CN114204680B - Multi-type automatic detection equipment fusion remote diagnosis system and method - Google Patents
Multi-type automatic detection equipment fusion remote diagnosis system and method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
- H04N23/81—Camera processing pipelines; Components thereof for suppressing or minimising disturbance in the image signal generation
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Abstract
The invention belongs to the technical field of power equipment diagnosis, and particularly provides a fusion remote diagnosis system and a fusion remote diagnosis method for multi-type automatic detection equipment. The invention also provides a fusion remote diagnosis method of the multi-type automatic detection equipment. According to the embodiment of the invention, the data acquisition terminal is arranged to remotely acquire the detection data of the to-be-detected electric equipment, so that a professional can remotely detect the electric equipment at different positions in an office, and the detection period is effectively shortened; when the user authority is verified, the accuracy of the subsequently obtained feature data is improved in the noise reduction processing process of the face image of the user, so that the accuracy of the user authority verification is improved, and the safety of a diagnosis system is ensured.
Description
Technical Field
The invention belongs to the technical field of power equipment diagnosis, and particularly relates to a system and a method for fusing remote diagnosis of multi-type automatic detection equipment.
Background
The structure of the power equipment is complex, and faults are easy to occur during operation, so that the fault monitoring needs to be carried out on the power equipment, and the safe operation of the power equipment is maintained.
With the continuous expansion of the scale of the power system, the power supply level of China is increasingly improved, and the power consumption requirements of various users are better met. Meanwhile, the requirement of people on the reliability of power supply is also increasing day by day. One important aspect of improving the reliability of power supply is to reduce the occurrence of power equipment faults and to diagnose the power equipment faults quickly so as to repair the power equipment in time and restore the normal operation of the power system as soon as possible. The emergence of fault diagnosis technology opens up a new way for improving the reliability and the safety of power supply of power equipment.
Different special detection equipment that different power equipment needs to use at present detects, consequently, needs professional to go to the scene of difference in person and detect power equipment to lead to the detection cycle relatively long.
Disclosure of Invention
The invention aims to provide a system and a method for fusing multi-type automatic detection equipment with remote diagnosis, which are used for solving the problem that the detection period is long because professional technicians are required to go to different sites to detect the electric equipment because different special detection equipment is required for detecting different electric equipment at present.
In order to achieve the above purpose, the present invention provides the following technical solutions.
In a first aspect, an embodiment of the present invention provides a multi-type automated detection device fusion remote diagnosis system, where the diagnosis system includes a detection data acquisition terminal, a cloud server, and a remote diagnosis terminal, where:
the data acquisition terminal is communicated with the cloud server and used for acquiring detection data of the to-be-detected power equipment and transmitting the detection data to the cloud server;
the cloud server is used for receiving detection data sent by the detection data acquisition terminal and detection results sent by the remote diagnosis terminal, storing the detection data and the detection results, and sending the detection data to the remote diagnosis terminal;
the remote diagnosis terminal is communicated with the cloud server and used for receiving the detection data sent by the cloud server; visually displaying the detection data; acquiring a detection result input by a user, and sending the detection result to the cloud server; the step of acquiring the detection result input by the user comprises the step of verifying the authority of the user; when the user acquires the right, acquiring a detection result input by the user; the verifying the authority of the user specifically includes: acquiring a face image of the user; acquiring feature data contained in the face image; and judging whether the user passes the identity authentication or not based on the characteristic data.
As a further limitation of the technical solution of the preferred embodiment of the present invention, the remote diagnosis terminal includes:
a communication module for communicating with the cloud server;
the display module is used for visually displaying the detection data;
and the input module is used for acquiring the detection result input by the user.
As a further limitation of the technical solution of the preferred embodiment of the present invention, the data acquisition terminal includes a detection data acquisition module and a detection picture acquisition module, wherein:
the detection data acquisition module is used for acquiring the equipment measurement data of the to-be-detected power equipment;
and the detection picture acquisition module is used for acquiring a field detection video.
As a further limitation of the technical solution of the preferred embodiment of the present invention, the input module includes:
the authority verification submodule is used for acquiring feature data contained in the face image; judging whether the user passes identity authentication or not based on the feature data;
and the result acquisition submodule is used for acquiring the detection result input by the user when the user acquires the right.
