Thermal power generating unit operator operation behavior evaluation and guidance system based on big data analysis

Abstract

The invention relates to the technical field of thermal power generating unit operation safety, in particular to a thermal power generating unit operator operation behavior evaluation and guidance system based on big data analysis. Comprising a multi-source operation data analysis module, an operation time sequence compliance analysis module, a dynamic operation evaluation module, an equipment state coupling analysis module and an intelligent operation guidance generation module. Wherein the multi-source operation data analysis module is used for collecting original data; the operation time sequence compliance analysis module is used for calculating and generating a time sequence compactness evaluation value reflecting the operation rhythm compliance degree and calculating an equipment aging influence compensation value; the dynamic operation evaluation module is used for outputting a comprehensive operation compliance index; according to the method, the operation behavior data and the equipment state information are fused, quantitative identification of the operation rhythm compliance and the risk level in the operation process of the thermal power generating unit is realized, and the grading guidance strategy is generated based on the evaluation result, so that the operation safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power unit operation safety, and in particular to a thermal power unit operator operation behavior evaluation and guidance system based on big data analysis. Background Art

[0002] In the field of thermal power generation, the safe and efficient operation of units is of great significance to the overall power supply. In the traditional operation and maintenance model, operators mostly rely on historical data from distributed control systems and paper-based standard operating procedures to execute instructions, but it is difficult to quickly integrate and analyze multi-source data, nor can it quantify the potential impact of operational behavior on equipment status in real time. At the same time, equipment aging problems will cause inconsistent operational responses, resulting in operational plans based on a single time interval being unable to meet the long-term stability requirements of the unit. Frequent emergency adjustments or abnormal operations often pose safety risks. As the scale of power systems continues to expand and operating conditions become increasingly complex, the timely integration and dynamic evaluation of multi-source operational data are becoming urgent needs.

[0003] Existing technologies still lack an overall solution that can form an effective linkage in multi-dimensional operational behavior monitoring, equipment health status assessment, and dynamic risk identification. It is difficult to conduct unified quantitative indicator analysis for different compliance levels and equipment aging status and output targeted operational guidance; when there are many operating instructions and equipment operating conditions are changeable, relying solely on fixed standard timing and manual judgment is often prone to problems such as operational rhythm deviation and increased equipment loss, and in serious cases, it may cause accidents. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a thermal power unit operator operation behavior evaluation and guidance system based on big data analysis.

[0005] A thermal power unit operator operation behavior assessment and guidance system based on big data analysis includes a multi-source operation data analysis module, an operation sequence compliance analysis module, a dynamic operation assessment module, an equipment status coupling analysis module, and an intelligent operation guidance generation module; wherein:

[0006] Multi-source operation data analysis module: collects raw data from the distributed control system historical database, operation instruction recorder, and equipment life monitoring unit, extracts the current continuous operation instruction sequence and its actual execution time point, and simultaneously obtains the remaining life ratio of related equipment;

[0007] Operation Timing Compliance Analysis Module: This module receives operation instruction sequences and execution time points, calculates the actual execution intervals between adjacent operation instructions, compares them with the corresponding theoretical interval requirements in standard operating procedures, and generates a timing tightness evaluation value that reflects the degree of compliance with the operation rhythm. It also calculates the compensation value for the impact of equipment aging based on the remaining life of the equipment.

[0008] Dynamic Operation Assessment Module: This module integrates the timing tightness evaluation value with the equipment aging impact compensation value to generate a comprehensive operation compliance index. When the comprehensive operation compliance index falls below a preset threshold, a deviation alarm is triggered and abnormal operation nodes that exceed the allowable fluctuation range are marked.

[0009] Equipment status coupling analysis module: For the abnormal operation node, it retrieves the real-time vibration data and temperature change data of the corresponding equipment, calculates the comprehensive impact of the operation behavior on the equipment status, and generates a high-risk operation indicator when the impact exceeds the safety limit;

[0010] Intelligent operation guidance generation module: used to select a matching guidance strategy level from the pre-built knowledge base based on the comprehensive operation compliance index range and the status of high-risk operation identification, and output at least one guidance plan.

[0011] Optionally, the multi-source operation data parsing module includes an operation instruction extraction unit, an execution time parsing unit, and a lifespan ratio synchronization unit; wherein:

[0012] Operation instruction extraction unit: used to obtain all original operation records within the specified evaluation period from the distributed control system historical database and operation instruction recorder, analyze the control variables, operation action identifiers and target device numbers in the operation records, combine the operation time tags, classify them according to the device number, and generate a continuous operation instruction sequence within the current period in chronological order;

[0013] Execution time parsing unit: used to receive the operation instruction sequence and extract the corresponding actual execution time point. The execution time point is provided by the system timestamp field in the operation log record. For multi-channel instruction concurrency scenarios, the operation time is automatically aligned with the device response feedback time to form a timing correspondence table;

[0014] Life ratio synchronization unit: used to collect the current remaining life data of the target equipment from the equipment life monitoring unit, and match it with the equipment number corresponding to the classified operation instruction, and output the remaining life ratio of the equipment corresponding to each operation instruction. The remaining life ratio is quantified as the ratio of the current service life to the expected design life, and is retained to two decimal places.

