PTA chain plate operation prompting and central control current monitoring safe operation device

Abstract

The invention discloses a PTA chain plate operation prompting and central control current monitoring safe operation device, and relates to the technical field of PTA chain plate operation optimization, and the device comprises a field operation indication unit which is used for constructing a chain plate operation situation model based on a composite signal of a chain plate frequency converter and a start-stop switch, outputting a chain plate operation state control instruction, and outputting a central control current control instruction; a chain plate operation state indication is generated through an anti-explosion flickering alarm indicating lamp; the signal conversion and transmission unit is used for acquiring chain plate operation current data according to the current signal of the horizontal section chain plate frequency converter, and transmitting a standardized current signal through a DCS (Distributed Control System) port; and the central control dynamic monitoring unit is used for calculating a current anomaly measurement index based on the standardized current signal, and establishing a multi-stage alarm triggering mechanism of the real-time running current trend of the chain plate by using a dynamic early warning threshold system. According to the invention, abnormal running of the chain plate can be found in time, and the running safety and reliability of the PTA chain plate are improved.

Description

Technical Field

[0001] The invention relates to the technical field of PTA chain plate operation optimization, and in particular to a PTA chain plate operation prompt and central control current monitoring safety operation device. Background Art

[0002] With the continuous development of modern industrial production technology and the continuous improvement of automation level, the conveying system, as a key link in the industrial production process, plays an increasingly important role in ensuring production efficiency, ensuring product quality and maintaining production safety; in many industries such as petrochemicals, chemicals, polyester, especially in the polyester (PET) production process, the stability and reliability of the material conveying system directly affect the overall operation status of the production line. As an important synthetic material, the production process of polyester has extremely high requirements for the accuracy and continuity of raw material transportation. The conveying system not only needs to have high-efficiency material transportation capabilities, but also needs to ensure safety and controllability during operation. This has become an important direction for innovation in modern industrial production technology.

[0003] Traditional chain conveyor systems usually adopt independent control modes, and the operating status mainly depends on the direct observation and manual control of the on-site operators. In actual production, operators need to control the operation of the chain through the on-site start switch. The key operating parameters of the equipment, such as operating speed, load size, motor current, etc., often need to be tediously read and analyzed manually through the frequency converter. This traditional control method not only increases the workload of operators, but also lays potential risks for potential safety hazards in the production process; for PET production, which requires extremely high production precision, this manual control mode is particularly backward and unreliable.

[0004] The existing chain conveyor system has many shortcomings, which seriously restricts the safety and efficiency of production. First, the existing technology lacks effective central control and real-time monitoring means, making it difficult to accurately control the operating status of the equipment. During high-frequency operation, the chain often has problems such as unstable operation, abnormal load increase, motor overload, etc., and may even cause the chain to get stuck or break; more seriously, the chain accidentally falls off and enters the PTA silo, which may cause the discharge valve to get stuck, and then cause serious failures in the slurry mixing system; secondly, the existing technology lacks a clear operation indication system on site, and operators often start feeding without fully confirming the operating status of the chain. This blind operation will cause the chain running current to fluctuate violently instantly, triggering the motor overload protection and causing the equipment to shut down urgently, which will not only affect production continuity, but may also cause equipment damage and safety accidents.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In response to the problems in the related technology, the present invention proposes a PTA chain plate operation prompt and central control current monitoring safety operation device, which has the advantages of realizing dual safety monitoring through the chain plate operation status model and current trend warning, timely discovering the abnormal operation of the chain plate, and improving the safety and reliability of the PTA chain plate operation, thereby solving the problem of lack of effective central control and real-time monitoring means in the prior art, which makes it difficult to accurately control the operation status of the chain plate. During high-frequency operation, the chain plate often has abnormal load increase and there is a lack of a clear operation indication system on site. Operators often feed materials without fully confirming the operation status of the chain plate, resulting in instantaneous and violent fluctuations in the chain plate operation current, triggering the motor overload protection or even emergency shutdown.

[0007] To this end, the specific technical solution adopted by the present invention is as follows:

[0008] A PTA chain plate operation prompt and central control current monitoring safety operation device, the PTA chain plate operation prompt and central control current monitoring safety operation device comprises:

[0009] The on-site operation indication unit is used to construct a chain plate operation status model based on the composite signal of the chain plate inverter and the start-stop switch, and use the chain plate operation status model to output the chain plate operation status control instruction, and generate the chain plate operation status indication through the explosion-proof flashing alarm indicator light;

[0010] The signal conversion and transmission unit is used to obtain the chain plate running current data according to the current signal of the horizontal section chain plate inverter, and transmit the standardized current signal through the DCS port;

[0011] The central control dynamic monitoring unit is used to calculate the current anomaly measurement index based on the transmitted standardized current signal, and use the dynamic early warning threshold system to establish a multi-level alarm trigger mechanism for the real-time operating current trend of the chain plate.

[0012] Furthermore, the on-site operation indication unit includes:

[0013] The chain plate operation feature module is used to obtain the chain plate operation multi-dimensional feature vector according to the operating frequency and start-stop switch state of the PTA conveyor chain plate inverter by using the frequency feature extraction algorithm and state conversion logic;

[0014] The operation situation model module is used to build a chain plate operation situation model based on the multi-dimensional feature vector of the chain plate operation, and generate a risk warning level by dynamically adjusting the feature weight;

[0015] The display signal mapping module is used to generate the indicator light display signal according to the risk warning level by using the risk-indicator light parameter mapping rules and the multi-state mapping table.

[0016] Furthermore, according to the operating frequency and start / stop switch status of the PTA conveyor chain inverter, the frequency feature extraction algorithm and the multidimensional feature vector extraction algorithm are used to obtain the chain operation multidimensional feature vector, including:

[0017] Based on the PTA conveyor chain inverter, the operating frequency data is collected to obtain the frequency change sequence, and the frequency feature mapping table is generated through the frequency feature extraction algorithm;

[0018] According to the start-stop switch state signal, the switch state feature vector is obtained by using the signal state encoding method, and the state change sequence is generated through the state transition logic;

[0019] Based on the frequency feature mapping table and state change sequence, the principal component analysis algorithm is used to obtain the composite feature information, and the multi-dimensional feature vector of the chain plate operation is generated through the feature fusion technology.

[0020] Furthermore, based on the multi-dimensional feature vector of the chain plate operation, a chain plate operation situation model is constructed, and by dynamically adjusting the feature weights, the risk warning levels are generated, including:

[0021] According to the multi-dimensional feature vector of the chain plate operation, a chain plate operation situation mapping model is established using a machine learning algorithm, and adaptive evaluation parameters are generated by dynamically adjusting the feature weights;

[0022] Based on the chain board operation situation mapping model, the operation situation score is obtained by using a multi-dimensional scoring algorithm, and the situation risk index is generated through risk association analysis;

[0023] According to the situation risk index, the risks are classified and the risk warning level is obtained.

[0024] Furthermore, the expression of the running situation score is:

[0025]

[0026] Where, Score is the total score of the chain plate operation status; W i are weight coefficients of each dimension, W1 is the weight coefficient of state stability; W2 is the weight coefficient of state continuity; W3 is the weight coefficient of switching regularity; S i is the score of each dimension, S1 is the frequency stability score; S2 is the state continuity score; S3 is the switching regularity score;

[0027] The expression of situation risk index is:

[0028]

[0029] In the formula, r iis the correlation between the ith risk factor and the reference sequence; x0(k) is the reference sequence; xi(k) is the comparison sequence; ρ is the resolution coefficient; n is the sequence length; k is the point in the sequence; min is the minimum difference of all points in all sequences; max is the maximum difference of all points in all sequences.

[0030] Further, according to the risk warning level, using the risk-indicator parameter mapping rule and the multi-state mapping table, generating the indicator display signal includes:

[0031] Based on the risk warning level, the indicator light display mode parameters are obtained using the risk-indicator light parameter mapping rules;

[0032] Generate an indicator light control strategy based on the indicator light display mode parameters and in combination with a multi-state mapping table;

[0033] Based on the indicator light control strategy, the indicator light display signal is generated by driving the signal converter and transmitted to the explosion-proof flashing alarm indicator light.

[0034] Furthermore, the central control dynamic monitoring unit includes:

[0035] The abnormality measurement index module is used to obtain the current fluctuation characteristics and calculate the current abnormality measurement index according to the standardized current signal transmitted by the DCS port using the improved dynamic time planning algorithm;

[0036] Dynamic warning threshold module, which is used to obtain abnormal operation warning parameters based on current abnormality measurement indicators and establish a dynamic warning threshold system by using multi-dimensional threshold analysis;

[0037] The multi-level alarm trigger module is used to obtain the evaluation results of the real-time operation current trend of the chain plate according to the dynamic early warning threshold system, and set a multi-level alarm trigger mechanism for the real-time operation current trend of the chain plate.

[0038] Furthermore, according to the standardized current signal transmitted by the DCS port, the improved dynamic time planning algorithm is used to obtain the current fluctuation characteristics and calculate the current anomaly measurement indicators including:

[0039] Based on the standardized current signal, the sliding window sampling algorithm is used to obtain the current time series data, and the standardized current sequence is established through data preprocessing;

[0040] According to the normalized current sequence, the improved dynamic time planning algorithm is used to obtain the current trend characteristics, and the trend characteristic vector is generated through trend matching;

[0041] Based on the trend feature vector, the current trend anomaly index is obtained using the anomaly measurement algorithm.

