Multi-channel grounding monitoring method for intelligent workstation static electricity monitor

Abstract

The present application relates to the technical field of grounding monitoring, and discloses a multi-channel grounding monitoring method for an intelligent workstation electrostatic monitor, the method comprising: performing infrared scanning on the workstation area and comparing the workstation layout template to obtain workstation configuration data and read electrostatic monitoring parameters; performing time-division multiplexing matrix design on multiple monitoring channels to obtain a time-division multiplexing configuration scheme; compiling switch control timing for high-speed solid-state relays to obtain a switch control timing table and executing intelligent switch control to realize monitoring channel switching; inputting electrostatic signals of multiple monitoring channels into a high-precision analog-to-digital converter for parallel sampling and denoising to obtain pre-processed electrostatic data; constructing a dynamic threshold model for each monitoring component and executing a multi-level alarm strategy. The present invention can dynamically adjust the monitoring strategy according to the workstation characteristics, realize efficient collection and processing of multi-channel data, and provide an accurate electrostatic risk assessment and early warning mechanism.

Description

Technical Field

[0001] The present application relates to the technical field of grounding monitoring, and in particular to a multi-path grounding monitoring method for an intelligent workstation electrostatic monitor. Background Art

[0002] Electrostatic discharge has always been a key factor affecting product quality and production efficiency. With the miniaturization and high integration of electronic components, their sensitivity to static electricity is increasing, making workstation static electricity monitoring particularly important. Traditional static electricity monitoring methods often use a single or fixed monitoring point, which is difficult to fully cover the complex workstation environment, resulting in inaccurate and in-time static electricity risk assessment.

[0003] At present, multi-channel grounding monitoring faces many challenges in practical applications. Multiple monitoring channels are prone to mutual interference, affecting the accuracy of data collection. Real-time processing and analysis of large amounts of data place high demands on the system's computing power. The electrostatic characteristics of different workstations vary greatly, and fixed monitoring parameters are difficult to adapt to diverse production environments. Summary of the invention

[0004] The present application provides a multi-channel grounding monitoring method for an intelligent workstation electrostatic monitor. The present invention can dynamically adjust the monitoring strategy according to the workstation characteristics, realize efficient collection and processing of multi-channel data, and provide accurate electrostatic risk assessment and early warning mechanism, thereby comprehensively improving the level of workstation electrostatic management.

[0005] In a first aspect, the present application provides a multi-path grounding monitoring method for an intelligent workstation electrostatic monitor, the multi-path grounding monitoring method for an intelligent workstation electrostatic monitor comprising:

[0006] Perform infrared scanning on the workstation area and compare it with the workstation layout template to obtain workstation configuration data, and read electrostatic monitoring parameters based on the workstation configuration data;

[0007] Based on the electrostatic monitoring parameters, a time division multiplexing matrix is ​​designed for multiple monitoring channels to obtain a time division multiplexing configuration scheme;

[0008] According to the time division multiplexing configuration scheme, the switch control timing sequence of the high-speed solid-state relay is compiled to obtain a switch control timing table, and intelligent switch control is performed based on the switch control timing table to realize monitoring channel switching;

[0009] The electrostatic signals of multiple monitoring channels are input into a high-precision analog-to-digital converter for parallel sampling and denoising to obtain pre-processed electrostatic data;

[0010] Based on the pre-processed electrostatic data, a dynamic threshold model is constructed for each monitoring component to obtain a warning threshold, and a multi-level alarm strategy is executed according to the warning threshold.

[0011] A second aspect of the present application provides a multi-channel grounding monitoring device for an intelligent workstation electrostatic monitor, the multi-channel grounding monitoring device for an intelligent workstation electrostatic monitor comprising:

[0012] A reading module is used to perform infrared scanning on the workstation area and compare the workstation layout template to obtain workstation configuration data, and read electrostatic monitoring parameters based on the workstation configuration data;

[0013] A design module, used to perform time division multiplexing matrix design on multiple monitoring channels based on the electrostatic monitoring parameters to obtain a time division multiplexing configuration scheme;

[0014] A switching module is used to compile a switch control timing sequence for a high-speed solid-state relay according to the time division multiplexing configuration scheme, obtain a switch control timing table, and perform intelligent switch control based on the switch control timing table to realize monitoring channel switching;

[0015] A processing module, used for inputting the electrostatic signals of multiple monitoring channels into a high-precision analog-to-digital converter for parallel sampling and denoising to obtain pre-processed electrostatic data;

[0016] The execution module is used to construct a dynamic threshold model for each monitoring component based on the pre-processed electrostatic data, obtain a warning threshold, and execute a multi-level alarm strategy according to the warning threshold.

[0017] The third aspect of the present application provides an electronic device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the electronic device executes the above-mentioned multi-path grounding monitoring method of the intelligent workstation electrostatic monitor.

[0018] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the multi-path grounding monitoring method of the intelligent workstation electrostatic monitor.

[0019] Compared with the prior art, the present application has the following beneficial effects: through infrared scanning and workstation layout template comparison, automatic identification and parameter matching of workstation configuration are realized, and the adaptability and deployment efficiency of the system are improved. The time-division multiplexing matrix design is adopted to optimize the multi-channel data acquisition strategy, effectively reduce the interference between channels, and improve the accuracy and efficiency of data acquisition. Intelligent switch control based on high-speed solid-state relays realizes accurate channel switching and reduces signal loss and interference during switching. Through high-precision analog-to-digital conversion and multi-stage denoising processing, the signal-to-noise ratio and reliability of electrostatic data are significantly improved. Adopting an adaptive dynamic threshold model, an accurate assessment of electrostatic risks under different workstations and environmental conditions is achieved. Multi-level alarms are performed according to dynamic thresholds, which improves the accuracy and timeliness of alarms and reduces false alarms and missed alarms. Through time series analysis and cluster analysis of electrostatic data, an electrostatic performance degradation model is constructed. The present invention can dynamically adjust the monitoring strategy according to the characteristics of the workstation, realize efficient acquisition and processing of multi-channel data, and provide accurate electrostatic risk assessment and early warning mechanism, thereby comprehensively improving the level of workstation electrostatic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0022] Figure 1 It is a flow chart of a multi-path grounding monitoring method of an intelligent workstation electrostatic monitor provided by an embodiment of the present invention;

[0023] Figure 2 It is a schematic block diagram of the structure of a multi-path grounding monitoring device of an intelligent workstation electrostatic monitor provided by an embodiment of the present invention;

[0024] Figure 3 It is a schematic block diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0027] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0028] It should be further understood that the term "and / or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. Figure 1 , an embodiment of a multi-path grounding monitoring method of an intelligent workstation electrostatic monitor in an embodiment of the present application includes:

[0029] Step 100: Perform infrared scanning on the workstation area and compare it with the workstation layout template to obtain workstation configuration data, and read electrostatic monitoring parameters based on the workstation configuration data;

[0030] It is understandable that the execution subject of the present application can be a multi-channel grounding monitoring device of a smart workstation static monitor, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0031] Specifically, the workstation area is scanned by infrared to obtain infrared image data, which reflects the temperature distribution, equipment contour and other physical features of the workstation area. The infrared image data is processed by an image segmentation algorithm, which includes a method based on edge detection, threshold segmentation or deep learning technology to divide the infrared image into several parts to extract the contour information of the workstation components. Feature extraction is performed on the contour information of the workstation components. The feature extraction is based on shape features, texture features and other significant attributes that can describe objects, and the feature vector of the component is constructed through these features. Based on the component feature vector, each workstation component is identified to obtain a specific component identification result. The identified components are compared with the preset workstation layout template, and the specific layout of the current workstation is determined by similarity calculation. The similarity score is obtained by calculating the similarity between the component identification result and the features of the preset workstation layout template. The higher the similarity score, the better the matching degree between the current workstation and the template. Based on the similarity score, the most matching workstation layout template is selected to obtain the type information of the current workstation, indicating the specific purpose, layout method and related electrostatic risk of the workstation. Based on the station type information and component identification results, the station configuration data is generated, including the specific layout information of the current station, the location of each component, and the type and status of each type of equipment. In order to ensure that the system can quickly access these configuration data in the subsequent monitoring process, the station configuration data is stored in the system's fast access memory to form a station configuration record. Based on the station configuration record, the corresponding electrostatic monitoring parameter set is retrieved from the electrostatic parameter library. The electrostatic parameter library includes the corresponding electrostatic monitoring requirements and historical data under various station types. By finding the electrostatic monitoring parameters that match the current station type, the required electrostatic indicators are effectively provided for the monitoring process. The electrostatic monitoring parameter set is compensated for environmental factors. The accumulation and release of static electricity are affected by environmental factors, such as humidity, temperature, airflow, etc., which will affect the generation and accumulation of electrostatic charges. By compensating for environmental factors in the electrostatic monitoring parameters, the monitoring is more accurate, the electrostatic parameter fluctuations caused by environmental changes are eliminated, and accurate electrostatic monitoring parameters are obtained.