[0015] Optionally, the operation timing compliance analysis module includes an execution interval calculation unit, a theoretical interval matching unit, and a rhythm compliance evaluation unit; wherein:

[0016] Execution interval calculation unit: used to receive the operation instruction sequence and the corresponding execution time point in the current cycle, pair two adjacent instructions according to the time sequence of operation execution, and calculate the actual execution interval D of each pair of instructions using the difference method i ;

[0017] Theoretical interval matching unit: Based on the category of each operation instruction and the target device number, retrieve the theoretical execution interval of this type of operation under the corresponding device from the standard operating procedure table, construct a set of theoretical reference values corresponding to the actual execution interval, and calculate the execution interval deviation V of each pair of operation instructions i ;

[0018] Rhythm compliance evaluation unit: Used to count all execution interval deviation values, calculate the normalized average deviation ratio, and output the evaluation value of the operation rhythm compliance degree. The formula is: where, R represents the evaluation value of the rhythm compliance degree; N represents the number of pairs of operation instructions in the current cycle; is the average ratio of all deviation values relative to the standard interval.

[0019] Optionally, the operation timing compliance analysis module further includes an aging compensation calculation unit and a life weight allocation unit; where:

[0020] Life weight allocation unit: Used to receive the remaining life ratio of the device output by the multi-source operation data parsing module, and look up the corresponding aging sensitivity weight coefficient W according to the device operation category;

[0021] Aging compensation calculation unit: Used to combine the remaining life ratio of the device and the aging sensitivity weight coefficient, and calculate the device aging impact compensation value A based on the exponential mapping method. The expression is: A = W·(1 - e -k·L ), where, a represents the device aging impact compensation value; W represents the aging sensitivity weight coefficient corresponding to the device; L represents the current remaining life ratio of the device; k is an adjustment coefficient.

[0022] Optionally, the dynamic operation evaluation module includes an exponential fusion calculation unit, a compliance judgment unit and an abnormal node marking unit; where: [[ID=二十七]]

[0023] Exponential fusion calculation unit: Used to receive the timing tightness evaluation value and the device aging impact compensation value output by the operation timing compliance analysis module, and calculate the comprehensive operation compliance index by using the weighted fusion method. The formula is: I = α·R + (1 - α)·(1 - A), where, I represents the comprehensive operation compliance index, and the range is [0, 1]; R is the timing tightness evaluation value; A is the device aging impact compensation value; α is the weighting coefficient;

[0024] Compliance judgment unit: Used to compare the comprehensive operation compliance index I with the compliance threshold T set in the system. When the inequality condition I < T is satisfied, immediately trigger an operation deviation alarm and mark the current operation cycle as a compliance risk state;

[0025] Abnormal node marking unit: After the compliance judgment unit triggers an alarm, it is used to retrieve all operation instruction sequences and corresponding execution time interval sequences in the current cycle, identify the instruction pairs with the largest interval error deviation, and record their operation numbers, time points and corresponding interval deviation values ​​as abnormal operation nodes, forming a traceable operation deviation record set.

[0026] Optionally, the device status coupling analysis module includes a status data calling unit, an impact degree analysis unit, and a risk identification generation unit; wherein:

[0027] Status data calling unit: used to receive abnormal operation node information output by the dynamic operation assessment module, and based on the target device number corresponding to the node, retrieve the real-time vibration data and temperature change data of the device within a preset time window before and after the abnormal node execution in the distributed monitoring platform;

[0028] Impact degree analysis unit: used to receive real-time status data after the call, and calculate the deviation degree of the abnormal operation on the dynamic response amplitude, thermal stability and vibration mode of the equipment based on the state changes before and after the abnormal operation. In combination with the equipment operating conditions and basic design parameters, the change amplitude is compared with the standard baseline, and the disturbance intensity of the operation behavior on the equipment state is comprehensively evaluated to form the operation impact degree index, which is used to measure the comprehensive impact degree caused by the operation behavior;

[0029] Risk identification generation unit: used to make judgments based on the operation impact index and the system's preset equipment safety fluctuation limit. When any indicator exceeds the limit, the current abnormal operation node is deemed to have high risk potential and a high-risk operation identification is generated.

[0030] Optionally, the impact degree analysis unit includes:

[0031] The state change extraction subunit is used to extract the monitoring data segments corresponding to the fixed time length before and after the abnormal operation node from the real-time vibration data and temperature change data of the equipment obtained by the state data call unit. The maximum change amplitude, mean offset and change rate in the corresponding data segments are calculated respectively to form a multi-dimensional state change vector that includes mechanical vibration disturbance, thermal stability change and response trend.

[0032] Benchmark offset calculation subunit: used to compare the extracted multi-dimensional state change vector with the historical characteristic mean of the device under steady-state operating conditions, and calculate the relative offset rate P on each state dimension j ;

[0033] Disturbance intensity summary subunit: It is used to summarize the relative deviation rate of each dimension into a single disturbance intensity index, and use weighted average to form the comprehensive operation impact degree. The calculation formula is: Among them, Z represents the operation impact degree index; w j is the disturbance weight coefficient of the j-th status index, satisfying ∑w j = 1; n is the total number of status index dimensions.

