# Research on International Law Data Integrity Guarantee Based on Antiterrorism Prediction Algorithm

Authors: Huang Ru Qing
Journal: Journal of Mathematics
 (2022)

---

## Abstract

In order to improve the quality of international law data, this paper designs a method to ensure the integrity of international law data based on an antiterrorism prediction algorithm. On the basis of introducing random function and deep learning technology, the prediction model set is split through the trusted seed model, the model selection is completed through iterative optimization, and then new terrorism-related factors are added to obtain the new prediction model set, so as to complete the antiterrorism data prediction. Based on the antiterrorism prediction data obtained by the above process, on the basis of identifying incomplete data, the antiterrorism data integrity guarantee method in international law database is designed through the process of determining data integrity operation parameters, eliminating worthless feature items, processing noise dimension reduction, and adding feature series of data integrity. Experimental results show that the frequency range and data sensitivity of international law data can be optimized by using this method, the audit process of data integrity is less time consuming, and the data integrity after processing can reach 0.967.


---

## Body

## 1. Introduction

Internationally recognized international law in which several states participate refers to the sum of the rule of law applicable between sovereign states and other international personality entities, also known as public international law, which is the law that distinguishes private international law or conflict of laws [1]. International law is also distinct from domestic law, which is the internal law of a state that regulates the conduct of individuals and other legal entities within its jurisdiction.As a national code of conduct, the emergence and existence of international law must have a certain material basis, which is the communicative behavior and relations between states [2]. In the field of international law, the sum of such relations between states is traditionally called “international community,” which is the social basis for the emergence and existence of international law. Since the middle of the 20th century, the understanding of the relations between countries has shown a trend towards the “international community.” The term “international community” is used more and more frequently in academic research and practice of international relations and international law [3]. In the writing of international law, the scope of this community usually points to the whole world, not limited to a group composed of a few countries. This allows international societies to overlap geographically, and the social basis of international law evolves from international society to local politics.With the development of the international legal system, after collecting case analysis and reports in the field of public international law, relevant organizations have constructed international law databases, including data of international courts, domestic courts, and ad hoc courts [4]. For the first time, this kind of database fully collects and analyzes international case law and provides in-depth analysis and guidance from legal experts. In the process of constructing such a database, however, due to factors such as network node deviation, susceptible to incomplete data, and with the extension of network transmission time, the data information keeps increasing; not only will the international law cause incomplete data balance level to continue declining, it can also severely affect information parameter aggregation behavior. In order to avoid the occurrence of the above situation, it is essential to ensure the integrity of international law data [5].At present, in terms of data integrity guarantee research, [6] proposes a method of cloud storage data integrity audit and guarantee, which summarizes the advantages and disadvantages of existing data audit protocols from the three aspects of data ownership proof, data recovery proof, and data ownership proof. The performance of typical protocols is evaluated in terms of technical principle, time cost, reliability, and detection probability. It is found that most audit protocols can only be used in specific scenarios, and it is urgent to further study universal audit protocols with balanced performance in all aspects. Audit protocols matching cloud storage, fog storage, blockchain, and other technologies are still in the exploratory stage. Finally, this method predicts the future development trend of outsourcing data auditing from three aspects: cloud audit protocol, alliance chain audit protocol, and component pool audit protocol.In [7], a method to ensure the integrity of outsourced spatial database was proposed, which improved on the existing verification tree structure and proposed a spatial verification data structure VSS-Tree. SS-tree is used as the basic structure and additional verification information, and boundary ball is used to divide regions to increase the degree of nodes, effectively reduce the height of the verification tree, and avoid unnecessary disk access, so as to improve the processing efficiency of spatial query verification. This ensures that the querying user can verify that the data in the query result is real and contains all data that meets the criteria.Reference [8] proposed multicopy integrity based on DDCT list audit and security methods. In this method, first, DDCT list is introduced to solve the problem of dynamic data operation; at the same time, the block number copy, the version number, and the information such as timestamp are stored in the table; next, to resist malicious cloud service provider attacks, a copy of the data signature verification algorithm is designed based on timestamp. Then, the concept of the duplicate block, including blockhead and block body, is proposed. The blockhead stores the signature information of duplicate data based on timestamp identification and authentication, and the block body stores the encrypted duplicate data. Finally, a third-party audit institution audits the integrity of multicopy data in the cloud by adopting the signature authentication algorithm based on duplicate timestamps.However, in practical application, it is found that the frequency range and sensitivity of data are not ideal after the application of the above traditional methods, the integrity audit process takes a long time, and the data integrity needs to be further improved. Based on the above analysis, this study takes antiterrorism behavior as the analysis basis and designs an antiterrorism prediction algorithm to ensure the integrity of antiterrorism data in the international law database.The following is a summary of the research: Section1 contains the introduction. Section 2 discusses the literature review and background. Section 3 discusses the international law data integrity guarantee based on the antiterrorism prediction algorithm. In Section 4, experiment and analysis of the proposed concepts are provided. Finally, the conclusion brings the paper to an end in Section 5.


