CN114401520A - Method and system for detecting thermodynamic diagram of wireless network signal - Google Patents
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Abstract
The invention discloses a wireless network signal detection thermodynamic diagram method and a wireless network signal detection thermodynamic diagram system. The method comprises the following steps: converting basic data and real data; step two, calculating a heat value; step three, rendering a thermodynamic diagram; and step four, converting the rendered thermodynamic diagram into a vector diagram. The method adopts a large number of house-type cases to carry out intelligent training on the rendering model, has high calculation speed and higher result accuracy, and can embody the coverage range of wireless signals more truly.
Description
Technical Field
The invention belongs to the technical field of wireless network signal detection, and particularly relates to a wireless network signal detection thermodynamic diagram method and system.
Background
With the popularization of wireless networks, more and more families use WiFi, but due to the problem of non-specification installation, wireless signals in a room cannot be reasonably used. In the prior art, only simple thermodynamic diagram rendering can be performed, and a near-real wireless signal coverage range cannot be obtained.
Chinese patent CN202010955730.2 discloses a thermodynamic diagram generation method and an electronic device, the method comprising: identifying network equipment corresponding to a to-be-processed area, and acquiring equipment information corresponding to the network equipment, wherein the equipment information comprises network signal intensity corresponding to a mobile terminal, and the mobile terminal is a terminal associated with the network equipment; determining terminal distribution information corresponding to the network equipment according to the network signal intensity corresponding to the mobile terminal; and generating the thermodynamic diagram of the area to be processed according to the terminal distribution information corresponding to the network equipment, realizing automatic generation of the thermodynamic diagram and improving the efficiency of generating the thermodynamic diagram. But the space of the device and the signal attenuation brought by various situations are not considered, and the accuracy is not enough.
The invention discloses a Chinese patent CN202110208502.3, and relates to the technical field of industrial scheduling, in particular to a 5G industrial Internet of things gateway and a control method thereof. In the invention, a scheduling control end firstly acquires map information of a scheduling area and acquires scheduling information of a current round task; further acquiring an optimal WiFi signal intensity thermodynamic diagram of the gateway of the Internet of things; generating an antenna direction adjusting instruction of the gateway of the Internet of things; after the gateway of the Internet of things executes the antenna direction adjusting instruction, the scheduling control end obtains the optimal path of the mobile equipment based on the thermodynamic diagram of the optimal WiFi signal strength; and finally, sending the optimal path to the mobile equipment through the gateway of the Internet of things. Before scheduling, the Internet of things gateway is adjusted according to the scheduling task, an optimal coverage range is obtained, and the mobile equipment and the fixed equipment can be ensured to be continuously and stably communicated with a scheduling control terminal. However, the obtained map information may be different from the actual map information, so that the actual coverage area cannot be presented.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a wireless network signal detection thermodynamic diagram method and a wireless network signal detection thermodynamic diagram system.
In order to achieve the purpose, the invention adopts the following technical scheme:
a wireless network signal detection thermodynamic diagram method comprises the following steps:
converting basic data and real data;
step two, calculating a heat value;
step three, rendering a thermodynamic diagram;
and step four, converting the rendered thermodynamic diagram into a vector diagram.
The vector diagram may facilitate compatibility with different devices.
For further explanation of the present invention, the real data is the actual area and the coverage of the actual router; the basic data are a home graph and a virtual router frequency.
The invention further explains that the actual area is identified and calculated by identifying the area, the length and the width on the house type graph and the house type internal wall body through the convolutional neural network, and the specific flow comprises the following steps:
11) collecting one part of unprocessed sample, and copying one part;
12) filling each unprocessed sample into a square according to requirements, converting the size of the square into the size of 800 multiplied by 800 pixels, filling the insufficient part with white, and forming a sample set by all converted samples;
13) identifying sample set features by using a labeling tool, wherein the actual black and gray shapes are used as features;
14) and randomly dividing the sample set into a test set and a training set according to the ratio of 2:8 and inputting the test set and the training set into a deep learning recognition model network.