[0034] Optionally, the risk identification generation unit includes:

[0035] Threshold comparison sub-unit: used to receive the operation impact degree index output by the impact degree analysis unit, call the preset device safety fluctuation limit value in the device safety parameter library, compare the current operation impact degree with the limit value, and calculate the over-limit degree value U;

[0036] Risk level determination sub-unit: based on the numerical range of the over-limit degree value U, when U>0, it means that the operation has caused abnormal disturbance, and the risk level is divided according to the preset threshold;

[0037] Identification output sub-unit: used to generate the corresponding risk identification information packet according to the risk level determination result, including the risk level, the associated abnormal operation node number, the impact degree index and the over-limit deviation amount.

[0038] Optionally, the dividing the risk level according to the preset threshold includes:

[0039] If 0 < U ≤ θ1, it is determined as a low risk;

[0040] If θ1 < U ≤ θ2, it is determined as a medium risk;

[0041] If U > θ2, it is determined as a high risk; where θ1 and θ2 are the risk division thresholds set by the system, satisfying 0 < θ1 < θ2.

[0042] Optionally, the intelligent operation guidance generation module includes a compliance interval identification unit, a risk level identification unit and a policy level matching unit; where:

[0043] Compliance interval identification unit: used to receive the comprehensive operation compliance index I output by the dynamic operation evaluation module, and divide I into the corresponding compliance interval level according to the分级阈值 set by the system, including the fully compliant interval, the slightly deviated interval, the moderately deviated interval and the severely deviated interval, and each level corresponds to a predefined compliance degree level code;

[0044] Risk level identification unit: used to receive the high-risk operation identification information output by the device status coupling analysis module, extract the risk level corresponding to the operation node, and make a combined judgment with the compliance interval level code to form a unique operation risk scenario identification;

[0045] Strategy level matching unit: Based on the operational risk scenario identification, the matching strategy level is searched in the pre-built multi-level knowledge strategy library. The strategy library contains an index table of operation guidance schemes divided by two factors: risk level and compliance level. When the scenario identification completely matches a certain level in the strategy library, the corresponding operation suggestion level is extracted, including control strategy adjustment suggestions, equipment operation constraint suggestions and operation frequency limitation schemes, and at least one currently applicable structured guidance scheme is output to the operation interface.

[0046] Beneficial effects of the present invention:

[0047] The present invention realizes dynamic evaluation and precise guidance of the operating behavior of thermal power unit operators through technical means such as multi-source operation data analysis, timing compliance analysis and equipment status coupling; the system synchronously associates operating instructions with the remaining life of the equipment, real-time vibration data, and temperature change data, and uses unified evaluation indicators to quantify the coupling relationship between operating rhythm and equipment health status, overcoming the limitation of traditional monitoring methods that cannot comprehensively evaluate the impact of equipment aging in real time, and greatly improving the real-time and precise control of the unit operation process.

[0048] The present invention, through the integration of timing tightness evaluation values, aging impact compensation values ​​and equipment risk identification, can timely determine and distinguish different levels of deviations or risks, and generate safer and more efficient operation suggestions in high-risk scenarios; it realizes the overall closed-loop process from "multi-source data acquisition-timing compliance analysis-equipment status coupling-intelligent decision feedback", significantly enhancing the safety and flexibility of unit operation, and avoiding potential accidents or energy waste caused by improper operation or equipment aging. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 Schematic diagram of a system for evaluating and guiding the operational behavior of operators of thermal power units according to an embodiment of the present invention;

[0051] Figure 2 Schematic diagram of a device status coupling analysis module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0053] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0054] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0055] like Figure 1-Figure 2 As shown, a thermal power unit operator operation behavior evaluation and guidance system based on big data analysis includes a multi-source operation data analysis module, an operation timing compliance analysis module, a dynamic operation evaluation module, an equipment status coupling analysis module, and an intelligent operation guidance generation module; wherein:

[0056] Multi-source operation data analysis module: collects raw data from the distributed control system historical database, operation instruction recorder, and equipment life monitoring unit, extracts the current continuous operation instruction sequence and its actual execution time point, and simultaneously obtains the remaining life ratio of related equipment;

[0057] Operation Timing Compliance Analysis Module: This module receives operation instruction sequences and execution time points, calculates the actual execution intervals between adjacent operation instructions, compares them with the corresponding theoretical interval requirements in standard operating procedures, and generates a timing tightness evaluation value that reflects the degree of compliance with the operation rhythm. It also calculates the compensation value for the impact of equipment aging based on the remaining life of the equipment.

[0058] Dynamic Operation Assessment Module: This module integrates the timing tightness evaluation value with the equipment aging impact compensation value to generate a comprehensive operation compliance index. When the comprehensive operation compliance index falls below a preset threshold, a deviation alarm is triggered and abnormal operation nodes that exceed the allowable fluctuation range are marked.

[0059] Equipment status coupling analysis module: For abnormal operation nodes, it retrieves the real-time vibration data and temperature change data of the corresponding equipment, calculates the comprehensive impact of the operation behavior on the equipment status, and generates a high-risk operation indicator when the impact exceeds the safety limit;

[0060] Intelligent operation guidance generation module: used to select a matching guidance strategy level from the pre-built knowledge base based on the comprehensive operation compliance index range and the status of high-risk operation identification, and output at least one guidance plan.

