Blockchain-based software development test data security verification method

Abstract

The application relates to the technical field of data transmission, in particular to a software development test data security verification method based on a block chain, which comprises the following steps: acquiring each string sequence of a user transaction software platform; constructing a word item singular factor of each word item, and calculating the uniqueness division weight of each string sequence; constructing a reliable feature vector of each word item of a unique data sequence, and calculating a non-confidence factor and a reliable significant coefficient; extracting each clustering cluster of all word items in the unique data sequence by adopting a K-means algorithm; thereby acquiring abstract data to generate signature data; and transmitting the digital signature and the user transaction software platform data to the block chain to complete the security verification of the user transaction software platform data. The application can realize optimized security verification of the digital signature of the user transaction software platform, and improve the security of the to-be-transmitted user transaction software platform data.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of data transmission, in particular to a software development test data security verification method based on a blockchain. BACKGROUND

[0002] The blockchain technology is a data storage and transmission technology based on cryptography and distributed computing, which verifies and stores data through a block chain data structure, generates and updates data through a distributed node consensus algorithm, guarantees the security of transmission data and access control through cryptography, programs and operates data through an intelligent contract composed of automatic script code, is a new distributed basic framework, has the characteristics of decentralization, data tamper resistance and user identity anonymity, and has been widely applied to financial platform systems.

[0003] However, in the development process of the existing financial platform system, user transaction software platform data is transmitted to the blockchain network, and the original data to be transmitted may be attacked, so that before the data is uploaded to the blockchain, the user's transaction data has a large security risk, therefore, it is necessary to verify the user transaction software platform data uploaded to the blockchain by using a digital information security verification method, and a digital signature method is usually used for digital information security verification, and the strong correlation between the digest data obtained by the traditional RSA digital signature algorithm leads to low security of the obtained digital signature, which is easy to be cracked by attackers. SUMMARY

[0004] In order to solve the above technical problems, the application provides a software development test data security verification method based on a blockchain to solve the existing problems.

[0005] The software development test data security verification method based on the blockchain provided by the application adopts the following technical scheme:

[0006] One embodiment of the application provides a software development test data security verification method based on a blockchain, which comprises the following steps:

[0007] Collecting user transaction software platform original data, including but not limited to transaction receiver, transaction nature, transaction amount, transaction time, transaction location;

[0008] obtaining each string sequence of the user transaction software platform data; forming a matching group of each term in the string sequence and its left and right adjacent terms; obtaining a term singularity factor of each term according to the number of occurrences of each term in the string sequence and the matching group of each term; obtaining a singularity factor sequence of the string sequence according to the term singularity factor of each term; obtaining a unique partition weight of each string sequence according to the distribution of each singularity factor sequence and the relationship therebetween; obtaining a unique data sequence and a reliable feature vector of each term in the unique data sequence;

[0009] For each term in the unique data sequence, constructing a non-confidence factor of each term according to the ED edit distance between the text strings of each term and the ED edit distance between the binary encodings after the text string conversion; obtaining a reliable significant coefficient of each term according to the relationship between the reliable feature vectors of each term and the non-confidence factor; clustering all terms in the unique data sequence using a K-means algorithm, and taking the absolute value of the difference between the reliable significant coefficients of two terms as the clustering distance; extracting summary data according to the reliable significant coefficients of the terms in the clustering cluster;

[0010] Combining the summary data to generate signature data; attaching a digital signature to the user transaction software platform original data and transmitting it to a blockchain node for storage; the blockchain node combines the received user transaction software platform original data and the digital signature to complete the security verification of the user transaction software platform data.

[0011] Preferably, the obtaining each string sequence of the user transaction software platform data comprises:

[0012] Performing named entity recognition on the user transaction software platform original data by a named entity recognition tool; and counting the character length of each identified named entity, and taking the length of the largest named entity as J;

[0013] According to the division of consecutive one character as a term, the string sequence is obtained According to the division of consecutive two characters as a term, the string sequence is obtained and so on, until the string sequence is obtained by dividing consecutive J characters as a term .

