Equipment fault level evaluation method and device, equipment, medium and program product

Abstract

The invention discloses an equipment fault level evaluation method and device, equipment, a medium and a program product, and the method comprises the steps: determining the initial operation fault level of power plant equipment based on the current operation monitoring data and historical operation monitoring data of the power plant equipment, and obtaining the associated equipment information of the power plant equipment, correcting the initial operation fault level based on the associated equipment information, obtaining a target operation fault level, obtaining appearance state information of the power plant equipment, determining an appearance fault level of the power plant equipment based on the appearance state information, and performing fault state evaluation on the power plant equipment based on the target operation fault level and the appearance fault level; according to the invention, fault grade analysis is carried out on the power plant equipment from the operation fault dimension and the appearance fault dimension, so that multi-dimensional fault assessment of the power plant equipment is realized, the accuracy of equipment fault assessment is effectively improved, the abnormity and potential problems of the equipment are found in time, and the operation efficiency and safety of the power plant are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power plants, and in particular to a method, device, equipment, medium and program product for evaluating equipment failure level. Background Art

[0002] As the main energy supply industry, the power industry has been under increasing load on power plant equipment as the demand for electricity continues to increase. In recent years, safety accidents have occurred frequently due to the chaotic management of power plant personnel and the rough management of power plant equipment. At present, the fault assessment method of power plant equipment is single, which cannot accurately detect equipment with abnormal faults, resulting in the inability to timely discover potential problems of equipment. Therefore, power plants are at great risk of safety accidents. Summary of the invention

[0003] The main purpose of the present invention is to provide a method, device, equipment, medium and program product for evaluating equipment fault level, aiming to solve the technical problem that the prior art cannot accurately detect equipment with fault anomalies, resulting in the inability to timely discover potential problems of the equipment.

[0004] To achieve the above object, the present invention provides a method for evaluating equipment failure level, the method comprising the following steps:

[0005] Determining an initial operation fault level of the power plant equipment based on current operation monitoring data and historical operation monitoring data of the power plant equipment;

[0006] Acquiring associated equipment information of the power plant equipment, and correcting the initial operation fault level based on the associated equipment information to obtain a target operation fault level;

[0007] Acquiring appearance status information of the power plant equipment, and determining an appearance fault level of the power plant equipment based on the appearance status information;

[0008] A fault level assessment is performed on the power plant equipment based on the target operating fault level and the appearance fault level.

[0009] Optionally, determining the initial operation fault level of the power plant equipment based on current operation monitoring data and historical operation monitoring data of the power plant equipment includes:

[0010] Determine the current operating state and the historical operating state of the power plant equipment based on the current operating monitoring data and the historical operating monitoring data of the power plant equipment;

[0011] Determine operation difference information of the power plant equipment according to the current operation state and the historical operation state;

[0012] Determine the current operation duration and the historical operation duration of the staff corresponding to the power plant equipment according to the current operation monitoring data and the historical operation monitoring data;

[0013] Determining operation difference information of the power plant equipment based on the current operation duration and the historical operation duration;

[0014] An initial operation fault level of the power plant equipment is determined according to the operation difference information and the operation difference information.

[0015] Optionally, the determining the operation difference information of the power plant equipment based on the current operation duration and the historical operation duration includes:

[0016] Acquiring image monitoring data of the power plant equipment;

[0017] Performing feature analysis on the image monitoring data to obtain current operation behavior information and historical operation behavior information of the staff corresponding to the power plant equipment;

[0018] Modifying the current operation duration and the historical operation duration based on the operation behavior information and the historical operation behavior information;

[0019] The operation difference information of the power plant equipment is determined according to the corrected current operation duration and the corrected historical operation duration.

[0020] Optionally, the acquiring the associated equipment information of the power plant equipment, and correcting the initial operation fault level based on the associated equipment information to obtain a target operation fault level includes:

[0021] Obtaining equipment network information of the power plant to which the power plant equipment belongs;

[0022] Determining attribute information and dependency information of each device in the power plant based on the device network information;

[0023] Determine the connection relationship and hierarchical relationship of each device according to the attribute information and the dependency information;

[0024] Building a device topology structure of the power plant based on the connection relationship and the hierarchical relationship;

[0025] The associated equipment information of the power plant equipment is acquired according to the equipment topology structure, and the initial operation fault level is corrected based on the associated equipment information to obtain a target operation fault level.

[0026] Optionally, acquiring the appearance status information of the power plant equipment and determining the appearance fault level of the power plant equipment based on the appearance status information includes:

[0027] Acquiring image monitoring data of the power plant equipment;

[0028] Segmenting the image monitoring data to obtain a plurality of image frames;

[0029] Classifying the multiple image frames based on timestamp information of each image frame to obtain a current image frame set and a historical image frame set of the power plant equipment;

[0030] Performing feature extraction on the current image frame set and the historical image frame set to obtain a current appearance feature vector and a historical appearance feature vector of the power plant equipment;

[0031] Acquire appearance change feature information of each component of the power plant equipment based on the current appearance feature vector and the historical appearance feature vector;

[0032] The appearance status information of the power plant equipment is acquired according to the appearance change characteristic information, and the appearance fault level of the power plant equipment is determined based on the appearance status information.

