CN114125401A - Case site wireless information acquisition method and system - Google Patents
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Abstract
The invention discloses a case site wireless information acquisition method and system, relating to the technical field of information acquisition, and comprising an acquisition method and an acquisition system; the collector disclosed by the invention integrates a plurality of sensors and wireless modules in a modular form, so that the on-site temperature and humidity of the smuggled case, satellite positioning information, full-system base station information, WIFI signals, unmanned aerial vehicle aerial data, monitoring video and other information elements can be quickly and accurately collected, and the requirement of a custom smuggling department on the on-site electronic information 'full-time-space' collection is comprehensively met; performing data structured conversion according to an industrial standard, uploading the data to a cloud server through a 5G network with high bandwidth, high reliability and low time delay, and directly importing the generated database file into a national public security on-site investigation information system; the method has the characteristics of simple and convenient operation, strong cruising ability and the like; the method saves communication cost, coordination links and work flow, and simultaneously maximally utilizes scientific and technological means to obtain case related information to serve actual combat case solving.
Description
Technical Field
The invention relates to the technical field of information acquisition, in particular to a case site wireless information acquisition method and system.
Background
Information gathering refers to the work of preparing information resources for published production, including the collection and processing of information. It is the direct basis and important basis for topic selection planning. The extension of the last step of the information collection becomes the beginning of the topic planning. The information acquisition system is constructed on the basis of a network information mining engine, can help people to acquire the latest information from different Internet sites in the shortest time, and can timely release the information to the sites within the first time after classification and format unification. Thereby improving the timeliness of information and saving or reducing the workload.
The existing survey data acquisition systems on the market are mutually independent in equipment for acquiring wireless signals and environmental data, and the existing survey personnel need to carry the equipment when going out, so that the existing survey personnel are not beneficial to developing work; moreover, since the research and development time is earlier, only 2G, 3G and 4G mobile communication networks are supported, the data of the base station of the currently developed and commercial 5G mobile network cannot be collected, the general economic condition of the suspects of the smuggling cases is better, the used mobile phones are often updated and updated faster, and the important data can be ignored when the data of the base station of the 5G cannot be collected; adverse to data analysis; only GPS positioning is supported, so that errors exist in positioning of the site position; the type of collected data is limited, only wireless signal collection is supported, and the type of media data such as live photos, monitoring videos and unmanned aerial vehicle data is not supported, so that a case live wireless information collection method and a case live wireless information collection system are provided to solve the problems.
Disclosure of Invention
The invention aims to solve the problems that in the prior art, the existing survey data acquisition systems on the market are mutually independent, and the existing survey personnel need to carry complicated equipment when going out, so that the existing survey data acquisition systems are not beneficial to developing work; moreover, since the research and development time is earlier, only 2G, 3G and 4G mobile communication networks are supported, the data of the base station of the currently developed and commercial 5G mobile network cannot be collected, the general economic condition of the suspects of the smuggling cases is better, the used mobile phones are often updated and updated faster, and the important data can be ignored when the data of the base station of the 5G cannot be collected; adverse to data analysis; only GPS positioning is supported, so that errors exist in positioning of the site position; the method and the system for acquiring the wireless information of the case site have the advantages that the acquired data types are limited, only wireless signal acquisition is supported, and the media data types such as site photos, monitoring videos and unmanned aerial vehicle data are not supported.
In order to achieve the purpose, the invention adopts the following technical scheme:
a case site wireless information acquisition method comprises the following steps:
collecting data in a modular mode;
shooting a monitoring video through monitoring equipment, and shooting a scene picture by an unmanned aerial vehicle and a camera;
collecting the acquired data through a UART/IIC/USB data bus;
the collected data are packaged and uploaded to an operation end through a Bluetooth or 2.4G wireless network;
sending the obtained monitoring video and the obtained on-site picture to an operation end;
the computer automatically acquires and refreshes data;
and sending the acquired data to a database defined according to an industry standard for storage.
