CN115295009B - Independent data-based self-elimination data instant analysis automobile diagnosis system - Google Patents
Independent data-based self-elimination data instant analysis automobile diagnosis system Download PDFInfo
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- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
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- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
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Abstract
The invention relates to the technical field of automobile diagnosis systems, in particular to an automobile diagnosis system for analyzing data in real time based on irrelevant data self-exclusion. The device comprises a noise waveform image drawing unit and an irrelevant variable eliminating unit. According to the invention, a noise waveform image drawing unit draws different types of noise images measured at a detection point, a noise type storage module calls waveform image information corresponding to different types of noise, irrelevant noise variable elimination is carried out by a irrelevant variable eliminating unit, the irrelevant noise waveform images measured at the detection point are identified and then eliminated, the eliminated waveform image information is transmitted to a detection waveform identification unit, the detection waveform identification unit combines waveform image information corresponding to different types of noise again to obtain noise image information corresponding to the detection point, the influence of the irrelevant variable on the noise measurement of the detection point is discharged, the detection accuracy of automobile noise is improved, and the accurate diagnosis of automobile fault reasons of an automobile owner is facilitated.
Description
Technical Field
The invention relates to the technical field of automobile diagnosis systems, in particular to an automobile diagnosis system for analyzing data in real time based on irrelevant data self-exclusion.
Background
The automobile diagnosis is an inspection for determining the technical condition of an automobile and finding out fault parts and reasons under the condition of not disassembling, and comprises the detection and diagnosis of an automobile engine, the detection and diagnosis of an automobile chassis, the detection and diagnosis of an automobile body and accessories, the detection and diagnosis of automobile exhaust pollutants and noise and the like.
In the automobile noise detection process, most of the existing modes are manually detected, but the efficiency of the detection mode is lower, the hearing of human ears is limited, the detection accuracy is greatly reduced, when a sound detection instrument is used for detecting detection points, although the detection accuracy can be improved, in the noise detection process of the detection points, the rest positions of the automobile can also emit noise, the sound can be transmitted through a medium, the detection instrument of the detection points can also detect the noise emitted by the rest positions of the automobile, the noise confusion can easily occur in the later period, the noise corresponding to the detection points can not be found out, and therefore an automobile diagnosis system for analyzing the automobile immediately based on the independent data self-exclusion data is urgently needed.
Disclosure of Invention
The present invention is directed to a system for analyzing an automobile diagnostic system based on independent data and self-exclusion data in real time, so as to solve the problems of the background art.
In order to achieve the purpose, the system comprises an automobile noise data acquisition unit, wherein the automobile noise data acquisition unit is used for acquiring noise data acquired by noise detection points at different positions of an automobile, the output end of the automobile noise data acquisition unit is connected with a noise waveform image drawing unit, the noise waveform image drawing unit draws a corresponding noise waveform image according to the noise data, the output end of the noise waveform image drawing unit is connected with a detected waveform identification unit, the output end of the detected waveform identification unit is connected with an independent variable eliminating unit, the input end of the independent variable eliminating unit is connected with a noise type storage module, the output end of the noise type storage module is connected with the input end of the detected waveform identification unit, the noise type storage module is used for storing different noise waveform data of each noise detection point of the automobile to generate stored data information, the detected waveform identification unit calls corresponding noise waveform information from the stored data information according to the detection points to determine the type of the noise waveform corresponding to the detection points, and the independent variable eliminating unit eliminates the measured independent variable noise waveform at the detection points.
As a further improvement of the technical solution, the noise waveform image drawing unit includes a noise type distinction drawing module, the noise type distinction drawing module distinguishes different types of noise according to a magnitude of noise waveform change and draws a corresponding noise waveform image, an output end of the noise type distinction drawing module is connected with a waveform change trend determination module, and the waveform change trend determination module determines a change trend of each different type of noise waveform image.
As a further improvement of the technical solution, the output end of the waveform trend determining module is connected with a corresponding noise tone image drawing module, and the corresponding noise tone image drawing module is used for combining tones corresponding to noise waveform images measured at the detection points.
As a further improvement of the technical solution, the output end of the noise waveform image drawing unit is connected to the input end of the noise type storage module, and the noise type storage module is used for storing and acquiring each noise waveform image.
