Method, device and equipment for diagnosing health state of pulverized coal furnace and storage medium

Abstract

The invention discloses a pulverized coal furnace health state diagnosis method, device and equipment and a storage medium, relates to the technical field of computers, is applied to a pulverized coal furnace system, and comprises the following steps: acquiring original pulverized coal furnace operation data from a distributed control system connected with the pulverized coal furnace system based on a preset interface; based on a preset multi-dimensional pulverized coal furnace health assessment index system, a preset soft measurement model, a health degree scoring model and original pulverized coal furnace operation data, performing multi-dimensional health state quantification; analysis is carried out based on the health state quantification result and the historical health state quantification result, and dominant and hidden faults are identified by using the analysis result; determining a current health state diagnosis result based on the fault identification result and the health state quantification result, and visually displaying the health state diagnosis result based on a preset diagnosis rule base; the health state diagnosis result comprises a radar map. According to the invention, problems existing in existing related schemes can be solved, and reliability and accuracy of health diagnosis of the pulverized coal furnace are improved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, equipment, and storage medium for diagnosing the health status of a pulverized coal boiler. Background Technology

[0002] Currently, large-scale pulverized coal boiler systems are typically equipped with DCS (Distributed Control System) systems, capable of real-time monitoring of thousands of operating parameters (such as temperature, pressure, flow rate, oxygen content, etc.). However, existing health diagnostic solutions based on monitoring data have the following problems:

[0003] (1) Alarm proliferation and isolation: Threshold alarms are set for a single parameter, resulting in a large number of alarms that are independent of each other, making it difficult to quickly locate the root cause;

[0004] (2) Lack of overall status assessment: To answer questions such as "What is the current overall health status of the pulverized coal boiler?", we can only rely on the personal experience of relevant staff to make judgments;

[0005] (3) Hidden faults are difficult to detect: For faults that occur slowly, such as ash accumulation, slow changes in coal quality, and instrument drift, they are often not detected until they have developed to a serious degree;

[0006] (4) Low level of visualization: Traditional interfaces mainly use trend charts and lists, and the information is presented in a scattered manner, which is not intuitive enough.

[0007] Therefore, how to integrate multi-source information for intelligent diagnosis and present the overall health status of pulverized coal boilers in an intuitive way is a problem that technical personnel in the field urgently need to solve. Summary of the Invention

[0008] In view of this, the purpose of this invention is to provide a method, apparatus, equipment, and storage medium for diagnosing the health status of a pulverized coal boiler, which can solve the problems existing in the existing related solutions, thereby improving the efficiency, reliability, and accuracy of pulverized coal boiler health diagnosis, reducing the false judgment rate, avoiding the waste of human and time resources of relevant personnel, and thus improving the operational stability, safety, and economy of the pulverized coal boiler. The specific solution is as follows:

[0009] Firstly, this application provides a health status diagnosis method for a pulverized coal boiler, applied to a pulverized coal boiler system, comprising:

[0010] Based on a preset interface, the original pulverized coal boiler operating data corresponding to the pulverized coal boiler system is obtained from the distributed control system connected to the pulverized coal boiler system.

[0011] Based on a pre-set multi-dimensional pulverized coal boiler health assessment index system, a pre-set soft measurement model, a health score model, and the original pulverized coal boiler operating data, the health status is quantified in multiple dimensions to determine the quantification results. The multiple dimensions include heat exchange efficiency, air leakage, steam drum stability, coal quality, instrument health, steam-water system, and auxiliary equipment status.

[0012] Based on the quantitative results of the health status and the corresponding historical quantitative results of the pulverized coal boiler system, an analysis is performed, and the corresponding analysis results are used to identify explicit and implicit faults in order to determine the fault identification results.

[0013] Based on the quantitative results of the health status and the fault identification results, the current health status diagnosis result corresponding to the pulverized coal boiler system is determined, and the health status diagnosis result is visualized based on a preset diagnosis rule base; the health status diagnosis result includes a radar chart.

[0014] Optionally, the step of quantifying the health status in multiple dimensions based on a preset multi-dimensional pulverized coal boiler health assessment index system, a preset soft measurement model, a health score model, and the original pulverized coal boiler operating data to determine the health status quantification result includes:

[0015] Based on the preset multi-dimensional pulverized coal boiler health assessment index system, the preset soft measurement model, and the original pulverized coal boiler operating data, the current parameter values ​​of the multi-dimensional pulverized coal boiler health assessment parameters are determined; the multi-dimensional pulverized coal boiler health assessment parameters include air preheater leakage coefficient, heat exchanger ash accumulation index, feedwater flow rate-steam flow rate difference, and coal quality score.

[0016] Based on the preset multi-dimensional pulverized coal boiler health assessment strategy, health scoring model, and the current parameter values, the health status is quantified in multiple dimensions to determine the health status quantification result.

[0017] Optionally, determining the current parameter values ​​of the multi-dimensional pulverized coal boiler health assessment parameters based on a preset multi-dimensional pulverized coal boiler health assessment index system, a preset soft measurement model, and the original pulverized coal boiler operating data includes:

[0018] Based on the original pulverized coal boiler operating data, the current flue gas temperature difference of the heat exchanger is obtained, and combined with the theoretical flue gas temperature difference under the design conditions and the first preset soft measurement model, the current parameter value of the ash accumulation index of the heat exchanger is determined; the heat exchanger includes a superheater, an economizer and an air preheater;

[0019] Based on the original pulverized coal boiler operating data, the oxygen content on the furnace side and the oxygen content on the flue gas side are obtained;

[0020] Based on the oxygen content on the furnace side, the oxygen content on the flue gas side, and the second preset soft measurement model, the excess air coefficient at the flue gas outlet and the excess air coefficient at the furnace outlet are analyzed and compared to determine the current parameter value of the air preheater leakage coefficient.

[0021] Based on the original pulverized coal boiler operating data, obtain the current parameter values ​​of the steam drum water level change frequency and / or steam drum pressure change frequency;

[0022] Based on the original pulverized coal boiler operating data, the boiler evaporation rate, blower inlet air temperature, furnace outlet flue gas temperature, coal mill current, exhaust gas temperature, and feedwater temperature are obtained.

[0023] Based on the third preset soft measurement model, the boiler evaporation rate, the blower inlet air temperature, the furnace outlet flue gas temperature, the coal mill current, the exhaust gas temperature, and the feed water temperature, the current parameter value of the coal quality score is determined;

[0024] Based on the instrument data in the original pulverized coal boiler operating data, the current parameter values ​​of multiple instrument parameters are determined; the instrument data includes boiler water conductivity, boiler water pH and boiler water phosphate content, wind speed and damper opening;

[0025] Based on the original pulverized coal boiler operating data, the feedwater flow rate and steam flow rate are obtained to determine the current parameter value of the feedwater flow rate - steam flow rate difference;

[0026] Based on the original pulverized coal boiler operating data, the current flue gas temperature value at the target location in the steam-water system is obtained; the steam-water system is the steam-water system in the pulverized coal boiler system.

