A method and system for real-time data acquisition and processing in a distributed control system

Abstract

This application relates to the field of data acquisition and processing technology, and provides a real-time data acquisition and processing method and system for a distributed control system. The method includes real-time acquisition of data streams through terminal sensors, analysis of the probability distribution of data change rates using a sliding window, and intelligent generation of sensitivity thresholds based on dynamic factors such as device power consumption and network load. When data mutations exceed the threshold, the system automatically extracts key change segments and generates three-dimensional features of time, period, and change amount, while simultaneously fusing frequency domain analysis results to enhance anti-interference capabilities. The urgency of events is calculated based on the spatiotemporal correlation of spectral energy entropy and characteristic parameters, ultimately forming a priority-ordered processing queue and dynamically allocating bandwidth. This method improves the system's response speed to sudden events, data identification accuracy, and stability in complex environments through multi-dimensional data fusion and adaptive resource adjustment.

Description

Technical Field

[0001] This application relates to the field of data acquisition and processing technology, and more specifically, to a real-time data acquisition and processing method and system for a distributed control system. Background Technology

[0002] The content in this section provides only background information related to this application and may not constitute prior art.

[0003] In the transmission of digital information, the distributed control system continuously senses equipment status and environmental information through node devices distributed throughout the field. These data collection points act like the nerve endings of the system, capturing changes in real time. Data is rapidly aggregated to the processing nodes via a dedicated industrial network. Throughout the transmission and processing flow, the system strictly ensures time consistency, providing an accurate basis for judgment. The aggregation points quickly clean, transform, and initially refine the raw data. Finally, the integrated real-time information is sent to the decision-making center, supporting the immediate generation and issuance of control commands. The entire process is interconnected, swift, and smooth, and can cope with anomalies such as network interruptions, ensuring uninterrupted operation of core functions.

[0004] In the prior art, Chinese patent with authorization announcement number CN104464254B discloses a distributed data synchronization acquisition device and method. The method deploys multiple data acquisition nodes to achieve synchronous data acquisition from different devices, and analyzes and processes the acquired data through a centralized processing unit.

[0005] However, this patent has some drawbacks. First, its data processing method is relatively simple, relying mainly on simple synchronous acquisition and centralized processing, lacking dynamic monitoring of data change rates and adaptive adjustment of sensitivity thresholds. This can lead to an inability to capture changes in key data in a timely manner when facing complex and ever-changing industrial environments, thus affecting the system's response speed and accuracy. Second, the patent cannot effectively deal with noise interference in the data, thereby affecting the accuracy of data processing. In addition, its event priority assessment and resource allocation mechanism is relatively simple, failing to fully consider the impact of factors such as equipment and networks on event processing, resulting in unreasonable resource allocation and affecting the overall performance of the system.

[0006] To address the aforementioned problems, this invention proposes a real-time data acquisition and processing method and system for a distributed control system, thereby improving the system's real-time performance, accuracy, and anti-interference capability. Summary of the Invention

[0007] To address the aforementioned technical problems, this application aims to provide a real-time data acquisition and processing method for a distributed control system. By introducing a dynamic sensitivity threshold, frequency domain analysis, and an event priority evaluation model based on resource attenuation coefficients, the method improves the system's real-time performance, accuracy, and anti-interference capability.

[0008] The objective of this application is achieved through the following technical solution:

[0009] In a first aspect, the present invention provides a real-time data acquisition and processing method for a distributed control system, comprising:

[0010] The target data stream is continuously collected by sensors deployed on terminal nodes, and the probability density distribution of the instantaneous rate of change of the target data stream is statistically analyzed using a sliding time window.

[0011] The preset percentile value of the probability density distribution is selected as the basic sensitivity threshold. The dynamic adjustment coefficient is calculated based on the remaining power of the corresponding device, the network load factor and the historical transmission energy consumption. The basic sensitivity threshold and the dynamic adjustment coefficient are weighted and fused to generate the target sensitivity threshold.

[0012] When any instantaneous rate of change exceeds the target sensitivity threshold, the difference sequence of adjacent sampling points of the instantaneous rate of change is calculated, and segments with change amplitudes greater than the amplitude threshold are filtered out by a preset amplitude threshold; continuous change points in the same direction within the segment are merged to generate a feature triplet containing the start timestamp, duration period, and cumulative change amount;

[0013] Frequency domain analysis is performed on the target data stream. The effective spectral features of the current data window are obtained by Fourier transform of the sliding window. The effective spectral features are spatiotemporally aligned with the feature triplet. The signal-to-noise ratio of each frequency band in the spectral features is used as the dynamic weighting coefficient. The anti-interference enhanced feature vector is generated by weighted fusion.

[0014] Based on the anti-interference enhanced feature vector, a dynamic event priority evaluation model is constructed. The evaluation model includes aligning the energy entropy of each frequency band in the feature vector with the duration period in the feature triplet, calculating the temporal mutation intensity by the ratio of the fluctuation amplitude of the spectral energy entropy to the duration period, multiplying the temporal mutation intensity with the cumulative change to obtain the initial urgency, constructing a resource attenuation coefficient by combining the remaining power of the current node and the network load factor, and multiplying the initial urgency by the resource attenuation coefficient to generate an event urgency score.

[0015] Events are sorted in descending order of their urgency scores to generate a weighted event processing queue; the bandwidth allocation of the transmission channel is dynamically adjusted based on the urgency scores of each event in the event processing queue.

[0016] Furthermore, before calculating the difference sequence of adjacent sampling points of the instantaneous rate of change, the following steps are also included:

[0017] The target data sequence is filtered by moving average to eliminate transient interference.