[0033] Optionally, the performing fault level assessment on the power plant equipment based on the target operation fault level and the appearance fault level includes:

[0034] Acquiring equipment characteristic information of the power plant equipment;

[0035] generating level weight information of the power plant equipment based on the equipment characteristic information;

[0036] Aggregating the target operation fault level and the appearance fault level based on the level weight information to obtain a target fault parameter;

[0037] Performing aging analysis on the power plant equipment according to the target fault parameters to obtain aging trend information of the power plant equipment;

[0038] Obtaining historical maintenance information of the power plant equipment;

[0039] A fault status assessment is performed on the power plant equipment based on the historical maintenance information and the aging trend information.

[0040] In addition, to achieve the above-mentioned purpose, the present invention also proposes a device for evaluating a device fault level, the device fault level evaluating device comprising:

[0041] An operation fault analysis module, used to determine an initial operation fault level of the power plant equipment based on current operation monitoring data and historical operation monitoring data of the power plant equipment;

[0042] An operation fault correction module, used for obtaining associated equipment information of the power plant equipment, and correcting the initial operation fault level based on the associated equipment information to obtain a target operation fault level;

[0043] An appearance fault analysis module, used to obtain appearance status information of the power plant equipment, and determine the appearance fault level of the power plant equipment based on the appearance status information;

[0044] The equipment fault assessment module is used to assess the fault level of the power plant equipment based on the target operation fault level and the appearance fault level.

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for evaluating equipment fault level, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for evaluating equipment fault level as described above.

[0046] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the device fault level assessment method as described above are implemented.

[0047] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the device fault level assessment method as described above are implemented.

[0048] The present invention determines the initial operation fault level of the power plant equipment based on the current operation monitoring data and the historical operation monitoring data of the power plant equipment, obtains the associated equipment information of the power plant equipment, and corrects the initial operation fault level based on the associated equipment information to obtain the target operation fault level, obtains the appearance status information of the power plant equipment, and determines the appearance fault level of the power plant equipment based on the appearance status information, and performs fault level evaluation on the power plant equipment based on the target operation fault level and the appearance fault level; because the present invention performs operation fault analysis on the power plant equipment from the equipment operation dimension, and corrects the initial operation fault level obtained by the operation fault analysis in combination with the associated equipment information of the power plant equipment, thereby ensuring the accuracy of the operation fault analysis results, and then performs appearance fault analysis on the power plant equipment from the equipment appearance dimension, thereby realizing multi-dimensional fault evaluation of the power plant equipment, effectively improving the accuracy of equipment fault evaluation, and timely discovering equipment abnormalities and potential problems, thereby improving the operation efficiency and safety of the power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0051] Figure 1 It is a flow chart of the first embodiment of the method for evaluating the equipment fault level of the present invention;

[0052] Figure 2 It is a flow chart of the second embodiment of the method for evaluating the equipment fault level of the present invention;

[0053] Figure 3 It is a structural block diagram of a first embodiment of the device for evaluating equipment fault level of the present invention;

[0054] Figure 4 It is a structural diagram of a device fault level assessment device for a hardware operating environment involved in an embodiment of the present invention.

[0055] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0056] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0057] The present invention provides a method for evaluating the fault level of a device. Figure 1 , Figure 1 It is a flowchart of the first embodiment of the method for evaluating the equipment fault level of the present invention.

[0058] In this embodiment, the device fault level assessment method includes the following steps:

[0059] Step S10: determining an initial operation fault level of the power plant equipment based on the current operation monitoring data and the historical operation monitoring data of the power plant equipment.

[0060] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a terminal electronic device capable of realizing the above functions, etc. The following takes the terminal device as an example to illustrate this embodiment and the following embodiments.

[0061] It should be noted that the power plant equipment may be equipment in the power plant such as boiler equipment, transformer equipment, power generation equipment and control equipment. The above-mentioned current operation monitoring data may be the operation data collected in the current monitoring period, for example, the monitoring period may be a time period such as 1 hour, 2 hours, 1 day, etc. The above-mentioned historical operation monitoring data may be the operation data collected in the historical monitoring period, for example, the current operation monitoring data is the operation data of the power plant equipment collected on July 24, and the historical operation monitoring data may be the operation data of the power plant equipment collected before July 24 (such as July 23, July 22, etc.).

[0062] It can be understood that the terminal device can collect the current operation monitoring data of the power plant equipment through sensors or equipment systems, collect historical operation monitoring data by obtaining the historical operation logs of the power plant equipment, pre-process the current operation monitoring data and the historical operation monitoring data, obtain the current operation information and the historical operation information, compare and analyze the current operation information with the historical operation information, and determine the initial operation level of the power plant equipment.

[0063] In some embodiments, the terminal device can perform feature analysis based on current operation monitoring data to obtain operating feature information of the power plant equipment in the current monitoring period (such as operating trends, operating parameter fluctuation ranges, and other characteristics), and perform feature analysis based on historical operation monitoring data to obtain operating feature information of the power plant equipment in historical monitoring periods. By comparing and analyzing the current operating feature information with the historical operating feature information, the initial operating fault level can be determined.

[0064] In order to improve information processing capabilities, in some embodiments, an intelligent assistant can be installed on the terminal device. The intelligent assistant can be a program built based on natural language processing technology of the Generative Pre-trained Transformer (GPT) model. The intelligent assistant can receive user input text, parse the input text into a computer-understandable data format, and then use a pre-trained neural network model to generate response text.