[0042] Furthermore, based on the current anomaly measurement indicators, multi-dimensional threshold analysis is used to obtain the abnormal operation warning parameters, and a dynamic warning threshold system is established, including:

[0043] According to the current anomaly measurement indicators, the current trend statistical analysis is used to obtain the trend anomaly pattern, and the trend warning parameters are generated through pattern clustering;

[0044] Based on the trend warning parameters, the adaptive algorithm is used to obtain the trend adjustment factor, and the trend warning update mechanism is established through parameter optimization;

[0045] According to the trend warning update mechanism, multi-dimensional threshold analysis is used to obtain the trend classification warning standard, and through threshold mapping, a dynamic trend warning system is generated.

[0046] Furthermore, according to the dynamic early warning threshold system, the evaluation result of the real-time operation current trend of the chain plate is obtained, and a multi-level alarm trigger mechanism of the real-time operation current trend of the chain plate is set, including:

[0047] Based on the dynamic warning threshold system, the trend assessment algorithm is used to obtain the current trend risk level, and through trend prediction, an operation trend assessment model is established;

[0048] According to the running trend assessment model, the trend alarm rules are applied to obtain the trend alarm conditions, and the trend alarm strategy is generated through trend prediction;

[0049] Based on the trend alarm strategy, a hierarchical alarm signal is generated through a multi-level alarm trigger mechanism, and the hierarchical alarm signal is pushed using the DCS port.

[0050] Further, based on the PTA conveyor chain plate inverter, the operating frequency data is collected, and then the operating abnormality evaluation index is obtained according to the operating frequency data to determine whether the frequency change sequence is abnormal;

[0051] The specific method for obtaining the operation abnormality evaluation index is as follows:

[0052] Acquire operating frequency data, wherein the operating frequency data includes average operating frequency, frequency change rate, operating frequency fluctuation value and frequency alarm number;

[0053] Acquire reference operating frequency data from a preset database, wherein the reference operating frequency data includes a frequency setting value, a change rate deviation, a fluctuation value deviation, and an alarm number limit;

[0054] The operation frequency data and the corresponding reference operation frequency data are processed to obtain the operation abnormality evaluation index.

[0055] Further, the current fluctuation characteristics are obtained, and then the fluctuation abnormality evaluation index is obtained according to the current fluctuation data to determine whether the current fluctuation characteristics are stable;

[0056] The specific method for obtaining the fluctuation anomaly assessment index is as follows:

[0057] Acquire current fluctuation data, wherein the current fluctuation data includes current mean value, fluctuation amplitude, current change rate, harmonic content and current stable time;

[0058] Acquire reference current fluctuation data from a preset database, wherein the reference current fluctuation data includes rated current, fluctuation amplitude deviation, current change rate deviation and harmonic content limit;

[0059] The current stabilization time is denormalized and a fluctuation anomaly evaluation index is obtained based on the current fluctuation data and the reference current fluctuation data.

[0060] Further, current time series data is obtained, and then the current time series evaluation index is obtained according to the current time series data and combined with the current fluctuation data to determine whether the current sequence is normalized;

[0061] The specific method for obtaining the current timing evaluation index is as follows:

[0062] Acquire the current time series data and the current mean value and the current change rate in the current fluctuation data, wherein the current time series data includes the number of abnormal current values ​​and the current variance;

[0063] Obtaining the maximum value of the abnormal current value and the rated current and current change rate deviation in the reference current fluctuation data;

[0064] The current timing data and the maximum value of the abnormal current value, the current mean value and the current change rate in the current fluctuation data, and the rated current and the current change rate deviation in the reference current fluctuation data are processed to obtain the current timing evaluation index.

[0065] The beneficial effects of the present invention are:

[0066] (1) By constructing a linkage mechanism between the chain plate operation status model and the dynamic early warning threshold system, the present invention realizes dual monitoring and protection of the PTA chain plate operation status; the on-site operation indication unit obtains the multi-dimensional feature vector of the chain plate operation in real time based on the chain plate inverter operation frequency and the start-stop switch status, and constructs the operation status model by dynamically adjusting the feature weight, so as to achieve accurate grasp of the chain plate operation status; the central control dynamic monitoring unit obtains the current fluctuation characteristics based on the horizontal section chain plate inverter current signal through an improved dynamic time planning algorithm, and establishes a dynamic early warning system by multi-dimensional threshold analysis, so as to realize real-time monitoring of the chain plate operation current trend; the two monitoring units form a mutually corroborating and complementary safety monitoring network through data bridging with the signal conversion and transmission unit, which can not only detect chain plate operation abnormalities in a timely manner, but also accurately predict potential faults, greatly improving the safety and reliability of PTA chain plate operation.

[0067] (2) In view of the problems existing in the prior art, such as the single means of chain plate operation monitoring and inaccurate state control, the present invention adopts a hierarchical intelligent algorithm system to fundamentally improve the accuracy and reliability of chain plate operation status monitoring; at the operation feature extraction level, the present invention obtains the multi-dimensional feature vector of chain plate operation through the frequency feature extraction algorithm and the state conversion logic, and combines the principal component analysis algorithm for feature fusion, thereby realizing comprehensive monitoring of the chain plate operation status; at the situation assessment level, the present invention innovatively proposes a quantitative evaluation method based on the total score of the operation situation, and realizes accurate evaluation of the operation status through weighted calculation of the three dimensions of state stability, state continuity, and switching regularity; at the warning triggering level, an improved dynamic time planning algorithm is used to obtain the current fluctuation characteristics, and a dynamic warning system is established through multi-dimensional threshold analysis. Combined with the current trend statistical analysis and adaptive algorithm, a scientific trend warning update mechanism is established; this monitoring system based on multiple algorithms not only realizes the timely discovery of minor abnormalities in chain plate operation, but also ensures the accuracy and timeliness of the warning through the establishment of a dynamic warning threshold system, thereby effectively avoiding problems such as abnormal chain plate load and unstable operation.

[0068] (3) Through the integrated application of explosion-proof flashing alarm indicator lights and DCS current monitoring, the present invention achieves seamless connection between on-site operation and central control supervision at the engineering practice level. The explosion-proof flashing alarm indicator lights are installed on the front wall of the feeding area, and provide on-site operators with a clear reference to the chain plate operation status through intuitive status indications; the DCS system establishes a complete trend monitoring and early warning disposal mechanism by real-time collection and analysis of chain plate operation current data; the organic combination of the two monitoring methods not only meets the explosion-proof safety requirements in the PTA production process, but also realizes full supervision from the site to the central control. This integrated application model greatly improves the operating environment of the production site and improves the work efficiency of operators. At the same time, through standardized monitoring processes and disposal mechanisms, it effectively reduces chain plate operation failures caused by human judgment errors, providing reliable technical support for the safe and stable operation of the PTA production process.

[0069] (4) By obtaining the operating frequency data and the reference operating frequency data from the preset database, the operating frequency data and the corresponding reference operating frequency data are processed to obtain the operating abnormality evaluation index to determine whether the frequency change sequence is abnormal. This improves the monitoring of the PTA chain operation and ensures the reliability of the frequency change sequence obtained based on the operating frequency data collected by the PTA conveyor chain inverter.

[0070] (5) By acquiring current fluctuation data and obtaining reference current fluctuation data from a preset database, the current stabilization time is denormalized and a fluctuation anomaly evaluation index is obtained based on the current fluctuation data and the reference current fluctuation data to determine whether the current fluctuation characteristics are stable. This achieves a more accurate evaluation of the current fluctuation characteristics and ensures the availability of the current fluctuation characteristics.

[0071] (6) By obtaining the current time series data and the current mean value and the current change rate in the current fluctuation data, and then obtaining the maximum number of abnormal current values ​​and the rated current and current change rate deviation in the reference current fluctuation data, the current time series data and the maximum number of abnormal current values ​​and the current mean value and the current change rate in the current fluctuation data and the rated current and current change rate deviation in the reference current fluctuation data are processed to obtain the current time series evaluation index, which not only improves the reliability of the current time series data, but also ensures the standardization of the current sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] 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 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 work.

[0073] Figure 1 It is a principle block diagram of a PTA chain plate operation prompt and central control current monitoring safety operation device according to an embodiment of the present invention;

[0074] Figure 2 It is a specific implementation diagram of a PTA chain plate operation prompt and central control current monitoring safety operation device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0075] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0076] According to an embodiment of the present invention, a PTA chain plate operation prompt and central control current monitoring safe operation device is provided.

[0077] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a PTA chain plate operation prompt and central control current monitoring safety operation device is provided, and the PTA chain plate operation prompt and central control current monitoring safety operation device includes:

[0078] The on-site operation indication unit 101 is used to construct a chain plate operation status model based on the composite signal of the chain plate inverter and the start-stop switch, and output a chain plate operation status control instruction using the chain plate operation status model, and generate a chain plate operation status indication through an explosion-proof flashing alarm indicator light;

[0079] The signal conversion and transmission unit 201 is used to obtain the chain plate operation current data according to the current signal of the horizontal section chain plate inverter, and transmit the standardized current signal through the DCS port;

[0080] The central control dynamic monitoring unit 301 is used to calculate the current anomaly measurement index based on the transmitted standardized current signal, and use the dynamic early warning threshold system to establish a multi-level alarm trigger mechanism for the real-time operation current trend of the chain plate.

[0081] In one embodiment, the field operation indication unit 101 includes:

[0082] The chain plate operation feature module (not shown in the figure) is used to obtain the chain plate operation multi-dimensional feature vector according to the operating frequency and start-stop switch state of the PTA conveyor chain plate inverter by using the frequency feature extraction algorithm and state conversion logic;

[0083] The operation situation model module (not shown in the figure) is used to construct a chain plate operation situation model based on the multi-dimensional feature vector of the chain plate operation, and generate a risk warning level by dynamically adjusting the feature weight;

[0084] The display signal mapping module (not shown in the figure) is used to generate an indicator light display signal according to the risk warning level by using the risk-indicator light parameter mapping rules and the multi-state mapping table.

