CN116781870A - Remote microwave monitoring method and system - Google Patents
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Abstract
The invention discloses a remote microwave monitoring method and a system, which belong to the technical field of data transmission, wherein the method comprises the following steps: acquiring forest image data by an acquisition end; compressing forest image data; encoding the compressed forest image data; modulating the code to obtain a microwave signal; encrypting the microwave signal; the acquisition end transmits the encrypted microwave signals to the monitoring center through the relay equipment; the monitoring center decrypts the received microwave signals; demodulating the decrypted microwave signal; decoding the demodulated code; performing image recognition on the decoded forest image data, and monitoring whether fire risks exist; and when the fire risk is monitored, an alarm signal is sent out. Through long-range microwave technology, the collection end can pass through a plurality of relay devices and transmit forest images to monitoring center in real time, and monitoring center monitors whether there is the conflagration risk, and the coverage area of fire condition control is wider, can not receive the influence of weather factor, and the cost is lower.
Description
Technical Field
The invention belongs to the technical field of data transmission, and particularly relates to a remote microwave monitoring method and system.
Background
Forest is one of important ecosystems on earth, has rich biodiversity, can absorb carbon dioxide, generate oxygen, maintain important functions such as hydrologic cycle. Forest fires can destroy vegetation and soil, and can also release a large amount of carbon dioxide and other harmful gases, which negatively affects climate change and environmental health. Meanwhile, forest fires often pose a threat to human society and residential areas, leading to casualties and property loss. The forest fire prevention measures are adopted, so that the occurrence and spread of fire disasters can be reduced, and the life and property safety of people is ensured. Therefore, forest fire prevention has important significance.
In the prior art, forest fire prevention is often carried out by adopting a mode of collecting remote sensing images through an unmanned aerial vehicle, the unmanned aerial vehicle can disregard topographic barriers, and the unmanned aerial vehicle reaches an area which is difficult to reach by the traditional monitoring means, so that the unmanned aerial vehicle has high flexibility. However, unmanned aerial vehicle monitoring is limited by time of flight and range, coverage is relatively small, unmanned aerial vehicle monitoring is susceptible to weather factors, and unmanned aerial vehicle purchase and maintenance costs are high, which is not conducive to popularization.
Disclosure of Invention
The invention provides a remote microwave monitoring method and a remote microwave monitoring system, which aim to solve the technical problems that in the prior art, a remote sensing image acquisition mode of an unmanned aerial vehicle is adopted to perform forest fire prevention, the unmanned aerial vehicle is limited by flight time and range, the coverage area is relatively small, the unmanned aerial vehicle is easily influenced by weather factors, and moreover, the unmanned aerial vehicle has high purchase and maintenance cost and is not beneficial to popularization.
First aspect
The invention provides a remote microwave monitoring method, which comprises the following steps:
s101: acquiring forest image data by an acquisition end;
s102: the acquisition end compresses forest image data;
s103: the acquisition end encodes the compressed forest image data;
s104: the acquisition end modulates the code to obtain a microwave signal;
s105: the acquisition end encrypts the microwave signal;
s106: the acquisition end transmits the encrypted microwave signals to the monitoring center through the relay equipment;
s107: the monitoring center decrypts the received microwave signals;
s108: the monitoring center demodulates the decrypted microwave signal;
s109: the monitoring center decodes the demodulated code;
s110: the monitoring center performs image recognition on the decoded forest image data and monitors whether fire risks exist or not;
s111: and when the monitoring center monitors the fire risk, an alarm signal is sent out.
Second aspect
The present invention provides a remote microwave monitoring system for performing the remote microwave monitoring method of the first aspect.
Compared with the prior art, the invention has at least the following beneficial technical effects:
according to the invention, the acquisition end can transmit the forest image to the monitoring center in real time through the plurality of relay devices by the remote microwave technology, the monitoring center performs image recognition on the forest image, whether fire risks exist or not is monitored, the coverage area of fire monitoring is wider, the influence of weather factors can be avoided, the cost of a camera of the acquisition end is lower compared with that of an unmanned aerial vehicle, and the popularization is facilitated.
