# Copyright Transaction Mode and Copyright Protection Risk Analysis of Green Industry from the Perspective of Information Environment

Authors: Jian Liu; Xuemin Deng
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

At present, the requirements of the current environment and background require us to have higher requirements for the data mode of copyright transaction. According to the previous way to conduct copyright transactions, it will cause a lot of unnecessary trouble, and according to the establishment of the copyright transaction database, we analyse the data types and patterns of copyright transactions and related copyright transaction protection measures and risk prevention. According to the copyright transaction database, we adopt BS network model, CIF copyright transaction data set model, CI data set model, data interactive query technology, and block matching technology to deal with the copyright transaction data model and copyright transaction risk prevention under the background of Informa ionization as follows: (1) we use BS network model, CIF copyright transaction data set model, and CI data set model to analyse and detect the copyright transaction data before transaction, which better reflects the network model effect of self-enhancing operation rate of copyright transaction data and effectively avoids overfitting problem. (2) By using interactive data query technology, the data presented in the preparation work before the transaction is more detailed, the samples are normalized, and the collected transaction data are tested for accuracy, which can eliminate potential dangers. (3) The method of similarity feature reconstruction of copyright transaction data set completes the extraction of association matching features of copyright transaction data system, which provides strong support for us to prevent risks and effectively match data in copyright transaction.


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## Body

## 1. Introduction

This paper studies whether DF rules are related to the change of information environment before earnings announcement, and finds that the introduction of DF rules reduces earnings volatility and accelerates the convergence of prices before earnings announcement to the level after earnings announcement. Evidence suggests that uneven, accurate, and uneven changes in analysts’ forecasts have increased the number of forward-looking disclosures. On the whole, it has not been found that the introduction of FD regulations will lead to the deterioration of the information environment before earnings announcement [1]. This paper will examine the impact of IFRS implementation on the information environment of financial analysts. In order to control mixed and concurrent events, we use companies that voluntarily implement IFRS at least two years before the mandatory date as control samples. The absolute prediction error and prediction deviation of this control sample will produce significant results only under the mandatory implementation system, and the mandatory implementation system and enterprise reporting incentives play a very important role in determining the mandatory impact [2]. The Internet is a tool to support teaching and research in a university environment. Multi-application hypermedia system provides hypertext extension in a unified user environment and embeds other media besides text. To improve the construction of a consistent application framework, an example of an intermedia session is provided [3]. With the rapid development of information technology, various advanced technical means are constantly emerging in teaching, which promotes the reform of education and teaching. Especially with the combination of mobile Internet cloud technology and education, teachers need to make corresponding changes. This paper puts forward some views on teachers’ transformation under the new information environment [4]. Data from the National Nursery Survey are used to assess the impact of price effects and marketing characteristics, which play an important role in whether firms sell specific plant categories, headquarters sales, and total sales of commercial nurseries and greenhouses for multiple plant categories. Demand factors also affect sales, and our results show that pricing factors play an important role in plant species sales and total sales [5]. The global environment encourages palm oil and other industries to reduce negative environmental impacts and assess the green level of palm oil industry according to energy conservation. This paper presents a new system tool, which can not only visualize the potential improvement potential of green performance but also represent the general situation of industry according to the actual situation [6]. Industrialization of environmental protection is one of the main trends of environmental protection development. In theoretical research and practical work, the concepts of environmental protection industry, environmental industry, and green industry are increasingly studied and applied. Environmental law defines the relevant concepts to adapt to the development of environmental industry, so it is necessary to study the construction and development of its relevant legal system. Green industry and legal research on green industry are the concrete implementation of this concept [7]. Environmental management and corporate social responsibility have become important work improvement focuses, pursuing international standards and developing green industrial system. Development, testing, and evaluation of green industrial system standards. The integrated quality management system puts forward the problems existing in environmental management and the methods to solve them. Therefore, the research on green industrial system is helpful for practitioners and researchers to guide the development of green industrial system [8]. In this paper, two new blind watermarking methods for 3D mesh targets are proposed. The first method is robust to uniform calibration, and the second method is robust to geometric simplification and mesh simplification attacks. The vertices of the 3D mesh object are geometrically deformed. When the pseudorandom watermark signal is projected onto the 3D mesh object, the pseudorandom watermark signal is projected without changing the topological structure of the vertices. Embedding the watermark into the target for rotation and translation can ensure the firmness of the watermark for translation and rotation [9]. We propose a practical scheme for copyright protection of electronic watermark images. Here, I will focus on the copyright protection of common pictures taken by cameras. By using robust watermark to extract pre-embedded watermark, the copyright owner of the image is displayed. Besides the picture content itself, it also makes copyright protection possible. Better protection performance can be obtained by using channel coding technology [10]. Some recent technological changes will reduce the revenue of digital products, while others will reduce the cost of bringing creative works to market. I do not know if copyright protection now provides little incentive for innovation. Over time, its superior quality should lead to an increase in sales and usage, and the index shows that it will increase after that. Researchers and policy makers should pay attention to producer surplus and consumer surplus when considering the degree of copyright protection [11]. In the image watermarking system, the ownership verification does not need the availability of the original image, which solves the copyright protection problem. We are committed to statistical methods to obtain the basic model of decision theory for effective detector structure design. In particular, a statistical description of the original image may not exist. In view of the known and unknown statistical characteristics, different modelling methods are proposed. The watermark can symbolize the information, and the performance of the watermark system is evaluated by the false alarm probability, the detection probability when the watermark exists, and the error probability when extracting the information possessed by the watermark [12]. We have set an optimal level of copyright protection for each producer and the whole society. For a single producer, the best level is no protection, and the gross profit of the producer after deducting development expenses is zero, or completely protected. On the other hand, the optimal degree of society depends largely on the distribution of enterprise development costs. It also shows that the increase of copyright protection may increase or decrease the social welfare loss caused by insufficient utilization of copyright, but the social welfare loss caused by insufficient production of copyright will decrease [13]. This paper discusses the trade-off between knowledge development and exploration activities. The study of mining and exploration activities is not new, and they are regarded as two ends of a continuum. With regard to the evidence that enterprises are faced with in carrying out exploration and mining activities, extensive component reuse and re-engineering programmes have been undertaken to develop new technological innovations and exploration activities within the enterprise, such as the development of professional skills of technicians [14]. At that time, economists and philosophers discussed the rationality of economic planning in market economy, and the concept of tacit knowledge became the key. The foundations of laissez-faire capitalism critically juxtaposed their findings with succinct reviews of management theory and ethnography. We find that the dependence of hidden knowledge as a collective heritage on hidden knowledge is reflected in the theme and trade secrets, so the research results of hidden knowledge need to be protected [15].