[0061] The multi-source operation data analysis module includes an operation instruction extraction unit, an execution time analysis unit, and a life ratio synchronization unit; wherein:

[0062] Operation instruction extraction unit: used to obtain all original operation records within the specified evaluation period from the distributed control system (DCS) historical database and operation instruction recorder, analyze the control variables, operation action identifiers and target device numbers in the operation records, combine the operation time tags, classify them according to the device number, and generate a continuous operation instruction sequence within the current period in chronological order;

[0063] Execution time parsing unit: This unit receives a sequence of operation instructions and extracts the corresponding actual execution time. The execution time is provided by the system timestamp field in the operation log record. For multi-channel instruction concurrency scenarios, the unit automatically aligns the operation time with the device response feedback time to form a timing correspondence table.

[0064] Life ratio synchronization unit: used to collect the current remaining life data of the target equipment from the equipment life monitoring unit, match it with the equipment number corresponding to the classified operation instruction, and output the remaining life ratio of the equipment corresponding to each operation instruction. The remaining life ratio is quantified as the ratio of the current service life to the expected design life, and is retained to two decimal places; through the above unit, the multi-source operation data analysis module can achieve accurate synchronization and fusion of the time dimension and object dimension of the operation behavior data and the equipment life data, providing highly accurate data input for subsequent operation compliance analysis and risk identification.

[0065] The operation timing compliance analysis module includes an execution interval calculation unit, a theoretical interval matching unit, and a rhythm compliance evaluation unit; wherein:

[0066] Execution interval calculation unit: used to receive the operation instruction sequence and the corresponding execution time point in the current cycle, pair two adjacent instructions according to the time sequence of operation execution, and calculate the actual execution interval D of each pair of instructions using the difference method i , the formula is: D i =E i+1 -E i , where D i represents the actual execution interval of the i-th pair of adjacent operation instructions; E i Indicates the execution time of the i-th operation instruction, in seconds;

[0067] Theoretical interval matching unit: Based on the category and target device number of each operation instruction, the theoretical execution interval of the operation of the corresponding device is retrieved from the standard operating procedure table, a theoretical reference value set corresponding to the actual execution interval is constructed, and the execution interval deviation V of each pair of operation instructions is calculated. i , the formula is: V i =|D i -S i |, where V i represents the execution interval deviation of the i-th pair of operation instructions; S i Indicates the corresponding theoretical interval in the standard operating procedure, in seconds;

[0068] Rhythm compliance evaluation unit: used to count all execution interval deviation values, calculate the normalized average deviation ratio, and output the operation rhythm compliance evaluation value. The formula is: Among them, R represents the rhythm compliance evaluation value, and its value range is [0, 1]. The closer R is to 1, the more the operation rhythm conforms to the standard regulations. N represents the number of operation instruction pairs in the current cycle. is the average ratio of all deviation values ​​to the standard interval; through the above units, the operation timing compliance analysis module can quantitatively compare the actual operation time characteristics with the standard procedures, and output a measurable rhythm evaluation index R, which serves as an important basic quantity for subsequent compliance evaluation.

[0069] The operation timing compliance analysis module also includes an aging compensation calculation unit and a life weight allocation unit; wherein:

[0070] Life weight allocation unit: used to receive the remaining life ratio of the equipment output by the multi-source operation data analysis module, and obtain the corresponding aging sensitivity weight coefficient W according to the equipment operation category through a table lookup, which is used to reflect the degree of change in the operational response of different types of equipment during the process of life reduction;

[0071] Table 1 Comparison of equipment operation categories and aging sensitivity weight coefficients

[0072]

[0073]

[0074] Instructions for using Table 1 above:

[0075] 1. Determine the target device operation category;

[0076] 2. Find the corresponding category code and device name from the comparison table;

[0077] 3. Read the weight coefficient W and use it in the calculation of the aging effect compensation value formula.

[0078] Definition of weight values: W = 0.50-0.65 is low aging sensitivity, suitable for equipment with simple structure or low frequency of use; W = 0.70-0.85 is moderate sensitivity, suitable for most rotating or actuators; W>0.85 is high sensitivity, mainly for high-frequency or precision control devices such as valves and proportional control components.

[0079] Aging compensation calculation unit: used to combine the remaining life ratio of the equipment and the aging sensitivity weight coefficient, and calculate the equipment aging impact compensation value A based on the exponential mapping method. The expression is: A=W·(1-e -k·L ), where A represents the compensation value for the impact of device aging; W represents the aging sensitivity weight coefficient corresponding to the device; L represents the current remaining life ratio of the device, ranging from 0 to 1; k is the adjustment coefficient, which reflects the nonlinear response slope of the aging sensitivity. Through the above units, the operation timing compliance analysis module can fully consider the impact of device aging factors on operation execution response, improve the adaptability and accuracy of timing analysis results, and provide a more objective basis for subsequent evaluation results.