In a second aspect, in another preferred embodiment provided by the present invention, there is provided a multi-type automatic detection device fusion remote diagnosis method, including:
the method comprises the steps that a data acquisition terminal acquires detection data of the to-be-detected power equipment and transmits the detection data to a cloud server;
the cloud server receives detection data sent by the detection data acquisition terminal and a detection result sent by the remote diagnosis terminal, stores the detection data and the detection result, and sends the detection data to the remote diagnosis terminal;
the remote diagnosis terminal receives detection data sent by the cloud server, wherein the detection data comprises equipment measurement data and a field detection video; the remote diagnosis terminal carries out visual display on the detection data; the remote diagnosis terminal acquires a detection result input by a user, and sends the detection result to the cloud server; the step of acquiring the detection result input by the user comprises the step of verifying the authority of the user; when the user acquires the right, acquiring a detection result input by the user; the verifying the authority of the user specifically includes: acquiring a face image of the user; acquiring feature data contained in the face image; and judging whether the user passes the identity authentication or not based on the characteristic data.
As a further limitation of the technical solution of the preferred embodiment of the present invention, the transmission method of the live detection video includes: detecting a changed picture in the video stream in real time;
and sending the changed picture to the cloud server.
As a further limitation of the technical solution of the preferred embodiment of the present invention, the step of acquiring the feature data included in the face image includes:
converting the face image into a grayscale image;
carrying out noise reduction processing on the gray level image to obtain a noise reduction image;
and acquiring feature data contained in the noise-reduced image by using an image feature acquisition algorithm.
As a further limitation of the technical solution of the preferred embodiment of the present invention, said determining whether the user passes the identity authentication based on the feature data includes:
calculating the similarity between the feature data and the feature data of the face image of the user with the input module use authority;
when the similarity is larger than a preset similarity threshold, determining that the user passes identity authentication; otherwise, determining that the user is not authenticated.
As a further limitation of the technical solution of the preferred embodiment of the present invention, the step of performing noise reduction processing on the grayscale image includes:
performing wavelet decomposition on the gray level image to obtain a high-frequency coefficient HF and a low-frequency coefficient LF;
if HF is larger than or equal to st, the high-frequency coefficient is processed as follows:
obtaining the processed high-frequency coefficient:
if HF < st, the high frequency coefficient is processed as follows:
obtaining a processed high-frequency coefficient:
in the formula, cHF represents a coefficient obtained after the HF is subjected to the treatment, and st represents a preset judgment threshold; hg denotes the value of the selection function,
ds 1 、ds 2 、ds 3 respectively representing a preset first processing coefficient, a preset second processing coefficient, a preset third processing coefficient, ds 1 ∈(0.79,0.91)，ds 2 ∈(0.9,1.1)，ds 3 ∈(0.05,0.15)；
Performing wavelet reconstruction on the cHF and the LF to obtain a first intermediate image;
performing edge detection on the gray level image to obtain a set U1 of edge pixel points in the gray level image;
respectively carrying out noise reduction processing on the edge pixel points in the U1 by using a non-local mean noise reduction algorithm to obtain the noise-reduced pixel values of the edge pixel points;
calculating the absolute value of the difference value of the pixel values of the edge pixel points before and after noise reduction;
judging whether the absolute value is larger than a preset absolute value threshold value,
if so, performing noise reduction processing on the edge pixel points on the gray image by using a bilateral noise reduction algorithm to obtain a second intermediate image;
the noise-reduced image is obtained using:
lowIg(x,y)＝w 1 ×midIgoe(x,y)+w 2 ×midIgto(x,y)；
where lowIg denotes a noise-reduced image, w 1 And w 2 Indicating a preset weight parameter, (x, y) indicating the position of the pixel point, midIgoe indicating the pixel point in the first intermediate image, midIgto indicating the pixel point in the second intermediate image and belonging to the set U1, midIgoe (x, y) indicating the pixel value of the pixel point with the position (x, y) in the first intermediate image, midIgto (x, y) indicating the pixel value of the pixel point with the position (x, y) in the second intermediate image, and lowIg (x, y) indicating the pixel value of the pixel point with the position (x, y) in the noise-reduced image.
