CN113779005A

CN113779005A - Defect evaluation method and device for primary equipment and storage medium

Info

Publication number: CN113779005A
Application number: CN202110882870.6A
Authority: CN
Inventors: 夏成文
Original assignee: Shenzhen Digital Power Grid Research Institute of China Southern Power Grid Co Ltd
Current assignee: Southern Power Grid Digital Grid Research Institute Co Ltd; Shenzhen Digital Power Grid Research Institute of China Southern Power Grid Co Ltd
Priority date: 2021-08-02
Filing date: 2021-08-02
Publication date: 2021-12-10

Abstract

The embodiment of the disclosure provides a defect assessment method and device for primary equipment and a storage medium, and belongs to the technical field of power grids. The defect evaluation method of the primary equipment comprises the following steps: acquiring initial defect data of primary equipment; preprocessing the initial defect data to obtain actual defect data; and inputting the actual defect data into a preset defect evaluation model for evaluation to obtain defect evaluation data. Through the technical scheme provided by the embodiment of the disclosure, the accuracy and efficiency of the defect evaluation of primary equipment can be improved.

Description

Defect evaluation method and device for primary equipment and storage medium

Technical Field

The present disclosure relates to the field of power grid technologies, and in particular, to a method and an apparatus for evaluating defects of primary devices, and a storage medium.

Background

The automatic management level of the equipment defects of the current power grid is not high, the equipment defects frequently occur, the defect data cannot be accurately recorded, and other factors cause a plurality of hidden dangers in the safe and stable operation of the power grid. In practice, due to the fact that the quality of description data of defects filled by patrolmen is low and the defect description is inconsistent with the actual defects of the equipment, misjudgment of the defects of the equipment and invalid equipment patrol maintenance work are caused frequently, patrol efficiency and defect elimination efficiency of business personnel are reduced, and management and control difficulty of power grid equipment is increased.

Most of the existing equipment defect diagnosis data are output according to the format of an instrument manufacturer or recorded according to the experience of a diagnostician, and the state diagnosis data format is eight-door, so that the standardization degree is poor, and the data are not beneficial to efficient utilization and analysis and diagnosis.

Disclosure of Invention

The main purpose of the present disclosure is to provide a method and an apparatus for evaluating defects of a primary device, and a storage medium, which can improve the accuracy and efficiency of evaluating defects of a primary device.

To achieve the above object, a first aspect of the present disclosure provides a method for evaluating defects of a primary device, including:

acquiring initial defect data of primary equipment;

preprocessing the initial defect data to obtain actual defect data;

and inputting the actual defect data into a preset defect evaluation model for evaluation to obtain defect evaluation data.

In some embodiments, the preprocessing the initial defect data to obtain actual defect data includes:

denoising and filtering the initial defect data;

carrying out variance analysis on the initial defect data subjected to denoising and filtering;

and eliminating the defect data subjected to the variance analysis, and removing abnormal defect data to obtain actual defect data.

In some embodiments, the inputting the actual defect data into a preset defect evaluation model for evaluation to obtain defect evaluation data includes:

inputting the actual defect data to the defect review model;

performing curve fitting on the actual defect data through a comprehensive evaluation algorithm of the defect evaluation model to obtain variation trend data;

and evaluating according to the change trend data to obtain the defect evaluation data.

In some embodiments, the defect evaluation data further includes a defect grade, and the inputting the actual defect data into a preset defect evaluation model for evaluation to obtain defect evaluation data further includes:

and carrying out threshold analysis on the actual defect data to obtain the defect grade.

In some embodiments, the method further comprises: training a defect assessment model specifically comprises:

acquiring a defect sample set;

and inputting the defect sample set into an initial evaluation model for training until the initial evaluation model converges to obtain the defect evaluation model.

In some embodiments, the method further comprises:

and visually displaying the defect evaluation data.

In some embodiments, the visually presenting the defect review data includes:

performing preliminary processing on the defect evaluation data;

performing secondary processing on the primarily processed defect evaluation data;

and performing three-dimensional visual display on the defect evaluation data subjected to secondary processing.