[0080] The dynamic operation assessment module includes an index fusion calculation unit, a compliance judgment unit, and an abnormal node marking unit; among which:

[0081] Index fusion calculation unit: used to receive the timing tightness evaluation value and the device aging impact compensation value output by the operation timing compliance analysis module, and calculate the comprehensive operation compliance index using a weighted fusion method. The formula is: I = α·R + (1-α)·(1-A), where I represents the comprehensive operation compliance index, ranging from [0, 1]; R is the timing tightness evaluation value; A is the device aging impact compensation value; α is the weighting coefficient used to adjust the weight of the two. Specifically, α is set as a function related to the remaining life ratio L of the device and the operation instruction frequency F in the current operation cycle, as follows: Where L is the remaining lifespan of the device; F is the number of operation instructions per unit time in the current evaluation period, in units of instructions per minute; β is the aging weight adjustment factor, which is an empirically set value in the range of [0.5, 2] and is adjusted according to the aging sensitivity of the device.

[0082] Compliance judgment unit: used to compare the comprehensive operation compliance index I with the set compliance threshold T within the system. When the inequality condition I < T is satisfied, it immediately triggers an operation deviation alarm and marks the current operation cycle as a compliance risk state;

[0083] Abnormal node marking unit: After the compliance judgment unit triggers an alarm, it is used to retrieve all the operation instruction sequences and the corresponding execution time interval sequences within the current cycle, identify the instruction pair with the largest interval error deviation among them, and uniformly record its operation number, time point, and the corresponding interval deviation value as an abnormal operation node, forming a traceable operation deviation record set. Through the above units, the dynamic operation evaluation module can integrate the accuracy of timing execution and the impact of equipment aging, output a unified comprehensive evaluation result, and based on this result, achieve dynamic judgment and precise abnormal identification, providing a clear deviation source for subsequent equipment state coupling analysis.

[0084] The equipment state coupling analysis module includes a state data calling unit, an impact degree analysis unit, and a risk identification generation unit; among which: [[ID=__]]

[0085] State data calling unit: used to receive the abnormal operation node information output by the dynamic operation evaluation module, and based on the target equipment number corresponding to the node, retrieve the real-time vibration data and temperature change data of the equipment within a preset time window before and after the execution of the abnormal node in the distributed monitoring platform. The data includes the instantaneous vibration amplitude, spectral characteristics, axial acceleration collected by the sensor, and the temperature change trend sequence, ensuring the synchronization of the time domain and the equipment state;

[0086] Impact degree analysis unit: used to receive the called real-time state data, calculate the deviation degree of the abnormal operation from the equipment's dynamic response amplitude, thermal stability, and vibration mode according to the state changes before and after the abnormal operation occurs, and compare the change amplitude with the standard baseline in combination with the equipment operating conditions and basic design parameters, comprehensively evaluate the disturbance intensity of the operation behavior on the equipment state, and form an operation impact degree index to measure the comprehensive impact degree caused by the operation behavior;

[0087] Risk identification generation unit: used to judge according to the operation impact degree index and the preset equipment safety fluctuation limit value of the system. When any index exceeds the limit value, it is determined that the current abnormal operation node has a high risk potential, generates a high-risk operation identification, and outputs it to the subsequent intelligent operation guidance module as a decision input. Through the above units, the equipment state coupling analysis module can achieve real-time closed-loop analysis from operation anomalies to equipment dynamic responses, accurately identify high-disturbance operation behaviors, and provide a risk basis supported by equipment operating conditions for guiding strategy output.

[0088] The impact degree analysis unit includes:

[0089] The state change extraction subunit is used to extract the monitoring data segments corresponding to the fixed time length (in seconds) before and after the abnormal operation node from the real-time vibration data and temperature change data of the equipment obtained by the state data call unit. The maximum change amplitude, mean offset and change rate in the corresponding data segments are calculated respectively to form a multi-dimensional state change vector that includes mechanical vibration disturbance, thermal stability change and response trend.

[0090] Benchmark offset calculation subunit: used to compare the extracted multi-dimensional state change vector with the historical characteristic mean of the device under steady-state operating conditions, and calculate the relative offset rate P on each state dimension j , the formula is: Among them, P j represents the relative deviation rate of the jth state indicator; X j is the current state change value; B j is the reference baseline value of this indicator under steady state;

[0091] Disturbance intensity summary subunit: It is used to summarize the relative deviation rate of each dimension into a single disturbance intensity index, and use weighted average to form the comprehensive operation impact degree. The calculation formula is: Among them, Z represents the operation impact index; w j is the disturbance weight coefficient of the j-th state index, satisfying ∑w j =1; n is the total number of status indicator dimensions; through the above subunits, the impact degree analysis unit can quantify the disturbance amplitude of the equipment status from the vibration and temperature changes caused by the operation, and output a standardized impact degree index Z, providing a comparable and traceable basis for the identification of high-risk operations.

[0092] The risk identification generation unit includes:

[0093] The threshold comparison subunit is used to receive the operation impact index output by the impact analysis unit, call the equipment safety fluctuation limit preset in the equipment safety parameter library, compare the current operation impact with the limit, and calculate the limit value U. The calculation formula is: U = ZK, where U represents the deviation of the operation behavior beyond the safety limit; Z is the operation impact index; K is the safety fluctuation limit corresponding to the equipment type, and the unit is the same as Z.

[0094] Risk level determination subunit: Based on the numerical range of the over-limit value U, when U>0, it indicates that the operation has caused abnormal disturbance, and the risk level is divided according to the preset threshold;

[0095] Identification output subunit: It is used to generate corresponding risk identification information packets according to the risk level determination result, including risk level, associated abnormal operation node number, impact degree index and over-limit deviation amount; through the above subunit structure, the risk identification generation unit can quantitatively compare the disturbance index with the safety limit, clearly divide the operation risk level, and form a standardized high-risk operation identification, realizing the logical closed-loop from state quantification to risk classification, and providing an accurate risk input basis for subsequent operation optimization.

