CN116385706A - Signal detection method and system based on image recognition technology - Google Patents
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Abstract
The invention relates to the technical field of signal detection, and particularly discloses a signal detection method and a signal detection system based on an image recognition technology, wherein the method comprises the steps of acquiring an equipment image, recognizing the equipment image and determining the running condition of corresponding equipment; determining target equipment, a gateway related to the target equipment and data acquisition frequency according to the running condition; acquiring transmission data of target equipment at the gateway based on the data acquisition frequency, and generating a data fluctuation map based on a preset arrangement rule; and identifying the data fluctuation graph, determining a fluctuation period, and generating inspection guide based on the fluctuation period. The method comprises the steps of determining the transmission data acquisition frequency of each device according to the running condition, acquiring transmission data based on the data acquisition frequency, and storing the transmission data in the form of an image; at the moment, the pixel point analysis is carried out on the transmission data, so that the transmission fluctuation can be rapidly positioned, the speed is extremely high, and the efficiency is extremely high.
Description
Technical Field
The invention relates to the technical field of signal detection, in particular to a signal detection method and system based on an image recognition technology.
Background
Along with the progress of computer hardware technology and network technology, a plurality of intelligent production devices are arranged in the existing workshop, the intelligent production devices collect and process data in real time in the working process, then send the data to other main bodies based on a preset gateway, and the data are very stable in most time periods, but have some interference to influence the production process of the intelligent production devices, so that data fluctuation occurs; how to quickly locate the time period with fluctuation in a plurality of data is a technical problem to be solved by the technical scheme of the invention.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a signal detection method and a system based on an image recognition technology, which solve the problem of how to rapidly locate a time period with fluctuation in a plurality of data.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
acquiring an equipment image, identifying the equipment image, and determining the running condition of corresponding equipment;
determining target equipment, a gateway related to the target equipment and data acquisition frequency according to the running condition;
acquiring transmission data of target equipment at the gateway based on the data acquisition frequency, and generating a data fluctuation map based on a preset arrangement rule;
and identifying the data fluctuation graph, determining a fluctuation period, and generating inspection guide based on the fluctuation period.
As a further scheme of the invention: the step of acquiring the equipment image, identifying the equipment image and determining the operation condition of the corresponding equipment comprises the following steps:
acquiring an area image according to a preset camera, identifying an equipment area in the camera based on preset equipment characteristics, and adjusting camera acquisition parameters to acquire an equipment image;
acquiring illumination intensity and air humidity, and determining a correction filter according to the illumination intensity and the air humidity;
adjusting the equipment image according to the correction filter to obtain an image to be detected;
and identifying the image to be detected, and determining the running condition of the corresponding equipment.
As a further scheme of the invention: the step of identifying the image to be detected and determining the operation condition of the corresponding equipment comprises the following steps:
sequencing the images to be detected according to the time sequence, and sequentially carrying out logic operation on adjacent images to be detected;
determining a change contour according to a logic operation result, and determining an indicator light area according to the shape of the change contour;
acquiring the color value average value of the indicator light area in each image to be detected, arranging the color value average values according to time sequence, and fitting a color value curve; the number of the color value curves is at least one;
and determining the running condition of the corresponding equipment according to the color value curve.
As a further scheme of the invention: the step of obtaining the transmission data of the target device at the gateway based on the data acquisition frequency and generating the data wave diagram based on a preset arrangement rule comprises the following steps:
acquiring data packet parameters of the target equipment, which contain time points, at the gateway based on the data acquisition frequency; the data packet parameters comprise data packet types and data volumes;
determining a data filling interval according to the time point, reading a preset characteristic value according to the data packet type, and determining the number of pixel points according to the data quantity; wherein the data filling interval is an angular range, the angular range being a function of time;
determining a data area corresponding to the data packet at the tail part of the data filling interval to obtain a data fluctuation diagram; the value of the pixel point in the data area is the characteristic value; the area of the data area is determined by the number of pixel points.