Drawings
The above features, technical features, advantages and implementation of the present invention will be further described in the following description of preferred embodiments with reference to the accompanying drawings in a clear and easily understood manner.
FIG. 1 is a schematic flow chart of a remote microwave monitoring method provided by the invention;
FIG. 2 is a schematic diagram of a remote microwave transmission structure provided by the present invention;
fig. 3 is a schematic structural diagram of microwave data transmission provided by the invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
For simplicity of the drawing, only the parts relevant to the invention are schematically shown in each drawing, and they do not represent the actual structure thereof as a product. Additionally, in order to simplify the drawing for ease of understanding, components having the same structure or function in some of the drawings are shown schematically with only one of them, or only one of them is labeled. Herein, "a" means not only "only this one" but also "more than one" case.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
In this context, it should be noted that the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, unless otherwise explicitly stated and defined. Either mechanically or electrically. Can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, in the description of the present invention, the terms "first," "second," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Example 1
In one embodiment, referring to fig. 1 of the specification, a schematic flow chart of a remote microwave monitoring method provided by the invention is shown. Referring to fig. 2 of the drawings, there is shown a schematic structural diagram of a remote microwave transmission provided by the present invention; referring to fig. 3 of the specification, a schematic structural diagram of microwave data transmission provided by the invention is shown.
The invention provides a remote microwave monitoring method, which comprises the following steps:
s101: the acquisition end acquires forest image data.
The acquisition end can be a camera or a video camera.
The plurality of acquisition ends are connected with the monitoring center through the relay equipment in a remote microwave communication mode.
S102: the acquisition end compresses forest image data.
Specifically, the forest image data may be compressed by discrete cosine transform, wavelet transform, predictive coding, or the like.
In one possible implementation manner, in order to improve the compression effect of the forest image data, S102 specifically includes substeps S1021 to S1027:
s1021: image frames of forest image data are extracted.
S1022: each image frame is decomposed into an R-channel image, a G-channel image, and a B-channel image.
Among them, a color image is generally composed of three basic color channels of red (R), green (G), and blue (B).
S1023: singular value decomposition is performed on the R channel image:
wherein R represents an R channel image, U is an orthogonal matrix,for a diagonal matrix, diag is the diagonal matrix symbol,i number values on the diagonal of the diagonal matrix, which may be referred to as singular values, V is an orthogonal matrix, V T Represents the transpose of V.
The singular value decomposition (Singular Value Decomposition, SVD) is an important matrix decomposition method, which can decompose a matrix into products of three matrices, and represents the spatial transformation of the original matrix, the importance degree of the singular value and the representation of the data under a new coordinate system.
S1024: extracting the first k columns of the orthogonal matrix U to perform dimension reduction processing on the orthogonal matrix U, extracting the first k rows of the orthogonal matrix V to perform dimension reduction processing on the orthogonal matrix V, and reserving the first k singular valuesAnd (3) zeroing the rest singular values to obtain a compressed R channel image.
Wherein the ranking of singular values represents the importance of a feature, the top ranked singular values represent the primary feature in the data, and the bottom ranked singular values represent the secondary feature in the data. The first k largest singular values are selected and the remaining singular values are set to zero. This corresponds to preserving the most important features in the data, while ignoring the smaller features.
Further, the first k columns of the orthogonal matrix U and the first k rows of the orthogonal matrix V are extracted, so that only the k features are reserved, which is equivalent to projecting data into a k-dimensional subspace, and dimension reduction processing of the data is realized.
S1025: and obtaining compressed G channel images and B channel images according to the same processing mode as the R channel images.
S1026: and combining the compressed R channel image, the compressed G channel image and the compressed B channel image to obtain a compressed image frame.
S1027: and combining the compressed image frames into compressed forest image data.
According to the invention, the forest image data is compressed through singular value decomposition, so that the main characteristics of the image can be extracted, the dimension of the data is reduced, the size of the data is further reduced, the microwave transmission efficiency is improved, the key information of the image is reserved, and the image is ensured not to be distorted.
S103: the acquisition end encodes the compressed forest image data.
Specifically, the acquisition end can encode the compressed forest image data in a mode of Huffman coding, equal-length coding, arithmetic coding, differential coding and the like.