And 11) copying one part of the image to compare with the final image trained by the model, and observing whether the deformation and other problems exist in the calculation and conversion process.
The input end of the deep learning identification model network adopts mosaic data to enhance and enrich the detection data set, so that the accuracy is kept while the weight is reduced; the deep learning identification model network adopts an R-CNN structure, and different trunk layers carry out parameter aggregation on different detection layers; the output end of the deep learning identification model network adopts CIOU Loss operation to ensure that the regression Loss is more accurate and faster in convergence when the regression Loss is overlapped with the target frame or even has an inclusion relation.
The invention further explains that the frequency of the virtual router and the coverage area of the actual router are converted in equal proportion to obtain the router frequency.
The invention further discloses that the calculation of the thermal value mainly comprises the calculation of WiFi coverage gradient values, shelter attenuation values and thermal value color ranges of different power routers.
The thermodynamic diagram rendering method mainly comprises the steps that a heat value rendering is carried out by cutting four quadrants by taking a WiFi point as an origin according to a house type diagram converted by a convolutional network neural recognition, and the attenuation of the heat value is comprehensively judged along with the expansion of a rendering distance and the existence of a shelter between a current pixel point and the origin in the rendering process.
A wireless network signal detection thermodynamic diagram system, comprising: the system comprises a data conversion module, a heating power value calculation rule module and a rendering module; the data conversion module is used for converting basic data and actual data; the heat value calculation rule module is used for making WiFi coverage range gradient values, shelter attenuation calculation rules and heat value color ranges of the routers with different powers; and the rendering module is used for calculating the heat value and rendering the color by matching the heat value.
The invention further discloses that the data conversion module identifies the area, the length and the width on the house type graph and the house type internal wall through the convolutional neural network, identifies and calculates the area to obtain the actual area, and performs equal proportion conversion on the frequency of the virtual router and the coverage area of the actual router to obtain the router frequency.
The invention further explains that the rendering module and the heating power value calculation rule module provide a service interface for the outside, and a user submits a house type diagram, a WiFi brand, power and a WiFi coordinate system to the service interface, then submits the house type diagram, the WiFi brand and the power to a rendering model for calculation and rendering, and finally obtains a rendering result and returns the rendering result to the user.
The rendering model is trained by acquiring a house type graph and photo deep learning rendering thermodynamic diagram model, and the rendering model is iterated continuously to finally obtain a rendering model which is closest to the actual situation.
The invention has the following beneficial effects:
the invention carries out the conversion between the house type graph and the actual area and the conversion between the actual router coverage area and the virtual router frequency by using the intelligent training model, carries out the rendering of the heat value on the converted house type graph, finally obtains a rendering thermodynamic diagram closest to the actual situation, has high calculation speed and high result accuracy, and can truly embody the wireless signal coverage area.
Drawings
Fig. 1 is a flow chart of a wireless network signal detection thermodynamic diagram.
Detailed Description
The invention will be further explained with reference to the drawings.
Example 1:
a wireless network signal detection thermodynamic diagram method comprises the following steps:
converting basic data and real data;
step two, calculating a heat value;
step three, rendering a thermodynamic diagram;
and step four, converting the rendered thermodynamic diagram into a vector diagram.
Example 2:
a wireless network signal detection thermodynamic diagram method comprises the following steps:
converting basic data and real data;
the actual data is the actual area and the coverage area of the actual router; the basic data are a home graph and a virtual router frequency.
