# Research on the Construction Path of Characteristic Tourism Resource Development System and Platform under the Background of Rural Revitalization

Authors: Xiaomei Kang
Journal: Mobile Information Systems
 (2022)

---

## Abstract

In order to improve the development effect of tourism resources with rural characteristics, this paper combines the digital communication signal modulation technology to process the related data of tourism resources. Moreover, before the quadrature demodulation of the tourism information signal is carried out in this paper, the symbol rate is estimated, and the signal-to-noise ratio and the symbol rate of the digital communication signal are estimated to improve the development effect of tourism resources. The estimation of the signal symbol rate is obtained by statistical analysis of the instantaneous characteristics of the signal in the time domain, and it can also be obtained by measuring the length of the adjacent transition points of the instantaneous amplitude. In addition, this paper constructs a rural characteristic tourism resource development and management system based on communication signal modulation technology. Finally, through test research, this paper verifies that the rural characteristic tourism resource development platform proposed in this paper has a good effect on the development and management of rural characteristic tourism resources.


---

## Body

## 1. Introduction

Tourism resources and tourist attractions are synonymous expressions of the same thing (tourist objects), and it is generally believed that tourism resources are Western tourist attractions [1]. From the perspective of linguistics and etymology, the complex relationship between tourism resources and tourist attractions is a divergence arising from the mixing of two sets of discourse systems at home and abroad at the beginning of domestic tourism research. Therefore, Yusof and others believed that tourism resources were originally intended to be tourist attractions, and the reinterpretation of the definition of “resources” gave different meanings to “tourism resources,” which led to disputes with tourism attractions [2]. The meaning of a word depends on the word itself and the understanding of the word. The rupture and discontinuity of tourism resources and the disputes over their relationship with tourism attractions are ultimately due to differences in attitudes and understandings of the connotation, boundaries and essential attributes of tourism objects under different discourse backgrounds. Although the above-mentioned scholars’ analysis revealed some facts and academic theories of China’s tourism resources research in the early years, which has its important value and reason, it still ignores the discourse of tourism resources research [3].The original meaning of “resources” refers to the natural mineral resources buried underground for development and utilization and later extended to all natural and social things with economic value and available for use. From the etymology and literal understanding, tourism resources obviously refer to all the elements with economic value and tourism development, which are economic concepts and have instrumental rationality and value judgment [4]; tourism attractions are the name of “things,” which are simple and clear. It points to “things,” emphasizing the objective properties and attractive properties of things without a clear value orientation. Except for the types of objects, the attractiveness of tourist attractions is rarely debated [5]. From the perspective of core objects, tourism resources include tourism attractions with development value and other resource elements; and tourism attractions include both “objects” that can be developed and elements that are not suitable for tourism development [6]. Therefore, according to the existing academic understanding, tourism attractions and tourism resources have both commonalities and differences, resulting in the above-mentioned debate on the relationship between tourism resources and tourism attractions, as well as differences in understanding of tourism objects and their attractiveness. Tourists do not need to think about and care about the economy and development standards of things [7]. Literature [8] clarifies the attractiveness of tourist attractions and especially emphasizes tourists as the target of attraction, and they all take tourists as an important component into the tourist attraction system. Literature [9] emphasizes that tourist attractions can make tourists experience motivation only when their markers are positively related to tourists’ needs. However, attraction is subjective, nonfunctional, and even irrational. Therefore, the term tourist attraction profoundly reflects the individuality in Western society.Rural tourism developers and local relevant departments and governments should realize that the development of local tourism resources should not blindly pursue economic benefits but should pay more attention to ecological benefits, social benefits, and cultural benefits. Pay equal attention to resources, fully tap the local cultural heritage and cultural characteristics, and regard it as a major point to attract tourists [10]. In the development process of the rural tourism industry, it is necessary to take the concept of sustainable development as a guide to realize the integration of different types of resources in the future. In the past, many rural tourism resource development projects had low resource utilization rate and poor overall construction level. They did not hesitate to destroy ecological resources and waste natural resources for economic benefits. This kind of development method at the expense of the natural environment is not desirable. Even if a large economic benefit is obtained in the short term, the ecological damage caused by it is difficult to repair [11]. Therefore, when developing tourism resources, based on the characteristics of local villages, we should analyze characteristic resources, classify resources, and formulate resource development plans and long-term business plans in a targeted manner [12]. Social experts can be invited to evaluate the feasibility of various resource development, minimize the negative impact caused by resource development, reduce the damage to the local natural ecological environment, implement the concept of sustainable development, and truly realize rural revitalization [13].Developers should be aware of the disadvantages brought about by the homogenization of tourism products, try their best to plan the villages to be developed with innovative thinking, and seek characteristics that are different from other rural tourism spots based on the characteristics of local villages. We should closely follow the pace of the times, observe and investigate popular tourism products of popular attractions, carefully analyze their characteristics and advantages, find out why they are popular and loved by tourists, analyze the diverse needs of tourists, and implement them into local rural tourism. In the industry, local tourism products are given a new look, and tourism products with distinctive characteristics and in line with the aesthetics of tourists are developed [14]. By collecting information on local rural products, we will vigorously develop native products, attract tourists with their unique flavors, drive the consumption of native products, and bring new economic growth points to local rural tourism. At the same time, it should rely on the local natural and cultural environment, such as tracing history and culture, and making it into corresponding popular science books, works of art, endow it with profound cultural characteristics, and face the mass market with innovative eyes [15].Villages have their own specific development history, which can be used to create different types of tourism themes and make them into multiple tourism sections. For example, some areas with significant revolutionary significance can be featured with the theme of red tourism, especially the life, agricultural tools, or buildings that played an important role in the revolutionary base during the revolutionary period. When visitors visit, introduce them to the local events, wars, the use of these agricultural tools and their functions at that time and can also allow tourists to personally operate these agricultural tools, experience the life of the year, and create a refreshing visual experience for tourists through the creation of various corresponding situations, enriching the rural tourism. new cognition, and gain historical knowledge and experience revolutionary culture. Under this premise, the scope, content, and value of rural tourism have been further expanded, and the cultural connotation and spiritual level of rural tourism have been improved. In the follow-up, under the unintentional or intentional publicity of tourists, it will attract more tourists to travel and further promote the local economic development [16].With a vast land and abundant resources, villages in different regions have their own different characteristics and rich tourism resources, but because of their strong independence and lack of mutual connection, they lack advantages in complementary resources. In the process of developing rural resources, the same area needs to focus on strengthening the connection between scenic spots, so as to achieve resource sharing and information sharing, so as to create more characteristic and high-quality tourist routes, and provide tourists with one-stop services to meet the needs of tourists. Diversified needs [17]: implement modern management in rural areas, increase capital investment, strengthen the construction of various convenient infrastructures, improve the service level and service ability of tourism practitioners, improve their cultural literacy and personal quality, and provide tourists with better services. Local township and county-level governments should also strive to attract more social capital and investment, so that the development of local rural resources can obtain more human and material support [18].In order to improve the development effect of tourism resources with rural characteristics, this paper combines digital communication signal modulation technology to process tourism resources-related data.