[0032] Step 200: Based on the electrostatic monitoring parameters, a time division multiplexing matrix is ​​designed for multiple monitoring channels to obtain a time division multiplexing configuration scheme;

[0033] Specifically, the electrostatic monitoring parameters are parsed, and the sampling frequency requirements, priority and interference sensitivity of each monitoring channel are extracted to obtain the channel characteristic data. The channel characteristic data includes the sampling frequency requirements of each channel, which directly affect the real-time monitoring. At the same time, the priority reflects that some channels need more attention in the entire monitoring process, and the interference sensitivity helps to avoid the mutual influence between different monitoring channels due to electromagnetic interference. Based on the channel characteristic data, the monitoring channels are sorted to obtain the initial channel sorting result. This sorting is based on the sampling frequency, priority and interference sensitivity to determine the preliminary monitoring order of each channel. The time slice is allocated for the initial channel sorting result, and the least common multiple cycle is calculated according to the sampling frequency requirements of each channel to obtain the length of the basic time slice. The length of the basic time slice defines the time window obtained by each channel. Based on the basic time slice length and the priority of each channel, a weighted allocation calculation is performed to obtain the time slice allocation scheme for each monitoring channel, ensuring that high-priority channels obtain more time slice resources to meet the real-time requirements of electrostatic monitoring. The time slice allocation scheme is conflict detected by identifying the channel combination with electromagnetic interference to obtain a potential interference list. Electromagnetic interference is a common problem in multi-channel monitoring systems. By reordering the monitoring channels, the distribution of channels is optimized to minimize interference, and the optimized channel ordering is obtained. A preliminary form of the time-division multiplexing matrix is ​​generated based on the optimized channel ordering. The initial time-division multiplexing matrix is ​​constructed by matrix generation based on the optimized channel ordering, where the rows of the matrix represent time slices and the columns represent monitoring channels, and an original time-division multiplexing matrix is ​​obtained. The original time-division multiplexing matrix is ​​filled, and each monitoring channel is assigned to a different time slice to ensure that each channel can be sampled in an appropriate time to form a filled time-division multiplexing matrix. In order to evaluate the performance of the matrix, based on the filled time-division multiplexing matrix, the number of channel switching times and the total energy consumption in each time slice are calculated, and a series of performance evaluation indicators are obtained to measure the efficiency of the time-division multiplexing matrix in practical applications, especially the number of channel switching times and the total energy consumption directly affect the stability and energy efficiency of the system. According to the performance evaluation indicators, the time-division multiplexing matrix is ​​optimized to minimize energy consumption, and a time-division multiplexing matrix with minimized energy consumption is obtained. The time-slice balancing process is performed on the time-division multiplexing matrix with minimized energy consumption to ensure that the sampling interval of the key channels does not exceed the maximum allowable value, to prevent the sampling frequency of some key monitoring channels from being reduced due to uneven distribution, which affects the accuracy of monitoring, and to obtain the balanced time-division multiplexing matrix. Based on the balanced time-division multiplexing matrix, the channel switching sequence and the configuration data of the timer are generated to form an execution instruction set. The execution instruction set is converted into the configuration parameters of the hardware registers, including the setting of the timer initial value, the configuration of the interrupt vector table, and the transmission configuration of DMA (direct memory access), to obtain the hardware-level configuration data.The hardware-level configuration data is integrity checked and encrypted to ensure that the configuration data is not damaged or tampered with during transmission and storage, and a securely encapsulated configuration data packet is obtained. The securely encapsulated configuration data packet and its related metadata are stored in non-volatile memory to form a time-division multiplexing configuration scheme.

[0034] Step 300: According to the time division multiplexing configuration scheme, the switch control timing sequence of the high-speed solid-state relay is compiled to obtain a switch control timing table, and intelligent switch control is performed based on the switch control timing table to realize monitoring channel switching;

[0035] It should be noted that the time division multiplexing configuration scheme is parsed, and the switching points of the time slice and the corresponding monitoring channel configuration information are extracted from it to obtain the original switching sequence, which describes the relationship between each time slice and the channel. The original switching sequence is optimized for switching timing, the optimal switching path is calculated, and the optimized switching sequence is obtained. Based on the optimized switching sequence, a specific switching action is assigned to each high-speed solid-state relay to generate a relay action mapping table. In the relay action mapping table, the switching state of each relay is associated with the time slice and channel requirements. In order to ensure that the switching action maintains synchronization and accuracy in actual execution, the relay action mapping table is analyzed for timing, and the initial switch control timing table is obtained by calculating the timestamp of each switch action. Since the high-speed solid-state relay will be affected by mechanical characteristics when performing switching actions, such as switch jitter and other phenomena, the initial switch control timing table is jitter compensated. By calculating the mechanical characteristic parameters of each relay, the compensated timing data is obtained, thereby reducing the impact of mechanical jitter on system stability. Based on the compensated timing data, pre-trigger and delayed trigger time are added to each switch action, so as to ensure that the action of each relay can better adapt to the timing requirements of the entire monitoring system during actual switching, and the adjusted switch control timing table is obtained. The adjusted timing table is analyzed for safety. In the safety analysis, the existing switch conflicts and short circuit risks are identified, and the risk points are recorded in the risk point list. Based on the risk point list, the switch control timing is partially adjusted, and appropriate protection intervals are inserted to avoid short circuits or other faults caused by the simultaneous action of relay switches, and the optimized switch control timing table is obtained. The optimized switch control timing table is converted into an executable instruction sequence of the microcontroller, including timer configuration, interrupt service program, and I / O port control instructions, and the underlying execution code is obtained. In order to improve the execution efficiency, the underlying execution code is optimized at the instruction level to generate a high-efficiency switch control program. The high-efficiency switch control program can reduce the processing delay of the microcontroller, make the switch action more accurate and rapid, and thus improve the real-time and reliability of the overall monitoring. Based on the efficient switch control program, hardware description language code is generated to define the state machine and control logic, and a configuration bitstream suitable for FPGA is obtained. FPGA can realize the parallel control of high-speed solid-state relays. By downloading the FPGA configuration bitstream to the field programmable gate array, the hardware management of the switch control timing table is realized, ensuring that the control of the relay has higher concurrency performance and accuracy. The switch control timing table is loaded into the scheduler of the real-time operating system, and the appropriate priority and preemption strategy are set for it to realize the dynamic scheduling scheme. The dynamic scheduling scheme enables the system to flexibly adjust the execution order of the switch control according to the actual situation, so as to achieve a rapid response to various emergencies.Based on the dynamic scheduling scheme, the execution engine of the intelligent switch control is started to process the switch control instructions from the timing table in real time, and finally realize the switching of each monitoring channel.

[0036] Step 400, inputting the electrostatic signals of multiple monitoring channels into a high-precision analog-to-digital converter for parallel sampling and denoising to obtain pre-processed electrostatic data;

[0037] Specifically, the electrostatic signals of multiple monitoring channels are synchronously sampled and triggered to obtain the electrostatic signals of all channels at the same time point, thereby ensuring the consistency and synchronization between the sampling data of each channel, and obtaining the original sampling data. The original sampling data is input into a high-precision analog-to-digital converter for quantization processing, converted from analog signals to digital signals, and the first electrostatic signal data is formed. These data are represented by high-precision floating-point numbers to describe the voltage value of the electrostatic signal. The first electrostatic signal data is converted into a data format, and the floating-point number is converted into a fixed-point number to obtain the second electrostatic signal data. Based on the second electrostatic signal data, the signal-to-noise ratio of each channel is calculated, the quality of the channel is evaluated, and the channel quality evaluation result is obtained. Based on the channel quality evaluation result, the parameters of the adaptive filter are optimized, and the filter parameters preset by the channel are obtained by calculating the optimal filter coefficient. The adaptive filter dynamically adjusts the filtering characteristics according to the actual situation of the channel to minimize noise interference. Based on the filter parameters preset by the channel, the second electrostatic signal data is digitally filtered to obtain the electrostatic data after preliminary filtering. The filtered electrostatic data is subjected to spectrum analysis to obtain the characteristic spectrum of the noise. The filtered electrostatic data is subjected to secondary filtering through the noise characteristic spectrum obtained by the spectrum analysis to obtain the denoised electrostatic data. Perform multi-scale decomposition on the denoised electrostatic data, analyze the frequency components of the electrostatic signal at different time scales, and obtain more comprehensive time-frequency characteristics of the electrostatic signal. Perform threshold processing on the time-frequency characteristics to remove some small but unimportant signal fluctuations and obtain standard electrostatic data. Based on the standard electrostatic data, calculate the baseline drift trend of each channel and obtain the baseline correction parameters. Perform baseline drift compensation through the baseline correction parameters to ensure the stability and accuracy of the electrostatic data and obtain stabilized electrostatic data. Perform outlier detection on the stabilized electrostatic data, identify and mark the abnormal data points. Perform local interpolation processing on the stabilized electrostatic data based on the marking results to generate corrected electrostatic data. Match the corrected electrostatic data with the calibration curves of each channel and perform nonlinear correction to eliminate the deviation caused by the nonlinear characteristics of the equipment during the electrostatic signal acquisition process, and obtain the final preprocessed electrostatic data.

[0038] Step 500: Based on the pre-processed electrostatic data, a dynamic threshold model is constructed for each monitoring component to obtain a warning threshold, and a multi-level alarm strategy is executed according to the warning threshold.