The actual area is identified and calculated by identifying the area, the length and the width on the house type graph and the house type internal wall body through the convolutional neural network, and the specific flow comprises the following steps:
11) collecting one part of unprocessed sample, and copying one part;
12) filling each unprocessed sample into a square according to requirements, converting the size of the square into the size of 800 multiplied by 800 pixels, filling the insufficient part with white, and forming a sample set by all converted samples;
13) identifying sample set features by using a labeling tool, wherein the actual black and gray shapes are used as features;
14) randomly dividing a sample set into a test set and a training set according to a ratio of 2:8 and inputting the test set and the training set into a deep learning identification model network;
and 11) copying one part of the image to compare with the final image trained by the model, and observing whether the deformation and other problems exist in the calculation and conversion process.
The input end of the deep learning identification model network adopts mosaic data to enhance and enrich the detection data set, so that the accuracy is kept while the weight is reduced; the deep learning identification model network adopts an R-CNN structure, and different trunk layers carry out parameter aggregation on different detection layers; the output end of the deep learning identification model network adopts CIOU Loss operation to ensure that the regression Loss is more accurate and faster in convergence when the regression Loss is overlapped with the target frame or even has an inclusion relation.
And carrying out equal proportion conversion on the frequency of the virtual router and the coverage range of the actual router to obtain the router frequency.
Step two, calculating a heat value;
and the calculation of the thermal value mainly comprises the calculation of WiFi coverage gradient values, shelter attenuation values and thermal value color ranges of the routers with different powers.
Step three, rendering a thermodynamic diagram;
the thermodynamic diagram rendering is mainly performed according to a house type diagram after convolutional network neural recognition conversion, a thermodynamic diagram model takes a WiFi point as an origin, and four quadrants are cut to perform thermodynamic value rendering; and comprehensively judging the attenuation strength of the thermal force value along with the expansion of the rendering distance and the existence of a shelter between the current pixel point and the origin in the rendering process.
And step four, converting the rendered thermodynamic diagram into a vector diagram.
Example 3:
a wireless network signal detection thermodynamic diagram system, comprising: the system comprises a data conversion module, a heating power value calculation rule module and a rendering module; the data conversion module is used for converting basic data and actual data; the heat value calculation rule module is used for making WiFi coverage range gradient values, shelter attenuation calculation rules and heat value color ranges of the routers with different powers; and the rendering module is used for calculating the heat value and rendering the color by matching the heat value.
Example 4:
a wireless network signal detection thermodynamic diagram system, comprising: the system comprises a data conversion module, a heating power value calculation rule module and a rendering module; the data conversion module is used for converting basic data and actual data; the data conversion module identifies the area, the length and the width on the house type graph and the house type internal wall through the convolutional neural network, identifies and calculates the area to obtain the actual area, and performs equal proportion conversion on the frequency of the virtual router and the coverage area of the actual router to obtain the router frequency.
And the heat value calculation rule module is used for making WiFi coverage range gradient values, shelter attenuation calculation rules and heat value color ranges of different power routers.
And the rendering module is used for calculating the heat value and rendering the color by matching the heat value.
The rendering module and the heating power value calculation rule module provide a service interface for the outside, and after a user submits a house type diagram, a WiFi brand, power and a WiFi coordinate system to the service interface, the house type diagram, the WiFi brand, the power and the WiFi coordinate system are submitted to the rendering model for calculation and rendering, and finally a rendering result is obtained and returned to the user.
The rendering model is trained by acquiring a house type image and photo deep learning rendering thermodynamic image model, and model iteration is continuously performed to finally obtain a rendering model closest to the actual situation.
The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made thereto by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should be considered as falling within the scope of the present invention.
Claims (10)
1. A wireless network signal detection thermodynamic diagram method is characterized by comprising the following steps:
converting basic data and real data;
step two, calculating a heat value;
step three, rendering a thermodynamic diagram;
and step four, converting the rendered thermodynamic diagram into a vector diagram.
2. The method of claim 1, wherein the method comprises: the actual data is the actual area and the coverage area of the actual router; the basic data are a home graph and a virtual router frequency.