As a further scheme of the invention: the step of identifying the data fluctuation map, determining a fluctuation period, and generating inspection guide based on the fluctuation period comprises the following steps:
sequentially calculating the first-order difference of each pixel point in the data fluctuation graph, and judging whether data fluctuation exists at each pixel point;
when data fluctuation exists, calculating a second-order difference at the pixel point to obtain fluctuation amplitude;
when the fluctuation amplitude reaches a preset fluctuation threshold value, marking pixel points;
counting marked pixel points, and when the number of the pixel points belonging to the same data area reaches a preset quantity threshold, reading a data filling interval corresponding to the data area and the position of the data area in the data filling interval to determine a fluctuation period;
and sending the fluctuation time period to a detection end, and receiving the detection guide fed back by the detection end.
As a further scheme of the invention: the calculation formula of the first order difference is as follows:
the calculation formula of the second-order difference is as follows:
in the method, in the process of the invention,
for->
First order difference,/->
For->
Is a second order difference of (2); the second-order differential calculation matrix is as follows:
the technical scheme of the invention also provides a signal detection system based on the image recognition technology, which comprises the following steps:
the condition judging module is used for acquiring equipment images, identifying the equipment images and determining the running condition of corresponding equipment;
the parameter determining module is used for determining target equipment, a gateway related to the target equipment and data acquisition frequency according to the running condition;
the fluctuation graph generation module is used for acquiring transmission data of the target equipment at the gateway based on the data acquisition frequency and generating a data fluctuation graph based on a preset arrangement rule;
and the fluctuation graph identification module is used for identifying the data fluctuation graph, determining a fluctuation period and generating inspection guide based on the fluctuation period.
As a further scheme of the invention: the condition determination module includes:
the image acquisition unit is used for acquiring an area image according to a preset camera, identifying an equipment area in the camera based on preset equipment characteristics, and adjusting camera acquisition parameters to acquire an equipment image;
the filter determining unit is used for acquiring illumination intensity and air humidity and determining a correction filter according to the illumination intensity and the air humidity;
the image adjusting unit is used for adjusting the equipment image according to the correction filter to obtain an image to be detected;
and the judging and executing unit is used for identifying the image to be detected and determining the running condition of the corresponding equipment.
As a further scheme of the invention: the fluctuation graph generation module comprises:
the data interception unit is used for acquiring data packet parameters of the target equipment, which contain time points, at the gateway based on the data acquisition frequency; the data packet parameters comprise data packet types and data volumes;
the mapping unit is used for determining a data filling interval according to the time point, reading a preset characteristic value according to the data packet type and determining the number of pixel points according to the data quantity; wherein the data filling interval is an angular range, the angular range being a function of time;
the data area determining unit is used for determining a data area corresponding to the data packet at the tail part of the data filling interval to obtain a data fluctuation diagram; the value of the pixel point in the data area is the characteristic value; the area of the data area is determined by the number of pixel points.
As a further scheme of the invention: the fluctuation map identification module comprises:
the fluctuation judging unit is used for sequentially calculating the first-order difference of each pixel point in the data fluctuation graph and judging whether data fluctuation exists at each pixel point or not;
the amplitude calculation unit is used for calculating the second-order difference at the pixel point when the data fluctuation exists, so as to obtain the fluctuation amplitude;
when the fluctuation amplitude reaches a preset fluctuation threshold value, marking pixel points;
the time interval positioning unit is used for counting marked pixel points, and when the number of pixel points belonging to the same data area reaches a preset number threshold, reading a data filling interval corresponding to the data area and the position of the data area in the data filling interval to determine a fluctuation time interval;
and the guide generation unit is used for sending the fluctuation time period to the detection end and receiving the detection guide fed back by the detection end.
(III) beneficial effects
The invention provides a signal detection method and system based on an image recognition technology. Compared with the prior art, the method has the following beneficial effects:
the method comprises the steps of acquiring an equipment image through a camera, carrying out image recognition on the equipment image, determining the running condition of equipment, determining the transmission data acquisition frequency of each equipment according to the running condition, acquiring transmission data based on the data acquisition frequency, and storing the transmission data in an image form; at the moment, the pixel point analysis is carried out on the transmission data, so that the transmission fluctuation can be rapidly positioned, the speed is extremely high, and the efficiency is extremely high.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block flow diagram of a signal detection method based on image recognition technology.
Fig. 2 is a first sub-flowchart of a signal detection method based on an image recognition technology.
Fig. 3 is a second sub-flowchart of a signal detection method based on an image recognition technique.
Fig. 4 is a third sub-flowchart of a signal detection method based on an image recognition technique.