[0096] The risk levels are divided according to the preset thresholds, including:

[0097] If 0 < U ≤ θ1, it is determined as a low risk;

[0098] If θ1 < U ≤ θ2, it is determined as a medium risk;

[0099] If U > θ2, it is determined as a high risk; where θ1 and θ2 are the risk division thresholds set by the system, and 0 < θ1 < θ2.

[0100] The intelligent operation guidance generation module includes a compliance interval identification unit, a risk level identification unit and a policy level matching unit; where:

[0101] Compliance interval identification unit: It is used to receive the comprehensive operation compliance index I output by the dynamic operation evaluation module, and divide I into the corresponding compliance interval levels according to the grading thresholds set by the system, including a full compliance interval, a mild deviation interval, a moderate deviation interval and a severe deviation interval. Each level corresponds to a predefined compliance degree level code, which is used to guide the policy matching call;

[0102] The specific method for dividing the compliance interval levels is as follows:

[0103] Three division threshold parameters are preset, namely the λ1 mild deviation threshold, the λ2 moderate deviation threshold, and the λ3 severe deviation threshold; and it satisfies: 0 < λ3 < λ2 < λ1 < 1. According to the value of the comprehensive operation compliance index I, it is divided into the following four interval levels:

[0104] When I ≥ λ1, it is divided into the full compliance interval, and the compliance level code is L0;

[0105] When λ2 ≤ I < λ1, it is divided into the mild deviation interval, and the compliance level code is L1;

[0106] When λ3 ≤ I < λ2, it is divided into the moderate deviation interval, and the compliance level code is L2;

[0107] When I < λ3, it is divided into the severe deviation interval, and the compliance level code is L3.

[0108] Risk level identification unit: This unit receives high-risk operation identification information output by the device status coupling analysis module, extracts the risk level corresponding to the operation node, and combines it with the compliance interval level code to form a unique operation risk scenario identifier. This identifier, composed of the compliance level and the risk level, is used to guide the knowledge base policy selection logic.

[0109] Strategy level matching unit: Based on the operational risk scenario identification, the matching strategy level is searched in the pre-built multi-level knowledge strategy library. The strategy library contains an index table of operational guidance plans divided by two factors: risk level and compliance level. When the scenario identification is completely matched with a certain level in the strategy library, the corresponding operational suggestion level is extracted, including control strategy adjustment suggestions, equipment operation constraint suggestions and operation frequency limitation plans, and at least one currently applicable structured guidance plan is output to the operation interface. Through the above unit, the intelligent operation guidance generation module can combine the operational behavior compliance and equipment operation risk, dynamically match the operational guidance strategy with the appropriate accuracy level, realize the rapid closed loop from behavior evaluation to strategy feedback, and improve the pertinence and effectiveness of abnormal behavior response.

[0110] The operation of outputting the guidance plan is as follows:

[0111] Assume the risk level is Rx, and the specific value is:

[0112] R0 means no risk;

[0113] R1 is low risk;

[0114] R2 is medium risk;

[0115] R3 is high risk;

[0116] And by combining the risk level code and the compliance level code to form a unique operational risk scenario identifier S ij , where i represents the compliance level code number (0 to 3); j represents the risk level code number (0 to 3). The combination of the two forms a unique operational risk scenario identifier, which is used to index the operational guidance plan below;

[0117] Table 2 Operational Guidance Program Index

[0118]

[0119]

[0120] As can be seen from Table 2 above, through the scene identification S ijMatching indexes can be performed, and the operation guidance level is generated by weighted merging of risk level and compliance status to generate more detailed guidance levels, such as level 0 to level 6; the larger the number, the higher the overall risk or the more serious the deviation, and more stringent control measures are required; instruction frequency control is recommended for adjusting the execution interval of operation instructions or limiting the maximum instruction frequency to reduce system operation pressure from a scheduling perspective; equipment operation and maintenance plan recommendations are used to perform maintenance, troubleshooting or replacement of key components based on the equipment health status and aging compensation results to prevent further risks; personnel supervision strategy recommendations are used for training, centralized supervision or special assessment of operators to standardize operating behavior and enhance safety awareness.

[0121] The strategy level matching unit identifies S according to the joint risk scenario ij By querying this index table, the corresponding operation guidance level, instruction frequency control suggestions, equipment operation and maintenance plan and personnel supervision strategy can be directly located, and an executable guidance plan can be output in a structured manner. At the same time, the system retains an expansion interface, which can be further subdivided or supplemented according to the unit equipment type and operation process characteristics to ensure that the strategy is adaptable and targeted.

