Failure early warning method and device for executing mechanism of power plant

Abstract

The invention provides a power plant execution mechanism failure early warning method and device, and the method comprises the steps: screening a corresponding relation between an instruction signal and a feedback signal from an I / O list based on a KKS coding rule, and generating a signal matching table; historical operation data of instruction signals and feedback signals of all execution mechanisms in a preset time window are obtained in batches through an SIS system data interface, the deviation absolute value of the instruction signals and the feedback signals is calculated, and a deviation data set is generated; integral operation and differential calculation are carried out on the deviation data set, and performance evaluation indexes reflecting the accumulated deviation and sudden change characteristics of the executing mechanism are generated; the performance evaluation indexes are visually displayed in a histogram form, and the corresponding relation between the execution mechanism numbers and the performance evaluation indexes is constructed; and screening the performance evaluation indexes according to a preset deviation threshold value and an integral threshold value, performing highlight marking on the execution mechanisms exceeding the threshold values in a visual interface, and outputting KKS codes of the execution mechanisms.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of state monitoring of power plant actuators, and particularly relates to a power plant actuator failure early warning method and device. BACKGROUND

[0002] Data analysis shows that actuator defects have become an important bottleneck restricting equipment reliability, accounting for 22% of the total number of defects in the thermal control profession, and the defect proportion has shown a continuous upward trend. According to the 1:29:300 accident model of the Heinrich rule, there is a larger potential hidden danger behind the apparent defects. In particular, the defects of actuators in important systems have a significant safety risk multiplier effect, which may cause unit non-stop, equipment damage and personal injury, and seriously threaten production safety.

[0003] Actuators have strong concealment in the early stage of degradation, and usually only the regulation history of the actuator is analyzed after failure occurs, and the judgment of the degradation degree mainly depends on the on-site experience of thermal workers. At the same time, the amount of data generated during the operation of the plant actuator is extremely large, and it is difficult to achieve comprehensive and timely coverage by relying on manual screening. If only a few important devices are monitored, it is easy to form a blind area of equipment management and leave safety hazards. SUMMARY

[0004] The present application aims to at least partially solve one of the problems in the related art.

[0005] To this end, a first object of the present application is to propose a power plant actuator failure early warning method.

[0006] A second object of the present application is to propose a power plant actuator failure early warning device.

[0007] A third object of the present application is to propose a computer device.

[0008] A fourth object of the present application is to propose a non-transitory computer readable storage medium.

[0009] To achieve the above objects, a power plant actuator failure early warning method according to a first aspect of the present application comprises: S1, based on the KKS coding rule, screening the corresponding relationship between the command signal and the feedback signal from the I / O list to generate a signal matching table containing the Chinese name of the actuator, the command signal identifier and the feedback signal identifier; S2, batch acquiring the historical operation data of the command signal and the feedback signal of each actuator within a preset time window through the SIS system data interface, calculating the absolute value of the deviation of the command signal and the feedback signal and generating a deviation data set; S3 performs integral and difference calculations on the deviation dataset to generate performance evaluation indicators that reflect the cumulative deviation and abrupt change characteristics of the actuator. S4 visualizes the performance evaluation indicators in the form of bar charts and establishes the correspondence between the actuator number and the performance evaluation indicators; S5 filters performance evaluation indicators based on preset deviation and integral thresholds, and highlights actuators that exceed the thresholds in the visualization interface and outputs their KKS codes.

[0010] In one embodiment of the present invention, S2 includes: S11, When filtering instruction signals based on KKS encoding rules, the signal type is limited to analog output and the KKS code ends with XQ01; S12 generates the corresponding feedback signal identifier by replacing AA with CG in the instruction signal KKS code.

[0011] In one embodiment of the present invention, S2 includes: S21 calls the dynamic sampling formula through the PI data interface. The parameters include the signal KKS code, start time, end time, sampling interval, sampling method, and server address. S22 directly calculates the absolute value of the difference between the command signal and the feedback signal as a numerical type and stores it in the worksheet, avoiding calculation errors caused by residual formulas.

[0012] In one embodiment of the present invention, S3 further includes: S31 uses integral calculation to quantify the cumulative deviation of the actuator, and the integral value is judged to be abnormal when it exceeds 25000; S32 identifies sudden deviations through differential calculations, with the maximum value exceeding... The initial warning was triggered.