[0027] Based on the original pulverized coal boiler operating data, the current values ​​of auxiliary machine current and auxiliary machine bearing temperature are obtained; the auxiliary machines include blowers, induced draft fans and coal mills.

[0028] Optionally, the step of quantifying the health status in multiple dimensions based on a preset multi-dimensional pulverized coal boiler health assessment strategy, a health scoring model, and the current parameter values ​​to determine the health status quantification result includes:

[0029] Based on the first health rating model, the current parameter value of the heat exchanger ash accumulation index, the number of heat exchangers in the pulverized coal boiler system, and the preset ash accumulation threshold, the health status of the heat exchange efficiency dimension is quantified to determine the first health status quantification result.

[0030] Based on the second health rating model, the current parameter value of the air preheater leakage coefficient, the preset upper limit value of leakage and the preset lower limit value of leakage, the health status of the leakage status dimension is quantified to determine the second health status quantification result.

[0031] Based on the third health rating model, the current parameter values ​​of the steam drum water level mutation frequency and / or the steam drum pressure mutation frequency, and the first historical parameter values ​​within the first preset period, parameter value mutation events are detected, and the corresponding mutation event detection results are used to quantify the health status of the steam drum stability dimension to determine the third health status quantification result.

[0032] Based on the fourth health score model and the current parameter value of the coal quality score, the health status of the coal quality status dimension is quantified to determine the fourth health status quantification result.

[0033] Based on the fifth health score model, the current parameter value of the correlation of multiple instrument parameters, and the second historical parameter value within the second preset period, abnormal parameter value events are detected, and the health status of the instrument health dimension is quantified using the corresponding abnormal event detection results to determine the fifth health status quantification result.

[0034] Based on the sixth health rating model, the current parameter value of the difference between water flow rate and steam flow rate, and the current flue gas temperature value, the health status of the steam-water system is quantified to determine the sixth health status quantification result.

[0035] Based on the seventh health rating model, the current values ​​of the auxiliary machine current and the temperature of the auxiliary machine bearing, the detection results of abnormal and sudden events of parameter values ​​are obtained, and the health status of the auxiliary machine is quantified using the corresponding value detection results to determine the seventh health status quantification result.

[0036] Optionally, the analysis based on the quantitative results of the health status and the historical quantitative results of the pulverized coal boiler system, and the identification of explicit and implicit faults using the corresponding analysis results, includes:

[0037] The target detection result is determined based on the quantitative results of the health status.

[0038] If the target detection results indicate that the health status of several dimensions is abnormal, then based on the historical health status quantification results within the third preset period, the target detection results, and the preset fault knowledge base, a multi-dimensional fault correlation analysis is performed to determine the explicit fault identification results.

[0039] Based on the quantitative results of the health status and the historical quantitative results of the health status within the fourth preset period, trend prediction and deviation analysis are performed to determine the first analysis result; wherein, the duration of the fourth preset period is longer than the duration of the third preset period;

[0040] Based on the analysis results, the preset latent fault identification rules, and the preset fault knowledge base, the latent fault identification result is determined.

[0041] Optionally, if the target detection result indicates that the health status of several dimensions is abnormal, then based on the historical health status quantification results within the third preset period, the target detection result, and the preset fault knowledge base, a multi-dimensional fault correlation analysis is performed, including:

[0042] If the target detection results indicate that the current health status of multiple dimensions is abnormal, based on the historical health status quantification results within the third preset period and the target detection results, a correlation analysis and propagation path analysis of the fault are performed to determine the second analysis result;

[0043] Based on the second analysis results and the preset fault knowledge base, root cause analysis is performed to determine the root cause analysis results;

[0044] Based on the root cause analysis results and the health status quantification results, action recommendations are determined.

[0045] Based on the root cause analysis results and the proposed measures, the results of identifying explicit faults are determined.

[0046] Optionally, the step of determining the current health status diagnosis result corresponding to the pulverized coal boiler system based on the health status quantification result and the fault identification result, and visually displaying the health status diagnosis result based on a preset diagnosis rule base, includes:

[0047] Based on the quantitative results of the health status and the fault identification results, a graphic diagram is drawn to determine the current health status diagnosis result corresponding to the pulverized coal boiler system; the health status diagnosis result includes a radar chart.

[0048] During the visualization of the health status diagnosis results, the radar graph is monitored to determine whether a preset pattern appears.

[0049] If the monitoring result is yes, then based on the preset diagnostic rule base, the corresponding root cause information is determined and displayed.

[0050] Secondly, this application provides a health status diagnostic device for a pulverized coal boiler, applied to a pulverized coal boiler system, comprising:

[0051] The data acquisition module is used to acquire the original pulverized coal boiler operation data corresponding to the pulverized coal boiler system from the distributed control system connected to the pulverized coal boiler system based on a preset interface.

[0052] The multi-dimensional health quantification module is used to quantify the health status in multiple dimensions based on a preset multi-dimensional pulverized coal boiler health assessment index system, a preset soft measurement model, a health score model, and the original pulverized coal boiler operating data, so as to determine the health status quantification result. The multiple dimensions include heat exchange efficiency dimension, air leakage state dimension, steam drum stability dimension, coal quality state dimension, instrument health dimension, steam-water system dimension, and auxiliary machine state dimension.

[0053] The fault identification module is used to analyze the health status quantification results and the historical health status quantification results corresponding to the pulverized coal boiler system, and to use the corresponding analysis results to identify explicit and implicit faults in order to determine the fault identification results.

[0054] The diagnostic display module is used to determine the current health status diagnostic result corresponding to the pulverized coal boiler system based on the health status quantification result and the fault identification result, and to visualize the health status diagnostic result based on a preset diagnostic rule library; the health status diagnostic result includes a radar chart.

[0055] Thirdly, this application provides an electronic device, comprising:

[0056] Memory, used to store computer programs;

[0057] A processor is used to execute the computer program to implement the steps of the aforementioned pulverized coal boiler health status diagnosis method.

[0058] Fourthly, this application provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of the aforementioned pulverized coal boiler health status diagnosis method.