[0039] Specifically, the pre-processed static data is statistically analyzed, including calculating the mean and standard deviation of the static level to obtain a static threshold benchmark. The mean reflects the overall trend of the static level, while the standard deviation represents the amplitude of the static fluctuation. Based on the static threshold benchmark, the initial warning threshold is set, including three different levels of low, medium and high, to form an initial multi-level threshold. The pre-processed static data is subjected to a sliding window analysis to calculate the change trend of the static signal in a short period of time and obtain a dynamic adjustment factor. The sliding window analysis captures the fluctuation of the static signal in a short period of time by rolling the data in a continuous time period. The dynamic adjustment factor is applied to the initially set multi-level threshold for real-time adjustment to obtain a warning threshold that is more in line with the current monitoring environment. The warning threshold has an adaptive characteristic and can be adjusted in time according to the change of the static signal to cope with the static risk in different operation scenarios. Based on the warning threshold, the correlation between static anomaly and operation behavior is established to form a dynamic threshold model. Static anomaly is associated with specific operation behaviors, such as the movement of personnel and the contact of tools. By establishing the correlation between static anomaly and operation behavior, the cause of static change is judged, thereby effectively reducing the possibility of false alarms. The constructed dynamic threshold model is deployed to the real-time monitoring system, so that it can continuously receive the pre-processed static data and perform real-time threshold comparison and alarm triggering. The real-time monitoring system, combined with the dynamic threshold model, can determine whether the warning threshold is exceeded based on the real-time collected static data, thereby triggering the corresponding alarm mechanism. The multi-level alarm strategy triggers different levels of response measures based on the degree to which the static signal exceeds the warning threshold. For example, at a low level of alert, only the warning information needs to be recorded, while at medium and high levels, more severe responses such as sound alarms or automatic shutdowns are issued.

[0040] The pre-processed electrostatic data is segmented into time windows, and the entire electrostatic data stream is divided into several time series segments, so as to conduct a more detailed analysis of the changes in electrostatic signals in different time periods. Feature extraction is performed on the time series segments to separate trend components, periodic components and random components. The trend component reflects the long-term change trend of the electrostatic signal, the periodic component reveals the law of periodic fluctuations, and the random component contains unpredictable noise parts. Based on the trend, periodicity and random components, the pre-processed electrostatic data is seasonally decomposed to obtain de-seasonalized electrostatic data. The purpose of seasonal decomposition is to eliminate the errors caused by periodic factors so that the electrostatic signal can more accurately reflect the actual changes. The de-seasonalized electrostatic data is converted into frequency domain, and the time domain data is converted into frequency domain features to reveal the energy distribution of the signal at different frequencies, help identify the regular changes in the electrostatic signal, and the characteristics of high-frequency or low-frequency components. Cluster analysis is performed on the frequency domain features. By analyzing the similarity of different frequency components, the electrostatic data is divided into multiple initial clusters to help understand different types of electrostatic change patterns. Based on the initial clustering results, refined classification is performed to obtain the classification structure of the electrostatic change pattern. By extracting features from the classification structure of the electrostatic change pattern, a feature vector of each electrostatic change pattern is formed to describe the features of different electrostatic change patterns. Based on the feature vector of the electrostatic change pattern, the electrostatic change pattern recognition model is trained to obtain a classifier for identifying different electrostatic change patterns. The pattern classifier is used to classify continuous electrostatic data streams in real time in real-time monitoring to obtain the real-time pattern label corresponding to each time point. The real-time pattern label is associated with the historical data to obtain the conversion probability matrix between the electrostatic change patterns, which describes the probability of mutual conversion between different electrostatic change patterns, thereby inferring the evolution trend between different change patterns. Based on the electrostatic change pattern conversion probability matrix, the key factors that cause electrostatic performance degradation are extracted. These key factors are the main factors affecting the performance of electrostatic signals in long-term changes. Based on the key factors, a model of the change trend of electrostatic performance over time is constructed to obtain a preliminary degradation model to describe the change trend of electrostatic performance in the time dimension. The preliminary degradation model is multi-dimensionally associated with process parameters and environmental factors to obtain a composite degradation model that considers the influence of multiple factors. Process parameters and environmental factors, such as temperature, humidity and operating conditions, will affect the accumulation and release of static electricity. The composite degradation model is numerically simulated to obtain the statistical distribution of the degradation process. The statistical distribution can describe the probability of degradation speeds of different electrostatic performance. Based on the statistical distribution of the degradation process, the time distribution of the electrostatic performance reaching the critical value is calculated to obtain the remaining service life prediction model. This model can predict the possible time when the electrostatic performance reaches the critical value based on the current electrostatic performance status. The remaining service life prediction model is combined with real-time monitoring data to obtain dynamically adjusted degradation prediction results.The dynamically adjusted prediction results can reflect the changes in electrostatic performance in real time and be updated according to the latest monitoring data, thereby improving the accuracy and reliability of the prediction. Based on the dynamically adjusted degradation prediction results, the risk assessment index of electrostatic performance degradation is generated to quantify the risk level of electrostatic performance degradation. Based on the risk assessment index, maintenance decision recommendations are generated, such as when to perform maintenance or what preventive measures to take, to ensure that the electrostatic risk is within a controllable range, completing the construction and application of the electrostatic performance degradation model.

[0041] In the embodiment of the present application, the automatic identification and parameter matching of the station configuration are realized by infrared scanning and station layout template comparison, and the adaptability and deployment efficiency of the system are improved. The time-division multiplexing matrix design is adopted to optimize the multi-channel data acquisition strategy, effectively reduce the interference between channels, and improve the accuracy and efficiency of data acquisition. Intelligent switch control based on high-speed solid-state relays realizes accurate channel switching and reduces signal loss and interference during switching. Through high-precision analog-to-digital conversion and multi-stage denoising processing, the signal-to-noise ratio and reliability of electrostatic data are significantly improved. Adopting an adaptive dynamic threshold model, an accurate assessment of electrostatic risks under different stations and environmental conditions is realized. Multi-level alarms are performed according to dynamic thresholds, which improves the accuracy and timeliness of alarms and reduces false alarms and missed alarms. Through time series analysis and cluster analysis of electrostatic data, an electrostatic performance degradation model is constructed. The present invention can dynamically adjust the monitoring strategy according to the station characteristics, realize efficient collection and processing of multi-channel data, and provide accurate electrostatic risk assessment and early warning mechanism, thereby comprehensively improving the level of station electrostatic management.

[0042] In a specific embodiment, the process of executing step 100 may specifically include the following steps:

[0043] Perform infrared scanning on the workstation area to obtain infrared image data; input the infrared image data into the image segmentation algorithm for processing to obtain the contour information of the workstation components;

[0044] Extract features of the workstation component contour information to obtain a component feature vector; perform component recognition based on the component feature vector to obtain a component recognition result;

[0045] Calculate the similarity between the component recognition result and the preset workstation layout template to obtain a similarity score; select the most matching workstation layout template based on the similarity score to obtain workstation type information;

[0046] Generate station configuration data according to the station type information and component identification results; store the station configuration data in the system fast access memory to obtain the station configuration record;

[0047] Based on the workstation configuration record, a corresponding electrostatic monitoring parameter set is retrieved from an electrostatic parameter library; an environmental factor compensation calculation is performed on the electrostatic monitoring parameter set to obtain an electrostatic monitoring parameter.

[0048] Specifically, an infrared scan is performed on the workstation area to obtain infrared image data. Infrared scanning captures the thermal radiation information of each component in the workstation and generates an infrared image that reflects the temperature distribution. The infrared image data is input into the image segmentation algorithm to separate the components in the image from the background and obtain the contour information of each component in the workstation. Image segmentation uses a threshold segmentation-based method, such as the Otsu method, which determines the optimal segmentation threshold by performing a histogram analysis on the grayscale values ​​of the image pixels, thereby separating the different components in the workstation. Feature extraction is performed on the contour information of the workstation components to generate a feature vector for each component. A feature vector is a numerical description of the features of an object, including shape, texture, edge and other information. For example, suppose the aspect ratio of a component, the uniformity of the surface texture, and the complexity of the edge are extracted to construct a feature vector. , expressed as:

[0049] ;

[0050] in, Represents the aspect ratio of a component, defined by the following formula:

[0051] ;

[0052] in, Indicates the length of the component. Indicates the width of a component. The aspect ratio effectively describes the overall shape of a component and helps identify different types of components. Represents the surface texture uniformity of the component, and the texture uniformity is calculated by the gray level co-occurrence matrix (GLCM). Assume that the gray level co-occurrence matrix is , then the texture uniformity is expressed as:

[0053] ;

[0054] in, Indicates that the gray value in the image is and The co-occurrence probability of a pixel pair, Indicates the number of gray levels. The higher the uniformity characteristic, the smoother the surface of the part. It represents the edge complexity of the component, which is calculated by the ratio of edge length to area, and is expressed as:

[0055] ;

[0056] in, Indicates the length of the edge of the component, Represents the area of ​​a component. The higher the edge complexity, the more irregular the boundary of the component. The feature vector of the component is input into the machine learning model for component recognition. The recognition model uses methods such as support vector machine, random forest or convolutional neural network to identify the specific type of each component by classifying the feature vector. The recognition result is compared with the preset workstation layout template to determine the type of the current workstation. The similarity calculation is performed using cosine similarity or Euclidean distance. For example, for two feature vectors and , and its similarity is expressed as:

[0057] ;

[0058] in, represents the dot product of two vectors, and Represent the norms of two vectors respectively. The higher the similarity score, the more the configuration of the current workstation matches the preset template. Based on the similarity score, the most matching workstation layout template is selected to obtain the type information of the workstation. Based on the workstation type information and the recognition results of the components, the configuration data of the workstation is generated, including the type, location and other relevant information of each component in the workstation. The workstation configuration data is stored in the system's fast access memory to form a workstation configuration record. Based on the workstation configuration record, the corresponding electrostatic monitoring parameter set is retrieved from the electrostatic parameter library, including the electrostatic characteristics and monitoring requirements of each component in the workstation. In order to improve the accuracy of monitoring, the electrostatic monitoring parameter set is calculated for environmental factor compensation. For example, the temperature and humidity in the environment will affect the accumulation and release of static electricity. The electrostatic monitoring parameters are compensated according to the temperature and humidity conditions of the current environment. The compensated electrostatic monitoring parameters reflect the actual situation of static electricity in the workstation and ensure the accuracy and stability of monitoring. For example, assuming that the electrostatic potential of a component is affected by humidity, its electrostatic monitoring parameters Compensation is done by the following formula:

[0059] ;

[0060] in, represents the initial electrostatic potential, represents the humidity compensation coefficient, Indicates the current humidity. Indicates the reference humidity. In this way, the electrostatic monitoring parameters are obtained after compensation for environmental factors.

[0061] In a specific embodiment, the process of executing step 200 may specifically include the following steps:

[0062] Analyze the electrostatic monitoring parameters, extract the sampling frequency requirements, priority and interference sensitivity of each monitoring channel, and obtain channel characteristic data; sort the monitoring channels based on the channel characteristic data to obtain the initial channel sorting results;

[0063] The initial channel sorting results are allocated to time slices, and the lowest common multiple cycle is calculated according to the sampling frequency requirements of each channel to obtain the basic time slice length; based on the basic time slice length and the priority of each channel, a weighted allocation calculation is performed to obtain the time slice allocation plan for each monitoring channel;

[0064] Conduct conflict detection on the time slice allocation scheme, identify channel combinations that may have electromagnetic interference, and obtain a potential interference list; reorder the monitoring channels based on the potential interference list to obtain an optimized channel order;

[0065] The optimized channels are sorted and matrix generated to construct an initial time division multiplexing matrix, where the matrix rows represent time slices and the columns represent monitoring channels, thereby obtaining an original time division multiplexing matrix; the original time division multiplexing matrix is ​​filled, and each monitoring channel is assigned to a different time slice, thereby obtaining a filled time division multiplexing matrix;

[0066] Based on the filled time division multiplexing matrix, the number of channel switching and total energy consumption in each time slice are calculated to obtain the performance evaluation index; according to the performance evaluation index, optimization is performed to obtain the time division multiplexing matrix with minimized energy consumption;

[0067] Perform time slice balancing on the energy-minimized time division multiplexing matrix to ensure that the sampling interval of the key channel does not exceed the maximum allowed value, and obtain the balanced time division multiplexing matrix; based on the balanced time division multiplexing matrix, generate the channel switching sequence and timer configuration data to obtain the execution instruction set;

[0068] Convert the execution instruction set into hardware register configuration parameters, including timer initial value, interrupt vector table and DMA transfer configuration, to obtain hardware-level configuration data; perform integrity check and encryption on the hardware-level configuration data to obtain a securely encapsulated configuration data packet;

[0069] The securely encapsulated configuration data packet and its metadata are stored in non-volatile memory to form a time-division multiplexing configuration scheme.

[0070] Specifically, the electrostatic monitoring parameters are parsed, and the sampling frequency requirements, priority, and interference sensitivity of each monitoring channel are extracted to obtain channel characteristic data. The sampling frequency of each channel represents the frequency requirement for data sampling on the channel, and the priority indicates the importance of the channel in the entire monitoring system. The interference sensitivity reflects the sensitivity of the channel to electromagnetic interference from other signals. Based on the channel characteristic data, all monitoring channels are sorted to obtain the initial channel sorting results. The initial channel sorting results are allocated time slices, and the least common multiple period is calculated according to the sampling frequency requirements of each channel, which is used as the length of the basic time slice. The length of the basic time slice is defined as:

[0071] ;

[0072] in, Indicates the length of the basic time slice, represents the least common multiple, The sampling frequencies of each monitoring channel are respectively. The basic time slice length is used to ensure that each channel obtains an appropriate sampling frequency within the cycle. Based on the basic time slice length and the priority of each channel, the time slices are weighted and allocated to obtain the time slice allocation scheme for each monitoring channel. Channels with high priority obtain more time slices and achieve more frequent sampling. Conflict detection is performed on the time slice allocation scheme to identify channel combinations with electromagnetic interference and obtain a potential interference list. The presence of electromagnetic interference will cause distortion of the sampling data of some channels. By detecting conflicts in the time slice allocation scheme, it is determined which channels cause interference. Based on the potential interference list, the monitoring channels are reordered to optimize the distribution of channels and avoid simultaneous sampling of sensitive channels as much as possible to obtain the optimized channel order. The optimized channel order is generated into a matrix to construct the initial time division multiplexing matrix, in which the rows of the matrix represent the time slices and the columns represent the monitoring channels, and the original time division multiplexing matrix is ​​obtained. The original time division multiplexing matrix is ​​filled, and each monitoring channel is allocated to different time slices to obtain the filled time division multiplexing matrix. In this matrix, it is ensured that each channel works in different time slices to achieve reasonable resource allocation. Based on the filled time-division multiplexing matrix, the number of channel switching in each time slice and the total energy consumption of the entire system are calculated to obtain the performance evaluation index. The more channel switching times, the higher the energy consumption of the system. The goal is to minimize the number of switching times and total energy consumption by optimizing the performance evaluation index, and obtain the time-division multiplexing matrix with minimized energy consumption. The energy consumption is expressed as:

[0073] ;

[0074] in, represents the total energy consumption, Indicates the number of channels, Indicates The switching cost of each channel is Indicates The number of switching times of each channel is calculated. By optimizing the energy consumption formula, the energy consumption of the system during switching is reduced, thereby extending the service life of the equipment. The time-slice balancing process is performed on the time-division multiplexing matrix with minimized energy consumption to ensure that the sampling interval of the key channel does not exceed the maximum allowable value. The sampling interval of the key channel is too large, which will cause the lag of the monitoring data, so it needs to be balanced, and finally the balanced time-division multiplexing matrix is ​​obtained. Based on the balanced matrix, the channel switching sequence and the configuration data of the timer are generated to form an execution instruction set. The execution instruction set is converted into the configuration parameters of the hardware register, including the timer initial value, the interrupt vector table and the DMA (direct memory access) transmission configuration, to obtain the hardware-level configuration data. In the hardware implementation process, the timer initial value is used to set the length of each time slice, the interrupt vector table is used to perform the switching operation at the end of the time slice, and the DMA configuration is used to quickly transmit the monitoring data. The hardware-level configuration data is integrity checked and encrypted to ensure that the data is not tampered with during transmission and storage, forming a securely encapsulated configuration data packet. The securely encapsulated configuration data packet and its metadata are stored in a non-volatile memory to form the final time-division multiplexing configuration scheme.

[0075] In a specific embodiment, the process of executing step 300 may specifically include the following steps:

[0076] Analyze the time-division multiplexing configuration scheme, extract the time slice switching points and the corresponding channel configuration information, and obtain the original switching sequence; optimize the switch timing of the original switching sequence, calculate the optimal switching path, and obtain the optimized switching sequence;

[0077] Based on the optimized switching sequence, a switching action is assigned to each high-speed solid-state relay to obtain a relay action mapping table; a timing analysis is performed on the relay action mapping table to calculate the precise timestamp of each switching action to obtain an initial switch control timing table;

[0078] Perform switch jitter compensation on the initial switch control timing table, calculate the mechanical characteristic parameters of each relay, and obtain compensated timing data; based on the compensated timing data, add pre-trigger and delayed trigger time to each switch action to obtain an adjusted switch control timing table;

[0079] Perform safety analysis on the adjusted switch control timing table to identify potential switch conflicts and short circuit risks and obtain a risk point list; based on the risk point list, perform local adjustments to the switch control timing and insert protection intervals to obtain an optimized switch control timing table;

[0080] Convert the optimized switch control timing table into an instruction sequence executable by the microcontroller, including timer configuration, interrupt service program and I / O port control instructions, to obtain the bottom-level execution code; perform instruction-level optimization on the bottom-level execution code to obtain a high-efficiency switch control program;

[0081] Based on the efficient switch control program, generate hardware description language code, define the state machine and control logic, and obtain the FPGA configuration bit stream; download the FPGA configuration bit stream to the field programmable gate array to generate the switch control timing table;

[0082] The switch control timing table is loaded into the scheduler of the real-time operating system, and the priority and preemption strategy are set to obtain a dynamic scheduling plan; based on the dynamic scheduling plan, the intelligent switch control execution engine is started to process the switch control instructions in real time to realize the switching of monitoring channels.