[0013] In one embodiment of the present invention, S4 includes: S41, a bar chart is drawn with the combination of the Chinese name of the actuator and the KKS code as the X-axis and the integral value of the deviation as the Y-axis. The integral value of the normal actuator is concentrated below 10000. S42 uses three-dimensional coordinate mapping technology to synchronously associate the actuator number, maximum deviation value, and integral value with the chart data points, thereby achieving multi-dimensional localization of abnormal features.

[0014] In one embodiment of the present invention, S5 includes: S51, for integral values ​​exceeding 25000 and maximum deviations exceeding The implementing agencies are highlighted with a red background; S52 achieves early warning sensitivity control by dynamically adjusting threshold parameters. Lowering the threshold increases the false alarm rate, while raising the threshold reduces the false alarm rate.

[0015] In one embodiment of the present invention, the method further includes: S6 calls a large language model to generate executable VBA code. It describes the data processing logic through natural language instructions and automatically converts it into code modules containing dynamic sampling function calls, loop algorithm construction, and exception marking rules. The code structure is optimized to improve execution efficiency.

[0016] To achieve the above objectives, a second aspect of the present invention provides a power plant actuator failure early warning device, comprising: The signal matching table generation module filters the correspondence between instruction signals and feedback signals from the I / O list based on the KKS encoding rules, and generates a signal matching table containing the Chinese name of the actuator, the instruction signal identifier, and the feedback signal identifier. The historical data acquisition and deviation calculation module acquires historical operating data of command signals and feedback signals of each actuator within a preset time window in batches through the SIS system data interface, calculates the absolute value of the deviation between command signals and feedback signals, and generates a deviation dataset. The performance evaluation index generation module performs integral and differential calculations on the deviation dataset to generate performance evaluation indices that reflect the cumulative deviation and abrupt change characteristics of the actuator. The visualization and correspondence construction module visualizes the performance evaluation indicators in the form of bar charts and constructs the correspondence between the execution agency number and the performance evaluation indicators. The threshold filtering and early warning marking module filters performance evaluation indicators based on preset deviation thresholds and integral thresholds. For actuators that exceed the thresholds, the module highlights them in the visualization interface and outputs their KKS codes.

[0017] A method and apparatus for early warning of actuator failure in power plants according to an embodiment of the present invention has the following significant advantages: This invention enables real-time, panoramic monitoring of the operating status of actuators. Through a unique sensing and data analysis mechanism, it can cover all critical actuators without blind spots, breaking through the blind spots of traditional spot checks and periodic maintenance, and providing a comprehensive and continuous data foundation for equipment health management.

[0018] This significantly improves the reliability and safety of equipment operation. By providing precise early warnings before failures occur, this device offers a valuable window for maintenance intervention, effectively preventing unplanned downtime and even safety accidents caused by sudden actuator malfunctions, thus ensuring continuous and stable production.

[0019] This invention has driven a fundamental shift in equipment management from a "preventative" to a "predictive" approach. The core value of this invention lies in transforming passive management into proactive management, and from periodic maintenance to on-demand maintenance. It no longer simply prevents failures, but accurately predicts their occurrence—a significant paradigm shift in the field of industrial operations and maintenance.

[0020] This effectively reduces operation and maintenance labor and management costs. The automated and intelligent early warning mechanism reduces reliance on regular manual inspections, freeing up specialized manpower. Simultaneously, maintenance strategies based on accurate predictions avoid over-maintenance and waste of redundant spare parts, achieving cost reduction and efficiency improvement.

[0021] This invention comprehensively improves equipment management performance. In summary, the overall benefits of this invention are reflected in increased overall equipment efficiency, extended mean time between failures (MTBF), and optimized lifecycle management costs, creating direct economic benefits and competitive advantages for enterprises.

[0022] To achieve the above objectives, a third aspect of this application provides a computer device, including a processor and a memory; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, for implementing a power plant actuator failure early warning method as described in the first aspect embodiment.

[0023] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a power plant actuator failure early warning method as described in the first aspect embodiment.