[0065] In the specific implementation, the intelligent assistant uses the neural network in deep learning technology and is trained through a large amount of corpus data, so that it can learn the structure, grammatical rules and semantic information of the language. After the user enters the text, the intelligent assistant will first segment and parse the text, convert it into a computer-readable vector form, and then send it to the pre-trained neural network model for inference. During the inference process, the intelligent assistant will predict the most likely next response based on the text entered by the user and the previous context. This prediction process is based on the model's learning of a large amount of corpus data, so it can largely restore the way humans express natural language and generate highly coherent and natural response text. Finally, the intelligent assistant returns the generated response text to the user to complete a dialogue interaction. Throughout the process, the intelligent assistant will continue to learn and optimize to provide more accurate and user-friendly answers.

[0066] For example, the terminal device obtains the current operation monitoring data and the historical operation monitoring data of the power plant equipment, pre-processes the current operation monitoring data and the historical operation monitoring data, converts the current operation monitoring data and the historical operation monitoring data into monitoring information text in natural language form, analyzes and parses the monitoring information text, and then inputs it into a pre-built fault assessment model for operation fault assessment to determine the initial operation fault level of the power plant equipment.

[0067] Step S20: acquiring associated equipment information of the power plant equipment, and correcting the initial operation fault level based on the associated equipment information to obtain a target operation fault level.

[0068] It should be noted that the associated equipment information may be relevant information of equipment associated with the power plant equipment to be evaluated for fault status. For example, equipment A is the power plant equipment to be evaluated for fault status, and equipment B is associated with equipment A. Therefore, the operating information and equipment information of equipment B are the associated equipment information of equipment A.

[0069] It should be noted that there is correlation between the various equipment in the power plant, and there may be mutual influence between the equipment. Therefore, in order to improve the accuracy of operation fault analysis, this embodiment corrects the initial operation fault level by combining the associated equipment information of the power plant equipment to obtain a more accurate target operation fault level, thereby introducing the dimension of equipment correlation when evaluating equipment operation faults, thereby more comprehensively evaluating the equipment status.

[0070] In some embodiments, the terminal device can build the equipment network structure of the power plant by obtaining the physical connection information and logical dependency information between the various devices in the power plant, obtain the associated equipment information of the associated equipment of the power plant equipment to be evaluated for fault based on the equipment network structure, and then correct the initial fault operation level based on the associated equipment information to obtain the target operation fault level.

[0071] For example, based on the current operation monitoring data and historical operation monitoring data of device A, the terminal device determines that the initial operation fault level of device A is level 3 (e.g., level 1 means that the device is fully functional and has excellent performance; level 2 means that the device is fully functional and has slightly reduced performance; level 3 means that some operation characteristics of the device fluctuate abnormally, but do not affect temperature operation; level 4 means that the function of the device is normal in the short term, but there are slight defects in performance; level 5 means that the device has some harmful faults and abnormal performance). The terminal device obtains the device association information of device A to determine whether there are abnormalities in the operation stability and function of device B associated with device A. If there are abnormalities in device B, it is determined that device A affects device B, and the initial fault operation level of device A can be corrected to level 4.

[0072] Furthermore, in order to accurately evaluate the operating fault level of the power plant equipment, the above step S20 may include:

[0073] Step S201: Acquire equipment network information of the power plant to which the power plant equipment belongs;

[0074] Step S202: determining attribute information and dependency information of each device in the power plant based on the device network information;

[0075] Step S203: determining the connection relationship and hierarchical relationship of each device according to the attribute information and the dependency information;

[0076] Step S204: constructing a device topology structure of the power plant based on the connection relationship and the hierarchical relationship;

[0077] Step S205: Acquire the associated equipment information of the power plant equipment according to the equipment topology structure, and correct the initial operation fault level based on the associated equipment information to obtain the target operation fault level.

[0078] It should be noted that the device network information may be the device network structure information in the power plant. The above attribute information may be the device ID, IP address, MAC address, host name, device type, physical address, communication transmission protocol, communication path and other information of the device. The above dependency information may include the control dependency, transmission dependency, connection dependency, function dependency and other dependency information of the device.

[0079] It can be understood that the terminal device can determine the connection relationship and hierarchical relationship between each device based on the attribute information and dependency information, build nodes and edges based on the connection relationship and hierarchical relationship, take each device in the power plant as a node, and the connection relationship between the devices as an edge, build a device topology structure (such as a tree structure, etc.) based on the nodes and edges, determine the associated devices that are associated with the power plant equipment based on the device topology structure, and obtain the associated device information of the associated devices.

[0080] Step S30: Acquire appearance status information of the power plant equipment, and determine the appearance fault level of the power plant equipment based on the appearance status information.

[0081] It should be noted that the appearance status information may be the appearance fault status information of the power plant equipment. For example, the appearance status information may include the appearance neatness, shell integrity, degree of surface deformation, stability of components such as connectors and operating parts, etc. of the power plant equipment.

[0082] It can be explained that the appearance of power plant equipment reflects the fault status of the equipment to a certain extent. If the operating fault status of the power plant equipment is good, but the appearance status is poor, it indicates that the equipment has a certain fault risk. Therefore, in order to more accurately evaluate the fault status of the equipment, this embodiment introduces the appearance fault analysis dimension to analyze the fault level of the power plant equipment.