## 2. Estimation of Related Parameters for Modulation Type Identification of Digital Communication Signals

### 2.1. Signal-to-Noise Ratio Estimation Algorithm for Digital Communication Signals

The main function of the signal-to-noise ratio is to directly reflect the quality information of the communication signal. In the process of digital communication signal processing, signal-to-noise ratio, as a key indicator, is an essential prior knowledge to measure the accuracy of signal processing method selection. In the adaptation process, the signal-to-noise ratio is usually used as the main parameter to measure the quality of the channel.The SNR estimation can be divided into constant envelope signal SNR estimation and nonconstant envelope signal SNR estimation according to the different modulation modes of the received signal. In contrast, the constant envelope signal-to-noise ratio estimation is simple and can be expressed as:(1)SNR=10logEan2varan.In formula (1), an is the instantaneous envelope of the signal. It can be seen that the estimation of the signal-to-noise ratio of the constant envelope only needs to analyze the signal envelope. The expected square value of the instantaneous envelope of the signal is regarded as the useful signal power, and the variance value of the instantaneous envelope of the signal is regarded as the noise power. Then, by definition, the signal-to-noise ratio of the constant envelope signal can be found.For the signal-to-noise ratio of the nonconstant envelope signal, the method in formula (1) cannot be simply used for estimation, but the singular value decomposition method of the autocorrelation matrix is used. After the useful signal passes through the Gaussian white noise channel, it is sampled without distortion by the signal receiver, and the sampled signal is(2)yn=xn+ωn.In formula (2), xn is the sampling sequence of the useful signal, ωn is the sampling sequence of Gaussian white noise, the mean value of the signal is 0, and the variance is denoted as σw2. The signal sequence xn and the noise sequence ωn are independently distributed, and the autocorrelation matrix of the signal yn is(3)Ryy=EynynH=Exn+wnxn+wnH=ExnxnH+EwnwnH.In formula (3), Rxx and Rww are the autocorrelation matrix of useful signal xn and Gaussian white noise signal ωn, respectively. It can be seen that the autocorrelation matrix Ryy of the received signal yn is equal to the sum of the autocorrelation matrix of the useful signal xn and the Gaussian white noise signal ωn. Since Ryy, Rxx, and Rww are all symmetric matrices, the singular value decomposition of them has the following:(4)Ryy=VΛYVH,Rxx=VΛxVH,Rww=σw2I.I is a unit matrix of order m, V is an orthogonal matrix, Λx is a diagonal matrix, which can be expressed as Λx=diagr1,r2,…,rp,0,…,0max, m is the order of the diagonal matrix Λx, the diagonal elements need to satisfy r1≥r2≥⋯≥rp, and the value of p is less than m.According to the properties of matrix transformation, formula (4) can be further expressed as:(5)VΛYVH=VΛxVH+VΛwVH=VΛx+ΛwVH.Among them,(6)Λy=diaga1,a2,amm×m=r1+σw2⋯0⋱rp+σw2⋮σw2⋱0⋯σw2.It can be seen that the signal subspace is ap-dimensional subspace formed by r1,r2,⋯,rp, and the noise subspace is a m−p-dimensional subspace formed by σw2. The p-dimensional signal subspace and the m−p-dimensional noise subspace constitute the signal space. On the basis of the spatial decomposition of the signal, the power of the received signal yn can be decomposed into the sum of the power Px of the useful signal xn and the power Pw of the noise signal ωn. The powers of the useful and noise signals can be expressed as:(7)Px=∑k=1Prk=∑k=1Pak−σw2,Pw=m×σw2.According to the definition of SNR, as long as the dimensionp of the signal subspace and the variance σw2 of white Gaussian noise are determined, the SNR of the received signal can be estimated. The value of p can be estimated according to the minimum length criterion (MDL) described in information theory:(8)MDLn=Nm−nlnΛn+12n2m−nlnN,p=argminnMDLn.In formula (11), N is the number of samples, n is the trial dimension, and Λn can be expressed as:(9)Λn=1m−n∑k=n+1mrk∏k=n+1mrk1/m−n.Therefore, the SNR estimation for nonconstant envelope signals can be performed as follows:First, the algorithm calculates the value of the autocorrelation matrixRyy of the received signal yn, performs singular value decomposition on the autocorrelation matrix Ryy, and obtains m eigenvalues a1,a2,⋯,am, which satisfy a1≥a2≥⋯≥am.Secondly, the MDL criterion is used to estimate the dimension of the signal subspace, calculate the variance valueσw2 of the Gaussian white noise signal from the formula (10), and bring it into the formula (11) to obtain the estimated value of the signal-to-noise ratio.(10)σw2=1m−n∑k=p+1mak,(11)SNR^=10lgPxPw=10lg∑k=1Pak−σw2m×σw2.The above is the estimation of the signal-to-noise ratio of the received signal through the minimum distance (MDL) algorithm. It can be seen that the estimation result of the dimension of the signal subspace will have a direct impact on the accuracy of the SNR estimation. In addition to this, there is the normalized ratio method, which is also a commonly used signal subspace dimension estimation method.The normalized ratio method is defined as:(12)vl=b⌢12+b⌢22+⋯+b⌢l2b12+b22+⋯+bl21/2,1≤l≤N.In the practical application of this algorithm, a value close to 1 must be determined first. Whenl satisfies the smallest integer whose vl is greater than or equal to this value, it can be considered that the eigenvalues before l are all “important.” Therefore, l can be determined as the dimension of the signal subspace.Through the simulation analysis of 2FSK, in the additive white Gaussian noise channel, the signal frequency of the carrier is 1600 Hz, and the sampling frequency of the signal is 18000 Hz. Among them, the length of the observation data is 200 symbols, and the dimension of the autocorrelation matrix is 50. When the signal-to-noise ratio of the received signal changes between −5 and 20, through MATLAB simulation, the influence of the MDL algorithm and the normalized ratio method on the analysis results is shown in Figure1:Figure 1
Influence of MDL algorithm and normalized ratio method on analysis results.
(a)(b)It can be seen that when the signal-to-noise ratio of the received signal is large, the estimation error produced by the normalized ratio method is also large. The normalized ratio method is to complete the estimation of the signal-to-noise ratio by estimating the noise variance. When the signal-to-noise ratio is small, the estimated error can be ignored, but when the signal-to-noise ratio is large, the estimated signal-to-noise ratio will deviate greatly from the real value. Therefore, the SNR estimation based on the MDL algorithm is superior to the normalized ratio algorithm. When using the minimum length algorithm to estimate the signal-to-noise ratio, the influence of the length of the observation data on the estimation result should also be considered. The longer the observation data is, the closer the obtained estimation result is to the true value.