As a further improvement of the technical scheme, the output end of the independent variable eliminating unit is connected with a novel noise waveform identifying unit, and the novel noise waveform identifying unit is used for identifying each noise waveform image obtained by detecting a detection point and finding out a novel noise waveform image which is not stored with stored data information.
As a further improvement of the technical solution, the independent variable rejecting unit is bidirectionally connected with a similar waveform analyzing unit, an input end of the similar waveform analyzing unit is connected with an output end of the detection waveform identifying unit, and the similar waveform analyzing unit is used for comparing a noise waveform image most similar to the novel noise waveform image by combining the novel noise waveform image with each noise waveform image measured at the detection point.
As a further improvement of the technical solution, an input end of the similar waveform analyzing unit is connected with an output end of the independent variable eliminating unit.
As a further improvement of the technical scheme, the output end of the novel noise waveform identification unit is connected with the input end of the noise type storage module.
As a further improvement of the technical scheme, the similar waveform analysis unit comprises a variation trend point comparison module, the variation trend point comparison module is used for comparing and processing trend points of two noise waveform images, the output end of the variation trend point comparison module is connected with a variation trend point threshold value presetting module, the variation trend point threshold value presetting module is used for presetting a variation trend point coincidence quantity threshold value, and the output end of the variation trend point threshold value presetting module is connected with a comparison information output module.
As a further improvement of the technical solution, the similar waveform analyzing unit adopts a trend point comparison algorithm, and an algorithm formula thereof is as follows:
wherein the content of the first and second substances,
to compare the various sets of trend points in the noise waveform image,
to is that
To compare various trend points in the noise waveform image,
for each set of trend points in the new noise waveform image,
to is that
For each trend point in the new noise waveform image,
a function for judging the coincidence quantity of the variation trend points after the two images are compared,
the weighted amount of the change trend points after the comparison of the two images,
the threshold value of the coincidence quantity of the variation trend points is the coincidence quantity of the variation trend points
Less than threshold of coincidence quantity of variation trend points
Function for determining the amount of coincidence of change tendency points
The output is 0, the two images are not similar, and when the change trend points coincide with each other
Not less than the threshold value of the coincidence quantity of the variation trend points
Function of determining the amount of coincidence of change tendency points
The output is 1 and the two images are similar.
Compared with the prior art, the invention has the beneficial effects that:
1. in the vehicle diagnosis system based on the independent data self-elimination data real-time analysis, a noise waveform image drawing unit draws different types of noise images measured by a detection point, a noise type storage module retrieves waveform image information corresponding to different types of noise, an independent noise variable elimination unit eliminates independent noise variables, the independent noise waveform images measured by the detection point are identified and then eliminated, the eliminated waveform image information is transmitted to a detection waveform identification unit, the detection waveform identification unit combines the waveform image information corresponding to different types of noise again to obtain the noise image information corresponding to the detection point, the influence of the independent variables on the detection point noise measurement is discharged, the vehicle noise detection accuracy is improved, and a vehicle owner can accurately diagnose the fault cause of the vehicle.
2. In this from getting rid of data instant analysis vehicle diagnostic system based on irrelevant data, each noise waveform image is gathered in the storage of noise type storage module, and after the manual judgement is accomplished, the noise that should not store carries out the sign, stores to noise type storage module afterwards to supply the later stage to carry out the noise and compare, reduce manual operation, improve noise detection efficiency.
3. In this be based on irrelevant data from getting rid of data instant analysis vehicle diagnostic system, draw novel noise waveform image through similar waveform analysis unit, and compare novel noise waveform image and the noise waveform image that detects the correspondence, if two images are similar, it shows that this check point appears the foreign matter or the check point leads to structural deformation because of the striking, the tone quality of original noise has been changed, noise waveform image changes this moment, and novel noise waveform image is the check point corresponding noise waveform image after changing promptly, for the later stage carries out the noise tone analysis, can reduce the scope that this check point goes wrong in advance simultaneously, improve vehicle diagnosis efficiency.