[0083] In some embodiments, the terminal device can collect the appearance image data of the power plant equipment, obtain the current appearance status information of the power plant equipment based on the appearance image data, compare the current appearance status information with the initial appearance status information of the power plant equipment, determine the appearance status information of the power plant equipment such as the appearance neatness, shell integrity, surface deformation, color consistency, surface coating integrity, component stability, surface identification clarity, and then rate the appearance fault of the power plant equipment based on the appearance status information to determine the appearance fault level of the power plant equipment. For example, if there is a large deformation in the appearance of the power plant equipment, the rating is level 3; if there are slight scratches or dents on the appearance, the rating is level 2; if the appearance is intact without obvious scratches and deformation, the rating is level 1.

[0084] Furthermore, in order to accurately evaluate the appearance fault of the power plant equipment, the above step S30 may include:

[0085] Step S301: Acquire image monitoring data of the power plant equipment;

[0086] Step S302: Segment the image monitoring data to obtain multiple image frames;

[0087] Step S303: classifying the multiple image frames based on the timestamp information of each image frame to obtain a current image frame set and a historical image frame set of the power plant equipment;

[0088] Step S304: extracting features from the current image frame set and the historical image frame set to obtain a current appearance feature vector and a historical appearance feature vector of the power plant equipment;

[0089] Step S305: acquiring appearance change feature information of each component of the power plant equipment based on the current appearance feature vector and the historical appearance feature vector;

[0090] Step S306: acquiring appearance status information of the power plant equipment according to the appearance change characteristic information, and determining the appearance fault level of the power plant equipment based on the appearance status information.

[0091] It should be noted that the image monitoring data includes current image monitoring data in a current monitoring period and historical image monitoring data in historical monitoring periods.

[0092] It is understandable that the terminal device segments the image monitoring data, for example, it can segment according to event intervals, segment the video stream according to set time intervals (such as every second, every minute), and obtain multiple image frames; the terminal device can also segment according to event triggers, and segment when specific events (such as equipment startup, stop, failure) occur to obtain image frames at critical moments.

[0093] It should be noted that the terminal device can obtain the current appearance feature vector and the historical appearance feature vector of the power plant equipment through feature extraction, perform feature matching based on the current appearance feature vector and the historical appearance feature vector, match the feature points of the current image frame set with the historical image frame set, calculate the feature similarity between the current image frame and the historical image frame based on the feature point matching results (for example, based on Euclidean distance or Hamming distance calculation), and obtain the appearance change feature information of each component of the power plant equipment based on the feature similarity.

[0094] Step S40: evaluating the fault level of the power plant equipment based on the target operation fault level and the appearance fault level.

[0095] It can be understood that the terminal equipment can analyze the operating dimension fault characteristic state of the power plant equipment based on the target operating fault level, analyze the appearance dimension fault characteristic state of the power plant equipment according to the appearance fault level, conduct a comprehensive assessment of the fault state of the power plant equipment based on the operating dimension fault characteristic state and the appearance dimension fault characteristic state, determine the final fault state of the power plant equipment, and determine the fault level of the power plant equipment according to the inverse value of the fault state.

[0096] In some embodiments, the terminal device determines the current functional integrity and operational stability of the power plant equipment based on the target operational fault level, determines the degree of appearance aging and damage of the power plant equipment based on the appearance fault level, and performs fault assessment on the power plant equipment from two dimensions: operational fault and appearance fault.

[0097] For example, if the functions of the power plant equipment are complete, the operation is stable, and there is slight damage to the appearance, the fault status of the power plant equipment is assessed as no fault risk, and only regular maintenance of the power plant equipment is required.

[0098] If the functions of the power plant equipment are complete and the operation is stable, but there is obvious damage or dents on the appearance or the position deviation of the connectors is large, it is judged that there is a small possibility of failure risk, and it is necessary to arrange for maintenance of the power plant equipment in the near future to avoid production accidents in the power plant due to equipment abnormality.

[0099] If the functions of power plant equipment are incomplete or the operation is unstable, then regardless of whether the appearance is damaged, it is determined that there is a high risk of failure, and it is necessary to immediately arrange for maintenance of the power plant equipment to avoid production accidents in the power plant due to equipment abnormalities.

[0100] Furthermore, in order to accurately evaluate the fault status of the power plant equipment, the above step S40 may include:

[0101] Step S401: Acquire equipment characteristic information of the power plant equipment;

[0102] Step S402: generating level weight information of the power plant equipment based on the equipment characteristic information;

[0103] Step S403: Aggregating the target operation fault level and the appearance fault level based on the level weight information to obtain a target fault parameter;

[0104] Step S404: performing aging analysis on the power plant equipment according to the target fault parameters to obtain aging trend information of the power plant equipment;

[0105] Step S405: Acquire historical maintenance information of the power plant equipment;

[0106] Step S406: performing a fault status assessment on the power plant equipment based on the historical maintenance information and the aging trend information.

[0107] It should be noted that the equipment characteristic information may include equipment installation information, equipment type, equipment material information, etc. Based on the equipment characteristic information, this embodiment can determine whether the appearance wear and deformation of the power plant equipment has an impact on the operation of the equipment. For example, a power plant equipment whose appearance wear and deformation has a greater impact on the operation can be set with a larger appearance fault level weight; a power plant equipment whose appearance wear and deformation has a smaller impact on the operation can be set with a smaller appearance fault level weight.