[0085] In one embodiment, according to the operating frequency and start / stop switch state of the PTA conveyor chain inverter, a frequency feature extraction algorithm and a multidimensional feature vector extraction algorithm are used to obtain the chain operation multidimensional feature vector, including:

[0086] S111, collecting operating frequency data based on the PTA conveyor chain inverter, obtaining a frequency change sequence, and generating a frequency feature mapping table through a frequency feature extraction algorithm;

[0087] Specifically, frequency sampling is performed through the PTA conveyor chain inverter, with a sampling frequency of 100 Hz, to continuously collect chain operation frequency data; the data acquisition accuracy is 0.1 Hz, the sampling time for each group is 1 minute, and 6000 frequency sampling points are obtained; the sampling data is transmitted to the data preprocessing module in real time through the RS485 communication interface.

[0088] Specifically, the method for obtaining the frequency change sequence is as follows: first, perform median filtering on the original frequency data, set the filter window to 5 data points, and remove sampling noise; then, calculate the average frequency value with 1 second as the time window (i.e., 100 sampling points) to obtain 60 frequency feature points; then, calculate the frequency difference between adjacent feature points to obtain 59 frequency changes; finally, arrange the frequency changes in chronological order to form a frequency change sequence.

[0089] Specifically, the process of generating a frequency feature mapping table through a frequency feature extraction algorithm is as follows: first, based on the frequency change sequence, the frequency statistical characteristics are calculated, including the mean, standard deviation, peak factor and skewness coefficient; second, the frequency change sequence is subjected to time-frequency analysis using wavelet transform. In this embodiment, the db4 wavelet is selected, and the number of decomposition layers is 3 to extract the energy characteristics of each frequency band; then, the time domain characteristics of the frequency sequence are calculated, including the maximum value, minimum value, root mean square value and waveform factor; finally, all characteristic parameters are normalized and mapped to the [0,1] interval to generate a frequency feature mapping table.

[0090] Specifically, the frequency feature mapping table includes the following feature items:

[0091] ①Basic characteristics of frequency:

[0092] Current frequency value f(t);

[0093] Frequency change rate df / dt;

[0094] Frequency fluctuation range [fmin,fmax];

[0095] ②Statistical characteristics:

[0096] Mean μ = 35 ± 0.5 Hz (horizontal segment);

[0097] Standard deviation σ = 0.8;

[0098] Peak factor Kp=3.5;

[0099] Skewness coefficient Ks=0.6;

[0100] ③ Frequency band energy characteristics:

[0101] Low frequency band [0-1Hz]: El = 0.65;

[0102] Mid-frequency band [1-2Hz]: Em=0.25;

[0103] High frequency band [2-3Hz]: Eh=0.10;

[0104] ④Time domain characteristics:

[0105] Maximum value fmax=37Hz;

[0106] Minimum value fmin = 33Hz;

[0107] RMS value frms = 35.5Hz;

[0108] Waveform factor Kw=1.2.

[0109] S112, according to the start-stop switch state signal, using the signal state coding method, obtain the switch state feature vector, and generate the state change sequence through the state conversion logic;

[0110] Specifically, according to the start-stop switch status signal, the switch signal is collected through the 24V DC signal acquisition module, and the sampling frequency is 10Hz; the signal preprocessing includes: anti-interference filtering, using an RC low-pass filter circuit, with a cut-off frequency of 1Hz; signal de-jittering, setting a filter time constant of 20ms.

[0111] Specifically, the process of obtaining the switch state feature vector using the signal state encoding method is as follows: first, the switch state signal is converted into a three-dimensional vector, the running state is encoded as [1,0,0], the stopped state is encoded as [0,1,0], and the fault state is encoded as [0,0,1]; then, the state judgment rule is set, requiring that the state duration is greater than 100ms to be recognized as a valid state; finally, the encoded vector is input into the state feature buffer, and the cache depth is 10 states.

[0112] Specifically, the method of generating a state change sequence through state transition logic is: first, record the timestamp and duration of the state vector to form a triple of <timestamp, state vector, duration>; second, detect the transition type between adjacent states. In this embodiment, normal startup is [0,1,0]->[1,0,0], normal stop is [1,0,0]->[0,1,0], and fault shutdown is [1,0,0]->[0,0,1]; finally, store the state transition information in chronological order and save the most recent 10 state transition records.

[0113] S113. Based on the frequency feature mapping table and the state change sequence, the principal component analysis algorithm is used to obtain the composite feature information, and the multi-dimensional feature vector of the chain plate operation is generated through the feature fusion technology.

[0114] Specifically, based on the frequency feature mapping table generated by S111 and the state change sequence generated by S112, the process of feature fusion is as follows: first, the feature items in the frequency feature mapping table are organized into a feature matrix F with a dimension of 12×1; secondly, the state features and transition features in the state change sequence are organized into a feature matrix S with a dimension of 6×1; then, a combined feature matrix C=[F;S] is constructed with a dimension of 18×1.

[0115] Specifically, the steps of obtaining composite feature information using the principal component analysis algorithm are as follows: first, standardize the combined feature matrix C to obtain the standardized matrix Z; second, calculate the covariance matrix R=Z'Z; then, solve the characteristic equation |R-λI|=0 to obtain the eigenvalue λi and eigenvector ui; then, select the first three principal components with a contribution rate of 85% to form a dimensionality reduction matrix P.

[0116] Specifically, the method of generating a multi-dimensional feature vector of chain operation through feature fusion technology is as follows: first, multiply the standardized matrix Z by the dimensionality reduction matrix P to obtain the principal component feature Y=ZP; secondly, design the feature weight vector w=[0.4,0.4,0.2], corresponding to the frequency feature, state feature and conversion feature; finally, calculate the weighted feature vector V=Yw to obtain the three-dimensional chain operation feature vector.

[0117] In one embodiment, based on the multi-dimensional feature vector of the chain plate operation, a chain plate operation situation model is constructed, and by dynamically adjusting the feature weights, the risk warning levels are generated, including:

[0118] S121. According to the multi-dimensional feature vector of the chain plate operation, a chain plate operation situation mapping model is established by using a machine learning algorithm, and adaptive evaluation parameters are generated by dynamically adjusting feature weights;

[0119] Specifically, according to the multi-dimensional feature vector of chain operation generated in step S113, the process of using support vector machine (SVM) to establish the chain operation status mapping model is as follows: first, construct a training data set, including 1000 groups of labeled data, of which 700 groups are used for training and 300 groups are used for verification; then, select the radial basis kernel function (RBF), the kernel parameter γ=0.1, and the penalty parameter C=1.0; then, use the cross-validation method to optimize the model parameters, and the verification accuracy reaches more than 95%.

[0120] Specifically, the method of generating adaptive evaluation parameters by dynamically adjusting feature weights is as follows: first, set the initial feature weight vector w0=[0.4, 0.4, 0.2]; second, calculate the model prediction accuracy acc every 24 hours; then, according to the change in accuracy, use the gradient descent method to update the weights with a step size of α=0.05, and the update formula is w(t+1)=w(t)+α×∇acc; finally, normalize the updated weights.

[0121] It should be noted that the gradient descent method is an iterative optimization algorithm. Its basic principle is to adjust the parameters along the direction of the gradient descent of the objective function to find the local minimum of the objective function. The algorithm first calculates the gradient (derivative) of the current point, and then moves in the opposite direction of the gradient with a certain step size, and repeats this process until it converges to the local optimal solution. In this embodiment, the prediction accuracy acc is used as the objective function, and the gradient ∇acc of acc to the weight vector w is calculated, and the weight is updated along the gradient direction with a fixed step size α=0.05, thereby realizing adaptive optimization of the feature weights, so that the operating status model can more accurately reflect the actual operating status of the chain plate. This is a prior art and will not be elaborated here.

[0122] S122. Based on the chain plate operation situation mapping model, a multi-dimensional scoring algorithm is used to obtain an operation situation score, and a situation risk index is generated through risk association analysis;

[0123] Specifically, based on the chain plate operation status mapping model established in step S121, the steps of obtaining the operation status score using a multi-dimensional scoring algorithm are as follows: first, setting the scoring dimensions, which in this embodiment include frequency stability (40 points), state continuity (30 points) and switching regularity (30 points); then, converting the mapping model output value to the [0,1] interval through a sigmoid function; then, calculating the weighted score according to the weight of each dimension, and setting the score interval to 0-100 points.

[0124] Specifically, the method for generating a situation risk index through risk association analysis is as follows: first, establish a risk factor library, including frequency overlimit, frequent start and stop, and abnormal shutdown; second, calculate the correlation between the current operating situation and each risk factor. In this embodiment, the gray correlation method is used, and the resolution coefficient is 0.5; then, set the risk weighting coefficient β=[0.4,0.3,0.3]; finally, calculate the comprehensive risk index R=Σ(βi×ri), and the update cycle is 1 minute.

[0125] S123. According to the situation risk index, the risk is classified into different levels to obtain the risk warning level.

[0126] Specifically, according to the situation risk index generated in step S122, the process of risk grading is as follows: first, set the risk threshold. In this embodiment, 0-20 points are set as the safe operation range, 20-50 points are set as the slight risk range, 50-80 points are set as the medium risk range, and 80-100 points are set as the high risk range; secondly, a 3-second judgment cycle is adopted, and the risk level change is required to last for more than 2 seconds to trigger the warning level update; finally, the risk warning level is stored in the cache, and the warning records for the last 5 minutes are kept.