## 2. Description of Green Industry Development under Information Environment

### 2.1. Necessity of Green Industry Development under Information Environment

The connotation of green industry development is under the information environment. Green industry development means that green industry institutions consider environmental protection factors when providing green industry services, and provide funding sources for projects that contribute to environmental improvement. The main purpose of green industry development is to guide producers and consumers in the whole society to form a green development concept, guide rational production and consumption, promote the establishment of an environment-friendly and resource-saving society, and, at the same time, continuously develop their own industries. The development of green industry needs the support of various emerging technologies, and the research and development of technology means a large amount of capital investment. The development of green industry has solved the dilemma of general industry development. It reduces the probability of industrial failure caused by financial difficulties, provides financing channels for China’s existing green industries, and creates more possibilities for their sources of funds. Flow funds to green industries and related fields so that green industries and projects have enough development funds to better expand their scale. At the same time, developing green industry can also help general industries break through barriers, complete the integration and utilization of internal resources, improve the effectiveness of resource allocation, and help green projects and industries improve efficiency and competitiveness, so as to achieve sustainable and healthy development.


### 2.2. Requirements for Green Industry Development under Information Environment

Developing green industry is the objective requirement of practising the new development concept in the information age. In the information age, realizing green development and sustainable development is placed in an important strategic position. Green development is also one of the five principles in the new development concept, focusing on solving the problem of harmonious coexistence between humans and nature. People began to realize the importance of green environmental protection gradually, and spontaneous organizations advocating environmental protection came into being to jointly carry out environmental protection actions. In such an environment, the concept of green development has become a development thought recognized and followed by all walks of life, which is inevitable for human society to develop to a certain level. Under the background of the information age, the development of green industry is the concrete embodiment of the objective requirements of practising this development concept, which contributes to China’s green development in the field of green finance and provides a steady stream of power for the green economy. We join the green development team with confidence. When these nongreen enterprises begin to attach importance to environmental protection and reduce pollution, they will gradually complete the transformation.


### 2.3. Development Status of Green Industry under Information Environment

At present, the development of green industry in China is in a relatively preliminary stage. First of all, from the overall scale, the market scale of China’s green industry development is constantly expanding, and the overall market demand is constantly stimulated. From the perspective of product types, China’s green industry development products are increasingly diversified, but there is still a problem of unbalanced development. At present, some countries in China are still one of the major green bond issuers in the world. Although other products, such as green insurance, started late and accounted for a relatively small proportion in the whole market scale, they have entered the fast lane of development and are developing towards a good trend, promoting the diversified development of China’s green industry. Green industry development institutions, especially some banks located in underdeveloped areas in the central and western regions, lack awareness of green finance and have not applied the concept of green development to the whole process of business development. If we do not have a deep understanding of the concept of green industry development products and services, the penetration rate will be very low. This will also lead to the fact that most groups in China do not know enough about the importance of green industries and green projects, and there are few green financial respondents, and the development of green industries in the whole green financial market is not active.


## 2.1. Necessity of Green Industry Development under Information Environment

The connotation of green industry development is under the information environment. Green industry development means that green industry institutions consider environmental protection factors when providing green industry services, and provide funding sources for projects that contribute to environmental improvement. The main purpose of green industry development is to guide producers and consumers in the whole society to form a green development concept, guide rational production and consumption, promote the establishment of an environment-friendly and resource-saving society, and, at the same time, continuously develop their own industries. The development of green industry needs the support of various emerging technologies, and the research and development of technology means a large amount of capital investment. The development of green industry has solved the dilemma of general industry development. It reduces the probability of industrial failure caused by financial difficulties, provides financing channels for China’s existing green industries, and creates more possibilities for their sources of funds. Flow funds to green industries and related fields so that green industries and projects have enough development funds to better expand their scale. At the same time, developing green industry can also help general industries break through barriers, complete the integration and utilization of internal resources, improve the effectiveness of resource allocation, and help green projects and industries improve efficiency and competitiveness, so as to achieve sustainable and healthy development.


## 2.2. Requirements for Green Industry Development under Information Environment

Developing green industry is the objective requirement of practising the new development concept in the information age. In the information age, realizing green development and sustainable development is placed in an important strategic position. Green development is also one of the five principles in the new development concept, focusing on solving the problem of harmonious coexistence between humans and nature. People began to realize the importance of green environmental protection gradually, and spontaneous organizations advocating environmental protection came into being to jointly carry out environmental protection actions. In such an environment, the concept of green development has become a development thought recognized and followed by all walks of life, which is inevitable for human society to develop to a certain level. Under the background of the information age, the development of green industry is the concrete embodiment of the objective requirements of practising this development concept, which contributes to China’s green development in the field of green finance and provides a steady stream of power for the green economy. We join the green development team with confidence. When these nongreen enterprises begin to attach importance to environmental protection and reduce pollution, they will gradually complete the transformation.


## 2.3. Development Status of Green Industry under Information Environment

At present, the development of green industry in China is in a relatively preliminary stage. First of all, from the overall scale, the market scale of China’s green industry development is constantly expanding, and the overall market demand is constantly stimulated. From the perspective of product types, China’s green industry development products are increasingly diversified, but there is still a problem of unbalanced development. At present, some countries in China are still one of the major green bond issuers in the world. Although other products, such as green insurance, started late and accounted for a relatively small proportion in the whole market scale, they have entered the fast lane of development and are developing towards a good trend, promoting the diversified development of China’s green industry. Green industry development institutions, especially some banks located in underdeveloped areas in the central and western regions, lack awareness of green finance and have not applied the concept of green development to the whole process of business development. If we do not have a deep understanding of the concept of green industry development products and services, the penetration rate will be very low. This will also lead to the fact that most groups in China do not know enough about the importance of green industries and green projects, and there are few green financial respondents, and the development of green industries in the whole green financial market is not active.


## 3. Information Green Products Copyright Transaction Processing

### 3.1. Copyright Transaction Data Range Detection

With the development of copyright transaction management technology, more and more copyright transaction data management systems appear. Usually, the intelligent development and design of copyright transaction data system is realized under B/F interactive framework protocol, and its core is data collection, query, and analysis in copyright transaction data system. The green development of copyright industry lies in promoting the green growth of copyright industry; that is, “green” is the regulation of the specific development mode of copyright industry, and “development” is the goal that the industry must achieve when practising the green concept. We promote the green growth of copyright industry. It is necessary to continuously improve the supply quality of copyright products and services. It is necessary to widely implement green growth management in the industrial system and pay attention to the effectiveness of industrial green growth management. In order to realize the detection of data distribution range, joint time series analysis is adopted to obtain the timestamp proofreading representation of copyright trading system data as follows:(1)xt=1−log2pt∑n=1an.Under theB/F interactive architecture protocol, we can focus on monitoring the effectiveness of industrial green growth management and calculating the distribution results of data distribution range, and its function is to promote the green growth of copyright industry and improve the effectiveness of management, promote the development of green industry towards diversification, and improve the group’s understanding of green industry and green projects.Pt is the internal frequency of copyright trading system data, e is the timestamp code defining copyright trading system data, t is the time point of receiving data, and an is the sample signal in the system. In the structure mapping of fuzzy information, the model network structure is used to compress the data, and the key string representation of the proofread copyright transaction data system is as follows:(2)L=Pt∑n=1aneδ2μθlog2m+1+L1−A.Due to the large scale of interactive big data distribution and poor feature clustering in copyright transaction data system, the query accuracy of interactive big data in copyright transaction data system is not high. Therefore, optimizing the interactive big data query technology of copyright transaction data system is of great significance in improving the management ability of copyright transaction data system. In the formula,δ is heterogeneity, which is the key data buffer memory size of copyright transaction data system, and μ is the similarity coefficient of data characteristics of copyright transaction data system. By using block-matching technology, the range detection expression of interactive big data of copyright transaction data system is obtained as follows:(3)H=∑n=1ane+δ2μθxt.