To achieve the above object, a second aspect of the present disclosure provides a defect-evaluating apparatus of a primary device, including:

the defect data acquisition module is used for acquiring initial defect data of the primary equipment;

the preprocessing module is used for preprocessing the initial defect data to obtain actual defect data;

and the defect evaluation module is used for inputting the actual defect data into a preset defect evaluation model for evaluation to obtain defect evaluation data.

To achieve the above object, a third aspect of the present disclosure provides another defect-assessment apparatus for a primary device, including:

at least one memory;

at least one processor;

at least one program;

the program is stored in a memory and a processor executes the at least one program to implement the method of the present disclosure as described in the above first aspect.

To achieve the above object, a fourth aspect of the present disclosure proposes a storage medium that is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform:

a method as described in the first aspect above.

According to the defect assessment method and device for the primary equipment and the storage medium, the initial defect data of the primary equipment are obtained and preprocessed to obtain the actual defect data, and then the actual defect data are input into the preset defect assessment model to be assessed to obtain the defect assessment data, so that the accuracy and efficiency of the defect assessment of the primary equipment can be improved.

Drawings

Fig. 1 is a flowchart of a defect assessment method of a primary device according to an embodiment of the present disclosure.

Fig. 2 is a flowchart of step 102 of fig. 1.

Fig. 3 is a flowchart of step 103 of fig. 1.

Fig. 4 is a partial flowchart of a defect assessment method for a primary device according to another embodiment of the present disclosure.

Fig. 5 is a partial flowchart of a defect assessment method for a primary device according to another embodiment of the present disclosure.

Fig. 6 is a schematic hardware configuration diagram of a defect assessment apparatus of a primary device according to an embodiment of the present disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the disclosure and are not intended to limit the disclosure.

It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of the disclosure only and is not intended to be limiting of the disclosure.

First, several nouns involved in the present disclosure are resolved:

primary equipment: the electric power generation system is an electric device directly participating in production, transmission and distribution of electric energy, and is also an electric device for transmission, flow, conversion and distribution of power and high-voltage current of a power plant, for example, energy conversion equipment such as a generator, a transformer and a motor, switch equipment such as a circuit breaker, a disconnecting switch, a load switch and a high-voltage fuse, current-carrying equipment such as a bus, an insulator and a cable, mutual inductors such as a voltage mutual inductor and a current mutual inductor, a power reactor and a lightning arrester. Primary equipment is mainly used for high voltage, high current loops.

Secondary equipment: the present invention relates to auxiliary electrical equipment for monitoring, controlling, protecting and adjusting the working conditions of primary equipment, for example, comprehensive automation equipment such as a voltmeter, an ammeter, a power meter and the like, relays (voltage relay, current relay, intermediate relay), a line protection measurement and control cabinet, a main transformer protection measurement and control cabinet, an electric energy metering screen, a frequency-voltage emergency control device, an electric energy quality monitoring cabinet and the like, an integrated power system such as a direct current power supply complete set device, an alternating current Uninterruptible Power Supply (UPS) and the like, and communication equipment such as an optical terminal, a comprehensive wiring cabinet, data communication equipment and the like. The secondary equipment does not directly participate in the production and point distribution processes of electric energy, but plays an important role in ensuring the normal and orderly work of the main equipment and the running economic benefit of the main equipment. The secondary equipment is mainly used in low-voltage and low-current loops. Particularly, the voltage transformer and the current transformer can belong to primary equipment and secondary equipment.

The primary equipment of the power grid is complex in structure, high in integration level and complex and changeable in operation environment, and is often influenced by external bad working conditions and system scheduling mode changes, so that the difficulty of defect assessment of the primary equipment is greatly increased. The current defect evaluation mainly depends on manual judgment, and the accuracy and the evaluation efficiency need to be improved urgently.

Based on this, the embodiments of the present disclosure provide a technical solution of a defect assessment method for primary equipment, which can improve accuracy and efficiency of defect assessment for the primary equipment.