[0059] As can be seen, this application is applied to a pulverized coal boiler system. Based on a preset interface, it obtains the original pulverized coal boiler operating data corresponding to the pulverized coal boiler system from the distributed control system connected to the pulverized coal boiler system. Based on a preset multi-dimensional pulverized coal boiler health assessment index system, a preset soft measurement model, a health score model, and the original pulverized coal boiler operating data, it performs multi-dimensional health status quantification to determine the health status quantification result. The multi-dimensional dimensions include heat exchange efficiency, air leakage status, steam drum stability, coal quality status, instrument health, steam-water system, and auxiliary equipment status. Based on the health status quantification result and the historical health status quantification result corresponding to the pulverized coal boiler system, it analyzes and uses the corresponding analysis results to identify explicit and implicit faults to determine the fault identification result. Based on the health status quantification result and the fault identification result, it determines the current health status diagnosis result corresponding to the pulverized coal boiler system, and visualizes the health status diagnosis result based on a preset diagnosis rule base. The health status diagnosis result includes a radar chart. In other words, this application first obtains raw pulverized coal boiler operating data from the connected distributed control system. Then, combining a preset multi-dimensional pulverized coal boiler health assessment index system, a preset soft sensor model, and a health scoring model, it determines the multi-dimensional quantitative results of the health status. Next, using the quantitative results of the health status and the corresponding historical quantitative results of the pulverized coal boiler system, it identifies explicit and implicit faults. Based on the fault identification results and the quantitative results of the health status, it determines the current health status diagnosis result and visualizes the diagnosis result based on a preset diagnostic rule base. This approach solves the problems existing in related solutions, thereby improving the efficiency, reliability, and accuracy of pulverized coal boiler health diagnosis, reducing the false positive rate, avoiding the waste of human and time resources of relevant personnel, and ultimately improving the operational stability, safety, and economy of the pulverized coal boiler. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0061] Figure 1 A flowchart of a health status diagnosis method for a pulverized coal boiler is provided in this application;

[0062] Figure 2 A schematic diagram of a health status diagnostic device for a pulverized coal boiler provided in this application;

[0063] Figure 3This application provides a structural diagram of an electronic device. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0065] The existing health diagnosis schemes based on monitoring data have the following problems: (1) Alarm proliferation and isolation: Threshold alarms are set for single parameters, resulting in a large number of alarms that are independent of each other, making it difficult to quickly locate the root cause; (2) Lack of overall status assessment: To answer questions such as "What is the current overall health status of the pulverized coal boiler?", one can only rely on the personal experience of relevant staff to make a judgment; (3) Hidden faults are difficult to detect: For faults that occur slowly, such as ash accumulation, slow changes in coal quality, and instrument drift, they are often only discovered when they have developed to a serious level; (4) Low level of visualization: Traditional interfaces are mainly based on trend charts and lists, and the information is presented in a scattered manner and is not intuitive enough.

[0066] Therefore, this application provides a health status diagnosis scheme for pulverized coal boilers, which can solve the problems existing in the existing related schemes, thereby improving the efficiency, reliability and accuracy of pulverized coal boiler health diagnosis, reducing the misjudgment rate, avoiding the waste of human and time resources of relevant personnel, and thus improving the operational stability, safety and economy of pulverized coal boilers.

[0067] See Figure 1 As shown, this invention discloses a health status diagnosis method for a pulverized coal boiler, applied to a pulverized coal boiler system, comprising:

[0068] Step S11: Based on a preset interface, obtain the original pulverized coal boiler operation data corresponding to the pulverized coal boiler system from the distributed control system connected to the pulverized coal boiler system.

[0069] In this embodiment, it is necessary to first obtain the operating data of the pulverized coal furnace system. Specifically, the original operating data of the pulverized coal furnace system can be obtained from the distributed control system connected to the pulverized coal furnace system in real time or according to the corresponding sampling period and preset interface. The sampling period can be configured according to actual needs and can be a regular period or an irregular period.

[0070] Step S12: Based on the preset multi-dimensional pulverized coal boiler health assessment index system, preset soft measurement model, health score model and the original pulverized coal boiler operation data, perform multi-dimensional health status quantification to determine the health status quantification result; the multi-dimensional dimensions include heat exchange efficiency dimension, air leakage state dimension, steam drum stability dimension, coal quality state dimension, instrument health dimension, steam-water system dimension and auxiliary machine state dimension.

[0071] In this embodiment, after obtaining the original pulverized coal boiler operating data, a series of high-level characteristic parameters are calculated based on a predefined mathematical model and algorithm. These values ​​are then used to quantify the health status in multiple dimensions. Specifically, based on a preset multi-dimensional pulverized coal boiler health assessment index system, a preset soft measurement model, and the original pulverized coal boiler operating data, the current parameter values ​​of the multi-dimensional pulverized coal boiler health assessment parameters are determined. These multi-dimensional pulverized coal boiler health assessment parameters include the air preheater leakage coefficient, heat exchanger ash accumulation index, feedwater flow rate-steam flow rate difference, and coal quality score. Based on a preset multi-dimensional pulverized coal boiler health assessment strategy, a health scoring model, and the current parameter values, multi-dimensional health status quantification is performed to determine the health status quantification result. A data integration interface exists between the health scoring model and the distributed control system for data transmission.

[0072] It is important to understand that, regarding the determination of parameter values, as shown in Table 1, this embodiment requires the use of a soft sensor model to calculate advanced characteristic parameters, including the heat exchanger ash accumulation index, air preheater leakage coefficient, real-time coal quality score, and feedwater-steam flow difference. Specifically: based on the original pulverized coal boiler operating data, the current flue gas temperature difference of the heat exchanger is obtained, and combined with the theoretical flue gas temperature difference under the design conditions and the first preset soft sensor model, the current parameter value of the heat exchanger ash accumulation index is determined; the heat exchanger includes a flow meter... The system includes a boiler, economizer, and air preheater; based on the original pulverized coal boiler operating data, the oxygen content on the furnace side and the oxygen content on the flue gas side are obtained; based on the oxygen content on the furnace side, the oxygen content on the flue gas side, and a second preset soft measurement model, the excess air coefficient at the flue gas outlet and the excess air coefficient at the furnace outlet are analyzed and compared to determine the current parameter value of the air preheater leakage coefficient; based on the original pulverized coal boiler operating data, the current parameter values ​​of the steam drum water level change frequency and / or steam drum pressure change frequency are obtained; based on the original pulverized coal boiler operating data... The system acquires data including boiler evaporation rate, blower inlet air temperature, furnace outlet flue gas temperature, pulverizer current, exhaust gas temperature, and feedwater temperature. Based on a third preset soft measurement model, the system determines the current parameter value of the coal quality score. Based on instrument data from the original pulverized coal boiler operating data, the system determines the current parameter value of the correlation between multiple instrument parameters. The instrument data includes boiler water conductivity, boiler water pH and phosphate content, wind speed, and damper opening. Based on the original pulverized coal boiler operating data, the system acquires feedwater flow rate and steam flow rate to determine the current parameter value of the feedwater flow rate-steam flow rate difference. Based on the original pulverized coal boiler operating data, the system acquires the current flue gas temperature value at the target location in the steam-water system. The steam-water system is the steam-water system within the pulverized coal boiler system. Based on the original pulverized coal boiler operating data, the system acquires the current parameter values ​​of the auxiliary equipment current and auxiliary equipment bearing temperature. The auxiliary equipment includes a blower, an induced draft fan, and a pulverizer.