### 3.2. Feature Extraction of Copyright Transaction Data

It is slightly biased to judge the copyright trading industry as an absolute green product. Based on this, this paper analyses the connotation of the development of copyright trading industry, examines the driving force of the green development of copyright trading industry, and puts forward the development path of copyright trading industry, so as to help the high-quality development of copyright trading industry.B is the frequency of data feature points, and T is the dimension of data feature points. Information matching of copyright transaction data is carried out according to the discriminant result of feature classification attributes, and the entropy of feature classification information is obtained as follows:(4)v=2T1+2H−ABlog2m.In order to realize the feature extraction of interactive big data information in copyright trading system, according to the classical equivalence relationship between various groups of data in copyright trading system, the representation of feature classification attribute discriminant function of interactive big data in copyright trading system is obtained as follows:(5)I=v⋅2λ+K.The interactive big data query methods of copyright transaction data system mainly include system interactive big data query method based on rough set feature matching, copyright transaction data system interactive big data query method based on similarity feature analysis, and so on. In the formula,K is the power bandwidth of the signal, and h is the characteristic interference coefficient. Considering the balance of the output of the copyright transaction data system, the frequency domain equalization expression of the signal is as follows:(6)D=2λ+Klog2d+Iw.First of all, we need to consider the output balance of copyright transaction data system, according to the system interactive big data query method of rough set feature matching, the bearing operation of feature interference coefficient is carried out, and the feature extraction of interactive big data information of copyright trading system is realized, and then, the feature matching data in the interactive big data query method of copyright trading data system can be captured.d is the iteration steps of system query, and w is the interactive big data output bit rate of copyright transaction data system. Based on the steady-state identification technology, the ambiguity function representation of data sampling in copyright transaction data system is as follows:(7)G=D⋅log2d+Iw∑k=1δkd−N.First, we need to have the feature interference coefficient obtained from the interactive big data query method of copyright transaction data system. Then, the steady-state frequency domain coefficients are obtained by further operation, and the iterative steps are obtained by querying the copyright transaction data system based on the steady-state frequency domain coefficients so that we can complete the purpose.Rm is the query response rate of copyright transaction data system, and N is the time-varying rate of tracking channel. The fuzzy features are constrained and controlled, and the modal function representation of the interactive big data feature distribution points of the copyright transaction data system is as follows:(8)M=∑k=1δkd−N+Rm.First of all, we need to calculate the query response rate of the data of the publishing rights transaction data system. By tracking the time-varying rate of the channel, the fuzzy features are constrained and controlled, and then, the modality of the interactive big data feature distribution points in the copyright transaction data system is analysed. The fuzzy degree of the data in the copyright transaction data system can be reduced by using the feature clustering method when querying the spatial matching degree.di is the access bit rate of the copyright transaction data system; according to the ambiguity function of the copyright transaction data system, N is the spatial matching degree of the system data query, and the fuzzy feature extraction expression of the interactive big data of the copyright transaction data system is obtained by using the feature clustering method in the database as follows:(9)Ht=∑k=1δkd−N+1Π∑i=1di.In order to build a quantitative feature analysis model of interactive big data in copyright transaction data system, the system information is matched according to the joint self-similarity feature of copyright transaction data system. The feature matching coefficient of rough quantization isT. In the classification attribute set, according to the sample training results of copyright transaction data system, the feature clustering threshold of discrete data samples is obtained, and the association matching feature extraction of copyright transaction data system is completed according to the above analysis. According to the similarity characteristics of target samples of copyright transaction data system, the operation training of interactive big data of copyright transaction data system is realized, and the expression is as follows:(10)Z=1Π∑i=1diM+∑n=1cnT−Ht.


### 3.3. Optimization of Data Interactive Query in Copyright Trading System

The big data interactive query network model of copyright transaction data system promotes comprehensive colour transformation and high-quality development. In the new stage, we must follow the basic requirements of saving resources and protecting the environment. By formulating implementation strategies and methods,cn is an effective data sample of copyright transaction data system and matches the feature mapping parameters of discrete samples. The captured scale information distribution function is expressed as follows:(11)F=∑n=1cnT−Ht+σ2−ptθZ2.First, we can use the captured scale information and distribution function to query the effective data samples of big data interactive system data and analyse the discrete samples that save resources and protect the environment through the feature extraction and clustering analysis model of big data, so as to obtain the feature mapping parameters of discrete samples.The above methods all establish feature extraction and cluster analysis models of interactive big data in copyright transaction data system; according to the characteristic distributed fusion and clustering results of interactive big data in copyright transaction data system, the optimized query of interactive big data in copyright transaction data system is realized. However, the data precision of interactive big data query technology in copyright transaction data system is low, and its accuracy cannot meet the application requirements in this field.θ is the spatial-spectral density of the data distribution of the copyright transaction data system, and the sample information of the copyright transaction data system is comprehensively evaluated and decided, and the depth representation of the obtained output layer is as follows:(12)E=12Πσ2−ptθZ2−sinθ2dl.By establishing the feature extraction and cluster analysis model of copyright transaction data system, after optimizing the interactive data of the system, the preliminary evaluation decision is made according to the distributed accuracy of features, and then, the output layer variable values of the interactive big data of the copyright transaction data system are analysed through the established cluster analysis model, and then, the comprehensive evaluation decision of the system sample information is further put forward to reflect the participation degree.ϕ is the ambiguity frequency shift of copyright transaction data system. Using joint autocorrelation mapping, according to the fuzzy clustering system of interactive big data in copyright transaction data system, the expression of fuzzy feature matching function is as follows:(13)s=∫1sinϕ2dφ+E2Πσ2−ptθZ2.First, we should complete the feature extraction and clustering model analysis of interactive big data in copyright transaction data system, solve the problem of distributed feature fusion and clustering results of interactive big data in the system, and solve the problem of low data precision. According to the spatial-spectral density of distribution, the joint correlation mapping can be extracted.In any blockφ, combined with the matching results of interactive big data of copyright transaction data system, the representation mode of big data interactive query network model of copyright transaction data system is as follows:(14)st=∫1sinϕ2dφ+∑j=1ZjT−E2Π2.In the network model of query system, the prescribed fusion rules are used for interactive big data fusion of copyright transaction data system, so as to realize interactive query of big data. The convergence control function of interactive big data query in copyright transaction data system is expressed as follows:(15)Ft=∑j=1ZjT+h−f2−sin2Πt.