Preferably, the data packing step is as follows:
sequencing each signal according to the size of the cut-off time, and carrying out data packing on the signals with small cut-off time first;
selecting the first information, and calculating the minimum byte required for packaging the signal message frame and the maximum byte required for packaging all the signal message frames;
selecting the next signal, and calculating whether the signal can be packed into the message frame which is the same as the previous information;
repeating the previous step until all the signals are packed to form a search tree;
and after the search tree is formed, reversing the search tree, and respectively adding the bandwidth utilization rate obtained by each search tree branch to find out a minimum search tree branch.
Preferably, the acquired monitoring videos and the live photos acquired by the unmanned aerial vehicle and the camera through the wireless network card are sent to the operation end through a copy-C interface.
Preferably, the photo processing steps are as follows:
normalizing the input image;
performing a series of convolution and pooling operations on the image to extract image characteristic information;
predicting the label distribution of the feature vectors in the feature sequence by adopting a bidirectional recurrent neural network;
and the problem of alignment of input data and a given label is solved by adopting connection time sequence classification, and interval characters and repeated characters are removed from an output result.
Preferably, the image enhancement step of the photo shot by the unmanned aerial vehicle is as follows:
carrying out gray level transformation on the image;
carrying out histogram equalization on the image after gray level transformation;
carrying out spatial domain sharpening enhancement on the equalized image;
and finally, homomorphic filtering is carried out.
Preferably, the specific processing steps of histogram equalization are as follows:
counting each gray level r of the original histogramkN is the number of pixelsk(k＝0,1,2,....,255)；
Calculating the probability P of the occurrence of a pixelr(rk)＝nkN, wherein n is the total number of pixels;
calculating cumulative histograms
And find Tk＝int[tk*(N-1)+0.5]Wherein N is 256;
according to the obtained TkWill be the original gray value rkMapping to a new grey value Sk＝TrkAnd obtaining a processed image.
A wireless information acquisition system for case sites comprises:
the modularized data acquisition module: the system is used for acquiring data in a modular mode;
video recording and shooting module: the system is used for shooting monitoring videos through monitoring equipment, and shooting field pictures through the unmanned aerial vehicle and the camera;
the data summarization module: the UART/IIC/USB data bus is used for collecting the acquired data;
a summary data upload module: the data processing device is used for packaging the summarized data and uploading the data to an operation end through a Bluetooth or 2.4G wireless network;
the video recording and shooting processing uploading module: the monitoring system is used for sending the acquired monitoring video and the acquired on-site photos to the operation end;
a data acquisition refresh module: the system is used for automatically acquiring and refreshing data by a computer;
a data saving module: and the data acquisition module is used for sending the acquired data to a database defined according to the industry standard for storage.
Preferably, the video recording and shooting processing uploading module comprises a photo processing module and an image enhancement module; the photo processing module includes:
a normalization processing unit: the image normalization processing module is used for normalizing the input image;
an image feature extraction unit: the image processing device is used for performing a series of convolution and pooling operations on the image and extracting image characteristic information;
a label distribution prediction unit: the label distribution of the feature vectors in the feature sequence is predicted by adopting a bidirectional recurrent neural network;
an alignment solving unit: the method is used for solving the alignment problem of input data and a given label by adopting connection time sequence classification, and interval characters and repeated characters are removed from an output result.
Preferably, the image enhancement module comprises:
a gradation conversion unit: the system is used for carrying out gray level transformation processing on an image;
an equalization processing unit: the histogram equalization processing device is used for carrying out histogram equalization processing on the image after gray level transformation;
a sharpening enhancement unit: the image processing device is used for carrying out spatial domain sharpening enhancement processing on the equalized image;
homomorphic filtering unit: the homomorphic filtering processing is used for carrying out homomorphic filtering processing on the image.