[0014] Preferably, the obtaining a term singularity factor of each term according to the number of occurrences of each term in the string sequence and the matching group of each term comprises:

[0015] Counting the number of occurrences of each term in the string sequence and the maximum number of occurrences of the term; calculating the absolute value of the difference between the number of occurrences of each term and the maximum number of occurrences; obtaining the ratio of the absolute value to the total number of terms in the string sequence, denoted as the first ratio;

[0016] counting the number of times each word item corresponding to the matching value in the string sequence appears in all string sequences, denoted as the matching occurrence number of each word item, obtaining the sum value of the matching occurrence number of all word items in the string sequence, and taking the negative number of the ratio of the matching occurrence number of each word item to the sum value as the exponent of the exponential function with the natural constant as the base;

[0017] taking the product of the calculation result of the exponential function and the first ratio as the word item singularity factor of each word item.

[0018] Preferably, the method for obtaining the singularity factor sequence of the string sequence according to the word item singularity factor of each word item comprises: composing the word item singularity factor of all word items in the string sequence into the singularity factor sequence of the string sequence.

[0019] Preferably, the method for obtaining the uniqueness division weight of each string sequence according to the distribution of each singularity factor sequence and the relationship therebetween comprises:

[0020] for the singularity factor sequence corresponding to each string sequence;

[0021] calculating the Shannon entropy of the singularity factor sequence, counting the maximum value of the Shannon entropy of all singularity factor sequences, calculating the sum value of the DTW distance between the singularity factor sequence and all other singularity factor sequences, and taking the sum value as the exponent of the exponential function with the natural constant as the base;

[0022] calculating the sum of the word item singularity factors of all word items in the string sequence, calculating the absolute value of the difference between the Shannon entropy and the maximum value of the Shannon entropy, and multiplying the ratio of the sum of the word item singularity factors to the absolute value of the difference by the calculation result of the exponential function to obtain the uniqueness division weight of the corresponding string sequence.

[0023] Preferably, the method for obtaining the uniqueness data sequence and the reliable feature vector of each word item in the uniqueness data sequence comprises:

[0024] taking the string sequence with the maximum uniqueness division weight as the uniqueness data sequence;

[0025] for each word item of the uniqueness data sequence, counting the occurrence frequency of each word item in the uniqueness data sequence, calculating the sum of the ED edit distances between each word item and all other word items in the text string, denoted as the first sum value, calculating the sum of the ED edit distances between the binary encoding of each word item after UTF-8 conversion and the binary encoding of all other word items, denoted as the second sum value, and composing the frequency, the first sum value and the second sum value into the reliable feature vector of each word item.

[0026] Preferably, the non-confidence factor of each word item comprises:

[0027] For each term of the unique data sequence, calculate the ED edit distance between the text string of each term and the text string of the rest terms, denoted as a first edit distance; calculate the ED edit distance between the binary code of the text string of each term after conversion and the binary code of the rest terms, denoted as a second edit distance; calculate the ratio of the first edit distance and the second edit distance;

[0028] Obtain the sum value of the ratio of each term; and take the product of the sum value of each term and the occurrence frequency as the non-confidence factor of each term.

[0029] Preferably, the reliable significant coefficient of each term is obtained according to the relationship between the reliable feature vectors of each term and the non-confidence factor, and the reliable significant coefficient of each term comprises:

[0030] For each term of the unique data sequence;

[0031] Calculate the ratio of the non-confidence factor of each term and the maximum value of the non-confidence factor in all terms of the unique data sequence, take the inverse of the ratio as the index of the exponential function with the natural constant as the base number; calculate the sum value of the Pearson correlation coefficients of the reliable feature vectors of each term and all other terms; and take the product of the inverse of the sum value and the calculation result of the exponential function as the reliable significant coefficient of each term.

[0032] Preferably, the abstract data is extracted according to the reliable significant coefficient of each term in the clustering cluster, and the method comprises the following steps: calculating the mean value of the reliable significant coefficient of each term in each clustering cluster; and taking each term with the reliable significant coefficient greater than the corresponding mean value in each clustering cluster as the abstract data.