[0083] Specifically, the time division multiplexing configuration scheme is parsed, the time slice switching points and the corresponding channel configuration information are extracted, and the original switching sequence is obtained. The switching point of each time slice represents the end of sampling of a certain channel and the beginning of sampling of the next channel. The original switching sequence contains the time nodes and related configurations of all channel switches. In order to minimize the energy consumption and switching delay of the switch, the switch timing of the original switching sequence is optimized, and the optimal switching path is obtained by calculation to form an optimized switching sequence. Based on the optimized switching sequence, specific switching actions are assigned to each high-speed solid-state relay, and a relay action mapping table is generated. In this mapping table, the switching action of each relay corresponds to the channel requirements of the time slice to ensure that the switching order is consistent with the state change of the relay. In order to ensure the synchronization and accuracy of the switching action, the relay action mapping table is time-series analyzed, and the initial switch control timing table is obtained by calculating the precise timestamp of each switching action. The timestamp calculation is completed by the following formula:

[0084] ;

[0085] in, Indicates The time point of the next switch, is the initial time, Indicates The time interval required for the switch. Perform switch jitter compensation on the initial switch control timing table, calculate the mechanical characteristic parameters of each relay, and obtain the compensated timing data. For example, the mechanical response time of the relay has a certain delay and fluctuation. In order to ensure the stability of the switching, each timestamp is compensated according to the mechanical characteristics. Based on the compensated timing data, add pre-trigger and delayed trigger time for each switch action. This means that before executing the switch action, it is necessary to trigger in advance at an appropriate time to ensure that the relay can complete the action within the required time slice and obtain the adjusted switch control timing table. The pre-trigger and delayed trigger time are determined by the following formula:

[0086] ;

[0087] in, is the adjusted trigger time, is the original timestamp, is the pre-trigger time, is the delay trigger time. This formula ensures that the switch operation has a certain time margin to cope with the uncertainty of relay action. The safety analysis of the adjusted switch control timing table is carried out to identify the existing switch conflicts and short circuit risks, and obtain a list of potential risk points. By analyzing the risk point list, it is found which relay actions overlap in time or cause short circuit problems due to circuit characteristics. Based on the analysis results, the switch control timing is partially adjusted, and necessary protection intervals are inserted to avoid conflicts between switches, and the optimized switch control timing table is obtained. The optimized switch control timing table is converted into an executable instruction sequence of the microcontroller, including the configuration of the timer, the interrupt service program, and the control instructions of the 1 / O port, and the underlying execution code is obtained. The underlying execution code is optimized at the instruction level to obtain an efficient switch control program. Instruction optimization usually involves removing redundant operations and reducing the cycle of instruction execution to ensure that each switch action can be completed quickly and accurately. Based on the efficient switch control program, the hardware description language (HDL) code is generated to define the state machine and control logic of the entire system, and the bit stream for FPGA (field programmable gate array) configuration is obtained. FPGA can realize parallel control and efficient logic execution of relays. By downloading the FPGA configuration bitstream into the field programmable gate array, the timing table of switch control is generated, and the timing control logic is implemented in hardware. After the FPGA is configured, the generated switch control timing table is loaded into the scheduler of the real-time operating system, and the priority and preemption strategy are set to obtain a dynamic scheduling scheme. The scheduling scheme enables the system to flexibly adjust the switch execution order of the relay according to the real-time situation, so as to cope with various emergencies and maintain the stability of the system. Based on the dynamic scheduling scheme, the execution engine of the intelligent switch control is started to process the switch control instructions from the timing table in real time to realize the dynamic switching of the monitoring channel.

[0088] In a specific embodiment, the process of executing step 400 may specifically include the following steps:

[0089] Perform synchronous sampling and triggering on the electrostatic signals of multiple monitoring channels to obtain original sampling data; input the original sampling data into a high-precision analog-to-digital converter for quantization to obtain first electrostatic signal data;

[0090] Performing data format conversion on the first electrostatic signal data, converting the floating point number into a fixed point number representation, and obtaining the second electrostatic signal data; calculating the signal-to-noise ratio of each channel based on the second electrostatic signal data, and obtaining a channel quality assessment result;

[0091] Adaptively optimize the filter parameters of the channel quality assessment result, calculate the optimal filter coefficient, and obtain the filter parameters preset by the channel; based on the filter parameters preset by the channel, digitally filter the second electrostatic signal data to obtain filtered electrostatic data;

[0092] Performing spectrum analysis on the filtered electrostatic data to obtain a noise characteristic spectrum; performing secondary filtering on the filtered electrostatic data based on the noise characteristic spectrum to obtain denoised electrostatic data;

[0093] Perform multi-scale decomposition on the denoised electrostatic data to obtain the time-frequency characteristics of the electrostatic signal; perform threshold processing on the time-frequency characteristics to obtain standard electrostatic data;

[0094] Based on the standard electrostatic data, the baseline drift trend of each channel is calculated to obtain the baseline correction parameters; the baseline correction parameters are applied to the standard electrostatic data to perform baseline drift compensation to obtain stabilized electrostatic data;

[0095] Performing outlier detection on the stabilized electrostatic data to obtain an abnormal data marker; performing local interpolation processing on the stabilized electrostatic data based on the abnormal data marker to obtain corrected electrostatic data;

[0096] The corrected electrostatic data are matched with the calibration curve of each channel and nonlinear correction is performed to obtain the preprocessed electrostatic data.

[0097] Specifically, the electrostatic signals of multiple monitoring channels are synchronously sampled and triggered to obtain raw sampling data. The raw sampling data is input into a high-precision analog-to-digital converter for quantization to convert the analog signal into a digital signal to obtain the first electrostatic signal data. The analog-to-digital conversion process is to map the continuously changing analog signal into a discrete digital value, and its accuracy depends on the bit width and sampling frequency of the ADC. After obtaining the first electrostatic signal data, these data usually exist in the form of floating-point numbers, and the processing of floating-point numbers is relatively complex and requires large computing resources in many embedded systems. Therefore, the data format of the first electrostatic signal data is converted to convert the floating-point number into a fixed-point number to obtain the second electrostatic signal data. The fixed-point format can reduce the demand for storage space and increase the processing speed in an embedded environment. Assume that the floating-point number of the first electrostatic signal data is , the corresponding fixed-point number is expressed as , converted by the following formula:

[0098] ;

[0099] in, is the original floating point number, is the converted fixed-point number, is the number of decimal places of the fixed-point number, Indicates a rounding operation. This conversion method retains the precision of floating-point numbers to a fixed number of decimal places. Based on the second electrostatic signal data, the signal-to-noise ratio of each channel is calculated to evaluate the quality of the channel. The signal-to-noise ratio is calculated using the following formula:

[0100] ;

[0101] in, Indicates The signal-to-noise ratio of each channel, represents the signal power, Represents the noise power. By calculating the signal-to-noise ratio, the data quality of each channel is evaluated, and which channels are subject to greater noise interference are identified. Based on the channel quality evaluation results, the parameters of the adaptive filter are optimized to calculate the optimal filter coefficients and obtain the filter parameters preset for the channel. Based on the filter parameters, the second electrostatic signal data is digitally filtered to obtain filtered electrostatic data. The filtering process uses a linear time-invariant digital filter, and the filter transfer function is:

[0102] ;

[0103] in, is the transfer function of the filter, and are the numerator and denominator coefficients of the filter respectively, and is the order of the filter. By designing a suitable and , to achieve effective suppression of noise. Perform spectral analysis on the filtered electrostatic data to obtain the frequency domain characteristics of the signal and obtain the characteristic spectrum of the noise. Spectral analysis is completed by fast Fourier transform, which can convert the time domain signal to the frequency domain and identify the intensity of different frequency components in the signal. Based on the noise characteristic spectrum, the filtered electrostatic data is filtered twice to remove the noise in a specific frequency band to obtain the denoised electrostatic data. The denoised electrostatic data is multi-scale decomposed by wavelet transform to obtain the time-frequency characteristics of the electrostatic signal. Multi-scale decomposition can decompose the signal into different frequency scales to help analyze the details and overall trends of the signal. Threshold processing is performed on the obtained time-frequency characteristics to remove some small but unimportant signal fluctuations to obtain standard electrostatic data. The threshold processing process is to set the signal below the threshold to zero according to the preset threshold, thereby retaining the main electrostatic characteristics. Based on the standard electrostatic data, it is necessary to calculate the baseline drift trend of each channel to obtain the baseline correction parameters. The baseline drift is the overall offset of the electrostatic signal caused by environmental factors or equipment aging. In order to eliminate this offset, the signal needs to be baseline corrected. Assume that the baseline drift is , the correction parameters are obtained by linear fitting and , so that:

[0104] ;

[0105] Apply the baseline correction parameters to the standard electrostatic data, perform baseline drift compensation, and obtain stabilized electrostatic data. In order to ensure the accuracy of the data, perform outlier detection on the stabilized electrostatic data and mark the abnormal points in the data. Based on the abnormal data marking, perform local interpolation processing on the stabilized electrostatic data to obtain corrected electrostatic data. Local interpolation is performed by polynomial fitting or spline interpolation to generate reasonable estimates at the locations of the outliers. Match the corrected electrostatic data with the calibration curves of each channel, perform nonlinear correction, and obtain preprocessed electrostatic data. Nonlinear correction can eliminate nonlinear errors in the sensor or system, making the final electrostatic data more accurate. For example, use quadratic polynomial fitting to correct the data, assuming the calibration curve is:

[0106] ;

[0107] in, is the corrected electrostatic data, is the original data, , , are the coefficients of the fit.