## 2. Background

International law has the following characteristics:(1)
International law provides a set of rules of conduct for states in dealing with their foreign relations, as well as their rights and obligations under international law(2)
International law is mandatory, but the way of enforcement is different from that of domestic law; however, the special way of enforcement is still mandatory(3)
Some important international treaties have clearly defined the effect of international law(4)
International practice has proved that international law, as the law between states, is not only recognized by all countries in the world but also abided by, by all countries [9]The specific division of international law is shown in Figure1.Figure 1
The division of international law.According to its scope of application, international law can be divided into general international law and special international law. General international law is the international law binding all countries, and special international law is the international law binding two or a few countries [10]. Geographically, there are universal international law and regional international law. Universal international law is the international law binding all countries in the world, while regional international law is the international law binding only countries in a certain region, such as “American international law” and “Latin American international law.” These are manifestations of the diversity and complexity of relations between states. However, in essence, only general and universal international law is generally referred to as international law, and the so-called special international law or regional international law must be restricted by general and universal international law [11].Theorists and practitioners in the field of international law, law, and international relations are trying to explore some questions about its nature, function, and status. For example, is international law equally binding all states? Does international law have common pursuit that transcends states? Why is international law so weak? Why does international law often pale in comparison with political power and powerful states? Is it possible that international law, like domestic law, will become more systematic, binding, enforceable, and authoritative? In order to explain the nature, state, application methods, and operation characteristics of international law, some research results have tried to put forward a lot of interpretations, among which the fragmentation of international law is discussed. In addition to the exploration of specific problems, there are also some attempts on methods and perspectives, for example, the school of “new” natural law, which combines international law with ethics, and the interdisciplinary study of international law and international relations [12]. It can be seen that these question-based explorations and discussions on perspectives and methods can help us better understand what international law is, what state it is in, and why it is in this state and can help us to distinguish right from wrong.The construction of an international law database plays an important role in case analysis and reports, latest updated articles, international organizations, and international cooperation in the field of public international law, as well as in-depth research on hot issues with a wide coverage [13]. Take Max Planck Encyclopedia of Public International Law as an example. It is a comprehensive and in-depth electronic reference library in the field of international law, including many latest articles on international law, international organizations, and international cooperation, as well as a wide range of hot issues. The database provides quick and advanced search methods and can be navigated by alphabetical lists of titles, authors, and topics. The homepage of Max Planck Encyclopedia of Public International Law is shown in Figure 2.Figure 2
Max Planck Encyclopedia of Public International Law homepage.It can be said that the establishment and application of the international law database have promoted the development and reform of international law to a great extent. In the process of constructing such a database, however, due to factors such as network node deviation, susceptible to incomplete data, and with the extension of network transmission time, the data information keeps increasing; not only will the international law cause incomplete data balance level to continue declining, it can also severely affect information parameter aggregation behavior [14]. In order to avoid the above situation, it is essential to ensure the integrity of international law data. Therefore, this study designs an international law data integrity guarantee method based on an antiterrorism prediction algorithm. The design process of this method is described in detail below.


## 3. International Law Data Integrity Guarantee Based on the Antiterrorism Prediction Algorithm

Based on the analysis of antiterrorism behavior and the design of antiterrorism prediction algorithm, this study designs the integrity guarantee method of antiterrorism data in an international law database.


### 3.1. Analysis of Antiterrorism Prediction Algorithm

With the accelerated development of global informatization, international terrorist activities have been constantly upgraded, and in recent years, they have been rampant and have extremely bad effects. In the face of the rapid transformation and upgrading of international terrorism, the antiterrorism situation has become increasingly severe, seriously affecting the social order and causing huge casualties and property losses [15]. Under the guidance of international law, it has become a consensus that intelligence leads the fight against terrorism in the face of severe challenges in the new situation. The post-event analysis of international terrorism can often be found in advance. In fact, there is a lot of information about terrorist attacks. The key is the lack of prediscovery mechanisms and tools, which are required by the covered big data. However, antiterrorism prediction is faced with challenges such as difficult multidomain data fusion, small sample data, and few model evaluation methods. Therefore, how to use big data and antiterrorism prediction deep integration is the key link to win terrorist activities. Big data is used to mine and predict terrorism-related intelligence, build a new antiterrorism prediction model, and realize timely prediction, early warning, and prevention of terrorist organization behavior, which is of great significance to the study of antiterrorism prediction [16].The application of the antiterrorism prediction algorithm can improve the decision-making ability to predict terrorist organizations and terrorists. In practical application, the purpose of the antiterrorism prediction algorithm is to predict when and where terrorist organizations and terrorists will create terrorist incidents, and then give decision-making suggestions to antiterrorism agencies, so as to avoid the occurrence of terrorist incidents as far as possible. Facts prove that antiterrorism prediction algorithms are often better than traditional manual analysis.A tactical prediction algorithm and a target locking algorithm are part of the traditional antiterrorism prediction algorithm. It is a calculation process that involves feeding one set of numerical values into an algorithm model and receiving a different set of numerical values as a result. Because the antiterrorism prediction algorithm is essentially a machine learning algorithm, it is based on the machine learning working concept; that is, it continuously acquires new knowledge and skills by simulating human learning behavior, reorganizing its own knowledge structure, and improving its own performance in order to improve its own prediction level [17].In general, the overall thinking of the traditional antiterrorism prediction algorithm mainly includes problem perception, data preparation, directional mining, evaluation and prediction, and feedback optimization. The process is shown in Figure3.Figure 3
The whole idea of traditional antiterrorism prediction algorithm.In Figure3, the core of the whole prediction algorithm is data mining and feedback optimization. Through data mining, the antiterrorism prediction model is built, and the accuracy of the prediction model is improved through several times of feedback optimization. However, antiterrorism prediction is faced with many challenges, such as difficult multidomain data fusion, small sample data, and few evaluation methods. Therefore, under the existing conditions, the practical feasibility of such methods is weak and the prediction universality is poor. At the same time, the research results obtained by investing a lot of manpower and material resources are easy to overturn or eliminate, and the cost is huge.In view of the shortcomings of existing technologies, this study uses a random algorithm to optimize the traditional antiterrorism prediction process. The specific steps are as follows:Step 1.
Collect the required terrorism-related data based on the perception of terrorism-related data.Step 2.
Use deductive method to mine the behavior data of historical terrorist organizations.Step 3.
Obtain the trusted seed modelS through deep learning.Step 4.
Assume that the prediction result of then-th seed model is Rn, while the actual terrorist act is v. On the basis of the preset reliability c, input the terrorism-related factor vector, when(1)c>DRnai|vai=Rnaivai,where D⋅ represents the data divergence, and the similarity evaluation of the two models is carried out, so the seed model S is called the trusted seed model.Step 5.
On the basis of several credible seed models, a random function fission prediction model is used to predict the parameter valueM. The random function used here is the Monte Carlo algorithm.Step 6.
The model setM′ was tested on the test set, and the models with poor prediction results were removed to screen out the model set M′ with relatively excellent performance.Step 7.
Save the prediction model setM′, and make antiterrorism prediction according to the prediction model set.Step 8.
Once a new terrorism-related factorf appears, return to Step 3 and add f to obtain a new prediction model set M″; the process is as follows:(2)M″=DRnai|vai×S+fM′+cM.
The above antiterrorism prediction algorithm still adopts the data acquisition and mining part of the existing antiterrorism research, which retains the advantages of traditional research. Meanwhile, it makes up for the disadvantages of the current research through the idea of a seed model, improves the prediction accuracy, and supports the addition of new terrorism-related factors, thus improving the scalability of the algorithm.