### 2.2. Analysis of Symbol Rate Estimation Algorithm for Digital Communication Signals

The symbol rate is an important parameter of digital communication signals, and the symbol rate must be estimated before the signal is orthogonally demodulated. The estimation of the signal symbol rate can be obtained either by statistical analysis of the instantaneous characteristics of the signal in the time domain or by measuring the length of the adjacent transition points of the instantaneous amplitude.The symbol rate is an important parameter for analyzing the characteristics of the signal. The symbol is used to represent each bit in the digital signal, and it can also be directly called “bit.” The symbol transmission rate can also be called the code transmission rate, which refers to the number of changes in symbols per second or the number of binary symbols transmitted per second. The binary baseband pulse stream is represented by a set of random pulse sequences as:(13)xt=∑nangt−nTB,an is the information code, TB is the symbol width, and gt is the signal waveform, which can be further expressed as:(14)gt−nTs=g1t−nTB,withProbabilityP,g2t−nTB,withProbability1−P.The power spectrum of the baseband pulse sequence consists of two parts: continuous spectrum and discrete spectrum:(15)Pf=fBP1−PG1f−G2f2+fB2∑m=−∞∞PG1mfB+1−PG2mfB2δf−mfB.Among them,G1f and G2f are the corresponding frequency spectra of waveforms g1t and g2t, respectively, and fB=1/TB is the symbol rate. The shape of the continuous spectrum is determined by the baseband pulse waveform, and the shape of the discrete spectrum is determined by the baseband pulse waveform and the statistical characteristics of the transmitted information code. If g1t is a rectangular pulse with signal amplitude A and width TB, then G1f=ATBSaπfTB.If the signal is a unipolar pulse signal with equal probability, that is,gt=g1t,g2t=0,P=1/2,A=1, its double-sideband power spectrum is(16)Pf=14TBSaπfTB2+14δf.If the signal is a bipolar pulse signal with equal probability, that is,gt=g1t=−g2t,P=1/2,A=1, its double-sideband power spectrum is expressed as:(17)Pf=TBSa2πfTB.In the above, we analyzed non-return-to-zero (NRZ) code sequences with a symbol duty cycle (ratio of symbol pulse width to symbol spacing) of 1. Moreover, in the case whereg1t and g2t occur with equal probability, the power spectra of the unipolar pulse signal and the bipolar pulse signal are analyzed. It can be seen that an does not have a synchronous time component with Ts. Therefore, in this case, the symbol rate information cannot be directly obtained through the power spectrum.If the pulse width of the symbol is changed, the duty cycle of the sequence symbol is less than 1, that is, the non-return-to-zero code is changed to the return-to-zero code (RZ). The duty cycle is represented byα, 0<α<1, and the pulse width of g1t is αTB. The power spectral density function of the binary random code when the signal is 0 or 1 with equal probability can be expressed as:(18)Pf=fB4αTBSaπfαTB2+fB24∑m=−∞∞αTBSaπαmfBTB2δf−mfB=α24TBSa2πfTB+α24∑m=−∞∞Sa2πmαδf−mfB.The value off is an integer multiple of fB, that is, f=mfB. It can be seen that the power spectrum of the unipolar return-to-zero (RZ) code has an obvious discrete spectrum at the symbol rate, and the signal symbol rate can be extracted by searching for the position of the discrete spectral line.The power spectral density plots of unipolar non-return-to-zero (NRZ) codes and unipolar return-to-zero (RZ) codes are simulated by MATLAB. It can be seen that the signal sampling frequency is 30 MHz, the symbol rate is 2500 B, and the duty cycle of the return-to-zero code isα=1/2. The simulation results are shown in Figures 2 and 3:Figure 2
NRZ code power spectral density diagram.
(a)(b)Figure 3
RZ code power spectral density diagram.
(a)(b)As shown in the figure, when the frequency value of the signal is an integral multiple of the symbol rate, the NRZ code has no frequency component, so the symbol rate value cannot be obtained for the NRZ code. However, at the frequency valuef=±fB of the RZ code, there are discrete spectral lines, and at the frequency value f=±2fB, discrete spectral lines with lower intensity can also be observed. Therefore, by searching the position of the discrete spectrum, the symbol rate value can be extracted.


### 2.3. Communication Signal Demodulation

Modulation and demodulation are the key technologies of communication signal transmission. Both parties of the communication demodulate the received signal through the agreed carrier frequency, modulation pattern, symbol rate, and other parameters to recover useful information. In the communication confrontation, because the receiver is passive, it cannot know the modulation parameters of the transmitter in advance. To obtain useful information, only passive demodulation can be obtained. This passive demodulation is called blind demodulation.


#### 2.3.1. Instantaneous Frequency Extraction Method

The symbol width of the MFSK signal isTB, and the symbol rate is fB=1/TB. The MFSK signal is sampled by a signal with a sampling frequency of f to obtain a real signal xi. The frequency interval of xi is fsep. The instantaneous frequency of the signal can be expressed as:(19)fi=φwi+1−φwi2π,fs.The algorithm performs median filtering on the instantaneous frequency sequencefi. If the filter window width n is an odd number, the algorithm takes n=2k+1, that is, extracts n numbers from the input frequency sequence fi, arranges them in order of size, and takes the value in the middle as the output value of the filter. Then, the algorithm obtains new samples and repeats the above calculation process. The filter output yi is mathematically expressed as:(20)fMedi=Medfi|n=Medianfi−k,…,fi,…,fi+k.After filtering, pulses with a period less thann/2 are suppressed, and pulses greater than n/2 are retained. The sequence signal fi is the filtered instantaneous frequency sequence, and it is normalized. If(21)fji−fsep2≤fMedi<fji+fsep2.The quantization result is(22)yi=j.Finally, the quantized output sequenceyi is obtained. The symbol width is measured on the quantized output sequence, and the demodulated output is obtained according to the measurement result. The demodulation method of the instantaneous frequency is simple and practical, but it has high requirements on the signal-to-noise ratio. When the signal-to-noise ratio is low, it is necessary to find other methods to demodulate the signal.