[0018] Furthermore, the step of obtaining the effective spectral characteristics of the current data window through the Fourier transform of the sliding window specifically includes:

[0019] The spectral characteristics of the current data window are obtained by using the Fourier transform of the sliding window.

[0020] Extract the amplitude and frequency distribution of the dominant frequency component from the spectral features;

[0021] By comparing the equipment's reference noise spectrum, the inherent noise components of the system are separated using spectral subtraction.

[0022] By combining temperature and electromagnetic intensity parameters collected by environmental sensors, a three-level wavelet packet decomposition and dynamic energy ratio detection method is used to identify environmental noise frequency bands.

[0023] A noise suppression weight matrix is ​​generated based on the energy distribution ratio of the inherent noise components to the ambient noise frequency bands;

[0024] The dominant frequency component is multiplied by the noise suppression weight matrix to output the optimized effective spectral characteristics.

[0025] Furthermore, the step of obtaining the spectral characteristics of the current data window through the Fourier transform of the sliding window specifically includes:

[0026] The target data within the current time window is extended forward to cover the data area at the end of the previous window by a preset percentage, forming a new window with overlapping data.

[0027] Perform a smooth transition on the first and last 5% of the data area in the new window;

[0028] The processed window data is subjected to standard spectrum transformation calculation. Based on the frequency components with energy intensity exceeding the set standard in the spectrum of the previous window and their distribution positions, the frequency components with sudden increase in energy intensity in the spectrum of the current window are identified and their positions are calibrated. Frequency components that appear repeatedly at the same frequency position in at least two consecutive windows are selected and merged to form the spectrum features of the current data window.

[0029] Furthermore, the steps for dynamically adjusting the bandwidth ratio of the transmission channel specifically include:

[0030] When the urgency score of an event exceeds the upper limit of the preset urgency threshold range, the reserved bandwidth resource channel for low-priority events will be automatically suspended, and the resource channel will be allocated to events with urgency scores exceeding the limit.

[0031] Configure a fixed step size adjustment strategy corresponding to the urgency score range, perform incremental adjustment of bandwidth resources according to the preset step size value matched by the value range, and approve the adjustment amount in real time through a two-way negotiation protocol between nodes;

[0032] Deploy a centralized coordinator to monitor the overall network load status. When the remaining bandwidth capacity of a node is lower than the system's preset capacity threshold, freeze the bandwidth resource request operations of low-priority events, and at the same time allocate minimum guaranteed bandwidth to events with an urgency score higher than the guarantee threshold.

[0033] Furthermore, the formula for generating the target sensitivity threshold is as follows:

[0034]

[0035] in, The probability density distribution function The inverse function (quantile function); Preset percentile values; This represents the current remaining battery percentage of the device. The attenuation coefficient; This refers to the real-time occupancy of the TCP / IP protocol stack buffer. This represents the total capacity of the buffer. Energy sensitivity coefficient This represents the average energy consumption per unit of data volume over the past 24 hours. Based on the basic threshold weight, To dynamically adjust the coefficient weights, satisfying .

[0036] Furthermore, after dynamically adjusting the bandwidth ratio of the transmission channel, it also includes:

[0037] The event urgency score is compared with a preset threshold. When the score exceeds the threshold, an alarm signal is automatically triggered. At the same time, the event's feature vector data is marked as high priority and pushed to the cloud server for storage.

[0038] Secondly, the present invention provides a real-time data acquisition and processing system for a distributed control system, comprising:

[0039] The data stream acquisition and probability analysis module is used to continuously acquire target data streams through sensors deployed on terminal nodes and to statistically analyze the probability density distribution of the instantaneous rate of change of the target data stream using a sliding time window.

[0040] The dynamic threshold generation module is used to select a preset percentile value of the probability density distribution as the basic sensitivity threshold, calculate the dynamic adjustment coefficient based on the remaining power of the corresponding device, the network load factor and the historical transmission energy consumption, and generate the target sensitivity threshold by weighted fusion of the basic sensitivity threshold and the dynamic adjustment coefficient.

[0041] The feature triplet extraction module is used to calculate the difference sequence of adjacent sampling points of instantaneous change rate when any instantaneous change rate exceeds the target sensitivity threshold, and to filter the segments with change amplitude greater than the preset amplitude threshold by means of a preset amplitude threshold; and to merge continuous change points in the same direction within the segment to generate feature triplet containing start timestamp, duration period and cumulative change amount.

[0042] The frequency domain enhancement processing module is used to perform frequency domain analysis on the target data stream. It obtains the effective spectral features of the current data window through the Fourier transform of the sliding window, aligns the effective spectral features with the feature triplet in time and space, and uses the signal-to-noise ratio of each frequency band in the spectral features as dynamic weighting coefficients to generate an anti-interference enhanced feature vector through weighted fusion.

[0043] The priority assessment modeling module is used to construct a dynamic event priority assessment model based on the anti-interference enhanced feature vector. The assessment model includes aligning the energy entropy of each frequency band in the feature vector with the duration period in the feature triplet, calculating the temporal mutation intensity by the ratio of the fluctuation amplitude of the spectral energy entropy to the duration period, multiplying the temporal mutation intensity by the cumulative change to obtain the initial urgency, constructing a resource attenuation coefficient by combining the current node's remaining power and the network load factor, and multiplying the initial urgency by the resource attenuation coefficient to generate an event urgency score.

[0044] The bandwidth dynamic scheduling module is used to generate a weighted event processing queue by sorting events in descending order of their urgency scores; and to dynamically adjust the bandwidth allocation of the transmission channel based on the urgency scores of each event in the event processing queue.

[0045] Thirdly, the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the steps corresponding to the method in the first aspect.