[0024] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a power plant actuator failure early warning method according to an embodiment of the present invention; Figure 2 This is an architecture diagram of a power plant actuator failure early warning method according to an embodiment of the present invention; Figure 3 This is a schematic diagram showing the operating status of all actuators in the plant according to an embodiment of the present invention; Figure 4 This is a schematic diagram showing the abnormal execution mechanism according to an embodiment of the present invention; Figure 5This is a structural diagram of a power plant actuator failure early warning device according to an embodiment of the present invention; Figure 6 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] The following description, with reference to the accompanying drawings, describes a method and apparatus for early warning of failure of actuators in power plants according to an embodiment of the present invention.

[0029] Figure 1 This is a flowchart of a power plant actuator failure early warning method according to an embodiment of the present invention, such as... Figure 1 As shown, it includes: S1, based on the KKS encoding rules, filters the correspondence between instruction signals and feedback signals from the I / O list, and generates a signal matching table containing the Chinese name of the actuator, the instruction signal identifier, and the feedback signal identifier; S2, through the SIS system data interface, batch acquires the historical operating data of the command signals and feedback signals of each actuator within a preset time window, calculates the absolute value of the deviation between the command signals and feedback signals, and generates a deviation dataset; S3 performs integral and difference calculations on the deviation dataset to generate performance evaluation indicators that reflect the cumulative deviation and abrupt change characteristics of the actuator. S4 visualizes the performance evaluation indicators in the form of bar charts and establishes the correspondence between the actuator number and the performance evaluation indicators; S5 filters performance evaluation indicators based on preset deviation and integral thresholds, and highlights actuators that exceed the thresholds in the visualization interface and outputs their KKS codes.

[0030] An embodiment of the present invention provides a method for early warning of failure of power plant actuators, which can realize early warning of performance degradation of power plant actuators, improve equipment operation reliability and safety, and reduce the risk of unplanned downtime and operation and maintenance costs.

[0031] The following describes in detail, with reference to the accompanying drawings, a method for early warning of failure of power plant actuators according to an embodiment of the present invention.

[0032] This invention utilizes historical data from the SIS system to achieve batch data processing and visualization through programming. For example... Figure 2 As shown, the steps of the present invention are as follows: The unit I / O register is reviewed, and all paired commands and feedback signals are selected to form a command feedback list. Historical data is retrieved via the SIS data interface and processed in batches through programming. A loop algorithm is constructed: it reads a set of command feedback signals at a time, extracting sampled data within a preset time range; abnormal peaks are identified: integral calculations (reflecting cumulative deviations) and differential calculations (capturing abrupt changes) are performed on the data at each timestamp. Status is analyzed and evaluated: the results of the command feedback deviation analysis of the actuators are statistically analyzed and charted, with out-of-limit data highlighted. By traversing all signal groups and summarizing the analysis results, the system can quantitatively assess the degree of degradation of each actuator, providing data support for preventative measures.

[0033] S10. Organize the I / O list and generate an instruction-feedback mapping table. Based on the KKS encoding rules, filter all instruction signals from the I / O list. These signals are characterized by being analog outputs (4-20mA) and ending with "XQ01" in their KKS code. After obtaining all instruction signals, match them one by one with their corresponding feedback signals. Feedback signals are characterized by analog inputs (4-20mA), and their KKS code is obtained by replacing "AA" with "CG" in the instruction signal's KKS code (e.g., if the instruction signal's KKS code is 10HHL61AA104XQ01, the feedback signal would be 10HHL61CG104XQ01). Record the Chinese name of the actuator, the instruction signal, and the feedback signal in columns A, B, and C of Worksheet 1, respectively, as the basic data for subsequent processing.

[0034] S20. Obtain historical operating data of the actuator through the SIS system data interface. Set the sampling duration to 24 hours and the sampling interval to 10 seconds, collecting a total of 8641 data points for each signal. Establish a dynamic calculation area in Worksheet 2, where column A stores the sampled data of the command signal, column B stores the sampled data of the feedback signal, and column C records the absolute value of the difference between the two. Call the PI data interface and construct a dynamic sampling formula, filling the collected data into columns A and B, and simultaneously calculating the difference and filling it into column C. Further calculate the maximum and cumulative values ​​of the difference, recording them in columns D and E of Worksheet 1 respectively. After completion, clear the data in the dynamic calculation area to avoid calculation errors caused by formula residue.