[0122] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0123] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A thermal power unit operator operation behavior evaluation and guidance system based on big data analysis, characterized in that: It includes a multi-source operation data analysis module, an operation timing compliance analysis module, a dynamic operation evaluation module, an equipment status coupling analysis module, and an intelligent operation guidance generation module; among which: Multi-source operation data analysis module: collects raw data from the distributed control system historical database, operation instruction recorder, and equipment life monitoring unit, extracts the current continuous operation instruction sequence and its actual execution time point, and simultaneously obtains the remaining life ratio of related equipment; Operation Timing Compliance Analysis Module: This module receives operation instruction sequences and execution time points, calculates the actual execution intervals between adjacent operation instructions, compares them with the corresponding theoretical interval requirements in standard operating procedures, and generates a timing tightness evaluation value that reflects the degree of compliance with the operation rhythm. It also calculates the compensation value for the impact of equipment aging based on the remaining life of the equipment. Dynamic Operation Assessment Module: This module integrates the timing tightness evaluation value with the equipment aging impact compensation value to generate a comprehensive operation compliance index. When the comprehensive operation compliance index falls below a preset threshold, a deviation alarm is triggered and abnormal operation nodes that exceed the allowable fluctuation range are marked. Equipment status coupling analysis module: For the abnormal operation node, it retrieves the real-time vibration data and temperature change data of the corresponding equipment, calculates the comprehensive impact of the operation behavior on the equipment status, and generates a high-risk operation indicator when the impact exceeds the safety limit; Intelligent operation guidance generation module: used to select a matching guidance strategy level from the pre-built knowledge base based on the comprehensive operation compliance index range and the status of high-risk operation identification, and output at least one guidance plan.

2. The thermal power unit operator operation behavior evaluation and guidance system based on big data analysis according to claim 1 is characterized in that: The multi-source operation data analysis module includes an operation instruction extraction unit, an execution time analysis unit, and a life ratio synchronization unit; wherein: Operation instruction extraction unit: used to obtain all original operation records within the specified evaluation period from the distributed control system historical database and operation instruction recorder, analyze the control variables, operation action identifiers and target device numbers in the operation records, combine the operation time tags, classify them according to the device number, and generate a continuous operation instruction sequence within the current period in chronological order; Execution time parsing unit: used to receive the operation instruction sequence and extract the corresponding actual execution time point. The execution time point is provided by the system timestamp field in the operation log record. For multi-channel instruction concurrency scenarios, the operation time is automatically aligned with the device response feedback time to form a timing correspondence table; Life ratio synchronization unit: used to collect the current remaining life data of the target equipment from the equipment life monitoring unit, and match it with the equipment number corresponding to the classified operation instruction, and output the remaining life ratio of the equipment corresponding to each operation instruction. The remaining life ratio is quantified as the ratio of the current service life to the expected design life, and is retained to two decimal places.

3. The thermal power unit operator operation behavior evaluation and guidance system based on big data analysis according to claim 1 is characterized in that: The operation timing compliance analysis module includes an execution interval calculation unit, a theoretical interval matching unit, and a rhythm compliance evaluation unit; wherein: Execution interval calculation unit: used to receive the operation instruction sequence and the corresponding execution time point in the current cycle, pair two adjacent instructions according to the time sequence of operation execution, and calculate the actual execution interval D of each pair of instructions using the difference method i ; Theoretical interval matching unit: Based on the category and target device number of each operation instruction, the theoretical execution interval of the operation of the corresponding device is retrieved from the standard operating procedure table, a theoretical reference value set corresponding to the actual execution interval is constructed, and the execution interval deviation V of each pair of operation instructions is calculated. i ; Rhythm compliance evaluation unit: used to count all execution interval deviation values, calculate the normalized average deviation ratio, and output the operation rhythm compliance evaluation value. The formula is: Where R represents the rhythm compliance evaluation value; N represents the number of operation instruction pairs in the current cycle; is the average ratio of all deviation values ​​to the standard interval.

4. The thermal power unit operator operation behavior evaluation and guidance system based on big data analysis according to claim 1 is characterized in that: The operation timing compliance analysis module further includes an aging compensation calculation unit and a life weight allocation unit; wherein: Lifetime Weight Allocation Unit: It is used to receive the remaining lifetime ratio of the device output by the multi-source operation data parsing module, and look up the corresponding aging sensitivity weight coefficient W according to the device operation category in a table. Aging compensation calculation unit: used to combine the remaining life ratio of the equipment and the aging sensitivity weight coefficient, and calculate the equipment aging impact compensation value A based on the exponential mapping method. The expression is: A=W·(1-e -k·L ), where a represents the compensation value for the impact of equipment aging; W represents the corresponding aging sensitivity weight coefficient of the equipment; L represents the current remaining life ratio of the equipment; and k is the adjustment coefficient.

5. The thermal power unit operator operation behavior evaluation and guidance system based on big data analysis according to claim 1 is characterized in that: The Dynamic Operation Evaluation Module includes an Exponential Fusion Calculation Unit, a Compliance Judgment Unit, and an Abnormal Node Marking Unit; among which: Exponential Fusion Calculation Unit: It is used to receive the timing tightness evaluation value and the device aging impact compensation value output by the Operation Timing Compliance Analysis Module, and calculate the comprehensive operation compliance index by using a weighted fusion method. The formula is: I = α·R + (1 - α)·(1 - A), where I represents the comprehensive operation compliance index, and the range is [0, 1]; R is the timing tightness evaluation value; A is the device aging impact compensation value; α is the weighting coefficient. Compliance Judgment Unit: It is used to compare the comprehensive operation compliance index I with the compliance threshold T set in the system. When the inequality condition I < T is satisfied, it immediately triggers an operation deviation alarm and marks the current operation cycle as a compliance risk state. Abnormal Node Marking Unit: After the Compliance Judgment Unit triggers an alarm, it is used to retrieve all the operation instruction sequences and the corresponding execution time interval sequences within the current cycle, identify the instruction pair with the largest interval error deviation, and uniformly record its operation number, time point, and the corresponding interval deviation value as an abnormal operation node, forming a traceable operation deviation record set.