[0108] In a specific embodiment, the process of executing step 500 may specifically include the following steps:

[0109] Perform statistical analysis on the pre-processed static data, calculate the mean and standard deviation of the static level, and obtain a static threshold benchmark; based on the static threshold benchmark, set the initial warning threshold, including low, medium, and high levels, and obtain an initial multi-level threshold;

[0110] Perform sliding window analysis on the pre-processed electrostatic data, calculate the short-term change trend, and obtain the dynamic adjustment factor; apply the dynamic adjustment factor to the initial multi-level threshold, make real-time adjustments, and obtain the warning threshold;

[0111] Based on the warning threshold, the correlation between static electricity anomaly and operating behavior is established to obtain a dynamic threshold model;

[0112] The dynamic threshold model is deployed into the real-time monitoring system, which continuously receives the pre-processed electrostatic data, performs real-time threshold comparison and alarm triggering, and completes the execution of the multi-level alarm strategy of the dynamic threshold.

[0113] Specifically, statistical analysis is performed on the pre-processed static data to evaluate the overall situation and fluctuation characteristics of the static level. The mean and standard deviation of the static level are calculated to describe the center position and dispersion of the data and obtain the static threshold benchmark. Based on the mean and standard deviation indicators, the initial warning threshold of static electricity is set, including low, medium and high levels, to obtain the initial multi-level threshold. When the static level exceeds a certain value, the corresponding alarm is triggered to prevent the potential risks of static electricity to equipment and personnel. For example, the low warning threshold is set to , the middle warning threshold is , the high warning threshold is , thus achieving graded warning of different risk levels of electrostatic signals. Based on the initial threshold setting, the pre-processed electrostatic data is subjected to sliding window analysis to capture the short-term trend of electrostatic signals and obtain dynamic adjustment factors. Sliding window analysis is a method of analyzing the data features in each window by defining a fixed-size window on the data and moving it step by step. Set the window size to , calculate the mean and change range of the data in the window in each time period to obtain the short-term change trend. The short-term change trend is expressed as the rate of change of the mean value in the window ;

[0114] in, Indicates at time The short-term trend of Display Window Inner Time The mean of is the time difference. The short-term change trend is used as a dynamic adjustment factor and applied to the initial multi-level threshold. The threshold of each level is adjusted in real time to obtain a dynamic warning threshold. Based on the dynamically adjusted warning threshold, the correlation between static anomalies and operating behaviors is established, and a dynamic threshold model is constructed. Static anomalies are often associated with specific operating behaviors, such as people walking around at workstations, tools and equipment contact, etc. By combining the time series data of static signals and operating behaviors, the correlation between static signal changes and specific operations is established. Assume that a certain operating behavior occurs at time point happens, and at the point in time After the significant change of the electrostatic signal is detected, it is considered that the operation behavior causes electrostatic anomaly. Through multiple correlation analyses, the trigger probability model of electrostatic anomaly is obtained. It is used to describe the degree of electrostatic level under different operating conditions. The dynamic threshold model is deployed in the real-time monitoring system to continuously receive the pre-processed electrostatic data, and perform real-time threshold comparison and alarm triggering, so as to complete the multi-level alarm strategy execution of the dynamic threshold. In the real-time monitoring system, the dynamic threshold model can compare the electrostatic data at each time point. When the electrostatic signal is detected to exceed the dynamically adjusted warning threshold, the corresponding alarm measures are triggered according to different levels of over-limit conditions. For example, when the electrostatic signal exceeds the low warning threshold, a sound alarm is issued, and when the electrostatic signal exceeds the medium or high warning threshold, a stronger alarm mechanism is triggered, such as a red light warning or automatic equipment stop.

[0115] In a specific embodiment, the process of executing the multi-path grounding monitoring method of the intelligent workstation electrostatic monitor may specifically include the following steps:

[0116] The pre-processed electrostatic data is segmented into time windows to obtain multiple time series segments; the features of the time series segments are extracted to obtain trend, periodicity and random components;

[0117] Based on the trend, periodicity and random components, the pre-processed electrostatic data is seasonally decomposed to obtain de-seasonalized electrostatic data; the de-seasonalized electrostatic data is transformed into the frequency domain to obtain the frequency domain characteristics of the electrostatic data;

[0118] Perform cluster analysis on frequency domain features to obtain initial clustering results; perform refined classification based on the initial clustering results to obtain the classification structure of electrostatic change patterns;

[0119] Extracting features of the classification structure of the electrostatic change pattern to obtain a feature vector of the electrostatic change pattern; training an electrostatic change pattern recognition model based on the feature vector of the electrostatic change pattern to obtain a pattern classifier;

[0120] Using the pattern classifier, the continuous electrostatic data stream is classified in real time to obtain the real-time pattern label; the real-time pattern label is correlated with the historical data to obtain the electrostatic change pattern conversion probability matrix;

[0121] The probability matrix of electrostatic change mode conversion is analyzed to extract the key factors of electrostatic performance degradation. Based on the key factors, a model of the change trend of electrostatic performance over time is constructed to obtain a preliminary degradation model.

[0122] Conduct multi-dimensional correlation analysis between the preliminary degradation model and process parameters and environmental factors to obtain a composite degradation model that takes into account the influence of multiple factors; perform numerical simulation on the composite degradation model to obtain the statistical distribution of the degradation process;

[0123] Based on the statistical distribution of the degradation process, the time distribution of the electrostatic performance reaching the critical value is calculated to obtain the remaining service life prediction model; the remaining service life prediction model is combined with the real-time monitoring data to obtain the dynamically adjusted degradation prediction result;

[0124] The risk assessment index of electrostatic performance degradation is generated according to the dynamically adjusted degradation prediction results, and the maintenance decision suggestion is generated based on the risk assessment index to complete the construction of the electrostatic performance degradation model.

[0125] Specifically, the preprocessed electrostatic data is segmented into time windows, and the original electrostatic signal is divided into several time series segments. Feature extraction is performed on each time series segment to extract trend, periodicity and random components. The trend component reflects the overall change direction of the electrostatic signal over time, the periodic component reveals the periodic fluctuation characteristics in the signal, and the random component describes the unpredictable random fluctuations in the signal. By decomposing the time series, the characteristics and potential structure of the signal can be understood more clearly. Based on the extracted trend, periodicity and random components, the preprocessed electrostatic data is seasonally decomposed to obtain deseasonalized electrostatic data. Seasonal decomposition takes the form of an additive model:

[0126] ;

[0127] in, represents the raw electrostatic data, Represents the trend component, represents the periodic component, Represents the random component. By removing the periodic component , get de-seasonalized electrostatic data , so that subsequent analysis can focus more on long-term changes and random fluctuations. The frequency domain characteristics of the static data are obtained by frequency domain conversion of the deseasonalized static data. By converting the time domain signal to the frequency domain, the distribution of different frequency components in the signal can be revealed. The frequency domain conversion uses the fast Fourier transform, and its formula is:

[0128] ;

[0129] in, Indicates frequency The signal component on represents deseasonalized time domain data, represents the total number of data points, is an imaginary unit. Through fast Fourier transform, the energy distribution of the signal at different frequencies is obtained to form the frequency domain characteristics of the electrostatic data. Cluster analysis is performed based on the frequency domain characteristics to obtain the initial clustering results. Signal segments with similar frequency domain characteristics are grouped together to reveal similar patterns in the electrostatic signal. For example, using -Mean clustering algorithm clusters signal points in the frequency domain feature space into different clusters. Based on the initial clustering results, refined classification is performed to obtain the classification structure of the electrostatic change pattern. By clustering similar electrostatic signals together, potential electrostatic change patterns can be identified. Feature extraction is performed on the classification structure of the electrostatic change pattern to obtain the feature vector of the electrostatic change pattern. Feature vector is a numerical description of each electrostatic variation mode, where Represents specific features in the pattern, such as the amplitude of the frequency component, the time interval of occurrence, etc. Based on the feature vector, the electrostatic change pattern recognition model is trained to obtain a pattern classifier. The pattern classifier is used to detect the change pattern of the electrostatic signal in real time, so as to classify the continuous electrostatic data stream in real time and obtain the real-time pattern label at each moment. The real-time pattern label is associated with the historical data to obtain the transition probability matrix of the electrostatic change pattern. Transition probability matrix It is expressed as:

[0130] ;