[0073] Table 1. Multi-dimensional Health Assessment Indicators for Pulverized Coal Boilers

[0074]

[0075] Regarding the quantification of health status, in this embodiment, the obtained current parameter values ​​are compared with their respective thresholds, design values, or ideal trends, and mapped to a health score of 0-100 using linear or nonlinear functions. Specifically: based on the first health scoring model, the current parameter value of the heat exchanger ash accumulation index, the number of heat exchangers in the pulverized coal boiler system, and a preset ash accumulation threshold, the health status of the heat exchange efficiency dimension is quantified to determine the first health status quantification result; based on the second health scoring model, the current parameter value of the air preheater leakage coefficient, a preset upper limit for leakage, and a preset lower limit for leakage, the health status of the leakage state dimension is quantified to determine the second health status quantification result; based on the third health scoring model, the current parameter values ​​of the steam drum water level mutation frequency and / or the steam drum pressure mutation frequency, and the first historical parameter values ​​within a first preset period, parameter value mutation events are detected, and the corresponding mutation event detection results are used to quantify the health status of the steam drum stability dimension. The following steps are taken to determine the third health status quantification result: Based on the fourth health scoring model and the current parameter value of the coal quality score, the health status of the coal quality status dimension is quantified to determine the fourth health status quantification result; Based on the fifth health scoring model, the current parameter value of the correlation of multiple instrument parameters, and the second historical parameter value within the second preset period, abnormal parameter value events are detected, and the corresponding abnormal event detection results are used to quantify the health status of the instrument health dimension to determine the fifth health status quantification result; Based on the sixth health scoring model, the current parameter value of the difference between the feedwater flow rate and the steam flow rate, and the current flue gas temperature value, the health status of the steam-water system dimension is quantified to determine the sixth health status quantification result; Based on the seventh health scoring model, the current parameter values ​​of the auxiliary machine current and the auxiliary machine bearing temperature, abnormal parameter value events and sudden events are detected, and the corresponding value detection results are used to quantify the health status of the auxiliary machine status dimension to determine the seventh health status quantification result.

[0076] Understandably, based on Table 1, the relevant implementation steps for determining the multi-dimensional parameter values ​​and quantifying health status can be summarized as follows:

[0077] (1) Heat exchange efficiency dimension.

[0078] ① Calculation of characteristic parameters:

[0079] If the temperature difference across the same side of the heat exchanger decreases (defined as the same side for flue gas, and the left and right sides for both sides), it indicates a decline in heat exchange and potential coking and ash accumulation. Based on the temperature drop trend on the flue gas side of the heat exchanger, a heat exchanger ash accumulation index is defined. The ash accumulation index is defined as follows:

[0080] .

[0081] In the formula, This represents the theoretical temperature difference of the flue gas under design conditions. ; This indicates the real-time measured difference in flue gas temperature. .

[0082] ② Health Calculation:

[0083] Each pulverized coal boiler has multiple heat exchangers (superheater, economizer, air preheater, etc.). If the ash accumulation index of any heat exchanger exceeds the set value, 20 points will be deducted from the heat exchange efficiency score.

[0084] Heat exchange efficiency score = 100 - 20 × number of ash-accumulated heat exchangers.

[0085] (2) Air leakage status dimension.

[0086] ① Calculation of characteristic parameters:

[0087] The air preheater leakage coefficient is calculated using the excess air coefficient at the flue gas exhaust and the excess air coefficient at the furnace outlet. Let the oxygen content on furnace side A be... The oxygen content on side B of the furnace is The oxygen content at the smoke exhaust point is .

[0088] Average oxygen content at furnace outlet :

[0089] ;

[0090] Excess air coefficient at furnace outlet :

[0091] ;

[0092] Excess air coefficient at the smoke exhaust point :

[0093] ;

[0094] Air preheater leakage coefficient :

[0095] .

[0096] ② Health Calculation:

[0097] When the air leakage coefficient is lower than the set value, the air leakage status score = 100 - 20 × air leakage coefficient / set value; when the air leakage coefficient is higher than the set value, the air leakage status score = 80 - 80 × (air leakage coefficient - set value) / set value.

[0098] (3) Steam drum stability dimension.

[0099] ① Calculation of characteristic parameters:

[0100] The characteristic parameters are the steam drum water level and steam drum pressure, which can be obtained directly from the original pulverized coal boiler operating data without additional calculation.

[0101] ② Health Calculation:

[0102] A full score is awarded if there are no sudden changes in steam drum pressure / water level within a unit of time (e.g., two hours). If a sudden change occurs, 20 points are deducted from the full score of 100 points, and so on, to obtain the steam drum stability score.

[0103] (4) Media state dimension.

[0104] ① Calculation of characteristic parameters:

[0105] The detailed calculation formula for the coal quality score of a pulverized coal boiler is as follows: Assume the following parameters: boiler evaporation rate C, feedwater temperature D, flue gas temperature at furnace outlet (left) E, flue gas temperature at furnace outlet (right) F, current of mill A G, current of mill B H, primary desuperheating water flow rate (left) I, primary desuperheating water flow rate (right) J, secondary desuperheating water flow rate (left) K, secondary desuperheating water flow rate (right) L, exhaust gas temperature (left) M, exhaust gas temperature (right) N, inlet air temperature of blower A O, and exhaust gas oxygen content P.

[0106] The following calculations are required in sequence: Total desuperheating water volume Q: Q = I + J + K + L; Furnace average temperature R: R = (E + F) / 2; Flue gas average temperature S:

[0107] ;

[0108] Smoke exhaust loss T:

[0109] ;

[0110] Traffic correction sub-item U:

[0111] ;

[0112] Coefficient V:

[0113] ;

[0114] Deduction item 1:

[0115] ;

[0116] Deduction item two:

[0117] ;

[0118] Deduction item three:

[0119] ;

[0120] Finally, the total score Z is obtained: Z = 100 - WXYU.

[0121] ② Health Calculation:

[0122] The total coal quality score Z is directly used as the medium condition score.

[0123] (5) Instrument health dimension.

[0124] ① Calculation of characteristic parameters:

[0125] The characteristic parameters consist of multiple sets of correlated parameters (such as boiler water conductivity / pH (acidity / alkalinity) / phosphate content, wind speed / damper opening, etc.). Each set of parameters is correlated with each other. For example, boiler water conductivity / pH / phosphate content increase and decrease synchronously, and wind speed and damper opening are positively correlated. These instrument data can be obtained directly from the original pulverized coal boiler operating data, and then algorithms are used to determine whether there are any anomalies in the relationships between each set of parameters.

[0126] ② Health Calculation:

[0127] For each abnormality occurring within a unit of time (e.g., two hours), 20 points are deducted from the maximum score of 100, thus obtaining the instrument health score.