[0108] In some embodiments, the terminal device may use time series analysis methods (such as ARIMA, exponential smoothing) to analyze the changing trends of equipment fault parameters to obtain aging trend information of power plant equipment; it may also use machine learning algorithms (such as linear regression, decision tree) to predict the aging trend of equipment.

[0109] It should be noted that the historical maintenance information may be historical maintenance record information of power plant equipment. For example, the historical maintenance information may include maintenance date, maintenance content, maintenance times, maintenance cost and other information.

[0110] In some embodiments, the terminal device can determine the maintenance score of the power plant equipment based on historical maintenance information, determine the fault score of the power plant equipment based on aging trend information, and then set corresponding weights respectively to perform a comprehensive score, and evaluate the fault status of the power plant equipment based on the comprehensive score result, for example: comprehensive score = fault score * 0.7 + maintenance score * 0.3.

[0111] This embodiment determines the initial operation fault level of the power plant equipment based on the current operation monitoring data and historical operation monitoring data of the power plant equipment, obtains the associated equipment information of the power plant equipment, and corrects the initial operation fault level based on the associated equipment information to obtain the target operation fault level, obtains the appearance status information of the power plant equipment, and determines the appearance fault level of the power plant equipment based on the appearance status information, and performs fault level evaluation on the power plant equipment based on the target operation fault level and the appearance fault level; since this embodiment performs operation fault analysis on the power plant equipment from the equipment operation dimension, the initial operation fault level obtained from the operation fault analysis is corrected in combination with the associated equipment information of the power plant equipment to ensure the accuracy of the operation fault analysis results, and then performs appearance fault analysis on the power plant equipment from the equipment appearance dimension, thereby realizing multi-dimensional fault evaluation of the power plant equipment, effectively improving the accuracy of equipment fault evaluation, and timely discovering equipment abnormalities and potential problems, thereby improving the operation efficiency and safety of the power plant.

[0112] refer to Figure 3 , Figure 3 It is a flow chart of the second embodiment of the method for evaluating the equipment fault level of the present invention.

[0113] Based on the above first embodiment, in this embodiment, step S10 further includes:

[0114] Step S101: determining the current operating state and the historical operating state of the power plant equipment based on the current operating monitoring data and the historical operating monitoring data of the power plant equipment.

[0115] It should be noted that the above-mentioned current operation monitoring data may include real-time sensor data (such as temperature, pressure, vibration, current, voltage, etc.), control system data, energy consumption data, etc. The above-mentioned historical operation monitoring data may include past sensor data, control system data, maintenance records, fault records, etc.

[0116] It is understandable that the terminal device can perform data cleaning, denoising and fusion on the current operation monitoring data and the historical operation monitoring data to obtain reliable candidate data and extract key characteristic parameters from the candidate data. The above key characteristic parameters can independently affect the fault state of the equipment. For example, for boiler equipment, it may include parameters such as temperature, pressure, flow rate, etc.

[0117] In some embodiments, the terminal device extracts the characteristics of the current operation data by preprocessing and feature analysis on the current operation monitoring data; extracts the characteristics of the historical operation data by preprocessing and feature analysis on the historical operation monitoring data; determines the current operating status of the power plant equipment based on the characteristics of the current operation data, and determines the historical operating status based on the characteristics of the historical operation data.

[0118] Step S102: determining operation difference information of the power plant equipment according to the current operation status and the historical operation status.

[0119] It is understandable that the terminal device can analyze the operating differences between the current operation process and the historical operation process of the power plant equipment based on the current operation status and the historical operation status, and identify the operating abnormalities of the power plant equipment.

[0120] In some embodiments, the terminal device can obtain operation difference information by aligning the current operation data and the historical operation data according to timestamps, and using a time series analysis method to compare the operation trend differences between the current and historical data based on the aligned current operation data and historical operation data.

[0121] Step S103: determining the current operation duration and the historical operation duration of the staff corresponding to the power plant equipment according to the current operation monitoring data and the historical operation monitoring data.

[0122] It should be noted that the current operation duration may be the duration used by the staff corresponding to the power plant equipment to operate the power plant equipment in the current monitoring cycle. The above-mentioned historical operation duration may be the duration used by the staff corresponding to the power plant equipment to operate the power plant equipment in one or more historical monitoring cycles. For example, the staff used 120 seconds to operate the power plant equipment to complete operation task A in the current monitoring cycle; the staff used 90 seconds to operate the power plant equipment to complete operation task A in the first historical monitoring cycle; the staff used 86 seconds to operate the power plant equipment to complete operation task A in the second historical monitoring cycle. It can be seen that the current operation duration of the power plant equipment is significantly higher than the historical operation duration, so it is determined that there may be an abnormality in the power plant equipment.

[0123] In some embodiments, the terminal device can determine the current operation time by analyzing the current operation monitoring data to determine the time spent by the staff from the start of the operation to the end of the operation in the current monitoring cycle; and determine the historical operation time by analyzing the historical operation monitoring data to determine the time spent by the staff from the start of the operation to the end of the operation in the historical monitoring cycle.

[0124] In some embodiments, the terminal device can determine the current operation time and historical operation time of the staff corresponding to the power plant equipment by collecting image monitoring data of the power plant equipment, preprocessing the image monitoring data, and performing data segmentation, target detection, and behavioral feature analysis on the preprocessed image monitoring data.