[0131] in, Indicates that from electrostatic mode Switch to mode The probability of Indicates the historical data from the mode Switch to mode The number of times, Indicates electrostatic mode The total number of occurrences. Through the conversion probability matrix, the conversion relationship between different electrostatic modes is quantified to reveal the dynamic change characteristics of electrostatic performance. The conversion probability matrix of electrostatic change mode is analyzed to extract the key factors of electrostatic performance degradation. For example, high-frequency conversion between certain modes indicates that the electrostatic performance of the equipment is degrading. Based on the key factors, a trend model of electrostatic performance over time is constructed to obtain a preliminary degradation model. The preliminary degradation model describes the downward trend of electrostatic performance in the time dimension. In order to improve the accuracy of the model, the preliminary degradation model is analyzed with process parameters and environmental factors in multiple dimensions to obtain a composite degradation model that considers the influence of multiple factors. For example, ambient temperature, humidity, equipment operation time, etc. will affect the generation and accumulation of static electricity. By combining these factors with the preliminary degradation model, the degradation process of electrostatic performance can be more comprehensively reflected. The composite degradation model is numerically simulated to obtain the statistical distribution of the degradation process. The statistical distribution describes the probability of electrostatic performance degradation under different conditions. Based on the statistical distribution of the degradation process, the time distribution of electrostatic performance reaching the critical value is calculated to obtain a prediction model for the remaining service life. The prediction model is used to estimate the possibility that electrostatic performance will reach an unacceptable level in the future, providing early warning of the electrostatic condition of the equipment. The remaining service life prediction model is combined with real-time monitoring data to obtain dynamically adjusted degradation prediction results. As real-time data is continuously updated, the prediction model will also be dynamically adjusted to provide more accurate electrostatic performance warnings. Based on the dynamically adjusted degradation prediction results, risk assessment indicators for electrostatic performance degradation are generated. For example, the probability that the equipment exceeds the electrostatic critical value within the next T time is used as a key indicator for risk assessment. If the probability exceeds the preset threshold, maintenance decision recommendations are automatically generated, such as arranging equipment downtime maintenance or performing anti-static treatment, thereby preventing potential harm caused by static electricity to equipment and operators.

[0132] The above describes the multi-path grounding monitoring method of the intelligent workstation electrostatic monitor in the embodiment of the present application. The following describes the multi-path grounding monitoring device of the intelligent workstation electrostatic monitor in the embodiment of the present application. Figure 2 In one embodiment of the present application, a multi-path grounding monitoring device of an intelligent workstation electrostatic monitor includes:

[0133] The reading module 11 is used to perform infrared scanning on the workstation area and compare the workstation layout template to obtain the workstation configuration data, and read the electrostatic monitoring parameters based on the workstation configuration data;

[0134] A design module 12 is used to design a time division multiplexing matrix for multiple monitoring channels based on electrostatic monitoring parameters to obtain a time division multiplexing configuration scheme;

[0135] The switching module 13 is used to compile the switch control timing of the high-speed solid-state relay according to the time division multiplexing configuration scheme, obtain the switch control timing table, and perform intelligent switch control based on the switch control timing table to realize the switching of the monitoring channel;

[0136] The processing module 14 is used to input the electrostatic signals of multiple monitoring channels into a high-precision analog-to-digital converter for parallel sampling and denoising to obtain pre-processed electrostatic data;

[0137] The execution module 15 is used to construct a dynamic threshold model for each monitoring component based on the pre-processed electrostatic data, obtain a warning threshold, and execute a multi-level alarm strategy according to the warning threshold.

[0138] Through the cooperation of the above-mentioned components, the automatic identification and parameter matching of the workstation configuration are realized through infrared scanning and workstation layout template comparison, which improves the adaptability and deployment efficiency of the system. The time-division multiplexing matrix design is adopted to optimize the multi-channel data acquisition strategy, effectively reduce the interference between channels, and improve the accuracy and efficiency of data acquisition. Based on the intelligent switch control of high-speed solid-state relays, accurate channel switching is realized, and the signal loss and interference during the switching process are reduced. Through high-precision analog-to-digital conversion and multi-stage denoising processing, the signal-to-noise ratio and reliability of electrostatic data are significantly improved. The adaptive dynamic threshold model is adopted to realize the accurate assessment of electrostatic risks under different workstations and environmental conditions. Multi-level alarms are performed according to dynamic thresholds, which improves the accuracy and timeliness of alarms and reduces false alarms and missed alarms. Through time series analysis and cluster analysis of electrostatic data, an electrostatic performance degradation model is constructed. The present invention can dynamically adjust the monitoring strategy according to the characteristics of the workstation, realize efficient collection and processing of multi-channel data, and provide accurate electrostatic risk assessment and early warning mechanism, thereby comprehensively improving the level of workstation electrostatic management.

[0139] See also Figure 3 , Figure 3 This is a schematic block diagram of the structure of an electronic device 300 provided in an embodiment of the present application. The electronic device 300 includes a processor 301 and a memory 302. The processor 301 and the memory 302 are connected via a device bus 203, wherein the memory 302 may include a non-volatile storage medium and an internal memory.

[0140] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor 301, the processor 301 can execute any of the above-mentioned multi-path grounding monitoring methods of the intelligent workstation electrostatic monitor.

[0141] The processor 301 is used to provide computing and control capabilities to support the operation of the entire electronic device 300 .

[0142] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 301, the processor 301 can execute any of the above-mentioned multi-path grounding monitoring methods of the intelligent workstation electrostatic monitor.

[0143] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the present application scheme, and does not constitute a limitation on the electronic device 300 involved in the present application scheme. The specific electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0144] It should be understood that the processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0145] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the electronic device 300 described above can refer to the corresponding process of the multi-path grounding monitoring method of the aforementioned intelligent workstation electrostatic monitor, and will not be repeated here.

[0146] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors implement a multi-path grounding monitoring method for an intelligent workstation electrostatic monitor as provided in an embodiment of the present application.

[0147] The computer-readable storage medium may be an internal storage unit of the electronic device 300 in the aforementioned embodiment, such as a hard disk or memory of the electronic device 300. The computer-readable storage medium may also be an external storage device of the electronic device 300, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the electronic device 300.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0149] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable an electronic device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0150] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-path grounding monitoring method for an intelligent workstation electrostatic monitor, characterized in that: The method comprises: Perform infrared scanning on the workstation area and compare it with the workstation layout template to obtain workstation configuration data, and read electrostatic monitoring parameters based on the workstation configuration data; Based on the electrostatic monitoring parameters, a time division multiplexing matrix is ​​designed for multiple monitoring channels to obtain a time division multiplexing configuration scheme; specifically comprising: parsing the electrostatic monitoring parameters, extracting the sampling frequency requirements, priority and interference sensitivity of each monitoring channel, and obtaining channel characteristic data; sorting the monitoring channels based on the channel characteristic data to obtain an initial channel sorting result; allocating time slices for the initial channel sorting result, calculating the least common multiple period according to the sampling frequency requirements of each channel, and obtaining a basic time slice length; performing weighted allocation calculation based on the basic time slice length and the priority of each channel to obtain a time slice allocation scheme for each monitoring channel; performing conflict detection on the time slice allocation scheme to identify channel combinations that may have electromagnetic interference and obtain a potential interference list; reordering the monitoring channels based on the potential interference list to obtain an optimized channel sorting; performing matrix generation on the optimized channel sorting to construct an initial time division multiplexing matrix, in which matrix rows represent time slices and columns represent monitoring channels, and obtaining the original time division multiplexing matrix. Using a matrix; filling the original time-division multiplexing matrix, allocating each monitoring channel to a different time slice, and obtaining a filled time-division multiplexing matrix; based on the filled time-division multiplexing matrix, calculating the number of channel switching times and total energy consumption in each time slice, and obtaining a performance evaluation index; optimizing according to the performance evaluation index, and obtaining a time-division multiplexing matrix with minimized energy consumption; performing time-slice balancing on the time-division multiplexing matrix with minimized energy consumption, ensuring that the sampling interval of the key channel does not exceed the maximum allowable value, and obtaining a balanced time-division multiplexing matrix; based on the balanced time-division multiplexing matrix, generating a channel switching sequence and timer configuration data, and obtaining an execution instruction set; converting the execution instruction set into hardware register configuration parameters, including timer initial value, interrupt vector table and DMA transfer configuration, and obtaining hardware-level configuration data; performing integrity check and encryption on the hardware-level configuration data, and obtaining a securely encapsulated configuration data packet; storing the securely encapsulated configuration data packet and its metadata in a non-volatile memory, and forming a time-division multiplexing configuration scheme; According to the time division multiplexing configuration scheme, the switch control timing sequence of the high-speed solid-state relay is compiled to obtain a switch control timing table, and intelligent switch control is performed based on the switch control timing table to realize monitoring channel switching; The electrostatic signals of multiple monitoring channels are input into a high-precision analog-to-digital converter for parallel sampling and denoising to obtain pre-processed electrostatic data; Based on the pre-processed electrostatic data, a dynamic threshold model is constructed for each monitoring component to obtain a warning threshold, and a multi-level alarm strategy is executed according to the warning threshold.

