CN116989842A - Equipment fault diagnosis method and device based on big data and storage medium - Google Patents
Equipment fault diagnosis method and device based on big data and storage medium Download PDFInfo
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Abstract
The application provides a device fault diagnosis method and device based on big data and a storage medium, wherein the device fault diagnosis method comprises the following steps: receiving a routing inspection point setting request, and generating a routing inspection route based on the positions of a plurality of routing inspection points and the execution sequence of the plurality of routing inspection points; issuing the inspection route to an inspection robot, sending an instruction for executing the inspection route to the inspection robot, enabling the inspection robot to move along the inspection route to reach all inspection points, and collecting inspection data at the inspection points; acquiring a preset template image, and modifying a pixel value of each corresponding rendering area based on inspection data acquired by an inspection robot at each inspection point to obtain an inspection state image; and inputting the inspection state image into a preset classifier, and outputting the current state level. According to the application, the inspection state image reflecting the current overall state of the equipment is rendered and generated, and then the inspection state image is input into the classifier to output state parameters, so that the overall operation state of the equipment can be accurately reflected, and the inspection efficiency is higher.
Description
Technical Field
The present application relates to the field of equipment security supervision, and in particular, to a method, an apparatus, and a storage medium for diagnosing equipment failure based on big data.
Background
With the progress of technology, large-scale equipment is becoming more common in the application of today's society, however, large-scale equipment is because of self volume is great, and the structure is comparatively complicated, often needs the manual work to patrol and examine to ensure that the machine normally operates.
With the advancement of equipment management digitization, the traditional manual inspection method cannot meet the requirement of equipment maintenance gradually, and the digitization means is required to be used, so that the equipment inspection efficiency and quality are improved, the hidden danger of equipment is found in time, and the good equipment state is ensured. In the existing digital inspection method, equipment such as a sensor is used for checking a certain position of a machine, whether the position is problematic or not is judged through data of the sensor, however, for large-scale equipment, the whole operation condition of the equipment cannot be represented by single position equipment parameters, and the whole operation condition of the large-scale equipment is difficult to be represented by digital means in the prior art.
Prior art 1 (CN 108189924 a) discloses a hydroelectric inspection robot and an inspection method thereof. The robot comprises a control device, a traveling device, an upper linkage platform, a lower linkage platform, a switching station, a dam body inspection device and an underground water turbine inspection device, wherein the ground switching station and the dam body inspection device comprise a high-definition camera, a three-dimensional laser navigation positioning device, a toxic and harmful inflammable gas detection sensor and a smoke sensor, the underground water turbine inspection device comprises a data receiving module for receiving water level data sent by water level sensors at various inspection points, a sound detection module for detecting the running condition of the water turbine and a temperature detection module for detecting the temperature of equipment, and the control device comprises a controller, an alarm and a wireless communication module. Technical drawbacks of prior art 1 include: in the inspection process, various parameters are collected, but the parameters of different types are not combined or processed in a combined way, so that the obtained inspection result is inaccurate, the state of an inspection object cannot be reflected from the whole body, and the inspection efficiency is low.
Disclosure of Invention
In order to solve the defects in the prior art, the application provides a device fault diagnosis method and device based on big data and a storage medium, which are used for integrally judging the running condition of the device.
The application adopts the following technical scheme.
The application provides a device fault diagnosis method based on big data, which comprises the following steps:
receiving a routing inspection point setting request, wherein the routing inspection point setting request comprises positions of a plurality of routing inspection points and execution sequences of the plurality of routing inspection points, and generating a routing inspection route based on the positions of the plurality of routing inspection points and the execution sequences of the plurality of routing inspection points;
the inspection route is issued to an inspection robot, an instruction for executing the inspection route is sent to the inspection robot, and the inspection robot moves along the inspection route to reach each inspection point and acquires inspection data at the inspection points in the process of executing the inspection route;
acquiring a preset template image, wherein each inspection point is correspondingly provided with a rendering area in the template image, and modifying a pixel value of each corresponding rendering area based on inspection data acquired by the inspection robot at each inspection point to obtain an inspection state image;
and inputting the inspection state image into a preset classifier, outputting the state grade of the current equipment, and judging the fault condition of the equipment according to the state grade.