[0125] Step S104: Determine the operation difference information of the power plant equipment based on the current operation duration and the historical operation duration.

[0126] It can be understood that this embodiment can determine the average operation time corresponding to the power plant equipment based on the historical operation time, and then determine the time difference between the current operation time and the average operation time, judge whether the time difference is within the preset normal difference range, and determine the operation difference information of the power plant equipment based on the judgment result.

[0127] Furthermore, in order to accurately analyze the operational differences of the power plant equipment, the above step S104 may include:

[0128] Step S1041: Acquire image monitoring data of the power plant equipment;

[0129] Step S1042: performing feature analysis on the image monitoring data to obtain current operation behavior information and historical operation behavior information of the staff corresponding to the power plant equipment;

[0130] Step S1043: modifying the current operation duration and the historical operation duration based on the operation behavior information and the historical operation behavior information;

[0131] Step S1044: determining the operation difference information of the power plant equipment according to the corrected current operation duration and the corrected historical operation duration.

[0132] It should be noted that the current operation behavior information may be information related to the operation behavior of the staff in the current monitoring period, for example, the current operation behavior information may include information such as the staff's posture and movement. The above-mentioned historical operation behavior information may be information related to the operation behavior of the staff in the historical monitoring period.

[0133] It is understandable that since the method of determining the operating time of staff from the operation monitoring data of power plant equipment is prone to errors, resulting in inaccurate operation time, this embodiment can analyze the image monitoring data of power plant equipment, analyze the operations and behaviors of staff from the image monitoring data, and determine the actual operation time of staff, so as to correct the current operation time and the historical operation time, thereby obtaining more accurate operation time.

[0134] In some embodiments, the terminal device can collect image data from the video surveillance system, ensure that each image has a timestamp, crop, scale, denoise and correct the lighting of the image, so as to obtain image monitoring data of the power plant equipment, perform target detection on the image monitoring data, identify the staff and power plant equipment in the image, extract the staff's posture information through a posture estimation algorithm, identify the staff's motion information through a motion recognition algorithm, and then extract the time series characteristics of the staff's operating behavior to obtain the staff's current operating behavior information and historical operating behavior information.

[0135] Step S105: determining the initial operation fault level of the power plant equipment according to the operation difference information and the operation difference information.

[0136] It should be noted that in order to more accurately analyze the operating fault status of power plant equipment, this embodiment not only analyzes the internal operating parameters of the equipment, but also analyzes based on the operation time of the staff outside the equipment. By analyzing whether there is a difference between the operation time of the staff in the current monitoring cycle and the previous historical operation time, it is analyzed whether the power plant equipment is stable in the actual working process. For example, if the internal operating parameters of the equipment are normal, but the current operation time of the staff in the current monitoring cycle is obviously longer than the historical operation time, it can be determined that there may be an abnormality in the power plant equipment.

[0137] This embodiment determines the current operating status and historical operating status of the power plant equipment based on the current operating monitoring data and historical operating monitoring data of the power plant equipment, determines the operating difference information of the power plant equipment according to the current operating status and the historical operating status, determines the current operation time and historical operation time of the staff corresponding to the power plant equipment according to the current operation monitoring data and the historical operation monitoring data, determines the operating difference information of the power plant equipment based on the current operation time and the historical operation time, and determines the initial operating fault level of the power plant equipment according to the operating difference information and the operating difference information; because this embodiment effectively analyzes the difference between the current operating status and the historical operating status of the power plant equipment through comprehensive analysis from the operating difference dimension of the equipment itself and the operating difference dimension of the staff corresponding to the power plant equipment, thereby accurately analyzing and evaluating the initial operating fault level of the power plant equipment, thereby improving the accuracy and efficiency of equipment fault evaluation, and avoiding the problem of deviation in subsequent evaluation due to inaccurate initial evaluation level.

[0138] In addition, an embodiment of the present invention further proposes a computer-readable storage medium, on which a device fault level assessment program is stored. When the device fault level assessment program is executed by a processor, the steps of the device fault level assessment method described above are implemented.

[0139] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0140] The computer-readable storage medium may be included in the device failure level assessment device; or may exist independently without being assembled into the device failure level assessment device.

[0141] In addition, an embodiment of the present invention further provides a computer program product, including a device failure level assessment program, which implements the steps of the device failure level assessment method described above when executed by a processor.

[0142] The specific implementation of the computer program product of the present invention is basically the same as the various embodiments of the above-mentioned equipment fault level assessment method, and will not be repeated here.

[0143] Reference Figure 3 , Figure 3 It is a structural block diagram of the first embodiment of the device for evaluating equipment fault level of the present invention.

[0144] like Figure 3 As shown, the device for evaluating the equipment fault level proposed in the embodiment of the present invention includes:

[0145] An operation fault analysis module 10, configured to determine an initial operation fault level of the power plant equipment based on current operation monitoring data and historical operation monitoring data of the power plant equipment;

[0146] An operation fault correction module 20, configured to obtain associated equipment information of the power plant equipment, and correct the initial operation fault level based on the associated equipment information to obtain a target operation fault level;

[0147] An appearance fault analysis module 30, used to obtain appearance status information of the power plant equipment, and determine the appearance fault level of the power plant equipment based on the appearance status information;

[0148] The equipment fault assessment module 40 is used to assess the fault level of the power plant equipment based on the target operation fault level and the appearance fault level.