[0035] Dynamic sampling formula: wsCalc.Range("C1:C8641").FormulaArray = _"=PISampDat("""&tag1&""", Sheet3!$B$1, Sheet3!$B$3, ""10s"", 0, ""192.192.192.192"")" The parameters include, in order: signal KKS code, start time, end time, sampling interval, sampling method, and server address.

[0036] S30. Construct a loop algorithm to iterate through all signal pairs in the instruction-feedback list, sequentially acquire sampled data, and fill the calculation results back into the corresponding positions in Worksheet 1. The number of loops depends on the number of signal pairs in the list; if data anomalies occur, the process will automatically interrupt. After the algorithm completes, the operating status data table of all actuators in the plant will be obtained.

[0037] For each timestamp, data undergoes integral calculation (reflecting cumulative deviation) and differential calculation (capturing abrupt changes). Specifically, integral calculation quantifies the cumulative deviation of the actuator; an integral value exceeding 25,000 is considered abnormal. Differential calculation identifies sudden deviations; a maximum value exceeding [a certain threshold] is considered abnormal. The initial warning was triggered.

[0038] S40. Draw statistical charts to visually display the actuator's operating status. Use the actuator number (its Chinese name and KKS code can be obtained from Worksheet 1) as the X-axis and the deviation value as the Y-axis (e.g., ...). Figure 3 By using three-dimensional coordinate mapping technology, the actuator number, maximum deviation, and integral value are synchronously linked to the chart data points, enabling multi-dimensional localization of abnormal characteristics. The chart shows that the operating data of most actuators are concentrated below 10,000; if the actuator's adjustment performance slightly declines, and there is a 3% difference between the command and feedback, its operating data will significantly exceed 25,000, making it easier to identify. This chart can comprehensively assess the operational health of all actuators in the plant.

[0039] S50: Highlight abnormal data to assist thermal engineers in quickly locating them. Actuators with a maximum command-feedback deviation greater than 50 and an integral value exceeding 25000 are highlighted in worksheet 1 with a red background (e.g., ...). Figure 4 This allows thermal engineers to quickly obtain the Chinese name and KKS code and proceed to the site for handling. The threshold can be adjusted according to actual operating conditions: lowering the threshold can improve the sensitivity of the warning, but may increase false alarms; raising the threshold can improve the accuracy of the alarm, but may miss some anomalies.

[0040] The system calls a large language model to generate executable VBA code. It describes the data processing logic through natural language instructions and automatically converts the code into code modules that include dynamic sampling function calls, loop algorithm construction, and exception marking rules. The code structure is optimized to improve execution efficiency.

[0041] Algorithm code and comments for this invention: Sub ValveCompare() Application.ScreenUpdating = False Application.Calculation = xlCalculationManual Dim wsData As Worksheet, wsCalc As Worksheet Dim i As Long, tag1 As String, tag2 As String Dim maxVal As Variant, sumVal As Variant Set wsData = ThisWorkbook.Sheets("Sheet2") Set wsCalc = ThisWorkbook.Sheets("Sheet1") Clear old results wsData.Range("B1:B300").ClearContents 'Construct a loop algorithm; the value depends on the number of executors.' For i = 1 To 300 Get the KKS codes of the current tag, command signal, and feedback signal. tag1 = wsCalc.Cells(i, 1).Value If tag1 = "" Then Exit For tag2 = wsCalc.Cells(i, 2).Value If tag2 = "" Then Exit For Clear the computing area wsCalc.Range("C:C, D:D, E:E").Clear Dynamically generate PI formula wsCalc.Range("C1:C8641").FormulaArray = _ "=PISampDat("""&tag1&""", Sheet3!$B$1, Sheet3!$B$3, ""10s"", 0, ""172.20.81.201"")" wsCalc.Range("D1:D8641").FormulaArray = _ "=PISampDat("""&tag2&""", Sheet3!$B$1, Sheet3!$B$3, ""10s"", 0, ""172.20.81.201"")" Force calculation while waiting for data to load. Application.Calculate DoEvents 'Calculate the difference (use the values ​​directly to avoid formula residue)' With wsCalc.Range("E1:E8641") .Formula = "=ABS(C1 - D1)" .Value = .Value End With Get maximum value / sum On Error Resume Next ' Skip null value errors maxVal = Application.WorksheetFunction.Max(wsCalc.Range("E1:E8641")) sumVal = Application.WorksheetFunction.Sum(wsCalc.Range("E1:E8641")) On Error GoTo 0 Write the result wsData.Cells(i, 2).Value = IIf(IsEmpty(maxVal), "N / A", maxVal) wsData.Cells(i, 3).Value = IIf(IsEmpty(sumVal), "N / A", sumVal) Next i Clear the computing area wsCalc.Range("C:C, D:D, E:E").Clear Application.Calculation = xlCalculationAutomatic Application.ScreenUpdating = True MsgBox "Processing complete!" End Sub To achieve the above embodiments, such as Figure 5 As shown, this embodiment also provides a power plant actuator failure early warning device 10, which includes a signal matching table generation module 100, a historical data acquisition and deviation calculation module 200, a performance evaluation index generation module 300, a visualization display and correspondence construction module 400, and a threshold screening and early warning marking module 500.