3. The method of claim 2, wherein the method comprises: the actual area is identified and calculated by identifying the area, the length and the width on the house type graph and the house type internal wall body through the convolutional neural network, and the specific flow comprises the following steps:
11) collecting one part of unprocessed sample, and copying one part;
12) filling each unprocessed sample into a square according to requirements, converting the size of the square into the size of 800 multiplied by 800 pixels, filling the insufficient part with white, and forming a sample set by all converted samples;
13) identifying sample set features by using a labeling tool, wherein the actual black and gray shapes are used as features;
14) and randomly dividing the sample set into a test set and a training set according to the ratio of 2:8 and inputting the test set and the training set into a deep learning recognition model network.
4. The method of claim 2, wherein the method comprises: and carrying out equal proportion conversion on the frequency of the virtual router and the coverage range of the actual router to obtain the router frequency.
5. The method of claim 1, wherein the method comprises: and the calculation of the thermal value mainly comprises the calculation of WiFi coverage gradient values, shelter attenuation values and thermal value color ranges of the routers with different powers.
6. The method of claim 1, wherein the method comprises: the thermodynamic diagram rendering method mainly comprises the steps that a heat value rendering is carried out by cutting four quadrants by taking a WiFi point as an original point according to a house type diagram after convolutional network neural recognition conversion, and the attenuation strength of the heat value is comprehensively judged along with the expansion of a rendering distance and the existence of a shelter between a current pixel point and the original point in the process of rendering.
7. A wireless network signal detection thermodynamic diagram system, comprising: the system comprises a data conversion module, a heating power value calculation rule module and a rendering module;
the data conversion module is used for converting basic data and actual data;
the heat value calculation rule module is used for making WiFi coverage range gradient values, shelter attenuation calculation rules and heat value color ranges of the routers with different powers;
and the rendering module is used for calculating the heat value and rendering the color by matching the heat value.
8. The wireless network signal detection thermodynamic diagram system of claim 7, wherein: the data conversion module identifies the area, the length and the width on the house type graph and the house type internal wall through the convolutional neural network, identifies and calculates the area to obtain the actual area, and performs equal proportion conversion on the frequency of the virtual router and the coverage area of the actual router to obtain the router frequency.
9. The wireless network signal detection thermodynamic diagram system of claim 7, wherein: the rendering module and the heating power value calculation rule module provide a service interface for the outside, and after a user submits a house type diagram, a WiFi brand, power and a WiFi coordinate system to the service interface, the house type diagram, the WiFi brand, the power and the WiFi coordinate system are submitted to the rendering model for calculation and rendering, and finally a rendering result is obtained and returned to the user.
10. The wireless network signal detection thermodynamic diagram system of claim 9, wherein: the rendering model is trained by acquiring a house type image and photo deep learning rendering thermodynamic image model, and model iteration is continuously performed to finally obtain a rendering model closest to the actual situation.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109712151A (en) * | 2018-12-31 | 2019-05-03 | 航天精一(广东)信息科技有限公司 | A kind of method of reverse color applying drawing thermodynamic chart |
| CN110059750A (en) * | 2019-04-17 | 2019-07-26 | 广东三维家信息科技有限公司 | House type shape recognition process, device and equipment |
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| US20210143923A1 (en) * | 2019-11-12 | 2021-05-13 | Plano Molding Company, Llc | System and method for monitoring and mapping signal strength |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109712151A (en) * | 2018-12-31 | 2019-05-03 | 航天精一(广东)信息科技有限公司 | A kind of method of reverse color applying drawing thermodynamic chart |
| CN110059750A (en) * | 2019-04-17 | 2019-07-26 | 广东三维家信息科技有限公司 | House type shape recognition process, device and equipment |
| US20210143923A1 (en) * | 2019-11-12 | 2021-05-13 | Plano Molding Company, Llc | System and method for monitoring and mapping signal strength |
| CN112183637A (en) * | 2020-09-29 | 2021-01-05 | 中科方寸知微(南京)科技有限公司 | Single-light-source scene illumination re-rendering method and system based on neural network |
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