2. The multi-path grounding monitoring method of the intelligent workstation electrostatic monitor according to claim 1 is characterized in that: The infrared scanning of the workstation area and the comparison of the workstation layout template to obtain the workstation configuration data, and the reading of the electrostatic monitoring parameters based on the workstation configuration data, include: Perform infrared scanning on the workstation area to obtain infrared image data; input the infrared image data into an image segmentation algorithm for processing to obtain contour information of the workstation components; Extracting features of the workstation component contour information to obtain a component feature vector; performing component recognition based on the component feature vector to obtain a component recognition result; Calculating the similarity between the component recognition result and a preset workstation layout template to obtain a similarity score; selecting the most matching workstation layout template based on the similarity score to obtain workstation type information; Generate workstation configuration data according to the workstation type information and the component identification result; store the workstation configuration data in a system fast access memory to obtain a workstation configuration record; Based on the workstation configuration record, a corresponding electrostatic monitoring parameter set is retrieved from an electrostatic parameter library; and environmental factor compensation calculation is performed on the electrostatic monitoring parameter set to obtain electrostatic monitoring parameters.

3. The multi-path grounding monitoring method of the intelligent workstation electrostatic monitor according to claim 1 is characterized in that: According to the time division multiplexing configuration scheme, the high-speed solid-state relay is subjected to switch control timing compilation to obtain a switch control timing table, and intelligent switch control is performed based on the switch control timing table to realize monitoring channel switching, including: Parsing the time division multiplexing configuration scheme, extracting the time slice switching point and the corresponding channel configuration information, and obtaining the original switching sequence; optimizing the switch timing of the original switching sequence, calculating the optimal switching path, and obtaining the optimized switching sequence; Based on the optimized switching sequence, a switching action is assigned to each high-speed solid-state relay to obtain a relay action mapping table; a timing analysis is performed on the relay action mapping table to calculate the precise timestamp of each switching action to obtain an initial switch control timing table; Perform switch jitter compensation on the initial switch control timing table, calculate the mechanical characteristic parameters of each relay, and obtain compensated timing data; based on the compensated timing data, add pre-trigger and delayed trigger time to each switch action to obtain an adjusted switch control timing table; Performing a safety analysis on the adjusted switch control timing table to identify potential switch conflicts and short circuit risks and obtain a risk point list; based on the risk point list, locally adjusting the switch control timing and inserting a protection interval to obtain an optimized switch control timing table; Converting the optimized switch control timing table into an instruction sequence executable by a microcontroller, including timer configuration, interrupt service program and I / O port control instructions, to obtain a bottom-level execution code; performing instruction-level optimization on the bottom-level execution code to obtain a high-efficiency switch control program; Based on the high-efficiency switch control program, generate hardware description language code, define the state machine and control logic, and obtain FPGA configuration bit stream; download the FPGA configuration bit stream to the field programmable gate array to generate a switch control timing table; The switch control timing table is loaded into the scheduler of the real-time operating system, and the priority and preemption strategy are set to obtain a dynamic scheduling scheme; based on the dynamic scheduling scheme, the intelligent switch control execution engine is started to process the switch control instructions in real time to realize the switching of the monitoring channel.

4. The multi-path grounding monitoring method of the intelligent workstation electrostatic monitor according to claim 3 is characterized in that: The electrostatic signals of multiple monitoring channels are input into a high-precision analog-to-digital converter for parallel sampling and denoising to obtain pre-processed electrostatic data, including: Performing synchronous sampling and triggering on the electrostatic signals of multiple monitoring channels to obtain original sampling data; inputting the original sampling data into a high-precision analog-to-digital converter for quantization to obtain first electrostatic signal data; Performing data format conversion on the first electrostatic signal data, converting the floating point number into a fixed point number representation, and obtaining the second electrostatic signal data; calculating the signal-to-noise ratio of each channel based on the second electrostatic signal data, and obtaining a channel quality assessment result; Performing adaptive filter parameter optimization on the channel quality assessment result, calculating the optimal filter coefficient, and obtaining the filter parameters preset by the channel; performing digital filtering on the second electrostatic signal data based on the filter parameters preset by the channel, and obtaining filtered electrostatic data; Performing spectrum analysis on the filtered electrostatic data to obtain a noise characteristic spectrum; performing secondary filtering on the filtered electrostatic data based on the noise characteristic spectrum to obtain denoised electrostatic data; Performing multi-scale decomposition on the denoised electrostatic data to obtain time-frequency characteristics of the electrostatic signal; performing threshold processing on the time-frequency characteristics to obtain standard electrostatic data; Based on the standard electrostatic data, the baseline drift trend of each channel is calculated to obtain a baseline correction parameter; the baseline correction parameter is applied to the standard electrostatic data to perform baseline drift compensation to obtain stabilized electrostatic data; Performing outlier detection on the stabilized electrostatic data to obtain an abnormal data mark; performing local interpolation processing on the stabilized electrostatic data based on the abnormal data mark to obtain corrected electrostatic data; The corrected electrostatic data is matched with the calibration curve of each channel, and nonlinear correction is performed to obtain preprocessed electrostatic data.

5. The multi-path grounding monitoring method of the intelligent workstation electrostatic monitor according to claim 4 is characterized in that: Based on the pre-processed electrostatic data, a dynamic threshold model is constructed for each monitoring component to obtain a warning threshold, and a multi-level alarm strategy is executed according to the warning threshold, including: Performing statistical analysis on the pre-processed static data, calculating the mean and standard deviation of the static level, and obtaining a static threshold benchmark; setting an initial warning threshold based on the static threshold benchmark, including three levels of low, medium, and high, and obtaining an initial multi-level threshold; Performing sliding window analysis on the pre-processed electrostatic data, calculating the short-term change trend, and obtaining a dynamic adjustment factor; applying the dynamic adjustment factor to the initial multi-level threshold, performing real-time adjustment, and obtaining a warning threshold; Based on the warning threshold, a correlation relationship between static electricity anomaly and operating behavior is established to obtain a dynamic threshold model; The dynamic threshold model is deployed in a real-time monitoring system to continuously receive pre-processed electrostatic data, perform real-time threshold comparison and alarm triggering, and complete the execution of a multi-level alarm strategy for dynamic thresholds.

6. The multi-path grounding monitoring method of the intelligent workstation electrostatic monitor according to claim 5, characterized in that: The method further comprises: Performing time window segmentation on the preprocessed electrostatic data to obtain a plurality of time series segments; performing feature extraction on the time series segments to obtain trend, periodicity and random components; Based on the trend, periodicity and random components, the pre-processed electrostatic data is seasonally decomposed to obtain de-seasonalized electrostatic data; the de-seasonalized electrostatic data is frequency-domain converted to obtain frequency-domain features of the electrostatic data; Performing cluster analysis on the frequency domain features to obtain initial clustering results; performing refined classification based on the initial clustering results to obtain a classification structure of the electrostatic change pattern; Extracting features from the classification structure of the electrostatic change pattern to obtain an electrostatic change pattern feature vector; training an electrostatic change pattern recognition model based on the electrostatic change pattern feature vector to obtain a pattern classifier; Using the pattern classifier, the continuous electrostatic data stream is classified in real time to obtain a real-time pattern label; the real-time pattern label is correlated with the historical data to obtain an electrostatic change pattern conversion probability matrix; Analyze the electrostatic change mode conversion probability matrix to extract key factors of electrostatic performance degradation; based on the key factors, construct a model of electrostatic performance change trend over time to obtain a preliminary degradation model; Performing a multi-dimensional correlation analysis on the preliminary degradation model, process parameters and environmental factors to obtain a composite degradation model that takes into account the influence of multiple factors; performing a numerical simulation on the composite degradation model to obtain a statistical distribution of the degradation process; Based on the statistical distribution of the degradation process, the time distribution of the electrostatic performance reaching the critical value is calculated to obtain a remaining service life prediction model; the remaining service life prediction model is combined with the real-time monitoring data to obtain a dynamically adjusted degradation prediction result; A risk assessment index of electrostatic performance degradation is generated according to the dynamically adjusted degradation prediction result, and a maintenance decision suggestion is generated based on the risk assessment index to complete the construction of the electrostatic performance degradation model.

7. A multi-path grounding monitoring device for an intelligent workstation electrostatic monitor, characterized in that: A method for performing multi-path grounding monitoring of an intelligent workstation electrostatic monitor according to any one of claims 1 to 6, the device comprising: A reading module is used to perform infrared scanning on the workstation area and compare the workstation layout template to obtain workstation configuration data, and read electrostatic monitoring parameters based on the workstation configuration data; A design module, used to perform time division multiplexing matrix design on multiple monitoring channels based on the electrostatic monitoring parameters to obtain a time division multiplexing configuration scheme; A switching module is used to compile a switch control timing sequence for a high-speed solid-state relay according to the time division multiplexing configuration scheme, obtain a switch control timing table, and perform intelligent switch control based on the switch control timing table to realize monitoring channel switching; A processing module, used for inputting the electrostatic signals of multiple monitoring channels into a high-precision analog-to-digital converter for parallel sampling and denoising to obtain pre-processed electrostatic data; The execution module is used to construct a dynamic threshold model for each monitoring component based on the pre-processed electrostatic data, obtain a warning threshold, and execute a multi-level alarm strategy according to the warning threshold.

8. An electronic device, characterized in that: The electronic device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the electronic device executes the multi-path grounding monitoring method of the smart workstation electrostatic monitor according to any one of claims 1-6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the multi-path grounding monitoring method of the intelligent workstation electrostatic monitor according to any one of claims 1-6 is implemented.

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