[0149] Furthermore, the operation fault analysis module 10 is also used to determine the current operation status and historical operation status of the power plant equipment based on the current operation monitoring data and historical operation monitoring data of the power plant equipment; determine the operation difference information of the power plant equipment according to the current operation status and the historical operation status; determine the current operation time and historical operation time of the staff corresponding to the power plant equipment according to the current operation monitoring data and the historical operation monitoring data; determine the operation difference information of the power plant equipment based on the current operation time and the historical operation time; determine the initial operation fault level of the power plant equipment according to the operation difference information and the operation difference information.

[0150] Furthermore, the operation fault analysis module 10 is also used to obtain image monitoring data of the power plant equipment; perform feature analysis on the image monitoring data to determine current operation behavior information and historical operation behavior information of the staff corresponding to the power plant equipment; correct the current operation time and the historical operation time based on the operation behavior information and the historical operation behavior information; and determine the operation difference information of the power plant equipment based on the corrected current operation time and the corrected historical operation time.

[0151] Furthermore, the operation fault correction module 20 is also used to obtain equipment network information of the power plant to which the power plant equipment belongs; determine the attribute information and dependency information of each device in the power plant based on the equipment network information; determine the connection relationship and hierarchical relationship of each device according to the attribute information and the dependency information; construct the equipment topology structure of the power plant based on the connection relationship and the hierarchical relationship; obtain the associated equipment information of the power plant equipment according to the equipment topology structure, and correct the initial operation fault level based on the associated equipment information to obtain the target operation fault level.

[0152] Furthermore, the appearance fault analysis module 30 is also used to obtain image monitoring data of the power plant equipment; segment the image monitoring data to obtain multiple image frames; classify the multiple image frames based on the timestamp information of each image frame to obtain a current image frame set and a historical image frame set of the power plant equipment; perform feature extraction on the current image frame set and the historical image frame set to obtain a current appearance feature vector and a historical appearance feature vector of the power plant equipment; obtain appearance change feature information of each component of the power plant equipment based on the current appearance feature vector and the historical appearance feature vector; obtain appearance status information of the power plant equipment based on the appearance change feature information, and determine the appearance fault level of the power plant equipment based on the appearance status information.

[0153] Furthermore, the equipment fault assessment module 40 is also used to obtain equipment characteristic information of the power plant equipment; generate level weight information of the power plant equipment based on the equipment characteristic information; aggregate the target operation fault level and the appearance fault level based on the level weight information to obtain target fault parameters; perform aging analysis on the power plant equipment according to the target fault parameters to determine aging trend information of the power plant equipment; obtain historical maintenance information of the power plant equipment; and perform fault status assessment on the power plant equipment based on the historical maintenance information and the aging trend information.

[0154] This embodiment determines the initial operation fault level of the power plant equipment based on the current operation monitoring data and historical operation monitoring data of the power plant equipment, obtains the associated equipment information of the power plant equipment, and corrects the initial operation fault level based on the associated equipment information to obtain the target operation fault level, obtains the appearance status information of the power plant equipment, and determines the appearance fault level of the power plant equipment based on the appearance status information, and performs fault level evaluation on the power plant equipment based on the target operation fault level and the appearance fault level; since this embodiment performs operation fault analysis on the power plant equipment from the equipment operation dimension, the initial operation fault level obtained from the operation fault analysis is corrected in combination with the associated equipment information of the power plant equipment to ensure the accuracy of the operation fault analysis results, and then performs appearance fault analysis on the power plant equipment from the equipment appearance dimension, thereby realizing multi-dimensional fault evaluation and fault level evaluation of the power plant equipment, effectively improving the accuracy of equipment fault evaluation, and timely discovering equipment abnormalities and potential problems, thereby improving the operation efficiency and safety of the power plant.

[0155] The device for evaluating the equipment fault level provided by the present application adopts the device fault level evaluation method in the above embodiment, and can solve the technical problem of equipment fault level evaluation. Compared with the prior art, the beneficial effects of the device for evaluating the equipment fault level provided by the present application are the same as the beneficial effects of the device fault level evaluation method provided by the above embodiment, and the other technical features in the device for evaluating the equipment fault level are the same as the features disclosed in the above embodiment method, which will not be described in detail here.

[0156] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0157] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0158] In addition, for technical details not fully described in this embodiment, reference can be made to the device fault level assessment method provided in any embodiment of the present invention, and will not be repeated here.

[0159] The present application provides a device for evaluating a device fault level, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the device fault level evaluation method in the above-mentioned embodiment one.

[0160] Reference below Figure 4 , which shows a schematic diagram of the structure of a device failure level assessment device suitable for implementing the embodiment of the present application. The device failure level assessment device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The device fault level assessment device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0161] like Figure 4 As shown, the device failure level assessment device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the device failure level assessment device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the device fault level assessment device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a device fault level assessment device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0162] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0163] The device fault level assessment device provided by the present application adopts the device fault level assessment method in the above embodiment, which can solve the technical problem of device fault level assessment. Compared with the prior art, the beneficial effects of the device fault level assessment device provided by the present application are the same as the beneficial effects of the device fault level assessment method provided by the above embodiment, and the other technical features in the device fault level assessment device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0164] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0165] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0166] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the device fault level assessment method in the above-mentioned embodiment.

