation baseline when a preset tolerance condition is met; performing state evaluation based on the switched baseline, outputting a prediction residual and an early warning control instruction; when the prediction residual is out of limit, extracting historical characteristics to construct a sample set, performing online gradient fine-tuning iteration on a weight matrix of an evaluation rule in an edge computing node, and updating; finally, combining an initial severity level of the instruction with queue backlog waiting time to calculate a dynamic priority, and preferentially issuing the instruction to a bottom logic controller through non-blocking scheduling. The application solves the state false alarm problem in the work order switching period, realizes adaptive correction of the evaluation model on equipment degradation, and shortens the transmission delay of the emergency control instruction.

Description

Technical Field

[0001] This invention relates to the field of industrial automation and intelligent manufacturing technology, specifically to a method for intelligent analysis and early warning of visual inspection data in production areas. Background Technology

[0002] In industrial manufacturing processes, production lines typically deploy vision inspection terminals and sensors to monitor and analyze the operating status of equipment. With increasing demands for production flexibility, production lines need to frequently switch production work orders. During work order switching, existing control systems usually update the status warning assessment baseline and judgment thresholds simultaneously upon receiving a new business message. However, changes in the mechanical state of physical equipment, such as rotational speed or temperature, have physical response times. There is a time lag between the issuance of system commands and the actual transition of equipment operating conditions. This causes the actual perceived data during process flow to easily exceed the newly set judgment thresholds, leading to false status alarms during production.

[0003] Meanwhile, production line equipment inevitably experiences mechanical wear and parameter drift during long-term operation. Existing condition warning and assessment mechanisms mostly rely on rule-based models trained offline and deployed in a fixed manner, whose computational weights are difficult to adapt to changes in underlying characteristics caused by physical degradation of the equipment. When the actual operating baseline of the equipment deviates, the static assessment model will lead to a persistently large prediction residual. Currently, error correction usually relies on stopping the machine to recollect data and manually recalibrating, which makes it difficult to maintain the continuity and adaptability of the monitoring and warning mechanism.

[0004] Furthermore, when the early warning model determines equipment malfunction and generates control commands, existing industrial communication gateways generally employ a conventional first-in-first-out (FIFO) queuing mechanism for message transmission. When a large amount of routine logs or monitoring data is concurrently transmitted on the production line network, data backlog can easily occur in the underlying communication buffer queue. This blocking data scheduling method forces emergency safety protection commands to queue sequentially, causing delays in the issuance of critical control commands during network congestion, and failing to trigger timely safety protection actions of the underlying logic controller under extreme operating conditions. Summary of the Invention

[0005] The technical problem solved by this invention is that existing production line vision and perception data analysis methods are prone to false status reports during production work order switching, the fixed status assessment rules are difficult to adapt to long-term physical degradation of equipment, and the transmission of downlink control commands is restricted by the underlying blocking queue, resulting in execution delays for emergency control commands.

[0006] To address the above problems, the present invention provides the following technical solution: A method for intelligent analysis and early warning of visual inspection data in production areas includes: receiving and parsing business messages issued by the manufacturing execution system to obtain work order status features containing target operating condition parameters; acquiring real-time sensing data during production line operation and matching and comparing the real-time sensing data with the target operating condition parameters based on preset tolerance conditions; when the real-time sensing data meets the tolerance conditions, smoothly switching the early warning assessment baseline according to the work order status features; performing real-time status assessment based on the real-time sensing data and the switched early warning assessment baseline, and outputting the predicted residual and corresponding early warning control instructions; responding to the predicted residual meeting the preset error exceedance condition, extracting downsampled status feature records within the historical time window as a sample set, performing online gradient fine-tuning iteration of the weight matrix of the assessment rules at the edge computing node, and updating the verified new weight matrix to the status assessment calculation; obtaining the initial severity level of the early warning control instructions, dynamically calculating the sending priority of the early warning control instructions in combination with the network queue waiting time, and non-blockingly scheduling the instructions to the underlying logic controller to execute safety protection actions.

[0007] In the message parsing step, the uplink data stream of the network port is monitored and frames are reassembled using a sliding window to extract the payload. Semantic parsing and memory mapping are then performed on the payload to obtain various control fields. For missing fields or illegal characters, the average parameter value from historical periods without anomalies is extracted as a safe default value for replacement, thereby constructing a work order status feature vector corresponding to the current production work order.

[0008] In the baseline switching mechanism, real-time sensing data is monitored cyclically, and its relative error with the target operating condition parameters is calculated. When the relative error is less than the set tolerance threshold within the set continuous acquisition cycle, it is determined that the production line equipment has completed the operating condition switch, triggering the system to change the early warning assessment baseline and threshold of the old work order to the early warning assessment baseline and threshold that match the new production work order.

[0009] During the sample set construction process, multi-source time-series sequences collected within the time window that led to the prediction residual exceeding the limit were extracted and divided into multiple time blocks according to their time span. For time blocks where the number of effective data points met the lower limit requirement, the trend mean, extreme values, and fluctuation variance were extracted and concatenated to generate downsampled state feature row vectors. For abnormal time blocks with insufficient effective data points, feature row vectors from previous effective time blocks were used to fill the gaps and low-quality mask markers were added. Data carrying low-quality mask markers were filtered, and the retained features were assembled into a sample set. Based on this, historical feature data was extracted from the local time-series database, and the feature mean of the portion exceeding the constant was unidirectionally accumulated using the warning baseline constant as the cutoff value to calculate the cumulative degradation of the production line equipment.

[0010] Before model fine-tuning iterations, a debouncing mechanism is pre-set. When a single prediction residual exceeds the upper limit of the tolerance interval, a debouncing timer is activated. Only when the prediction residuals consistently exceed the upper limit within a set evaluation period is the error exceeding the limit confirmed, and an update enable pulse is generated. The fine-tuning iteration process uses a target loss function with a penalty regularization term, calculates the error gradient matrix, and determines the update step size by combining it with the momentum accumulation matrix. A double-ended clamping constraint function is used to constrain the intermediate transition matrix generated by parameter updates within a numerical boundary between zero and one. The new weight matrix is ​​compared with the mean square residuals on an independent validation dataset. After successful validation, an exponential moving average mechanism is used to smoothly merge and replace the old weight matrix running online.

[0011] During the control command issuance phase, the warning control command is intercepted and the initial severity level constant is parsed. The current backlog waiting time is obtained by querying the underlying communication buffer queue. The dynamic priority index is calculated by combining the initial severity level constant, backlog waiting time, and time sensitivity coefficient, and the highest value is truncated using an extreme value protection function. A heap-based priority queue is maintained within the downlink communication gateway, and queued packets are reordered according to the dynamic priority index. An asynchronous multi-threaded mechanism is used to deliver high-priority emergency stop packets to the underlying logic controller first.

[0012] This invention provides a method for intelligent analysis and early warning of visual inspection data in production areas. It has the following beneficial effects:

[0013] 1. This invention matches and compares the status characteristics of work orders issued by the manufacturing execution system with real-time sensing data. Only when the real-time sensing data continuously meets preset tolerance conditions will a smooth switch be performed on the early warning assessment baseline. This mechanism solves the problem of the physical time difference between the issuance of work order switching instructions by the production system and the actual change in the operating status of the underlying physical equipment. It avoids false alarms caused by premature hard switching of baseline parameters and improves the accuracy of production line status monitoring during the transition phase of operating conditions.

[0014] 2. When the predicted residual continuously exceeds the tolerance range, this invention extracts historical downsampling state feature records to construct a sample set. At the edge computing nodes, it uses a target loss function including a penalty regularization term to perform online gradient fine-tuning iteration on the weight matrix, and constrains the numerical update boundary through a double-ended clamping function. This scheme enables the state assessment calculation model to locally adaptively correct for physical wear and parameter drift caused by long-term operation of industrial equipment, reducing the dependence on manual offline data re-collection and model calibration, and maintaining the reliability of the early warning mechanism under long-term operation.

[0015] 3. This invention dynamically calculates the transmission priority index of the warning control command by combining the initial severity level of the warning control command with the backlog waiting time of the network buffer queue, and then uses a priority queue within the downlink communication gateway to reorder and non-blockingly schedule the delivery of queued messages. This design ensures that, under network communication congestion, urgent security protection commands can bypass the queuing sequence of regular log data and be delivered to the underlying logic controller first, shortening the control response delay under extreme abnormal conditions and ensuring the operational safety of the underlying physical devices. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall system structure and data flow of the present invention; Figure 2 This is a schematic diagram of the operation flow of the dynamic feedback adjustment module of the present invention; Figure 3 This is a flowchart illustrating the logic of the business collaboration scheduling module of the present invention running the uplink work order decoder. Detailed Implementation

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] See attached document Figure 1 , Figure 1 This is a schematic diagram of the structure of an intelligent analysis and early warning system for production line visual inspection data according to an embodiment of the present invention. The present invention provides an intelligent analysis and early warning system for production line visual inspection data, which may include: a feature extraction module 100, a matrix construction module 200, a drift calculation module 300, a degradation assessment module 400, and an early warning update module 500.

[0019] The technical solution of this invention is implemented based on the hardware physical environment of edge computing nodes and industrial vision components. The industrial vision components include industrial cameras and strobe light sources, used to acquire complete field-of-view images of workpieces and fixed fixture backgrounds one by one during continuous production.

[0020] Edge computing nodes establish data communication links with industrial vision components to receive sequences of images from the field of view. They also establish data interaction channels with the production line control system and manufacturing execution system via the industrial Ethernet protocol, enabling them to output early warning command signals and receive equipment maintenance confirmation signals.