The embodiment of the present disclosure provides a method and an apparatus for evaluating defects of a primary device, and a read storage medium, which are specifically described in the following embodiments.

The defect evaluation method of the primary device provided by the embodiment of the disclosure can be applied to a terminal, a server side and a container mechanism. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, smart watch, or the like; the server side can be configured into an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and cloud servers for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network) and big data and artificial intelligence platforms; but is not limited to the above form.

Fig. 1 is an alternative flowchart of a defect assessment method for a primary device according to an embodiment of the present disclosure, where the method in fig. 1 includes steps 101 to 103.

Step 101, acquiring initial defect data of primary equipment;

step 102, preprocessing initial defect data to obtain actual defect data;

and 103, inputting the actual defect data into a preset defect evaluation model for evaluation to obtain defect evaluation data.

In step 101 of some embodiments, the initial defect data includes at least defective device information, inspection data, personnel data, and the like; wherein the defective device information includes at least a defect type; the detection data may include, but is not limited to, data types in the form of text, audio, images, and video. Taking the primary device as a GIS device as an example for explanation, the defect types of the primary device may include, but are not limited to: metal particle insulation defects, insulation cavities, tip-to-plate insulation defects, and insulation edge foreign matter.

Referring to fig. 2, in some embodiments step 102 includes:

step 201, carrying out denoising and filtering processing on initial defect data;

202, carrying out variance analysis on the initial defect data subjected to denoising and filtering;

and step 203, removing the defect data subjected to the variance analysis, and removing abnormal defect data to obtain actual defect data.

Specifically, in step 201, a median denoising filter algorithm may be used for processing. In step 202, variance analysis is performed on the initial defect data after the denoising and filtering process to determine normal defect data and abnormal defect data, and a confidence interval is solved, wherein the normal defect data is determined if the confidence interval is within the data, and the abnormal defect data is determined if the data is not within the confidence interval. In step 203, the abnormal defect data not in the confidence interval is removed to obtain normal defect data, i.e. actual defect data.

Referring to FIG. 3, the defect review data includes defect trend data, and in some embodiments, step 103 includes:

step 301, inputting actual defect data into a preset defect evaluation model;

step 302, performing curve fitting on actual defect data through a comprehensive evaluation algorithm of a defect evaluation model to obtain variation trend data;

and step 303, evaluating according to the variation trend data to obtain defect evaluation data.

In some embodiments, the defect review model performs defect review on the primary equipment through a composite review algorithm. The comprehensive evaluation algorithm may include, but is not limited to, AHP analytic hierarchy process, entropy method, TOPSIS algorithm, fuzzy comprehensive evaluation method, and the like. In a specific application scene, the comprehensive evaluation algorithm adopts an entropy method; the entropy method is an objective weighting method, is not influenced by human factors, and can effectively avoid the indexes caused by the human subjective factors by calculating the index weight based on the data characteristics.

In some embodiments, the defect review data further includes a defect level, and step 103 further includes:

Specifically, the threshold value is selected to meet the national or industry standard requirements. In one embodiment, taking the diagnosis of dissolved gas in oil as an example, if the gas content or the absolute gas production rate of one of the gases exceeds a threshold, a value of 1 is displayed, otherwise, a value of 0 is displayed, wherein 1 indicates that the standard is exceeded (the defect level is 1 level), and 0 indicates that the standard is not exceeded (the defect level is 0 level). The threshold values of the gas content value and the gas production rate value are implemented according to IEC and the national electric power industry standard. Other parameters also determine the threshold value according to the relevant criteria.

In another embodiment, when the actual defect data is image data, the defect area in the actual defect data may be calculated, and the defect area may be used as a failure area, and the defect degree of the primary equipment is evaluated according to the relationship between the failure area and the area threshold, for example, the failure area exceeds a preset area threshold, and then the failure area and the area threshold are determined. In more detail, a further refinement of the defect level may be determined according to the size ratio between the failure area and the area threshold, for example: if the failure area is 3 times of the area threshold, determining that the defect grade is 1 grade serious; if the failure area is 4 times the area threshold, it can be determined that the defect level is very serious of level 1.