4. In the independent data-based self-exclusion data instant analysis automobile diagnosis system, a novel noise waveform image is compared with a noise waveform image corresponding to independent noise through a similar waveform analysis unit, the independent noise waveform image most similar to the novel noise waveform image is judged, meanwhile, a detection point corresponding to the most similar independent noise waveform image is judged, when an inspector performs noise detection on the detection point, if the noise waveform image corresponding to the detection point stored with stored data information cannot be found, the novel noise waveform image is used as the noise waveform image corresponding to the detection point, and the inspector is helped to analyze the noise waveform images corresponding to different detection points more quickly.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a flow chart of a noise waveform image rendering unit of the present invention;
FIG. 3 is a flow chart of a similar waveform analyzing unit according to the present invention.
The various reference numbers in the figures mean:
10. an automobile noise data acquisition unit;
20. a noise waveform image drawing unit; 210. a noise type distinguishing and drawing module; 220. a waveform change trend determination module; 230. a corresponding noise tone image drawing module;
30. a detection waveform identification unit;
40. an irrelevant variable eliminating unit;
50. a noise type storage module;
60. a novel noise waveform identification unit;
70. a similar waveform analyzing unit; 710. a variation trend point comparison module; 720. a threshold value presetting module of the variation trend point; 730. and a comparison information output module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-3, an automobile noise data acquisition unit 10 is provided for acquiring noise data acquired from noise detection points at different positions of an automobile, an output end of the automobile noise data acquisition unit 10 is connected with a noise waveform image drawing unit 20, the noise waveform image drawing unit 20 draws a corresponding noise waveform image according to the noise data, an output end of the noise waveform image drawing unit 20 is connected with a detected waveform identification unit 30, an output end of the detected waveform identification unit 30 is connected with an independent variable rejection unit 40, an input end of the independent variable rejection unit 40 is connected with a noise type storage module 50, an output end of the noise type storage module 50 is connected with an input end of the detected waveform identification unit 30, the noise type storage module 50 is used for storing different noise waveform data of each noise detection point of the automobile to generate stored data information, the detected waveform identification unit 30 calls corresponding noise waveform information from the stored data information according to the detection point to determine a type of the noise waveform corresponding to the detection point, and the independent variable rejection unit 40 rejects noise at the detection point.
When the automobile noise data acquisition unit 10 is used specifically, noise data acquired by noise detection points at different positions of an automobile are acquired, in the detection process, the detection points and other positions generate noise when the automobile is started, for example, when exhaust pipe noise is detected, the detection points are preset at the inner end of an exhaust pipe, but an engine also generates noise in the automobile starting process, so that the detection points generate a plurality of different types of noise, the automobile noise data acquisition unit 10 transmits the acquired noise data to the noise waveform image drawing unit 20, the noise waveform image drawing unit 20 draws corresponding noise waveform images according to the noise data, the sound quality of different noises is different, the sound quality of the noise is determined by waveforms, the noise waveform images drawn by the noise waveform image drawing unit 20 can distinguish the acquired different noise types to generate waveform image information, transmitting the waveform image information to a detected waveform identification unit 30, simultaneously storing different noise waveform data of each noise detection point of the automobile by a noise type storage module 50 to generate waveform image information corresponding to different types of noise, transmitting the waveform image information corresponding to different types of noise to an irrelevant variable eliminating unit 40 and the detected waveform identification unit 30, calling corresponding noise waveform information from the stored data information by the detected waveform identification unit 30 to obtain a noise waveform image corresponding to the detection point, eliminating the waveform image information corresponding to the irrelevant noise of the detection point by the irrelevant variable eliminating unit 40 combining the noise waveform image and the waveform image corresponding to the different types of noise to obtain a final waveform image of the detection point, and obtaining whether the size and the frequency of the noise are abnormal or not according to the final waveform image of the detection point, in the invention, a noise waveform image drawing unit 20 draws different types of noise images measured at a detection point, because the tone quality of each noise is different, the waveforms of the different types of noise waveform images in the drawn images are different, at the moment, waveform image information corresponding to different types of noise is called from a noise type storage module 50, irrelevant noise variable elimination is carried out through an irrelevant variable eliminating unit 40, namely, the irrelevant noise waveform images measured at the detection point are identified through waveform comparison and then eliminated, the eliminated waveform image information is transmitted to a detected waveform identifying unit 30, the detected waveform identifying unit 30 combines the waveform image information corresponding to different types of noise again to obtain the noise image information corresponding to the detection point, the influence of the irrelevant variable on the noise measurement at the detection point is eliminated, the detection accuracy of automobile noise is improved, and the automobile fault reason can be accurately diagnosed by an automobile owner.