Fig. 5 is a block diagram of the constitution of a signal detection system based on the image recognition technology.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the application solves the problem of how to quickly locate the time period with fluctuation in a plurality of data by providing the signal detection method and the system based on the image recognition technology.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments.
Example 1:
as shown in fig. 1, the present invention provides a signal detection method based on an image recognition technology, which includes:
fig. 1 is a flow chart of a signal detection method based on an image recognition technology, and in an embodiment of the invention, the method includes:
step S100: acquiring an equipment image, identifying the equipment image, and determining the running condition of corresponding equipment;
the technical scheme of the invention is applied to the workshop of the Internet of things, wherein a plurality of devices are arranged in the workshop of the Internet of things, and a data transmission process exists among the devices; the images of the devices are acquired, the images are identified, the running condition of the devices can be judged, the running condition of the devices is mainly represented by the indicator lights on the devices, the indicator lights continuously flash in the working process of the devices, and the flashing state can reflect the working state of the devices.
Step S200: determining target equipment, a gateway related to the target equipment and data acquisition frequency according to the running condition;
the method comprises the steps that unstable equipment is positioned according to the running condition and serves as target equipment, data of all the equipment are transmitted through a preset gateway in an Internet of things workshop, the target equipment is determined, meanwhile, the gateway related to the target equipment is also positioned, and the gateway is determined when the workshop is built; further, the data acquisition frequency is the acquisition frequency of the transmission data of the target equipment at the gateway, and the more abnormal the running condition is, the higher the data acquisition frequency is.
Step S300: acquiring transmission data of target equipment at the gateway based on the data acquisition frequency, and generating a data fluctuation map based on a preset arrangement rule;
according to the determined data acquisition frequency, the transmission data of the target equipment are acquired at the gateway, and the acquired transmission data are required to be converted into graph data, which is a place with a different technical scheme.
Step S400: identifying the data fluctuation map, determining a fluctuation period, and generating inspection guidance based on the fluctuation period;
after the image data are converted into the image data, the abnormal time period can be rapidly determined by means of the existing image recognition technology, after the abnormal time period is determined, what data are queried, what processing is performed is preset by a manager, and the execution main body of the invention directly reads the corresponding inspection guide.
Fig. 2 is a first sub-flowchart of a signal detection method based on an image recognition technology, where the steps of acquiring an image of a device, recognizing the image of the device, and determining an operation status of a corresponding device include:
step S101: acquiring an area image according to a preset camera, identifying an equipment area in the camera based on preset equipment characteristics, and adjusting camera acquisition parameters to acquire an equipment image;
the existing cameras almost all have an angle adjusting function, equipment areas are determined according to preset equipment characteristics in area images acquired by the cameras, and then the detection directions of the cameras are adjusted, so that the cameras directly acquire the equipment images.
Step S102: acquiring illumination intensity and air humidity, and determining a correction filter according to the illumination intensity and the air humidity;
the influence of the environment on the image, especially the illumination intensity, is great, the correction filter is preset according to the illumination intensity, and the influence of the illumination intensity can be removed according to the correction filter, so that the image identification process is more accurate; besides the illumination intensity, the air humidity also has a certain influence on the image, and when the air humidity is high, for example, fog exists, the image needs to be corrected at the moment; wherein, illumination intensity and air humidity are obtained by a preset sensor.
Step S103: adjusting the equipment image according to the correction filter to obtain an image to be detected;
and adjusting the equipment image according to the correction filter, and eliminating the environmental influence as much as possible.
Step S104: identifying the image to be detected, and determining the running condition of corresponding equipment;
and identifying the image to be detected, and determining the operation condition of the corresponding equipment.
Further, the step of identifying the image to be detected and determining the operation condition of the corresponding device includes:
sequencing the images to be detected according to the time sequence, and sequentially carrying out logic operation on adjacent images to be detected;
determining a change contour according to a logic operation result, and determining an indicator light area according to the shape of the change contour;
acquiring the color value average value of the indicator light area in each image to be detected, arranging the color value average values according to time sequence, and fitting a color value curve; the number of the color value curves is at least one;
and determining the running condition of the corresponding equipment according to the color value curve.