#### 2.3.2. Short-Time Fourier Transform Method

After the MFSK signal is sampled by a sampling signal with a sampling frequency offs, a real signal xn with a frequency interval of fsep is obtained. The symbol width of the MFSK signal is TB, and the symbol rate is fB=1/TB.If the window functionwn is a Hanning window, the window width is N. At the same time, the selection of N should meet the requirements of signal processing. If N is too large, intersymbol crosstalk will be caused. If N is too small, useful information will be lost. Generally, N=1∼2fB is selected. The MFSK signal undergoes a windowed short-time Fourier transform as:(23)Xk,m=STFTxnwn−m.The square of its modulus is as follows:(24)XPk,m=Xk,m2.The subscript corresponding to the maximum value of the spectrum iski, and the frequency corresponding to the maximum value of the spectrum is as follows:(25)fi=kiN,fs.For the sliding window functionwn with the step size P along the sequence xn, the selection of the sliding step size d should fully consider its influence on the estimation of the signal parameters. Therefore, the general sliding step size is P=0.1∼0.5TB. By repeating the above process, the instantaneous frequency sequence fi can be obtained. Then, the instantaneous frequency fi is quantized, filtered, and symbol detected by the same method, and the demodulation of the MFSK signal can be realized.Fast Fourier transform is usually implemented with FFT. If the frequency interval of the FFT isΔf, I is the ratio of the frequency interval to the frequency resolution, and I=fsep/Δf is the fast Fourier transform demodulation signal, I generally takes a value between 8 and 16. When the value is too large and Δf is too small, the computational load is too large. When the value is too small and Δf is too large, the requirement cannot be met.MPSK is a multi-ary phase-modulated signal that must be processed by coherent demodulation. Therefore, the communication countermeasure reconnaissance must first try to obtain the two parameters of carrier frequency and symbol rate to realize the demodulation of MPSK signal. The general steps of MPSK signal demodulation are to firstly identify the modulation type of the intercepted signal and determine its modulation pattern; second, determine the value ofM and determine the hexadecimal number of the modulated signal. Then, the basic parameters of the signal are estimated, and the carrier frequency and symbol rate of the signal are mastered. Finally, the coherent demodulator is used to demodulate the signal MPSK.(1)
Extract the signal carrier frequencyBecause in the phase-modulated digital signal, when the symbol information is distributed with equal probability, the transmitted signal does not include the carrier component. Therefore, it is necessary to restore the carrier components first. Commonly used methods include square rate method carrier extraction, autocorrelation method carrier extraction, and high-order cyclic cumulant method carrier extraction. Taking the 2PSK signal as an example, the effectiveness of the square rate method carrier extraction is illustrated.During the duration of one symbol, the 2PSK signal is expressed as:(26)st=cosωct+θ0,ProbabilityisP,−cosωct+θ0,Probabilityis1−P.If the equal probability distribution, that is,P=1/2, the power spectrum is as follows:(27)Sω=TB4Saω+ωc2TB2+Saω−ωc2TB2.In the above formula,TB is the symbol width. It can be seen that there is no signal carrier component in the power spectrum of the 2PSK signal with equal probability distribution. The square of the 2PSK signal has(28)s2t=121+cos2ωct+2θ0.After filtering and removing the DC component,(29)s2t=12cos4πfct+2θ0.A single frequency signal is obtained, and the frequency of the signal is2fc, which is 2 times the carrier frequency. By extracting, analyzing, and judging the single-frequency signal, the carrier frequency of the 2PSK signal can be obtained. Similarly, when M is 4, 8, …, it can also be handled in the same way.(2)
Blind demodulation of MPSK signalThe carrier frequency of the MPSK signal has been correctly estimated, because the estimation of the symbol rate, the judgment of the signal modulation type and the determination of theM value can be obtained by referring to the previous contents. According to these known parameters, and the final demodulation of MPSK signal can be realized, the principle is shown in Figure 4.(i)
The algorithm extracts the signal carrier frequency and determines theM value of the MPSK signal;(ii)
The algorithm uses the carrier frequencyfc to set the local oscillator (NCO) to construct a digital quadrature demodulator. At the same time, the signal bandwidth is determined according to the analysis results of the power spectrum of the signal, and the parameters of the low-pass filter are set accordingly. The carrier is tracked through the carrier synchronization loop and the initial demodulation of the signal is obtained.(iii)
The algorithm estimates the signal symbol rate and uses it to set the low-pass filter (LPF) and control the sampling clock. The algorithm performs low-pass filtering and sampling judgment on the two signals outputted by the initial demodulation and finally completes the demodulation of the MPSK signal. Taking the binary PSK signal as an example, the MATLAB simulation of the signal modulation and demodulation process is performed, as shown in Figure5.Figure 4
The principle of blind demodulation of MPSK signal. The specific steps of demodulation are given in the figure.It can be seen that the key to the blind demodulation of MPSK signal is the estimation of the carrier frequency and symbol rate of the signal. Only in this way can the signal information be accurately demodulated and the baseband signal can be recovered.Figure 5
2PSK modulation and demodulation process.
(a)(b)(c)(d)(e)(f)