[0128] (6) Soft drink system dimension.

[0129] ① Calculation of characteristic parameters:

[0130] The characteristic parameters are the difference between the feedwater flow rate and the steam flow rate (ΔF) and the flue gas temperature at various key locations in the steam-water system. In the formula, This indicates the steam flow rate, in t / h. Let F represent the water flow rate, t / h. The absolute value of the difference between F and F is used to obtain ΔF.

[0131] ② Health Calculation:

[0132] If ΔF exceeds the set value, 20 points will be deducted from the full score of 100. If the smoke temperature is abnormal at the same time, another 20 points will be deducted. The score of the soda system will be obtained in this way.

[0133] (7) Auxiliary machine status dimension.

[0134] ① Calculation of characteristic parameters:

[0135] The characteristic parameters are the current and bearing temperature of the blower / induced draft fan / coal mill, etc.

[0136] ② Health Calculation:

[0137] Full marks are awarded if there are no sudden current changes or abnormal temperatures. If any auxiliary machine experiences a sudden current change and the bearing temperature is abnormal, 40 points are deducted. The auxiliary machine status score is obtained accordingly.

[0138] In this way, the system integrates soft measurement technology and uses easily measurable parameters to calculate difficult-to-measure parameters such as dust accumulation index and air leakage coefficient in real time, providing a data foundation for health score. This accurately maps physical parameters of different dimensions and ranges into standardized health scores, thereby improving the comprehensiveness of diagnosis.

[0139] Step S13: Analyze the health status quantification results and the historical health status quantification results corresponding to the pulverized coal boiler system, and use the corresponding analysis results to identify explicit and implicit faults in order to determine the fault identification results.

[0140] In this embodiment, trend analysis, correlation analysis, and soft sensing techniques will be used to perform historical health status data for early identification and warning of slowly occurring faults such as dust accumulation, air leakage, instrument failure, and initial leakage. Multi-parameter fusion analysis will be used to classify multiple related alarms into higher-level subsystem health status issues, thereby reducing the number of alarms, improving alarm quality, and enhancing the foresight of fault identification. Specifically: Detection will be performed based on the quantified health status results to determine the target detection result; if the target detection result indicates an anomaly in the current health status of several dimensions, multi-dimensional fault correlation analysis will be performed based on the historical quantified health status results within a third preset period, the target detection result, and a preset fault knowledge base to determine the explicit fault identification result; trend prediction and deviation analysis will be performed based on the quantified health status results and the historical quantified health status results within a fourth preset period to determine the first analysis result; wherein the duration of the fourth preset period is longer than the duration of the third preset period; and the implicit fault identification result will be determined based on the analysis result, preset implicit fault identification rules, and the preset fault knowledge base.

[0141] It is important to understand that, regarding the multi-dimensional fault correlation analysis of explicit faults, in this embodiment, if the target detection result indicates an abnormality in the current health status of multiple dimensions, a fault correlation analysis and propagation path analysis are performed based on the historical health status quantification results within a third preset period and the target detection result to determine the second analysis result; based on the second analysis result and a preset fault knowledge base, root cause analysis is performed to determine the root cause analysis result; based on the root cause analysis result and the health status quantification result, action recommendation information is determined; and based on the root cause analysis result and the action recommendation information, the explicit fault identification result is determined. In other words, in this embodiment, when performing correlation analysis, the root cause of the fault is analyzed by analyzing the propagation and evolution path of the fault, and action recommendation information is provided based on the corresponding root cause.

[0142] Step S14: Based on the health status quantification result and the fault identification result, determine the current health status diagnosis result corresponding to the pulverized coal boiler system, and visualize the health status diagnosis result based on the preset diagnosis rule library; the health status diagnosis result includes a radar chart.

[0143] In this embodiment, N key dimensions are selected, such as the 7 dimensions shown in Table 1, and their health scores are plotted on a radar chart. Dimensions with excessively low scores (e.g., <60) are highlighted with an alarm, and users can click on the dimension to drill down to a detailed diagnostic analysis page. Specifically, based on the quantitative results of the health status and the fault identification results, a graphical representation is created to determine the current health status diagnosis result corresponding to the pulverized coal boiler system. The health status diagnosis result includes a radar chart. During the visualization of the health status diagnosis result, the radar chart is monitored to determine if a preset pattern appears, thus determining the monitoring result. If the monitoring result is positive, the corresponding root cause information is determined and displayed based on a preset diagnostic rule base. In other words, the system has a built-in diagnostic rule base; when the radar chart exhibits a specific pattern (e.g., a single dimension depression, or multiple adjacent dimensions simultaneously depression), it can automatically suggest possible root causes, thereby improving intuitiveness.

[0144] It is important to understand that the displayed radar chart interface should include the following elements:

[0145] 1) Real-time status line: A contour line connecting the scores of N dimensions;

[0146] 2) Health baseline: The outermost N-sided polygon represents the perfect score (100 points);

[0147] 3) Alarm threshold line: The inner N-sided polygon (such as the 80-point line) will prompt attention when the outline shrinks to within this line;

[0148] 4) Color coding: Green (healthy, >80), Yellow (sub-healthy, 60-80), Red (alarm, <60);

[0149] 5) Interactive elements: Hover over to display dimension details; click to jump to the detailed analysis interface for that dimension.

[0150] In addition, the radar chart display interface in this application has a linkage interface with the sound and light alarm and voice broadcast system, which is used to realize alarm while displaying a comprehensive multi-dimensional health status. The displayed radar chart interface also has interactive functions, such as drill-down and hover prompts, which allow users to drill down from the comprehensive display interface to the detailed analysis interface of specific faults, thereby improving interactivity.

[0151] In summary, this embodiment establishes an indicator system covering multiple independent dimensions, including heat exchange, air leakage, stability, coal quality, instrumentation, water vapor, and auxiliary equipment, converting the parameters of each dimension into standardized health scores. These health scores from multiple dimensions are then integrated and displayed in a radar chart format, visually presenting the overall status and weak points through contour lines, color coding, and threshold lines. In this way, (1) the problem of isolated alarms is solved: through multi-parameter fusion analysis, multiple related alarms are attributed to the health status of higher-level subsystems, reducing the number of alarms and improving the quality of alarms; (2) the problem of missing status assessment is solved: a multi-dimensional quantified assessment system for the health of pulverized coal boilers is constructed, transforming complex operating data into concise and clear health scores, and realizing an objective and quantitative evaluation of the overall status of pulverized coal boilers; (3) the problem of early warning of hidden faults is solved: through trend analysis, correlation analysis and soft measurement technology, early identification and early warning of slowly occurring faults such as ash accumulation, air leakage, instrument failure, and initial leakage are carried out; (4) the problem of unintuitive visualization is solved: radar charts are used as the main visualization tool, integrating the health scores of multiple dimensions into one chart, making the health status and weak links of pulverized coal boilers clear at a glance.