[0046] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps corresponding to the method in the first aspect.

[0047] In summary, the technical solutions of this application have at least the following advantages and beneficial effects:

[0048] This invention continuously collects data streams through terminal sensors, analyzes the probability distribution characteristics of instantaneous change rates using a sliding time window, and generates adaptive sensitivity thresholds by combining dynamic parameters such as device power and network load, achieving accurate capture of abnormal changes. Through difference sequence analysis and merging of continuous unidirectional change points, triplet data with time-dimensional characteristics are extracted. Simultaneously, frequency domain analysis is used to extract spectral features and align them with spatiotemporal features, constructing an anti-interference feature vector with dynamic signal-to-noise ratio weighting. Based on the calculation of spectral energy entropy and the intensity of temporal abrupt changes in duration, a priority evaluation model is constructed in conjunction with resource status, ultimately forming a weighted event processing queue and dynamically allocating transmission bandwidth. This method, through multi-dimensional feature fusion and dynamic resource adaptation mechanisms, improves the system's real-time response to sudden events, data discrimination accuracy, and anti-interference capability in complex environments. Attached Figure Description

[0049] Figure 1 A flowchart illustrating a real-time data acquisition and processing method for a distributed control system provided by the present invention;

[0050] Figure 2 A schematic diagram of the structure of a real-time data acquisition and processing system for a distributed control system provided by the present invention;

[0051] Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0053] like Figure 1 As shown in the embodiment of this application, a real-time data acquisition and processing method for a distributed control system includes:

[0054] S101 continuously collects target data streams through sensors deployed on terminal nodes and uses a sliding time window to statistically analyze the probability density distribution of the instantaneous rate of change of the target data streams.

[0055] Specifically, target data streams are continuously acquired at a fixed sampling frequency using various types of sensors (such as temperature, pressure, or vibration sensors) deployed on terminal nodes. The sampling interval is set to milliseconds to seconds depending on the application scenario. The sliding time window uses a dynamically adjustable window length, typically set to 10–30 sampling periods, with a window sliding step of a single sampling interval to ensure data continuity. When calculating the instantaneous rate of change within the window, the backward difference method is used to obtain the numerical change gradient of adjacent sampling points, forming a rate of change time series.

[0056] Furthermore, probability density distribution statistics are achieved through a kernel density estimation algorithm. A Gaussian kernel function is used to smooth all instantaneous rate-of-change data within the window, generating a continuous probability distribution curve. This curve reflects the dynamic characteristics of the current monitored parameter within the time window. For example, in industrial equipment vibration monitoring, the rate of change is concentrated in the ±0.5 m / s² range under normal operating conditions, while a long-tailed distribution appears under abnormal operating conditions. Its advantage lies in using probability density distribution to quantify the statistical regularity of the system's dynamic behavior, which is more adaptable than a fixed threshold. Simultaneously, the sliding window mechanism achieves a balance between real-time data processing and historical correlation, avoiding false triggering of single-point mutations. The calculation formula is as follows:

[0057] (1)

[0058] in, Instantaneous rate of change The probability density function; The number of sampling periods contained in the sliding time window (i.e., the length of the sliding window). This represents the number of sampling points within the window. ; This is a bandwidth parameter that controls the smoothness. For the first in the window The instantaneous rate of change of each sampling point; Input variable (rate of change value); This represents the sampling point number (integer index) corresponding to the starting time of the sliding window.

[0059] S102, Select the preset percentile value of the probability density distribution as the basic sensitivity threshold, calculate the dynamic adjustment coefficient based on the remaining power of the corresponding device, the network load factor and the historical transmission energy consumption, and generate the target sensitivity threshold by weighted fusion of the basic sensitivity threshold and the dynamic adjustment coefficient.

[0060] Specifically, firstly, a preset percentile value (such as the 90th percentile) is extracted from the probability density distribution curve generated in step S101 as the basic sensitivity threshold. This value reflects the statistical boundary characteristics of the monitored parameter under normal operating conditions. The calculation of the dynamic adjustment coefficient integrates three key parameters: remaining power of the equipment, network load factor, and historical transmission energy consumption. The remaining power of the equipment is quantified using an exponential decay model to ensure reduced data acquisition intensity when the power is low. The network load factor is calculated by real-time monitoring of the buffer occupancy rate of the TCP / IP protocol stack. Historical transmission energy consumption is weighted based on the average energy consumption per unit of data volume over the past 24 hours. The weighted fusion process is implemented using a fuzzy logic controller, with specific values ​​automatically adapted according to the equipment type, ultimately generating the target sensitivity threshold. This step improves the traditional fixed threshold to a dynamic threshold system with environmental awareness, achieving adaptive data acquisition optimization. The specific calculation process is as follows:

[0061] Basic sensitivity threshold for

[0062] (2)

[0063] in, The probability density distribution function The inverse function (quantile function); Preset percentile values ​​(e.g., 90th percentile corresponds to) (0.9)

[0064] Dynamic adjustment coefficient for:

[0065] (3)

[0066] in, The remaining power factor of the equipment. Network load factor This represents the historical transmission energy consumption factor.

[0067] Remaining power factor The expression is:

[0068] (4)

[0069] in, The current remaining battery percentage of the device (0≤ ≤1), This is the attenuation coefficient.

[0070] Historical transmission energy consumption factor The expression is:

[0071] (6)

[0072] in, Energy sensitivity coefficient This represents the average energy consumption per unit of data over the past 24 hours.

[0073] Target sensitivity threshold The expression is:

[0074] (7)

[0075] in, Based on the basic threshold weight, To dynamically adjust the coefficient weights, satisfying .