In one possible implementation, in order to improve the coding efficiency and the transmission efficiency, S103 specifically includes sub-steps S1031 to S1033:
s1031: performing wavelet transformation on the compressed forest image data to obtain wavelet coefficients y:
where x represents the compressed forest image data,representing a wavelet transform function.
Among them, wavelet transformation is a mathematical tool for signal and image analysis. It provides the ability to describe and analyze local features of a signal or image by decomposing the signal or image into wavelet coefficients of different scales and frequencies.
Further, the wavelet transform may convert the image data from the time domain to the frequency domain, such that the image data can be represented with fewer coefficients. By wavelet transformation, redundant information of image data can be decomposed and suppressed, thereby realizing a higher compression rate.
S1032: scalar quantization is carried out on the wavelet coefficient to obtain discrete index coefficient q:
wherein ,representing scalar functions>Representing the step size.
Wherein these wavelet coefficients may be scalar quantized to a limited set of discrete levels for further compression and encoding. By quantization, the range of wavelet coefficients will be divided into a discrete set of levels, each level represented by a respective index. These discrete index coefficients q may be more efficiently encoded and stored to enable compression and representation of the wavelet coefficients. The quantized wavelet coefficients q will be used in subsequent encoding and decoding processes for data transmission, storage or reconstruction.
In the scalar quantization process, the accuracy and level of quantization may be controlled according to a specific quantization table or quantization parameter. The important wavelet coefficients will be kept at a higher level of precision while the less important coefficients will be quantized to a lower level of precision, thus enabling compression of the data while preserving as much important information of the image as possible.
S1033: the index coefficients are compressed into a bitstream.
Specifically, the number of bits required for each index coefficient is determined, depending on the number of quantization levels. Each index coefficient is converted to its binary representation and encoded into a bit stream in terms of the number of bits. This may be done using bit operations or binary conversion algorithms. The bit streams of each index coefficient are sequentially combined to form an overall bit stream.
After the index coefficient is compressed into the bit stream, the volume of the data can be further reduced, and the transmission efficiency of the data can be improved. The bit stream can more efficiently store and transmit data, thereby reducing transmission delay and resource occupation.
In the invention, the compressed forest image data is subjected to wavelet transformation, scalar quantization and bit stream compression, so that higher compression rate can be realized, the data volume can be reduced, important information can be reserved, and the data transmission efficiency can be improved. These benefits make the storage, transmission and processing of image data more efficient and convenient.
S104: the acquisition end modulates the codes to obtain microwave signals.
Specifically, the acquisition end may modulate the code by adopting modulation modes such as Amplitude Shift Keying (ASK) modulation, frequency Shift Keying (FSK) modulation, phase Shift Keying (PSK) modulation, quadrature Amplitude Modulation (QAM) modulation, and the like, so as to obtain the microwave signal.
In one possible implementation, S104 is specifically: the code is modulated by quadrature amplitude modulation to obtain a microwave signal.
Among them, quadrature amplitude modulation (Quadrature Amplitude Modulation, QAM) is a complex modulation technique that modulates digital data simultaneously onto the amplitude and phase of a signal. It combines the characteristics of amplitude modulation and phase modulation to allow more information to be transmitted within a limited signal bandwidth.
S105: the acquisition end encrypts the microwave signal.
It should be noted that, the security and confidentiality of data transmission can be increased by encrypting the microwave signal by the acquisition end.
In one possible embodiment, S105 specifically includes substeps S1051 to S1057:
s1051: selecting two large prime numbers a and b, and calculating and />：
S1052: an integer e is randomly selected such that the random number e satisfies:
wherein ,representing the random numbers e and->Mutually good quality.
S1053: calculating the inverse of the random number e:
wherein mod represents the modulo operator;
s1054: taking (G, e) as a private key and (G, d) as a public key EK.
It should be noted that the generation of the private and public keys using the above-described method may provide a secure encryption and authentication mechanism that protects the confidentiality and integrity of the forest image data and ensures that only authorized persons may access and manipulate the forest image data.