[0033] Preferably, the signature data is generated in combination with the abstract data, and the method comprises the following steps: calculating the abstract value of the abstract data by using a hash function; obtaining the key pair of the user transaction software platform data by using the RSA digital signature algorithm; and performing digital signature on the abstract value in combination with the private key in the key pair.

[0034] The present application has at least the following beneficial effects:

[0035] The application obtains each character string division sequence through user transaction software platform original data, obtains a unique division weight according to the data characteristics of each term in each character string division sequence, determines a unique data sequence based on the unique division weight, obtains a reliable significant coefficient of each term through the reliability characteristics between each term in the unique data sequence, clusters the unique data sequence based on the reliable significant coefficient, and selects a term with a larger reliable significant coefficient in each cluster as the abstract data of the user transaction software platform original data. The beneficial effect is that the abstract data is ensured to be as unique as possible while avoiding generating a digital signature with all user transaction software platform original data as abstract data. The digital signature generated according to the determined abstract data solves the problem that the abstract data obtained by the traditional RSA digital signature algorithm has strong correlation and is easy to be cracked by attackers, and the block chain node can better verify the security of the user transaction software platform data to be transmitted according to the application. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0037] Figure 1 The flowchart of the software development test data security verification method based on block chain provided by the present application. DETAILED DESCRIPTION

[0038] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object, the specific implementation, structure, features and effects of the software development test data security verification method based on block chain according to the present application are described in detail as follows by combining with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0040] The specific scheme of the software development test data security verification method based on block chain provided by the present application is specifically described below in combination with the drawings.

[0041] One embodiment of the present application provides a blockchain-based software development test data security verification method, specifically, the following blockchain-based software development test data security verification method is provided, please refer to Figure 1 The method comprises the following steps:

[0042] In step S001, the original data required by a user to be transmitted to a blockchain network is acquired, and the acquired original data is preprocessed.

[0043] The user transaction software platform original data to be transmitted to the blockchain is acquired through a transaction database, and the user transaction software platform original data includes transaction receiver, transaction nature, transaction amount, transaction time, transaction location, transaction notes and other related data. In order to prevent errors caused by network transmission fluctuations during data acquisition, and to prevent data without actual semantic information (such as punctuation marks) in the user transaction software platform original data, the acquired user transaction software platform original data to be transmitted is subjected to data cleaning. Since data cleaning is a known technology, this embodiment will not be described in more detail.

[0044] At this point, the user transaction software platform original data can be acquired through the transaction database and subjected to data cleaning, serving as a basis for subsequent analysis.

[0045] In step S002, a unique data sequence is obtained from the user transaction software platform original data to be transmitted to the blockchain network, and the summary data of the user transaction software platform original data is obtained through the unique data sequence.

[0046] The summary data is determined according to the characteristics of the user transaction software platform original data for subsequent construction of the digital signature of the user transaction software platform data.

[0047] In the process of acquiring the digital signature through the user transaction software platform data to be transmitted, if the data similarity in different user transaction software platform original data is strong and the regularity is strong, it may be easier for an attacker to crack the digital signature, and it may also be possible to crack other digital signatures with strong similarity based on the cracked digital signature, resulting in a security risk of user transaction software platform data not transmitted to the blockchain being leaked and tampered with in advance.

[0048] First, the raw data from the user's trading software platform is processed using the named entity recognition tool Stanford NER. Stanford NER takes the raw data as input and outputs the identified named entities in the text data. It iterates through all identified named entities and calculates their character length, denoteing the length of the longest named entity as J. Next, the acquired user trading software platform data is segmented into strings, with each consecutive character treated as a term to obtain a string sequence. The string sequence is obtained by dividing the string into two consecutive characters as a term. And so on, until according to the continuous Divide the string sequence by dividing each J character into a single term. The beneficial effect of string segmentation based on the above method is that it avoids the fact that the terms obtained by traditional word segmentation tools already have a high degree of rule similarity.