In some embodiments, the defect review data further includes a defect eligibility type, and step 103 further includes:

and performing threshold analysis on the actual defect data to obtain a defect qualified type. The defect qualified types comprise qualified types, monitoring required types and unqualified types. Specifically, in an actual application scene, a gray scale map of actual defect data is obtained, a pipeline outer anticorrosive layer structure of primary equipment is detected to obtain the gray scale map, a black area in the gray scale map is represented as an area with good bonding quality, a white area is an area with poor bonding quality, the gray scale map is processed to obtain a digital image, wherein a dark red area represents that the area is unqualified and needs to be retired, an orange area represents that the area is unqualified and needs to be maintained, a yellow area represents that the area needs to be monitored, and a green area represents that the area is qualified.

In a specific application scenario, please refer to table 1, where table 1 is defect evaluation data of a primary device, where a transformer is taken as an example for description, and 5 transformers are respectively transformer 1, transformer 2, transformer 3, transformer 4, and transformer 5, and a transformer 2 is taken as an example for description, the defect severity of the transformer 2 is 91, the device aging factor is 0.8, the device load factor is 0.6, the defect factor is 0.8, the thermal aging factor is 0.8, the insulation aggregation factor is 1, the qualified type of the transformer 2 is monitored, the risk level is low, and the risk reason is insulating paper resistance.

TABLE 1

In some embodiments, the defect evaluation data further includes defect positions and defect occurrence frequency, and please refer to table 2, which illustrates the defect of the oil-filled transformer, where the defect positions are 217 times of the occurrence frequency of the respirator, 159 times of the occurrence frequency of the oil pipeline, and 194 times of the occurrence frequency of the valve.

In some embodiments, the defect evaluation data further includes a defect cause and a frequency of occurrence of defects, which are described by taking the defect of the oil-filled transformer as an example, the defect cause is that the frequency of occurrence of defects due to environmental factors (where the environmental factors include high temperature, high humidity, and the like) is 231 times, the quality control of the defect cause includes that the quality of raw materials of the product is unqualified, the quality control of the product is not in place, the maintenance is not performed according to the rule, and the like, and the defect cause is that the frequency of occurrence of defects due to the unqualified quality of raw materials of the product is 159 times.

Location of defect	Frequency of occurrence of defect
		Breathing apparatus	217
Oil-way pipeline	159
		Valve gate	194

TABLE 2

TABLE 3

In some embodiments, the defect evaluation data further includes a plurality of defect types and defect portions, please refer to table 4, which takes the defect of the transformer as an example for description, and the defect type is the terminal heating, and the corresponding defect portion is the bushing; aiming at the defect type of a cooling system, the defect parts comprise an oil-submerged pump, a cooling fan, a radiator and a cold control box; and aiming at the defect type of oil leakage, the sent defect part comprises an oil conservator.

Referring to fig. 4, the defect evaluation method of the primary equipment further includes: training a defect assessment model specifically comprises:

step 401, acquiring a defect sample set;

and step 402, inputting the defect sample set into the initial evaluation model for training until the initial evaluation model converges to obtain the defect evaluation model.

Specifically, the embodiments of the present disclosure use the cross entropy in tf. To minimize the loss function, embodiments of the present disclosure need to compute the derivative of the loss function with respect to the model variables, pass the found derivative values into the optimizer tf.

In some embodiments, the performance of the model in evaluating defects may be evaluated by an evaluator tf, keras, metrics, which can compare the predicted results of the defect evaluation model with the actual results and output the ratio of the number of samples with correct prediction to the total number of samples.