Compared with the prior art, the invention has the beneficial effects that:
the collector disclosed by the invention integrates a plurality of sensors and wireless modules in a modular form, so that the on-site temperature and humidity of the smuggled case, satellite positioning information, full-system base station information, WIFI signals, unmanned aerial vehicle aerial data, monitoring video and other information elements can be quickly and accurately collected, and the 'full-time-space' collection requirement of a custom smuggled department on-site electronic information is comprehensively met; and performing data structured conversion according to an industrial standard, uploading the data to a cloud server through a 5G network with high bandwidth, high reliability and low time delay, and directly importing the generated database file into a national field investigation information system. The method has the characteristics of simple and convenient operation, strong cruising ability and the like; the method saves communication cost, coordination links and work flow, and simultaneously maximally utilizes scientific and technological means to obtain case related information to serve actual combat case solving.
Drawings
FIG. 1 is a schematic overall flow chart of a case site wireless information acquisition method according to the present invention;
FIG. 2 is a schematic view of a data packing step of the case site wireless information acquisition method according to the present invention;
FIG. 3 is a schematic view of a photo processing procedure of a case site wireless information collection method according to the present invention;
FIG. 4 is a schematic view of the image enhancement step of the case site wireless information acquisition method according to the present invention;
FIG. 5 is a schematic flow chart illustrating specific processing steps of histogram equalization in a case field wireless information acquisition method according to the present invention;
fig. 6 is a schematic view of an overall structure of a wireless information acquisition system for a case site according to the present invention.
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.
Referring to fig. 1-5, a case site wireless information acquisition method comprises the following steps:
s1: collecting data in a modular mode, wherein the modular mode comprises an integrated temperature and humidity sensor, a full-standard base station drive test chip and a full-standard WI-FI chip;
s2: shooting a monitoring video through monitoring equipment, and shooting a scene picture by an unmanned aerial vehicle and a camera;
s3: collecting the acquired data through a UART/IIC/USB data bus;
s4: the collected data are packaged and uploaded to an operation end through a Bluetooth or 2.4G wireless network; the data packing steps are as follows:
s401: sequencing each signal according to the size of the cut-off time, and carrying out data packing on the signals with small cut-off time first;
s402: selecting the first information, and calculating the minimum byte required for packaging the signal message frame and the maximum byte required for packaging all the signal message frames;
s403: selecting the next signal, and calculating whether the signal can be packed into the message frame which is the same as the previous information;
s404: repeating the previous step until all the signals are packed to form a search tree;
s405: after the search tree is formed, the search tree is inverted, the bandwidth utilization rate obtained by each search tree branch is respectively added, and a minimum search tree branch is found out;
s5: sending the obtained monitoring video and the scene pictures shot by the unmanned aerial vehicle and the camera which are obtained through the wireless network card to an operation end through a Tpye-C interface; the photo processing steps are as follows:
s501: normalizing the input image;
s502: performing a series of convolution and pooling operations on the image to extract image characteristic information;
s503: predicting the label distribution of the feature vectors in the feature sequence by adopting a bidirectional recurrent neural network;
s504: the problem of alignment of input data and a given label is solved by adopting connection time sequence classification, and interval characters and repeated characters are removed from an output result;
the man-machine shot picture image enhancement steps are as follows:
s511: the image is grey-scale transformed, which is a transformation that maps the pixel value r of the processing to a value s, described as: s ═ P [ r ], where P [ ] is the gray scale transformation function;
s512: histogram equalization is carried out on the image after gray level transformation, and if the gray level of the image is normalized and distributed in the range of r being more than or equal to 0 and less than or equal to 1, the gray level can be transformed as follows:
s＝T(r)