[0049] The data elements in each string sequence have different lengths. When generating digital signatures from digest data, the digest data must uniquely represent the original data; that is, the digest data itself needs to maintain high representativeness and low similarity. The i-th term in the j-th string sequence is merged with its left and right adjacent terms in the sequence to obtain a matching group for the term. To determine the optimal continuous character partitioning sequence, the security between data elements in the sequence is further analyzed. Based on the above analysis, this invention calculates the term singularity factor of each term in each string sequence and constructs a singularity factor sequence for the corresponding string sequence based on the term singularity factors of all terms in each string sequence, thus obtaining the unique partitioning weight of each string sequence.

[0050]

[0051]

[0052] In the above formula, Let be the term singularity factor of the i-th term in the j-th string sequence; , These represent the number of occurrences of the i-th word in the j-th string sequence and the maximum number of occurrences of a word in the j-th string sequence, respectively. Let be the total number of terms in the j-th string sequence; This represents the number of times the matching group corresponding to the i-th term in the j-th string sequence appears across all string sequences;

[0053] The larger the ratio of the difference between the number of occurrences of the i-th word in the j-th string sequence and the maximum number of occurrences of any word in the string sequence to the total number of words in the string sequence, the better. The larger the value, the greater the difference between the frequency of the i-th term in the j-th string and the frequency of the maximum term, indicating that the term is more unique in the string sequence. Simultaneously, the greater the frequency of the matching group corresponding to the i-th term in the j-th string sequence across all string sequences, the more unique the term. The larger the value, the greater the correlation between the i-th term matching group in the j-th string sequence and each string sequence, and the lower the uniqueness of the term in the string sequence. The smaller the value, the more singular the term becomes. The larger.

[0054] Assign weights to the uniqueness of the j-th string sequence; Let be the Shannon entropy of the singular factor sequence of the j-th string sequence; The maximum value among the singular factor sequences of all string sequences is the Shannon entropy. Let j be the j-th string sequence; Let be the sum of the DTW distances between the singular factor sequence of the j-th string sequence and the singular factor sequences of all other string sequences except itself. Here, the singular factor sequence is the sequence composed of the singular factors of all terms in the corresponding string sequence.

[0055] It should be noted that, in the fractional operation for uniqueness partitioning weights in this embodiment, when encountering a denominator of 0, a parameter adjustment factor can be added to the denominator to prevent the denominator from being 0 in order to ensure the calculation result is meaningful. This parameter adjustment factor is a very small positive number. In this embodiment, the value of the parameter adjustment factor can be 0.01. Its specific value can be set by the implementer according to the actual situation, and this embodiment does not impose specific limitations.

[0056] When the singularity factor of all terms in the j-th string sequence is larger, that is... The larger the value, the higher the uniqueness of all terms in the string sequence. Furthermore, the higher the uniqueness of the j-th string sequence obtained by dividing the original data of the user's trading software platform into j consecutive characters; simultaneously, the smaller the difference between the Shannon entropy of the singular factor sequence of the j-th string sequence and the maximum Shannon entropy of the singular factor sequences of all string sequences, the higher the uniqueness of the string sequence. The smaller the value, the greater the disorder among the data elements in the singularity factor sequence of the j-th string sequence, and the higher the uniqueness of the string sequence. The uniqueness partitioning weight of the string sequence... the greater the DTW distance between the singular factor sequence of the jth string sequence and the singular factor sequence of all the other string sequences except itself, the greater the difference between the jth string sequence and the other string sequences, the higher the data uniqueness of the string sequence obtained by dividing the original data of the user transaction software platform according to consecutive j characters, and the more reliable the digital signature of the user transaction software platform data obtained based on the string sequence. the greater the difference between the jth string sequence and the other string sequences, the higher the data uniqueness of the string sequence obtained by dividing the original data of the user transaction software platform according to consecutive j characters, and the more reliable the digital signature of the user transaction software platform data obtained based on the string sequence.

[0057] Since the length of the largest named entity identified in the original data of the user transaction software platform obtained in the above steps is denoted as J, when calculating the uniqueness division weight of the above string sequence, only the uniqueness division weight of the jth string sequence needs to be calculated, and the string sequence is to avoid the inability to calculate the number of occurrences of the term matching group in the jth string sequence in all character sequences, i.e., the inability to calculate .