Compared with the prior art, the embodiment of the invention remotely acquires the detection data of the to-be-detected power equipment by setting the data acquisition terminal, so that a professional can remotely detect the power equipment at different positions in an office, and the commuting process of the professional is reduced, so that the detection period is effectively shortened; when the user authority is verified, in the noise reduction process of the face image of the user, two noise reduction paths are set, one path is used for carrying out noise reduction on a common noise point through wavelet decomposition, the other path is used for obtaining a suspected noise point in an edge pixel point through a non-local mean value, namely whether the absolute value is larger than an edge pixel point corresponding to a preset absolute value threshold value, then carrying out noise reduction on the suspected noise point, and then carrying out weighting fusion on the pixel value of the pixel point in the set U1 and the pixel value of the pixel point in the first intermediate image in a weighting mode, so that the edge detail information of the noise reduction image is enhanced while noise is reduced, the accuracy of subsequently obtained feature data is improved, the accuracy of the user authority verification is improved, and the safety of a diagnosis system is ensured.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a block diagram of a multi-type automated inspection device fusion remote diagnosis system according to an embodiment of the present invention;
fig. 2 is a block diagram of a data acquisition terminal in a remote diagnosis system integrated with multi-type automatic detection devices according to an embodiment of the present invention;
fig. 3 is a block diagram of a remote diagnosis terminal in a system integrated with multi-type automatic detection devices according to an embodiment of the present invention;
FIG. 4 is a flow chart of an implementation of a method for integrating remote diagnosis with multi-type automatic detection equipment according to an embodiment of the present invention;
FIG. 5 is a sub-flowchart of a method for integrating remote diagnosis with multi-type automatic detection equipment according to an embodiment of the present invention;
FIG. 6 is another sub-flowchart of a multi-type automated inspection device fusion remote diagnosis method according to an embodiment of the present invention;
FIG. 7 is a further sub-flowchart of a method for integrating remote diagnosis with multi-type automatic detection equipment according to an embodiment of the present invention;
fig. 8 is a block diagram of an input module in a system for integrating multiple types of automatic detection devices with remote diagnosis according to an embodiment of the present invention;
fig. 9 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further 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 invention and are not intended to limit the invention. Specific implementations of the present invention are described in detail below with reference to specific embodiments.
For example, the remote diagnosis terminal in this embodiment may specifically be a mobile phone, a smart watch, a tablet computer, and other devices.
The display device of the remote diagnosis terminal can be a touch display screen.
Different special detection equipment that different power equipment needs to use at present detects, consequently, needs professional technical personnel to go to the scene in person and carry out different scene and detect power equipment to lead to the detection cycle relatively long.
In order to solve the above problem, the embodiments of the present invention provide the following solutions.
FIG. 1 is a block diagram illustrating a multi-type automated inspection device incorporating a remote diagnosis system according to an embodiment of the present invention;
as shown in fig. 1, in an embodiment provided by the present invention, a multi-type automated detection device fusion remote diagnosis system is provided, the diagnosis system includes a detection data acquisition terminal 100, a cloud server 200, and a remote diagnosis terminal 300, wherein: the detection data acquisition terminal 100 and the cloud server 200, and the remote diagnosis terminal 300 and the cloud server 200 are communicated through a network; the network may be a medium for providing a communication link between the test data acquiring terminal 100 and the cloud server 200 and between the remote diagnosis terminal 300 and the cloud server 200. In particular, the network may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
In the embodiment of the present invention, the data acquiring terminal 100 is in communication with the cloud server, and is configured to acquire detection data of an electric power device to be detected, and transmit the detection data to the cloud server;
in this embodiment of the present invention, the cloud server 200 is configured to receive the detection data sent by the detection data obtaining terminal and the detection result sent by the remote diagnosis terminal, store the detection data and the detection result, and send the detection data to the remote diagnosis terminal;
in the embodiment of the present invention, the remote diagnosis terminal 300 is in communication with the cloud server, and is configured to receive the detection data sent from the cloud server; visually displaying the detection data; and acquiring a detection result input by a user, and sending the detection result to the cloud server.
In the embodiment of the present invention, the acquiring of the detection result input by the user includes a step of verifying the authority of the user; when the user acquires the right, acquiring a detection result input by the user; the verifying the authority of the user specifically includes: acquiring a face image of the user; acquiring feature data contained in the face image; and judging whether the user passes the identity authentication or not based on the characteristic data.