S1055: constructing a chaotic mapping relation formula of the random number e and the inverse element d:
wherein n represents the number of verification times, lambda 1 、λ 2 、λ 3 、λ 4 and λ5 The chaotic parameters are represented and are all constants.
It should be noted that, the function of the chaotic mapping formula is to protect the random number e and the inverse d, and in each subsequent verification, the random number e and the inverse d will change. In the prior art, the chaotic mapping relation is that the range of chaotic parameters is discontinuous, a plurality of periodic windows exist in a parameter space, the chaotic behavior is fragile, and when the parameters are interfered, the chaotic behavior is easy to disappear, so that the problem of chaotic degradation occurs. The method comprises the steps of initializing two parameter polynomials, folding any value into a fixed range through modular operation, generating chaotic mapping from a nonlinear polynomial, generating two-dimensional chaotic mapping with robust chaos, and overcoming the defects in the conventional chaotic mapping relation.
S1056: performing exclusive or operation on the HASH value HASH of the microwave signal and the public key EK through the following formula to obtain a key K:
wherein ,ki The value representing the ith bit of the key K, hash i The value of the ith bit, ek, representing the HASH value HASH i The value representing the ith bit of the public key EK.
It should be noted that, by performing exclusive-or operation on the hash value of the microwave signal and the public key, randomness of the key can be increased, and security of the key can be improved, so as to increase resistance to attack.
S1057: the microwave signal is encrypted by a key K.
According to the invention, the acquisition end encrypts the microwave signal, so that the safety and confidentiality of data transmission can be improved, unauthorized access and interception are prevented, sensitive information is protected from being revealed, meanwhile, the attack resistance of the system and the integrity of data are increased, and the safety and reliability of forest image data in the transmission process are ensured.
S106: the acquisition end transmits the encrypted microwave signals to the monitoring center through the relay equipment.
The relay device is a device for enhancing a signal transmission range, expanding a coverage area, and providing a relay function of signal transmission in a data transmission process. The relay device can amplify and enhance the strength of the microwave signal to ensure that the signal is not distorted or weakened by signal attenuation during transmission. By amplifying the signal, the relay device can extend the transmission distance, ensuring that the signal can reach the monitoring center.
S107: the monitoring center decrypts the received microwave signal.
It should be noted that, the decryption process is the inverse of the encryption process, and in order to avoid repetition, the present invention is not repeated.
S108: the monitoring center demodulates the decrypted microwave signal.
In one possible implementation, S108 is specifically: the decrypted microwave signal is demodulated by quadrature amplitude inverse modulation.
It should be noted that, the demodulation process is the inverse of the modulation process, and the present invention is not repeated.
S109: the monitoring center decodes the demodulated code.
In one possible embodiment, S109 specifically includes substeps S1091 to S1094:
s1091: the index coefficient q is obtained from the bit stream.
S1092: inverse quantization is performed according to the index coefficient to obtain a reconstructed wavelet coefficient。
。
S1093: performing wavelet inverse transformation according to the reconstructed wavelet coefficient to obtain reconstructed forest image data：
wherein ,representing the wavelet inverse transform function.
S1094: and performing dequantization processing on the reconstructed forest image data.
In image coding, quantization is a process of converting continuous wavelet coefficients into discrete index coefficients. Dequantization is the process of converting discrete index coefficients back to continuous wavelet coefficients. The purpose of dequantization is to recover the wavelet coefficients prior to encoding for subsequent inverse transformation and reconstruction of the image data.
It should be noted that, the decoding process is the inverse of the encoding process, and the present invention is not repeated.
In one possible embodiment, the remote microwave monitoring method further comprises:
a sample dataset X is acquired, the sample dataset comprising a plurality of training images X.
Constructing codec loss functions：
wherein ,representing wavelet transform variable parameters, +.>Representing dequantized variable parameters, ">Representing the bit rate.
And training the coding and decoding modes through a sample data set with the aim of minimizing the loss function to obtain the optimal variable parameters of wavelet transformation and dequantization variable parameters.