### 3.2. Design of International Law Data Integrity Guarantee Method

Based on the antiterrorism prediction data obtained from the above process, the integrity assurance method of antiterrorism data in the international law database is designed.


#### 3.2.1. Identifying Incomplete Data

The incomplete data identification process is composed of three processing steps: incomplete data subspace extraction, statistical element reconstruction, and separable amplitude calculation. The specific design process is as follows.(1) Subspaces of Incomplete Data Extracted by Factor Analysis. Data subspace is a relatively fuzzy storage set of information parameters, and the larger the information coverage area involved in the subspace, the more the incomplete data samples to be screened [18]. Therefore, in order to obtain accurate extraction results of incomplete data subspace, the factor analysis method is used to integrate the obtained historical data into multiple information parameter sets; then, according to the multivariate statistical algorithm, the original set space is improved; and finally, all the information parameters are put into the subspace set structure.It is assumed thatg1 and g2, respectively, represent two different parameter value factors of incomplete data information and that g2>g1 is satisfied. It is stipulated that Δt represents the unit extraction time of data information parameters. In general, the physical value range of this indicator always exists between 0 and 1. By combining the above physical quantities, the subspace extraction results of incomplete data can be expressed as(3)h=ω×Δt2g2−g1∂q¯,where ∂ represents the reasonably defined term of incomplete data, ω represents the weight value of single extraction of data information parameters, and q¯ represents the mean value of single transmission of incomplete data information.(2) Reconstructed Statistic. Statistics are standard conditions for measuring the transmission capacity of data information parameters. The higher the level of numerical quantity of statistical element nodes is, the stronger the constraint ability of the applied algorithm is [19]. With the increase of incomplete data screening, the number of statistical element nodes will decrease obviously due to the influence of computing, which will lead to the continuous decline of screening application ability. In order to solve this problem, the statistical element nodes are reconstructed in this study. On the one hand, the changing trend of the screening amount of incomplete data is avoided, and on the other hand, the computational amount of data information can be effectively controlled.Supposeη0 represents the minimum constraint value of statistical element coefficient, ηn represents the maximum constraint value of statistical element coefficient, and N represents the actual iterative transmission times of incomplete data information. Simultaneous formula (3) can be used to express the statistical element reconstruction result as(4)z=e×h∑η0ηnN×b¯2,where e represents the transmission characteristic value of incomplete data information parameters, p represents the extraction permission value of information parameters, and b¯ represents the mean existence of statistical element nodes.(3) Separable Amplitude Quantity. In the process of identifying incomplete data, the data cannot maintain the invariable existence state but will show obvious turbulent change with the increase of information parameter storage level. In this case, there is always a certain numerical comparison between the parameters of selectable and nonselectable data, which can separate the amplitude.Separable amplitude is a piece of dynamic data information matching result. As the amount of data to be screened increases, the numerical coordination relationship is always in the existing state of absolute change. Therefore, in order to obtain effective calculation results of separable amplitude, it is necessary to accurately grasp the parameter value level of incomplete data information and, on this basis, to complete the overall planning and coordination of related information parameters.Assuming thatα and β are two different amplitude nodes, jα represents the magnitude of amplitude separation when the coefficient is α, and jβ represents the magnitude of amplitude separation when the coefficient is β. Simultaneous formula (3) can be used to calculate the result of separable magnitude expressed as(5)S=z2+yk1+k2jβ−jα2,where y represents the basic separation conditions of amplitude parameters, and k1 and k2 represent two different incomplete data items to be counted as index definitions. To sum up, the identification and processing of incomplete international law data are completed.