[0076] Substituting formulas (2), (3), (4), (5), and (6) into formula (7) yields:

[0077] (8)

[0078] S103, when any instantaneous rate of change exceeds the target sensitivity threshold, calculate the difference sequence of adjacent sampling points of instantaneous rate of change, and filter the segments with change amplitude greater than the amplitude threshold by using a preset amplitude threshold; merge the continuous same-direction change points within the segment to generate a feature triplet containing the start timestamp, duration period and cumulative change amount.

[0079] Specifically, when the instantaneous rate of change of the sensor exceeds the target sensitivity threshold, the system first calculates the difference sequence between adjacent sampling points of the instantaneous rate of change. This difference sequence reflects the local fluctuation characteristics of the monitored parameter. Significant ranges of change amplitude are filtered out using a preset amplitude threshold (e.g., 0.3 m / s² in industrial vibration monitoring), effectively removing random noise interference. For the selected ranges, the system automatically merges consecutive points of change in the same direction (e.g., a pressure sensor showing an upward trend for five consecutive sampling periods), generating a feature triplet containing the start timestamp, duration, and cumulative change. This triplet fully records the spatiotemporal characteristics and intensity evolution of the abnormal event.

[0080] The technical principle is as follows: the difference sequence calculation uses the backward difference method to maintain consistency with the algorithm in step S101; the amplitude threshold setting needs to be combined with the physical dimensions of the specific application scenario, for example, 0.2MPa is set for oil pipeline pressure monitoring; the merging of points of change in the same direction adopts the sliding window matching algorithm to ensure accurate division of event boundaries. Taking wind turbine vibration monitoring as an example, when the vibration change rate exceeds the threshold due to blade bearing wear, the system will capture a feature triplet that lasts for 8 seconds with a cumulative change of 4.7m / s², accurately reflecting the fault development process. Thus, the amount of invalid data transmission is reduced through the dynamic threshold triggering mechanism; the feature triplet structure upgrades the event description dimension from single-point alarm to process tracking, improving the accuracy of fault diagnosis; and the accurate recording of timestamps and duration periods provides basic data for the spatiotemporal alignment of subsequent steps. This step is highly compatible with the data compression requirements of distributed systems, and each terminal node can independently complete feature extraction, thereby reducing network transmission load.

[0081] The calculation process is as follows:

[0082] Difference sequence for:

[0083] (9)

[0084] in, For the first The difference in the instantaneous rate of change of each sampling point. The first sliding window The instantaneous rate of change of each sampling point The starting sampling point number of the sliding window. This is the length of the sliding window.

[0085] The amplitude threshold filtering condition is set as follows:

[0086] (10)

[0087] in, This is the preset amplitude threshold.

[0088] The start timestamp in the feature triple is:

[0089] (11)

[0090] in, The start time of the abnormal event. The time of the first sampling point to meet the amplitude threshold.

[0091] The duration of the characteristic triplet is:

[0092] (12)

[0093] in, The duration of the abnormal event; This refers to the sequence number of the final sampling point in a continuously changing segment in the same direction. This refers to the starting sampling point number of the continuously changing segment in the same direction; The sampling interval is denoted as .

[0094] Cumulative change in characteristic triples:

[0095] (13)

[0096] in, The algebraic sum of all differences within the segment; This is an index variable, representing the sequence number of the sampling point; Indicates the first The difference in the instantaneous rate of change of a sampling point is defined as the difference in the rate of change of adjacent sampling points.

[0097] It is important to note that before calculating the difference sequence of adjacent sampling points of instantaneous rate of change, a moving average filter can be applied to the target data sequence to eliminate instantaneous interference.

[0098] S104: Perform frequency domain analysis on the target data stream, obtain the effective spectral features of the current data window through the Fourier transform of the sliding window, align the effective spectral features with the feature triplet in time and space, use the signal-to-noise ratio of each frequency band in the spectral features as dynamic weighting coefficients, and generate an anti-interference enhanced feature vector through weighted fusion.

[0099] This step performs anti-interference enhanced feature vector generation on the target data stream. Its core lies in constructing a composite feature representation with noise suppression capabilities through sliding window frequency domain analysis and spatiotemporal alignment with feature triples. Specifically, the effective spectral features of the current data window are obtained through sliding window Fourier transform: the target data within the current time window is extended forward to cover a predetermined proportion of the data area at the end of the previous window, forming a new window with overlapping data. This design utilizes the continuity of historical data to avoid the spectral leakage phenomenon that occurs at the boundaries of conventional sliding windows. Subsequently, a smooth transition processing is applied to the data areas at the beginning and end edges of the new window, and a window function weighting technique is used to suppress high-frequency interference components introduced by data jumps, ensuring the stability of the spectral analysis.

[0100] Next, standard spectral transformation calculations are performed on the preprocessed window data. Based on the frequency components with energy intensities exceeding a set standard in the previous window spectrum and their distribution locations, a dynamic frequency tracking template is established. By comparing the spatial distribution relationship between the frequency components with abnormally increased energy in the current window spectrum and this template, frequency position calibration is completed. Frequency components that repeatedly appear at the same frequency position in at least two consecutive windows are selected and merged to form an effective spectral feature reflecting the true dynamics of the system. This mechanism effectively filters out transient random noise and retains fault characteristic frequencies with time persistence.