In the invention, the variable parameters of wavelet transformation and the variable parameters of dequantization are adjusted through training so that the coding and decoding process is more suitable for the characteristics of the processed forest image data. By optimizing the encoding and decoding modes, the efficiency and quality of image compression can be improved, and the information loss can be reduced. Further, by constructing the codec loss function, which includes the bit rate as an important consideration, the bit rate required for transmission and storage of data can be reduced as much as possible while maintaining the image quality. The efficiency, the accuracy and the reliability of the remote microwave monitoring system are improved, and the requirements of fire risk monitoring are better met.
S110: and the monitoring center performs image recognition on the decoded forest image data and monitors whether fire risks exist.
Specifically, the monitoring center can perform image recognition on the decoded forest image data through a yolo algorithm, a support vector machine, a deep learning algorithm and the like, and monitor whether fire risks exist.
In one possible implementation, S110 specifically includes substeps S1101 to S110B:
s1101: a sample data set Y is acquired, the sample data set Y comprising a plurality of fire images.
S1102: data enhancement is performed on the sample data set:
wherein m represents a fused image, y 1 Representing an existing first image, y, in a sample dataset 2 Representing the second image already in the sample dataset and μ representing the fusion coefficient.
Specifically, the first image may be a flame image, and the second image may be a smoke, fog, cloud, or the like image.
It should be noted that the initial fire of the forest fire is often relatively small and is easily shielded. The diversity of tree species in forests and other obscurants such as smoke, fog, clouds, etc. create diversity of obscurants that all affect the effect of the model in identifying fires. The invention adopts the mutual fusion among pictures to simulate the case of the fire being blocked, generates more blocking samples to be brought into the model for learning, and leads the final network model to have good recognition effect on other blocking conditions.
S1103: and extracting a gray level co-occurrence matrix of the fire image.
The gray level co-occurrence matrix is a statistical method for describing the texture characteristics of the image. It captures texture information in an image by analyzing gray value relationships between pixels. The gray level co-occurrence matrix is a two-dimensional matrix in which each element represents a co-occurrence frequency of two pixels having a pair of specific gray level values in a specific direction and distance. Specifically, the gray level co-occurrence matrix counts the frequency of gray values of each pair of pixels in the image, and can capture the spatial relationship and the relative distribution between gray levels.
S1104: extracting image features of fire images through gray level co-occurrence matrix, wherein the image features comprise contrast e 1 Correlation e 2 Energy e 3 And homogeneity e 4 。
In one possible implementation, S1104 is specifically:
the contrast e of the fire image is extracted by the following formula 1 Correlation e 2 Energy e 3 And homogeneity e 4 ：
Wherein PG represents the gray level co-occurrence matrix, P (i, j) represents the pixel value of the ith row and the jth column in the gray level co-occurrence matrix, u i Representing the mean value of the pixel values of the ith row, u j Represents the mean value of the pixel values of the j-th column, S i Represents standard deviation of pixel values of the ith row, S j Representing the standard deviation of the pixel values of the j-th column.
Wherein the features of contrast, correlation, energy and homogeneity may provide a description of the texture of the fire image. Fire images typically have unique textural features, such as flames, smoke, fire lights, etc., which can be captured by image features extracted by a gray scale co-occurrence matrix. These features help to distinguish the fire image from the normal image and provide texture information about the fire image. By extracting the image features of the gray level co-occurrence matrix, the texture features and the statistical information of the fire image can be effectively described, and comprehensive feature description and quantitative analysis are provided, so that the fire image is identified and analyzed.
S1105: feature fusion is carried out on each image feature to obtain a fusion feature value E:
wherein θ represents a fusion parameter, α 1 Weights representing contrast, alpha 2 Weights representing correlations, alpha 3 Representing the weight of the energy, alpha 4 Weights indicating homogeneity.
Wherein fusing different image features may provide a more comprehensive and accurate image description. Each image feature can capture the information of the image from different angles, and by fusing the image features, the advantages of each feature can be comprehensively utilized, so that more comprehensive and more accurate feature expression can be obtained.
In one possible embodiment, the weight α of the contrast ratio 1 Weight of correlation alpha 2 Weight of energy alpha 3 Weight of homogeneity alpha 4 The determination mode of (a) is as follows:
by comparing contrast, correlation, energy and homogeneity in pairs, a discrimination matrix A is established by combining a nine-level scale method:
wherein ,aij Represents the importance level of the ith image feature relative to the jth image feature, a ij Can be determined by nine-pole scaling, n=4.