[0058] The string sequence with the largest uniqueness division weight obtained is denoted as the uniqueness data sequence T. When considering the data reliability of each term in the uniqueness division sequence T, the text difference between each term in the sequence can be reflected, but since the original data of the user transaction software platform and the digital signature are transmitted in binary data during transmission to the blockchain, the difference between the binary encodings of the terms with smaller text differences before transcoding may become larger, which may pose a certain security risk for subsequent selection of abstract data to generate reliable digital signatures. Based on the above analysis, the occurrence frequency of the kth term in the uniqueness data sequence , the sum of the ED edit distances between the kth term and all the other terms in the text string , and the sum of the ED edit distances between the kth term and all the other terms in the uniqueness data sequence after UTF-8 transcoding , the reliable feature vector of the kth term in the uniqueness data sequence is denoted as The reliable feature vector can quantify the similarity and importance between terms from multiple angles, capture the semantic, spelling, and structural characteristics of each term in the text information, reflect the semantic structure and contextual association of the term in the text, and help improve the discrimination and understanding of the term. Based on the above analysis, the reliable saliency coefficient RS of the corresponding term is obtained:

[0059]

[0060]

[0061] is a non-confidence factor of the kth term in the unique data sequence; is the frequency of the kth term in the unique data sequence; K is the total number of terms in the unique data sequence; respectively are text strings corresponding to the kth and bth terms (in the formula ); is the ED edit distance between the text strings of the kth and bth terms; respectively are binary encodings of the text strings of the kth and bth terms after conversion; is the ED edit distance between the binary encodings of the kth and bth terms. It should be noted that, in the calculation process of the non-confidence factor, when the denominator is 0, i.e., the kth term is the same as the bth term, in order to ensure that the calculation result is meaningful, the embodiment sets the calculation result of to 1.

[0062] When the frequency of the kth term in the unique data sequence is greater, i.e., is greater, it indicates that the uniqueness of the kth term for the unique data sequence is lower, and the kth term should not be used as the summary data to generate the digital signature. Meanwhile, when the ratio of the ED edit distance between the text strings of the kth term and all other terms in the sequence and the ED edit distance between the binary data strings of the kth term and all other terms in the sequence is greater, i.e., is greater, it indicates that the text character difference between the kth term and all other terms in the sequence is greater, but the binary encoding difference between the kth term and all other terms in the sequence after conversion is smaller. Using the kth term as the summary data is more likely to cause security risks to the subsequently generated digital signature, and the non-confidence factor of the kth term is greater.

[0063] is a reliable saliency coefficient of the kth term in the unique data sequence; is the maximum value of the non-confidence factor of all terms in the unique data sequence; respectively are reliable feature vectors corresponding to the kth and bth terms in the unique data sequence; is the Pearson correlation coefficient between the reliable feature vectors corresponding to the kth and bth terms. It should be noted that, in the process of performing the fraction operation of the reliable saliency coefficient, when the denominator is 0, in order to ensure that the calculation result is meaningful, a tuning factor can be added to the denominator for addition to prevent the denominator from being 0. The tuning factor is a very small positive number. In the embodiment, the value of the tuning factor can be 0.01. The specific value can be set by the implementer according to the actual situation, and the embodiment of the application does not make specific limitations.

[0064] The smaller the ratio between the non-confidence factor of the kth term in the unique data sequence and the maximum non-confidence factor among the terms in the sequence, the higher the reliability of the kth term, and the more likely it is to be used as summary data to generate a digital signature. The smaller the ratio between the non-confidence factor of the kth term in the unique data sequence and the maximum non-confidence factor among the terms in the sequence, the higher the reliability of the kth term, and the more likely it is to be used as summary data to generate a digital signature. The smaller the ratio between the non-confidence factor of the kth term in the unique data sequence and the maximum non-confidence factor among the terms in the sequence, the higher the reliability of the kth term, and the more likely it is to be used as summary data to generate a digital signature. The smaller the ratio between the non-confidence factor of the kth term in the unique data sequence and the maximum non-confidence factor among the terms in the sequence, the higher the reliability of the kth term, and the more likely it is to be used as summary data to generate a digital signature. The greater the reliable significance coefficient of the term, The greater the reliable significance coefficient of the term,