[0101] Subsequently, the amplitude and frequency distribution of the dominant frequency component are extracted from the effective spectral features. Spectral subtraction is then performed using the equipment's reference noise spectrum to separate the system's inherent mechanical noise components. Simultaneously, temperature and electromagnetic intensity parameters collected by environmental sensors are integrated. A three-level wavelet packet decomposition technique is employed to peel away the signal frequency bands layer by layer, and the dominant environmental noise frequency band is quantitatively identified using dynamic energy ratio detection. Based on the energy proportion distribution of inherent noise and environmental noise in the frequency domain, a noise suppression weight matrix is ​​generated in the frequency dimension. This matrix assigns high weight values ​​to fault characteristic frequency bands, implementing strong attenuation of noise-accumulated frequency bands. Finally, the amplitude of the dominant frequency component and the noise suppression weight matrix are weighted and combined to output the optimized effective spectral features, achieving targeted enhancement of fault characteristics.

[0102] The beneficial effects are as follows: The effective spectral features are spatiotemporally aligned with the feature triplet generated in step S103. The alignment process uses the start timestamp recorded in the feature triplet as a reference to locate the corresponding time interval for spectral analysis, ensuring the spatiotemporal consistency between time-domain events and frequency-domain features. The signal-to-noise ratio (SNR) of each frequency band in the spectral features is used as a dynamic weighting coefficient to perform weighted fusion on the cumulative change parameters in the feature triplet: fault features corresponding to high SNR frequency bands are given higher weights, while low SNR frequency bands are weighted lower, enhancing the anti-interference capability of the feature vector.

[0103] The specific calculation process is as follows:

[0104] The formula for sliding window spectral analysis is as follows:

[0105] (14)

[0106] in, For the first Complex spectral values ​​of each frequency component; For the first in the window Time-domain data of each sampling point; Hanning window function coefficients (dimensionless); The length of the window; Frequency index; It is an imaginary number.

[0107] The effective frequency band selection criteria are:

[0108] (15)

[0109] in, For the effective frequency band index set; This is the energy threshold ratio; This is the vector of all frequency amplitudes.

[0110] The signal-to-noise ratio weighting formula is:

[0111] (16)

[0112] (17)

[0113] in, For frequency band Normalized weights; For frequency band Signal-to-noise ratio; The noise floor power; For indexing.

[0114] (18)

[0115] in, For the final feature vector, The starting timestamp, For a continuous cycle, This represents the cumulative change. For frequency indexing.

[0116] S105, based on the anti-interference enhanced feature vector, constructs a dynamic event priority assessment model; the assessment model includes aligning the energy entropy of each frequency band in the feature vector with the duration period in the feature triplet, calculating the temporal mutation intensity by the ratio of the fluctuation amplitude of the spectral energy entropy to the duration period; multiplying the temporal mutation intensity with the cumulative change to obtain the initial urgency, constructing a resource attenuation coefficient by combining the current node's remaining power and the network load factor, and multiplying the initial urgency by the resource attenuation coefficient to generate an event urgency score.

[0117] Specifically, the energy entropy of each frequency band in the anti-interference enhancement feature vector is first time-aligned with the duration period parameter recorded in the feature triplet to ensure that the frequency domain statistical characteristics and the time domain event process are on the same time reference. The technical principle is that spectral energy entropy characterizes the complexity fluctuation characteristics of the signal's frequency domain components, while the duration period reflects the temporal continuity of abnormal events; aligning the two enables spatiotemporal feature fusion. By calculating the ratio of the aligned spectral energy entropy fluctuation amplitude to the duration period, a temporal mutation intensity index is obtained. This index quantifies the degree of abnormal mutation in frequency domain characteristics per unit time, effectively distinguishing between sudden faults and gradual degradation.

[0118] Subsequently, the intensity of the temporal mutation is multiplied by the cumulative change in the feature triplet to generate the initial urgency parameter. The mathematical principle behind this multiplication is that the cumulative change reflects the overall impact of the abnormal event, while the intensity of the temporal mutation characterizes the severity of the event's development. Multiplying the two allows for a comprehensive assessment of the overall hazard level of the event. For example, in a wind turbine monitoring scenario, if a blade crack causes a sudden increase in the energy entropy of the vibration signal in a specific frequency band (high temporal mutation intensity), and the crack propagation causes the cumulative change to continue to increase, the product of the two will significantly increase the initial urgency value, accurately matching the actual risk level of mechanical damage.

[0119] Furthermore, a resource attenuation coefficient is constructed by combining the remaining power of the current node with the network load factor. The design principle of the resource attenuation coefficient is based on the resource constraint characteristics of distributed systems: when the remaining power of a node is below a safety threshold or the network load is too high, the processing intensity of low-priority events must be suppressed to ensure critical functions. This coefficient is dynamically generated through fuzzy logic rules. For example, the remaining power is given a non-linear weight using an exponential attenuation model, and the network load factor is calculated based on the real-time buffer occupancy rate of the transport layer protocol stack. The two work together to determine the resource availability level. Finally, the initial urgency is multiplied by the resource attenuation coefficient to generate an event urgency score. This operation achieves a dual optimization objective—it retains the technical severity assessment of the event itself while embedding the current resource state constraints of the node, so that the score result conforms to the actual scheduling requirements of the distributed system.

[0120] The specific calculation formula is as follows:

[0121] Spectral Energy Entropy The expression is:

[0122] (19)

[0123] in, The spectral energy entropy characterizes the complexity of the frequency domain components; For frequency band Normalized weights, This is the set of valid frequency band indices.

[0124] Temporal mutation strength The expression is:

[0125] (20)

[0126] in, The intensity of temporal abrupt change is the fluctuation amplitude of the frequency domain entropy per unit time. The spectral energy entropy of the current window; The spectral energy entropy of the previous window; The periodicity is a characteristic triplet.

[0127] (twenty one)

[0128] in, Initial urgency level; This represents the cumulative change in the characteristic triplet.

[0129] Resource decay coefficient The expression is:

[0130] (twenty two)

[0131] in, This is the resource attenuation coefficient. Equipment remaining power factor This is the network load factor.