Type of device	Type of defect	Defective part	Defective component
				Transformer device	Terminal block heating	Sleeve pipe	Wire clamp
Transformer device	Cooling system defect	Oil-submersible pump	Oil flow relay
				Transformer device	Cooling system defect	Oil-submersible pump	Valve gate
Transformer device	Cooling system defect	Cooling fan	Cooling fan
				Transformer device	Cooling system defect	Heat radiator	Heat radiator
Transformer device	Cooling system defect	Heat radiator	Oil-way pipeline
				Transformer device	Cooling system defect	Heat radiator	Valve gate
Transformer device	Cooling system defect	Cold control box	Relay with a movable contact
				Transformer device	Cooling system defect	Cold control box	Power supply air switch
Transformer device	Oil leakage	Oil conservator	Oil level indicator
				Transformer device	Oil leakage	Oil conservator	Oil-way pipeline
Transformer device	Oil leakage	Oil conservator	Flange

TABLE 4

In an application scenario, the method for evaluating the defect of the primary equipment further comprises the following steps: marking the initial image data in the step 101 to obtain an intermediate data set with image labels, dividing the intermediate data set into a defect sample set and a defect test set, inputting the defect sample set into an initial evaluation model, and training until the initial evaluation model is converged to obtain a defect evaluation model; and inputting the defect test set into a defect evaluation model for test evaluation to obtain test defect evaluation data.

Referring to fig. 5, in some embodiments, the method for defect review of a primary device further comprises:

visually displaying the defect evaluation data; the method specifically comprises the following steps:

step 501, performing primary processing on the defect evaluation data;

step 502, performing secondary processing on the primarily processed defect evaluation data;

and 503, performing three-dimensional visual display on the defect evaluation data subjected to the secondary processing.

In some embodiments, the preliminary processing on the target data in step 501 specifically includes:

precipitating and cleaning the defect evaluation data;

and structuring the cleaned defect evaluation data.

Further, the preliminary processing on the defect estimation data in step 501 further includes:

and extracting and analyzing the defect evaluation data after the structured processing.

Specifically, through the preliminary treatment to the target data, carry out deposit, washing, structurization processing, extraction and analysis to the target data promptly for the target data is more intelligent, accord with three-dimensional visual effect more.

Optionally, the secondary processing is performed on the defect evaluation data after the primary processing in step 502, which specifically includes:

and performing data fusion and twinning treatment on the analyzed defect evaluation data.

Specifically, the defect evaluation data after the data fusion and twinning processing is displayed in a three-dimensional visualization manner in the defect evaluation data step 503. By carrying out data fusion and twin processing on defect evaluation, the defect evaluation data has the effect and function in a real scene in a three-dimensional visual scene, so that the digital twin of the three-dimensional visual scene and the real scene can be realized, the space and the time are integrated, and the user experience is improved.

In some embodiments, the defect review method for a primary device further comprises:

constructing a three-dimensional model according to a preset defect type of primary equipment;

solving the three-dimensional model to obtain the current defect type;

and visually displaying the current defect type.

Specifically, as the different preset defect types may have a large difference, the corresponding three-dimensional models are respectively constructed according to the different preset defect types, so as to obtain a three-dimensional model for each preset defect type, so as to more intuitively show the different defect types.

In a specific application scenario, the defect evaluation data includes defect occurrence time, and the defect evaluation method of the primary device further includes:

forming the defect evaluation data into message information according to the defect type;

classifying according to the message information;

and dynamically displaying the classified message information according to the defect grade and the defect occurrence time sequence.

Through dynamic display, relevant workers can visually check the defect occurrence condition conveniently, the relevant workers are helped to quickly locate the defect part, and the primary equipment with the defect is quickly processed. In addition, the related message information can be played back.

The technical scheme provided by the embodiment of the disclosure takes data as drive, and combines with an artificial intelligence related algorithm to carry out intelligent evaluation on the defects of primary equipment, and can eliminate the influence of artificial subjective factors and provide the accuracy and efficiency of evaluation.