[0021] See attached document Figure 2 , Figure 2This is a flowchart illustrating an intelligent analysis and early warning method for visual inspection data in a production area according to an embodiment of the present invention. The present invention provides an intelligent analysis and early warning method for visual inspection data in a production area, comprising the following steps:

[0022] S100, the feature extraction module 100 sets a fixed non-detection reference area in the field of view image, extracts the reference optical feature vector, calculates the light source drift based on the deviation of the reference optical feature vector relative to the initial reference of the system, extracts the original geometric feature vector and the original optical feature vector of the target workpiece, performs differential correction on the original optical feature vector using the light source drift, and concatenates the original geometric feature vector and the corrected optical feature vector to generate a multidimensional heterogeneous feature vector sequence. S200, the matrix construction module 200 receives a multidimensional heterogeneous feature vector sequence, establishes a data sliding window of a set length in the internal storage space, calculates the sample covariance matrix of the multidimensional heterogeneous feature vector sequence within the data sliding window, performs eigenvalue decomposition on the sample covariance matrix, extracts the largest eigenvalue as the heterogeneous feature coupling drift scalar, and generates the main eigenvalue time series sequence. S300, the drift calculation module 300 receives the time series of principal feature values, sets up a short-period sliding window and a long-period sliding window to run in parallel, calculates the local variance of the principal feature values ​​in the short-period sliding window to obtain the transient fluctuation component, performs linear fitting using the least squares method in the long-period sliding window to obtain the change slope as the long-term gradual component, and combines the transient fluctuation component and the long-term gradual component to generate a multi-scale feature drift vector; S400, the degradation assessment module 400 receives multi-scale feature drift vectors, converts the multi-scale feature drift vectors into fuzzy state input vectors through a preset membership function, configures a fuzzy weight relation matrix containing multiple potential equipment fault source dimensions, performs matrix multiplication operation on the fuzzy state input vectors and the fuzzy weight relation matrix, outputs the degradation confidence scores corresponding to each potential equipment fault source, and generates equipment degradation assessment vectors. S500, the early warning update module 500 calculates the first difference of the degradation confidence score and obtains the corresponding degradation rate. When the degradation confidence score exceeds the set absolute confidence threshold or the degradation rate exceeds the set degradation rate threshold, an early warning signal is generated for the fault source of the target equipment and sent to the production line control system. S600, the early warning update module 500 receives the discrete maintenance confirmation signal from the manufacturing execution system, generates a real label vector based on the discrete maintenance confirmation signal, traces the historical equipment deterioration assessment vector before the maintenance occurred to calculate the diagnostic error, and uses the diagnostic error and the historical fuzzy state input vector to update the fuzzy weight relationship matrix online through the gradient descent algorithm to form a closed-loop self-learning working data stream of weight parameters.

[0023] See attached document Figure 3 , Figure 3 This is a timing diagram illustrating the principle of feature extraction module initialization according to an embodiment of the present invention.

[0024] In this embodiment, in order to establish a stable comparison benchmark, the feature extraction module 100 performs benchmark feature initialization calibration before the production line visual inspection system is put into operation. Specifically, the benchmark feature initialization process includes the following sub-steps:

[0025] S101, Set the non-detection reference area. The feature extraction module 100 receives the initial idle field-of-view image acquired by the industrial vision component during the system calibration phase. As a preferred method, the system delineates a specific physical region in the initial idle field-of-view image that is independent of the workpiece's motion trajectory as a non-detection reference area. Typically, the non-detection reference area can be set as the surface of a fixed mechanical fixture at the edge of the production line conveyor belt, or as a diffuse reflection calibration block pre-installed at the edge of the field of view. To reduce inherent errors caused by material aging, the diffuse reflection calibration block preferably uses industrial-grade Teflon material with stable surface albedo. The non-detection reference area substantially maintains constant spatial pixel coordinates during subsequent continuous production inspection cycles and avoids the feed and transfer paths of the workpiece being measured to prevent occlusion.

[0026] After defining the aforementioned reference area, the operation proceeds to S102 to extract the initial ambient light state vector. Within the set system initialization time window, the feature extraction module 100 controls the industrial camera and stroboscopic light source to continuously trigger under the set production operating parameters, acquiring multiple frames of reference background images. The length of the initialization time window is determined based on the acquisition frame rate of the industrial camera and the typical fluctuation period of the workshop ambient light, typically set to cover a time span of tens to hundreds of consecutive images to ensure the statistical significance of the sampled data. The feature extraction module 100, based on the set spatial pixel coordinates, extracts the image matrix of the non-detection reference area from each frame of the reference background image and calculates the optical feature parameters within the image matrix. To comprehensively quantify the ambient light state, the optical feature parameters are configured to include the global grayscale mean of the region pixels and the local texture contrast value based on the grayscale co-occurrence matrix. For the calculation of the grayscale co-occurrence matrix and the extraction of texture contrast, those skilled in the art can directly call the standard operators in the underlying computer vision library; the calculation principles and processes are well-known technologies in this field and will not be elaborated here.

[0027] The system then executes step S103 to construct a reference optical feature vector. From a general data processing perspective, single-frame images often contain sensor thermal noise or other high-frequency random interference; directly using them as a reference can easily introduce initial measurement bias. Therefore, the feature extraction module 100 performs an arithmetic average of the optical feature parameters extracted from multiple reference background images within the initialization time window to filter out transient electrical noise from the image sensor. Based on the parameters after the above averaging process, the system generates a reference optical feature vector. The definition formula for the reference optical eigenvector is as follows:

[0028] In the formula, The total number of features representing the selected optical feature parameters, which is a natural number greater than or equal to 2, and the specific value depends on the number of optical feature types actually extracted. This represents the arithmetic mean of the optical feature parameters of the i-th dimension within the initialization time window. The value is ; Represents the transpose of a matrix. Reference optical eigenvectors. This characterizes the reference photoresponse baseline provided by the production line visual inspection system under ideal and stable external ambient light conditions, without physical degradation of the equipment. The feature extraction module 100 extracts the reference optical feature vector... It is stored in the system memory and used to provide absolute reference coordinates for calculating the ambient light drift in subsequent detection cycles.

[0029] In this embodiment, the ambient light source in the production line is typically affected by physical attenuation of the light source hardware or interference from fluctuations in external workshop lighting. These interferences manifest as changes in the global illumination field, thus partially superimposed on the image features of the workpiece under test. To mitigate the unintended impact of this common-mode interference on the detection results, the feature extraction module 100 executes dynamic differential correction logic during continuous operation. Specifically, the differential correction process includes the following sub-steps:

[0030] S104, obtain the light source drift amount for the current detection cycle. Let the sequence number of the current continuous detection cycle be... , It is a natural number greater than 0. After acquiring the field-of-view image at the current moment, the feature extraction module 100 locates the non-detection reference region according to the initially set spatial coordinates. Using the same parameter extraction algorithm as the baseline feature initialization, the system calculates the current real-time background optical feature vector of the region. Furthermore, the feature extraction module 100 calculates the real-time background optical feature vector and the reference optical feature vector stored in the system memory. The vector difference is used to obtain the light source drift deviation vector. The specific formula for calculating vector deviation is as follows:

[0031] In the formula, It is an n-dimensional column vector with the same dimensions as the initial reference vector. The numerical components of each dimension quantitatively characterize the change in the global ambient light source of the production line at the current moment relative to the initial state.

[0032] Correspondingly, for the actual product under test, the system executes S105 to extract the original optical features of the target workpiece. Since the workpiece area and the non-detection reference area within the field of view are under approximately the same global illumination field, the variation in the workpiece's surface albedo and the fluctuations in ambient light jointly determine the texture and brightness characteristics of its actual image. The feature extraction module 100 performs image segmentation on the target workpiece detection area in the current field of view image, extracts feature parameters related to the workpiece surface and optical state, and constructs the original optical feature vector. The internal elements and eigendimensionality of the original optical eigenvectors maintain a strict correspondence with those of the reference optical eigenvectors, i.e., they are both... The structure is a column vector. For image segmentation of the workpiece region and extraction of basic feature parameters, those skilled in the art can use conventional low-level image processing library functions such as threshold segmentation and edge detection. The principles of image region cropping and numerical calculation are well-known technologies in this field and will not be elaborated here.

[0033] S106, Perform common-mode drift differential cancellation operation. In industrial machine vision imaging theory, common-mode noise exists; that is, fluctuations in the global ambient light source typically cause a linear superposition effect on the optical feature values ​​of different regions within the same physical field of view. Based on this physical premise, the feature extraction module 100 treats the light source drift deviation vector as an environmental interference common-mode term and performs a subtraction cancellation operation on the corresponding dimension from the original optical feature vector of the target workpiece, ultimately outputting the corrected optical feature vector. .

[0034] As a preferred approach, considering the varying sensitivities of different feature parameters to changes in illumination, and the potential for illegal feature values ​​due to direct subtraction, the feature extraction module 100 incorporates a compensation operator and non-negative activation logic in the cancellation operation. The specific correction formula configuration is as follows:

[0035] In the formula, The optical feature vector of the workpiece surface after removing common-mode interference from ambient light; This is the raw optical feature vector extracted directly from the image; This is the light source drift deviation vector; To set dimensions This is a diagonal matrix of order, where the elements on the main diagonal correspond to correction and compensation coefficients for various optical features. These coefficients are pre-calibrated using empirical sensitivity curves of the corresponding features against ambient light intensity, and their values ​​are typically set within a range of... It is a non-negative truncation operator used to force the negative dimension after the difference operation to zero, preventing subsequent matrix calculation errors caused by over-compensation of the bias.

[0036] Through the above decoupling steps, the corrected optical feature vector By removing most environmental factors at the data level, the numerical fluctuations tend to reflect the changes in the physical material state of the workpiece surface itself, reducing the probability of misjudgment in subsequent degradation assessment algorithms.

[0037] After obtaining the corrected optical features in the preceding steps, the feature extraction module 100 needs to combine the geometric dimension data reflecting the workpiece processing quality to provide composite data input for subsequent equipment degradation assessment. Typically, physical degradation of production line equipment often leads to changes in the micro-cutting texture of the workpiece surface and deviations in macroscopic contour dimensions. Comprehensive extraction of these two types of heterogeneous data can more objectively reflect the true service status of the manufacturing equipment. In specific implementation, the heterogeneous data assembly and splicing process of the feature extraction module 100 includes the following sub-steps:

[0038] S107, Extract and construct the original geometric feature vector. The feature extraction module 100 simultaneously extracts geometric morphological parameters reflecting the workpiece machining quality within the target workpiece detection area. In this embodiment, the geometric morphological parameters include key dimensional measurements of the workpiece machining surface and the form and position tolerances of the boundary contour. The system uses edge detection and sub-pixel positioning algorithms to locate key feature points of the workpiece, calculates the geometric morphological parameters accordingly, and organizes them into an original geometric feature vector. Original geometric feature vectors Manifestation A column vector of dimension, where This represents the total number of pre-defined geometric feature parameters, and its value is a natural number greater than or equal to 1. For the specific algorithms of image edge detection and sub-pixel localization size measurement, those skilled in the art can utilize existing computer vision measurement libraries; the calculation principles are well-known in the field and will not be elaborated upon here.

[0039] After obtaining the geometric morphological parameters, the original geometric feature vector is taken into account. It possesses the physical dimension of length, while the corrected optical feature vector... The data streams possess dimensions of light intensity or grayness, and these two dimensions typically differ by orders of magnitude in their numerical distribution ranges. To reduce the risk of numerical bias, where large numerical features dominate the evolution direction of the covariance matrix in subsequent multidimensional covariance calculations, the system executes step S108 to perform dimensional normalization on the heterogeneous data streams.