#### 3.2.2. Data Integrity Assurance

In order to guarantee the data integrity of international law, the operation parameters of data integrity must be determined first. Therefore, the text classification training is used to adjust the feature items of the data, some worthless feature items are removed by feature dimension, and the noise of international law data is removed by feature dimension reduction.Assuming that the feature item of the data isci and the total number of data text categories is ui, the calculation process of obtaining the data’s worthless features is as follows:(6)φci,ui=Pci∩uiXciXui.Among them, the frequency of feature itemci in international law data text can be expressed as Pci∩ui.If the training sample of the complete feature item of data isXci, the expression of the complete category item of data contained in the training sample is Xui. Therefore, when there is a parallel relationship between the corresponding data text category ui and the feature term, the ratio of Pci∩ui presents a linear positive relationship, and there is a correlation between the integrity of the data in the feature term and Pci∩ui. When Pci∩ui increases, data integrity also increases.On this basis, feature terms of international law data are reduced in dimension, proportional relationship is set on the basis of data feature terms, data feature dimensions are calculated in combination with feature terms, and mutual information is used to improve the classification accuracy of data integrity feature dimension.Knowing the chi-square value between data integrity features and text categories, as well as the relationship between feature dimensionality reduction, the total calculation is performed according to the data features, so that the total number of dimensionality reductions of text features can be obtained to determine the integrity of international data. The relevant test criteria of text categories need to be classified and identified. The process is as follows:(7)ωc=φci,uiε×ϑ,where the word frequency parameter of international law data is ε and the number of integrity feature items in the data feature category is ϑ. Then, feature items with integrity word frequency in data are separated from documents, and word frequency feature categories are introduced and associated. According to the weight parameters of high-frequency feature items, the weight values of low-frequency feature items of international law data are reduced [20]. Integrity operation parameters are determined as follows according to data integrity feature items:(8)γ=ωc×Pci−∑i=1mPcim.Among them, the frequency expression of feature items of international law data isPci. The data integrity guarantee mechanism of international law is established according to the integrity operation parameters obtained.First of all, the international law data in the sample set is decomposed intom sets, and the number of text sets after classification is mm−1/2. The correlation coefficient in the corresponding classification training texts is calculated by the corresponding classification collection. According to the integrity operation parameter integrity guarantee range obtained above, the formula is as follows:(9)F=x1y1+x2y2+⋯+xmymx12+x22+⋯xm2y12+y22+⋯+ym2,where xm and ym are vector values in the sample set of international law data. After processing the decomposed international law data text set according to the scope of text classification, the integrity feature item matrix is obtained as follows:(10)m′=mm−12x1y1x1y2⋯x1ymx2y2x2y2⋯x2ym⋮⋮⋱⋮xmymxmy2⋯xmym.On this basis, according to the setting of the detection prerequisites of integrity feature items by the detection function, the results are as follows:(11)τci=limn⟶mτcinn,where the parameter value of the integrity feature function is τ and the measured international law data integrity feature category is τx1,x2,…,xn. When the parameter value of the characteristic function is τn=τ and limn⟶mKfin=Kx1,x2,…,xn, the positive definite relation formula is used to calculate the curve amplitude of international law data integrity, and at the same time, the overall characteristics of the data sample are identified. Assuming that the width of the integrity function of international law data is σ, vector values xm and ym are used to calculate the integrity characteristics of the data as a whole, and the process is as follows:(12)T=0.5xm×κ×σ+0.5ym×κ−1×σ.The new international law data sequence equation is obtained by summation of data integrity characteristic series as follows:(13)O=∑κ=1Nzκ×T.Thus, a complete international law data series can be obtained; that is, international law data integrity assurance processing is completed.


## 3.1. Analysis of Antiterrorism Prediction Algorithm

With the accelerated development of global informatization, international terrorist activities have been constantly upgraded, and in recent years, they have been rampant and have extremely bad effects. In the face of the rapid transformation and upgrading of international terrorism, the antiterrorism situation has become increasingly severe, seriously affecting the social order and causing huge casualties and property losses [15]. Under the guidance of international law, it has become a consensus that intelligence leads the fight against terrorism in the face of severe challenges in the new situation. The post-event analysis of international terrorism can often be found in advance. In fact, there is a lot of information about terrorist attacks. The key is the lack of prediscovery mechanisms and tools, which are required by the covered big data. However, antiterrorism prediction is faced with challenges such as difficult multidomain data fusion, small sample data, and few model evaluation methods. Therefore, how to use big data and antiterrorism prediction deep integration is the key link to win terrorist activities. Big data is used to mine and predict terrorism-related intelligence, build a new antiterrorism prediction model, and realize timely prediction, early warning, and prevention of terrorist organization behavior, which is of great significance to the study of antiterrorism prediction [16].The application of the antiterrorism prediction algorithm can improve the decision-making ability to predict terrorist organizations and terrorists. In practical application, the purpose of the antiterrorism prediction algorithm is to predict when and where terrorist organizations and terrorists will create terrorist incidents, and then give decision-making suggestions to antiterrorism agencies, so as to avoid the occurrence of terrorist incidents as far as possible. Facts prove that antiterrorism prediction algorithms are often better than traditional manual analysis.A tactical prediction algorithm and a target locking algorithm are part of the traditional antiterrorism prediction algorithm. It is a calculation process that involves feeding one set of numerical values into an algorithm model and receiving a different set of numerical values as a result. Because the antiterrorism prediction algorithm is essentially a machine learning algorithm, it is based on the machine learning working concept; that is, it continuously acquires new knowledge and skills by simulating human learning behavior, reorganizing its own knowledge structure, and improving its own performance in order to improve its own prediction level [17].In general, the overall thinking of the traditional antiterrorism prediction algorithm mainly includes problem perception, data preparation, directional mining, evaluation and prediction, and feedback optimization. The process is shown in Figure3.Figure 3
The whole idea of traditional antiterrorism prediction algorithm.In Figure3, the core of the whole prediction algorithm is data mining and feedback optimization. Through data mining, the antiterrorism prediction model is built, and the accuracy of the prediction model is improved through several times of feedback optimization. However, antiterrorism prediction is faced with many challenges, such as difficult multidomain data fusion, small sample data, and few evaluation methods. Therefore, under the existing conditions, the practical feasibility of such methods is weak and the prediction universality is poor. At the same time, the research results obtained by investing a lot of manpower and material resources are easy to overturn or eliminate, and the cost is huge.In view of the shortcomings of existing technologies, this study uses a random algorithm to optimize the traditional antiterrorism prediction process. The specific steps are as follows:Step 1.
Collect the required terrorism-related data based on the perception of terrorism-related data.Step 2.
Use deductive method to mine the behavior data of historical terrorist organizations.Step 3.
Obtain the trusted seed modelS through deep learning.Step 4.
Assume that the prediction result of then-th seed model is Rn, while the actual terrorist act is v. On the basis of the preset reliability c, input the terrorism-related factor vector, when(1)c>DRnai|vai=Rnaivai,where D⋅ represents the data divergence, and the similarity evaluation of the two models is carried out, so the seed model S is called the trusted seed model.Step 5.
On the basis of several credible seed models, a random function fission prediction model is used to predict the parameter valueM. The random function used here is the Monte Carlo algorithm.Step 6.
The model setM′ was tested on the test set, and the models with poor prediction results were removed to screen out the model set M′ with relatively excellent performance.Step 7.
Save the prediction model setM′, and make antiterrorism prediction according to the prediction model set.Step 8.
Once a new terrorism-related factorf appears, return to Step 3 and add f to obtain a new prediction model set M″; the process is as follows:(2)M″=DRnai|vai×S+fM′+cM.
The above antiterrorism prediction algorithm still adopts the data acquisition and mining part of the existing antiterrorism research, which retains the advantages of traditional research. Meanwhile, it makes up for the disadvantages of the current research through the idea of a seed model, improves the prediction accuracy, and supports the addition of new terrorism-related factors, thus improving the scalability of the algorithm.