Further, the inspection data comprise loudness data and/or temperature data, if the inspection data are loudness data and temperature data, the preset template image comprises a loudness template image and a temperature template image, and the pixel value of each corresponding rendering area is modified based on the inspection data collected by the inspection robot at each inspection point;
modifying pixel values of each rendering area in the loudness template image based on the collected loudness data to obtain a first channel image of the inspection state image;
modifying pixel values of each rendering area in the temperature template image based on the acquired temperature data to obtain a second channel image of the inspection state image;
and if the inspection data is one of loudness data or temperature data, the inspection state image is a single-channel image which only modifies the pixel value of each rendering area in the loudness template image or the temperature template image.
Further, the inspection data further includes distance data, the distance data is the distance between the inspection robot and the equipment to be inspected, which is measured by the distance meter after the inspection robot reaches the inspection points, and in the step of measuring the distance between the inspection robot and the equipment to be inspected, each inspection point is provided with a distance measuring direction, and parameters in the distance measuring direction are measured by the distance meter.
Further, if the inspection data includes distance data, in the step of modifying the pixel value of each corresponding rendering area based on the inspection data collected by the inspection robot at each inspection point, weighting calculation is performed on the loudness data and/or the temperature data based on the distance data, so as to obtain the pixel value of the rendering area corresponding to each inspection point.
Further, the loudness data and/or temperature data is weighted based on the distance data according to the following formula:
H＝δC
where C represents loudness data or temperature data, δ represents distance data, and H represents pixel values.
Further, the generating the inspection route based on the positions of the plurality of inspection points and the execution sequence of the plurality of inspection points further includes:
sequentially numbering the inspection points based on the execution sequence of the plurality of inspection points;
obtaining a topographic map of a current inspection scene, marking the position of each inspection point in the topographic map, and calculating an inspection sub-path between every two adjacent numbered inspection points by using an A-Star algorithm;
combining the plurality of patrol sub-paths to obtain a patrol route.
Further, in the step of moving the inspection robot along the inspection route to each inspection point and collecting inspection data at the inspection point, the stay time is calculated based on the historical state data of the equipment to be inspected, and the inspection robot reaches the inspection point and stays at the inspection point for measuring the stay time to inspect the data.
Further, calculating the residence time based on the historical state data of the equipment to be inspected further comprises:
calculating the average value of the state grades of the equipment to be inspected for the previous n times, and if the average value is larger than a preset residence threshold value, the residence time is a first time length; and if the average value is smaller than or equal to a preset stay threshold value, the stay time is a second duration.
Further, calculating basic pixel parameters of a preset template image based on historical state data of equipment to be patrolled and examined, including:
calculating an average value of state grades of the equipment to be inspected for the previous n times, and if the average value is larger than a preset state threshold value, the basic pixel parameter is a first pixel value; and if the average value is smaller than or equal to a preset state threshold value, the basic pixel parameter is a second pixel value.
The application also provides a device fault diagnosis device based on big data, which comprises:
the system comprises a routing inspection route generation module, a routing inspection route generation module and a routing inspection module, wherein the routing inspection route generation module receives a routing inspection point setting request, the routing inspection point setting request comprises positions of a plurality of routing inspection points and execution sequences of the plurality of routing inspection points, and the routing inspection route is generated based on the positions of the plurality of routing inspection points and the execution sequences of the plurality of routing inspection points;
the inspection route issuing module issues the inspection route to an inspection robot and gives an instruction for executing the inspection route to the inspection robot, and the inspection robot moves along the inspection route to reach each inspection point and acquires inspection data at the inspection points in the process of executing the inspection route;
the inspection state image drawing module is used for obtaining a preset template image, each inspection point is correspondingly provided with a rendering area in the template image, and the pixel value of each corresponding rendering area is modified based on inspection data collected by the inspection robot at each inspection point to obtain an inspection state image;
and the state grade calculation module inputs the inspection state image into a preset classifier and outputs the current state grade.
The application also provides a terminal, which comprises a processor and a storage medium; the storage medium is used for storing instructions;
the processor is configured to operate according to the instruction to perform the steps of the big data based equipment fault diagnosis method.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the big data based device failure diagnosis method.
Compared with the prior art, the application has at least the following technical effects:
1. the application can construct the inspection route based on the set inspection points, and collect inspection data by utilizing the inspection robot to reach each inspection point, integrally reflect the running state of the equipment, and has higher inspection efficiency;
2. the scheme can calculate the current state level of the equipment from two aspects of loudness data and temperature data by combining the multi-category parameters, so that the accuracy of state calculation is improved;
3. according to the application, the distance data is used as the weight to carry out weighted calculation, and the loudness data or the pixel value corresponding to the temperature data is adjusted, so that the errors of temperature and loudness caused by the distance between the inspection robot and equipment to be inspected are reduced;
4. the application combines the historical state grade to judge the probability of faults, takes the historical state grade as a reference, and improves the calculation accuracy of the state grade.