The identification process of the images to be detected is described, firstly, the images to be detected are ordered according to the time sequence, and adjacent images to be detected are sequentially compared, so that the process can rapidly locate the change area; then, the outline of the change area can determine the area of the indicator light, and the halation of the indicator light is approximately circular in the image; and finally, calculating the color value average value of the indicator light area in each image to be detected, and obtaining the change condition of the indicator light of a certain device, namely the change curve, wherein the characteristics of the change curve represent the running condition of the device. The characteristics of the profile may be based on existing functional processing means, such as fourier transforms.
Fig. 3 is a second sub-flowchart of a signal detection method based on an image recognition technology, where the step of obtaining transmission data of a target device at the gateway based on the data acquisition frequency and generating a data wave pattern based on a preset arrangement rule includes:
step S301: acquiring data packet parameters of the target equipment, which contain time points, at the gateway based on the data acquisition frequency; the data packet parameters comprise data packet types and data volumes;
the data packet parameters acquired based on the gateway comprise data packet types and data volumes;
step S302: determining a data filling interval according to the time point, reading a preset characteristic value according to the data packet type, and determining the number of pixel points according to the data quantity; wherein the data filling interval is an angular range, the angular range being a function of time;
step S303: determining a data area corresponding to the data packet at the tail part of the data filling interval to obtain a data fluctuation diagram; the value of the pixel point in the data area is the characteristic value; the area of the data area is determined by the number of pixel points.
In one example of the technical scheme of the invention, the mode of converting transmission data into the data wave map is a rotary filling mode, a polar coordinate system is established at a central point, then the polar coordinate system is divided into different angle ranges, pixel point color values are sequentially determined according to data packet types along with time change, pixel point areas are determined according to data quantity, and then the data wave map is obtained by filling the data wave map into the corresponding angle ranges.
FIG. 4 is a third sub-flowchart of a signal detection method based on image recognition technology, wherein the steps of recognizing the data fluctuation graph, determining a fluctuation period, and generating inspection guidance based on the fluctuation period include:
step S401: sequentially calculating the first-order difference of each pixel point in the data fluctuation graph, and judging whether data fluctuation exists at each pixel point;
the first derivative can be understood as a first order derivative, which can reflect the change condition of the pixel point, namely judging whether the pixel point has the mutation of the color value; the reason why the first derivative is converted into the first derivative is that the values of the points in the image are discrete values, and the derivative of the discrete values exists in the form of a derivative.
Step S402: when data fluctuation exists, calculating a second-order difference at the pixel point to obtain fluctuation amplitude;
the second difference corresponds to a second derivative that reflects the rate of change of the color value, the greater the absolute value of the second derivative, the higher the rate of change of the color value change, which means that the color value does not change smoothly, but changes rapidly.
Step S403: when the fluctuation amplitude reaches a preset fluctuation threshold value, marking pixel points;
when the fluctuation amplitude reaches a preset condition, the relevant pixel point is regarded as a point to be detected and marked; the marked pixel points are points where there is a change and the degree of change is more severe.
Step S404: counting marked pixel points, and when the number of the pixel points belonging to the same data area reaches a preset quantity threshold, reading a data filling interval corresponding to the data area and the position of the data area in the data filling interval to determine a fluctuation period;
counting marked pixel points, calculating the pixel points in each data area, and when the pixel points reach preset conditions, reversely pushing the fluctuation time period according to the generation rule of the data area.
Step S405: sending the fluctuation time period to a detection end, and receiving an inspection guide fed back by the detection end;
after the fluctuation time period is determined, inquiring data corresponding to the fluctuation time period, and determining which devices need to be detected and how to detect the devices according to the data, namely the checking guide; the detection end can be a manual end containing an automatic processing algorithm.
The calculation formula of the first-order difference is as follows:
the calculation formula of the second-order difference is as follows:
in the method, in the process of the invention,
for->
First order difference,/->
For->
Is a second order difference of (2); the second-order differential calculation matrix is as follows:
the first-order difference means the square sum of two unidirectional differences; the second-order difference is the difference sum of the pixel point to be detected and other pixel points; the second-order difference is an eight-neighborhood representation, and if the calculation process is to be simplified, a four-neighborhood representation can be adopted, namely, calculation is performed by using only four adjacent elements, namely, upper, lower, left and right.