[0040] As a preferred approach, the feature extraction module 100 configures an independent data scaling operator before vector concatenation. Based on the mean and standard deviation of each feature dimension obtained from historical production and inspection data, the system uses a Z-score normalization algorithm with a smoothing term to normalize the original geometric feature vectors. With the corrected optical eigenvectors Perform a linear transformation dimension by dimension. Using any kk-th dimension feature element x from the feature column vector... k For example, its normalization calculation formula is defined as follows:

[0041] In the formula, These are the normalized feature elements; Input values ​​for the original feature elements; The arithmetic mean of the dimensional features within a defined historical statistical time window; The historical standard deviation of the dimension feature; To prevent extremely small smoothing constants with denominators of zero, their range is usually set to be [value missing]. This ensures that the system does not throw computational errors when the features are in a static state. Through the element-wise operations described above, the system generates dimensionless normalized geometric feature vectors. With normalized optical eigenvectors Through this operation, feature data that originally belonged to different physical domains were uniformly mapped to a numerical space with the same mean and variance.

[0042] After unifying the dimensions, the system calls the splicing mapping logic, i.e., enters step S109, to perform the splicing mapping of the multidimensional heterogeneous feature space. The feature extraction module 100 allocates a unified data structure container in its internal memory to store the normalized geometric feature vectors. With normalized optical eigenvectors The concatenation and combination operations are performed according to a fixed dimensional order. This generates a multidimensional heterogeneous feature vector corresponding to the current detection period. The specific splicing operation formula is defined as follows:

[0043] In the formula, Let be the multidimensional heterogeneous feature column vector after unified dimension fusion. It is a dd-dimensional column vector and satisfies . ; This represents the matrix transpose operation.

[0044] As the continuous production testing cycle progresses, the feature extraction module 100 buffers and records the generated features sequentially according to the timestamp. This forms a continuous, multidimensional, heterogeneous feature vector sequence. The sequence, as a standardized data output stream, is pushed into the subsequent internal storage space to support the construction and calculation of the time-series evolution matrix.

[0045] In this embodiment, the continuous production line inspection process requires the system to have the ability to process time-series data. The front-end feature extraction module 100 continuously outputs a multi-dimensional heterogeneous feature vector sequence. In order to capture the dynamic law of the evolution of the physical state of the equipment over time and to move away from isolated detection at a single moment, the matrix construction module 200 constructs a time window of a specific length in memory to obtain a feature set over a continuous time period for correlation analysis. In specific implementation, the process of establishing the data buffer structure includes the following sub-steps:

[0046] S201, Construct the underlying circular memory space. As a preferred approach, the matrix construction module 200 allocates a fixed-size contiguous memory segment in the internal storage space of the edge computing node, and constructs a circular buffer queue as a data sliding window based on it. Compared with a conventional linear moving array, the circular buffer structure, through the circular redirection of pointers, effectively reduces the probability of triggering global data block memory copy operations during data updates, thereby reducing the computational latency at the underlying level of the edge computing node. The matrix construction module 200 sets a maximum capacity length L for the sliding window. The capacity length L represents the number of frames of continuous feature vectors contained within the window. Its specific value is usually preset based on the equipment cycle time of the production line and the typical cycle of physical degradation to be monitored. For example, in a conventional machining part inspection scenario, it is usually a positive integer between 100 and 1000 to cover continuous processing states of several minutes to tens of minutes.

[0047] For data write operations, the system executes S202, performing cyclic writing and expiration of feature vectors. The matrix construction module 200 receives the current time based on the time tick. Multidimensional heterogeneous feature vectors The system internally maintains a write pointer to indicate the currently available storage address in the circular buffer queue. When a new feature vector... Upon arrival, the matrix construction module 200 stores it in the memory location corresponding to the write pointer and advances the write pointer one position according to the modulo operation logic.

[0048] Considering the possibility of occasional frame drops in industrial field camera hardware or communication links, a timestamp verification mechanism is configured here. If the feature vector is not effectively received in the current detection cycle, the system will retrieve the historical feature vector from the previous moment. Placeholder writes are performed, or linear interpolation is used with adjacent valid data frames to ensure that data points in the circular buffer queue maintain a strictly equidistant distribution on the physical timeline, preventing temporal misalignment from distorting the evolutionary pattern. When the amount of data loaded in the circular buffer queue reaches its maximum capacity L, the continuously advancing write pointer automatically wraps back to the queue's starting address, and newly arriving feature vectors directly overwrite the oldest historical feature data in the queue, thus achieving a physical sliding update of the time window. The specific code implementation of the underlying memory allocation and pointer logic operations of the circular buffer queue can be completed by those skilled in the art by calling the memory management interface of a standard programming language; the specific operation steps are well-known in the field and will not be elaborated here.

[0049] Because the circular buffer queue is empty during actual system initialization or restart reset, to prevent insufficient sample size within the window from distorting subsequent statistical algorithms, the matrix construction module 200 is typically in a data warm-up and storage period before the amount of data received in the queue reaches the set capacity length L, and does not trigger subsequent matrix operation logic. Once the buffer queue is full for the first time, the system immediately executes S203 to extract the real-time window data matrix.

[0050] Within each set calculation cycle, the matrix construction module 200, based on the current write pointer position, backtracks in reverse chronological order and reads the most recently loaded data from the buffer queue. The system constructs a two-dimensional data matrix suitable for subsequent matrix operations by horizontally concatenating and arranging these independent column vectors in chronological order, resulting in a multidimensional heterogeneous feature vector. The specific mathematical expression of the data matrix is ​​as follows:

[0051] In the formula, For the current moment The extracted sliding window data matrix has the following dimensions: Rank, among which The total dimension of a single multidimensional heterogeneous feature vector; to They represent from the current moment respectively Back to Moments are continuously distributed on the timeline A multidimensional heterogeneous feature vector.

[0052] Based on the aforementioned buffering and assembly mechanisms, the matrix construction module 200 maintains a dynamic dataset with a stable sample size in memory space in real time. The dataset structurally unifies discrete data from different detection periods, providing the necessary data format preparation for subsequent extraction of the temporal correlation evolution patterns between multi-dimensional geometric and optical features.

[0053] In this embodiment, single-dimensional features are often insufficient to fully reflect the hidden degradation of production line equipment. In actual manufacturing environments, when critical components of production line equipment experience physical deterioration, it typically leads to correlated deviations and fluctuations in the geometric dimensions and surface optical texture of the processed workpiece. For example, continuous tool wear not only causes the workpiece's dimensional tolerances to gradually exceed limits but also increases the surface roughness, thereby altering its optical contrast under a stroboscopic light source. Based on this, the matrix construction module 200 is configured to capture this cross-physical domain correlation evolution trend. In specific implementation, the multidimensional covariance calculation process includes the following sub-steps:

[0054] S204, performs local centering of the data within the sliding window. Matrix construction module 200 reads the two-dimensional data matrix extracted in the previous steps. To extract second-order statistics reflecting feature fluctuations, the system calculates the arithmetic mean of each feature dimension over the time axis within the sliding window. Furthermore, the system uses the mean vector to shift each column of the data matrix, generating a locally centered data matrix. The mathematical expression for local centralization is as follows:

[0055] In the formula, For the same dimension as a single heterogeneous feature vector dimensional mean column vector; This represents the maximum capacity length of the sliding window; Representative data matrix The Column vectors correspond to the multidimensional heterogeneous features of a specific historical moment within a sliding window; For length is A row vector of all 1s; After centralized processing A data matrix of order 1, in which the algebraic sum of the elements in each row is equal to zero.

[0056] Based on the aforementioned zero-mean data benchmark, the system executes S205 to deduce the multidimensional feature coupling covariance matrix. The matrix construction module 200 utilizes the centralized data matrix... Matrix multiplication is performed to calculate the initial sample covariance matrix. In real-world industrial applications, when the production line is in a highly stable state, some feature parameters may remain approximately static for extended periods. This extremely low-fluctuation input can easily lead to a rank deficiency in the initial sample covariance matrix, causing it to tend towards a singular matrix state, which in turn triggers numerical calculation anomalies in the subsequent eigenvalue decomposition process. To prevent algorithmic dead zones, as a preferred approach, the system introduces a diagonal perturbation term for regularization compensation during the calculation process. The final generated multidimensional feature-coupled covariance matrix... The calculation formula is defined as follows:

[0057] In the formula, Generated for the current sliding window at the corresponding time. Real symmetric covariance matrix of order 1; It is the transpose of the centralized data matrix; This is the correction constant for the degrees of freedom in the statistical unbiased estimate; To prevent matrix singularities, the regularization perturbation coefficients are typically configured with a range of values ​​of [value range missing]. The smallest positive constants within the interval are used to maintain the full rank of the matrix without changing the main evolutionary characteristics of the system; for A unit matrix of order.

[0058] The covariance matrix generated by the above matrix operations The elements on the main diagonal reflect the discrete variance of each heterogeneous feature, while the elements off the diagonal quantitatively characterize the strength of the time-varying coupling correlation between geometric and optical features. For the underlying code calculation implementation of matrix multiplication and basic statistics, those skilled in the art can directly call the relevant matrix operation functions in existing mathematical operation libraries. The compilation and execution principles are well-known technologies in this field and will not be elaborated upon here.

[0059] In this embodiment, the multidimensional feature coupling covariance matrix generated in the preceding steps contains the full coupling relationship between high-dimensional features. In actual production line manufacturing environments, a single physical degradation mechanism of equipment often simultaneously triggers multiple geometric dimensional deviations and changes in surface texture contrast in specific directions. This multidimensional representation based on the same physical root cause usually results in strong information redundancy between features in different physical domains. Directly processing high-dimensional data not only significantly increases the computing power overhead of edge computing nodes but may also introduce noise interference caused by small fluctuations. In order to extract the key information that dominates the evolution of equipment state, the matrix construction module 200 is configured with feature dimensionality reduction solution logic. In specific implementation, the dimensionality reduction solution process includes the following sub-steps:

[0060] S206, Perform eigenvalue decomposition of the covariance matrix. Matrix construction module 200 obtains the multidimensional feature-coupled covariance matrix corresponding to the current sliding window time step. The system is symmetric about real matrices. Numerical decomposition of eigenvalues ​​and eigenvectors is performed to extract an orthogonal basis reflecting the energy distribution of the data space. Its mathematical expression for matrix decomposition is as follows:

[0061] In the formula, for The multidimensional feature coupling covariance matrix of order 1; For inclusion The eigenvector matrix of n column vectors, where each column vector represents an orthogonal transformation direction in the original high-dimensional space; It is composed of corresponding eigenvalues A diagonal matrix of order 1.

[0062] Considering the potential truncation errors from low-level floating-point operations, a very small number of eigenvalues ​​might appear as tiny negative numbers approaching zero, which could cause logical anomalies in subsequent calculations. As a preferred approach, the system... All eigenvalues ​​on the diagonal Perform a non-negative truncation operation, that is, set The corrected eigenvalues ​​are then sorted in descending order of their numerical values, and the eigenvector matrix is ​​adjusted accordingly. The column vectors in the matrix are rearranged synchronously. For the specific numerical solution algorithm of matrix eigenvalue decomposition, those skilled in the art can directly call existing basic mathematical libraries to implement it; its computational principles are well-known in the field and will not be elaborated here.