## 2.1. Signal-to-Noise Ratio Estimation Algorithm for Digital Communication Signals

The main function of the signal-to-noise ratio is to directly reflect the quality information of the communication signal. In the process of digital communication signal processing, signal-to-noise ratio, as a key indicator, is an essential prior knowledge to measure the accuracy of signal processing method selection. In the adaptation process, the signal-to-noise ratio is usually used as the main parameter to measure the quality of the channel.The SNR estimation can be divided into constant envelope signal SNR estimation and nonconstant envelope signal SNR estimation according to the different modulation modes of the received signal. In contrast, the constant envelope signal-to-noise ratio estimation is simple and can be expressed as:(1)SNR=10logEan2varan.In formula (1), an is the instantaneous envelope of the signal. It can be seen that the estimation of the signal-to-noise ratio of the constant envelope only needs to analyze the signal envelope. The expected square value of the instantaneous envelope of the signal is regarded as the useful signal power, and the variance value of the instantaneous envelope of the signal is regarded as the noise power. Then, by definition, the signal-to-noise ratio of the constant envelope signal can be found.For the signal-to-noise ratio of the nonconstant envelope signal, the method in formula (1) cannot be simply used for estimation, but the singular value decomposition method of the autocorrelation matrix is used. After the useful signal passes through the Gaussian white noise channel, it is sampled without distortion by the signal receiver, and the sampled signal is(2)yn=xn+ωn.In formula (2), xn is the sampling sequence of the useful signal, ωn is the sampling sequence of Gaussian white noise, the mean value of the signal is 0, and the variance is denoted as σw2. The signal sequence xn and the noise sequence ωn are independently distributed, and the autocorrelation matrix of the signal yn is(3)Ryy=EynynH=Exn+wnxn+wnH=ExnxnH+EwnwnH.In formula (3), Rxx and Rww are the autocorrelation matrix of useful signal xn and Gaussian white noise signal ωn, respectively. It can be seen that the autocorrelation matrix Ryy of the received signal yn is equal to the sum of the autocorrelation matrix of the useful signal xn and the Gaussian white noise signal ωn. Since Ryy, Rxx, and Rww are all symmetric matrices, the singular value decomposition of them has the following:(4)Ryy=VΛYVH,Rxx=VΛxVH,Rww=σw2I.I is a unit matrix of order m, V is an orthogonal matrix, Λx is a diagonal matrix, which can be expressed as Λx=diagr1,r2,…,rp,0,…,0max, m is the order of the diagonal matrix Λx, the diagonal elements need to satisfy r1≥r2≥⋯≥rp, and the value of p is less than m.According to the properties of matrix transformation, formula (4) can be further expressed as:(5)VΛYVH=VΛxVH+VΛwVH=VΛx+ΛwVH.Among them,(6)Λy=diaga1,a2,amm×m=r1+σw2⋯0⋱rp+σw2⋮σw2⋱0⋯σw2.It can be seen that the signal subspace is ap-dimensional subspace formed by r1,r2,⋯,rp, and the noise subspace is a m−p-dimensional subspace formed by σw2. The p-dimensional signal subspace and the m−p-dimensional noise subspace constitute the signal space. On the basis of the spatial decomposition of the signal, the power of the received signal yn can be decomposed into the sum of the power Px of the useful signal xn and the power Pw of the noise signal ωn. The powers of the useful and noise signals can be expressed as:(7)Px=∑k=1Prk=∑k=1Pak−σw2,Pw=m×σw2.According to the definition of SNR, as long as the dimensionp of the signal subspace and the variance σw2 of white Gaussian noise are determined, the SNR of the received signal can be estimated. The value of p can be estimated according to the minimum length criterion (MDL) described in information theory:(8)MDLn=Nm−nlnΛn+12n2m−nlnN,p=argminnMDLn.In formula (11), N is the number of samples, n is the trial dimension, and Λn can be expressed as:(9)Λn=1m−n∑k=n+1mrk∏k=n+1mrk1/m−n.Therefore, the SNR estimation for nonconstant envelope signals can be performed as follows:First, the algorithm calculates the value of the autocorrelation matrixRyy of the received signal yn, performs singular value decomposition on the autocorrelation matrix Ryy, and obtains m eigenvalues a1,a2,⋯,am, which satisfy a1≥a2≥⋯≥am.Secondly, the MDL criterion is used to estimate the dimension of the signal subspace, calculate the variance valueσw2 of the Gaussian white noise signal from the formula (10), and bring it into the formula (11) to obtain the estimated value of the signal-to-noise ratio.(10)σw2=1m−n∑k=p+1mak,(11)SNR^=10lgPxPw=10lg∑k=1Pak−σw2m×σw2.The above is the estimation of the signal-to-noise ratio of the received signal through the minimum distance (MDL) algorithm. It can be seen that the estimation result of the dimension of the signal subspace will have a direct impact on the accuracy of the SNR estimation. In addition to this, there is the normalized ratio method, which is also a commonly used signal subspace dimension estimation method.The normalized ratio method is defined as:(12)vl=b⌢12+b⌢22+⋯+b⌢l2b12+b22+⋯+bl21/2,1≤l≤N.In the practical application of this algorithm, a value close to 1 must be determined first. Whenl satisfies the smallest integer whose vl is greater than or equal to this value, it can be considered that the eigenvalues before l are all “important.” Therefore, l can be determined as the dimension of the signal subspace.Through the simulation analysis of 2FSK, in the additive white Gaussian noise channel, the signal frequency of the carrier is 1600 Hz, and the sampling frequency of the signal is 18000 Hz. Among them, the length of the observation data is 200 symbols, and the dimension of the autocorrelation matrix is 50. When the signal-to-noise ratio of the received signal changes between −5 and 20, through MATLAB simulation, the influence of the MDL algorithm and the normalized ratio method on the analysis results is shown in Figure1:Figure 1
Influence of MDL algorithm and normalized ratio method on analysis results.
(a)(b)It can be seen that when the signal-to-noise ratio of the received signal is large, the estimation error produced by the normalized ratio method is also large. The normalized ratio method is to complete the estimation of the signal-to-noise ratio by estimating the noise variance. When the signal-to-noise ratio is small, the estimated error can be ignored, but when the signal-to-noise ratio is large, the estimated signal-to-noise ratio will deviate greatly from the real value. Therefore, the SNR estimation based on the MDL algorithm is superior to the normalized ratio algorithm. When using the minimum length algorithm to estimate the signal-to-noise ratio, the influence of the length of the observation data on the estimation result should also be considered. The longer the observation data is, the closer the obtained estimation result is to the true value.