[0152] Therefore, this application first obtains raw pulverized coal boiler operating data from the connected distributed control system. Then, combining a preset multi-dimensional pulverized coal boiler health assessment index system, a preset soft measurement model, and a health scoring model, it determines the multi-dimensional quantitative results of the health status. Next, using the quantitative results of the health status and the corresponding historical quantitative results of the pulverized coal boiler system, it identifies explicit and implicit faults. Based on the fault identification results and the quantitative results of the health status, it determines the current health status diagnosis result and visualizes the diagnosis result based on a preset diagnostic rule base. This approach solves the problems existing in related solutions, thereby improving the efficiency, reliability, intuitiveness, comprehensiveness, fault identification foresight, accuracy, and interactivity of pulverized coal boiler health diagnosis, reducing the false positive rate, avoiding the waste of human and time resources of relevant personnel, and ultimately improving the operational stability, safety, and economy of the pulverized coal boiler.

[0153] See Figure 2 As shown in the illustration, this application also discloses a health status diagnostic device for a pulverized coal boiler, applied to a pulverized coal boiler system, comprising:

[0154] The data acquisition module 11 is used to acquire the original pulverized coal boiler operation data corresponding to the pulverized coal boiler system from the distributed control system connected to the pulverized coal boiler system based on a preset interface.

[0155] The multidimensional health quantification module 12 is used to quantify the health status in multiple dimensions based on a preset multidimensional pulverized coal boiler health assessment index system, a preset soft measurement model, a health score model, and the original pulverized coal boiler operating data, so as to determine the health status quantification result; the multiple dimensions include heat exchange efficiency dimension, air leakage state dimension, steam drum stability dimension, coal quality state dimension, instrument health dimension, steam-water system dimension, and auxiliary machine state dimension.

[0156] The fault identification module 13 is used to analyze the health status quantification results and the historical health status quantification results corresponding to the pulverized coal boiler system, and to use the corresponding analysis results to identify explicit and implicit faults in order to determine the fault identification results.

[0157] The diagnostic display module 14 is used to determine the current health status diagnostic result corresponding to the pulverized coal boiler system based on the health status quantification result and the fault identification result, and to visualize the health status diagnostic result based on a preset diagnostic rule library; the health status diagnostic result includes a radar chart.

[0158] In some specific embodiments, the multidimensional health quantification module 12 may specifically include:

[0159] The parameter value determination submodule is used to determine the current parameter values ​​of multi-dimensional pulverized coal boiler health assessment parameters based on the preset multi-dimensional pulverized coal boiler health assessment index system, the preset soft measurement model, and the original pulverized coal boiler operating data. The multi-dimensional pulverized coal boiler health assessment parameters include air preheater leakage coefficient, heat exchanger ash accumulation index, feedwater flow rate-steam flow rate difference, and coal quality score.

[0160] The quantification result determination submodule is used to perform multi-dimensional health status quantification based on the preset multi-dimensional pulverized coal boiler health assessment strategy, health scoring model and the current parameter value, so as to determine the health status quantification result.

[0161] In some specific embodiments, the parameter value determination submodule may specifically include:

[0162] The first parameter value determination unit is used to obtain the current flue gas temperature difference value of the heat exchanger based on the original pulverized coal boiler operating data, and determine the current parameter value of the ash accumulation index of the heat exchanger by combining the theoretical flue gas temperature difference value under the design conditions and the first preset soft measurement model; the heat exchanger includes a superheater, an economizer and an air preheater;

[0163] The oxygen acquisition unit is used to acquire the oxygen content on the furnace side and the oxygen content on the flue gas side based on the original pulverized coal boiler operating data.

[0164] The second parameter value determination unit is used to analyze and compare the excess air coefficient at the flue gas outlet and the excess air coefficient at the furnace outlet based on the oxygen content on the furnace side, the oxygen content on the flue gas side, and the second preset soft measurement model, so as to determine the current parameter value of the air preheater leakage coefficient.

[0165] The third parameter value determination unit is used to obtain the current parameter values ​​of the steam drum water level change frequency and / or steam drum pressure change frequency based on the original pulverized coal boiler operating data.

[0166] The data acquisition unit is used to acquire boiler evaporation rate, blower inlet air temperature, furnace outlet flue gas temperature, coal mill current, exhaust gas temperature and feedwater temperature based on the original pulverized coal boiler operating data.

[0167] The fourth parameter value determination unit is used to determine the current parameter value of the coal quality score based on the third preset soft measurement model, the boiler evaporation rate, the blower inlet air temperature, the furnace outlet flue gas temperature, the coal mill current, the flue gas temperature, and the feed water temperature.

[0168] The fifth parameter value determination unit is used to determine the current parameter value of the correlation of multiple instrument parameters based on the instrument data in the original pulverized coal boiler operation data; the instrument data includes boiler water conductivity, boiler water pH and boiler water phosphate content, wind speed and damper opening;

[0169] The sixth parameter value determination unit is used to obtain the feedwater flow rate and steam flow rate based on the original pulverized coal boiler operating data, so as to determine the current parameter value of the feedwater flow rate-steam flow rate difference;

[0170] The flue gas temperature acquisition unit is used to acquire the current flue gas temperature value at a target location in the steam-water system based on the original pulverized coal boiler operating data; the steam-water system is the steam-water system in the pulverized coal boiler system.

[0171] The seventh parameter value determination unit is used to obtain the current parameter values ​​of auxiliary machine current and auxiliary machine bearing temperature based on the original pulverized coal boiler operating data; the auxiliary machine includes a blower, an induced draft fan and a coal mill.

[0172] In some specific embodiments, the quantization result determination submodule may specifically include:

[0173] The first result determination unit is used to quantify the health status of the heat exchange efficiency dimension based on the first health score model, the current parameter value of the heat exchanger ash accumulation index, the number of heat exchangers in the pulverized coal boiler system, and the preset ash accumulation threshold, so as to determine the first health status quantification result.

[0174] The second result determination unit is used to quantify the health status of the leakage state dimension based on the second health score model, the current parameter value of the air preheater leakage coefficient, the preset upper limit value of leakage and the preset lower limit value of leakage, so as to determine the second health status quantification result.

[0175] The third result determination unit is used to detect parameter value mutation events based on the third health score model, the current parameter values ​​of the steam drum water level mutation frequency and / or the steam drum pressure mutation frequency and the first historical parameter values ​​within the first preset period, and to quantify the health status of the steam drum stability dimension using the corresponding mutation event detection results, so as to determine the third health status quantification result.

[0176] The fourth result determination unit is used to quantify the health status of coal quality based on the fourth health score model and the current parameter value of the coal quality score, so as to determine the fourth health status quantification result.