[0063] After eigenvalue decomposition and sorting, to eliminate redundant secondary information branches, the system enters S207, performing principal component truncation based on the cumulative variance contribution rate. The matrix construction module 200 calculates the cumulative variance contribution rate of the first k k eigenvalues ​​based on the descendingly ordered eigenvalue sequence. The contribution rate quantitatively characterizes the pre-retention state. The proportion of original data information that each principal component can cover. To prevent computational crashes caused by the sum of all eigenvalues ​​being zero in extreme cases, the system introduces a zero-prevention smoothing term into the calculation formula, defined as follows:

[0064] In the formula, To retain the previous Cumulative variance contribution rate for each eigenvector; and These are the numbers after being sorted in descending order. The and the first One eigenvalue; This represents the total number of dimensions of the original covariance matrix; Let be the target dimension for dimensionality reduction to be determined, and let its value satisfy . Positive integers; It is a very small positive floating-point constant, usually taking the value of It is specifically designed to maintain the validity of division logic when the system receives completely static, dead water data.

[0065] The system has a preset contribution rate threshold. The threshold value is typically set within the range of [0.85, 0.95], with the specific value pre-calibrated based on the balance between detection sensitivity and computational power limitations on-site. The matrix construction module 200 traverses in ascending order. Value, search satisfies The smallest Value. The system extracts the rearranged feature vector matrix. The front of the middle Each column vector is concatenated and combined column by column to construct... Projection matrix of order This dynamic threshold-based truncation mechanism enables the system to adaptively adjust the retained feature dimensions when the device state undergoes abrupt changes.

[0066] After constructing the dimension reduction space mapping relationship, the system executes S208 to complete the high-dimensional to low-dimensional mapping projection of multi-dimensional features. The matrix construction module 200 utilizes the generated projection matrix. For the locally centered data matrix generated in the previous steps Perform linear projection operations. Projection operations transfer data from... The original observation space of dimensionality is compressed to Generate a dimensionless feature matrix from the latent variable feature space. The latent variable here, in a physical sense, corresponds to the inherent degradation driving force of the equipment that cannot be directly measured. The specific projection calculation formula is as follows:

[0067] In the formula, For dimensional reduction eigenvalue matrix; Let be the transpose of the projection matrix, with dimensions . ; For local centralization 1-order data matrix; This represents the maximum capacity length of the sliding window.

[0068] After the above projection mapping, the output dimensionality-reduced feature matrix is... It can typically isolate non-dominant fluctuations caused by minor environmental disturbances or common-mode interference to a large extent. The data column vectors contained within it represent the main evolution trajectory of the production line equipment along the dominant physical degradation direction within the current time window, providing a high signal-to-noise ratio data foundation for subsequent modules to quantify the health status of the equipment.

[0069] In this embodiment, the degradation of production line equipment typically manifests as a superposition of long-term slow wear and tear and short-term abnormal fluctuations. To decouple these two different scales of evolution in the time dimension, the state assessment module 300 is configured with dual-track queue scheduling logic to differentially buffer the dimensionality-reduced features output from preceding steps. Specifically, the scheduling process includes the following sub-steps:

[0070] S301, establish a dual-timescale buffer queue. The state assessment module 300 divides the memory space of the edge computing node into two independent regions to construct a long-cycle baseline queue and a short-cycle real-time queue, respectively. Physically, the long-cycle baseline queue is used to reflect the macroscopic state baseline of the device over a long historical span, and its maximum capacity length is set to... Short-cycle real-time queues are used to capture recent transient evolution characteristics of the device, and their maximum capacity length is set to... To ensure sufficient differentiation in evolutionary characteristics between the two timescales, the system typically sets the capacity length to satisfy an equation. The proportionality constant The value range is usually configured as an integer within [5, 20], depending on the regular maintenance cycle and cycle frequency of the production line equipment.

[0071] After completing the queue structure initialization, the system proceeds to S302 to perform track-by-track writing and synchronous updating of feature data. The state evaluation module 300 acquires the dimensionality-reduced feature matrix generated in the preceding steps. Considering that the matrix covers the data of the entire historical sliding window, the system extracts the data corresponding to the current moment from it. The latest column vector This is used as a valid state representation for the current detection cycle. The system implements differentiated write strategies for these two queues with different positioning.

[0072] As a preferred approach, for short-cycle real-time queues designed to capture transient changes, the state evaluation module 300 will use column vectors... The data is directly pushed in according to the current timestamp. However, for long-period baseline queues, to prevent high-frequency noise from polluting the macroscopic baseline and to reduce memory redundancy, the system introduces local smoothing and downsampling operators before writing. Specifically, the system performs moving average filtering on multiple consecutively arriving column vectors, removing outliers that deviate from the mean by more than a set threshold, and then pushes the smoothed feature vectors into the long-period baseline queue according to the set downsampling interval. When the amount of data loaded in the buffer queue reaches its maximum configured capacity, the system uses a first-in-first-out mechanism to evict the oldest historical feature data in the queue, thereby maintaining the dynamic forward shift of the time window. For pointer maintenance and memory release operations of the underlying queue storage, those skilled in the art can call the standard data structure interface of the existing operating system to implement them. The principle of memory address mapping is a well-known technology in this field and will not be elaborated here.

[0073] Considering that the memory queue may be filled with a large number of invalid null or zero values ​​when the device is first started or restarted and reset, directly calling it to participate in subsequent evaluations would greatly increase the probability of serious miscalculations. As a preferred approach, the system is configured with a dynamic ready flag in S302. The status evaluation module 300 monitors the current loading depth of the long-cycle baseline queue in real time. Only when the loading depth reaches the set effective threshold will the system set the ready flag to true, thereby enabling subsequent downlink calculation logic.

[0074] When the queue ready flag is activated, the system periodically executes S303 to extract the dual-track time window data set. In each detection cycle, the state evaluation module 300 extracts continuous feature vector sets from the long and short cycle queues respectively, and horizontally concatenates them according to chronological order to assemble them into long-cycle feature matrices. and short-period characteristic matrix The mathematical expression for its assembly is as follows:

[0075] In the formula, The dimension extracted at the current moment is The long-period baseline characteristic matrix of order 1; For dimension A short-period real-time feature matrix of order 1; This represents the number of feature dimensions retained after the preceding dimensionality reduction solution; This represents the k-dimensional feature column vector recorded at a specific historical time point.

[0076] Through the aforementioned dual-track queue scheduling mechanism, the state assessment module 300 physically isolates long-term data reflecting macroeconomic trends from short-term data reflecting the current state. This structured data separation operation aims to reduce the risk of small fluctuations being over-smoothed in long-term averages, while also helping to reduce the interference of single-point disturbances on the judgment of short-term state evolution, providing multi-scale structured support for subsequent accurate comparison of the degree to which the current state of the equipment deviates from the normal baseline.

[0077] In this embodiment, after a preliminary dual-track queue scheduling, the system separates long-period data representing the macroscopic baseline of the device and short-period data representing the recent state in physical memory. To quantitatively assess the current health status of the device, the state assessment module 300 is configured with variance analysis logic to analyze the distribution differences between these two sets of data in the multidimensional latent variable space. In conventional data comparison applications, simply using Euclidean distance is often insufficient to eliminate the numerical scale differences and physical coupling correlations between different feature dimensions. Therefore, the system introduces a statistical distance measure based on covariance analysis. Specifically, the variance analysis process includes the following sub-steps:

[0078] S304, extract long-period baseline distribution parameters. State assessment module 300 obtains the dimensions generated in the preceding sequence. Long-period characteristic matrix The system calculates the arithmetic mean and covariance structure of latent variables across all dimensions within a long-term window to establish a multivariate statistical benchmark characterizing the normal service status of equipment. The specific formulas for calculating its distribution parameters are defined as follows:

[0079] In the formula, This is a k-dimensional mean column vector of long-period baseline data; Represents the first in the long-period characteristic matrix Column vectors; For long-period states The covariance matrix of order; For length is A row vector of all 1s; for The system is an identity matrix of order 1. Considering that during long-term steady-state operation, some latent variable features after dimensionality reduction may exhibit a low-fluctuation, near-static state, this can easily lead to the constructed baseline covariance matrix being in a non-full-rank singular state, resulting in computational crashes with zero denominators during subsequent inversion operations. To avoid inversion operation anomalies, as a preferred approach, the system introduces a diagonal perturbation term at the tail of the covariance operation. The range of values ​​for the anti-singular perturbation constant γ is typically configured as follows: This is used to ensure the invertibility of the matrix while avoiding interference with the original distribution characteristics.

[0080] After obtaining the baseline distribution parameters, the system executes step S305 to perform multivariate spatial distance calculation for short-period states. The state assessment module 300 reads the short-period feature matrix. The system calculates the Mahalanobis distance of each state feature vector within a short-period window relative to the center of the long-period baseline. This distance calculation method based on variance analysis can adaptively allocate penalty weights according to the historical variance of each dimension feature in a multi-dimensional space. Specifically, for feature dimensions with large historical fluctuations, the same absolute deviation will be assigned a smaller outlier weight; while for dimensions with highly stable historical performance, even a small deviation can produce significant distance accumulation, a mechanism that better reflects the actual evolution of physical device degradation. For any nth feature vector in the short-period feature matrix... column vectors Its multi-dimensional spatial distance The calculation formula is defined as follows:

[0081] In the formula, The calculated scalar distance value; It is the inverse of the long-period covariance matrix; Let be the index number of the column vector, and let its value satisfy . Positive integers. For the underlying numerical computation implementation of matrix inversion, those skilled in the art can directly call the standard function interfaces in existing mathematical operation libraries. The compilation and execution principles are well-known technologies in this field and will not be elaborated upon here.

[0082] After acquiring the continuous distance set within a short period, the system proceeds to S306 to generate a comprehensive state deviation index. Considering that the spatial distance calculated in a single measurement is prone to instantaneous fluctuations under complex electromagnetic interference or random mechanical vibration conditions, and that directly calculating the arithmetic mean is easily skewed by extreme outliers, the state assessment module 300 performs smooth aggregation of the distance set within the short period window based on a truncated mechanism. The system then... The distance values ​​are sorted in ascending order, and the proportion of values ​​at both ends of the distribution is removed. The data points are then used to calculate the expected value of the remaining middle section data, generating the comprehensive state deviation for the current detection cycle. :

[0083] In the formula, A quantitative indicator characterizing the overall deviation of the equipment at the current moment; This represents the maximum capacity length of the short-cycle real-time queue. The threshold for the unilateral truncation ratio is typically set to a value between 0.05 and 0.10. This represents the floor operation; Represents the number after ascending order. Distance values.