In the embodiment of the invention, the diagnostic system provided by the invention remotely acquires the detection data of the to-be-detected electric power equipment by arranging the data acquisition terminal, so that a user (namely, a professional technician) can remotely detect the electric power equipment at different positions in an office, and the commuting process of the professional technician is reduced, so that the detection period is effectively shortened.
FIG. 2 is a block diagram illustrating a remote diagnosis terminal in a system for integrating multiple types of automatic detection devices with remote diagnosis according to an embodiment of the present invention;
as shown in fig. 2, in the embodiment of the present invention, the remote diagnosis terminal 300 includes: a communication module 301 for communicating with the cloud server;
a display module 302, configured to perform visual display on the detection data;
the input module 303 is configured to obtain a detection result input by the user.
Specifically, different types of power devices correspond to different types of data acquisition terminals, for example, a device for measuring oil temperature, which is required to measure oil temperature, is required to detect a transformer; for capacitive devices, capacitance measurement is required.
Preferably, the inspection data includes device measurement data and live inspection video.
Fig. 3 shows a block diagram of a data acquisition terminal in a system integrating multiple types of automatic detection devices and remote diagnosis according to an embodiment of the present invention.
Further, as shown in fig. 3, in the embodiment of the present invention, the data acquiring terminal 100 includes a detection data acquiring module 101 and a detection screen acquiring module 102, where:
the detection data acquisition module 101 is configured to acquire device measurement data of the to-be-detected power device;
the detection picture acquiring module 102 is configured to acquire a field detection video.
Specifically, the field detection video is a video of the process of detecting the detection equipment, so that the professional can conveniently and remotely see the state change of the equipment to be detected after the equipment to be detected is detected.
Specifically, when transmitting the live detection video, only the data of the changed portion of the picture may be transmitted in order to save bandwidth.
Further, as shown in fig. 8, in the embodiment of the present invention, the input module 303 includes:
the authority verification submodule 3031 is configured to obtain feature data included in the face image; judging whether the user passes identity authentication or not based on the feature data;
and the result obtaining submodule 3032 is configured to obtain the detection result input by the user when the user obtains the right.
As a further limitation of the present invention, the method for transmitting live test video comprises: detecting a changed picture in the video stream in real time;
and sending the changed picture to the cloud server.
In the transmission process of the measured data of the equipment, in order to ensure the accuracy of the data, a corresponding checking means is required,
dividing a device measurement data segment to be transmitted into a plurality of 16-bit integers at a detection data acquisition terminal, and adding the integers; then, the carry of the front row is compensated to the back row, and the negation operation is carried out to obtain a check sum; the detection data acquisition terminal calculates a check sum before sending data, and sends the check sum to the cloud server together, the cloud server calculates the check sum in the same mode after receiving the equipment measurement data, and if the two check sums are different, data transmission errors are indicated, and the data needs to be retransmitted.
Preferably, the device measurement data includes temperature, current, voltage, resistance and vibration frequency.
Specifically, in addition to the measurement data listed above, there are data such as frequency, phase, water pressure, and air pressure. Different devices need to acquire different data.
Preferably, the cloud server is further configured to store the detection result.
According to the invention, the data acquisition terminal is arranged to remotely acquire the detection data of the to-be-detected electric equipment, so that a professional can remotely detect the electric equipment at different positions in an office, and the commuting process of the professional is reduced, so that the detection period is effectively shortened.
FIG. 4 is a flow chart of an implementation of a method for integrating remote diagnosis with multi-type automatic detection equipment according to an embodiment of the present invention;
as shown in fig. 4, in another preferred embodiment provided by the present invention, a multi-type automatic detection device fusion remote diagnosis method is provided, the diagnosis method includes:
step S401: the method comprises the steps that a data acquisition terminal acquires detection data of electric equipment to be detected and transmits the detection data to a cloud server;
step S402: the cloud server receives detection data sent by the detection data acquisition terminal and a detection result sent by the remote diagnosis terminal, stores the detection data and the detection result, and sends the detection data to the remote diagnosis terminal;
step S403: the remote diagnosis terminal receives detection data sent by the cloud server, wherein the detection data comprises equipment measurement data and a field detection video; the remote diagnosis terminal carries out visual display on the detection data; the remote diagnosis terminal obtains a detection result input by a user, and the remote diagnosis terminal sends the detection result to the cloud server.