[0127] In one embodiment, the expression of the running status score is:

[0128]

[0129] Where, Score is the total score of the chain plate operation status; W i are weight coefficients of each dimension, W1 is the weight coefficient of state stability, which is 0.4 in this embodiment; W2 is the weight coefficient of state continuity, which is 0.3 in this embodiment; W3 is the weight coefficient of switching regularity, which is 0.3 in this embodiment; S i are the scores of each dimension, S1 is the frequency stability score, which is calculated by the frequency feature mapping table; S2 is the state continuity score, which is calculated by the state change sequence; S3 is the switching regularity score, which is calculated by the state transition logic.

[0130] Specifically, the expression of the frequency stability score is:

[0131]

[0132] Where S1 is the frequency stability score; S f Score the frequency value. When the frequency is within the rated range of 35±2Hz, the value is 1. Otherwise, it is calculated according to the deviation ratio. v S is the frequency change rate score. When df / dt≤0.5Hz / s, the value is 1. Otherwise, it is calculated according to the excess ratio. r It is a score for the frequency fluctuation range. When the fluctuation range is ≤4Hz, the value is 1, otherwise it is calculated according to the excess ratio.

[0133] Specifically, the expression of the state continuity score is:

[0134]

[0135] Where S2 is the state continuity score; S t Score for state duration. If the running state duration is ≥ 30 min, the value is 1. Otherwise, it is calculated based on the time ratio. c Score for the number of state switching times. If the number of switching times within 30 minutes is ≤ 2, the value is 1. Otherwise, it is calculated according to the proportion of the number of times exceeding the limit. s It is a score for state stability. When the chain plate running state switches in the order of [0,1,0]->[1,0,0] (normal start) or [1,0,0]->[0,1,0] (normal stop), the value is 1. If an abnormal switch occurs such as [1,0,0]->[0,0,1] (fault shutdown), the value is calculated according to the following ratio: the number of normal switches in the most recent 10 state switches / 10.

[0136] Specifically, the expression of the switching regularity score is:

[0137]

[0138] Where S3 is the switching regularity score; R n For the switching normative score, the chain plate operation status switches to value 1 according to the normal start-stop sequence, otherwise it takes a value of 0; R t R is the switching time interval score. If the adjacent switching interval is ≥5min, the value is 1. Otherwise, it is calculated based on the time ratio. p Score the switching mode. If it meets the preset switching mode, the value is 1. Otherwise, it is calculated based on the compliance degree.

[0139] The expression of the situation risk index is:

[0140]

[0141] In the formula, r i is the correlation between the ith risk factor and the reference sequence; x0(k) is the reference sequence, which represents the characteristic sequence of the chain plate under the ideal operating state; xi(k) is the comparison sequence, which represents the characteristic sequence of the chain plate under the current operating state; ρ is the resolution coefficient, which is 0.5 in this embodiment; n is the sequence length; k is the point in the sequence; min is the minimum difference of all points in all sequences; max is the maximum difference of all points in all sequences.

[0142] In one embodiment, according to the risk warning level, using the risk-indicator parameter mapping rule and the multi-state mapping table, generating the indicator display signal includes:

[0143] S131. Based on the risk warning level, using the risk-indicator parameter mapping rule, obtain the indicator light display mode parameters;

[0144] Specifically, based on the risk warning level generated in step S123, the process of configuring the display parameters using the risk-indicator parameter mapping rules is as follows: first, load the preset risk-indicator parameter mapping rules; second, according to the current risk warning level, look up the table to obtain the corresponding display parameters; then, generate a display mode parameter data packet <color, brightness, flashing frequency>.

[0145] Specifically, the risk-indicator parameter mapping rules include:

[0146] Safe operation (0-20 points): {color: green, brightness: 500 lumens, flicker: no};

[0147] Minor risk (20-50 points): {color: yellow, brightness: 600 lumens, flicker: 0.5Hz};

[0148] Medium risk (50-80 points): {color: orange, brightness: 800 lumens, flicker: 2Hz};

[0149] High risk (80-100 points): {color: red, brightness: 1000 lumens, flicker: 5Hz}.

[0150] S132, generating an indicator light control strategy according to the indicator light display mode parameters and in combination with a multi-state mapping table;

[0151] Specifically, the process of generating the indicator light control strategy according to the indicator light display mode parameters generated in step S131 and in combination with the multi-state mapping table is as follows: first, the display mode parameters are converted into a control instruction sequence; second, the display mode is determined according to the multi-state mapping table; and then, a PWM control signal is generated to control the LED drive circuits of different colors in the explosion-proof flashing alarm indicator light.

[0152] Specifically, the multi-state mapping table defines the display mode under different operating states. In this embodiment:

[0153] The normal operating state is continuous display flashing, and the PWM duty cycle is 90%;

[0154] The warning state is periodic flashing, and the PWM duty cycle changes periodically between 0-90%;

[0155] The alarm state is fast flashing, and the PWM duty cycle switches quickly between 0-90%;

[0156] The fault state is alternate flashing, and the different color LEDs in the explosion-proof flashing alarm indicator light are lit alternately.

[0157] Specifically, the minimum interval time for state switching is set to 3 seconds, and a gradient time of 0.5 seconds is set during the switching process to avoid sudden changes in display.

[0158] S133, based on the indicator light control strategy, generate an indicator light display signal by driving a signal converter, and transmit the signal to the explosion-proof flashing alarm indicator light;

[0159] Specifically, based on the indicator light control strategy generated in step S132, the process of generating the indicator light display signal through the driving signal converter is: first, configuring the driver parameters (input voltage: DC24V, signal type: PWM, driving frequency: 100Hz); second, generating a standard PWM waveform according to the control strategy; and then, outputting the driving current through the LED driving circuit.

[0160] Specifically, the process of signal transmission to the explosion-proof flashing alarm indicator light includes: first, the RS485 communication protocol is adopted in this embodiment, and the baud rate is 9600bps for data transmission; secondly, a communication verification mechanism is set, and CRC16 verification is adopted in this embodiment; finally, it is ensured that the signal transmission delay does not exceed 100ms.

[0161] Specifically, the technical parameters of the explosion-proof flashing alarm indicator light used in this embodiment are: explosion-proof grade ExdIIBT4, rated power 20W, display angle 120°, service life 50,000 hours, protection grade IP65.

[0162] In one embodiment, the signal conversion and transmission unit 201 is used to obtain the chain plate operating current data according to the current signal of the horizontal segment chain plate inverter, and transmit the standardized current signal through the DCS port.

[0163] Specifically, the implementation process of the signal conversion and transmission unit 201 acquiring the chain running current data is as follows: first, the Hall current sensor (model: LEMHAL50-S, range: 0-50A, accuracy: ±0.5%) installed at the output end of the horizontal section chain inverter collects the chain running current signal in real time; secondly, the collected 4-20mA analog signal is connected to the signal acquisition module (in this embodiment, the SM331 analog input module is used, 8 channels, 16-bit resolution); then, the sampling period is set to 100ms, and the sampling accuracy is 0.1A to perform real-time collection of the current signal.

[0164] Specifically, the process of standardizing the collected current signal is as follows: first, a first-order RC low-pass filter circuit is used for signal filtering, and the cut-off frequency is set to 10Hz to eliminate power frequency interference; second, the 4-20mA current signal is converted into the actual current value according to a linear relationship. The conversion formula is: actual current value = (measured current - 4mA) × (10A / 16mA), where the normal operating current value is about 6A; then, the converted current value is range calibrated, and the calibration reference points are the no-load current of 5A and the full-load current of 8A, where 8A is set as the high alarm value for chain operation.

[0165] Specifically, the method for transmitting standardized current signals through the DCS port is as follows: first, the standardized current data is packaged into a communication frame, and the data frame format is: <timestamp (4 bytes) + current value (4 bytes) + check code (2 bytes)>; secondly, the data is transmitted to the communication interface of the DCS system (Siemens CP443-1 Ethernet communication processor) through the RS485 bus (baud rate: 9600bps, data bit: 8 bits, stop bit: 1 bit, check bit: none); then, the DCS system performs a CRC check after receiving the data, and if the check passes, the data is written into the variable current signal IT-03031.

[0166] Specifically, the protection requirements in this embodiment include equipment protection: all on-site equipment must meet explosion-proof requirements, and the explosion-proof level is not lower than ExdIIBT4; area isolation: the dust in the PTA feeding area is effectively isolated from the inverter area by an isolation wall; cable protection: armored shielded cable RVVP-2×1.0mm² is used, with a protection level of IP65; grounding protection: all equipment is grounded at a common point, and the grounding resistance is ≤4Ω.

[0167] Specifically, the current monitoring requirements in this embodiment include: a normal operating value of about 6A; a high alarm value of 8A; and a monitoring period of 100ms.

[0168] Specifically, the daily maintenance requirements in this embodiment include: equipment inspection cycle is once per shift; signal calibration cycle is once a month; dust prevention inspection is once a week; system backup is once a week.

[0169] Specifically, this embodiment stipulates that the signal transmission quality indicators include signal acquisition accuracy: ≤0.5%; data transmission delay: ≤100ms; data packet loss rate: ≤0.1%; system availability: ≥99.9%.

[0170] In one embodiment, the central control dynamic monitoring unit 301 includes:

[0171] The abnormality measurement index module (not shown in the figure) is used to obtain the current fluctuation characteristics and calculate the current abnormality measurement index according to the standardized current signal transmitted by the DCS port using the improved dynamic time planning algorithm;

[0172] The dynamic warning threshold module (not shown in the figure) is used to obtain the abnormal operation warning parameters based on the current abnormality measurement index and establish a dynamic warning threshold system by using multi-dimensional threshold analysis;

[0173] The multi-level alarm trigger module (not shown in the figure) is used to obtain the real-time operating current trend evaluation results of the chain plate according to the dynamic early warning threshold system, and set a multi-level alarm trigger mechanism for the real-time operating current trend of the chain plate.