[0084] Through the above variance analysis mechanism, the output comprehensive state deviation is... Numerically, this indicator quantitatively reflects the comprehensive correlation between the current microscopic physical morphology of production line equipment and the deviation of macroscopic processing quality from historical steady-state benchmarks. It provides quantitative data for subsequent stages to set early warning logic and trigger predictive maintenance commands.

[0085] In this embodiment, the comprehensive state deviation calculated in the preceding steps reflects the distance by which the equipment state deviates from the normal baseline at the current moment. However, the physical degradation process of equipment is usually non-linear, and relying solely on static distance amplitudes often makes it difficult to distinguish whether the equipment is in a slow wear-out phase or an accelerated deterioration phase. To assist subsequent predictive maintenance, the state assessment module 300 is configured to analyze the rate of change of the deviation index over time. Specifically, the calculation process of the trend slope includes the following sub-steps:

[0086] S307, Construct the historical evolution sequence of deviation. The state evaluation module 300 maintains an independent scalar buffer queue in the memory of the edge computing node. Based on the detection cycle, the system sequentially receives the comprehensive state deviation generated in each cycle. The data is then sequentially pushed into a queue. The system sets a trend fitting window length W for the queue. The window length is typically between 10 and 50, used to provide the number of samples required for fitting. When the deviation value loaded into the queue reaches W, the oldest data points are discarded as new data are moved in, forming a fixed-length time series dataset.

[0087] After acquiring the historical deviation sequence, the system executes S308 to perform abscissa normalization and least-squares trend fitting. In actual industrial edge computing environments, due to task scheduling mechanisms or network transmission jitter, the collected absolute timestamp sequences often exhibit fluctuations in time intervals. If absolute time is directly used as the fitting abscissa, the corresponding time difference will introduce additional computational noise, causing the slope calculation result to produce physically meaningless oscillations. As a preferred approach, the state evaluation module 300 performs local normalization of the time step before fitting. The system maps the first data point within the fitting window to a relative zero point and constructs an equally spaced increasing relative time series vector using a fixed beat constant as the step size.

[0088] Based on the normalized time series, the state assessment module 300 uses the least squares method to perform linear regression calculations on the deviation data within the window. To prevent division operations from crashing due to the horizontal axis difference approaching zero in extreme cases, the system introduces a regularization anti-zero constant into the calculation logic. Current trend slope The calculation formula is defined as follows:

[0089] In the formula, The trend slope extracted at the current moment. A value greater than zero indicates that the equipment is in a state of continuous deterioration, while a value less than or equal to zero usually corresponds to the equipment's state falling back after shutdown or tool replacement. The set length of the trend fitting window; Represents the normalized first A relative time step; for The arithmetic mean of a relative time step; This represents the deviation of the historical aggregated state stored in the buffer queue; Within the fitting window The arithmetic mean of the deviations; To prevent singular perturbation constants, they are typically configured as follows: This is used to avoid division by zero errors.

[0090] The slope output from a single window fitting may still contain high-frequency noise under complex operating conditions. Therefore, the system enters S309 to generate an exponentially smoothed evolution rate. The state evaluation module 300 uses the smoothed slope value from the previous time step to calculate the original slope at the current time step. Low-pass filtering is performed to generate the final smooth trend slope. The calculation logic is as follows:

[0091] In the formula, The output evolution rate index is the result of smoothing. This is a historical smoothing indicator from the previous moment; To smooth the weighting coefficients, their value range is set to... Within the range, the specific values ​​are pre-calibrated based on the typical decay time constant of the equipment.

[0092] In actual operation, when the system has just started up or has undergone a reset and restart, due to the lack of historical data, the smoothed value of the previous moment in the above recursive formula is... The current state is undefined. Directly calling the function without proper handling could lead to out-of-bounds memory access or invalid calculation results. To prevent this algorithmic dead zone, the state evaluation module 300 is configured with a cold start initialization mechanism. Specifically, when the buffer queue is first full... Calculate the first original slope from the given data. At that time, the system directly Assigned to the initial smooth state This completes the initialization and oscillation of the smoothing filter.

[0093] Through the above calculation process, the condition assessment module 300 extracts the slope of the smooth trend characterizing the equipment's degradation rate. This parameter provides the derivative dimension of the condition input for subsequent dynamic extrapolation of the equipment's remaining service life.

[0094] In this embodiment, the comprehensive state deviation and smoothing trend slope output by the preceding state assessment module are both continuous real-valued variables. While these continuous values ​​possess clear physical quantitative characteristics, in actual industrial operations, operators typically face discrete and specific maintenance instructions. To establish a transitional mapping between the underlying quantitative values ​​and the upper-level macroscopic decisions, the early warning decision module 400 is equipped with a membership transformation function generator to convert state indicators with different dimensions and scales into fuzzy membership vectors reflecting the deterioration stage of the equipment. Specifically, the transformation process includes the following sub-steps:

[0095] S401 defines the physical domain and fuzzy subset of the state index. The early warning decision module 400 addresses the comprehensive state deviation. and smoothing trend slope A state partitioning space is established separately. Based on the typical degradation cycle of production line equipment, the system divides the equipment's operating conditions into three fuzzy subsets: stable period, degradation period, and critical period. Traditional step-threshold partitioning methods are prone to frequent jumps in judgment results between different states due to small measurement noise when the input value is near the boundary. As a preferred approach, the system uses a smooth, continuous nonlinear transition function to replace the hard-boundary piecewise logic, thereby preserving an appropriate fault tolerance margin in the state transition region.

[0096] After establishing the fuzzy subset architecture, the system proceeds to S402 to construct and execute dynamic membership transformation calculations. The early warning decision module 400 calls its internally configured transformation function library to perform nonlinear mapping on the input data. For the intermediate transitional state of the "degeneration period," the system uses a difference structure based on the generalized logistic curve to construct its membership function. This structure can generate a bell-shaped distribution with smooth decay at both ends in multidimensional space. Simultaneously, for the "stable period" and "critical period" at the ends of the universe of discourse, the system uses unilateral logistic curves to construct monotonically decreasing and monotonically increasing transformation boundaries, respectively. The corresponding mathematical expressions are as follows:

[0097] In the formula, , and These are the membership metrics for the current overall state deviation belonging to the stable period, the degradation period, and the critical period, respectively, with their values ​​ranging from [0,1]. The overall state deviation input at the current moment; and These are the central threshold parameters for the equipment's transition from the stable phase to the degradation phase, and from the degradation phase to the critical phase, respectively, and they must meet the following requirements. The algebraic constraints are determined in advance based on the statistical mean of the equipment's historical life cycle. and This is the conversion slope coefficient corresponding to the phase transition region, and its value range is typically configured as follows: The real constants within the range determine the width of the state transition band.

[0098] In real-world industrial edge computing scenarios, the input variables received by the system may be affected by occasional sensor disconnections or communication protocol parsing errors. Illegal data, such as non-numerical values ​​or values ​​approaching infinity, may occur. Directly substituting such unverified outliers into the underlying floating-point unit for exponential calculations can easily lead to arithmetic logic unit overflow, causing calculation errors or program interruptions. To ensure the robustness of algorithm execution, as a preferred approach, the early warning decision module 400 is configured with boundary clamping and data validity verification logic before performing exponential mapping. When an input value is identified as an invalid floating-point number or exceeding a preset physical limit domain, the system forcibly resets it to a valid boundary value within the domain, ensuring the safety and continuity of subsequent conversion processes. For the numerical approximation calculation of the underlying exponential function, those skilled in the art can directly call the standard mathematical library instructions supported by existing processors; the compilation and execution principles are well-known technologies in this field and will not be elaborated upon here.

[0099] Following the above calculation process, the system executes step S403 to generate a multi-dimensional comprehensive state membership vector. The early warning decision module 400 then uses similar logic to simultaneously smooth the trend slope. The transformation calculation is performed under each fuzzy subset. The system horizontally concatenates the membership values ​​of each state index corresponding to the stable period, degradation period, and critical period at the same time according to a set format to generate a comprehensive state membership vector for the current detection period. Vectors in numerical space represent the probability distribution of a device's current physical state belonging to various potential degradation stages, providing dimensionless standardized input data for subsequent modules to perform multi-rule reasoning and output corresponding maintenance strategies.

[0100] In this embodiment, the comprehensive state membership vector generated in the preceding steps reflects the fuzzy state distribution of deviation and trend slope in their respective dimensions. Traditional fuzzy inference mechanisms typically rely on a manually configured IFTHEN rule base. As the number of monitoring parameters increases, the number of rules increases significantly, and inference conflicts are easily generated under boundary conditions. To establish a direct mathematical mapping between multidimensional degradation features and deterministic maintenance actions, the early warning decision module 400 is equipped with a cross-domain matrix multiplier. In specific implementation, the calculation scheduling process of the multiplier includes the following sub-steps:

[0101] S404 performs the cross-domain outer product operation of the membership vector. The early warning decision module 400 obtains the deviation membership vector corresponding to the amplitude domain at the current time. and the slope membership vector corresponding to the rate domain The system performs a tensor outer product operation on the column vectors of these two independent dimensions to construct a joint state matrix that reflects the multi-dimensional state interaction characteristics. The mathematical expression for the outer product operation is as follows:

[0102] In the formula, For generated Order-state joint matrix; It is an m-order column vector containing membership degrees for the stable period, the degradation period, and the critical period; This is generated by transposing the membership vector in the rate domain. Row vector of order; The outer product operation represents vectors. Each element in the state joint matrix... In a physical sense, this characterizes the absolute wear level of the equipment. The state and the rate of deterioration are in the first The joint membership probability of states.

[0103] After obtaining the multidimensional cross-feature space, it is necessary to differentiate the severity of each state combination. For example, high deviation combined with a high evolution slope usually corresponds to a higher probability of component failure, while high deviation accompanied by zero slope may only stem from static baseline drift of the sensor. To this end, the system executes S405 to load the expert rule weight matrix for risk distribution mapping. The early warning decision module 400 is pre-configured in the physical storage area with... Order of regular weight matrix The value ranges of each element in the matrix are configured in... Within the specified range, the specific values ​​are calibrated offline based on historical data from failure mode and impact analysis of specific production line equipment.

[0104] To convert the two-dimensional joint probability in matrix form into a one-dimensional quantitative decision basis, the early warning decision module 400 then performs Hadamard product and dimension reduction aggregation of the matrix in S406. The system processes the joint state matrix... With the rule weight matrix The elements at corresponding positions are multiplied point by point, and then all elements of the resulting product matrix are summed globally to generate a scalar comprehensive early warning index. Considering the influence of the truncation error of the preceding nonlinear transformation, the sum of all elements of the state joint matrix may drift slightly in some cases. Therefore, the system introduces a normalization constant and a perturbation smoothing term into the denominator of the summation formula. The specific calculation formula is defined as follows:

[0105] In the formula, A comprehensive early warning index generated for the current detection cycle; and The corresponding matrix at the th Line number The numerical elements of the column; To prevent singular floating-point constants with denominators of zero, they are typically configured as follows: This is used to maintain normal program scheduling in the abnormal state where all input vectors are zero. For the instruction set optimization of the underlying matrix element-wise multiplication and accumulation loop, those skilled in the art can implement it based on the hardware acceleration library of the target edge computing chip. Its register-level operation principle is a well-known technology in this field and will not be elaborated upon here.