[0132] Emergency level rating The expression is:

[0133] (twenty three)

[0134] in, Rate the urgency of the final event.

[0135] S106: Generate a weighted event processing queue by sorting the events in descending order of their urgency scores; dynamically adjust the bandwidth ratio of the transmission channel based on the urgency scores of each event in the event processing queue.

[0136] This step aims to design a dynamic resource scheduling mechanism based on event urgency scores. Specifically, it generates weighted event processing queues by arranging events in descending order of urgency scores, and then dynamically adjusts the bandwidth allocation of transmission channels based on the urgency scores of each event in the queue. This achieves high efficiency and fairness in event processing within the distributed control system. In generating the weighted event processing queues, the system uses the urgency scores of each event as the queue weight benchmark, prioritizing high-scoring events by arranging them in descending order. The principle behind this is that the urgency score comprehensively considers the temporal abrupt change intensity, cumulative change, and node resource status of the event, accurately reflecting the technical severity and processing priority of the event. The beneficial effect is that this queue structure overcomes the drawbacks of the traditional first-come, first-served mechanism, ensuring that critical events (such as equipment failure) receive priority processing resources, preventing low-priority events from consuming system capacity, thereby improving overall response speed and resource utilization. For example, in a vibration monitoring scenario, if the event of a cracked device has an urgency score of 90 (highest priority), while the event of environmental temperature fluctuation has a score of 30 (lower priority), the system automatically places the blade crack event at the front of the queue, assigns it a higher weight, and prioritizes its data transmission task.

[0137] Furthermore, the specific steps for dynamically adjusting the bandwidth ratio of the transmission channel are as follows:

[0138] First, when the urgency score of an event exceeds the upper limit of the preset urgency threshold range, the system automatically suspends the reserved bandwidth resource channel usage of low-priority events and reallocates the resource channel to events with urgency scores exceeding the limit. The principle is that the upper limit of the preset urgency threshold range serves as a watershed for critical events, triggering a resource preemption mechanism. By interrupting the allocation of reserved bandwidth resource channels for low-priority events, it ensures that high-priority events exclusively occupy additional channel resources. The beneficial effect is to reduce the processing delay of high-urgency events and prevent the loss of critical data or response delays due to resource contention. For example, in the event of a sudden pressure change on a pressure sensor at a critical location, if the urgency score exceeds the upper limit threshold (such as being set to an upper limit of 80 points), the system immediately suspends the bandwidth channel of nearby temperature monitoring tasks and instead fully supports pressure data transmission, thereby accelerating fault diagnosis.

[0139] Second, the system is configured with a fixed-step adjustment strategy corresponding to the urgency score range. This strategy performs incremental bandwidth resource adjustments according to a preset step size matching the range, and the real-time adjustment amount is approved through a two-way negotiation protocol between nodes. The principle behind this is that the fixed-step adjustment strategy divides the urgency score into multiple ranges (e.g., high, medium, low), each with a preset step size (e.g., a large step size for high ranges and a small step size for low ranges). This achieves a smooth increase or decrease in bandwidth resources. Simultaneously, the two-way negotiation protocol between nodes ensures that the adjustment amount is interactively approved by adjacent nodes, avoiding network conflicts caused by unilateral decisions. The beneficial effects are maintaining the stability and predictability of bandwidth adjustments, preventing resource oscillations, and improving the collaborative efficiency of distributed nodes. For example, in vibration monitoring, if a node's event urgency score falls into the medium range (e.g., 50-70 points), the system increases bandwidth by 10% with a preset small step size, but the adjustment amount needs to be negotiated and approved with adjacent nodes to ensure balanced network load.

[0140] Third, a centralized coordinator is deployed to monitor the overall network load status. When the remaining bandwidth capacity of a node is lower than the system's preset capacity threshold, the coordinator freezes bandwidth resource requests for low-priority events and allocates minimum guaranteed bandwidth for events with urgency scores higher than the guarantee threshold. The principle is that the centralized coordinator aggregates bandwidth usage data from each node in real time. When it detects insufficient remaining bandwidth capacity, it prioritizes freezing the application process for low-priority events and forcibly reserves minimum bandwidth resources for high-scoring events. The beneficial effect is to globally optimize network resource allocation, prevent congestion, and ensure the baseline transmission capability of critical events. For example, when the remaining bandwidth capacity of all nodes in the network drops below the preset threshold, the coordinator freezes all new requests for events with scores lower than 20 points and allocates minimum guaranteed bandwidth for bearing overheating events with scores higher than the guarantee threshold (such as 60 points) to ensure that their data continues to be uploaded.

[0141] Furthermore, after dynamically adjusting the bandwidth ratio of the transmission channel, it also includes:

[0142] The event urgency score is compared with a preset threshold. When the score exceeds the threshold, an alarm signal is automatically triggered. At the same time, the event's feature vector data is marked as high priority and pushed to the cloud server for storage.

[0143] Specifically, the system uses a preset threshold comparison mechanism to evaluate the urgency score of an event in real time. When the score exceeds the preset threshold, the system automatically triggers a multi-level linkage response: first, it activates an audible and visual alarm signal to alert on-site operators; simultaneously, it marks the event-related anti-interference enhancement feature vector data as high priority and pushes it to a cloud server for persistent storage via a dedicated transmission channel. The underlying technology utilizes a pre-configured threshold judgment module in the distributed control system to compare the urgency score output by the dynamic event priority evaluation model in real time. When the score exceeds a dynamic threshold set according to the equipment's safe operation standards, an alarm and data grading processing flow is triggered.