Drawings
FIG. 1 is a schematic diagram of one embodiment of a big data based device fault diagnosis method of the present application;
FIG. 2 is a schematic diagram of another embodiment of the big data based device fault diagnosis method of the present application;
FIG. 3 is a schematic illustration of the execution of a patrol path;
fig. 4 is a schematic diagram of an embodiment of the equipment fault diagnosis device based on big data according to the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. The described embodiments of the application are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present application.
As shown in fig. 1 and 2, the present application provides a device fault diagnosis method based on big data, which comprises the following specific steps:
step 1, receiving a routing inspection point setting request, wherein the routing inspection point setting request comprises positions of a plurality of routing inspection points and execution sequences of the plurality of routing inspection points, and generating a routing inspection route based on the positions of the plurality of routing inspection points and the execution sequences of the plurality of routing inspection points;
in some embodiments of the application, the inspection staff can set different inspection points according to the inspection requirements, and enable the inspection robot to execute different inspection routes, so as to provide inspection flexibility.
As shown in fig. 2 and 3, in some embodiments of the present application, the step of generating the inspection route based on the positions of the plurality of inspection points and the execution sequence of the plurality of inspection points includes:
sequentially numbering the inspection points based on the execution sequence of the plurality of inspection points;
obtaining a topographic map of a current inspection scene, marking the position of each inspection point in the topographic map, and calculating an inspection sub-path between every two adjacent numbered inspection points by using an A-Star algorithm;
combining the plurality of patrol sub-paths to obtain a patrol route.
By adopting the scheme, the routing inspection sub-paths between every two adjacent numbered routing inspection points are calculated, and a plurality of routing inspection sub-paths are combined to obtain the routing inspection route, so that the path planning efficiency is improved.
In some embodiments of the present application, the topography of the inspection scene includes a safe area and a dangerous area, and different inspection speeds are set for the safe area and the dangerous area; when the inspection path is in a dangerous area, setting the running speed of the inspection robot to be a first speed; and setting the running speed of the inspection robot to be a second speed when the inspection path is in a safe area.
By adopting the scheme, when the inspection path is in a dangerous area, the running speed of the inspection robot is the first speed; when the inspection path is in the safety area, the running speed of the inspection robot is the second speed, so that the running safety of the inspection robot is improved.
In some embodiments of the present application, in the step of moving the inspection robot along the inspection route to each inspection point and collecting inspection data at the inspection point, a stay time is calculated based on historical state data of the equipment to be inspected, the inspection robot reaches the inspection point, and the stay time is stopped at the inspection point to measure the inspection data.
In a specific implementation, calculating the residence time based on the historical state data of the equipment to be inspected further includes: calculating the average value of the state grades of the equipment to be inspected for the previous n times, and if the average value is larger than a preset residence threshold value, the residence time is a first time length; and if the average value is smaller than or equal to a preset stay threshold value, the stay time is a second duration.
In the implementation process, specific parameters of the first time length and the second time length can be selected by a technician according to actual conditions on the premise that the first time length is longer than the second time length.
By adopting the scheme, the residence time is calculated based on the historical state data of the equipment to be inspected, and longer residence time can be provided for equipment with higher fault level, so that the data input precision is improved.
Step 2, issuing a routing inspection route to an inspection robot, and giving an instruction for executing the routing inspection route to the inspection robot, wherein the inspection robot moves along the routing inspection route to reach each inspection point and collects inspection data at the inspection points in the process of executing the routing inspection route;
in some embodiments of the present application, in the step of sending the inspection route to the inspection robot, the generated inspection route may be sent to the inspection robot through a device such as bluetooth, and if the inspection robot receives the information successfully, the host side feeds back the information successfully received to the host side, and the host side may send an instruction for executing the inspection route to the inspection robot;
the inspection robot can be a track robot or a four-wheel robot with a motor.
In the specific implementation process, the inspection robot moves along the inspection route to reach each inspection point and collects inspection data at the inspection points, the inspection data are collected through a sensor carried by the inspection robot, the sensor comprises but not limited to a loudness sensor, a temperature sensor or an image sensor, and the like, the loudness sensor and the temperature sensor can collect the loudness data and the temperature data respectively, the image sensor can be a camera for shooting images, and the images collected by the camera can be used for providing image references for staff.