### 3.4. Optimization of Copyright Transaction Data Transmission

With the development of copyright transaction management technology, more and more copyright transaction data management systems appear. Usually, the intelligent development and design of copyright transaction data system is realized underB/F interactive framework protocol, and its core is data collection, query, and analysis in copyright transaction data system. h represents the characteristic sampling frequency of the copyright transaction data system, and f represents the information transmission rate of the system data query. Based on this, the window width of the interactive big data transmission of the system is calculated, and the representation is as follows:(16)um=∑j=1ZjTy+q−Ft,where F is the time delay of data transmission and q is the hidden node of data transmission. The transmission frequency of interactive big data query data of copyright transaction data system is obtained by adopting adaptive copyright transaction data operation method, which is expressed as follows:(17)ϕ=−ΠFt2y+q+log3η+δ2c+q.With the development of copyright transaction management technology, more and more data management systems are produced to assist the operation and transaction.B/F interactive framework protocol is needed for data collection and query analysis in copyright transaction data system. Then, the time delay of data transmission and the hidden nodes during data transmission are determined, and the window width based on the information transmission rate of system data query is obtained according to the feasibility of copyright transaction data operation. Without it, the interactive big data transmission of transaction data system cannot be successfully completed.Using the hidden node of time delay when copyright data are traded, the way of interacting with big data to query data in copyright transaction data system is further improved by using adaptive method, as follows:(18)py=∑n=1,t=1μnr+log3η+δ2c+q.From the gradient error of data transmission,r is the step. Through the method of reconstructing the similarity features of the target sample set, the interactive big data model of the copyright transaction data system is deeply optimized, and the global optimal control function representation of the model is as follows:(19)Y=∑n=1,t=1μnr+o−pt+py2tΠ2.In view of the above problems, this paper puts forward to improve the interactive data query technology of copyright transaction data system. Through the simulation test, we can see that the technology proposed in this paper has superior performance in improving the interactive query ability of big data of copyright transaction data. The path of green development of copyright industry lies in establishing the goal of industrial development, that is, realizing the green growth of copyright trading industry.μn is the step size of adaptive calculation. Finally, the optimal model representation of interactive big data in copyright transaction data system is as follows:(20)Fk=Ft+∑n=1,t=1μnr−pt.The weights and biases in each layer are updated by backpropagation, and the updated parameters are simulated and practiced in the next cyclic iteration, so as to continuously improve the recognition rate and lose the function in the network model. The weight valuew and offset position b in the network model are processed by improving the operation rate, and the specific operation mode expression is as follows:(21)w2=w11−lμm−lδδwjw,b.Keeping the value unchanged, this operation is called one-sided implication. In BS network model, the gradient disappears because of the algorithm linkage differential rules. Solving the activation function brought by continuous multiplication can make the output of some data 0, thus obtaining the interdependence between parameters and effectively alleviating the occurrence of fitting in the process of copyright transaction data. Specifically,(22)b2=b1−lδδwjw,b.BS network feeds back the classification error to the latest updated parameters of the network model through backpropagation and minimizes the identification loss in the network model through continuous updating iteration. The specific treatment methods are as follows:(23)w2=w11−lηm−l∂∂wJw,b.In this way, the performance of the model is improved and the time of copyright transaction data processing is shortened. Copyright transaction data are self-enhanced by a coefficient greater than 1, which accelerates the approach speed of the network to the extreme point. As the learning progresses, the model is close to convergence, so at this stage, according to the relative rate of change of the cost function, the calculation rate is appropriately increased or decreased until the network converges or reaches the set number of repetitions. The utilization of then copyright database can be expressed as follows:(24)b2=b1−l∂∂bJw,b,ln=l0λn,1≤n≤b,ln−11−αx,n>b⋅ax≥0,ln−11−aαx,n>b⋅ax<0.


## 3.1. Copyright Transaction Data Range Detection

With the development of copyright transaction management technology, more and more copyright transaction data management systems appear. Usually, the intelligent development and design of copyright transaction data system is realized under B/F interactive framework protocol, and its core is data collection, query, and analysis in copyright transaction data system. The green development of copyright industry lies in promoting the green growth of copyright industry; that is, “green” is the regulation of the specific development mode of copyright industry, and “development” is the goal that the industry must achieve when practising the green concept. We promote the green growth of copyright industry. It is necessary to continuously improve the supply quality of copyright products and services. It is necessary to widely implement green growth management in the industrial system and pay attention to the effectiveness of industrial green growth management. In order to realize the detection of data distribution range, joint time series analysis is adopted to obtain the timestamp proofreading representation of copyright trading system data as follows:(1)xt=1−log2pt∑n=1an.Under theB/F interactive architecture protocol, we can focus on monitoring the effectiveness of industrial green growth management and calculating the distribution results of data distribution range, and its function is to promote the green growth of copyright industry and improve the effectiveness of management, promote the development of green industry towards diversification, and improve the group’s understanding of green industry and green projects.Pt is the internal frequency of copyright trading system data, e is the timestamp code defining copyright trading system data, t is the time point of receiving data, and an is the sample signal in the system. In the structure mapping of fuzzy information, the model network structure is used to compress the data, and the key string representation of the proofread copyright transaction data system is as follows:(2)L=Pt∑n=1aneδ2μθlog2m+1+L1−A.Due to the large scale of interactive big data distribution and poor feature clustering in copyright transaction data system, the query accuracy of interactive big data in copyright transaction data system is not high. Therefore, optimizing the interactive big data query technology of copyright transaction data system is of great significance in improving the management ability of copyright transaction data system. In the formula,δ is heterogeneity, which is the key data buffer memory size of copyright transaction data system, and μ is the similarity coefficient of data characteristics of copyright transaction data system. By using block-matching technology, the range detection expression of interactive big data of copyright transaction data system is obtained as follows:(3)H=∑n=1ane+δ2μθxt.