[0144] In practice, alarm signal generation employs a multimodal output strategy, including both audible alarm signals to attract attention and visual alarm information to accurately describe event characteristics. The alarm information integrates key parameters such as the start timestamp and duration from feature triplet records, as well as the dominant frequency component characteristics obtained from spectrum analysis, providing comprehensive diagnostic information for maintenance personnel. In the data marking and transmission phase, the system implements high-priority marking by modifying the priority flag bit in the data packet header and dynamically allocates dedicated transmission channels based on software-defined networking technology to ensure reliable transmission of critical data in congested network environments. After receiving the data, the cloud server stores it in a time-series database with fast retrieval capabilities and establishes an association index with the original sampled data to support subsequent in-depth analysis and tracing.

[0145] Based on the same inventive concept, such as Figure 2 As shown, the present invention provides a real-time data acquisition and processing system for a distributed control system, comprising:

[0146] The data stream acquisition and probability analysis module 201 is used to continuously acquire target data streams through sensors deployed on terminal nodes and to statistically analyze the probability density distribution of the instantaneous rate of change of the target data stream using a sliding time window.

[0147] The dynamic threshold generation module 202 is used to select a preset percentile value of the probability density distribution as the basic sensitivity threshold, calculate the dynamic adjustment coefficient based on the remaining power of the corresponding device, the network load factor and the historical transmission energy consumption, and generate the target sensitivity threshold by weighted fusion of the basic sensitivity threshold and the dynamic adjustment coefficient.

[0148] The feature triplet extraction module 203 is used to calculate the difference sequence of adjacent sampling points of instantaneous change rate when any instantaneous change rate exceeds the target sensitivity threshold, filter the segment with change amplitude greater than the preset amplitude threshold, merge the continuous same-direction change points in the segment, and generate feature triplet containing start timestamp, duration period and cumulative change amount;

[0149] The frequency domain enhancement processing module 204 is used to perform frequency domain analysis on the target data stream. It obtains the effective spectral features of the current data window through the Fourier transform of the sliding window, aligns the effective spectral features with the feature triplet in time and space, uses the signal-to-noise ratio of each frequency band in the spectral features as dynamic weighting coefficients, and generates an anti-interference enhanced feature vector through weighted fusion.

[0150] The priority assessment modeling module 205 is used to construct a dynamic event priority assessment model based on the anti-interference enhanced feature vector. The assessment model includes aligning the energy entropy of each frequency band in the feature vector with the duration period in the feature triplet, calculating the temporal mutation intensity by the ratio of the fluctuation amplitude of the spectral energy entropy to the duration period, multiplying the temporal mutation intensity with the cumulative change to obtain the initial urgency, constructing a resource attenuation coefficient by combining the remaining power of the current node and the network load factor, and multiplying the initial urgency by the resource attenuation coefficient to generate an event urgency score.

[0151] The bandwidth dynamic scheduling module 206 is used to generate a weighted event processing queue by sorting events in descending order of their urgency scores; and to dynamically adjust the bandwidth ratio of the transmission channel based on the urgency scores of each event in the event processing queue.

[0152] Based on the same inventive concept, this invention provides an electronic device, such as... Figure 3 As shown, it includes: a memory 302, a processor 301, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, it implements a real-time data acquisition and processing method for a distributed control system.

[0153] Based on the same inventive concept, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a real-time data acquisition and processing method for a distributed control system.

[0154] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A real-time data acquisition and processing method for a distributed control system, characterized in that, include: The target data stream is continuously collected by sensors deployed on the terminal node, and the probability density distribution of the instantaneous rate of change of the target data stream is statistically analyzed using a sliding time window. A preset percentile value of the probability density distribution is selected as the basic sensitivity threshold. A dynamic adjustment coefficient is calculated based on the remaining power of the corresponding device, the network load factor, and the historical transmission energy consumption. The basic sensitivity threshold and the dynamic adjustment coefficient are then weighted and fused to generate the target sensitivity threshold. When any instantaneous rate of change exceeds the target sensitivity threshold, the difference sequence of adjacent sampling points of the instantaneous rate of change is calculated, and segments with change amplitudes greater than the amplitude threshold are filtered out by a preset amplitude threshold; continuous same-direction change points within the segment are merged to generate a feature triplet containing a start timestamp, duration period, and cumulative change amount; Frequency domain analysis is performed on the target data stream. The effective spectral features of the current data window are obtained by Fourier transform of the sliding window. The effective spectral features are spatiotemporally aligned with the feature triplet. The signal-to-noise ratio of each frequency band in the spectral features is used as a dynamic weighting coefficient. An anti-interference enhanced feature vector is generated by weighted fusion. Based on the anti-interference enhanced feature vector, a dynamic event priority evaluation model is constructed. The evaluation model includes aligning the energy entropy of each frequency band in the feature vector with the duration period in the feature triplet, calculating the temporal mutation intensity by the ratio of the spectral energy entropy fluctuation amplitude to the duration period, multiplying the temporal mutation intensity with the cumulative change to obtain the initial urgency, constructing a resource attenuation coefficient by combining the current node's remaining power and the network load factor, and multiplying the initial urgency by the resource attenuation coefficient to generate an event urgency score. The events are sorted in descending order of their urgency scores to generate a weighted event processing queue; the bandwidth allocation of the transmission channel is dynamically adjusted based on the urgency scores of each event in the event processing queue.

2. The real-time data acquisition and processing method for a distributed control system according to claim 1, characterized in that, Before calculating the difference sequence of adjacent sampling points of the instantaneous rate of change, the method further includes: The target data sequence is filtered by moving average to eliminate transient interference.