The collected inspection data at least comprise one of loudness data or temperature data, and in the specific implementation process, if the temperature of the machine is higher than a temperature threshold value, only the temperature data are collected; if the loudness of the machine is higher than the loudness threshold value, only collecting loudness data; and if the machine does not have the condition that the temperature is higher than the temperature threshold value and the loudness is higher than the loudness threshold value, collecting loudness data and temperature data at the same time.
Preferably, the temperature threshold and the loudness threshold are set by a technician according to the actual condition of the diagnostic object, and the sensor is set according to the type of data to be acquired.
In some embodiments of the present application, the collected inspection data further includes distance data, where the distance data is a distance between the inspection robot and the equipment to be inspected measured by the distance meter after the inspection robot reaches the inspection point, and in the step of measuring the distance between the inspection robot and the equipment to be inspected, each inspection point is provided with a ranging direction, and parameters in the ranging direction are measured by the distance meter.
By adopting the scheme, the distance meter can be a laser distance meter, and the distance parameter in the distance measuring direction can be accurately measured.
In the specific implementation process, the laser range finder is adjusted to the ranging direction for ranging.
Step 3, acquiring a preset template image, wherein each inspection point is correspondingly provided with a rendering area in the template image, and modifying a pixel value of each corresponding rendering area based on inspection data acquired by the inspection robot at each inspection point to obtain an inspection state image;
in some embodiments of the present application, the preset template image includes a plurality of pixel grids, and the pixel value of each pixel grid is the same in the initial state; and in the rendering area, each inspection point corresponds to at least one pixel grid.
Preferably, the number of the pixel grids corresponding to each inspection point is equal.
Presetting a corresponding template image according to the type of the inspection data acquired in the step 2, setting the loudness template image when the inspection data comprise loudness data, and setting the temperature template image when the inspection data comprise temperature data.
In a specific implementation process, the loudness template image and the temperature template image are images including a plurality of pixel grids.
In some embodiments of the application, setting a template image according to the type of the acquired inspection data, and modifying pixel values of a corresponding rendering area in the template image according to the inspection data acquired by the inspection points;
if the inspection data comprise loudness data and temperature data, the preset template image comprises a loudness template image and a temperature template image, and the pixel value of each corresponding rendering area is modified based on the inspection data collected by the inspection robot at each inspection point;
modifying pixel values of each rendering area in the loudness template image based on the collected loudness data to obtain a first channel image of the inspection state image;
and obtaining a second channel image of the inspection state image for each of the temperature template images based on the acquired temperature data.
If the inspection data is only loudness data or temperature data, the inspection state image is a single-channel image which only modifies pixel values of each rendering area in the loudness template image or the temperature template image.
In a specific implementation process, the pixel value of the rendering area can be directly modified into the pixel value of the acquired temperature data parameter or loudness data parameter.
In some embodiments of the present application, if the inspection data further includes distance data, in the step of modifying the pixel value of each corresponding rendering area based on the inspection data collected by each inspection point, weighting calculation is further required to be performed on the loudness data and/or the temperature data based on the distance data, so as to obtain the pixel value of the rendering area corresponding to each inspection point.
In some embodiments of the present application, in the step of weighting the loudness data and/or temperature data based on the distance data, the loudness data and/or temperature data are respectively weighted based on the distance data based on the following formula:
H＝δC
where C represents loudness data or temperature data, δ represents distance data, and H represents pixel values.
By adopting the scheme, the distance data is used as the weight to carry out weighted calculation, the pixel value corresponding to the loudness data or the temperature data is adjusted, the errors of temperature and loudness caused by the distance between the inspection robot and equipment to be inspected are reduced, and the errors are expressed as the pixel value.
In the implementation process, if C represents the loudness data, H represents a pixel value of a rendering area in the loudness template image, that is, a pixel value of the rendering area of the first channel image; if C represents temperature data, H represents a pixel value of a rendering area in the temperature template image, that is, a pixel value of a rendering area of the second channel image.
And 4, inputting the inspection state image into a preset classifier, outputting the state grade of the current equipment, and judging the fault condition of the equipment according to the state grade.
In the specific implementation process, the preset classifiers are all classifiers which are trained in advance, and the classifiers can be SVM classifiers.
The inspection state image obtained in the step 3 is input into the classifier, and the present state grade of the equipment can be calculated through loudness data and temperature data, so that the accuracy of state calculation is improved.