[0167] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0168] The computer-readable storage medium may be included in the device failure level assessment device; or may exist independently without being assembled into the device failure level assessment device.

[0169] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the device failure level assessment device, the device failure level assessment device performs device failure level assessment.

[0170] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0171] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0172] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0173] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for evaluating equipment failure level, characterized in that: The equipment failure level assessment method comprises: Determining an initial operation fault level of the power plant equipment based on current operation monitoring data and historical operation monitoring data of the power plant equipment; Acquiring associated equipment information of the power plant equipment, and correcting the initial operation fault level based on the associated equipment information to obtain a target operation fault level; Acquiring appearance status information of the power plant equipment, and determining an appearance fault level of the power plant equipment based on the appearance status information; A fault level assessment is performed on the power plant equipment based on the target operating fault level and the appearance fault level.

2. The device failure level assessment method according to claim 1, characterized in that: The determining the initial operation fault level of the power plant equipment based on the current operation monitoring data and the historical operation monitoring data of the power plant equipment comprises: Determine the current operating state and the historical operating state of the power plant equipment based on the current operating monitoring data and the historical operating monitoring data of the power plant equipment; Determine operation difference information of the power plant equipment according to the current operation state and the historical operation state; Determine the current operation duration and the historical operation duration of the staff corresponding to the power plant equipment according to the current operation monitoring data and the historical operation monitoring data; Determining operation difference information of the power plant equipment based on the current operation duration and the historical operation duration; An initial operation fault level of the power plant equipment is determined according to the operation difference information and the operation difference information.

3. The device failure level assessment method according to claim 2, characterized in that: The determining the operation difference information of the power plant equipment based on the current operation duration and the historical operation duration includes: Acquiring image monitoring data of the power plant equipment; Performing feature analysis on the image monitoring data to obtain current operation behavior information and historical operation behavior information of the staff corresponding to the power plant equipment; Modifying the current operation duration and the historical operation duration based on the operation behavior information and the historical operation behavior information; The operation difference information of the power plant equipment is determined according to the corrected current operation duration and the corrected historical operation duration.

4. The device failure level assessment method according to any one of claims 1 to 3, characterized in that: The acquiring of the associated equipment information of the power plant equipment, and correcting the initial operation fault level based on the associated equipment information to obtain a target operation fault level, includes: Obtaining equipment network information of the power plant to which the power plant equipment belongs; Determining attribute information and dependency information of each device in the power plant based on the device network information; Determine the connection relationship and hierarchical relationship of each device according to the attribute information and the dependency information; Building a device topology structure of the power plant based on the connection relationship and the hierarchical relationship; The associated equipment information of the power plant equipment is acquired according to the equipment topology structure, and the initial operation fault level is corrected based on the associated equipment information to obtain a target operation fault level.

5. The device failure level assessment method according to any one of claims 1 to 3, characterized in that: The acquiring the appearance status information of the power plant equipment and determining the appearance fault level of the power plant equipment based on the appearance status information includes: Acquiring image monitoring data of the power plant equipment; Segmenting the image monitoring data to obtain multiple image frames; Classifying the multiple image frames based on timestamp information of each image frame to obtain a current image frame set and a historical image frame set of the power plant equipment; Performing feature extraction on the current image frame set and the historical image frame set to obtain a current appearance feature vector and a historical appearance feature vector of the power plant equipment; Acquire appearance change feature information of each component of the power plant equipment based on the current appearance feature vector and the historical appearance feature vector; The appearance status information of the power plant equipment is acquired according to the appearance change characteristic information, and the appearance fault level of the power plant equipment is determined based on the appearance status information.

6. The device failure level assessment method according to any one of claims 1 to 3, characterized in that: The performing fault level assessment on the power plant equipment based on the target operation fault level and the appearance fault level comprises: Acquiring equipment characteristic information of the power plant equipment; generating level weight information of the power plant equipment based on the equipment characteristic information; Aggregating the target operation fault level and the appearance fault level based on the level weight information to obtain a target fault parameter; Performing aging analysis on the power plant equipment according to the target fault parameters to obtain aging trend information of the power plant equipment; Obtaining historical maintenance information of the power plant equipment; A fault status assessment is performed on the power plant equipment based on the historical maintenance information and the aging trend information.

7. A device for evaluating equipment failure level, characterized in that: The equipment failure level assessment device comprises: An operation fault analysis module, used to determine an initial operation fault level of the power plant equipment based on current operation monitoring data and historical operation monitoring data of the power plant equipment; An operation fault correction module, used for obtaining associated equipment information of the power plant equipment, and correcting the initial operation fault level based on the associated equipment information to obtain a target operation fault level; An appearance fault analysis module, used to obtain appearance status information of the power plant equipment, and determine the appearance fault level of the power plant equipment based on the appearance status information; The equipment fault assessment module is used to assess the fault level of the power plant equipment based on the target operation fault level and the appearance fault level.

8. A device for evaluating equipment failure level, characterized in that: The device for evaluating the equipment failure level comprises: a memory, a processor, and a device failure level evaluation program stored in the memory and executable on the processor, wherein the device failure level evaluation program is configured to implement the device failure level evaluation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a device failure level assessment program, and when the device failure level assessment program is executed by a processor, the device failure level assessment method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a device failure level assessment program, and when the device failure level assessment program is executed by a processor, the steps of the device failure level assessment method according to any one of claims 1 to 6 are implemented.

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