[0177] The fifth result determination unit is used to detect abnormal parameter value events based on the fifth health score model, the current parameter value of the correlation of multiple instrument parameters, and the second historical parameter value within the second preset period, and to use the corresponding abnormal event detection results to quantify the health status of the instrument health dimension, so as to determine the fifth health status quantification result.

[0178] The sixth result determination unit is used to quantify the health status of the steam-water system based on the sixth health score model, the current parameter value of the difference between the water flow rate and the steam flow rate, and the current flue gas temperature value, so as to determine the sixth health status quantification result.

[0179] The seventh result determination unit is used to detect abnormal and sudden events of parameter values ​​based on the seventh health score model, the current of the auxiliary machine and the temperature of the auxiliary machine bearing, and to use the corresponding value detection results to quantify the health status of the auxiliary machine in the dimension of auxiliary machine status, so as to determine the seventh health status quantification result.

[0180] In some specific embodiments, the fault identification module 13 may specifically include:

[0181] The detection submodule is used to perform detection based on the quantified health status results to determine the target detection result;

[0182] The explicit fault identification submodule is used to determine the explicit fault identification result by performing multi-dimensional fault correlation analysis based on the historical health status quantification results within the third preset period, the target detection results, and the preset fault knowledge base if the target detection results indicate that there is an abnormality in the current health status of several dimensions.

[0183] The analysis submodule is used to perform trend prediction and deviation analysis based on the quantitative results of the health status and the historical quantitative results of the health status within the fourth preset period, so as to determine the first analysis result; wherein the duration of the fourth preset period is longer than the duration of the third preset period.

[0184] The latent fault identification submodule is used to determine the latent fault identification result based on the analysis results, the preset latent fault identification rules, and the preset fault knowledge base.

[0185] In some specific embodiments, the explicit fault identification submodule may specifically include:

[0186] The correlation analysis unit is used to perform correlation analysis and propagation path analysis of faults based on the historical health status quantification results within a third preset period and the target detection results if the target detection results indicate that the current health status of multiple dimensions is abnormal, so as to determine the second analysis result.

[0187] The root cause analysis unit is used to perform root cause analysis based on the second analysis result and the preset fault knowledge base to determine the root cause analysis result;

[0188] The recommendation information determination unit is used to determine the recommended measures information based on the root cause analysis results and the health status quantification results;

[0189] The identification result determination unit is used to determine the overt fault identification result based on the root cause analysis results and the proposed measures information.

[0190] In some specific embodiments, the diagnostic display module 14 may specifically include:

[0191] The diagnostic result determination unit is used to perform graphic drawing based on the health status quantification result and the fault identification result to determine the current health status diagnostic result corresponding to the pulverized coal boiler system; the health status diagnostic result includes a radar chart.

[0192] A visualization monitoring unit is used to monitor whether the current radar chart shows a preset pattern during the visualization display of the health status diagnosis results, so as to determine the monitoring results;

[0193] The root cause information display unit is used to determine and display the corresponding root cause information based on a preset diagnostic rule base if the monitoring result is yes.

[0194] Furthermore, embodiments of this application also disclose an electronic device, Figure 3 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0195] Figure 3 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the pulverized coal boiler health status diagnosis method disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0196] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0197] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0198] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the pulverized coal boiler health status diagnosis method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0199] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for diagnosing the health status of a pulverized coal boiler. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0200] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0201] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0202] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0203] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0204] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of diagnosing a health state of a coal-fired boiler, characterized by, The application is applied to a pulverized coal furnace system, comprising: Based on a preset interface, the original pulverized coal furnace operation data corresponding to the pulverized coal furnace system is acquired from a distributed control system connected with the pulverized coal furnace system; Based on a preset multi-dimensional pulverized coal furnace health evaluation index system, a preset soft measurement model, a health degree scoring model and the original pulverized coal furnace operation data, a multi-dimensional health state quantification is performed to determine a health state quantification result; the multi-dimensions include heat exchange efficiency dimension, air leakage state dimension, drum stability dimension, coal quality state dimension, instrument health dimension, steam-water system dimension and auxiliary machine state dimension; Based on the health state quantification result and the historical health state quantification result corresponding to the pulverized coal furnace system, analysis is performed, and corresponding analysis results are used to identify explicit faults and implicit faults to determine a fault identification result; Based on the health state quantification result and the fault identification result, a health state diagnosis result corresponding to the pulverized coal furnace system at present is determined, and the health state diagnosis result is visually displayed based on a preset diagnosis rule base; the health state diagnosis result includes a radar chart.

2. The method of diagnosing the health state of a coal powder furnace according to claim 1, characterized by, The multi-dimensional health state quantification based on the preset multi-dimensional pulverized coal furnace health evaluation index system, the preset soft measurement model, the health degree scoring model and the original pulverized coal furnace operation data to determine the health state quantification result comprises: Based on the preset multi-dimensional pulverized coal furnace health evaluation index system, the preset soft measurement model and the original pulverized coal furnace operation data, current parameter values of multi-dimensional pulverized coal furnace health evaluation parameters are determined; the multi-dimensional pulverized coal furnace health evaluation parameters include air preheater air leakage coefficient, heat exchanger ash deposition index, feedwater flow-steam flow difference and coal quality score; Based on the preset multi-dimensional pulverized coal furnace health degree evaluation strategy, the health degree scoring model and the current parameter values, multi-dimensional health state quantification is performed to determine the health state quantification result.

3. The method of diagnosing the health state of a coal powder furnace according to claim 2, characterized by, The determination of the current parameter values of the multi-dimensional pulverized coal furnace health evaluation parameters based on the preset multi-dimensional pulverized coal furnace health evaluation index system, the preset soft measurement model and the original pulverized coal furnace operation data comprises: Based on the original pulverized coal furnace operation data, a current heat exchanger flue gas temperature difference value is acquired, and combined with a theoretical flue gas temperature difference value under a design working condition and a first preset soft measurement model, a current parameter value of the heat exchanger ash deposition index is determined; the heat exchanger includes a superheater, an economizer and an air preheater; Based on the original pulverized coal furnace operation data, a furnace side oxygen content and a flue gas discharge side oxygen content are acquired; Based on the furnace side oxygen content, the flue gas discharge side oxygen content and a second preset soft measurement model, analysis and comparison of the excess air coefficient at the flue gas discharge and the excess air coefficient at the furnace outlet are performed to determine the current parameter value of the air preheater air leakage coefficient; Based on the original pulverized coal furnace operation data, a current parameter value of a steam drum water level mutation frequency and / or a steam drum pressure mutation frequency is acquired; Based on the original pulverized coal furnace operation data, a boiler evaporation capacity, an air inlet temperature of an air supply fan, a furnace outlet flue gas temperature, a coal mill current, a flue gas discharge temperature and a feedwater temperature are acquired; Determine a current parameter value of the coal quality score based on the third preset soft measurement model, the boiler evaporation capacity, the air inlet temperature of the air blower, the flue gas temperature at the furnace outlet, the current of the coal mill, the exhaust gas temperature, and the feed water temperature; Determine a current parameter value of the correlation of multiple instrument parameters based on instrument data in the original coal-fired boiler operation data; the instrument data includes the conductivity of the boiler water, the pH value and the phosphoric acid content of the boiler water, the air speed, and the air door opening degree; Obtain the feed water flow and the steam flow based on the original coal-fired boiler operation data to determine a current parameter value of the feed water flow-steam flow difference; Obtain a current flue gas temperature value at a target position in the steam-water system based on the original coal-fired boiler operation data; the steam-water system is a steam-water system in the coal-fired boiler system; Obtain current parameter values of the auxiliary machine current and the auxiliary machine bearing temperature based on the original coal-fired boiler operation data; the auxiliary machine includes an air blower, an induced draft fan, and a coal mill.