after transformation, the pixel value r of the original image is mapped to a new pixel value s, the transformation function satisfies the interval that r is more than or equal to 0 and less than or equal to 1, T is more than or equal to 0 and less than or equal to 1 (r) and is monotonically increased, the gray level after mapping transformation is ensured to be in an allowed range and the sequence is unchanged, the histogram equalization is to select a proper transformation function to ensure that the histogram of the processed image has flat distribution, and the transformation function is as follows:
l-1, where L is the total number of gray levels, Pr(rj) is the probability of occurrence of pixel rj, and it can be seen that histogram equalization actually uses the cumulative distribution function of the image as a transformation function;
the histogram equalization comprises the following specific processing steps:
s51201: counting each gray level r of the original histogramkN is the number of pixelsk(k＝0,1,2,....,255)；
S51202: calculating the probability P of the occurrence of a pixelr(rk)＝nkN, wherein n is the total number of pixels;
s51203: calculating cumulative histograms
And find Tk＝int[tk*(N-1)+0.5]Wherein N is 256;
s51204: according to the obtained TkWill be the original gray value rkMapping to new grey values
Obtaining a processed image;
s513: carrying out spatial domain sharpening enhancement on the equalized image, wherein the sharpening method is used for enhancing the image in the aspect of details and can be realized by adopting a first-order differential operator and a second-order differential operator in a spatial domain;
the Sobel operator is a first-order differential operator and is commonly used for edge extraction, and the result of the Sobel operator, namely the edge details of the extracted image, is added to the original image, so that the edge and the details can be highlighted, namely the image is sharpened; for image f (x, y), the Sobel operator approximation formula is as follows:
the spatial domain Laplace is a second-order differential operator, and the application of the spatial domain Laplace emphasizes the abrupt change of the gray level in the image and reduces the area with slow change of the gray level; in the aspect of sensitivity to image details, the second-order differential has the characteristic of being more sensitive than the first-order differential, particularly to gradient gradual change details;
the laplace transform of the image function f (x, y) is defined as:
adding the laplacian part to the original graph to obtain an enhanced image of g (x, y):
g(x,y)＝f(x,y)+▽2f(x,y)
s514: finally, homomorphic filtering is carried out, and the homomorphic filtering utilizes the illumination characteristics of the image to reduce the influence of uneven illumination on contrast enhancement; the theoretical basis for homomorphic filtering is an illumination-reflection model that treats an image as the product of two parts, illumination and reflection:
f(x,y)＝i(x,y)r(x,y)
the method mainly utilizes an illumination-reflection model to perform frequency domain processing on an image, improves the appearance of an image by simultaneously performing compression and contrast enhancement of a gray scale range, displays detail information of a dark area, has small influence on an image of a bright area, and improves the definition of the image.
Taking logarithm of two sides of the above formula to obtain:
Inf(x,y)＝Ini(x,y)+Inr(x,y)
and then Fourier transform is carried out:
Z(μ,ν)＝Fi(μ,ν)+Fr(μ,ν)
the frequency domain function Z (u, v) is processed by means of a filter function H (u, v) to obtain:
S(μ,ν)＝H(μ,ν)Z(μ,ν)＝H(μ,ν)Fi(μ,ν)+H(μ,ν)Fr(μ,ν)
performing an inverse fourier transform on S to obtain:
s(x,y)＝i′(x,y)+r′(x,y)
finally, performing an exponential operation on the above formula can generate an enhanced image g (x, y) meeting the requirement:
g(x,y)＝es(x,y)＝ei′(x,y).er′(x,y)；
s6: the computer automatically finishes more than three times of data acquisition and data refreshing within 60 seconds;
s7: and sending the acquired data to a database defined according to an industry standard for storage.
Referring to fig. 6, a wireless information collecting system for case sites includes:
the modularized data acquisition module: the system is used for acquiring data in a modular mode;
video recording and shooting module: the system is used for shooting monitoring videos through monitoring equipment, and shooting field pictures through the unmanned aerial vehicle and the camera;
the data summarization module: the UART/IIC/USB data bus is used for collecting the acquired data;
a summary data upload module: the data processing device is used for packaging the summarized data and uploading the data to an operation end through a Bluetooth or 2.4G wireless network;
the video recording and shooting processing uploading module: the monitoring system is used for sending the acquired monitoring video and the acquired on-site photos to the operation end;
a data acquisition refresh module: the system is used for automatically acquiring and refreshing data by a computer;
a data saving module: and the data acquisition module is used for sending the acquired data to a database defined according to the industry standard for storage.