## 3.2. Design of International Law Data Integrity Guarantee Method

Based on the antiterrorism prediction data obtained from the above process, the integrity assurance method of antiterrorism data in the international law database is designed.


### 3.2.1. Identifying Incomplete Data

The incomplete data identification process is composed of three processing steps: incomplete data subspace extraction, statistical element reconstruction, and separable amplitude calculation. The specific design process is as follows.(1) Subspaces of Incomplete Data Extracted by Factor Analysis. Data subspace is a relatively fuzzy storage set of information parameters, and the larger the information coverage area involved in the subspace, the more the incomplete data samples to be screened [18]. Therefore, in order to obtain accurate extraction results of incomplete data subspace, the factor analysis method is used to integrate the obtained historical data into multiple information parameter sets; then, according to the multivariate statistical algorithm, the original set space is improved; and finally, all the information parameters are put into the subspace set structure.It is assumed thatg1 and g2, respectively, represent two different parameter value factors of incomplete data information and that g2>g1 is satisfied. It is stipulated that Δt represents the unit extraction time of data information parameters. In general, the physical value range of this indicator always exists between 0 and 1. By combining the above physical quantities, the subspace extraction results of incomplete data can be expressed as(3)h=ω×Δt2g2−g1∂q¯,where ∂ represents the reasonably defined term of incomplete data, ω represents the weight value of single extraction of data information parameters, and q¯ represents the mean value of single transmission of incomplete data information.(2) Reconstructed Statistic. Statistics are standard conditions for measuring the transmission capacity of data information parameters. The higher the level of numerical quantity of statistical element nodes is, the stronger the constraint ability of the applied algorithm is [19]. With the increase of incomplete data screening, the number of statistical element nodes will decrease obviously due to the influence of computing, which will lead to the continuous decline of screening application ability. In order to solve this problem, the statistical element nodes are reconstructed in this study. On the one hand, the changing trend of the screening amount of incomplete data is avoided, and on the other hand, the computational amount of data information can be effectively controlled.Supposeη0 represents the minimum constraint value of statistical element coefficient, ηn represents the maximum constraint value of statistical element coefficient, and N represents the actual iterative transmission times of incomplete data information. Simultaneous formula (3) can be used to express the statistical element reconstruction result as(4)z=e×h∑η0ηnN×b¯2,where e represents the transmission characteristic value of incomplete data information parameters, p represents the extraction permission value of information parameters, and b¯ represents the mean existence of statistical element nodes.(3) Separable Amplitude Quantity. In the process of identifying incomplete data, the data cannot maintain the invariable existence state but will show obvious turbulent change with the increase of information parameter storage level. In this case, there is always a certain numerical comparison between the parameters of selectable and nonselectable data, which can separate the amplitude.Separable amplitude is a piece of dynamic data information matching result. As the amount of data to be screened increases, the numerical coordination relationship is always in the existing state of absolute change. Therefore, in order to obtain effective calculation results of separable amplitude, it is necessary to accurately grasp the parameter value level of incomplete data information and, on this basis, to complete the overall planning and coordination of related information parameters.Assuming thatα and β are two different amplitude nodes, jα represents the magnitude of amplitude separation when the coefficient is α, and jβ represents the magnitude of amplitude separation when the coefficient is β. Simultaneous formula (3) can be used to calculate the result of separable magnitude expressed as(5)S=z2+yk1+k2jβ−jα2,where y represents the basic separation conditions of amplitude parameters, and k1 and k2 represent two different incomplete data items to be counted as index definitions. To sum up, the identification and processing of incomplete international law data are completed.