Among them, nine-level Scale (Nine-Point Scale) is a Scale method for comparing and evaluating the relative importance, quality or degree of objects or concepts. Typically consists of a scale containing nine levels, each level being used to represent a different degree or degree. The evaluators need to select one of the nine classes to be most suitable for evaluating the importance degree of the ith image feature relative to the jth image feature according to own feeling or cognition.
Calculating eigenvectors and eigenvalues of the discrimination matrix A:
wherein lambda represents the eigenvalue of the discrimination matrix A, p represents the eigenvector of the discrimination matrix A, and the largest eigenvalue is marked as lambda max The eigenvector corresponding to the largest eigenvalue is denoted as p max ，。
For the feature vector p corresponding to the largest feature value max Normalization processing:
wherein the normalized vectorAre>Weights respectively representing the characteristics of the respective images, which can be denoted as +.>。
In the invention, a systematic method is provided for determining the weight, based on the pairwise comparison between criteria and expert judgment, a plurality of factors such as contrast, correlation, energy and homogeneity can be considered, and the importance of the factors is comprehensively considered, so that the contributions of different image features are more comprehensively evaluated and compared, and the decision process is more objective and scientific. Subjective bias and randomness can be reduced through weight determination, and a quantifiable basis is provided for decision making.
S1106: the fused eigenvalue E is mapped into the interval range of [0,1] using the Sigmod function:
wherein e represents a natural logarithm.
It should be noted that, through the operation of the Sigmoid function, the fusion eigenvalue may be converted into a probability value between 0 and 1. The purpose of this is to normalize the fusion characteristic value, so that the fusion characteristic value has probability interpretation, and subsequent judgment and processing are convenient.
S1107: the result output of converting the probability value into {0,1} using step function g (E):
wherein 1 indicates the presence of a fire and 0 indicates the absence of a fire.
S1108: constructing a loss function J (theta):
wherein n represents the number of fire images, E i Fusion characteristic value, g (E i ) Representing the output result of the step function.
It should be noted that, by constructing the loss function J (θ), the performance of the fire image recognition model can be measured. The loss function is an index for measuring the difference between the model prediction result and the real label, and the model can obtain better effect in the fire image recognition task by minimizing the loss function.
S1109: and solving the loss function J (theta) by using a gradient descent method, and calculating a fusion parameter theta.
In the invention, the gradient descent method is used for solving the loss function, so that the fusion parameter theta of the model can be optimized. The gradient descent method is a commonly used optimization algorithm, and the values of parameters are continuously updated by calculating the gradient direction of the loss function to the parameters, so that the values gradually approach to an optimal solution. Therefore, the fusion parameter theta of the model can be optimized, and the accuracy and stability of fire image identification are improved.
S110A: and acquiring real-time forest image data.
S110B: and carrying out image recognition on the real-time forest image data through a trained algorithm, and monitoring whether fire risks exist.
S111: and when the monitoring center monitors the fire risk, an alarm signal is sent out.
Specifically, once the monitoring center monitors the fire risk, the monitoring center can send out alarm signals in the modes of sound alarm, flashing alarm, short message telephone notification and the like. And automatically triggers the emergency system.
Compared with the prior art, the invention has at least the following beneficial technical effects:
according to the invention, the acquisition end can transmit the forest image to the monitoring center in real time through the plurality of relay devices by the remote microwave technology, the monitoring center performs image recognition on the forest image, whether fire risks exist or not is monitored, the coverage area of fire monitoring is wider, the influence of weather factors can be avoided, the cost of a camera of the acquisition end is lower compared with that of an unmanned aerial vehicle, and the popularization is facilitated.
Example 2
In one embodiment, the present invention provides a remote microwave monitoring system for performing the remote microwave monitoring method of embodiment 1.
The remote microwave monitoring system provided by the invention can realize the steps and effects of the remote microwave monitoring method in the embodiment 1, and in order to avoid repetition, the invention is not repeated.