FIG. 5 is a sub-flowchart of a multi-type automated inspection device fusion remote diagnosis method according to an embodiment of the present invention;
as shown in fig. 5, as a further limitation of the present invention, the step of the remote diagnosis terminal acquiring the detection result input by the user includes:
step S501: verifying the authority of the user;
step S502: and when the user acquires the right, acquiring a detection result input by the user.
FIG. 6 is another sub-flowchart of a multi-type automated inspection device fusion remote diagnosis method according to an embodiment of the present invention;
as shown in fig. 6, as a further limitation of the present invention, the step of verifying the user's right includes:
step S601: acquiring a face image of the user;
step S602: acquiring feature data contained in the face image;
step S603: and judging whether the user passes the identity authentication or not based on the characteristic data.
In the embodiment, the authority verification is performed on the input process of the detection result in an identity verification mode, so that the occurrence of data substitution is effectively avoided, and the corresponding detection personnel can be directly found out after an accident caused by inaccurate detection.
FIG. 7 is a further sub-flowchart illustrating a multi-type automated inspection device fusion remote diagnosis method according to an embodiment of the present invention;
as shown in fig. 7, as a further limitation of the present invention, the step of acquiring feature data included in the face image includes:
step S701: converting the face image into a grayscale image;
step S702: carrying out noise reduction processing on the gray level image to obtain a noise reduction image;
step S703: and acquiring feature data contained in the noise-reduced image by using an image feature acquisition algorithm.
Specifically, for example, the feature obtaining algorithm used in the embodiment of the present invention may be a HOG algorithm, a Harris algorithm, or the like, and specifically, the embodiment of the present invention is not limited.
As a further limitation of the present invention, the determining whether the user passes the authentication based on the feature data includes:
calculating the similarity between the feature data and the feature data of the face image of the user with the input module use authority;
when the similarity is larger than a preset similarity threshold, determining that the user passes identity authentication;
and when the similarity is not greater than a preset similarity threshold, determining that the user does not pass the identity authentication.
As a further limitation of the present invention, the step of performing noise reduction processing on the grayscale image includes:
performing wavelet decomposition on the gray level image to obtain a high-frequency coefficient HF and a low-frequency coefficient LF;
if HF is larger than or equal to st, the high-frequency coefficient is processed as follows:
obtaining the processed high-frequency coefficient:
if HF < st, the high frequency coefficient is processed as follows:
obtaining the processed high-frequency coefficient:
wherein cHF represents the coefficient obtained by subjecting HF to the above treatment, and st represents pre-treatmentSetting a judgment threshold value; hg denotes the value of the selection function,
ds 1 、ds 2 、ds 3 respectively representing a preset first processing coefficient, a preset second processing coefficient, a preset third processing coefficient, ds 1 ∈(0.79,0.91)，ds 2 ∈(0.9,1.1)，ds 3 ∈(0.05,0.15)；
Performing wavelet reconstruction on the cHF and the LF to obtain a first intermediate image;
performing edge detection on the gray level image to obtain a set U1 of edge pixel points in the gray level image;
respectively carrying out noise reduction processing on the edge pixel points in the U1 by using a non-local mean noise reduction algorithm to obtain the noise-reduced pixel values of the edge pixel points;
calculating the absolute value of the difference value of the pixel values of the edge pixel points before and after noise reduction;
judging whether the absolute value is larger than a preset absolute value threshold value,
if so, performing noise reduction processing on the edge pixel points on the gray image by using a bilateral noise reduction algorithm to obtain a second intermediate image;
the noise-reduced image is obtained using:
lowIg(x,y)＝w 1 ×midIgoe(x,y)+w 2 ×midIgto(x,y)；
where lowIg denotes a noise-reduced image, w 1 And w 2 Indicating a preset weight parameter, (x, y) indicating the position of the pixel point, midIgoe indicating the pixel point in the first intermediate image, midIgto indicating the pixel point in the second intermediate image and belonging to the set U1, midIgoe (x, y) indicating the pixel value of the pixel point with the position (x, y) in the first intermediate image, midIgto (x, y) indicating the pixel value of the pixel point with the position (x, y) in the second intermediate image, and lowIg (x, y) indicating the pixel value of the pixel point with the position (x, y) in the noise-reduced image.