3. The real-time data acquisition and processing method for a distributed control system according to claim 1, characterized in that, The step of obtaining the effective spectral features of the current data window through the Fourier transform of the sliding window specifically includes: The spectral characteristics of the current data window are obtained by using the Fourier transform of the sliding window. Extract the amplitude and frequency distribution of the dominant frequency component from the spectral features; By comparing the equipment's reference noise spectrum, the inherent noise components of the system are separated using spectral subtraction. By combining temperature and electromagnetic intensity parameters collected by environmental sensors, a three-level wavelet packet decomposition and dynamic energy ratio detection method is used to identify environmental noise frequency bands. A noise suppression weight matrix is ​​generated based on the energy distribution ratio of the inherent noise components to the environmental noise frequency band. The dominant frequency component is multiplied by the noise suppression weight matrix to output the optimized effective spectral characteristics.

4. The real-time data acquisition and processing method for a distributed control system according to claim 3, characterized in that, The step of obtaining the spectral characteristics of the current data window through the Fourier transform of the sliding window specifically includes: The target data within the current time window is extended forward to cover the data area at the end of the previous window by a preset percentage, forming a new window with overlapping data. The data areas at the beginning and end of the new window are processed to achieve a smooth transition. The processed window data is subjected to standard spectrum transformation calculation. Based on the frequency components with energy intensity exceeding the set standard in the spectrum of the previous window and their distribution positions, the frequency components with sudden increase in energy intensity in the spectrum of the current window are identified and their positions are calibrated. Frequency components that appear repeatedly at the same frequency position in at least two consecutive windows are selected and merged to form the spectrum features of the current data window.

5. The real-time data acquisition and processing method for a distributed control system according to claim 1, characterized in that, The step of dynamically adjusting the bandwidth ratio of the transmission channel specifically includes: When the urgency score of an event exceeds the upper limit of the preset urgency threshold range, the reserved bandwidth resource channel for low-priority events will be automatically suspended, and the resource channel will be allocated to events with urgency scores exceeding the limit. Configure a fixed step size adjustment strategy corresponding to the urgency score value range, perform incremental adjustment of bandwidth resources according to the preset step size value matched by the value range, and approve the real-time adjustment amount through a two-way negotiation protocol between nodes; Deploy a centralized coordinator to monitor the overall network load status. When the remaining bandwidth capacity of a node is lower than the system's preset capacity threshold, freeze the bandwidth resource request operations of low-priority events, and at the same time allocate minimum guaranteed bandwidth to events with an urgency score higher than the guarantee threshold.

6. The real-time data acquisition and processing method for a distributed control system according to claim 1, characterized in that, The formula corresponding to the target sensitivity threshold is: in, The probability density distribution function The inverse function (quantile function); Preset percentile values; This represents the current remaining battery percentage of the device. The attenuation coefficient; This refers to the real-time occupancy of the TCP / IP protocol stack buffer. This represents the total capacity of the buffer. Energy sensitivity coefficient This represents the average energy consumption per unit of data volume over the past 24 hours. Based on the basic threshold weight, To dynamically adjust the coefficient weights, satisfying .

7. The real-time data acquisition and processing method for a distributed control system according to claim 1, characterized in that, After dynamically adjusting the bandwidth ratio of the transmission channel, the method further includes: The urgency score of the event is compared with a preset threshold. When the score exceeds the threshold, an alarm signal is automatically triggered. At the same time, the feature vector data of the event is marked as high priority and pushed to the cloud server for storage.

8. A real-time data acquisition and processing system for a distributed control system, characterized in that, include: The data stream acquisition and probability analysis module is used to continuously acquire target data streams through sensors deployed on terminal nodes, and to statistically analyze the probability density distribution of the instantaneous rate of change of the target data stream using a sliding time window. The dynamic threshold generation module is used to select a preset percentile value of the probability density distribution as the basic sensitivity threshold, calculate the dynamic adjustment coefficient based on the remaining power of the corresponding device, the network load factor and the historical transmission energy consumption, and generate the target sensitivity threshold by weighted fusion of the basic sensitivity threshold and the dynamic adjustment coefficient. The feature triplet extraction module is used to calculate the difference sequence of adjacent sampling points of the instantaneous change rate when any instantaneous change rate exceeds the target sensitivity threshold, filter the segments with change amplitude greater than the preset amplitude threshold through a preset amplitude threshold, and merge the continuous same-direction change points in the segment to generate a feature triplet containing the start timestamp, duration period and cumulative change amount. The frequency domain enhancement processing module is used to perform frequency domain analysis on the target data stream, obtain the effective spectral features of the current data window through the Fourier transform of the sliding window, align the effective spectral features with the feature triplet in time and space, use the signal-to-noise ratio of each frequency band in the spectral features as dynamic weight coefficients, and generate an anti-interference enhanced feature vector through weighted fusion. The priority assessment modeling module is used to construct a dynamic event priority assessment model based on the anti-interference enhancement feature vector. The assessment model includes aligning the energy entropy of each frequency band in the feature vector with the duration period in the feature triplet, calculating the temporal mutation intensity by the ratio of the spectral energy entropy fluctuation amplitude to the duration period, multiplying the temporal mutation intensity with the cumulative change to obtain the initial urgency, constructing a resource attenuation coefficient by combining the current node's remaining power and the network load factor, and multiplying the initial urgency by the resource attenuation coefficient to generate an event urgency score. The bandwidth dynamic scheduling module is used to generate a weighted event processing queue by sorting the events in descending order of their urgency scores; and to dynamically adjust the bandwidth ratio of the transmission channel according to the urgency scores of each event in the event processing queue.

9. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a real-time data acquisition and processing method for a distributed control system as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a real-time data acquisition and processing method for a distributed control system as described in any one of claims 1 to 7.

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