## 2.2. Analysis of Symbol Rate Estimation Algorithm for Digital Communication Signals

The symbol rate is an important parameter of digital communication signals, and the symbol rate must be estimated before the signal is orthogonally demodulated. The estimation of the signal symbol rate can be obtained either by statistical analysis of the instantaneous characteristics of the signal in the time domain or by measuring the length of the adjacent transition points of the instantaneous amplitude.The symbol rate is an important parameter for analyzing the characteristics of the signal. The symbol is used to represent each bit in the digital signal, and it can also be directly called “bit.” The symbol transmission rate can also be called the code transmission rate, which refers to the number of changes in symbols per second or the number of binary symbols transmitted per second. The binary baseband pulse stream is represented by a set of random pulse sequences as:(13)xt=∑nangt−nTB,an is the information code, TB is the symbol width, and gt is the signal waveform, which can be further expressed as:(14)gt−nTs=g1t−nTB,withProbabilityP,g2t−nTB,withProbability1−P.The power spectrum of the baseband pulse sequence consists of two parts: continuous spectrum and discrete spectrum:(15)Pf=fBP1−PG1f−G2f2+fB2∑m=−∞∞PG1mfB+1−PG2mfB2δf−mfB.Among them,G1f and G2f are the corresponding frequency spectra of waveforms g1t and g2t, respectively, and fB=1/TB is the symbol rate. The shape of the continuous spectrum is determined by the baseband pulse waveform, and the shape of the discrete spectrum is determined by the baseband pulse waveform and the statistical characteristics of the transmitted information code. If g1t is a rectangular pulse with signal amplitude A and width TB, then G1f=ATBSaπfTB.If the signal is a unipolar pulse signal with equal probability, that is,gt=g1t,g2t=0,P=1/2,A=1, its double-sideband power spectrum is(16)Pf=14TBSaπfTB2+14δf.If the signal is a bipolar pulse signal with equal probability, that is,gt=g1t=−g2t,P=1/2,A=1, its double-sideband power spectrum is expressed as:(17)Pf=TBSa2πfTB.In the above, we analyzed non-return-to-zero (NRZ) code sequences with a symbol duty cycle (ratio of symbol pulse width to symbol spacing) of 1. Moreover, in the case whereg1t and g2t occur with equal probability, the power spectra of the unipolar pulse signal and the bipolar pulse signal are analyzed. It can be seen that an does not have a synchronous time component with Ts. Therefore, in this case, the symbol rate information cannot be directly obtained through the power spectrum.If the pulse width of the symbol is changed, the duty cycle of the sequence symbol is less than 1, that is, the non-return-to-zero code is changed to the return-to-zero code (RZ). The duty cycle is represented byα, 0<α<1, and the pulse width of g1t is αTB. The power spectral density function of the binary random code when the signal is 0 or 1 with equal probability can be expressed as:(18)Pf=fB4αTBSaπfαTB2+fB24∑m=−∞∞αTBSaπαmfBTB2δf−mfB=α24TBSa2πfTB+α24∑m=−∞∞Sa2πmαδf−mfB.The value off is an integer multiple of fB, that is, f=mfB. It can be seen that the power spectrum of the unipolar return-to-zero (RZ) code has an obvious discrete spectrum at the symbol rate, and the signal symbol rate can be extracted by searching for the position of the discrete spectral line.The power spectral density plots of unipolar non-return-to-zero (NRZ) codes and unipolar return-to-zero (RZ) codes are simulated by MATLAB. It can be seen that the signal sampling frequency is 30 MHz, the symbol rate is 2500 B, and the duty cycle of the return-to-zero code isα=1/2. The simulation results are shown in Figures 2 and 3:Figure 2
NRZ code power spectral density diagram.
(a)(b)Figure 3
RZ code power spectral density diagram.
(a)(b)As shown in the figure, when the frequency value of the signal is an integral multiple of the symbol rate, the NRZ code has no frequency component, so the symbol rate value cannot be obtained for the NRZ code. However, at the frequency valuef=±fB of the RZ code, there are discrete spectral lines, and at the frequency value f=±2fB, discrete spectral lines with lower intensity can also be observed. Therefore, by searching the position of the discrete spectrum, the symbol rate value can be extracted.


## 2.3. Communication Signal Demodulation

Modulation and demodulation are the key technologies of communication signal transmission. Both parties of the communication demodulate the received signal through the agreed carrier frequency, modulation pattern, symbol rate, and other parameters to recover useful information. In the communication confrontation, because the receiver is passive, it cannot know the modulation parameters of the transmitter in advance. To obtain useful information, only passive demodulation can be obtained. This passive demodulation is called blind demodulation.


### 2.3.1. Instantaneous Frequency Extraction Method

The symbol width of the MFSK signal isTB, and the symbol rate is fB=1/TB. The MFSK signal is sampled by a signal with a sampling frequency of f to obtain a real signal xi. The frequency interval of xi is fsep. The instantaneous frequency of the signal can be expressed as:(19)fi=φwi+1−φwi2π,fs.The algorithm performs median filtering on the instantaneous frequency sequencefi. If the filter window width n is an odd number, the algorithm takes n=2k+1, that is, extracts n numbers from the input frequency sequence fi, arranges them in order of size, and takes the value in the middle as the output value of the filter. Then, the algorithm obtains new samples and repeats the above calculation process. The filter output yi is mathematically expressed as:(20)fMedi=Medfi|n=Medianfi−k,…,fi,…,fi+k.After filtering, pulses with a period less thann/2 are suppressed, and pulses greater than n/2 are retained. The sequence signal fi is the filtered instantaneous frequency sequence, and it is normalized. If(21)fji−fsep2≤fMedi<fji+fsep2.The quantization result is(22)yi=j.Finally, the quantized output sequenceyi is obtained. The symbol width is measured on the quantized output sequence, and the demodulated output is obtained according to the measurement result. The demodulation method of the instantaneous frequency is simple and practical, but it has high requirements on the signal-to-noise ratio. When the signal-to-noise ratio is low, it is necessary to find other methods to demodulate the signal.