Compared with the prior art, the invention has at least the following beneficial technical effects:
according to the invention, the acquisition end can transmit the forest image to the monitoring center in real time through the plurality of relay devices by the remote microwave technology, the monitoring center performs image recognition on the forest image, whether fire risks exist or not is monitored, the coverage area of fire monitoring is wider, the influence of weather factors can be avoided, the cost of a camera of the acquisition end is lower compared with that of an unmanned aerial vehicle, and the popularization is facilitated.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. A remote microwave monitoring method, comprising:
s101: acquiring forest image data by an acquisition end;
s102: the acquisition end compresses the forest image data;
s103: the acquisition end encodes the compressed forest image data;
s104: the acquisition end modulates the codes to obtain microwave signals;
s105: the acquisition end encrypts the microwave signal;
s106: the acquisition end transmits the encrypted microwave signals to the monitoring center through the relay equipment;
s107: the monitoring center decrypts the received microwave signals;
s108: the monitoring center demodulates the decrypted microwave signal;
s109: the monitoring center decodes the demodulated code;
s110: the monitoring center performs image recognition on the decoded forest image data and monitors whether fire risks exist or not;
s111: and when the monitoring center monitors the fire risk, an alarm signal is sent out.
2. The remote microwave monitoring method according to claim 1, wherein S102 specifically comprises:
s1021: extracting an image frame of the forest image data;
s1022: decomposing each image frame into an R channel image, a G channel image and a B channel image;
s1023: singular value decomposition is performed on the R channel image:
；
wherein R represents an R channel image, U is an orthogonal matrix,for a diagonal matrix, diag is the diagonal matrix symbol,i number values on the diagonal of the diagonal matrix, which may be referred to as singular values, V is an orthogonal matrix, V T Represents the transpose of V;
s1024: extracting the first k columns of the orthogonal matrix U to perform dimension reduction processing on the orthogonal matrix U, extracting the first k rows of the orthogonal matrix V to perform dimension reduction processing on the orthogonal matrix V, and reserving the first k singular valuesZero is given to the rest singular values, and a compressed R channel image is obtained;
s1025: obtaining a compressed G channel image and a compressed B channel image according to the same processing mode as the R channel image;
s1026: combining the compressed R channel image, G channel image and B channel image to obtain a compressed image frame;
s1027: and combining the compressed image frames into compressed forest image data.
3. The remote microwave monitoring method according to claim 1, wherein S103 specifically comprises:
s1031: performing wavelet transformation on the compressed forest image data to obtain wavelet coefficients y:
；
where x represents the compressed forest image data,representing a wavelet transform function;
s1032: scalar quantization is carried out on the wavelet coefficient to obtain discrete index coefficient q:
；
wherein ,representing scalar functions>Representing a step size;
s1033: compressing the index coefficient into a bit stream;
the step S109 specifically includes:
s1091: obtaining an index coefficient q according to the bit stream;
s1092: performing inverse quantization according to the index coefficient to obtain a reconstructed wavelet coefficient；
；
S1093: performing wavelet inverse transformation according to the reconstructed wavelet coefficient to obtain reconstructed forest image data：
；
wherein ,representing a wavelet inverse transformation function;
s1094: and performing dequantization processing on the reconstructed forest image data.
4. A remote microwave monitoring method according to claim 3, further comprising:
acquiring a sample data set X, wherein the sample data set comprises a plurality of training images X;
constructing codec loss functions：
；
wherein ,representing wavelet transform variable parameters, +.>Representing dequantized variable parameters, ">Representing the bit rate;
and training the coding and decoding mode through the sample data set with the aim of minimizing the loss function to obtain the optimal variable parameters of wavelet transformation and dequantization variable parameters.
5. The remote microwave monitoring method according to claim 1, wherein S104 is specifically: modulating the code by quadrature amplitude modulation to obtain a microwave signal;
the step S108 specifically includes: the decrypted microwave signal is demodulated by quadrature amplitude inverse modulation.