Example 2:
a signal detection system based on image recognition technology, the system 10 comprising:
the condition judging module 11 is used for acquiring an equipment image, identifying the equipment image and determining the running condition of the corresponding equipment;
a parameter determining module 12, configured to determine a target device, a gateway related to the target device, and a data acquisition frequency according to the operation condition;
the fluctuation graph generation module 13 is used for acquiring transmission data of the target equipment at the gateway based on the data acquisition frequency and generating a data fluctuation graph based on a preset arrangement rule;
and the fluctuation graph identification module 14 is used for identifying the data fluctuation graph, determining a fluctuation period and generating inspection guidance based on the fluctuation period.
The condition determination module 11 includes:
the image acquisition unit is used for acquiring an area image according to a preset camera, identifying an equipment area in the camera based on preset equipment characteristics, and adjusting camera acquisition parameters to acquire an equipment image;
the filter determining unit is used for acquiring illumination intensity and air humidity and determining a correction filter according to the illumination intensity and the air humidity;
the image adjusting unit is used for adjusting the equipment image according to the correction filter to obtain an image to be detected;
and the judging and executing unit is used for identifying the image to be detected and determining the running condition of the corresponding equipment.
The surge map generation module 13 includes:
the data interception unit is used for acquiring data packet parameters of the target equipment, which contain time points, at the gateway based on the data acquisition frequency; the data packet parameters comprise data packet types and data volumes;
the mapping unit is used for determining a data filling interval according to the time point, reading a preset characteristic value according to the data packet type and determining the number of pixel points according to the data quantity; wherein the data filling interval is an angular range, the angular range being a function of time;
the data area determining unit is used for determining a data area corresponding to the data packet at the tail part of the data filling interval to obtain a data fluctuation diagram; the value of the pixel point in the data area is the characteristic value; the area of the data area is determined by the number of pixel points.
The surge map identification module 14 includes:
the fluctuation judging unit is used for sequentially calculating the first-order difference of each pixel point in the data fluctuation graph and judging whether data fluctuation exists at each pixel point or not;
the amplitude calculation unit is used for calculating the second-order difference at the pixel point when the data fluctuation exists, so as to obtain the fluctuation amplitude;
when the fluctuation amplitude reaches a preset fluctuation threshold value, marking pixel points;
the time interval positioning unit is used for counting marked pixel points, and when the number of pixel points belonging to the same data area reaches a preset number threshold, reading a data filling interval corresponding to the data area and the position of the data area in the data filling interval to determine a fluctuation time interval;
and the guide generation unit is used for sending the fluctuation time period to the detection end and receiving the detection guide fed back by the detection end.
It can be understood that the signal detection system based on the image recognition technology provided by the embodiment of the present invention corresponds to the signal detection method based on the image recognition technology, and the explanation, the examples, the beneficial effects and other parts of the content of the signal detection system based on the image recognition technology can refer to the corresponding content in the signal detection method based on the image recognition technology, which is not repeated herein.
In summary, compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of acquiring an equipment image through a camera, carrying out image recognition on the equipment image, determining the running condition of equipment, determining the transmission data acquisition frequency of each equipment according to the running condition, acquiring transmission data based on the data acquisition frequency, and storing the transmission data in an image form; at the moment, the pixel point analysis is carried out on the transmission data, so that the transmission fluctuation can be rapidly positioned, the speed is extremely high, and the efficiency is extremely high.
It should be noted that, from the above description of the embodiments, those skilled in the art will clearly understand that each embodiment may be implemented by means of software plus necessary general hardware platform. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments. In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The signal detection method based on the image recognition technology is characterized by comprising the following steps:
acquiring an equipment image, identifying the equipment image, and determining the running condition of corresponding equipment;
determining target equipment, a gateway related to the target equipment and data acquisition frequency according to the running condition;
acquiring transmission data of target equipment at the gateway based on the data acquisition frequency, and generating a data fluctuation map based on a preset arrangement rule;
and identifying the data fluctuation graph, determining a fluctuation period, and generating inspection guide based on the fluctuation period.
2. The method for detecting signals based on image recognition technology as recited in claim 1, wherein the step of acquiring an image of a device, recognizing the image of the device, and determining the operation condition of the corresponding device includes:
acquiring an area image according to a preset camera, identifying an equipment area in the camera based on preset equipment characteristics, and adjusting camera acquisition parameters to acquire an equipment image;
acquiring illumination intensity and air humidity, and determining a correction filter according to the illumination intensity and the air humidity;
adjusting the equipment image according to the correction filter to obtain an image to be detected;
and identifying the image to be detected, and determining the running condition of the corresponding equipment.