### 2.3.2. Short-Time Fourier Transform Method

After the MFSK signal is sampled by a sampling signal with a sampling frequency offs, a real signal xn with a frequency interval of fsep is obtained. The symbol width of the MFSK signal is TB, and the symbol rate is fB=1/TB.If the window functionwn is a Hanning window, the window width is N. At the same time, the selection of N should meet the requirements of signal processing. If N is too large, intersymbol crosstalk will be caused. If N is too small, useful information will be lost. Generally, N=1∼2fB is selected. The MFSK signal undergoes a windowed short-time Fourier transform as:(23)Xk,m=STFTxnwn−m.The square of its modulus is as follows:(24)XPk,m=Xk,m2.The subscript corresponding to the maximum value of the spectrum iski, and the frequency corresponding to the maximum value of the spectrum is as follows:(25)fi=kiN,fs.For the sliding window functionwn with the step size P along the sequence xn, the selection of the sliding step size d should fully consider its influence on the estimation of the signal parameters. Therefore, the general sliding step size is P=0.1∼0.5TB. By repeating the above process, the instantaneous frequency sequence fi can be obtained. Then, the instantaneous frequency fi is quantized, filtered, and symbol detected by the same method, and the demodulation of the MFSK signal can be realized.Fast Fourier transform is usually implemented with FFT. If the frequency interval of the FFT isΔf, I is the ratio of the frequency interval to the frequency resolution, and I=fsep/Δf is the fast Fourier transform demodulation signal, I generally takes a value between 8 and 16. When the value is too large and Δf is too small, the computational load is too large. When the value is too small and Δf is too large, the requirement cannot be met.MPSK is a multi-ary phase-modulated signal that must be processed by coherent demodulation. Therefore, the communication countermeasure reconnaissance must first try to obtain the two parameters of carrier frequency and symbol rate to realize the demodulation of MPSK signal. The general steps of MPSK signal demodulation are to firstly identify the modulation type of the intercepted signal and determine its modulation pattern; second, determine the value ofM and determine the hexadecimal number of the modulated signal. Then, the basic parameters of the signal are estimated, and the carrier frequency and symbol rate of the signal are mastered. Finally, the coherent demodulator is used to demodulate the signal MPSK.(1)
Extract the signal carrier frequencyBecause in the phase-modulated digital signal, when the symbol information is distributed with equal probability, the transmitted signal does not include the carrier component. Therefore, it is necessary to restore the carrier components first. Commonly used methods include square rate method carrier extraction, autocorrelation method carrier extraction, and high-order cyclic cumulant method carrier extraction. Taking the 2PSK signal as an example, the effectiveness of the square rate method carrier extraction is illustrated.During the duration of one symbol, the 2PSK signal is expressed as:(26)st=cosωct+θ0,ProbabilityisP,−cosωct+θ0,Probabilityis1−P.If the equal probability distribution, that is,P=1/2, the power spectrum is as follows:(27)Sω=TB4Saω+ωc2TB2+Saω−ωc2TB2.In the above formula,TB is the symbol width. It can be seen that there is no signal carrier component in the power spectrum of the 2PSK signal with equal probability distribution. The square of the 2PSK signal has(28)s2t=121+cos2ωct+2θ0.After filtering and removing the DC component,(29)s2t=12cos4πfct+2θ0.A single frequency signal is obtained, and the frequency of the signal is2fc, which is 2 times the carrier frequency. By extracting, analyzing, and judging the single-frequency signal, the carrier frequency of the 2PSK signal can be obtained. Similarly, when M is 4, 8, …, it can also be handled in the same way.(2)
Blind demodulation of MPSK signalThe carrier frequency of the MPSK signal has been correctly estimated, because the estimation of the symbol rate, the judgment of the signal modulation type and the determination of theM value can be obtained by referring to the previous contents. According to these known parameters, and the final demodulation of MPSK signal can be realized, the principle is shown in Figure 4.(i)
The algorithm extracts the signal carrier frequency and determines theM value of the MPSK signal;(ii)
The algorithm uses the carrier frequencyfc to set the local oscillator (NCO) to construct a digital quadrature demodulator. At the same time, the signal bandwidth is determined according to the analysis results of the power spectrum of the signal, and the parameters of the low-pass filter are set accordingly. The carrier is tracked through the carrier synchronization loop and the initial demodulation of the signal is obtained.(iii)
The algorithm estimates the signal symbol rate and uses it to set the low-pass filter (LPF) and control the sampling clock. The algorithm performs low-pass filtering and sampling judgment on the two signals outputted by the initial demodulation and finally completes the demodulation of the MPSK signal. Taking the binary PSK signal as an example, the MATLAB simulation of the signal modulation and demodulation process is performed, as shown in Figure5.Figure 4
The principle of blind demodulation of MPSK signal. The specific steps of demodulation are given in the figure.It can be seen that the key to the blind demodulation of MPSK signal is the estimation of the carrier frequency and symbol rate of the signal. Only in this way can the signal information be accurately demodulated and the baseband signal can be recovered.Figure 5
2PSK modulation and demodulation process.
(a)(b)(c)(d)(e)(f)


## 2.3.1. Instantaneous Frequency Extraction Method

The symbol width of the MFSK signal isTB, and the symbol rate is fB=1/TB. The MFSK signal is sampled by a signal with a sampling frequency of f to obtain a real signal xi. The frequency interval of xi is fsep. The instantaneous frequency of the signal can be expressed as:(19)fi=φwi+1−φwi2π,fs.The algorithm performs median filtering on the instantaneous frequency sequencefi. If the filter window width n is an odd number, the algorithm takes n=2k+1, that is, extracts n numbers from the input frequency sequence fi, arranges them in order of size, and takes the value in the middle as the output value of the filter. Then, the algorithm obtains new samples and repeats the above calculation process. The filter output yi is mathematically expressed as:(20)fMedi=Medfi|n=Medianfi−k,…,fi,…,fi+k.After filtering, pulses with a period less thann/2 are suppressed, and pulses greater than n/2 are retained. The sequence signal fi is the filtered instantaneous frequency sequence, and it is normalized. If(21)fji−fsep2≤fMedi<fji+fsep2.The quantization result is(22)yi=j.Finally, the quantized output sequenceyi is obtained. The symbol width is measured on the quantized output sequence, and the demodulated output is obtained according to the measurement result. The demodulation method of the instantaneous frequency is simple and practical, but it has high requirements on the signal-to-noise ratio. When the signal-to-noise ratio is low, it is necessary to find other methods to demodulate the signal.