## 3.2. Feature Extraction of Copyright Transaction Data

It is slightly biased to judge the copyright trading industry as an absolute green product. Based on this, this paper analyses the connotation of the development of copyright trading industry, examines the driving force of the green development of copyright trading industry, and puts forward the development path of copyright trading industry, so as to help the high-quality development of copyright trading industry.B is the frequency of data feature points, and T is the dimension of data feature points. Information matching of copyright transaction data is carried out according to the discriminant result of feature classification attributes, and the entropy of feature classification information is obtained as follows:(4)v=2T1+2H−ABlog2m.In order to realize the feature extraction of interactive big data information in copyright trading system, according to the classical equivalence relationship between various groups of data in copyright trading system, the representation of feature classification attribute discriminant function of interactive big data in copyright trading system is obtained as follows:(5)I=v⋅2λ+K.The interactive big data query methods of copyright transaction data system mainly include system interactive big data query method based on rough set feature matching, copyright transaction data system interactive big data query method based on similarity feature analysis, and so on. In the formula,K is the power bandwidth of the signal, and h is the characteristic interference coefficient. Considering the balance of the output of the copyright transaction data system, the frequency domain equalization expression of the signal is as follows:(6)D=2λ+Klog2d+Iw.First of all, we need to consider the output balance of copyright transaction data system, according to the system interactive big data query method of rough set feature matching, the bearing operation of feature interference coefficient is carried out, and the feature extraction of interactive big data information of copyright trading system is realized, and then, the feature matching data in the interactive big data query method of copyright trading data system can be captured.d is the iteration steps of system query, and w is the interactive big data output bit rate of copyright transaction data system. Based on the steady-state identification technology, the ambiguity function representation of data sampling in copyright transaction data system is as follows:(7)G=D⋅log2d+Iw∑k=1δkd−N.First, we need to have the feature interference coefficient obtained from the interactive big data query method of copyright transaction data system. Then, the steady-state frequency domain coefficients are obtained by further operation, and the iterative steps are obtained by querying the copyright transaction data system based on the steady-state frequency domain coefficients so that we can complete the purpose.Rm is the query response rate of copyright transaction data system, and N is the time-varying rate of tracking channel. The fuzzy features are constrained and controlled, and the modal function representation of the interactive big data feature distribution points of the copyright transaction data system is as follows:(8)M=∑k=1δkd−N+Rm.First of all, we need to calculate the query response rate of the data of the publishing rights transaction data system. By tracking the time-varying rate of the channel, the fuzzy features are constrained and controlled, and then, the modality of the interactive big data feature distribution points in the copyright transaction data system is analysed. The fuzzy degree of the data in the copyright transaction data system can be reduced by using the feature clustering method when querying the spatial matching degree.di is the access bit rate of the copyright transaction data system; according to the ambiguity function of the copyright transaction data system, N is the spatial matching degree of the system data query, and the fuzzy feature extraction expression of the interactive big data of the copyright transaction data system is obtained by using the feature clustering method in the database as follows:(9)Ht=∑k=1δkd−N+1Π∑i=1di.In order to build a quantitative feature analysis model of interactive big data in copyright transaction data system, the system information is matched according to the joint self-similarity feature of copyright transaction data system. The feature matching coefficient of rough quantization isT. In the classification attribute set, according to the sample training results of copyright transaction data system, the feature clustering threshold of discrete data samples is obtained, and the association matching feature extraction of copyright transaction data system is completed according to the above analysis. According to the similarity characteristics of target samples of copyright transaction data system, the operation training of interactive big data of copyright transaction data system is realized, and the expression is as follows:(10)Z=1Π∑i=1diM+∑n=1cnT−Ht.


## 3.3. Optimization of Data Interactive Query in Copyright Trading System

The big data interactive query network model of copyright transaction data system promotes comprehensive colour transformation and high-quality development. In the new stage, we must follow the basic requirements of saving resources and protecting the environment. By formulating implementation strategies and methods,cn is an effective data sample of copyright transaction data system and matches the feature mapping parameters of discrete samples. The captured scale information distribution function is expressed as follows:(11)F=∑n=1cnT−Ht+σ2−ptθZ2.First, we can use the captured scale information and distribution function to query the effective data samples of big data interactive system data and analyse the discrete samples that save resources and protect the environment through the feature extraction and clustering analysis model of big data, so as to obtain the feature mapping parameters of discrete samples.The above methods all establish feature extraction and cluster analysis models of interactive big data in copyright transaction data system; according to the characteristic distributed fusion and clustering results of interactive big data in copyright transaction data system, the optimized query of interactive big data in copyright transaction data system is realized. However, the data precision of interactive big data query technology in copyright transaction data system is low, and its accuracy cannot meet the application requirements in this field.θ is the spatial-spectral density of the data distribution of the copyright transaction data system, and the sample information of the copyright transaction data system is comprehensively evaluated and decided, and the depth representation of the obtained output layer is as follows:(12)E=12Πσ2−ptθZ2−sinθ2dl.By establishing the feature extraction and cluster analysis model of copyright transaction data system, after optimizing the interactive data of the system, the preliminary evaluation decision is made according to the distributed accuracy of features, and then, the output layer variable values of the interactive big data of the copyright transaction data system are analysed through the established cluster analysis model, and then, the comprehensive evaluation decision of the system sample information is further put forward to reflect the participation degree.ϕ is the ambiguity frequency shift of copyright transaction data system. Using joint autocorrelation mapping, according to the fuzzy clustering system of interactive big data in copyright transaction data system, the expression of fuzzy feature matching function is as follows:(13)s=∫1sinϕ2dφ+E2Πσ2−ptθZ2.First, we should complete the feature extraction and clustering model analysis of interactive big data in copyright transaction data system, solve the problem of distributed feature fusion and clustering results of interactive big data in the system, and solve the problem of low data precision. According to the spatial-spectral density of distribution, the joint correlation mapping can be extracted.In any blockφ, combined with the matching results of interactive big data of copyright transaction data system, the representation mode of big data interactive query network model of copyright transaction data system is as follows:(14)st=∫1sinϕ2dφ+∑j=1ZjT−E2Π2.In the network model of query system, the prescribed fusion rules are used for interactive big data fusion of copyright transaction data system, so as to realize interactive query of big data. The convergence control function of interactive big data query in copyright transaction data system is expressed as follows:(15)Ft=∑j=1ZjT+h−f2−sin2Πt.