In addition, the noise waveform image drawing unit 20 includes a noise type distinction drawing module 210, the noise type distinction drawing module 210 distinguishes different types of noise according to the magnitude of the noise waveform change and draws a corresponding noise waveform image, the output end of the noise type distinction drawing module 210 is connected with a waveform change trend determination module 220, and the waveform change trend determination module 220 determines the change trend of each different type of noise waveform image. When the noise type distinguishing and drawing module 210 is used specifically, the noise type distinguishing and drawing module 210 distinguishes different types of noise according to the amplitude of the noise waveform change, draws corresponding noise waveform images, generates noise waveform image information, and transmits the noise waveform image information to the noise type distinguishing and drawing module 210, the waveform change trend judging module 220 judges the change trend of each different type of noise waveform image according to the noise waveform image information, and obtains change trend time points, such as what time node images in the drawn noise waveform image are changed from an ascending trend to a descending trend, so that the later-stage noise comparison work corresponding to detection points is carried out, comparison basis is provided for detection personnel, and detection errors are reduced.
Further, the output end of the waveform change trend determining module 220 is connected with a corresponding noise tone image drawing module 230, the corresponding noise tone image drawing module 230 is configured to combine tones corresponding to the noise waveform images detected at the detection points, when the noise waveform image drawing module is used specifically, the corresponding noise tone image drawing module 230 combines tones corresponding to the noise waveform images detected at the detection points to obtain corresponding noise tone images, meanwhile, the waveform change trend determining module 220 generates change trend information of the noise waveform images, and transmits the change trend information of the noise waveform images to the corresponding noise tone image drawing module 230, the corresponding noise tone image drawing module 230 combines the change trend information of the noise waveform images to mark tones corresponding to the change trend time points, and then the tone corresponding to the change trend time point is a noise tone or a noise lowest point, and analyzes each noise tone highest point for performing noise tone determination at a later stage, and determining whether the position has noise abnormality.
Still further, the output end of the noise waveform image drawing unit 20 is connected to the input end of the noise type storage module 50, and the noise type storage module 50 is used for storing each acquired noise waveform image. Because in the noise detection process, the noise that noise type storage module 50 did not store before appears very easily in each noise that the check point surveyed, this just leads to when the corresponding noise of later stage check point discerns, the noise that did not store before can't be rejected, still need the manual work to distinguish, lead to noise detection effect greatly reduced, each noise waveform image is gathered in noise type storage module 50 storage this moment, after the manual work is judged to accomplish, the noise that should not store is identified, store to noise type storage module 50 afterwards, for the later stage carries out the noise and compares, manual operation is reduced, noise detection efficiency is improved.
Specifically, the output end of the independent variable rejecting unit 40 is connected with a novel noise waveform identifying unit 60, the novel noise waveform identifying unit 60 is used for identifying each noise waveform image obtained by detecting a detection point, and finding out a novel noise waveform image which is not stored in the stored data information from the novel noise waveform image, during specific use, the novel noise waveform identifying unit 60 identifies each noise waveform image obtained by detecting the detection point, compares the noise waveform image with the stored data information, finds out a noise waveform image which is not compared successfully, and takes the noise waveform image which is not compared successfully as the novel noise measured by the detection point, so as to be used for later reference.
In addition, the independent variable rejecting unit 40 is connected to the similar waveform analyzing unit 70 in a bidirectional manner, an input end of the similar waveform analyzing unit 70 is connected to an output end of the detected waveform identifying unit 30, and the similar waveform analyzing unit 70 is configured to compare a noise waveform image most similar to the new noise waveform image with each noise waveform image detected at the detection point. Because among the automobile diagnosis process, foreign matter appears very easily in the check point or the check point leads to structural deformation because of the striking, the noise tone quality that the check point corresponds this moment can change, can't find out the noise waveform image that corresponds with the check point from the storage data information, extract novel noise waveform image through similar waveform analysis unit 70, and compare novel noise waveform image and the noise waveform image that the detection corresponds, if two images are similar, it indicates that foreign matter appears in this check point or the check point leads to structural deformation because of the striking, the tone quality of original noise has been changed, noise waveform image changes this moment, and novel noise waveform image is the corresponding noise waveform image of check point after changing promptly, for the noise tone analysis of later stage, can reduce the scope that this check point goes wrong in advance simultaneously, improve automobile diagnosis efficiency.