[0174] In one embodiment, according to the standardized current signal transmitted by the DCS port, the improved dynamic time planning algorithm is used to obtain the current fluctuation characteristics and calculate the current abnormality metric index, including:

[0175] S311, based on the standardized current signal, using a sliding window sampling algorithm to obtain current time series data, and establishing a standardized current sequence through data preprocessing;

[0176] Specifically, based on the standardized current signal transmitted by the signal conversion transmission unit 201, the process of processing using the sliding window sampling algorithm is as follows: first, the sampling period is set to 500ms and the sliding window length is set to 10 minutes (i.e., 1200 sampling points); secondly, the data in each sampling window is subjected to wavelet denoising, and the db4 wavelet, 3-layer decomposition, and soft threshold method are used to remove high-frequency noise; then, the denoised data is standardized, and in this embodiment, the maximum minimization (min-max) standardization method is adopted to map to the [0,1] interval.

[0177] Specifically, the steps to establish a normalized current series through data preprocessing are: first, check the data integrity and use linear interpolation to fill in the missing data; second, eliminate outliers and set the threshold to mean ± 3 times the standard deviation; then, smooth the data using a 5-point moving average method; finally, generate timestamp-current value pair format data with a timestamp accuracy of milliseconds.

[0178] S312. According to the normalized current sequence, using an improved dynamic time planning algorithm, obtain current trend characteristics, and generate a trend characteristic vector through trend matching.

[0179] Specifically, based on the normalized current sequence generated in step S311, the process of obtaining the current trend characteristics using the improved dynamic time planning algorithm is as follows: first, aligning the current current sequence with the preset standard operating mode (normal operating current 6A±0.5A); second, calculating the bending metric matrix, using the Euclidean distance as the distance metric; then, solving the optimal path through dynamic planning to obtain the matching relationship between sequences.

[0180] Specifically, the method of generating trend feature vectors through trend matching is as follows: first, extract the characteristic parameters of the matching path, including path length, cumulative distance, and number of matching points; second, calculate trend similarity indicators, including peak-to-valley ratio, volatility, and duration; finally, combine and generate an 8-dimensional trend feature vector, which includes time domain and frequency domain features.

[0181] S313. Based on the trend feature vector, an abnormality measurement algorithm is used to obtain a current trend abnormality index.

[0182] Specifically, based on the trend feature vector generated in step S312, the process of obtaining the current trend anomaly index using the anomaly measurement algorithm is as follows: first, a normal operation feature library is established, which includes 100 sets of historical normal operation data; second, the Mahalanobis distance between the current feature vector and the feature library is calculated; then, the anomaly index calculation formula is set: anomaly index = 1 / (1+exp(-k×(d-d0))), where k is the sensitivity coefficient (value 2.0), d is the Mahalanobis distance, and d0 is the reference distance (value 3.0).

[0183] In one embodiment, based on the current abnormality metric, multi-dimensional threshold analysis is used to obtain operation abnormality warning parameters, and a dynamic warning threshold system is established, including:

[0184] S321, according to the current abnormality measurement index, using the current trend statistical analysis, obtaining the trend abnormality pattern, and generating the trend warning parameter through pattern clustering;

[0185] Specifically, according to the current anomaly metric calculated in step S313, the process of obtaining the trend anomaly pattern using current trend statistical analysis is as follows: first, the distribution characteristics of the statistical anomaly index, including mean, standard deviation, skewness, and kurtosis; second, the DBSCAN clustering algorithm is used with a density threshold of ε=0.1 and a minimum number of points MinPts=5 to cluster the anomaly patterns; then, the characteristic parameters of each cluster are extracted to form an anomaly pattern feature set.

[0186] Specifically, the method of generating trend warning parameters through pattern clustering is as follows: first, calculate the occurrence frequency and degree of harm of each abnormal pattern; second, establish a risk assessment matrix with the horizontal axis representing the occurrence frequency (level 1-5) and the vertical axis representing the degree of harm (level 1-5); finally, generate a trend warning parameter set, including risk level, warning time and disposal recommendations.

[0187] S322. Based on the trend warning parameters, the trend adjustment factor is obtained by using an adaptive algorithm, and a trend warning update mechanism is established through parameter optimization;

[0188] Specifically, based on the trend warning parameters generated in step S321, the process of using the adaptive algorithm to obtain the trend adjustment factor is as follows: first, set the initial adjustment factor α0=1.0; second, calculate the adjustment amount Δα=η×(1-accuracy) according to the warning accuracy feedback, and the learning rate η=0.1; then, update the adjustment factor αt+1=αt+Δα, and constrain the range [0.5,2.0].

[0189] Specifically, the method of establishing a trend warning update mechanism through parameter optimization is as follows: first, set the warning parameter update cycle to 4 hours; second, based on the warning effect in the last 24 hours, calculate the optimization direction of each parameter; finally, use the gradient descent method to update the warning parameters, and dynamically adjust the learning rate.

[0190] S323. According to the trend warning update mechanism, multi-dimensional threshold analysis is used to obtain trend classification warning standards, and a dynamic trend warning system is generated through threshold mapping.

[0191] Specifically, according to the trend warning update mechanism established in step S322, the process of obtaining the trend classification warning standard by using multi-dimensional threshold analysis is as follows: first, set the warning indicators of three dimensions: current value (based on 6A), change rate (based on 0.1A / min), and duration (based on 5min); secondly, establish the warning level classification standard: normal (green), attention (yellow), warning (orange), alarm (red); then, the specific parameters of each level are:

[0192] Green: current ≤7A and change rate ≤0.1A / min;

[0193] Yellow: 7A<current≤7.5A or 0.1A / min<change rate≤0.2A / min;

[0194] Orange: 7.5A<current≤8A or 0.2A / min<change rate≤0.3A / min;

[0195] Red: Current > 8A or rate of change > 0.3A / min.

[0196] In one embodiment, according to the dynamic early warning threshold system, obtaining the evaluation result of the real-time operation current trend of the chain plate and setting a multi-level alarm trigger mechanism of the real-time operation current trend of the chain plate include:

[0197] S331. Based on the dynamic warning threshold system, the trend assessment algorithm is used to obtain the current trend risk level, and an operation trend assessment model is established through trend prediction;

[0198] Specifically, based on the dynamic warning threshold system generated in step S323, the process of obtaining the current trend risk level using the trend assessment algorithm is as follows: first, the distance between the current operating parameters and the warning thresholds at each level is calculated in real time; second, the time dimension weight is added, with the recent data having a larger weight; then, a comprehensive assessment is performed to obtain the risk level (level 1-4).

[0199] Specifically, the method of establishing an operation trend evaluation model through trend prediction is as follows: first, use the ARIMA model with parameters (p=2, d=1, q=1) to predict the current trend in the next 10 minutes; second, calculate the relationship between the predicted value and the warning threshold; finally, generate a trend evaluation report.

[0200] S332, according to the running trend assessment model, using the trend alarm rule, obtaining the trend alarm condition, and generating the trend alarm strategy through trend prediction;

[0201] Specifically, according to the operation trend evaluation model established in step S331, the process of obtaining the trend alarm conditions using the trend alarm rules is as follows: first, the alarm trigger conditions are set, including ① current value out-of-limit alarm: the real-time value exceeds the threshold; ② change rate alarm: the change exceeds the threshold within 2 minutes; ③ trend prediction alarm: it is predicted that the limit will be exceeded within 10 minutes; secondly, the alarm release conditions are set, which in this embodiment is that the parameters return to normal and last for 3 minutes.

[0202] S333. Based on the trend alarm strategy, a hierarchical alarm signal is generated through a multi-level alarm trigger mechanism, and the hierarchical alarm signal is pushed using the DCS port.

[0203] Specifically, based on the trend alarm strategy generated in step S332, the process of generating an alarm signal through a multi-level alarm trigger mechanism is as follows: first, according to the horizontal section chain plate running current signal (IT-03031), a multi-level alarm trigger condition is set, including ① current trend warning: triggered when it is detected that the current value continues to rise within 10 minutes and exceeds 6.5A; ② high current alarm: triggered when the current value exceeds the set high alarm value of 8A; ③ current fluctuation alarm: triggered when the current value fluctuates by more than ±1A within 5 minutes.

[0204] Specifically, the multi-level alarm trigger mechanism includes: first, the system monitors the current signal in real time; second, when any of the above multi-level alarm trigger conditions is met, the DCS screen will simultaneously display: the specific alarm type (trend warning / high alarm / fluctuation alarm), current current value, current operation trend curve, alarm trigger time, and alarm duration.

[0205] Specifically, the method of using the DCS port to push the alarm signal is: first, generate a standard alarm data packet, including the alarm time, alarm type identification, current value when triggered, current trend data for the previous 10 minutes, and alarm status (trigger / continuous / released); second, push the alarm information to the central control monitoring screen through the OPC interface of the DCS system; finally, display the changes in the operating current in real time on the trend curve, and mark the alarm points to facilitate operators to conduct trend analysis and judgment.

[0206] Specifically, the alarm release conditions include: ① Trend warning release: the current value returns to the range of 6A±0.5A and lasts for 5 minutes; ② High alarm release: the current value drops below 8A and lasts for 3 minutes; ③ Fluctuation alarm release: the current value fluctuation returns to the range of ±0.5A and lasts for 5 minutes.

[0207] In one embodiment, the operating frequency data is collected based on the PTA conveyor chain inverter, and then the operating abnormality evaluation index is obtained according to the operating frequency data to determine whether the frequency change sequence is abnormal.