6. The thermal power unit operator operation behavior evaluation and guidance system based on big data analysis according to claim 1 is characterized in that: The Device State Coupling Analysis Module includes a State Data Retrieval Unit, an Impact Degree Analysis Unit, and a Risk Identification Generation Unit; among which: State Data Retrieval Unit: It is used to receive the abnormal operation node information output by the Dynamic Operation Evaluation Module, and retrieve the real-time vibration data and temperature change data of the device within a preset time window before and after the execution of the abnormal node based on the target device number corresponding to the node in the distributed monitoring platform. Impact Degree Analysis Unit: It is used to receive the retrieved real-time state data, calculate the deviation degree of the abnormal operation from the device's dynamic response amplitude, thermal stability, and vibration mode according to the state change situation before and after the abnormal operation occurs, and compare the change amplitude with the standard baseline in combination with the device operation conditions and basic design parameters, comprehensively evaluate the disturbance intensity of the operation behavior on the device state, and form an operation impact degree index to measure the comprehensive impact degree caused by the operation behavior. Risk Identification Generation Unit: It is used to judge according to the operation impact degree index and the device safety fluctuation limit preset by the system. When any index exceeds the limit, it is determined that the current abnormal operation node has high-risk potentiality and generates a high-risk operation identification.

7. The thermal power unit operator operation behavior evaluation and guidance system based on big data analysis according to claim 6 is characterized in that: The Impact Degree Analysis Unit includes: State Change Extraction Sub-Unit: It is used to extract the monitoring data segments corresponding to a fixed duration before and after the abnormal operation node from the device real-time vibration data and temperature change data obtained by the State Data Retrieval Unit, and calculate the maximum change amplitude, mean offset, and change rate in the corresponding data segments respectively, constituting a multi-dimensional state change vector including mechanical vibration disturbance, thermal stability change, and response trend. Benchmark offset calculation subunit: used to compare the extracted multi-dimensional state change vector with the historical characteristic mean of the device under steady-state operating conditions, and calculate the relative offset rate P on each state dimension j ; Disturbance intensity summary subunit: It is used to summarize the relative deviation rate of each dimension into a single disturbance intensity index, and use weighted average to form the comprehensive operation impact degree. The calculation formula is: Among them, Z represents the operation impact index; w j is the disturbance weight coefficient of the j-th state index, satisfying ∑w j =1; n is the total number of status indicator dimensions.

8. The thermal power unit operator operation behavior evaluation and guidance system based on big data analysis according to claim 7 is characterized in that: The Risk Identification Generation Unit includes: Threshold comparison subunit: It is used to receive the operation impact degree index output by the impact degree analysis unit, call the preset device safety fluctuation limit value in the device safety parameter library, compare the current operation impact degree with the limit value, and calculate the overlimit degree value U; Risk level determination subunit: Based on the numerical range of the overlimit degree value U, when U>0, it indicates that the operation has caused abnormal disturbance, and the risk level is determined according to the preset threshold; Identification output subunit: It is used to generate a corresponding risk identification information packet according to the risk level determination result, including the risk level, the associated abnormal operation node number, the impact degree index and the overlimit deviation amount.

9. The thermal power unit operator operation behavior evaluation and guidance system based on big data analysis according to claim 8 is characterized in that: The determination of the risk level according to the preset threshold includes: If 0<U≤θ1, it is determined as a low risk; If θ1<U≤θ2, it is determined as a medium risk; If U>θ2, it is determined as a high risk; where θ1 and θ2 are the risk division thresholds set by the system, and 0<θ1<θ2 is satisfied.

10. The thermal power unit operator operation behavior evaluation and guidance system based on big data analysis according to claim 5, characterized in that: The intelligent operation guidance generation module includes a compliance interval identification unit, a risk level identification unit and a policy level matching unit; among them: Compliance interval identification unit: It is used to receive the comprehensive operation compliance index I output by the dynamic operation evaluation module, and divide I into the corresponding compliance interval level according to the分级阈值set by the system, including a full compliance interval, a mild deviation interval, a moderate deviation interval and a severe deviation interval, and each level corresponds to a predefined compliance degree level code; Risk level identification unit: It is used to receive the high-risk operation identification information output by the device state coupling analysis module, extract the risk level corresponding to the operation node, and make a joint judgment with the compliance interval level code to form a unique operation risk scenario identification; Policy level matching unit: Based on the operation risk scenario identification, search for the matching policy level in the pre-constructed multi-level knowledge policy library. The policy library contains an operation guidance plan index table divided by two factors of risk level and compliance level; when the scenario identification completely matches a certain level in the policy library, extract the corresponding operation suggestion level, including control strategy adjustment suggestions, device operation constraint suggestions and operation frequency limit schemes, and output at least one currently applicable structured guidance plan to the operation interface.

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