Specifically, the existing image noise reduction process generally performs noise reduction based on a neighborhood, and such a noise reduction mode easily makes edge pixel points blurred. In the invention, two noise reduction paths are set, one path is used for carrying out noise reduction processing on a common noise point through wavelet decomposition, the other path is used for firstly obtaining a suspected noise point in an edge pixel point through a non-local mean value, namely whether an absolute value is larger than an edge pixel point corresponding to a preset absolute value threshold value or not, then carrying out noise reduction processing on the suspected noise point, and then carrying out weighting fusion on a pixel value of a pixel point in a set U1 and a pixel value of a pixel point in a first intermediate image in a weighting mode, so that the edge detail information of a noise reduction image is enhanced while the noise is reduced, the accuracy of subsequently obtained feature data is improved, and the safety of the invention is improved.
In summary, the embodiment of the invention remotely acquires the detection data of the to-be-detected power equipment by setting the data acquisition terminal, so that a professional can remotely detect the power equipment at different positions in an office, and the commuting process of the professional is reduced, thereby effectively shortening the detection period; when the user authority is verified, in the noise reduction process of the face image of the user, two noise reduction paths are set, one path is used for carrying out noise reduction on a common noise point through wavelet decomposition, the other path is used for obtaining a suspected noise point in an edge pixel point through a non-local mean value, namely whether the absolute value is larger than an edge pixel point corresponding to a preset absolute value threshold value, then carrying out noise reduction on the suspected noise point, and then carrying out weighting fusion on the pixel value of the pixel point in the set U1 and the pixel value of the pixel point in the first intermediate image in a weighting mode, so that the edge detail information of the noise reduction image is enhanced while noise is reduced, the accuracy of subsequently obtained feature data is improved, the accuracy of the user authority verification is improved, and the safety of a diagnosis system is ensured.
Fig. 9 is a block diagram schematically illustrating a computer device according to an embodiment of the present invention.
Further, as shown in fig. 9, in a further preferred embodiment provided by the present invention, a computer device is provided, and the computer device is configured to execute the processing flow of the multi-type automatic detection device fusion remote diagnosis method. The computer device comprises:
a memory 801 and one or more processors 802;
the memory 801 for storing one or more programs;
when executed by the one or more processors 802, the one or more programs cause the one or more processors 802 to implement the multi-type automated inspection device fusion remote diagnosis method as provided in the embodiments above.
Further, in the real-time embodiment provided by the present invention, the computer device further has a communication interface 803 for receiving a control command.
Further, in yet another preferred embodiment provided by the present invention, a storage medium containing computer executable instructions is provided, which when executed by a computer processor, is used for executing the multi-type automatic detection device fusion remote diagnosis method provided by the above embodiments.
In a typical configuration of an embodiment of the present invention, the terminal, the device serving the network, and the computing device include one or more processors (CPUs), input/output interfaces, network interfaces, and memories.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data.
Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the device described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (4)
1. The multi-type automatic detection equipment fusion remote diagnosis system is characterized by comprising a detection data acquisition terminal, a cloud server and a remote diagnosis terminal;
the data acquisition terminal is communicated with the cloud server and used for acquiring detection data of the to-be-detected power equipment and transmitting the detection data to the cloud server;
the cloud server is used for receiving detection data sent by the detection data acquisition terminal and detection results sent by the remote diagnosis terminal, storing the detection data and the detection results, and sending the detection data to the remote diagnosis terminal;
the remote diagnosis terminal is communicated with the cloud server and used for receiving the detection data sent by the cloud server; visually displaying the detection data; acquiring a detection result input by a user, and sending the detection result to the cloud server; the method comprises the steps of obtaining a detection result input by a user, wherein the step of obtaining the detection result input by the user comprises the step of verifying the authority of the user; when the user acquires the right, acquiring a detection result input by the user; the verifying the authority of the user specifically includes: acquiring a face image of the user; acquiring feature data contained in the face image; judging whether the user passes identity authentication or not based on the feature data;