The video recording and shooting processing uploading module comprises a photo processing module and an image enhancement module; the photo processing module includes:
a normalization processing unit: the image normalization processing module is used for normalizing the input image;
an image feature extraction unit: the image processing device is used for performing a series of convolution and pooling operations on the image and extracting image characteristic information;
a label distribution prediction unit: the label distribution of the feature vectors in the feature sequence is predicted by adopting a bidirectional recurrent neural network;
an alignment solving unit: the method is used for solving the alignment problem of input data and a given label by adopting connection time sequence classification, and interval characters and repeated characters are removed from an output result.
Wherein the image enhancement module comprises:
a gradation conversion unit: the system is used for carrying out gray level transformation processing on an image;
an equalization processing unit: the histogram equalization processing device is used for carrying out histogram equalization processing on the image after gray level transformation;
a sharpening enhancement unit: the image processing device is used for carrying out spatial domain sharpening enhancement processing on the equalized image;
homomorphic filtering unit: the homomorphic filtering processing is used for carrying out homomorphic filtering processing on the image.
An intelligent computer device comprising a memory and a processor, the memory having computer readable instructions stored therein which when executed by the processor implement the steps of the case field wireless information gathering method as claimed in any one of claims 1 to 6.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. A case site wireless information acquisition method is characterized by comprising the following steps:
collecting data in a modular mode;
shooting a monitoring video through monitoring equipment, and shooting a scene picture by an unmanned aerial vehicle and a camera;
collecting the acquired data through a UART/IIC/USB data bus;
the collected data are packaged and uploaded to an operation end through a Bluetooth or 2.4G wireless network;
sending the obtained monitoring video and the obtained on-site picture to an operation end;
the computer automatically acquires and refreshes data;
and sending the acquired data to a database defined according to an industry standard for storage.
2. The case site wireless information acquisition method and system according to claim 1, characterized in that the data packing step is as follows:
sequencing each signal according to the size of the cut-off time, and carrying out data packing on the signals with small cut-off time first;
selecting the first information, and calculating the minimum byte required for packaging the signal message frame and the maximum byte required for packaging all the signal message frames;
selecting the next signal, and calculating whether the signal can be packed into the message frame which is the same as the previous information;
repeating the previous step until all the signals are packed to form a search tree;
and after the search tree is formed, reversing the search tree, and respectively adding the bandwidth utilization rate obtained by each search tree branch to find out a minimum search tree branch.
3. The case site wireless information acquisition method and system according to claim 1, characterized in that the acquired monitoring video, the acquired scene photos taken by the unmanned aerial vehicle and the camera through the wireless network card are sent to the operation end through a Tpye-C interface.
4. The case site wireless information acquisition method and system according to claim 1, wherein the photo processing steps are as follows:
normalizing the input image;
performing a series of convolution and pooling operations on the image to extract image characteristic information;
predicting the label distribution of the feature vectors in the feature sequence by adopting a bidirectional recurrent neural network;
and the problem of alignment of input data and a given label is solved by adopting connection time sequence classification, and interval characters and repeated characters are removed from an output result.
5. The wireless case site information acquisition method and system according to claim 1, characterized in that the image enhancement steps of the photo shot by the unmanned aerial vehicle are as follows:
carrying out gray level transformation on the image;
carrying out histogram equalization on the image after gray level transformation;
carrying out spatial domain sharpening enhancement on the equalized image;
and finally, homomorphic filtering is carried out.
6. The case site wireless information acquisition method and system according to claim 5, wherein the specific processing steps of histogram equalization are as follows:
counting each gray level r of the original histogramkN is the number of pixelsk(k＝0,1,2,....,255)；
Calculating the probability P of the occurrence of a pixelr(rk)＝nkN, wherein n is the total number of pixels;
calculating cumulative histograms
And find Tk＝int[tk*(N-1)+0.5]Wherein N is 256;
according to the obtained TkWill be the original gray value rkMapping to new grey values
Resulting in a processed image.