In some embodiments of the application, the status level may be classified as level 1, level 2, level 3 or level 4, when the status level is level 1, the device is normal; when the state grade is grade 2, the equipment is less likely to have a fault problem; when the state grade is grade 3, the equipment is more likely to have a fault problem; when the status level is 4, the device is most likely to have a fault problem. And the inspection personnel can judge the fault condition of the equipment according to the state grade.
In some embodiments of the present application, the calculating step of calculating the basic pixel parameters of each pixel grid in the preset template image in step 3 based on the historical state data of the equipment to be patrolled includes:
calculating the average value of the state grades of the equipment to be inspected for the previous n times, and if the average value is larger than a preset state threshold value, the basic pixel parameter is a first pixel value; and if the average value is smaller than or equal to a preset state threshold value, the basic pixel parameter is a second pixel value.
The first pixel value and the second pixel value are both preset parameter values. In some embodiments of the present application, the first pixel value is greater than the second pixel value, and specific parameters of the first pixel value and the second pixel value may be set by a technician on the premise that the first pixel value is greater than the second pixel value.
By adopting the scheme, as the historical state level is higher, faults are easy to occur, and therefore in the scheme, if the average value is larger than the preset state threshold value, the basic pixel parameter is a first pixel value; if the average value is smaller than or equal to a preset state threshold value, the basic pixel parameter is a second pixel value, two basic pixel value selections are provided, the historical state level is used as a reference, and the calculation accuracy of the state level is improved.
By adopting the scheme, firstly, the scheme can construct a routing inspection route based on the set routing inspection points, the routing inspection robot is utilized to reach each routing inspection point to collect routing inspection data, each rendering area in the template image is utilized to render and generate the routing inspection state image reflecting the current overall state of the equipment, and then the routing inspection state image is input into the classifier to obtain the current state level of the equipment, the running state of the equipment is integrally reflected, and the routing inspection efficiency is higher.
As shown in fig. 4, the present application also provides a device fault diagnosis apparatus based on big data, the apparatus comprising:
the system comprises a routing inspection route generation module, a routing inspection route generation module and a routing inspection module, wherein the routing inspection route generation module receives a routing inspection point setting request, the routing inspection point setting request comprises positions of a plurality of routing inspection points and execution sequences of the plurality of routing inspection points, and the routing inspection route is generated based on the positions of the plurality of routing inspection points and the execution sequences of the plurality of routing inspection points;
the inspection route issuing module issues the inspection route to an inspection robot and gives an instruction for executing the inspection route to the inspection robot, and the inspection robot moves along the inspection route to reach each inspection point and acquires inspection data at the inspection points in the process of executing the inspection route;
the inspection state image drawing module is used for obtaining a preset template image, each inspection point is correspondingly provided with a rendering area in the template image, and the pixel value of each corresponding rendering area is modified based on inspection data collected by the inspection robot at each inspection point to obtain an inspection state image;
and the state grade calculation module inputs the inspection state image into a preset classifier and outputs the current state grade.
By adopting the scheme, the whole state of the equipment can be fed back into the inspection state image, the manual arrangement intervention is not needed, and further, the running state of the equipment is output through the classifier, so that the inspection efficiency is improved.
Compared with the prior art, the application has the beneficial effects that the integral running state of the equipment can be accurately reflected by generating the inspection state image reflecting the current integral state of the equipment and inputting the inspection state image into the classifier to output the state parameter, and the inspection efficiency is higher.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (e.g., connected through the internet using an internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (12)
1. A method for diagnosing equipment faults based on big data, characterized in that the method comprises the steps of:
receiving a routing inspection point setting request, wherein the routing inspection point setting request comprises positions of a plurality of routing inspection points and execution sequences of the plurality of routing inspection points, and generating a routing inspection route based on the positions of the plurality of routing inspection points and the execution sequences of the plurality of routing inspection points;
the inspection route is issued to an inspection robot, an instruction for executing the inspection route is sent to the inspection robot, and the inspection robot moves along the inspection route to reach each inspection point and acquires inspection data at the inspection points in the process of executing the inspection route;
acquiring a preset template image, wherein each inspection point is correspondingly provided with a rendering area in the template image, and modifying a pixel value of each corresponding rendering area based on inspection data acquired by the inspection robot at each inspection point to obtain an inspection state image;
and inputting the inspection state image into a preset classifier, outputting the state grade of the current equipment, and judging the fault condition of the equipment according to the state grade.