[0065] At this point, the reliable significance coefficient of each term in the unique data sequence is obtained, and K-means algorithm is used to cluster all terms in the unique data sequence. The input of the K-means algorithm is all terms in the unique data sequence, and the number of clusters is set by the implementer. In this embodiment, it is set to 3. The absolute value of the difference between the reliable significance coefficients of two terms is used as the distance between the two terms in the clustering process. When the position of the cluster center no longer moves, the algorithm iteration is stopped. The output of the algorithm is the clustering result of the unique data sequence. Since the K-means clustering algorithm is a well-known technology, this embodiment will not be described in more detail.

[0066] When the reliable significance coefficient of each term in the cluster is greater, for the entire user transaction software platform raw data to be transmitted to the blockchain, using the terms in the cluster as summary data and generating a digital signature with it will result in higher security and greater resistance to attacks by attackers. Therefore, the mean of the reliable significance coefficients of the terms in each cluster is calculated; each term in the cluster whose reliable significance coefficient is greater than the corresponding mean is used as summary data for generating a digital signature of the user transaction software platform data.

[0067] Step S003, according to the obtained summary data, obtaining the summary value, and obtaining the digital signature of the user transaction software platform data through the RSA digital signature algorithm, for the blockchain node to verify the received user transaction software platform data.

[0068] According to the user transaction software platform data to be transmitted into the blockchain, more representative summary data is obtained, the key pair of the user transaction software platform data is obtained through the RSA digital signature algorithm, the summary data obtained is calculated using the hash function SHA-256 to obtain a summary value, and the summary value is digitally signed according to the private key in the key pair. Since the RSA digital signature algorithm and the hash function SHA-256 are well-known technologies, this embodiment will not be described in more detail.

[0069] The generated digital signature is then appended to the user transaction software platform original data and transmitted to the blockchain node for storage. The block node in the blockchain can perform security verification of the user transaction software platform data according to the received user transaction software platform original data and digital signature, thereby realizing a software development test data security verification method based on the blockchain.

[0070] It should be noted that the above-mentioned sequence of the embodiments of the application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or may be advantageous.