The embodiment of the present disclosure further provides a defect evaluation apparatus for primary equipment, which can implement the defect evaluation method for the primary equipment, and the apparatus includes:

The embodiment of the present disclosure further provides a defect assessment apparatus for primary equipment, including:

at least one memory;

at least one processor;

at least one program;

the programs are stored in the memory, and the processor executes the at least one program to implement the defect-assessment method of the primary appliance described above in the embodiments of the present disclosure. The electronic device may be any intelligent terminal including a mobile phone, a tablet computer, a Personal Digital Assistant (PDA for short), a Point of Sales (POS for short), a vehicle-mounted computer, and the like.

Referring to fig. 6, fig. 6 illustrates a hardware structure of a defect estimation apparatus of a primary device according to another embodiment, the defect estimation apparatus of the primary device includes:

the processor 601 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, and is configured to execute a relevant program to implement the technical solution provided by the embodiment of the present disclosure;

the memory 602 may be implemented in a form of a ROM (read only memory), a static storage device, a dynamic storage device, or a RAM (random access memory). The memory 602 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present disclosure is implemented by software or firmware, the relevant program codes are stored in the memory 602 and called by the processor 601 to execute the defect assessment method of the primary device according to the embodiments of the present disclosure;

an input/output interface 603 for implementing information input and output;

the communication interface 604 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g., USB, network cable, etc.) or in a wireless manner (e.g., mobile network, WIFI, bluetooth, etc.); and

a bus 605 that transfers information between the various components of the device (e.g., the processor 601, memory 602, input/output interfaces 603, and communication interfaces 604);

wherein the processor 601, the memory 602, the input/output interface 603 and the communication interface 604 are communicatively connected to each other within the device via a bus 605.

The embodiment of the present disclosure also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the defect assessment method of the primary device.

According to the defect evaluation method of the primary equipment, the defect evaluation device of the primary equipment and the storage medium provided by the embodiment of the disclosure, the initial defect data of the primary equipment is obtained, the initial defect data is preprocessed to obtain the actual defect data, and the actual defect data is input into the preset defect evaluation model to be evaluated to obtain the defect evaluation data, so that the accuracy and the efficiency of the defect evaluation of the primary equipment can be improved.

The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The embodiments described in the embodiments of the present disclosure are for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation to the technical solutions provided in the embodiments of the present disclosure, and it is obvious to those skilled in the art that the technical solutions provided in the embodiments of the present disclosure are also applicable to similar technical problems with the evolution of technology and the emergence of new application scenarios.

The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.

The terms "first," "second," "third," "fourth," and the like in the description of the disclosure and in the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It is to be understood that in the present disclosure, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.

In the several embodiments provided in the present disclosure, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present disclosure. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and therefore do not limit the scope of the claims of the embodiments of the present disclosure. Any modifications, equivalents and improvements within the scope and spirit of the embodiments of the present disclosure should be considered within the scope of the claims of the embodiments of the present disclosure by those skilled in the art.

Claims

1. A method for defect assessment of a primary device, comprising:

acquiring initial defect data of primary equipment;

preprocessing the initial defect data to obtain actual defect data;

2. The method of claim 1, wherein preprocessing the initial defect data to obtain actual defect data comprises:

denoising and filtering the initial defect data;

3. The method of claim 2, wherein inputting the actual defect data into a predetermined defect evaluation model for evaluation to obtain defect evaluation data comprises:

inputting the actual defect data to the defect review model;

4. The method of claim 2, wherein the defect review data further includes a defect rating, and wherein inputting the actual defect data into a predetermined defect review model for review results in defect review data further comprises:

5. The method of any one of claims 1 to 4, further comprising: training a defect assessment model specifically comprises:

acquiring a defect sample set;

6. The method of any one of claims 1 to 4, further comprising:

and visually displaying the defect evaluation data.

7. The method of claim 6, wherein the visually presenting the defect review data comprises:

performing preliminary processing on the defect evaluation data;

8. A defect review apparatus for a primary device, comprising:

9. A defect review apparatus for a primary device, comprising:

at least one memory;

at least one processor;

at least one program;

the program is stored in the memory, the processor executing the at least one program to implement the method of any one of claims 1 to 7.

10. A storage medium that is a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform:

the method of any one of claims 1 to 7.