Further, the input end of the similar waveform analyzing unit 70 is connected with the output end of the independent variable eliminating unit 40. When the noise detection device is used specifically, the irrelevant variable eliminating unit 40 eliminates irrelevant noise measured at a detection point to generate irrelevant noise eliminating information, the irrelevant noise eliminating information is transmitted to the similar waveform analyzing unit 70, the novel noise waveform image is compared with a noise waveform image corresponding to the irrelevant noise through the similar waveform analyzing unit 70 to judge the irrelevant noise waveform image most similar to the novel noise waveform image, and meanwhile, the detection point corresponding to the most similar irrelevant noise waveform image is judged.
Still further, the output terminal of the novel noise waveform identification unit 60 is connected to the input terminal of the noise type storage module 50. After each check point all detects the completion, also namely each check point that can send the noise all finds corresponding noise waveform image, and still there is new noise in the image that the check point detected out, the novel noise waveform image that this new noise corresponds can't be accomplished the comparison with storage data information, then show that the car position failure beyond the check point appears, detect out corresponding position through the manual work, bind with the novel noise waveform image that corresponds, generate and bind information, and to bind information transmission to noise type storage module 50 and save, meet in the later stage and compare when the noise of accomplishing with novel noise waveform image, can judge that this position breaks down, further improve automobile diagnosis efficiency, help detection personnel reachs the fault point sooner.
In addition, the similar waveform analyzing unit 70 includes a trend point comparing module 710, the trend point comparing module 710 is configured to compare trend points of the two noise waveform images, an output end of the trend point comparing module 710 is connected to a trend point threshold presetting module 720, the trend point threshold presetting module 720 is configured to preset a trend point coincidence threshold, and an output end of the trend point threshold presetting module 720 is connected to a comparison information output module 730. When the noise waveform image comparison method is used specifically, the trend point comparison module 710 compares the trend points of the two noise waveform images, compares the trend point coincidence amount of the two noise waveform images, that is, if the two images at the same time point have the same type of variation amplitude, it indicates that the two images at the time point have the trend point coincidence, the trend point threshold value presetting module 720 presets a trend point coincidence amount threshold value, compares the trend point coincidence amount with the trend point coincidence amount threshold value, and determines that the two noise waveform images are similar if the trend point coincidence amount threshold value is exceeded, so as to generate noise waveform image similar information, and transmits the noise waveform image similar information to the comparison information output module 730, and outputs the comparison information through the comparison information output module 730, so as to improve the image comparison efficiency.
In addition, the similar waveform analyzing unit 70 adopts a trend point comparison algorithm, and the algorithm formula is as follows:
wherein the content of the first and second substances,
to compare various sets of trend points in the noise waveform image,
to is that
To compare various trend points in the noise waveform image,
for each set of trend points in the new noise waveform image,
to is that
For each trend point in the new noise waveform image,
a function for judging the coincidence quantity of the variation trend points after the two images are compared,
the weighted amount of the change trend points after the comparison of the two images,
the threshold value of the coincidence quantity of the variation trend points is the coincidence quantity of the variation trend points
Less than threshold of coincidence quantity of variation trend points
Function for determining the amount of coincidence of change tendency points
The output is 0, the two images are not similar, and when the change trend points coincide with each other
Not less than the threshold value of the coincidence quantity of the variation trend points
Function for determining the amount of coincidence of change tendency points
The output is 1 and the two images are similar.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. The vehicle diagnosis system for the real-time analysis of the self-exclusion data based on the irrelevant data comprises a vehicle noise data acquisition unit (10), wherein the vehicle noise data acquisition unit (10) is used for acquiring noise data acquired from noise detection points at different positions of a vehicle, and is characterized in that: the automobile noise data acquisition unit (10) is connected with a noise waveform image drawing unit (20) at the output end, the noise waveform image drawing unit (20) draws a corresponding noise waveform image according to noise data, the noise waveform image drawing unit (20) is connected with a detected waveform identification unit (30) at the output end, an independent variable rejecting unit (40) is connected at the output end of the detected waveform identification unit (30), a noise type storage module (50) is connected at the input end of the independent variable rejecting unit (40), the output end of the noise type storage module (50) is connected with the input end of the detected waveform identification unit (30), the noise type storage module (50) is used for storing different noise waveform data of each noise detection point of an automobile and generating stored data information, the detected waveform identification unit (30) calls corresponding noise waveform information from the stored data information according to the detection point and determines the noise waveform type corresponding to the detection point, and the independent variable rejecting unit (40) rejects the measured independent noise at the detection point;