6. The remote microwave monitoring method according to claim 1, wherein S105 specifically comprises:
s1051: selecting two large prime numbers a and b, and calculating and />：
；
S1052: an integer e is randomly selected such that the random number e satisfies:
；
wherein ,representing random number +.>And->Mutual quality;
s1053: calculating random numbersIs the inverse of:
；
wherein mod represents the modulo operator;
s1054: taking (G, e) as a private key and (G, d) as a public key EK;
s1055: constructing a chaotic mapping relation formula of the random number e and the inverse element d:
；
wherein n represents the number of verification times, lambda 1 、λ 2 、λ 3 、λ 4 and λ5 The chaotic parameters are represented and are constants;
s1056: performing exclusive or operation on the HASH value HASH of the microwave signal and the public key EK through the following formula to obtain a key K:
；
wherein ,ki The value representing the ith bit of the key K, hash i The value of the ith bit, ek, representing the HASH value HASH i A value representing the ith bit of the public key EK;
s1057: the microwave signal is encrypted by a key K.
7. The remote microwave monitoring method according to claim 1, wherein S110 specifically comprises:
s1101: acquiring a sample data set Y, wherein the sample data set Y comprises a plurality of fire images;
s1102: data enhancement is performed on the sample data set:
；
wherein m represents a fused image, y 1 Representing an existing first image, y, in said sample dataset 2 Representing an existing second image in the sample dataset, μ representing a fusion coefficient;
s1103: extracting a gray level co-occurrence matrix of the fire image;
s1104: extracting image features of the fire image through the gray level co-occurrence matrix, wherein the image features comprise contrast e 1 Correlation e 2 Energy e 3 And homogeneity e 4 ；
S1105: feature fusion is carried out on the image features to obtain a fusion feature value E:
；
wherein θ represents a fusion parameter, α 1 Weights representing contrast, alpha 2 Weights representing correlations, alpha 3 Representing the weight of the energy, alpha 4 Weights representing homogeneity;
s1106: the fused eigenvalue E is mapped into the interval range of [0,1] using the Sigmod function:
；
wherein e represents a natural logarithm;
s1107: the result output of converting the probability value into {0,1} using step function g (E):
；
wherein 1 indicates the presence of a fire, and 0 indicates the absence of a fire;
s1108: constructing a loss function J (theta):
；
wherein n represents the number of fire images, E i Fusion characteristic value, g (E i ) Representing the output result of the step function;
s1109: solving the loss function J (theta) by using a gradient descent method, and calculating a fusion parameter theta;
S110A: acquiring real-time forest image data;
S110B: and carrying out image recognition on the real-time forest image data through a trained algorithm, and monitoring whether fire risks exist.
8. The remote microwave monitoring method according to claim 7, wherein S1104 is specifically:
extracting the contrast e of the fire image by the following formula 1 Correlation e 2 Energy e 3 And homogeneity e 4 ：
；
Wherein P represents the gray level co-occurrence matrix, P (i, j) represents the pixel value of the ith row and the jth column in the gray level co-occurrence matrix, u i Representing the mean value of the pixel values of the ith row, u j Represents the mean value of the pixel values of the j-th column, S i Represents standard deviation of pixel values of the ith row, S j Representing the standard deviation of the pixel values of the j-th column.
9. The method of claim 7, wherein the contrast is weighted α 1 The weight of the correlation alpha 2 Weight alpha of the energy 3 Weight of the homogeneity alpha 4 The determination mode of (a) is as follows:
by comparing contrast, correlation, energy and homogeneity in pairs, a discrimination matrix A is established by combining a nine-level scale method:
；
wherein ,aij Represents the importance level of the ith image feature relative to the jth image feature, a ij The value of (2) can be determined by a nine-pole scale method, and n=4;
calculating the eigenvector and eigenvalue of the discrimination matrix A:
；
wherein lambda represents the eigenvalue of the discrimination matrix A, p represents the eigenvector of the discrimination matrix A, and the largest eigenvalue is marked as lambda max The eigenvector corresponding to the largest eigenvalue is denoted as p max ，；
For the feature vector p corresponding to the maximum feature value max Normalization processing:
；
wherein the normalized vectorAre>Weights respectively representing the characteristics of the respective images, which can be denoted as +.>。
10. A remote microwave monitoring system for performing the remote microwave monitoring method of any one of claims 1 to 9.
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