4. The method of diagnosing the health state of a coal powder furnace according to claim 3, characterized by, Quantify the health state in multiple dimensions based on the preset multi-dimensional coal-fired boiler health degree evaluation strategy, the health degree scoring model, and the current parameter values to determine a health state quantification result, including: Quantify the health state in the heat exchange efficiency dimension based on a first health degree scoring model, a current parameter value of the heat exchanger ash deposition index, the number of heat exchangers in the coal-fired boiler system, and a preset ash deposition threshold to determine a first health state quantification result; Quantify the health state in the air leakage state dimension based on a second health degree scoring model, a current parameter value of the air preheater air leakage coefficient, a preset upper limit value of air leakage, and a preset lower limit value of air leakage to determine a second health state quantification result; Detect parameter value mutation events based on a third health degree scoring model, a current parameter value of the frequency of steam drum water level mutation and / or the frequency of steam drum pressure mutation, and a first historical parameter value in a first preset period, and quantify the health state in the steam drum stability dimension based on the corresponding mutation event detection result to determine a third health state quantification result; Quantify the health state in the coal quality state dimension based on a fourth health degree scoring model and a current parameter value of the coal quality score to determine a fourth health state quantification result; Detect parameter value abnormal events based on a fifth health degree scoring model, a current parameter value of the correlation of multiple instrument parameters, and a second historical parameter value in a second preset period, and quantify the health state in the instrument health dimension based on the corresponding abnormal event detection result to determine a fifth health state quantification result; Quantify the health state in the steam-water system dimension based on a sixth health degree scoring model, a current parameter value of the feed water flow-steam flow difference, and a current flue gas temperature value to determine a sixth health state quantification result; Detect parameter value abnormal events and mutation events based on a seventh health degree scoring model, current parameter values of the auxiliary machine current and the auxiliary machine bearing temperature, and quantify the health state in the auxiliary machine state dimension based on the corresponding value detection result to determine a seventh health state quantification result.

5. The method of diagnosing the health state of a coal powder furnace according to claim 1, characterized by, The health state quantification result and the corresponding historical health state quantification result of the coal-fired boiler system are analyzed, and corresponding analysis results are used to identify explicit faults and implicit faults, including: Detecting based on the health state quantification result to determine a target detection result; If the target detection result indicates that the health state of the current several dimensions is abnormal, then based on the historical health state quantification result in the third preset period, the target detection result and the preset fault knowledge base, multi-dimensional fault correlation analysis is performed to determine an explicit fault identification result; Based on the health state quantification result, the historical health state quantification result in the fourth preset period, trend prediction and deviation analysis are performed to determine a first analysis result; wherein the time length of the fourth preset period is greater than the time length of the third preset period; Based on the analysis result, the preset implicit fault identification rule and the preset fault knowledge base, an implicit fault identification result is determined.

6. The method of diagnosing the health state of a coal powder furnace according to claim 5, characterized by, If the target detection result indicates that the health state of the current several dimensions is abnormal, then based on the historical health state quantification result in the third preset period, the target detection result and the preset fault knowledge base, multi-dimensional fault correlation analysis is performed, including: If the target detection result indicates that the health state of the current several dimensions is abnormal, based on the historical health state quantification result in the third preset period and the target detection result, the correlation analysis and propagation path analysis of the fault are performed to determine a second analysis result; Based on the second analysis result and the preset fault knowledge base, root cause analysis is performed to determine a root cause analysis result; Based on the root cause analysis result and the health state quantification result, a measure suggestion information is determined; Based on the root cause analysis result and the measure suggestion information, an explicit fault identification result is determined.

7. The method of diagnosing the health state of a coal powder furnace according to any one of claims 1 to 6, characterized by, Based on the health state quantification result and the fault identification result, a health state diagnosis result corresponding to the coal-fired boiler system is determined, and based on a preset diagnosis rule base, the health state diagnosis result is visually displayed, including: Based on the health state quantification result and the fault identification result, a graph is drawn to determine a health state diagnosis result corresponding to the coal-fired boiler system; the health state diagnosis result includes a radar chart; In the process of visually displaying the health state diagnosis result, it is monitored whether the current radar chart appears a preset shape to determine a monitoring result; If the monitoring result is yes, then based on a preset diagnosis rule base, corresponding root cause information is determined and displayed.

8. A health status diagnostic device for a pulverized coal boiler, characterized in that, Applied to a coal-fired boiler system, including: A data acquisition module is configured to acquire original coal-fired boiler operation data corresponding to the coal-fired boiler system from a distributed control system connected to the coal-fired boiler system based on a preset interface; A multi-dimensional health quantification module is configured to perform multi-dimensional health state quantification based on a preset multi-dimensional coal-fired boiler health evaluation index system, a preset soft measurement model, a health degree scoring model, and the original coal-fired boiler operation data, to determine a health state quantification result; the multi-dimensions include heat exchange efficiency dimension, air leakage state dimension, drum stability dimension, coal quality state dimension, instrument health dimension, steam-water system dimension, and auxiliary machine state dimension; A fault identification module is configured to analyze the health state quantification result and a historical health state quantification result corresponding to the coal-fired boiler system, and identify explicit faults and implicit faults by using corresponding analysis results, to determine a fault identification result; A diagnosis display module is configured to determine a health state diagnosis result corresponding to the coal-fired boiler system based on the health state quantification result and the fault identification result, and visually display the health state diagnosis result based on a preset diagnosis rule base; the health state diagnosis result includes a radar chart.

9. An electronic device, comprising: The computer program is stored in the memory and executed by the processor to implement the health state diagnosis method of the coal-fired boiler according to any one of claims 1 to 7. The computer program is stored in the memory and executed by the processor to implement the health state diagnosis method of the coal-fired boiler according to any one of claims 1 to 7. ​ 10. A computer-readable storage medium, characterized in that, ​

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