[0106] After obtaining the continuously output comprehensive early warning index, as a preferred method, the early warning decision module 400 performs hierarchical hard threshold discrimination in S407 to generate actuator instructions. The system internally has preventative maintenance thresholds. and emergency stop threshold And satisfy numerical constraints The specific values ​​of the aforementioned dual-layer thresholds are derived through reverse calculation based on the historical failure sample dataset of the device, and are typically configured in [the specified range]. Within the specified range. Considering the complex environmental interference in industrial settings, which can easily cause transient spikes in single detection data, triggering a shutdown command based solely on a single exceedance would lead to numerous false alarms. To eliminate control logic dead zones, the system incorporates time-window anti-jitter delay logic in the command triggering stage.

[0107] The early warning decision module 400 will cycle by cycle The comparison is performed with the aforementioned dual-layer threshold, and the number of consecutive periods exceeding the limit is statistically analyzed using internal registers. When When the system outputs an instruction to maintain the current machining program and clears the statistics register; when the value is continuous Each cycle satisfies At that time, the system issues preventative change orders with planned time windows to the manufacturing execution system via the industrial bus; when And continue During each cycle, the system directly pulls low the enable pin of the underlying programmable logic controller to trigger an emergency stop interrupt. The debouncing cycle parameter... and The value is set according to the equipment's processing cycle time and is usually configured as an integer between 3 and 5. By configuring a cross-domain matrix multiplication mechanism, the evaluation logic achieves the numerical mapping of discrete multidimensional fuzzy parameters to deterministic control commands without having to handle complex rule base traversal.

[0108] In this embodiment, although the pre-warning decision module can output a comprehensive warning index based on current characteristics, during the long-term service life of the equipment, the preset expert rule weights and static model parameters may gradually deviate from the actual physical boundary due to the natural aging of the mechanical structure or the seasonal drift of the working environment temperature. To enable the system to adapt to changes in operating conditions, a dynamic feedback adjustment module 500 is configured. This module integrates a differential calculation and threshold comparator to continuously monitor and evaluate the deviation between the model's predicted output and the actual state of the physical equipment. Specifically, the error monitoring and triggering process includes the following sub-steps:

[0109] S501, acquire the predicted sequence and the actual physical label. The dynamic feedback adjustment module 500 synchronously saves the historical comprehensive early warning index sequence output by the preceding early warning decision module in its internal buffer. Simultaneously, the system receives actual maintenance logs from the manufacturing execution system or actual wear data periodically recorded by external high-precision sensors via the industrial bus. Considering that the physical dimensions of the maintenance data and the dimensionless index output by the algorithm are not consistent, as a preferred approach, the system maps the aforementioned discrete physical maintenance data into normalized actual state labels based on their physical limit boundaries. This ensures that its value range is consistent with the comprehensive early warning index. The specific mathematical formula for the conversion is defined as follows:

[0110] In the formula, The transformed normalized true state labels; The actual physical measurement value at the current moment is entered externally; The baseline physical parameters for equipment in a brand new or recently overhauled state; These are the limiting physical parameters that indicate when equipment reaches the standards for scrapping or mandatory replacement. Through the above linear mapping, discrete data that originally had specific physical units are transformed into data at a certain limit. A series of real numbers in an interval.

[0111] Due to manual data entry processes or offline detection cycles, the timestamps of the actual status labels typically lag behind the real-time prediction timestamps of the edge computing nodes. Directly comparing data from different times introduces time misalignment errors, rendering the deviation calculation results unreliable. To address this issue, the system proceeds to S502, performing time-series alignment and residual measurement. The dynamic feedback adjustment module 500, based on the arrival timestamps of the actual labels, backtracks and retrieves historical prediction values ​​matching the same occurrence time from the historical prediction cache queue. ,in This represents the number of time alignment delay cycles.

[0112] During timing registration, to prevent logical dead zones caused by memory addressing anomalies, the system first checks the number of latency cycles. Does it exceed the maximum depth of the historical cache queue? .like This indicates that the physical maintenance data is severely outdated, and the corresponding historical predictions have been overwritten and discarded. In this case, the system actively discards the secondary difference calculation request and waits for the next set of maintenance data; if the conditions are met... The system then performs a difference operation on the two registered sets of data to calculate the absolute residual value at the current time. :

[0113] In the formula, The calculated scalar error magnitude; The warning index corresponding to the physical detection time in the historical prediction cache queue; This indicates the absolute value operation.

[0114] After calculating the residual, the system enters S503 to perform threshold comparison and trigger signal generation based on the hysteresis interval. If a single fixed threshold is directly used for comparison, slight fluctuations in the residual value near the threshold boundary can easily lead to repeated triggering of the feedback update signal, causing frequent start-stop cycles of subsequent parameter update algorithms and consuming the processing power of edge computing nodes. As a preferred approach, the dynamic feedback adjustment module 500 is configured with hysteresis comparison logic based on a dual-threshold approach. The system has a trigger upper limit threshold. and reset lower threshold And satisfy the constraints. The specific values ​​of these two thresholds are preset based on the system's tolerance for predictive errors, and are typically... Configured in the range [0.15, 0.25] Configured in Interval.

[0115] The dynamic feedback adjustment module 500 adjusts the residual at the current time. With internal update status register Logical judgment is performed based on the preceding state. It is in an idle state (logic 0) and satisfies When this occurs, the system sets the register to the active state (logic 1) and simultaneously outputs an update enable pulse to the subsequent module; when It is in an active state and meets the following conditions. When the system prediction accuracy has recovered to an acceptable level, the system will reset the registers to an idle state; if the residual is at... Within the hysteresis interval, the register retains the logical state of the previous moment. For the digital circuit implementation mechanism of the underlying comparator and status register, those skilled in the art can use standard logic gates or programmable gate arrays to build it; its working principle is well-known in the field and will not be elaborated here.

[0116] Through the aforementioned differential calculation and hysteresis threshold comparison mechanism, the dynamic feedback adjustment module 500 suppresses conventional measurement noise and transient fluctuation interference at the software level. This mechanism, without causing excessively frequent system responses, captures nodes where the model experiences substantial performance degradation, providing a triggering basis for subsequent adaptive parameter iteration.

[0117] In this embodiment, the comprehensive early warning index and various control commands generated by the preceding modules are essentially local logical variables residing in the memory of the edge computing node. These internal state information needs to be converted into network communication messages conforming to industry standards in order to be correctly parsed and responded to by the external manufacturing execution system or the underlying programmable logic controller. To establish an information link between the underlying quantitative values ​​and the external physical actuators, the system is configured with an alarm encoder and a downlink communication gateway. Specifically, the message encapsulation and distribution process includes the following sub-steps:

[0118] S504 is a digital protocol encapsulation for executing multi-source control commands. The system acquires the current cycle's status warning index, order change or emergency stop command flag, and feedback update pulses. The alarm encoder concatenates these discrete data units, along with the current absolute timestamp and device physical address, into a continuous byte stream vector according to the standard data structure of industrial control protocols. Considering that long-distance physical cable transmission in industrial settings is susceptible to bit flipping due to high-frequency electromagnetic interference from frequency converters, the encoder appends a cyclic redundancy check (CRC) code, such as the CRC16 checksum commonly used in the standard Modbus protocol, to the end of the data frame to ensure the integrity of the data frame during transmission.

[0119] After completing the basic data packet encapsulation, the system proceeds to S505 to calculate the dynamic transmission priority index. In multi-concurrent task scenarios, conventional first-in-first-out queues cannot guarantee the timely issuance of emergency stop commands, while pure absolute priority scheduling can easily lead to low-priority regular logs residing in memory for extended periods, causing task starvation. As a preferred approach, the alarm encoder dynamically calculates the priority index for each generated data frame. The system comprehensively considers the initial severity of the alarm and the waiting time of the packet in the queue, and its dynamic priority index... The calculation formula is defined as follows:

[0120] In the formula, For the first The dynamic priority scalar of each message at the current moment; The system presets an initial severity level constant based on business attributes. For example, an emergency stop command is assigned a value of 10, and a regular heartbeat packet is assigned a value of 1. This is the number of time steps a message has accumulated in the buffer queue since its generation. and These are the normalized coefficients used to balance the weight ratios, and they must satisfy mathematical constraints. This is the time gain constant, and its value is usually configured to be between [0.05, 0.2]. The function is for finding the minimum value; This is a preset saturation clamping upper limit constant used to truncate exponential divergence in case of abnormal delays, preventing overflow of underlying floating-point register operations. Its specific value is usually configured according to the system variable type, such as 255 or 65535. By introducing an exponential time compensation term and a saturation limit, the transmission priority of low-level data packets will gradually increase after prolonged delays, ensuring that all messages can be processed within a limited time.

[0121] After obtaining the dynamic priority of each message, the system executes S506, scheduling downlink communication in non-blocking mode and initiating retransmission verification. In synchronous blocking communication mechanisms, if the external lower-level device fails to return an acknowledgment response in a timely manner due to a sudden power outage or network congestion, the edge gateway's sending thread will be continuously suspended. This situation, where socket resources are locked for a long time, can cause subsequent emergency control commands to be congested within the sending end. To prevent potential risks, the downlink communication gateway adopts asynchronous non-blocking sending logic. The gateway's independent thread is based on priority. The system extracts and sends messages in descending order, and immediately releases system control after calling the underlying sending interface. For the asynchronous socket call mechanism of the underlying network protocol stack, those skilled in the art can implement it using standard operating system network programming interfaces; its compilation and execution principles are well-known in the field and will not be elaborated upon here.

[0122] To ensure the reliability of the communication loop, the downlink communication gateway is synchronously configured with a state machine retransmission verification mechanism. The gateway starts an independent timer logic when sending a data frame. If no valid acknowledgment packet is received from the target device within the set timeout threshold, the gateway determines that the transmission has been lost. At this time, the system pushes the unacknowledged message back to the front of the queue and increments the message retransmission counter. When the number of consecutive retransmissions of a critical instruction exceeds the preset maximum retransmission limit, the system raises the local hardware alarm pin, triggering an audible and visual alarm to prompt on-site engineers to inspect the physical lines. Through the above-mentioned encoding encapsulation and anti-blocking scheduling mechanism, the system achieves orderly scheduling from early warning decisions to physical actions in complex industrial network environments.