3. The method for detecting signals based on image recognition technology as set forth in claim 2, wherein the step of recognizing the image to be detected and determining the operation condition of the corresponding device includes:
sequencing the images to be detected according to the time sequence, and sequentially carrying out logic operation on adjacent images to be detected;
determining a change contour according to a logic operation result, and determining an indicator light area according to the shape of the change contour;
acquiring the color value average value of the indicator light area in each image to be detected, arranging the color value average values according to time sequence, and fitting a color value curve; the number of the color value curves is at least one;
and determining the running condition of the corresponding equipment according to the color value curve.
4. The method for detecting signals based on image recognition technology as set forth in claim 1, wherein the step of acquiring transmission data of the target device at the gateway based on the data acquisition frequency and generating a data wave pattern based on a preset arrangement rule includes:
acquiring data packet parameters of the target equipment, which contain time points, at the gateway based on the data acquisition frequency; the data packet parameters comprise data packet types and data volumes;
determining a data filling interval according to the time point, reading a preset characteristic value according to the data packet type, and determining the number of pixel points according to the data quantity; wherein the data filling interval is an angular range, the angular range being a function of time;
determining a data area corresponding to the data packet at the tail part of the data filling interval to obtain a data fluctuation diagram; the value of the pixel point in the data area is the characteristic value; the area of the data area is determined by the number of pixel points.
5. The method for detecting a signal based on an image recognition technique according to claim 1, wherein the step of recognizing the data wave pattern, determining a wave period, and generating the inspection guide based on the wave period comprises:
sequentially calculating the first-order difference of each pixel point in the data fluctuation graph, and judging whether data fluctuation exists at each pixel point;
when data fluctuation exists, calculating a second-order difference at the pixel point to obtain fluctuation amplitude;
when the fluctuation amplitude reaches a preset fluctuation threshold value, marking pixel points;
counting marked pixel points, and when the number of the pixel points belonging to the same data area reaches a preset quantity threshold, reading a data filling interval corresponding to the data area and the position of the data area in the data filling interval to determine a fluctuation period;
and sending the fluctuation time period to a detection end, and receiving the detection guide fed back by the detection end.
6. The method for detecting signals based on image recognition technology as set forth in claim 5, wherein the first order difference has a calculation formula:
the calculation formula of the second-order difference is as follows:
in the method, in the process of the invention,
for->
First order difference,/->
For->
Is a second order difference of (2); the second-order differential calculation matrix is as follows:
7. a signal detection system based on image recognition technology, the system comprising:
the condition judging module is used for acquiring equipment images, identifying the equipment images and determining the running condition of corresponding equipment;
the parameter determining module is used for determining target equipment, a gateway related to the target equipment and data acquisition frequency according to the running condition;
the fluctuation graph generation module is used for acquiring transmission data of the target equipment at the gateway based on the data acquisition frequency and generating a data fluctuation graph based on a preset arrangement rule;
and the fluctuation graph identification module is used for identifying the data fluctuation graph, determining a fluctuation period and generating inspection guide based on the fluctuation period.
8. The image recognition technology-based signal detection system of claim 7, wherein the condition determination module comprises:
the image acquisition unit is used for acquiring an area image according to a preset camera, identifying an equipment area in the camera based on preset equipment characteristics, and adjusting camera acquisition parameters to acquire an equipment image;
the filter determining unit is used for acquiring illumination intensity and air humidity and determining a correction filter according to the illumination intensity and the air humidity;
the image adjusting unit is used for adjusting the equipment image according to the correction filter to obtain an image to be detected;
and the judging and executing unit is used for identifying the image to be detected and determining the running condition of the corresponding equipment.
9. The image recognition technology-based signal detection system of claim 7, wherein the fluctuation map generation module comprises:
the data interception unit is used for acquiring data packet parameters of the target equipment, which contain time points, at the gateway based on the data acquisition frequency; the data packet parameters comprise data packet types and data volumes;
the mapping unit is used for determining a data filling interval according to the time point, reading a preset characteristic value according to the data packet type and determining the number of pixel points according to the data quantity; wherein the data filling interval is an angular range, the angular range being a function of time;
the data area determining unit is used for determining a data area corresponding to the data packet at the tail part of the data filling interval to obtain a data fluctuation diagram; the value of the pixel point in the data area is the characteristic value; the area of the data area is determined by the number of pixel points.