[0208] Specifically, the specific method for obtaining the operation abnormality evaluation index is as follows: obtain the operation frequency data within the time window, the operation frequency data includes the average operation frequency, the frequency change rate, the operation frequency fluctuation value and the number of frequency alarms; obtain the reference operation frequency data from the preset database, the reference operation frequency data includes the frequency setting value, the change rate deviation, the fluctuation value deviation and the alarm number limit; process the operation frequency data and the corresponding reference operation frequency data to obtain the operation abnormality evaluation index.

[0209] The specific restriction expression of the operation abnormality evaluation index is as follows:

[0210] ;

[0211] In the formula, Indicates the average operating frequency of the PTA conveyor chain. Indicates the frequency change rate of the PTA conveyor chain. Indicates the operating frequency fluctuation value of the PTA conveyor chain. Indicates the frequency alarm times of PTA conveyor chain. Indicates the frequency setting value, represents the rate of change deviation, represents the fluctuation value deviation, Indicates the alarm frequency limit. Indicates the abnormal operation evaluation index of the PTA conveyor chain.

[0212] Specifically, the frequency threshold is obtained from the preset database and compared with the operation abnormality assessment index: if the operation abnormality assessment index is less than the frequency threshold obtained from the preset database, it means that the operation frequency data is normal; if the operation abnormality assessment index is not less than the frequency threshold, it means that the operation frequency data is abnormal, and the corresponding frequency change sequence is also abnormal, and the PTA chain plate operation is stopped for equipment operation and maintenance.

[0213] Specifically, the algorithm combines the operating frequency data and the reference operating frequency data for comprehensive analysis to obtain the operating abnormality evaluation index, where the reference operating frequency data are not 0. As the average operating frequency approaches the frequency setting value, the operating abnormality evaluation index becomes smaller, indicating that the possibility of abnormal operating frequency of the PTA conveyor chain is smaller; when the frequency change rate is smaller than the change rate deviation, the operating abnormality evaluation index is also smaller, indicating that the possibility of abnormal operating frequency change of the PTA conveyor chain is smaller, and the operating frequency fluctuation value is analyzed in the same way; the lower the frequency alarm number is below the alarm number limit, the fewer times the frequency abnormality occurs, and the lower the possibility of abnormal operating frequency; the analysis of the operating abnormality evaluation index is helpful to accurately evaluate the operating conditions of the PTA conveyor chain, thereby ensuring the reliability of the frequency change sequence.

[0214] The parameters involved in the processing of the operation anomaly assessment index in this algorithm are interrelated and do not exist independently. Among them, the average operating frequency is the cumulative performance of the frequency change rate. The higher the frequency change rate, the more violent the frequency fluctuation, and the easier it is for the average operating frequency to deviate from the frequency setting value. The average operating frequency reflects the stable state over a long period of time. The operating frequency fluctuation value describes the deviation amplitude of the frequency in a short period of time. If the operating frequency fluctuation value is high for a long time, it may cause the average operating frequency to gradually deviate from the frequency setting value, indicating that there is an overall instability problem in the operating frequency. The number of frequency alarms mainly depends on whether the operating frequency exceeds the set range, and the deviation of the average operating frequency from the normal range may directly lead to the alarm triggering. If the average frequency deviates too much from the set value (for example, too high or too low), it may cause more frequent frequency alarms. An increase in the frequency change rate may make it easier for the frequency to quickly exceed the safety range, thereby triggering an alarm. If the frequency change rate is too high, the number of frequency alarms may show exponential growth. When the operating frequency fluctuation value exceeds the fluctuation value deviation, the system will record an alarm. The larger the operating frequency fluctuation value, the more frequency alarms will occur.

[0215] It should be explained that the operating frequency data are all obtained through the DCS port, and the frequency alarm times are monitored and triggered in real time by the PLC (Programmable Logic Controller), and the alarm log is recorded and integrated.

[0216] Specifically, the specific expression of the sigmoid function is as follows:

[0217] ;

[0218] in, represents the input of the function, Represents the output of a function.

[0219] The reference operating frequency data is obtained from a preset database. In a specific embodiment, the reference operating frequency data is set by a preset staff according to the specific operating conditions of the PTA chain plate.

[0220] Specifically, the frequency threshold is obtained from a preset database. In a specific embodiment, all the operation frequency data of the PTA chain plate operation abnormality in the historical data are substituted into the specific restriction expression of the operation abnormality evaluation index to obtain the corresponding data set, and the data set is averaged to obtain the frequency threshold.

[0221] In one embodiment, the current fluctuation characteristics are obtained, and then the fluctuation anomaly evaluation index is obtained according to the current fluctuation data to determine whether the current fluctuation characteristics are stable.

[0222] Specifically, the specific method for obtaining the fluctuation anomaly assessment index is as follows: obtain current fluctuation data within a time window, the current fluctuation data including current mean, fluctuation amplitude, current change rate, harmonic content and current stabilization time; obtain reference current fluctuation data from a preset database, the reference current fluctuation data including rated current, fluctuation amplitude deviation, current change rate deviation and harmonic content limit; de-unitize the current stabilization time and obtain the fluctuation anomaly assessment index based on the current fluctuation data and the reference current fluctuation data.

[0223] The specific limiting expression of the volatility anomaly assessment index is as follows:

[0224] ;

[0225] In the formula, Indicates the average current when the PTA chain plate is running, Indicates the fluctuation range of PTA chain plate during operation. Indicates the current change rate when the PTA chain plate is running, Indicates the harmonic content of the PTA chain plate during operation. Indicates the current stability time when the PTA chain plate is running. Indicates the rated current, Indicates the fluctuation range deviation, Indicates the current change rate deviation, Indicates the harmonic content limit, Represents the volatility anomaly assessment index.

[0226] Specifically, a fluctuation threshold is obtained from a preset database and compared with a fluctuation anomaly assessment index: if the fluctuation anomaly assessment index is less than the fluctuation threshold obtained from the preset database, it indicates that the current fluctuation characteristics are stable; if the fluctuation anomaly assessment index is not less than the fluctuation threshold, it indicates that the current fluctuation characteristics are unstable, and the PTA chain plate operation is stopped for circuit detection.

[0227] Specifically, the algorithm combines the reference current fluctuation data with the current fluctuation data for comprehensive analysis to obtain the fluctuation anomaly assessment index, where the reference current fluctuation data are not 0. As the current mean is closer to the rated current, the fluctuation anomaly assessment index is smaller, indicating that the current fluctuation is more stable. When the fluctuation amplitude is smaller than the fluctuation amplitude deviation, the fluctuation anomaly assessment index is also smaller, indicating that the fluctuation amplitude is more stable, and the current change rate is analyzed in the same way. As the harmonic content is lower than the harmonic content limit, the corresponding fluctuation anomaly assessment index is also smaller, indicating that the current fluctuation is more stable. Similarly, when the current is stable for a longer time, the fluctuation anomaly assessment index is also smaller, indicating that the current fluctuation is more stable. By analyzing the fluctuation anomaly assessment index, not only can the abnormal current situation be monitored in real time, but also the stability of the current fluctuation characteristics is guaranteed.

[0228] The parameters involved in the processing of the fluctuation anomaly assessment index in this algorithm are interrelated and do not exist independently. Among them, the current mean value reflects the overall load level, and the fluctuation amplitude describes the degree of deviation of the current around the mean value. If the current change rate continues to increase, the current mean value may deviate with the irregular fluctuation of the load, and harmonic interference may cause measurement errors of the current mean value, affecting the effective energy consumption evaluation of the load. If the current mean value fluctuates slightly, the PTA chain plate is more likely to remain in a stable state, thereby increasing the current stability time. The current change rate reflects the dynamic characteristics of the fluctuation amplitude. When the fluctuation amplitude is large, the current change rate may increase. High harmonic content may cause rapid fluctuations in current, thereby increasing the fluctuation amplitude. The smaller the fluctuation amplitude, the longer the PTA chain plate remains in the stable range. The higher the current change rate, the worse the current stability of the PTA chain plate, and the shorter the current stability time. When the harmonic content is high, the current waveform may be more irregular and the stability deteriorates, resulting in a decrease in the current stability time.

[0229] It should be explained that the current mean is obtained by continuously sampling the current using a current sensor (such as a Hall current sensor, CT current transformer), the fluctuation amplitude is obtained by recording the maximum and minimum current values ​​within the time window, and represents the current fluctuation amplitude. The current change rate is calculated using Python (NumPy), the harmonic content is obtained through a harmonic analyzer, and the current stabilization time is obtained through a data recorder (such as Yokogawa DL850).

[0230] The reference current fluctuation data is obtained from a preset database. In a specific embodiment, the reference current fluctuation data is set by a preset staff according to the specific operation conditions of the PTA chain plate.

[0231] Specifically, the fluctuation threshold is obtained from a preset database. In a specific embodiment, all current fluctuation data of abnormal operation of PTA chain plates in historical data are substituted into a specific restriction expression of the fluctuation anomaly evaluation index to obtain a corresponding data set, and the data set is averaged to obtain the fluctuation threshold.

[0232] In one embodiment, current time series data is obtained, and then the method further includes obtaining a current time series evaluation index based on the current time series data and in combination with current fluctuation data to determine whether the current sequence is normalized.

[0233] Specifically, the specific method for obtaining the current timing evaluation index is as follows: obtain the current timing data within the time window and the current mean and current change rate in the current fluctuation data, the current timing data including the number of abnormal current values ​​and the current variance; obtain the maximum number of abnormal current values ​​and the rated current and current change rate deviation in the reference current fluctuation data; process the current timing data and the maximum number of abnormal current values ​​and the current mean and current change rate in the current fluctuation data and the rated current and current change rate deviation in the reference current fluctuation data to obtain the current timing evaluation index.