[0071] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0072] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; modifying the technical solutions described in the above embodiments, or equivalently replacing some technical features, does not change the essence of the corresponding technical solutions out of the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1.A blockchain-based software development test data security verification method, characterized in that, The method comprises the following steps: Collecting user transaction software platform original data, including but not limited to transaction receiver, transaction nature, transaction amount, transaction time, transaction location; Obtaining each string sequence of the user transaction software platform data; forming a matching group of each term and its left and right adjacent terms in the string sequence; obtaining a term singularity factor of each term according to the number of occurrences of each term in the string sequence and the matching group of each term; obtaining a singularity factor sequence of the string sequence according to the term singularity factor of each term; obtaining a unique partition weight of each string sequence according to the distribution of each singularity factor sequence and the relationship therebetween; obtaining a unique data sequence and a reliable feature vector of each term in the unique data sequence; For each term in the unique data sequence, constructing a non-confidence factor of each term according to the ED edit distance between the text strings of each term and the ED edit distance between the binary codes after the text string coding; obtaining a reliable significant coefficient of each term according to the relationship between the reliable feature vectors of each term and the non-confidence factor; clustering all terms in the unique data sequence by using a K-means algorithm, and taking the absolute value of the difference between the reliable significant coefficients of two terms as the clustering distance measure; extracting summary data according to the reliable significant coefficients of the terms in the clustering cluster; Generating signature data in combination with the summary data; attaching a digital signature to the user transaction software platform original data and transmitting it to a blockchain node for storage; the blockchain node completes the security verification of the user transaction software platform data in combination with the received user transaction software platform original data and the digital signature. 2.The blockchain-based software development test data security verification method of claim 1, wherein, The method comprises the following steps: Performing named entity recognition on the user transaction software platform original data by using a named entity recognition tool; and counting the character length of each identified named entity, and taking the length of the largest named entity as J; The string sequence is obtained by dividing the string according to one character as a word item continuously The string sequence is obtained by dividing the string according to two characters as a word item continuously The string sequence is obtained by dividing the string according to 3J characters as a word item continuously . 3.The blockchain-based software development test data security verification method of claim 1, wherein, The method comprises the following steps: Counting the number of occurrences of each term in the string sequence and the maximum number of occurrences of the term; calculating the absolute value of the difference between the number of occurrences of each term and the maximum number of occurrences; obtaining the ratio of the absolute value to the total number of terms in the string sequence, denoted as the first ratio; Counting the number of occurrences of each term in the string sequence, denoted as the matching occurrence number of each term; obtaining the sum of the matching occurrence numbers of all terms in the string sequence; taking the negative of the ratio of the matching occurrence number of each term to the sum as the exponent of an exponential function with a natural constant as the base; The product of the calculation result of the exponential function and the first ratio is taken as the term singularity factor of each term. 4.The blockchain-based software development test data security verification method of claim 1, wherein, The method comprises the following steps: 5.The blockchain-based software development test data security verification method of claim 4, wherein, For each string sequence corresponding singularity factor sequence; ​ Calculate the Shannon entropy of the singular factor sequence, and count the maximum value of the Shannon entropy of all singular factor sequences. Calculate the sum of the DTW distance of the singular factor sequence and all other singular factor sequences, and take the sum as the exponent of the exponential function with the natural constant as the base number. Calculate the sum of the term singular factors of all terms in the string sequence; calculate the absolute value of the difference between the Shannon entropy and the maximum value of the Shannon entropy; multiply the ratio of the sum of the term singular factors and the absolute value of the difference by the calculation result of the exponential function as the uniqueness division weight of the corresponding string sequence. 6.The blockchain-based software development test data security verification method of claim 1, wherein, The acquisition of the unique data sequence and the reliable feature vector of each term in the unique data sequence comprises: The string sequence with the maximum uniqueness division weight is recorded as the unique data sequence. For each term in the unique data sequence, count the frequency of each term in the unique data sequence; calculate the sum of the ED edit distance between each term and the text string of the remaining all terms, and record it as the first sum; calculate the sum of the ED edit distance between the binary encoding of each term after UTF-8 conversion and the binary encoding of the remaining all terms, and record it as the second sum; the reliable feature vector of each term is composed of the frequency, the first sum, and the second sum. 7.The blockchain-based software development test data security verification method of claim 6, wherein, The non-confidence factor of each term comprises: For each term in the unique data sequence, calculate the ED edit distance between each term and the text string of the remaining terms, and record it as the first edit distance; calculate the ED edit distance between the binary encoding of each term text string after conversion and the binary encoding of the remaining terms, and record it as the second edit distance; calculate the ratio of the first edit distance and the second edit distance; Acquire the sum of the ratio of each term; take the product of the sum of each term and the frequency as the non-confidence factor of each term. 8.The blockchain-based software development test data security verification method of claim 1, wherein, The reliable significant coefficient of each term is obtained according to the relationship between the reliable feature vectors of each term and the non-confidence factor, comprising: For each term in the unique data sequence; Calculate the ratio of the non-confidence factor of each term to the maximum value of the non-confidence factor of all terms in the unique data sequence, take the opposite of the ratio as the exponent of the exponential function with the natural constant as the base number; calculate the sum of the Pearson correlation coefficients of the reliable feature vectors of each term and all other terms; take the product of the reciprocal of the sum and the calculation result of the exponential function as the reliable significant coefficient of each term. 9.The blockchain-based software development test data security verification method of claim 8, wherein, The reliable significant coefficient of each term is obtained according to the relationship between the reliable feature vectors of each term and the non-confidence factor, comprising: 10.The blockchain-based software development test data security verification method of claim 9, wherein, The reliable significant coefficient of each term is obtained according to the relationship between the reliable feature vectors of each term and the non-confidence factor, comprising: The signature data is generated by combining the abstract data, comprising: using a hash function to calculate the abstract value of the abstract data, using the RSA digital signature algorithm to obtain the key pair of the user transaction software platform data, and combining the private key in the key pair to perform digital signature on the abstract value.

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