the noise waveform image drawing unit (20) comprises a noise type distinguishing drawing module (210), the noise type distinguishing drawing module (210) distinguishes different types of noise according to the amplitude of noise waveform change and draws corresponding noise waveform images, the output end of the noise type distinguishing drawing module (210) is connected with a waveform change trend judging module (220), and the waveform change trend judging module (220) judges the change trend of noise waveform images of different types;
the output end of the waveform change trend judging module (220) is connected with a corresponding noise tone image drawing module (230), and the corresponding noise tone image drawing module (230) is used for combining tones corresponding to noise waveform images measured at the detection points.
2. The system of claim 1, wherein the system comprises: the output end of the noise waveform image drawing unit (20) is connected with the input end of the noise type storage module (50), and the noise type storage module (50) is used for storing and acquiring each noise waveform image.
3. The system of claim 1, wherein the system comprises: the output end of the irrelevant variable eliminating unit (40) is connected with a novel noise waveform identifying unit (60), and the novel noise waveform identifying unit (60) is used for identifying each noise waveform image obtained by detecting point detection and finding out the novel noise waveform image which is not stored in the stored data information.
4. The system of claim 3, wherein the system comprises: the irrelevant variable rejecting unit (40) is connected with a similar waveform analyzing unit (70) in a bidirectional mode, the input end of the similar waveform analyzing unit (70) is connected with the output end of the detection waveform identifying unit (30), and the similar waveform analyzing unit (70) is used for comparing a noise waveform image most similar to the novel noise waveform image by combining the novel noise waveform image and each noise waveform image measured at a detection point.
5. The system of claim 4, wherein the vehicle diagnostic system is further configured to analyze the vehicle on-demand based on the independent data and the exclusion data: the input end of the similar waveform analysis unit (70) is connected with the output end of the irrelevant variable eliminating unit (40).
6. The system of claim 3, wherein the system comprises: the output end of the novel noise waveform identification unit (60) is connected with the input end of the noise type storage module (50).
7. The system of claim 5, wherein the system comprises: similar waveform analysis unit (70) are including changing trend point and comparing module (710), changing trend point compares module (710) and is used for comparing the trend point of two noise waveform images and handles, changing trend point compares module (710) output and is connected with and changes trend point threshold value and predetermines module (720), changing trend point threshold value predetermines module (720) and is used for predetermineeing the threshold value of the coincidence quantity of change trend point, changing trend point threshold value predetermines module (720) output and is connected with and compares information output module (730).
8. The system of claim 7, wherein the vehicle diagnostic system is further configured to analyze the vehicle on-demand based on the independent data and the exclusion data: the similar waveform analysis unit (70) adopts a trend point comparison algorithm, and the algorithm formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
to compare various sets of trend points in the noise waveform image,
to is that
To compare various trend points in the noise waveform image,
for each set of trend points in the new noise waveform image,
to is that
For each trend point in the new noise waveform image,
a function for judging the coincidence quantity of the change trend points after the two images are compared,
the two images are compared, and the change trend points are superposed,
the threshold value of the coincidence quantity of the variation trend points is the coincidence quantity of the variation trend points
Less than threshold of coincidence quantity of variation trend points
Function of determining the amount of coincidence of change tendency points
The output is 0, the two images are not similar, and when the change trend points overlap
Not less than the threshold value of the coincidence quantity of the variation trend points
Function for determining the amount of coincidence of change tendency points
The output is 1 and the two images are similar.
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