the diagnosis system is used for realizing a multi-type automatic detection equipment fusion remote diagnosis method, and the diagnosis method comprises the following steps:
the method comprises the steps that a data acquisition terminal acquires detection data of the to-be-detected power equipment and transmits the detection data to a cloud server;
the cloud server receives detection data sent by the detection data acquisition terminal and a detection result sent by the remote diagnosis terminal, stores the detection data and the detection result, and sends the detection data to the remote diagnosis terminal;
the remote diagnosis terminal receives detection data sent by the cloud server, wherein the detection data comprises equipment measurement data and a field detection video; the remote diagnosis terminal carries out visual display on the detection data; the remote diagnosis terminal acquires a detection result input by a user, and sends the detection result to the cloud server;
the transmission method of the field detection video comprises the following steps: detecting a changed picture in a video stream in real time; sending the changed picture to the cloud server;
the step of acquiring feature data contained in the face image includes: converting the face image into a grayscale image; carrying out noise reduction processing on the gray level image to obtain a noise reduction image; acquiring feature data contained in the noise-reduced image by using an image feature acquisition algorithm;
the judging whether the user passes the identity authentication based on the feature data comprises: calculating the similarity between the feature data and the feature data of the face image of the user with the input module use authority; when the similarity is larger than a preset similarity threshold, determining that the user passes identity authentication; otherwise, determining that the user does not pass the identity authentication;
the step of performing noise reduction processing on the grayscale image includes:
performing wavelet decomposition on the gray level image to obtain a high-frequency coefficient HF and a low-frequency coefficient LF;
if HF is larger than or equal to st, the high-frequency coefficient is processed as follows:
obtaining the processed high-frequency coefficient:
if HF < st, the high frequency coefficient is processed as follows:
obtaining the processed high-frequency coefficient:
in the formula, cHF represents a coefficient obtained after the HF is subjected to the treatment, and st represents a preset judgment threshold; hg denotes the value of the selection function,
ds 1 、ds 2 、ds 3 respectively representing a preset first processing coefficient, a preset second processing coefficient, a preset third processing coefficient, ds 1 ∈(0.79,0.91)，ds 2 ∈(0.9,1.1)，ds 3 ∈(0.05,0.15)；
Performing wavelet reconstruction on the cHF and the LF to obtain a first intermediate image;
performing edge detection on the gray level image to obtain a set U1 of edge pixel points in the gray level image;
respectively carrying out noise reduction processing on the edge pixel points in the U1 by using a non-local mean noise reduction algorithm to obtain the noise-reduced pixel values of the edge pixel points;
calculating the absolute value of the difference value of the pixel values of the edge pixel points before and after noise reduction;
judging whether the absolute value is larger than a preset absolute value threshold value or not;
if so, performing noise reduction processing on the edge pixel points on the gray image by using a bilateral noise reduction algorithm to obtain a second intermediate image;
the noise-reduced image is obtained using:
lowIg(x,y)＝w 1 ×midIgoe(x,y)+w 2 ×midIgto(x,y)；
in the formula:
lowIg denotes a noise reduced image;
w 1 and w 2 Representing a preset weight parameter;
(x, y) represents the location of a pixel;
midgoe represents a pixel point in the first intermediate image;
midIgto represents a pixel point in the second intermediate image and belonging to the set U1;
midgoe (x, y) represents a pixel value of a pixel point at a position (x, y) in the first intermediate image;
midIgto (x, y) represents the pixel value of the pixel point with the position (x, y) in the second intermediate image;
lowIg (x, y) represents a pixel value of a pixel point at a position (x, y) in the noise-reduced image.
2. The multi-type automated detection device fusion remote diagnosis system according to claim 1, wherein the remote diagnosis terminal comprises:
a communication module for communicating with the cloud server;
the display module is used for visually displaying the detection data;
and the input module is used for acquiring the detection result input by the user.
3. The multi-type automated detection device fusion remote diagnosis system according to claim 2, wherein the data acquisition terminal comprises a detection data acquisition module and a detection picture acquisition module, wherein:
the detection data acquisition module is used for acquiring the equipment measurement data of the to-be-detected power equipment;
and the detection picture acquisition module is used for acquiring a field detection video.
4. The multi-type automated detection device fusion remote diagnosis system according to claim 2, wherein the input module comprises:
the authority verification submodule is used for acquiring feature data contained in the face image; judging whether the user passes identity authentication or not based on the feature data;
and the result acquisition submodule is used for acquiring the detection result input by the user when the user acquires the right.
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