## 3.4. Optimization of Copyright Transaction Data Transmission

With the development of copyright transaction management technology, more and more copyright transaction data management systems appear. Usually, the intelligent development and design of copyright transaction data system is realized underB/F interactive framework protocol, and its core is data collection, query, and analysis in copyright transaction data system. h represents the characteristic sampling frequency of the copyright transaction data system, and f represents the information transmission rate of the system data query. Based on this, the window width of the interactive big data transmission of the system is calculated, and the representation is as follows:(16)um=∑j=1ZjTy+q−Ft,where F is the time delay of data transmission and q is the hidden node of data transmission. The transmission frequency of interactive big data query data of copyright transaction data system is obtained by adopting adaptive copyright transaction data operation method, which is expressed as follows:(17)ϕ=−ΠFt2y+q+log3η+δ2c+q.With the development of copyright transaction management technology, more and more data management systems are produced to assist the operation and transaction.B/F interactive framework protocol is needed for data collection and query analysis in copyright transaction data system. Then, the time delay of data transmission and the hidden nodes during data transmission are determined, and the window width based on the information transmission rate of system data query is obtained according to the feasibility of copyright transaction data operation. Without it, the interactive big data transmission of transaction data system cannot be successfully completed.Using the hidden node of time delay when copyright data are traded, the way of interacting with big data to query data in copyright transaction data system is further improved by using adaptive method, as follows:(18)py=∑n=1,t=1μnr+log3η+δ2c+q.From the gradient error of data transmission,r is the step. Through the method of reconstructing the similarity features of the target sample set, the interactive big data model of the copyright transaction data system is deeply optimized, and the global optimal control function representation of the model is as follows:(19)Y=∑n=1,t=1μnr+o−pt+py2tΠ2.In view of the above problems, this paper puts forward to improve the interactive data query technology of copyright transaction data system. Through the simulation test, we can see that the technology proposed in this paper has superior performance in improving the interactive query ability of big data of copyright transaction data. The path of green development of copyright industry lies in establishing the goal of industrial development, that is, realizing the green growth of copyright trading industry.μn is the step size of adaptive calculation. Finally, the optimal model representation of interactive big data in copyright transaction data system is as follows:(20)Fk=Ft+∑n=1,t=1μnr−pt.The weights and biases in each layer are updated by backpropagation, and the updated parameters are simulated and practiced in the next cyclic iteration, so as to continuously improve the recognition rate and lose the function in the network model. The weight valuew and offset position b in the network model are processed by improving the operation rate, and the specific operation mode expression is as follows:(21)w2=w11−lμm−lδδwjw,b.Keeping the value unchanged, this operation is called one-sided implication. In BS network model, the gradient disappears because of the algorithm linkage differential rules. Solving the activation function brought by continuous multiplication can make the output of some data 0, thus obtaining the interdependence between parameters and effectively alleviating the occurrence of fitting in the process of copyright transaction data. Specifically,(22)b2=b1−lδδwjw,b.BS network feeds back the classification error to the latest updated parameters of the network model through backpropagation and minimizes the identification loss in the network model through continuous updating iteration. The specific treatment methods are as follows:(23)w2=w11−lηm−l∂∂wJw,b.In this way, the performance of the model is improved and the time of copyright transaction data processing is shortened. Copyright transaction data are self-enhanced by a coefficient greater than 1, which accelerates the approach speed of the network to the extreme point. As the learning progresses, the model is close to convergence, so at this stage, according to the relative rate of change of the cost function, the calculation rate is appropriately increased or decreased until the network converges or reaches the set number of repetitions. The utilization of then copyright database can be expressed as follows:(24)b2=b1−l∂∂bJw,b,ln=l0λn,1≤n≤b,ln−11−αx,n>b⋅ax≥0,ln−11−aαx,n>b⋅ax<0.


## 4. Copyright Transaction Protection-Related Operations

### 4.1. Copyright Protection Transaction Data Schema Query

Copyright transaction data query requires high performance of BS network model. If the usage rate of copyright transaction data is too high, the optimal solution will switch back and forth near the extreme point. The operation rate of copyright transaction data is too high, which will lead to local convergence of the optimal solution of the model. It can be seen that the utilization rate of copyright transaction data greatly affects the convergence effect of the network model. In previous experiments, the calculation rate of copyright transaction data in BS network model is mostly set according to experience, which varies greatly in different periods. Therefore, setting the computation rate of copyright transaction data in BS model as a single invariant will have an important impact on the computation performance of the model. Inspired by the adaptive adjustment algorithm, the self-enhancement algorithm of copyright transaction data execution rate proposed in this paper adjusts the execution rate according to the change of copyright transaction data in each stage of the model training process. Compared with uncertain copyright transaction data, the computation enhancement algorithm for copyright transaction data can improve the performance of the model and shorten the computation time. Copyright transaction protection data query requires database-based operations, as shown in Figure1 below:Figure 1
Implementation flow chart of copyright transaction data query.With iteration, the recognition accuracy of self-enhancing algorithm for copyright transaction data operation rate has been continuously improved. After reaching a certain level, the accuracy rate remains unchanged and the BS network model converges. Therefore, the algorithm can better reflect the effect of the self-enhancing network model of copyright transaction data operation rate. CIF copyright transaction data set is composed of ten groups of transaction collections, with a total of 70,000 transaction data pieces, each of which contains more than 5,000 variable values. The CI data set model is divided into six training empty fields, each batch contains 60,000 variable values, and the test set is composed of 5,000 variable values randomly selected from a large number of copyright transaction data. Before the model training, the copyright transaction data are processed, the accuracy of the collected transaction data is tested, and the data protection is measured by the coincidence of the data set, as shown in Table1 below:Table 1
Identification and protection rate of each method in copyright database.
ModelAccuracyData protection rateK-SVM79.610.236ResN-2091.250.157ResN-11093.570.245KeN82.180.236INet201085.780.251High-1992.460.114AlexN87.120.336The 9500 copyright transaction data collected are used as test data to verify the recognition accuracy of the model. Each sample is a variable value group of 68 × 68, which is 9 groups of 0 8. Before the BS network model, the samples are normalized. The self-reinforcement algorithm of copyright transaction data operation rate is used as the network model optimization algorithm, with 128 samples in each batch and 30 generations. DPout is introduced in the model training stage to avoid the influence of lamination on experimental results. The BS network model combined with the algorithm is tested in the data set, the data classification degree of the classification results is recorded, and the accuracy of the two groups is repeatedly compared, as shown in Figure2.Figure 2
Accuracy of copyright transaction data identification algorithm.In order to better analyse the classification degree of data classification results in copyright transactions, we need to introduce and eliminate negative useless data generated on the data set in the full connection layer of model training. Classification results of the data repeatedly compared with the accuracy of the test data so that we can better present the copyright transaction data classification results of the data classification degree.The BS network model of this algorithm has lower effect on CIF data set than benefit network. Compared with HY, RN, and other deep networks, the recognition ability of this algorithm is limited, so there is a certain gap. In the model training stage, the negative data from the experiment on the Alex network model data set are introduced and eliminated in the full connection layer, and the corresponding graphics are made. The collection accuracy and recognition error rate when collecting copyright data on the database while repeating are shown in Table2.Table 2
Error rate of data collection on copyright database.
Space 0Space 0Space 0Space 0Space 0Space 0Train1.2780.9870.8120.7350.6870.612Text1.1780.8560.6340.7820.6840.524


### 4.2. Test and Analysis of Copyright Transaction Data

In order to realize the application performance in interactive query of copyright transaction data, the simulation test and analysis of copyright transaction data are carried out, and the operation efficiency of learning different data in the transaction data model is set to be 0.93, the feature matching coefficient is 0.72, the database distribution scale is 2,550 times of operation space, and the sampling interval is 0.37 s. The training data set and test data set of copyright transaction database interaction are shown in Table3.Table 3
Copyright database interactive training test data set.
Training setData set sizeTest setData sizeDatabase component 13754Database component 12166Database component 23486Database component 24379Database component 35328Database component 36539Database component 43579Database component 47538According to the above simulation conditions and parameter settings, the interactive big data query technology of copyright transaction data system based on rough set feature matching and the designed technology are used to query the interactive big data of copyright transaction data system, and get the database access throughput comparison test results. According to the database access throughput performance comparison test, the experimental results show that the system response time is faster and the server configuration is higher under the application of the technology designed in this research. Based on this technology and the designed technology, the database access efficiency test is completed, and the transmission rate of each section accessed by the copyright transaction database is shown in Figure3.Figure 3
Comparison of processing of copyright transaction data at different distances.It is very necessary to deal with potential risks. At the same time, we are faced with the problem that the amount of copyright transaction data will be very large in the fluctuation range of transmission rate, but at the same time, it is indispensable that we need to strengthen the detection and protection of data in copyright protection. According to the detection and data encryption, in order to make the protection of copyright transactions more comprehensive, data fusion and joint similarity features can be carried out so that security protection can be in place.