[0123] In this embodiment, the evaluation logic of the edge computing node not only relies on the underlying sensor data but is also constrained by the macro-level production tasks. When the manufacturing execution system issues a new production work order, the target speed, processing load, or material properties of the equipment may change, rendering the original baseline for normal equipment operation inapplicable. To enable the underlying algorithm to perceive and adapt to changes in the upper-layer business logic, the business coordination scheduling module 600 is equipped with an uplink work order decoder. In specific implementation, the parsing and synchronization process of work order data includes the following sub-steps:

[0124] S601 performs uplink data stream listening and frame reassembly. The service coordination scheduling module 600 maintains a listening state on the designated network port, receiving service messages from the host computer. In industrial network environments based on control protocols, due to the buffering mechanism of network routing nodes, continuously sent data packets are prone to packet merging or partial packet phenomena at the receiving end. If data is extracted directly according to a fixed offset, there is a high probability of reading incomplete fields or out-of-bounds memory. As a preferred method, the uplink work order decoder is configured with frame reassembly logic based on a sliding window. The system dynamically extracts complete data frames in the receiving buffer according to the frame header identifier and message length field preset by the protocol, and performs independent verification and calculation. For the specific implementation of the underlying network data stream buffering and boundary positioning, those skilled in the art can develop it according to the corresponding socket programming specifications, which are well-known technologies in the field and will not be elaborated here.

[0125] After extracting the complete application layer data frame, the system proceeds to S602 to perform semantic parsing and memory mapping of business fields. Work orders issued by the manufacturing execution system typically use a text serialization format, containing business fields such as processing batch, target output, spindle speed setpoint, and material hardness. The upstream work order decoder iterates and matches these fields according to a pre-configured industrial equipment general information model dictionary. To avoid null pointer exceptions or illegal characters causing interruptions in the conversion function execution, the decoder is configured with a validity check mechanism before performing string-to-floating-point conversion. When a missing required field or an illegal numeric character is detected, the system triggers local exception handling logic. To avoid the risk of process mismatch caused by using statically hard-coded constants, the system extracts the average parameter value from the memory log during the previous alarm-free stable operating cycle on the device as a safe default value for replacement, maintaining the continuous operation of the decoding process.

[0126] To transform the processing parameters at the business level into mathematical inputs that can be directly used by the underlying algorithm, the system enters S603 to construct a work order feature normalization vector. Because the dimensions and ranges of variation of different physical processing parameters vary significantly, direct mixing and calculation would cause numerical scale imbalance. The business collaborative scheduling module 600 extracts the key numerical parameters after parsing and performs a linear mapping based on the equipment's rated physical extreme values. The normalization calculation formula for individual work order characteristics is defined as follows:

[0127] In the formula, To extract and convert the first The characteristic of the normalized chemical unit is configured in the range of [0,1]. These are the original values ​​of the actual processing parameters obtained from the parsed message; and These are the lower and upper limits of the allowable parameters of the equipment at the physical level, respectively, and their specific values ​​are pre-calibrated by the equipment's engineering design parameters; To prevent zero perturbation constants, they are typically configured as follows: This is used to prevent a division-by-zero logical dead zone caused when the physical upper and lower limits are configured equal or a read error occurs. The system sequentially concatenates the features from each dimension to generate a comprehensive work order status vector.

[0128] After obtaining the standardized work order status vector, the system executes S604 to initiate a smooth switching schedule for baseline parameters. In actual production line operations, there is a time window for mechanical adjustment or preheating when physical equipment switches from an old work order to a new one. If the underlying detection baseline is hard-switched immediately upon receiving the message, the misalignment between the measured value and the new baseline will generate false alarm signals. As a preferred approach, the uplink work order decoder introduces a delayed switching mechanism based on status feedback. After parsing a new work order, the system starts an internal waiting timer and continuously monitors the actual operating status feedback of the equipment. The decoder extracts the core process parameters defined in the new work order and obtains the real-time feedback values ​​transmitted from the underlying sensors. The system determines that the current state has entered the tolerance range under the following conditions:

[0129] In the formula, The allowable relative tolerance ratio is typically configured to be between 0.02 and 0.05. When the system is continuously... If the above tolerance conditions are met in a single acquisition cycle, or if the waiting time exceeds the preset adjustment timeout threshold, the decoder will formally submit the new work order status vector to the preceding status assessment module, driving the dynamic update of the relevant assessment baseline and threshold.

[0130] In this embodiment, in addition to handling real-time status monitoring and work order parsing, the edge computing node also needs to accumulate long-term operational patterns of the equipment. A single early warning or alarm is often the result of long-term, hidden degradation of physical equipment. To construct a complete equipment degradation lifecycle map and support subsequent offline parameter optimization and equipment-level diagnostic analysis, the business collaboration scheduling module 600 is internally configured with a historical situation tracing engine. The engine is used for efficient persistence of multi-source time-series data and correlation anomaly retrieval. In specific implementation, the data tracing and aggregation process includes the following sub-steps:

[0131] The S605 performs efficient downsampling and feature preservation of multi-source time-series data. Limited by the disk storage capacity of edge nodes in industrial settings, directly storing high-frequency acquired raw sensor data and real-time predictive indices can easily increase the system's storage burden and cause read / write blocking during high-concurrency queries. To reduce data volume while retaining critical state information, the system needs to downsample historical operating sequences. While simple sliding window averaging can effectively compress data in this stage, it may filter out transient spikes characterizing early localized equipment failures, resulting in insufficient extraction of historical anomaly features.

[0132] As a preferred approach, the historical situation tracing engine employs a joint statistical feature extraction matrix based on time blocks. For a length of... The downsampling time window, the system targets the first Data sequence within a time block Simultaneously, the system extracts the trend mean, extreme values, and variance of the data. Considering that occasional sensor disconnections may lead to missing valid data within the window, the system verifies the actual number of valid sampling points before performing calculations. .when When the joint feature matrix is ​​constructed, the corresponding formula is defined as follows:

[0133] In the formula, For the first The comprehensive feature row vector generated by each time block; For the first time window The original valid sampling point values; This is a function to find the maximum value. This is the arithmetic mean of the valid data within the window. If... In case of anomalies, the system proactively skips formula calculations, uses the feature vector of the previous valid time block for copying and filling, and marks the block's data quality as low. Through this joint preservation and anomaly filling mechanism, the system improves the data compression ratio while largely preserving the macro-trends and micro-anomalies of the equipment's operation.

[0134] After obtaining the downsampled feature matrix, the system proceeds to S606 to perform structured writing with timestamps and business tags. The system associates and concatenates the aforementioned feature row vectors with the current work order number, material batch, and absolute timestamp, packages them to generate structured historical status records, and writes them to the local lightweight time-series database. To prevent the database from experiencing performance degradation due to excessive expansion of single-table records, the engine employs a dual partitioning strategy based on time span and work order cycle to store data in shards. Specifically, the system uses the natural month as the first-level physical table partition and the work order number as the second-level logical index, physically isolating data from different processing tasks. The underlying time-series database's data sharding storage mechanism, B+ tree index construction, and read / write lock allocation can be implemented by those skilled in the art based on standard relational or non-relational database management systems; the underlying execution and scheduling principles are well-known technologies in the field and will not be elaborated upon here.

[0135] When the equipment triggers a maintenance command or receives a diagnostic query request from an external engineer, the system enters S607 to perform reverse backtracking and degradation measurement of historical operating status. The historical status tracing engine uses the timestamps of abnormal alarms as the basis for its operation. The system retrieves historical state data within a set time window by tracing back from the time-series database. To prevent out-of-bounds addressing caused by the starting point exceeding the earliest existing record time in the database, the system uses the actual timestamp of the calculated starting point. Constraints ,in For the preset backtracking time span, This is the earliest timestamp of valid data in the database.

[0136] To quantify the overall severity of equipment degradation over a given period, the engine calculates the cumulative unidirectional degradation of historical early warning indices. If the warning indices over a given time period are summed directly, the inherent baseline value during normal equipment operation will mask the true increase in deterioration. The system incorporates baseline subtraction and non-negative rectification mechanisms into its calculation logic, with the specific mathematical expression as follows:

[0137] In the formula, This is the scalar value for the calculated cumulative amount of degradation; For the first The average warning index recorded in each historical time block; The warning baseline constant calibrated for equipment in a brand new or healthy state, typically ranging from [value range missing]. It is a non-negative rectification function used to remove normal data fluctuations below the baseline; The physical time span step of the sampling time block is reduced. By calculating the cumulative degradation, the historical situation tracing engine maps the complex long-term time series evolution process into a single comparable quantitative indicator, providing data support for the host computer to perform macro-level equipment asset management and maintenance cycle optimization.

[0138] In this embodiment, the aforementioned dynamic feedback adjustment module triggers an update enable pulse after detecting that the prediction error continuously exceeds the tolerance range. Traditional industrial control systems, when facing model degradation, typically rely on engineers to manually calibrate parameters by stopping the system. This offline approach is difficult to meet the real-time requirements of continuous production. To achieve adaptive iteration of the edge-side evaluation model, the business collaborative scheduling module 600 is equipped with a gradient fine-tuning optimizer. The optimizer aims to correct the expert rule weight matrix in the preceding early warning decision module online based on the measured physical residuals. In practice, the online fine-tuning and updating process of parameters includes the following sub-steps:

[0139] S608 constructs a supervised fine-tuning sample set. As the dynamic feedback adjustment module outputs an update enable pulse, the gradient fine-tuning optimizer is activated to intervene in the processing flow. The system calls the historical state tracing engine through an internal interface to extract historical records within the time window that triggered the update. The optimizer then uses the historical state joint matrix... As the input feature tensor, the true state labels are processed using a unified scale. As the monitoring target, samples are assembled into a micro-batch sample set. To prevent the introduction of sudden noise interference, the system actively filters out historical data blocks marked as low-quality masks. To prevent dimensionality anomalies during underlying matrix operations, the system introduces a data volume verification mechanism here. A constant value for the micro-batch sample size is set. The value is typically configured as an integer between 16 and 64. This is based on the number of valid samples after filtering. Less than The system determines that the currently accumulated error samples are insufficient to support stable parameter iteration, actively terminates the current fine-tuning task, clears the pulse flag, and waits for data accumulation in the next cycle; when At that time, the system randomly selects Each sample is used for subsequent calculations to ensure the numerical stability of the sample set.