[0042] The signal matching table generation module 100 is used to filter the correspondence between instruction signals and feedback signals from the I / O list based on the KKS encoding rules, and generate a signal matching table containing the Chinese name of the actuator, the instruction signal identifier, and the feedback signal identifier. The historical data acquisition and deviation calculation module 200 is used to acquire historical operating data of command signals and feedback signals of each actuator within a preset time window in batches through the SIS system data interface, calculate the absolute value of the deviation between the command signals and feedback signals, and generate a deviation dataset. The performance evaluation index generation module 300 is used to perform integral and differential calculations on the deviation dataset to generate performance evaluation indices that reflect the cumulative deviation and abrupt change characteristics of the actuator. The visualization and correspondence construction module 400 is used to visualize the performance evaluation indicators in the form of bar charts and to construct the correspondence between the execution agency number and the performance evaluation indicators. The threshold filtering and early warning marking module 500 is used to filter performance evaluation indicators based on preset deviation thresholds and integral thresholds, and to highlight actuators that exceed the thresholds in the visualization interface and output their KKS codes.

[0043] Furthermore, the signal matching table generation module 100 described above is also used for: When filtering instruction signals based on KKS encoding rules, the signal type is limited to analog output and the KKS code ends with XQ01. The corresponding feedback signal identifier is generated by replacing AA with CG in the KKS code of the instruction signal.

[0044] Furthermore, the aforementioned historical data acquisition and deviation calculation module 200 is also used for: The dynamic sampling formula is called through the PI data interface. The parameters include the signal KKS code, start time, end time, sampling interval, sampling method, and server address. The absolute value of the difference between the command signal and the feedback signal is directly calculated as a numerical type and stored in the worksheet to avoid calculation errors caused by residual formulas.

[0045] Furthermore, the aforementioned performance evaluation index generation module 300 is also used for: The cumulative deviation of the actuator is quantified by integral calculation, and it is judged to be abnormal when the integral value exceeds 25,000; Sudden deviations are identified through differential calculations, with the maximum value exceeding... The initial warning was triggered.

[0046] Furthermore, the aforementioned visualization and correspondence construction module 400 is also used for: A bar chart was drawn with the combination of the Chinese name of the actuator and the KKS code as the X-axis and the integral value of the deviation as the Y-axis. The integral value of the normal actuator is concentrated below 10,000. By using three-dimensional coordinate mapping technology, the actuator number, maximum deviation value, and integral value are synchronously associated with the chart data points, enabling multi-dimensional localization of abnormal features.

[0047] Furthermore, the threshold filtering and early warning marking module 500 mentioned above is also used for: For integral values ​​exceeding 25000 and maximum deviations exceeding The implementing agencies are highlighted with a red background; Early warning sensitivity is controlled by dynamically adjusting threshold parameters. Lowering the threshold increases the false alarm rate, while raising the threshold reduces the false alarm rate.

[0048] Furthermore, device 10 also includes: The system calls a large language model to generate executable VBA code. It describes the data processing logic through natural language instructions and automatically converts the code into code modules that include dynamic sampling function calls, loop algorithm construction, and exception marking rules. The code structure is optimized to improve execution efficiency.

[0049] An embodiment of the present invention provides a power plant actuator failure early warning device, which can realize early warning of the performance degradation of power plant actuators, improve the reliability and safety of equipment operation, and reduce the risk of unplanned downtime and operation and maintenance costs.