### 4.3. Transaction Copyright Protection and Risk Management

The security protection of copyright transaction lies in dealing with potential risks. By analysing Figure3, during the experiment, the amount of copyright transaction data will be very large in the fluctuation range of disk transfer rate. The fluctuation range of disk transfer rate in the proposed method is relatively unstable trend edge. It is also indispensable to ensure the security of copyright transaction under the condition that the transmission rate of data query of copyright transaction data is high, the so-called strengthening data detection and protection in copyright protection. Before data transmission, we will first detect and encrypt the transaction data. In order to make the protection of copyright transactions more comprehensive, we will analyse and detect the data during copyright transaction in an all-round way. Once we find abnormal data and any risk matters that threaten the security of copyright transaction, we will upload the problem data for relevant feedback. The specific effect comparison and risk detection of copyright protection methods are shown in Figure 4.Figure 4
Analysis and comparison of copyright transaction protection paths.First, we can use functions to map copyright protection data in sample space to a high-dimensional feature space. Then, the information fusion and feature matching of interactive big data in copyright transaction data system are realized by technology combined with similarity feature analysis control method, and the data fusion can solve the problem of inaccurate balance distance results.In order to prevent the danger of copyright trading and test the precision of interactive big data query of copyright protection system in different methods, analysis of Figure5 shows that the method we use has a high precision rate for interactive big data query of copyright transaction data system, which plays a good role in copyright protection. When the maintenance rate reaches over 95% and the output balance is better, the interactive big data access query performance of copyright transaction data system is improved. In order to solve the problem of imprecise query distance and time-consuming caused by copyright protection, a novel clustering algorithm is established, which maps copyright protection data in sample space to a high-dimensional feature space by using functions. However, this technology ignores the steps of data fusion, which leads to the unsatisfactory interactive query effect and the precision rate cannot meet the requirements of deep processing of big data. The technology proposed in this paper realizes interactive big data information fusion and feature matching of copyright transaction data system through joint self-similarity feature analysis control method. In order to maintain copyright transaction data in the future, ensure the safety of copyright transaction, achieve ideal application effect, solve the problem of low precision rate, and improve the effect of copyright protection, the copyright data are detected and analysed, and then transferred and maintained. The specific comparison effect is shown in Figure 6.Figure 5
Response mode of copyright security change.Figure 6
Copyright transaction protection channel test.We should use functions to map copyright protection data in sample space to a high-dimensional feature space and test the precision of copyright protection system in different methods to solve the problem that the precision cannot meet the requirements of deep processing of big data. Analytic control method solves the problem that the interactive query effect of technology is not ideal for big data information fusion and feature matching.In Figure5, calk is a copyright data detection system clock signal and stun is a system maintenance data reset signal, which is an extracted copyright protection data information symbol. If the input signal is valid, the input signal notifies the system to start operation. When the analysis meets the judgment condition, the corresponding port is output. Only when the copyright security level in the mode changes, the output signal will judge the security level and quickly detect the peak value and change type of related data. The specific copyright protection operation flow is shown in Figure 5.Copyright data protection lies in realizing high-speed Walsh conversion. Under the condition of satisfying the system timing requirement, a minimum high-speed Walsh operation module is reused 262 times, which can greatly save the consumption of resources. An implementation block diagram of the high-speed Walsh conversion includes a control unit, an input/output selection unit, a calculation unit, and an intermediate cache RAM unit. The management of copyright transaction data plays a vital role in copyright protection. The specific discovery protection and migration process is shown in Figure7.Figure 7
Copyright protection data detection and protection process.


## 4.1. Copyright Protection Transaction Data Schema Query

Copyright transaction data query requires high performance of BS network model. If the usage rate of copyright transaction data is too high, the optimal solution will switch back and forth near the extreme point. The operation rate of copyright transaction data is too high, which will lead to local convergence of the optimal solution of the model. It can be seen that the utilization rate of copyright transaction data greatly affects the convergence effect of the network model. In previous experiments, the calculation rate of copyright transaction data in BS network model is mostly set according to experience, which varies greatly in different periods. Therefore, setting the computation rate of copyright transaction data in BS model as a single invariant will have an important impact on the computation performance of the model. Inspired by the adaptive adjustment algorithm, the self-enhancement algorithm of copyright transaction data execution rate proposed in this paper adjusts the execution rate according to the change of copyright transaction data in each stage of the model training process. Compared with uncertain copyright transaction data, the computation enhancement algorithm for copyright transaction data can improve the performance of the model and shorten the computation time. Copyright transaction protection data query requires database-based operations, as shown in Figure1 below:Figure 1
Implementation flow chart of copyright transaction data query.With iteration, the recognition accuracy of self-enhancing algorithm for copyright transaction data operation rate has been continuously improved. After reaching a certain level, the accuracy rate remains unchanged and the BS network model converges. Therefore, the algorithm can better reflect the effect of the self-enhancing network model of copyright transaction data operation rate. CIF copyright transaction data set is composed of ten groups of transaction collections, with a total of 70,000 transaction data pieces, each of which contains more than 5,000 variable values. The CI data set model is divided into six training empty fields, each batch contains 60,000 variable values, and the test set is composed of 5,000 variable values randomly selected from a large number of copyright transaction data. Before the model training, the copyright transaction data are processed, the accuracy of the collected transaction data is tested, and the data protection is measured by the coincidence of the data set, as shown in Table1 below:Table 1
Identification and protection rate of each method in copyright database.
ModelAccuracyData protection rateK-SVM79.610.236ResN-2091.250.157ResN-11093.570.245KeN82.180.236INet201085.780.251High-1992.460.114AlexN87.120.336The 9500 copyright transaction data collected are used as test data to verify the recognition accuracy of the model. Each sample is a variable value group of 68 × 68, which is 9 groups of 0 8. Before the BS network model, the samples are normalized. The self-reinforcement algorithm of copyright transaction data operation rate is used as the network model optimization algorithm, with 128 samples in each batch and 30 generations. DPout is introduced in the model training stage to avoid the influence of lamination on experimental results. The BS network model combined with the algorithm is tested in the data set, the data classification degree of the classification results is recorded, and the accuracy of the two groups is repeatedly compared, as shown in Figure2.Figure 2
Accuracy of copyright transaction data identification algorithm.In order to better analyse the classification degree of data classification results in copyright transactions, we need to introduce and eliminate negative useless data generated on the data set in the full connection layer of model training. Classification results of the data repeatedly compared with the accuracy of the test data so that we can better present the copyright transaction data classification results of the data classification degree.The BS network model of this algorithm has lower effect on CIF data set than benefit network. Compared with HY, RN, and other deep networks, the recognition ability of this algorithm is limited, so there is a certain gap. In the model training stage, the negative data from the experiment on the Alex network model data set are introduced and eliminated in the full connection layer, and the corresponding graphics are made. The collection accuracy and recognition error rate when collecting copyright data on the database while repeating are shown in Table2.Table 2
Error rate of data collection on copyright database.
Space 0Space 0Space 0Space 0Space 0Space 0Train1.2780.9870.8120.7350.6870.612Text1.1780.8560.6340.7820.6840.524