[0234] The specific limiting expression of the current timing evaluation index is as follows:

[0235] ;

[0236] In the formula, Indicates the average current when the PTA chain plate is running, Indicates the current change rate when the PTA chain plate is running, Indicates the number of abnormal current values ​​when the PTA chain plate is running. Indicates the current variance of the PTA chain plate operation, Indicates the rated current, Indicates the current change rate deviation, Indicates the maximum number of abnormal current values. Indicates the current timing evaluation index when the PTA chain plate is running.

[0237] Specifically, a timing threshold is obtained from a preset database and compared with a current timing evaluation index: if the current timing evaluation index is less than the timing threshold obtained from the preset database, it indicates that the current sequence is normalized; if the current timing evaluation index is not less than the timing threshold, the PTA chain plate operation is stopped for circuit detection.

[0238] Specifically, the algorithm combines the current timing data and the maximum number of abnormal current values, the current mean and current change rate in the current fluctuation data, and the rated current and current change rate deviation in the reference current fluctuation data for comprehensive analysis to obtain the current timing evaluation index. In the formula, as the current mean is closer to the rated current, the current timing evaluation index is smaller, indicating that the current timing is more standardized; when the current change rate is greater than the change rate deviation, the current timing evaluation index is larger, indicating that the current timing is less in compliance with the standard, and an increase in the number of abnormal current values ​​will also lead to an increase in the current timing evaluation index. Similarly, when the current variance is larger, the current timing evaluation index will also be larger, indicating that the current series is more unstable and less standardized; through the analysis of the current timing evaluation index, it is beneficial to ensure the stability of the current timing, while also ensuring the availability and standardization of the current series.

[0239] Specifically, assuming that the rated current is 100A, the current change rate deviation is 0.1, and the maximum number of abnormal current values ​​is 10, the data change table of the current timing evaluation index can be obtained, as shown in Table 1:

[0240] Table 1 Data change table of current timing evaluation index

[0241] It can be seen from Table 1 that as the current mean value approaches the rated current, the current change rate, the number of abnormal current values ​​and the current variance increase, and the current timing evaluation index also increases accordingly. For example, the current mean value increases from 80 in the first row to 100 in the third row, the current change rate increases from 0.0 in the first row to 0.4 in the fifth row, the number of abnormal current values ​​increases from 2 in the first row to 6 in the fifth row, and the current variance increases from 1.1 in the first row to 1.8 in the fifth row. The corresponding current timing evaluation index also increases from 0.330 in the first row to 0.994 in the fifth row, indicating that the larger the current timing evaluation index, the greater the possibility that the current timing is not standardized. Among them, when the current fluctuates frequently (that is, the number of abnormal current values ​​increases), the fluctuation amplitude will also increase, resulting in an increase in the current variance. For example, if the load often changes suddenly and the current fluctuates significantly, it will not only cause the current variance to increase, but also record more abnormal values, and the corresponding number of abnormal current values ​​will also increase. When the abnormal current values ​​are concentrated on a few fluctuations, the current variance may increase rapidly, while the number of abnormal current values ​​is stable. For example, if only the number of individual abnormal current values ​​deviates far, its impact on the current variance may be greater; by analyzing the current timing evaluation index, it is helpful to improve the reliability of the current timing and reduce the risk of errors in subsequent analysis.

[0242] It should be explained that the number of abnormal current values ​​is obtained through a sliding window, and the current variance is obtained by obtaining the current value within the time window from the current sensor and calculated through the variance formula.

[0243] The maximum value of the number of abnormal current values ​​is obtained from a preset database. In a specific embodiment, the maximum value of the number of abnormal current values ​​is set by a preset staff according to the specific operation conditions of the PTA chain plate.

[0244] Specifically, the timing threshold is obtained from a preset database. In a specific embodiment, all current timing data of abnormal operation of the PTA chain plate in the historical data and the current mean and current change rate in the current fluctuation data are substituted into the specific restriction expression of the current timing evaluation index to obtain the corresponding data set, and the data set is averaged to obtain the timing threshold.

[0245] Specifically, after judging whether the frequency change sequence is abnormal through the operation abnormality assessment index, if there is no abnormality in the specific operation of the PTA chain plate, continue to judge the current fluctuation and the current timing, and make assessments and judgments based on the fluctuation abnormality assessment index and the current timing assessment index respectively. There is a parameter correlation between the fluctuation abnormality assessment index and the current timing assessment index. The operation prompts of the PTA chain plate and the safe operation of the central control current monitoring are more accurately monitored and more precise safety warnings are given based on the current fluctuation and the current timing. This not only ensures the real-time monitoring of the PTA chain plate operation, but also realizes more comprehensive detection and warning of the PTA chain plate operation prompts and the central control current monitoring.

[0246] In order to facilitate understanding of the above technical solution of the present invention, a specific description is given below taking a certain X as an example:

[0247] In order to facilitate understanding of the above technical solution of the present invention, a specific description is given below taking a certain PTA production site as an example:

[0248] like Figure 2 As shown, at the PTA production site, firstly, an explosion-proof flashing alarm chain plate operation indicator light was installed on the front wall 2.5 meters above the feeding area. The indicator light was connected in series with the operation signal of the horizontal section chain plate inverter, so that the indicator light would light up when the chain plate was running and go out when it was stopped, thereby providing an intuitive operation status indication for the on-site operators; at the same time, we connected the operation current signal (IT-03031) of the chain plate horizontal section motor to the DCS port, and transmitted the signal to the central control monitoring system through the DCS terminal; finally, the device was integrated into the central control monitoring system to achieve more comprehensive and intelligent monitoring.

[0249] During the operation of the PTA chain, the on-site operation indication unit 101 is responsible for real-time monitoring of the operation status of the chain, and provides intuitive instructions to the operator through the flashing alarm operation indicator light. The unit constructs a chain operation status model through the composite signal of the chain inverter and the chain start-stop switch, and uses the chain operation status model to output chain operation status control instructions. These instructions generate chain operation status indications through explosion-proof flashing alarm indicators, providing operators with accurate operation status information; the signal conversion and transmission unit 201 is responsible for converting the operation current signal (IT-03031) of the chain horizontal section motor into a standardized current signal, and transmitting it to the central control monitoring system through the DCS port. The unit is connected to the central control monitoring system through the DCS port. The signal interface and data processing program of the frequency converter and DCS system realize signal conversion and transmission, ensuring the accuracy and real-time performance of the current signal; the central control dynamic monitoring unit 301 receives the transmitted standardized current signal and analyzes it using the software algorithm layer of the DCS monitoring system. The abnormal measurement index module obtains the current fluctuation characteristics through an improved dynamic time planning algorithm and calculates the current abnormality measurement index. Based on these indicators, the dynamic early warning threshold module uses multi-dimensional threshold analysis to obtain the operation abnormality early warning parameters and establishes a dynamic early warning threshold system. The multi-level alarm trigger module obtains the real-time operation current trend evaluation results of the chain plate according to the system, and sets up a multi-level alarm trigger mechanism for the real-time operation current trend of the chain plate.

[0250] Finally, the device realizes comprehensive monitoring of the operating status of the PTA chain plate. The on-site operation indication unit 101 provides intuitive operating status indication, the signal conversion and transmission unit 201 ensures the accurate transmission of the current signal, and the central control dynamic monitoring unit 301 realizes real-time monitoring and early warning of current trends through intelligent algorithms, which not only improves the accuracy of monitoring, but also enhances the intelligence level of the system, providing reliable technical guarantee for the safe and stable operation of the PTA production process.

[0251] In summary, with the help of the above technical scheme of the present invention, firstly, by constructing the linkage mechanism of the chain plate operation status model and the dynamic early warning threshold system, the present invention realizes the dual monitoring protection of the PTA chain plate operation status; the on-site operation indication unit 101 obtains the multi-dimensional feature vector of the chain plate operation in real time based on the operation frequency and the start-stop switch state of the chain plate inverter, and constructs the operation status model by dynamically adjusting the feature weight, so as to realize the accurate grasp of the chain plate operation status; the central control dynamic monitoring unit 301 obtains the current fluctuation characteristics based on the current signal of the horizontal section chain plate inverter through the improved dynamic time planning algorithm, and establishes a dynamic early warning system by multi-dimensional threshold analysis, so as to realize the real-time monitoring of the chain plate operation current trend; the two monitoring units form a mutually verified and complementary safety monitoring network through data bridging with the signal conversion transmission unit 201, which can not only detect the abnormal operation of the chain plate in time, but also accurately predict potential faults, greatly improving the safety and reliability of the PTA chain plate operation; secondly, in view of the problems of single chain plate operation monitoring means and inaccurate state control existing in the prior art, the present invention adopts a hierarchical intelligent algorithm system The system fundamentally improves the accuracy and reliability of chain plate operation status monitoring; at the operation feature extraction level, the present invention obtains the multi-dimensional feature vector of chain plate operation through frequency feature extraction algorithm and state conversion logic, combines the principal component analysis algorithm for feature fusion, and realizes comprehensive monitoring of chain plate operation status; at the situation assessment level, the present invention innovatively proposes a quantitative evaluation method based on the total score of operation status, and realizes accurate evaluation of operation status through weighted calculation of three dimensions: state stability, state continuity, and switching regularity; at the warning triggering level, an improved dynamic time planning algorithm is used to obtain current fluctuation characteristics, and a dynamic warning system is established through multi-dimensional threshold analysis. Combined with current trend statistical analysis and adaptive algorithm, a scientific trend warning update mechanism is established; this monitoring system based on multiple algorithms not only realizes the timely discovery of minor abnormalities in chain plate operation, but also ensures the accuracy and timeliness of warning through the establishment of a dynamic warning threshold system, thereby effectively avoiding problems such as abnormal chain plate load and unstable operation; finally, through the integrated application of explosion-proof flashing alarm indicator light and DCS current monitoring, the present invention realizes seamless connection between on-site operation and central control supervision at the engineering practice level. The explosion-proof flashing alarm indicator light is installed on the front wall of the feeding area, providing on-site operators with a clear reference to the chain plate operation status through intuitive status indication; the DCS system establishes a complete trend monitoring and early warning disposal mechanism by real-time collection and analysis of chain plate operation current data; the organic combination of the two monitoring methods not only meets the explosion-proof safety requirements in the PTA production process, but also realizes the whole process supervision from the site to the central control.This integrated application model has greatly improved the operating environment of the production site and increased the work efficiency of operators. At the same time, through standardized monitoring processes and disposal mechanisms, it has effectively reduced chain operation failures caused by human misjudgment, providing reliable technical support for the safe and stable operation of the PTA production process.