### 3.2.2. Data Integrity Assurance

In order to guarantee the data integrity of international law, the operation parameters of data integrity must be determined first. Therefore, the text classification training is used to adjust the feature items of the data, some worthless feature items are removed by feature dimension, and the noise of international law data is removed by feature dimension reduction.Assuming that the feature item of the data isci and the total number of data text categories is ui, the calculation process of obtaining the data’s worthless features is as follows:(6)φci,ui=Pci∩uiXciXui.Among them, the frequency of feature itemci in international law data text can be expressed as Pci∩ui.If the training sample of the complete feature item of data isXci, the expression of the complete category item of data contained in the training sample is Xui. Therefore, when there is a parallel relationship between the corresponding data text category ui and the feature term, the ratio of Pci∩ui presents a linear positive relationship, and there is a correlation between the integrity of the data in the feature term and Pci∩ui. When Pci∩ui increases, data integrity also increases.On this basis, feature terms of international law data are reduced in dimension, proportional relationship is set on the basis of data feature terms, data feature dimensions are calculated in combination with feature terms, and mutual information is used to improve the classification accuracy of data integrity feature dimension.Knowing the chi-square value between data integrity features and text categories, as well as the relationship between feature dimensionality reduction, the total calculation is performed according to the data features, so that the total number of dimensionality reductions of text features can be obtained to determine the integrity of international data. The relevant test criteria of text categories need to be classified and identified. The process is as follows:(7)ωc=φci,uiε×ϑ,where the word frequency parameter of international law data is ε and the number of integrity feature items in the data feature category is ϑ. Then, feature items with integrity word frequency in data are separated from documents, and word frequency feature categories are introduced and associated. According to the weight parameters of high-frequency feature items, the weight values of low-frequency feature items of international law data are reduced [20]. Integrity operation parameters are determined as follows according to data integrity feature items:(8)γ=ωc×Pci−∑i=1mPcim.Among them, the frequency expression of feature items of international law data isPci. The data integrity guarantee mechanism of international law is established according to the integrity operation parameters obtained.First of all, the international law data in the sample set is decomposed intom sets, and the number of text sets after classification is mm−1/2. The correlation coefficient in the corresponding classification training texts is calculated by the corresponding classification collection. According to the integrity operation parameter integrity guarantee range obtained above, the formula is as follows:(9)F=x1y1+x2y2+⋯+xmymx12+x22+⋯xm2y12+y22+⋯+ym2,where xm and ym are vector values in the sample set of international law data. After processing the decomposed international law data text set according to the scope of text classification, the integrity feature item matrix is obtained as follows:(10)m′=mm−12x1y1x1y2⋯x1ymx2y2x2y2⋯x2ym⋮⋮⋱⋮xmymxmy2⋯xmym.On this basis, according to the setting of the detection prerequisites of integrity feature items by the detection function, the results are as follows:(11)τci=limn⟶mτcinn,where the parameter value of the integrity feature function is τ and the measured international law data integrity feature category is τx1,x2,…,xn. When the parameter value of the characteristic function is τn=τ and limn⟶mKfin=Kx1,x2,…,xn, the positive definite relation formula is used to calculate the curve amplitude of international law data integrity, and at the same time, the overall characteristics of the data sample are identified. Assuming that the width of the integrity function of international law data is σ, vector values xm and ym are used to calculate the integrity characteristics of the data as a whole, and the process is as follows:(12)T=0.5xm×κ×σ+0.5ym×κ−1×σ.The new international law data sequence equation is obtained by summation of data integrity characteristic series as follows:(13)O=∑κ=1Nzκ×T.Thus, a complete international law data series can be obtained; that is, international law data integrity assurance processing is completed.


## 3.2.1. Identifying Incomplete Data

The incomplete data identification process is composed of three processing steps: incomplete data subspace extraction, statistical element reconstruction, and separable amplitude calculation. The specific design process is as follows.(1) Subspaces of Incomplete Data Extracted by Factor Analysis. Data subspace is a relatively fuzzy storage set of information parameters, and the larger the information coverage area involved in the subspace, the more the incomplete data samples to be screened [18]. Therefore, in order to obtain accurate extraction results of incomplete data subspace, the factor analysis method is used to integrate the obtained historical data into multiple information parameter sets; then, according to the multivariate statistical algorithm, the original set space is improved; and finally, all the information parameters are put into the subspace set structure.It is assumed thatg1 and g2, respectively, represent two different parameter value factors of incomplete data information and that g2>g1 is satisfied. It is stipulated that Δt represents the unit extraction time of data information parameters. In general, the physical value range of this indicator always exists between 0 and 1. By combining the above physical quantities, the subspace extraction results of incomplete data can be expressed as(3)h=ω×Δt2g2−g1∂q¯,where ∂ represents the reasonably defined term of incomplete data, ω represents the weight value of single extraction of data information parameters, and q¯ represents the mean value of single transmission of incomplete data information.(2) Reconstructed Statistic. Statistics are standard conditions for measuring the transmission capacity of data information parameters. The higher the level of numerical quantity of statistical element nodes is, the stronger the constraint ability of the applied algorithm is [19]. With the increase of incomplete data screening, the number of statistical element nodes will decrease obviously due to the influence of computing, which will lead to the continuous decline of screening application ability. In order to solve this problem, the statistical element nodes are reconstructed in this study. On the one hand, the changing trend of the screening amount of incomplete data is avoided, and on the other hand, the computational amount of data information can be effectively controlled.Supposeη0 represents the minimum constraint value of statistical element coefficient, ηn represents the maximum constraint value of statistical element coefficient, and N represents the actual iterative transmission times of incomplete data information. Simultaneous formula (3) can be used to express the statistical element reconstruction result as(4)z=e×h∑η0ηnN×b¯2,where e represents the transmission characteristic value of incomplete data information parameters, p represents the extraction permission value of information parameters, and b¯ represents the mean existence of statistical element nodes.(3) Separable Amplitude Quantity. In the process of identifying incomplete data, the data cannot maintain the invariable existence state but will show obvious turbulent change with the increase of information parameter storage level. In this case, there is always a certain numerical comparison between the parameters of selectable and nonselectable data, which can separate the amplitude.Separable amplitude is a piece of dynamic data information matching result. As the amount of data to be screened increases, the numerical coordination relationship is always in the existing state of absolute change. Therefore, in order to obtain effective calculation results of separable amplitude, it is necessary to accurately grasp the parameter value level of incomplete data information and, on this basis, to complete the overall planning and coordination of related information parameters.Assuming thatα and β are two different amplitude nodes, jα represents the magnitude of amplitude separation when the coefficient is α, and jβ represents the magnitude of amplitude separation when the coefficient is β. Simultaneous formula (3) can be used to calculate the result of separable magnitude expressed as(5)S=z2+yk1+k2jβ−jα2,where y represents the basic separation conditions of amplitude parameters, and k1 and k2 represent two different incomplete data items to be counted as index definitions. To sum up, the identification and processing of incomplete international law data are completed.