## 2.3.2. Short-Time Fourier Transform Method

After the MFSK signal is sampled by a sampling signal with a sampling frequency offs, a real signal xn with a frequency interval of fsep is obtained. The symbol width of the MFSK signal is TB, and the symbol rate is fB=1/TB.If the window functionwn is a Hanning window, the window width is N. At the same time, the selection of N should meet the requirements of signal processing. If N is too large, intersymbol crosstalk will be caused. If N is too small, useful information will be lost. Generally, N=1∼2fB is selected. The MFSK signal undergoes a windowed short-time Fourier transform as:(23)Xk,m=STFTxnwn−m.The square of its modulus is as follows:(24)XPk,m=Xk,m2.The subscript corresponding to the maximum value of the spectrum iski, and the frequency corresponding to the maximum value of the spectrum is as follows:(25)fi=kiN,fs.For the sliding window functionwn with the step size P along the sequence xn, the selection of the sliding step size d should fully consider its influence on the estimation of the signal parameters. Therefore, the general sliding step size is P=0.1∼0.5TB. By repeating the above process, the instantaneous frequency sequence fi can be obtained. Then, the instantaneous frequency fi is quantized, filtered, and symbol detected by the same method, and the demodulation of the MFSK signal can be realized.Fast Fourier transform is usually implemented with FFT. If the frequency interval of the FFT isΔf, I is the ratio of the frequency interval to the frequency resolution, and I=fsep/Δf is the fast Fourier transform demodulation signal, I generally takes a value between 8 and 16. When the value is too large and Δf is too small, the computational load is too large. When the value is too small and Δf is too large, the requirement cannot be met.MPSK is a multi-ary phase-modulated signal that must be processed by coherent demodulation. Therefore, the communication countermeasure reconnaissance must first try to obtain the two parameters of carrier frequency and symbol rate to realize the demodulation of MPSK signal. The general steps of MPSK signal demodulation are to firstly identify the modulation type of the intercepted signal and determine its modulation pattern; second, determine the value ofM and determine the hexadecimal number of the modulated signal. Then, the basic parameters of the signal are estimated, and the carrier frequency and symbol rate of the signal are mastered. Finally, the coherent demodulator is used to demodulate the signal MPSK.(1)
Extract the signal carrier frequencyBecause in the phase-modulated digital signal, when the symbol information is distributed with equal probability, the transmitted signal does not include the carrier component. Therefore, it is necessary to restore the carrier components first. Commonly used methods include square rate method carrier extraction, autocorrelation method carrier extraction, and high-order cyclic cumulant method carrier extraction. Taking the 2PSK signal as an example, the effectiveness of the square rate method carrier extraction is illustrated.During the duration of one symbol, the 2PSK signal is expressed as:(26)st=cosωct+θ0,ProbabilityisP,−cosωct+θ0,Probabilityis1−P.If the equal probability distribution, that is,P=1/2, the power spectrum is as follows:(27)Sω=TB4Saω+ωc2TB2+Saω−ωc2TB2.In the above formula,TB is the symbol width. It can be seen that there is no signal carrier component in the power spectrum of the 2PSK signal with equal probability distribution. The square of the 2PSK signal has(28)s2t=121+cos2ωct+2θ0.After filtering and removing the DC component,(29)s2t=12cos4πfct+2θ0.A single frequency signal is obtained, and the frequency of the signal is2fc, which is 2 times the carrier frequency. By extracting, analyzing, and judging the single-frequency signal, the carrier frequency of the 2PSK signal can be obtained. Similarly, when M is 4, 8, …, it can also be handled in the same way.(2)
Blind demodulation of MPSK signalThe carrier frequency of the MPSK signal has been correctly estimated, because the estimation of the symbol rate, the judgment of the signal modulation type and the determination of theM value can be obtained by referring to the previous contents. According to these known parameters, and the final demodulation of MPSK signal can be realized, the principle is shown in Figure 4.(i)
The algorithm extracts the signal carrier frequency and determines theM value of the MPSK signal;(ii)
The algorithm uses the carrier frequencyfc to set the local oscillator (NCO) to construct a digital quadrature demodulator. At the same time, the signal bandwidth is determined according to the analysis results of the power spectrum of the signal, and the parameters of the low-pass filter are set accordingly. The carrier is tracked through the carrier synchronization loop and the initial demodulation of the signal is obtained.(iii)
The algorithm estimates the signal symbol rate and uses it to set the low-pass filter (LPF) and control the sampling clock. The algorithm performs low-pass filtering and sampling judgment on the two signals outputted by the initial demodulation and finally completes the demodulation of the MPSK signal. Taking the binary PSK signal as an example, the MATLAB simulation of the signal modulation and demodulation process is performed, as shown in Figure5.Figure 4
The principle of blind demodulation of MPSK signal. The specific steps of demodulation are given in the figure.It can be seen that the key to the blind demodulation of MPSK signal is the estimation of the carrier frequency and symbol rate of the signal. Only in this way can the signal information be accurately demodulated and the baseband signal can be recovered.Figure 5
2PSK modulation and demodulation process.
(a)(b)(c)(d)(e)(f)


## 3. Rural Characteristic Tourism Resource Development System

Gridization of tourism resources is to spread the tourism resource data in the administrative area into a grid of suitable size according to a certain mathematical model, so as to realize the transformation from the statistical unit of the administrative area to the grid statistical unit. The grid method can integrate multi-source tourism resource data to build a spatial model and express the spatial differentiation law of tourism resources in regional units by scale. Moreover, it overcomes the uniform distribution of statistical index values of various tourism resources in each unit caused by the analysis of administrative regions. At the same time, it can also realize the dynamic research of tourism resource law based on grid time series data. Therefore, the grid of tourism resources data with administrative districts as a unit can be used as one of the means of spatial analysis and evaluation of tourism resources. In addition, the grid of tourism resources provides an ideal method for comprehensive analysis of tourism resources (Figure6).Figure 6
Gridding of rural characteristic tourism resources data.The elements of the rural landscape refer to the various backgrounds, materials, and symbols that constitute the form and meaning of the rural landscape and exist in the rural space, also known as the constituent elements of the rural landscape, as shown in Figure7.Figure 7
Element system of rural landscape.Tourism mainly includes six aspects: travel, food, accommodation, transportation, shopping, and entertainment. It involves tourism destination management departments, tourism agencies, tourism intermediaries, tourism transportation departments, tourism hotels, tourism restaurants and tourism enterprises, and other related departments and enterprises. Moreover, its resources have the characteristics of diversity, complexity, distribution, tangibility, intangibility, and uncertainty. In order to realize the sharing of tourism resources, this paper proposes a tourism resource management model as shown in Figure8.Figure 8
Rural tourism resource management system.After the above model is constructed, the effect of the rural characteristic tourism resource development platform proposed in this paper is verified, and the effect of resource development and resource management is carried out through the simulation model, and the experimental results shown in Figure9 are obtained.Figure 9
Development and management effects of rural characteristic tourism resources.Through the above research, it is verified that the rural characteristic tourism resource development platform proposed in this paper has a good rural characteristic tourism resource development and management effect.


## 4. Conclusion

In the research of tourism resources, whether it is the traditional research that emphasizes “the theoretical core of tourism resources is the attractive factor” and “tourism resources are the objects of tourists’ activities,” or emerging research that believes that “tourism resources can attract people to generate tourism motivation” and “attractiveness is the core value of tourism resources,” they all regard tourism objects as the core element. Moreover, they will all be “attractive to tourists as a major measure of tourism resources.” In order to improve the development effect of tourism resources with rural characteristics, this paper combines the digital communication signal modulation technology to process the related data of tourism resources. Through the test research, it is verified that the rural characteristic tourism resource development platform proposed in this paper has good rural characteristic tourism resource development and management effects.



---

*Source: 7500042-2022-08-25.xml*