[0252] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A PTA chain plate operation prompt and central control current monitoring safety operation device, characterized in that: The PTA chain plate operation prompt and central control current monitoring safety operation device includes: The on-site operation indication unit is used to construct a chain plate operation status model based on the composite signal of the chain plate inverter and the start-stop switch, and use the chain plate operation status model to output the chain plate operation status control instruction, and generate the chain plate operation status indication through the explosion-proof flashing alarm indicator light; The signal conversion and transmission unit is used to obtain the chain plate running current data according to the current signal of the horizontal section chain plate inverter, and transmit the standardized current signal through the DCS port; The central control dynamic monitoring unit is used to calculate the current anomaly measurement index based on the transmitted standardized current signal, and use the dynamic early warning threshold system to establish a multi-level alarm trigger mechanism for the real-time operating current trend of the chain plate.

2. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 1, characterized in that: The on-site operation indication unit comprises: The chain plate operation feature module is used to obtain the chain plate operation multi-dimensional feature vector according to the operating frequency and start-stop switch state of the PTA conveyor chain plate inverter by using the frequency feature extraction algorithm and state conversion logic; The operation situation model module is used to build a chain plate operation situation model based on the multi-dimensional feature vector of the chain plate operation, and generate a risk warning level by dynamically adjusting the feature weight; The display signal mapping module is used to generate the indicator light display signal according to the risk warning level by using the risk-indicator light parameter mapping rules and the multi-state mapping table.

3. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 2, characterized in that: The method of obtaining the multidimensional feature vector of the chain plate operation according to the operating frequency and the start / stop switch state of the PTA conveyor chain plate inverter and using the frequency feature extraction algorithm and the multidimensional feature vector extraction algorithm comprises: Based on the PTA conveyor chain inverter, the operating frequency data is collected to obtain the frequency change sequence, and the frequency feature mapping table is generated through the frequency feature extraction algorithm; According to the start-stop switch state signal, the switch state feature vector is obtained by using the signal state encoding method, and the state change sequence is generated through the state transition logic; Based on the frequency feature mapping table and state change sequence, the principal component analysis algorithm is used to obtain the composite feature information, and the multi-dimensional feature vector of the chain plate operation is generated through the feature fusion technology.

4. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 2, characterized in that: The chain plate operation situation model is constructed based on the multi-dimensional feature vector of the chain plate operation, and the risk warning level is generated by dynamically adjusting the feature weights, including: According to the multi-dimensional feature vector of the chain plate operation, a chain plate operation situation mapping model is established using a machine learning algorithm, and adaptive evaluation parameters are generated by dynamically adjusting the feature weights; Based on the chain board operation situation mapping model, the operation situation score is obtained by using a multi-dimensional scoring algorithm, and the situation risk index is generated through risk association analysis; According to the situation risk index, the risks are classified into different levels and the risk warning level is obtained.

5. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 4, characterized in that: The expression of the running status score is: ； Where, Score is the total score of the chain plate operation status; W i is the weight coefficient of each dimension, W1 is the weight coefficient of state stability; W2 is the weight coefficient of state continuity; W3 is the weight coefficient of switching regularity; S i is the score of each dimension, S1 is the frequency stability score; S2 is the state continuity score; S3 is the switching regularity score; The expression of the situation risk index is: ； In the formula, r i is the correlation between the ith risk factor and the reference sequence; x0(k) is the reference sequence; xi(k) is the comparison sequence; ρ is the resolution coefficient; n is the sequence length; k is the point in the sequence; min is the minimum difference of all points in all sequences; max is the maximum difference of all points in all sequences.

6. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 2, characterized in that: The generating of the indicator light display signal according to the risk warning level and using the risk-indicator light parameter mapping rule and the multi-state mapping table comprises: Based on the risk warning level, the indicator light display mode parameters are obtained using the risk-indicator light parameter mapping rules; Generate an indicator light control strategy based on the indicator light display mode parameters and in combination with a multi-state mapping table; Based on the indicator light control strategy, the indicator light display signal is generated by driving the signal converter and transmitted to the explosion-proof flashing alarm indicator light.

7. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 1, characterized in that: The central control dynamic monitoring unit comprises: The abnormality measurement index module is used to obtain the current fluctuation characteristics and calculate the current abnormality measurement index according to the standardized current signal transmitted by the DCS port using the improved dynamic time planning algorithm; Dynamic warning threshold module, which is used to obtain abnormal operation warning parameters based on current abnormality measurement indicators and establish a dynamic warning threshold system by using multi-dimensional threshold analysis; The multi-level alarm trigger module is used to obtain the evaluation results of the real-time operation current trend of the chain plate according to the dynamic early warning threshold system, and set a multi-level alarm trigger mechanism for the real-time operation current trend of the chain plate.

8. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 7, characterized in that: The method of obtaining the current fluctuation characteristics and calculating the current abnormality metric index based on the standardized current signal transmitted by the DCS port by using the improved dynamic time planning algorithm includes: Based on the standardized current signal, the sliding window sampling algorithm is used to obtain the current time series data, and the standardized current sequence is established through data preprocessing; According to the normalized current sequence, the improved dynamic time planning algorithm is used to obtain the current trend characteristics, and the trend characteristic vector is generated through trend matching; Based on the trend feature vector, the current trend anomaly index is obtained using the anomaly measurement algorithm.

9. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 7, characterized in that: The method of obtaining abnormal operation warning parameters based on current abnormality measurement indicators and establishing a dynamic warning threshold system by using multi-dimensional threshold analysis includes: According to the current anomaly measurement indicators, the current trend statistical analysis is used to obtain the trend anomaly pattern, and the trend warning parameters are generated through pattern clustering; Based on the trend warning parameters, the adaptive algorithm is used to obtain the trend adjustment factor, and the trend warning update mechanism is established through parameter optimization; According to the trend warning update mechanism, multi-dimensional threshold analysis is used to obtain the trend classification warning standard, and through threshold mapping, a dynamic trend warning system is generated.

10. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 7, characterized in that: The method of obtaining the evaluation result of the real-time operation current trend of the chain plate according to the dynamic early warning threshold system and setting a multi-level alarm trigger mechanism of the real-time operation current trend of the chain plate includes: Based on the dynamic warning threshold system, the trend assessment algorithm is used to obtain the current trend risk level, and through trend prediction, an operation trend assessment model is established; According to the running trend assessment model, the trend alarm rules are applied to obtain the trend alarm conditions, and the trend alarm strategy is generated through trend prediction; Based on the trend alarm strategy, a hierarchical alarm signal is generated through a multi-level alarm trigger mechanism, and the hierarchical alarm signal is pushed using the DCS port.

11. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 3, characterized in that: The method includes collecting operating frequency data based on the PTA conveyor chain plate inverter, and then obtaining an operating abnormality evaluation index based on the operating frequency data to determine whether the frequency change sequence is abnormal; The specific method for obtaining the operation abnormality evaluation index is as follows: Acquire operating frequency data, wherein the operating frequency data includes average operating frequency, frequency change rate, operating frequency fluctuation value and frequency alarm number; Acquire reference operating frequency data from a preset database, wherein the reference operating frequency data includes a frequency setting value, a change rate deviation, a fluctuation value deviation, and an alarm number limit; The operation frequency data and the corresponding reference operation frequency data are processed to obtain the operation abnormality evaluation index.

12. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 7, characterized in that: The obtaining of the current fluctuation characteristics further includes obtaining a fluctuation anomaly evaluation index according to the current fluctuation data to determine whether the current fluctuation characteristics are stable; The specific method for obtaining the fluctuation anomaly assessment index is as follows: Acquire current fluctuation data, wherein the current fluctuation data includes current mean value, fluctuation amplitude, current change rate, harmonic content and current stable time; Acquire reference current fluctuation data from a preset database, wherein the reference current fluctuation data includes rated current, fluctuation amplitude deviation, current change rate deviation and harmonic content limit; The current stabilization time is denormalized and a fluctuation anomaly evaluation index is obtained based on the current fluctuation data and the reference current fluctuation data.

13. A PTA chain plate operation prompt and central control current monitoring safety operation device according to claim 8, characterized in that: The current time series data is obtained, and then the current time series evaluation index is obtained according to the current time series data and combined with the current fluctuation data to determine whether the current sequence is normalized; The specific method for obtaining the current timing evaluation index is as follows: Acquire the current time series data and the current mean value and the current change rate in the current fluctuation data, wherein the current time series data includes the number of abnormal current values ​​and the current variance; Obtaining the maximum value of the abnormal current value and the rated current and current change rate deviation in the reference current fluctuation data; The current timing data and the maximum value of the abnormal current value, the current mean value and the current change rate in the current fluctuation data, and the rated current and the current change rate deviation in the reference current fluctuation data are processed to obtain the current timing evaluation index.

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