10. The image recognition technology-based signal detection system of claim 7, wherein the fluctuation map recognition module comprises:
the fluctuation judging unit is used for sequentially calculating the first-order difference of each pixel point in the data fluctuation graph and judging whether data fluctuation exists at each pixel point or not;
the amplitude calculation unit is used for calculating the second-order difference at the pixel point when the data fluctuation exists, so as to obtain the fluctuation amplitude;
when the fluctuation amplitude reaches a preset fluctuation threshold value, marking pixel points;
the time interval positioning unit is used for counting marked pixel points, and when the number of pixel points belonging to the same data area reaches a preset number threshold, reading a data filling interval corresponding to the data area and the position of the data area in the data filling interval to determine a fluctuation time interval;
and the guide generation unit is used for sending the fluctuation time period to the detection end and receiving the detection guide fed back by the detection end.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100068151A (en) * | 2008-12-12 | 2010-06-22 | 한국전자통신연구원 | Gateway with camera controller and camera control method for visual situation acknowledgment |
| JP2011123726A (en) * | 2009-12-11 | 2011-06-23 | Mitsubishi Electric Corp | Image processing device for monitoring |
| CN105997138A (en) * | 2015-03-31 | 2016-10-12 | 三星电子株式会社 | Ultrasound imaging apparatus and method of processing ultrasound image thereof |
| WO2021004402A1 (en) * | 2019-07-05 | 2021-01-14 | 深圳数字生命研究院 | Image recognition method and apparatus, storage medium, and processor |
| US10977772B1 (en) * | 2018-09-25 | 2021-04-13 | National Technology & Engineering Solutions Of Sandia, Llc | Unmanned aircraft system (UAS) detection and assessment via temporal intensity aliasing |
| WO2021068486A1 (en) * | 2019-10-12 | 2021-04-15 | 深圳壹账通智能科技有限公司 | Image recognition-based vision detection method and apparatus, and computer device |
| WO2021098030A1 (en) * | 2019-11-22 | 2021-05-27 | 网宿科技股份有限公司 | Method and apparatus for video encoding |
| CN113746758A (en) * | 2021-11-05 | 2021-12-03 | 南京敏宇数行信息技术有限公司 | Method and terminal for dynamically identifying flow protocol |
| CN114067340A (en) * | 2022-01-17 | 2022-02-18 | 山东北软华兴软件有限公司 | Intelligent judgment method and system for information importance |
-
2023
- 2023-06-06 CN CN202310658170.8A patent/CN116385706B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100068151A (en) * | 2008-12-12 | 2010-06-22 | 한국전자통신연구원 | Gateway with camera controller and camera control method for visual situation acknowledgment |
| JP2011123726A (en) * | 2009-12-11 | 2011-06-23 | Mitsubishi Electric Corp | Image processing device for monitoring |
| CN105997138A (en) * | 2015-03-31 | 2016-10-12 | 三星电子株式会社 | Ultrasound imaging apparatus and method of processing ultrasound image thereof |
| US10977772B1 (en) * | 2018-09-25 | 2021-04-13 | National Technology & Engineering Solutions Of Sandia, Llc | Unmanned aircraft system (UAS) detection and assessment via temporal intensity aliasing |
| WO2021004402A1 (en) * | 2019-07-05 | 2021-01-14 | 深圳数字生命研究院 | Image recognition method and apparatus, storage medium, and processor |
| WO2021068486A1 (en) * | 2019-10-12 | 2021-04-15 | 深圳壹账通智能科技有限公司 | Image recognition-based vision detection method and apparatus, and computer device |
| WO2021098030A1 (en) * | 2019-11-22 | 2021-05-27 | 网宿科技股份有限公司 | Method and apparatus for video encoding |
| CN113746758A (en) * | 2021-11-05 | 2021-12-03 | 南京敏宇数行信息技术有限公司 | Method and terminal for dynamically identifying flow protocol |
| CN114067340A (en) * | 2022-01-17 | 2022-02-18 | 山东北软华兴软件有限公司 | Intelligent judgment method and system for information importance |
Non-Patent Citations (1)
| Title |
|---|
| 高昂;刘明潇;孙东坡;曹震;陈云飞;: "基于图像识别的床面推移质分布密度检测方法", 人民黄河, no. 09 * |
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