2. The big data based equipment failure diagnosis method according to claim 1, wherein,
the inspection data comprise loudness data and/or temperature data, if the inspection data are loudness data and temperature data, a preset template image comprises a loudness template image and a temperature template image, and the pixel value of each corresponding rendering area is modified based on the inspection data collected by the inspection robot at each inspection point;
modifying pixel values of each rendering area in the loudness template image based on the collected loudness data to obtain a first channel image of the inspection state image;
modifying pixel values of each rendering area in the temperature template image based on the acquired temperature data to obtain a second channel image of the inspection state image;
and if the inspection data is one of loudness data or temperature data, the inspection state image is a single-channel image which only modifies the pixel value of each rendering area in the loudness template image or the temperature template image.
3. The big data based equipment failure diagnosis method according to claim 2, wherein,
the inspection data further comprise distance data, the distance data are distances between the inspection robot and equipment to be inspected, which are measured through the distance meter after the inspection robot reaches the inspection points, and in the step of measuring the distances between the inspection robot and the equipment to be inspected, each inspection point is provided with a distance measuring direction, and parameters in the distance measuring direction are measured through the distance meter.
4. The equipment fault diagnosis method based on big data as claimed in claim 3, wherein,
if the inspection data comprise distance data, in the step of modifying the pixel value of each corresponding rendering area based on the inspection data collected by the inspection robot at each inspection point, weighting calculation is performed on the loudness data and/or the temperature data based on the distance data, so as to obtain the pixel value of the rendering area corresponding to each inspection point.
5. The big data based equipment failure diagnosis method according to claim 4, wherein,
weighting the loudness data and/or temperature data based on the distance data according to the following formula:
H＝δC
where C represents loudness data or temperature data, δ represents distance data, and H represents pixel values.
6. The big data based equipment failure diagnosis method according to any one of claims 1 to 5, wherein,
the generating the inspection route based on the positions of the plurality of inspection points and the execution sequence of the plurality of inspection points further includes:
sequentially numbering the inspection points based on the execution sequence of the plurality of inspection points;
obtaining a topographic map of a current inspection scene, marking the position of each inspection point in the topographic map, and calculating an inspection sub-path between every two adjacent numbered inspection points by using an A-Star algorithm;
combining the plurality of patrol sub-paths to obtain a patrol route.
7. The big data based equipment failure diagnosis method according to claim 1, wherein,
and in the step of moving the inspection robot along the inspection route to reach each inspection point and collecting inspection data at the inspection points, calculating the stay time based on the historical state data of the equipment to be inspected, and measuring the inspection data at the inspection points by the aid of the inspection robot when the inspection robot reaches the inspection points.
8. The big data based equipment failure diagnosis method according to claim 7, wherein,
calculating the residence time based on historical state data of the equipment to be inspected further comprises:
calculating the average value of the state grades of the equipment to be inspected for the previous n times, and if the average value is larger than a preset residence threshold value, the residence time is a first time length; and if the average value is smaller than or equal to a preset stay threshold value, the stay time is a second duration.
9. The big data based equipment failure diagnosis method according to claim 6, wherein,
calculating basic pixel parameters of a preset template image based on historical state data of equipment to be patrolled and examined, wherein the basic pixel parameters comprise:
calculating an average value of state grades of the equipment to be inspected for the previous n times, and if the average value is larger than a preset state threshold value, the basic pixel parameter is a first pixel value; and if the average value is smaller than or equal to a preset state threshold value, the basic pixel parameter is a second pixel value.
10. A device failure diagnosis apparatus based on big data, the apparatus comprising:
the system comprises a routing inspection route generation module, a routing inspection route generation module and a routing inspection module, wherein the routing inspection route generation module receives a routing inspection point setting request, the routing inspection point setting request comprises positions of a plurality of routing inspection points and execution sequences of the plurality of routing inspection points, and the routing inspection route is generated based on the positions of the plurality of routing inspection points and the execution sequences of the plurality of routing inspection points;
the inspection route issuing module issues the inspection route to an inspection robot and gives an instruction for executing the inspection route to the inspection robot, and the inspection robot moves along the inspection route to reach each inspection point and acquires inspection data at the inspection points in the process of executing the inspection route;
and the inspection state image drawing module is used for acquiring a preset template image, each inspection point is correspondingly provided with a rendering area in the template image, and the pixel value of each corresponding rendering area is modified based on inspection data acquired by the inspection robot at each inspection point to obtain an inspection state image.
11. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-9.
12. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1-9.
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| CN117781897B (en) * | 2024-02-28 | 2024-05-07 | 常州市伟通机电制造有限公司 | Strip width inspection system and inspection method based on image acquisition |
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