[0140] After acquiring a small batch of samples, the system enters step S609 to construct a regularized loss function and calculate the weight gradient. The optimizer needs to quantify the deviation between the current weight matrix and the actual physical laws. If only the sum of squared prediction errors is used as the loss function, the updated weight matrix elements may experience abnormal numerical fluctuations due to the influence of local overfitting data from the industrial field, thus losing the original physical meaning of the expert rules. As a preferred approach, the gradient fine-tuning optimizer introduces a target loss function that includes an L2 regularization term. For the k-th sample in the current batch, the calculation formula for the loss function Lk is defined as follows:

[0141] In the formula, This is the predicted value of the comprehensive early warning index calculated using the current weight matrix as the forward propagation parameter; The true state label corresponding to the sample; The regularization penalty coefficient is typically configured in... Within the range; The initial expert rule weight matrix set when the equipment leaves the factory serves as a benchmark anchor point to prevent excessive deviation. The squared Frobenius norm of the matrix represents the normalization term. The normalization term constrains the optimization range of the fine-tuning space, ensuring that the updated weights do not deviate excessively from the initial physical baseline experience. Based on the chain rule, the system calculates the loss function with respect to each element of the weight matrix. The partial derivatives are used to generate the corresponding error gradient matrix. The gradient matrix, at the physical level, indicates the direction of parameter adjustment that will result in the fastest decrease in the current prediction residual. For the implementation mechanism of the chain expansion and automatic differentiation algorithm of the underlying matrix differentiation, those skilled in the art can develop it using the underlying operator library of the standard computational graph framework. The principles of computational graph establishment and backpropagation are well-known technologies in this field and will not be elaborated upon here.

[0142] After calculating the gradient matrix, the system proceeds to S610 to perform adaptive parameter optimization with boundary constraints. In incremental updates in industrial settings, traditional gradient descent methods using a fixed learning rate tend to converge slowly in areas with gentle error surfaces or experience gradient calculation out-of-bounds errors in areas with steep numerical values. The system is configured with adaptive parameter update logic incorporating a momentum term and implements strict physical boundary clamping constraints. The mathematical expression for the weight update in the next iteration is as follows:

[0143] In the formula, and These are the gradient momentum accumulation matrices for the current iteration and the next iteration, respectively, where the initial momentum matrix is ​​from the system initialization phase. It is set to be an all-zero matrix with the same dimension as the weight matrix; The momentum decay coefficient is typically configured as follows: The base learning rate constant, whose value range is usually set to [value range missing]. To calculate the intermediate transition matrix; This is the weight matrix currently in use; and Represents the first element in the corresponding matrix Line number The specific numerical elements of the column; This constitutes a double-ended clamping function. This constraint mechanism ensures that all elements of the weight matrix are numerically constrained within the effective probability interval [0,1] at any iteration depth, thus mitigating the risk of the underlying fuzzy inference logic collapsing due to parameter out-of-bounds errors to some extent.

[0144] After completing the parameter update calculation, the system executes S611, initiating a deployment schedule based on a smooth replacement of the validation set. Directly overwriting the online memory with the newly trained weights may lead to instability in subsequent production line monitoring due to occasional local overfitting. The gradient fine-tuning optimizer extracts independent validation datasets from the historical situation tracking engine that have not participated in this fine-tuning within the past week. The system then applies the new weights... Compared with the old weight Substitute each value into the forward computation path and calculate the mean squared residuals of both values ​​on the validation set. The evaluation formula is defined as follows:

[0145] In the formula, Input the matrix to be evaluated The first result Predicted values ​​for each validation sample; The true labels for the samples. System comparison. and The size. If This indicates that the fine-tuning failed to bring about a generalized improvement in global performance, and the system actively discarded the newly generated weight matrix and kept the old parameters unchanged online; if and only if Only then can the system approve the parameter update request.

[0146] To further reduce the switching impact caused by sudden parameter changes, the system adopts an exponential moving average mechanism to smoothly merge the old and new parameters, resulting in a merged online application matrix. The calculation is as follows: In the formula, To ensure smooth updates, the coefficient is typically set to a value of [value to be filled in]. This is the older version of the matrix currently running in memory; This forms the next-generation replacement matrix that is ultimately sent to the early warning decision module. Through the aforementioned closed-loop verification and smooth fusion mechanism, the system achieves stable tracking of the degradation trend of real equipment by the underlying evaluation algorithm without interrupting the real-time evaluation process of edge nodes.

[0147] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent analysis and early warning of visual inspection data in production areas, characterized in that, include Receive and parse business messages sent by the manufacturing execution system to obtain work order status characteristics containing target operating condition parameters; Acquire real-time sensing data during production line operation, and match and compare the real-time sensing data with the target operating condition parameters based on preset tolerance conditions; When the real-time sensing data meets the tolerance condition, the early warning assessment baseline is smoothly switched according to the work order status characteristics. Based on the real-time sensing data and the switched early warning assessment baseline, a real-time status assessment is performed, and the predicted residual and corresponding early warning control instructions are output. In response to the prediction residual satisfying the preset error excess condition, the downsampled state feature records within the historical time window are extracted as a sample set. The weight matrix of the evaluation rule is fine-tuned online at the edge computing node, and the verified new weight matrix is ​​updated to the state evaluation calculation. The initial severity level of the warning control command is obtained, and the sending priority of the warning control command is dynamically calculated in combination with the network queue waiting time. The command is then sent to the underlying logic controller in a non-blocking manner according to the sending priority to perform security protection actions.

2. The method according to claim 1, characterized in that, The process of receiving and parsing business messages issued by the manufacturing execution system to obtain work order status features containing target operating condition parameters specifically includes: The uplink data stream of the network port is monitored and reassembled using a sliding window to extract the payload; The payload is semantically parsed and memory-mapped to obtain various control fields of the current production work order; The control field is validated for legality. If a missing field or illegal character is detected, the average value of the parameters from the historical period without abnormal records is extracted and replaced as a safe default value. Based on the extracted or replaced business parameters, construct the work order status feature vector corresponding to the current production work order.

3. The method according to claim 1, characterized in that, When the real-time sensing data meets the tolerance condition, the early warning assessment baseline is smoothly switched based on the work order status characteristics, specifically including: The real-time sensing data is monitored cyclically, and the relative error between the real-time sensing data and the target operating condition parameters is calculated. When the relative error is detected to be less than the set tolerance threshold within the set continuous acquisition period, it is determined that the production line equipment has completed the physical transition to the state corresponding to the new production work order. The work order status feature vector is submitted, which drives the underlying analysis module to change the early warning evaluation baseline and threshold of the old work order to the early warning evaluation baseline and threshold that match the new production work order.

4. The method according to claim 1, characterized in that, The extracted downsampling state feature records within the historical time window are used as the sample set, specifically including: Extract the multi-source time series sequences collected within the time window that caused the predicted residual to exceed the limit; The multi-source time series is divided into multiple time blocks according to a preset time span; Verify the number of valid data points in each time block. For time blocks where the number of valid data points meets the lower limit requirement, extract their trend mean, extreme values ​​and fluctuation variance respectively, and concatenate them to generate downsampled state feature row vectors. For abnormal time blocks with insufficient valid data points, the downsampled state feature row vector of the most recent valid time block is used to fill the gap, and a low-quality mask is added. Filter the data carrying the low-quality mask markers, and assemble the retained downsampled state feature row vectors with the corresponding real state labels into a micro-batch sample set.

5. The method according to claim 4, characterized in that, After generating the micro-batch sample set, the method further includes: Historical feature data within a set time window is extracted by backtracking from the local time series database, and the actual calculation start time of the backtracking is constrained to the earliest record time of the valid data in the database. Using the warning baseline constant under the new health state as the benchmark cutoff value, after deducting normal fluctuation data, the characteristic mean of the part exceeding the warning baseline constant is unidirectionally accumulated along the physical time step; The cumulative degradation of the production line equipment within the historical time window is calculated to characterize the overall degradation level of the equipment.

6. The method according to claim 1, characterized in that, The online gradient fine-tuning iteration of the weight matrix of the evaluation rule at the edge computing node specifically includes: Based on the predicted values ​​and true state labels of the micro-batch sample set, a target loss function containing a penalty regularization term is constructed, wherein the penalty regularization term is used to constrain the deviation of the current weight matrix from the initial expert rule weight matrix. Calculate the error gradient matrix of the target loss function with respect to each element of the weight matrix; Initialize a momentum accumulation matrix with the same dimension as the weight matrix, and calculate the step size by combining the error gradient matrix and momentum decay coefficient. An intermediate transition matrix is ​​generated based on the set learning rate and the parameter update step size. The elements of the intermediate transition matrix are then constrained to an effective physical boundary of zero to one using a double-ended clamping constraint function, resulting in an updated weight matrix.

7. The method according to claim 6, characterized in that, The step of updating the verified new weight matrix to the state evaluation calculation specifically includes: Extract the independent validation dataset that did not participate in this gradient fine-tuning iteration; Substitute the new weight matrix and the old weight matrix currently running online into the forward computation link, and compare their mean squared residuals on the independent validation dataset. If the mean squared residual generated by the new weight matrix is ​​less than the mean squared residual generated by the old weight matrix, then the verification is deemed successful. An exponential moving average mechanism is used to smoothly merge the verified new weight matrix with the old weight matrix in proportion, generating the final online application matrix and distributing it for replacement.

8. The method according to claim 1, characterized in that, The process of obtaining the initial severity level of the early warning control command and dynamically calculating the sending priority of the early warning control command based on the network queue waiting time specifically includes: The warning control command output by the preceding module is intercepted, the command payload is parsed, and a preset initial severity level constant is obtained by mapping. Query the underlying communication buffer queue to obtain the current backlog waiting time of the early warning control command in the sending queue; The initial severity level constant, the backlog waiting time, and the preset time sensitivity coefficient are mathematically combined to calculate the dynamic priority index corresponding to the early warning control command. The dynamic priority index is clamped by truncating the highest value through an extreme value protection function to prevent floating-point calculations from going out of bounds due to prolonged network congestion.

9. The method according to claim 8, characterized in that, The step of non-blockingly scheduling and sending data to the underlying logic controller according to the sending priority to execute security protection actions specifically includes: Based on the secure transmission protocol, a header identifier and a cyclic redundancy check code are added to the early warning control command, and the command is encapsulated to generate a message to be sent. A priority queue based on a heap structure is maintained within the downlink communication gateway, and all queued messages are reordered according to the dynamic priority index. The asynchronous multithreading mechanism takes over the priority queue, directly depriving high-priority emergency stop messages of the transmission channel of low-priority regular logs, and sending them to the underlying logic controller first.

10. The method according to claim 1, characterized in that, Before extracting downsampling state feature records within the historical time window as a sample set in response to the predicted residual satisfying the preset error exceedance condition, the method further includes anti-shake verification logic: When the predicted residual calculated in a single operation exceeds the upper limit of the preset tolerance range, the system starts the anti-jitter timer and refuses to trigger the weight matrix update. The error exceedance condition is confirmed only when the predicted residual exceeds the upper limit of the tolerance range within a continuously set evaluation period. Then, an update enable pulse is generated to activate the online gradient fine-tuning iteration to avoid erroneous updates due to transient noise interference.

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