[0050] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 6As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the method described above.

[0051] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0052] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0053] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method of power plant actuator failure early warning, characterized by, Comprise: S1, based on KKS coding rules from I / O list screening instruction signal and feedback signal corresponding relationship, generating contains the signal matching table of the execution mechanism Chinese name, instruction signal identification and feedback signal identification; S2, through the SIS system data interface batch acquisition within a predetermined time window each execution mechanism instruction signal and feedback signal history operation data, calculate the absolute value of the deviation of instruction signal and feedback signal and generate deviation data set; S3, the integral operation and difference calculation of deviation data set, generate the performance evaluation index reflecting the cumulative deviation and mutation characteristics of the execution mechanism; S4, the performance evaluation index is visualized in the form of column chart, the corresponding relationship between the execution mechanism number and the performance evaluation index is constructed; S5, according to the preset deviation threshold and integral threshold, the performance evaluation index is screened, the execution mechanism exceeding the threshold is highlighted in the visualization interface and its KKS code is output.

2. The method of claim 1, wherein, The S1 comprises: S11, when screening instruction signal based on KKS coding rules, limit signal type to analog output and KKS code to end with XQ01; S12, by replacing AA in instruction signal KKS code with CG to generate corresponding feedback signal identification.

3. The method of claim 1, wherein, The S2 comprises: S21, by calling dynamic sampling formula through PI data interface, parameters include signal KKS code, start time, end time, sampling interval, sampling mode and server address; S22, the absolute value of the difference between instruction signal and feedback signal is directly calculated as numerical type and stored in worksheet, avoiding calculation error caused by formula residue.

4. The method of claim 1, wherein, The S3 further comprises: S31, the cumulative deviation of the execution mechanism is quantified by integral operation, and the integral value exceeding 25000 is determined as abnormal; S32, identify the sudden deviation by difference calculation, the maximum value exceeds trigger preliminary warning.

5. The method of claim 1, wherein, The S4 comprises: S41, draw column chart with execution mechanism Chinese name and KKS code combination as X axis and deviation integral value as Y axis, the integral value of normal execution mechanism is concentrated below 10000; S42, through three-dimensional coordinate mapping technology, execution mechanism number, maximum deviation value and integral value are synchronously associated to chart data point, realizing multidimensional positioning of abnormal characteristics.

6. The method of claim 1, wherein, The S5 comprises: S51, if the integral value exceeds 25000 and the maximum deviation exceeds the actuator, a red background highlight is used. S52, by dynamically adjusting threshold parameters, realize early warning sensitivity control, increase false positive rate when reduce threshold, reduce false negative rate when improve threshold.

7. The method of claim 1, wherein, Further comprise: S6, calling large language model to generate executable VBA code, through natural language instruction description data processing logic, the model is automatically converted into code module containing dynamic sampling function call, loop algorithm construction and abnormal marking rule, and the code structure is optimized to improve execution efficiency.

8. A power plant actuator failure early warning device, characterized by, Comprise: Signal matching table generation module, based on KKS coding rules from I / O list screening instruction signal and feedback signal corresponding relationship, generating contains the signal matching table of the execution mechanism Chinese name, instruction signal identification and feedback signal identification; History data acquisition and deviation calculation module, through the SIS system data interface batch acquisition within a predetermined time window each execution mechanism instruction signal and feedback signal history operation data, calculate the absolute value of the deviation of instruction signal and feedback signal and generate deviation data set; The performance evaluation index generation module performs integral operation and difference calculation on the bias data set to generate performance evaluation indexes reflecting cumulative bias and mutation characteristics of the actuator; The visual display and corresponding relationship construction module visually displays the performance evaluation indexes in the form of a column chart and constructs a corresponding relationship between the actuator number and the performance evaluation indexes; The threshold screening and early warning marking module screens the performance evaluation indexes according to preset bias thresholds and integral thresholds, highlights and outputs the KKS code of the actuators exceeding the thresholds in the visual interface.

9. A computer device, comprising: It comprises a processor and a memory: The processor runs a program corresponding to the executable program code stored in the memory by reading the executable program code, so as to realize the power plant actuator failure early warning method according to any one of claims 1-7.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the power plant actuator failure early warning method according to any one of claims 1-7.

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