## 4.2. Test and Analysis of Copyright Transaction Data

In order to realize the application performance in interactive query of copyright transaction data, the simulation test and analysis of copyright transaction data are carried out, and the operation efficiency of learning different data in the transaction data model is set to be 0.93, the feature matching coefficient is 0.72, the database distribution scale is 2,550 times of operation space, and the sampling interval is 0.37 s. The training data set and test data set of copyright transaction database interaction are shown in Table3.Table 3
Copyright database interactive training test data set.
Training setData set sizeTest setData sizeDatabase component 13754Database component 12166Database component 23486Database component 24379Database component 35328Database component 36539Database component 43579Database component 47538According to the above simulation conditions and parameter settings, the interactive big data query technology of copyright transaction data system based on rough set feature matching and the designed technology are used to query the interactive big data of copyright transaction data system, and get the database access throughput comparison test results. According to the database access throughput performance comparison test, the experimental results show that the system response time is faster and the server configuration is higher under the application of the technology designed in this research. Based on this technology and the designed technology, the database access efficiency test is completed, and the transmission rate of each section accessed by the copyright transaction database is shown in Figure3.Figure 3
Comparison of processing of copyright transaction data at different distances.It is very necessary to deal with potential risks. At the same time, we are faced with the problem that the amount of copyright transaction data will be very large in the fluctuation range of transmission rate, but at the same time, it is indispensable that we need to strengthen the detection and protection of data in copyright protection. According to the detection and data encryption, in order to make the protection of copyright transactions more comprehensive, data fusion and joint similarity features can be carried out so that security protection can be in place.


## 4.3. Transaction Copyright Protection and Risk Management

The security protection of copyright transaction lies in dealing with potential risks. By analysing Figure3, during the experiment, the amount of copyright transaction data will be very large in the fluctuation range of disk transfer rate. The fluctuation range of disk transfer rate in the proposed method is relatively unstable trend edge. It is also indispensable to ensure the security of copyright transaction under the condition that the transmission rate of data query of copyright transaction data is high, the so-called strengthening data detection and protection in copyright protection. Before data transmission, we will first detect and encrypt the transaction data. In order to make the protection of copyright transactions more comprehensive, we will analyse and detect the data during copyright transaction in an all-round way. Once we find abnormal data and any risk matters that threaten the security of copyright transaction, we will upload the problem data for relevant feedback. The specific effect comparison and risk detection of copyright protection methods are shown in Figure 4.Figure 4
Analysis and comparison of copyright transaction protection paths.First, we can use functions to map copyright protection data in sample space to a high-dimensional feature space. Then, the information fusion and feature matching of interactive big data in copyright transaction data system are realized by technology combined with similarity feature analysis control method, and the data fusion can solve the problem of inaccurate balance distance results.In order to prevent the danger of copyright trading and test the precision of interactive big data query of copyright protection system in different methods, analysis of Figure5 shows that the method we use has a high precision rate for interactive big data query of copyright transaction data system, which plays a good role in copyright protection. When the maintenance rate reaches over 95% and the output balance is better, the interactive big data access query performance of copyright transaction data system is improved. In order to solve the problem of imprecise query distance and time-consuming caused by copyright protection, a novel clustering algorithm is established, which maps copyright protection data in sample space to a high-dimensional feature space by using functions. However, this technology ignores the steps of data fusion, which leads to the unsatisfactory interactive query effect and the precision rate cannot meet the requirements of deep processing of big data. The technology proposed in this paper realizes interactive big data information fusion and feature matching of copyright transaction data system through joint self-similarity feature analysis control method. In order to maintain copyright transaction data in the future, ensure the safety of copyright transaction, achieve ideal application effect, solve the problem of low precision rate, and improve the effect of copyright protection, the copyright data are detected and analysed, and then transferred and maintained. The specific comparison effect is shown in Figure 6.Figure 5
Response mode of copyright security change.Figure 6
Copyright transaction protection channel test.We should use functions to map copyright protection data in sample space to a high-dimensional feature space and test the precision of copyright protection system in different methods to solve the problem that the precision cannot meet the requirements of deep processing of big data. Analytic control method solves the problem that the interactive query effect of technology is not ideal for big data information fusion and feature matching.In Figure5, calk is a copyright data detection system clock signal and stun is a system maintenance data reset signal, which is an extracted copyright protection data information symbol. If the input signal is valid, the input signal notifies the system to start operation. When the analysis meets the judgment condition, the corresponding port is output. Only when the copyright security level in the mode changes, the output signal will judge the security level and quickly detect the peak value and change type of related data. The specific copyright protection operation flow is shown in Figure 5.Copyright data protection lies in realizing high-speed Walsh conversion. Under the condition of satisfying the system timing requirement, a minimum high-speed Walsh operation module is reused 262 times, which can greatly save the consumption of resources. An implementation block diagram of the high-speed Walsh conversion includes a control unit, an input/output selection unit, a calculation unit, and an intermediate cache RAM unit. The management of copyright transaction data plays a vital role in copyright protection. The specific discovery protection and migration process is shown in Figure7.Figure 7
Copyright protection data detection and protection process.


## 5. Concluding Remarks

This paper deeply analyses the development and trading mode of copyright industry under the background of Informa ionization. How to make the copyright transaction in the context of information security transactions and transfer, and how to protect the security of copyright transactions, we put forward in the context of information security processing, according to the BS network model better reflect the copyright transaction data operation rate self-enhanced network model effect. Starting from the data analysis and detection of copyright transaction database, and detecting and encrypting the transaction data before data transmission can make the protection of copyright transaction more comprehensive. The data query of copyright transaction protection needs to be based on the operation of database. Therefore, the CIF copyright transaction data set and CI data set we use provide us with great support for copyright protection in operability. The comprehensive use of these methods and set model also makes us more effective in using copyright transaction data and more comprehensive in copyright transaction protection.



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*Source: 2539949-2022-07-07.xml*