7. A wireless information acquisition system for case sites is characterized by comprising:
the modularized data acquisition module: the system is used for acquiring data in a modular mode;
video recording and shooting module: the system is used for shooting monitoring videos through monitoring equipment, and shooting field pictures through the unmanned aerial vehicle and the camera;
the data summarization module: the UART/IIC/USB data bus is used for collecting the acquired data;
a summary data upload module: the data processing device is used for packaging the summarized data and uploading the data to an operation end through a Bluetooth or 2.4G wireless network;
the video recording and shooting processing uploading module: the monitoring system is used for sending the acquired monitoring video and the acquired on-site photos to the operation end;
a data acquisition refresh module: the system is used for automatically acquiring and refreshing data by a computer;
a data saving module: and the data acquisition module is used for sending the acquired data to a database defined according to the industry standard for storage.
8. The wireless case site information acquisition system according to claim 7, wherein the video recording and shooting processing uploading module comprises a photo processing module and an image enhancement module; the photo processing module includes:
a normalization processing unit: the image normalization processing module is used for normalizing the input image;
an image feature extraction unit: the image processing device is used for performing a series of convolution and pooling operations on the image and extracting image characteristic information;
a label distribution prediction unit: the label distribution of the feature vectors in the feature sequence is predicted by adopting a bidirectional recurrent neural network;
an alignment solving unit: the method is used for solving the alignment problem of input data and a given label by adopting connection time sequence classification, and interval characters and repeated characters are removed from an output result.
9. The wireless information acquisition system for case sites according to claim 8, wherein the image enhancement module comprises:
a gradation conversion unit: the system is used for carrying out gray level transformation processing on an image;
an equalization processing unit: the histogram equalization processing device is used for carrying out histogram equalization processing on the image after gray level transformation;
a sharpening enhancement unit: the image processing device is used for carrying out spatial domain sharpening enhancement processing on the equalized image;
homomorphic filtering unit: the homomorphic filtering processing is used for carrying out homomorphic filtering processing on the image.
10. An intelligent computer device, comprising a memory and a processor, wherein the memory stores computer readable instructions, and the processor executes the computer readable instructions to implement the steps of the case field wireless information collection method according to any one of claims 1 to 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115457451A (en) * | 2022-06-13 | 2022-12-09 | 广东众志检测仪器有限公司 | Monitoring method and device of constant temperature and humidity test box based on Internet of things |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101726493A (en) * | 2009-12-02 | 2010-06-09 | 中国建筑材料科学研究总院 | Method and device for detecting shrinkage and cracking performance of cement-based material |
| CN108446691A (en) * | 2018-06-09 | 2018-08-24 | 西北农林科技大学 | A kind of face identification method based on SVM linear discriminants |
| CN112215221A (en) * | 2020-09-22 | 2021-01-12 | 国交空间信息技术（北京）有限公司 | Automatic vehicle frame number identification method |
| CN112330132A (en) * | 2020-10-30 | 2021-02-05 | 梁山县公安局 | Public security law enforcement supervision and management system |
-
2021
- 2021-12-23 CN CN202111592929.4A patent/CN114125401A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101726493A (en) * | 2009-12-02 | 2010-06-09 | 中国建筑材料科学研究总院 | Method and device for detecting shrinkage and cracking performance of cement-based material |
| CN108446691A (en) * | 2018-06-09 | 2018-08-24 | 西北农林科技大学 | A kind of face identification method based on SVM linear discriminants |
| CN112215221A (en) * | 2020-09-22 | 2021-01-12 | 国交空间信息技术（北京）有限公司 | Automatic vehicle frame number identification method |
| CN112330132A (en) * | 2020-10-30 | 2021-02-05 | 梁山县公安局 | Public security law enforcement supervision and management system |
Non-Patent Citations (2)
| Title |
|---|
| 刘计平: "基于图像增强直方图", 《机械管理开发》 * |
| 肖木等: "LIN总线数据打包算法", 《低压电器》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115457451A (en) * | 2022-06-13 | 2022-12-09 | 广东众志检测仪器有限公司 | Monitoring method and device of constant temperature and humidity test box based on Internet of things |
| CN115457451B (en) * | 2022-06-13 | 2023-06-16 | 广东众志检测仪器有限公司 | Constant temperature and humidity test box monitoring method and device based on Internet of things |
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