## 3.2.2. Data Integrity Assurance

In order to guarantee the data integrity of international law, the operation parameters of data integrity must be determined first. Therefore, the text classification training is used to adjust the feature items of the data, some worthless feature items are removed by feature dimension, and the noise of international law data is removed by feature dimension reduction.Assuming that the feature item of the data isci and the total number of data text categories is ui, the calculation process of obtaining the data’s worthless features is as follows:(6)φci,ui=Pci∩uiXciXui.Among them, the frequency of feature itemci in international law data text can be expressed as Pci∩ui.If the training sample of the complete feature item of data isXci, the expression of the complete category item of data contained in the training sample is Xui. Therefore, when there is a parallel relationship between the corresponding data text category ui and the feature term, the ratio of Pci∩ui presents a linear positive relationship, and there is a correlation between the integrity of the data in the feature term and Pci∩ui. When Pci∩ui increases, data integrity also increases.On this basis, feature terms of international law data are reduced in dimension, proportional relationship is set on the basis of data feature terms, data feature dimensions are calculated in combination with feature terms, and mutual information is used to improve the classification accuracy of data integrity feature dimension.Knowing the chi-square value between data integrity features and text categories, as well as the relationship between feature dimensionality reduction, the total calculation is performed according to the data features, so that the total number of dimensionality reductions of text features can be obtained to determine the integrity of international data. The relevant test criteria of text categories need to be classified and identified. The process is as follows:(7)ωc=φci,uiε×ϑ,where the word frequency parameter of international law data is ε and the number of integrity feature items in the data feature category is ϑ. Then, feature items with integrity word frequency in data are separated from documents, and word frequency feature categories are introduced and associated. According to the weight parameters of high-frequency feature items, the weight values of low-frequency feature items of international law data are reduced [20]. Integrity operation parameters are determined as follows according to data integrity feature items:(8)γ=ωc×Pci−∑i=1mPcim.Among them, the frequency expression of feature items of international law data isPci. The data integrity guarantee mechanism of international law is established according to the integrity operation parameters obtained.First of all, the international law data in the sample set is decomposed intom sets, and the number of text sets after classification is mm−1/2. The correlation coefficient in the corresponding classification training texts is calculated by the corresponding classification collection. According to the integrity operation parameter integrity guarantee range obtained above, the formula is as follows:(9)F=x1y1+x2y2+⋯+xmymx12+x22+⋯xm2y12+y22+⋯+ym2,where xm and ym are vector values in the sample set of international law data. After processing the decomposed international law data text set according to the scope of text classification, the integrity feature item matrix is obtained as follows:(10)m′=mm−12x1y1x1y2⋯x1ymx2y2x2y2⋯x2ym⋮⋮⋱⋮xmymxmy2⋯xmym.On this basis, according to the setting of the detection prerequisites of integrity feature items by the detection function, the results are as follows:(11)τci=limn⟶mτcinn,where the parameter value of the integrity feature function is τ and the measured international law data integrity feature category is τx1,x2,…,xn. When the parameter value of the characteristic function is τn=τ and limn⟶mKfin=Kx1,x2,…,xn, the positive definite relation formula is used to calculate the curve amplitude of international law data integrity, and at the same time, the overall characteristics of the data sample are identified. Assuming that the width of the integrity function of international law data is σ, vector values xm and ym are used to calculate the integrity characteristics of the data as a whole, and the process is as follows:(12)T=0.5xm×κ×σ+0.5ym×κ−1×σ.The new international law data sequence equation is obtained by summation of data integrity characteristic series as follows:(13)O=∑κ=1Nzκ×T.Thus, a complete international law data series can be obtained; that is, international law data integrity assurance processing is completed.


## 4. Experiment and Analysis

In order to verify the practical application performance of the international law data integrity guarantee method designed above based on antiterrorism prediction algorithm, the following experimental process is designed.


### 4.1. Experimental Design

Experimental data were obtained from Max Planck Encyclopedia of Public International Law database. The experiment was built on Matlab v10.0 (64 bit) simulation platform, and the SR258 1U rack server was used to set up the data acquisition platform and console. A sample sequence of international law data is shown in Figure4.Figure 4
Sample distribution of international law data series.In order to improve the explicability of experimental results, the integrity assurance method of outsourcing spatial database (method of [7]) and the integrity audit and guarantee method of multicopy based on DDCT table (method of [8]) were taken as the comparison group. The method of this paper was taken as the experimental group for comparative test.


### 4.2. Experimental Results and Analysis

Firstly, using frequency range and sensitivity of data as indicators, the practicability of different evaluation methods is verified. The specific values of index parameters of different methods are shown in Table1.Table 1
Comparison of experimental parameter values.
Number of experimentsFrequency range of data usageData sensitivityThe proposed method100.940.88200.950.96300.910.91400.880.87500.930.91Method of [7]100.610.60200.300.73300.440.72400.660.65500.730.71Method of [8]100.510.67200.610.72300.640.75400.780.74500.520.78According to Table1, in the whole experiment, the frequency range of data used in the proposed method and the value level of data sensitivity index are always relatively close, there is no obvious fluctuation coefficient, and the value is very close to 1. However, there is a big gap between the frequency range of data use and the value of data sensitivity index of the two traditional methods and 1. Therefore, the method of this paper is more practical.Then, the time consumption of the integrity audit process of different methods is used as an indicator for comparative verification, and the results are shown in Figure5.Figure 5
Comparison of the time consumption of the integrity audit process of different methods.According to the results shown in Figure5, in the 30th experiment, the integrity audit process of the method of this paper takes 17 min, and that of the method of [7] takes 21 min. The integrity audit process of the method of [8] takes 26 min. In the 50th experiment, the integrity audit process of the method of this paper takes 20 min, and that of the method of [7] takes 23 min. The integrity audit process of the method of [8] takes 25 min. In the whole experiment, the integrity audit of the method of this paper takes only 19 min at most, indicating that the method of this paper has high timeliness.Finally, the validity of different evaluation methods is verified by using data integrity after the application of different methods. Statistical results of data integrity after the application of different methods are shown in Table2.Table 2
Comparison of data integrity after the application of different processing methods.
Number of experiments/timeMethod of this paperMethod of [7]Method of [8]100.9440.8610.829200.9670.8600.865300.9210.8250.856400.9250.8530.836500.9660.8420.844According to the results shown in Table2, data integrity varies between 0.921 and 0.967 after the application of the method of this paper. After the application of the method of [7], the data integrity varies between 0.825 and 0.861. After the application of the method of [8], the data integrity varies between 0.829 and 0.865. Furthermore, in the whole experiment process, the maximum data integrity after the application of the method of this paper can reach 0.967, indicating that compared with the two traditional methods, the proposed method is more effective.


## 4.1. Experimental Design

Experimental data were obtained from Max Planck Encyclopedia of Public International Law database. The experiment was built on Matlab v10.0 (64 bit) simulation platform, and the SR258 1U rack server was used to set up the data acquisition platform and console. A sample sequence of international law data is shown in Figure4.Figure 4
Sample distribution of international law data series.In order to improve the explicability of experimental results, the integrity assurance method of outsourcing spatial database (method of [7]) and the integrity audit and guarantee method of multicopy based on DDCT table (method of [8]) were taken as the comparison group. The method of this paper was taken as the experimental group for comparative test.


## 4.2. Experimental Results and Analysis

Firstly, using frequency range and sensitivity of data as indicators, the practicability of different evaluation methods is verified. The specific values of index parameters of different methods are shown in Table1.Table 1
Comparison of experimental parameter values.
Number of experimentsFrequency range of data usageData sensitivityThe proposed method100.940.88200.950.96300.910.91400.880.87500.930.91Method of [7]100.610.60200.300.73300.440.72400.660.65500.730.71Method of [8]100.510.67200.610.72300.640.75400.780.74500.520.78According to Table1, in the whole experiment, the frequency range of data used in the proposed method and the value level of data sensitivity index are always relatively close, there is no obvious fluctuation coefficient, and the value is very close to 1. However, there is a big gap between the frequency range of data use and the value of data sensitivity index of the two traditional methods and 1. Therefore, the method of this paper is more practical.Then, the time consumption of the integrity audit process of different methods is used as an indicator for comparative verification, and the results are shown in Figure5.Figure 5
Comparison of the time consumption of the integrity audit process of different methods.According to the results shown in Figure5, in the 30th experiment, the integrity audit process of the method of this paper takes 17 min, and that of the method of [7] takes 21 min. The integrity audit process of the method of [8] takes 26 min. In the 50th experiment, the integrity audit process of the method of this paper takes 20 min, and that of the method of [7] takes 23 min. The integrity audit process of the method of [8] takes 25 min. In the whole experiment, the integrity audit of the method of this paper takes only 19 min at most, indicating that the method of this paper has high timeliness.Finally, the validity of different evaluation methods is verified by using data integrity after the application of different methods. Statistical results of data integrity after the application of different methods are shown in Table2.Table 2
Comparison of data integrity after the application of different processing methods.
Number of experiments/timeMethod of this paperMethod of [7]Method of [8]100.9440.8610.829200.9670.8600.865300.9210.8250.856400.9250.8530.836500.9660.8420.844According to the results shown in Table2, data integrity varies between 0.921 and 0.967 after the application of the method of this paper. After the application of the method of [7], the data integrity varies between 0.825 and 0.861. After the application of the method of [8], the data integrity varies between 0.829 and 0.865. Furthermore, in the whole experiment process, the maximum data integrity after the application of the method of this paper can reach 0.967, indicating that compared with the two traditional methods, the proposed method is more effective.


## 5. Conclusion

In order to further optimize the quality of international law data, this paper studies the integrity guarantee method of international law data on the basis of designing antiterrorism prediction algorithm. Firstly, the prediction model set is split through the trusted seed model, the model selection is completed through iterative optimization, and then new terrorism-related factors are added to obtain the new prediction model set, so as to complete the antiterrorism data prediction. Based on the antiterrorism prediction data obtained by the above process, on the basis of identifying incomplete data, the antiterrorism data integrity guarantee method in international law database is designed through the process of determining data integrity operation parameters, eliminating worthless feature items, processing noise dimension reduction, and adding feature series of data integrity.On the basis of the above research, experimental data prove that this method takes less time to audit the data integrity and that the data integrity after processing can reach 0.967. The frequency range and sensitivity of international law data can be effectively optimized by using this method.



---

*Source: 3089545-2022-02-21.xml*