CN119030789A - A bill data integrity verification system and method - Google Patents

Abstract

The present invention relates to the field of data verification technology, and specifically to a bill data integrity verification system and method, the system comprising: a data hash processing module constructs a hash value of the data content based on the acquired bill data, calculates the time interval between each data item and its timestamp, associates the hash value and timestamp with a unique identifier, and generates a data integrity identifier. In the present invention, by inserting redundant data and monitoring errors in transmission in real time, it helps to improve the accuracy and stability of data transmission. The distribution and fluctuation range analysis of errors enables the system to dynamically adjust redundant data according to the actual transmission environment, reduces the probability of data loss, and ensures reliable transmission of data in complex environments. The verification mechanism is generated by a random number sequence to ensure continuous verification of the data stream, accurately detect error blocks in data transmission, and enhance the flexibility and efficiency of data verification.

Description

A bill data integrity verification system and method

Technical Field

The present invention relates to the technical field of data verification, and in particular to a bill data integrity verification system and method.

Background Art

The field of data verification technology uses multiple technologies such as checksums, hash functions, digital signatures or encryption algorithms to detect whether data has been tampered with or damaged during storage or transmission. In addition, data verification technology also involves implementing access control and using security protocols to protect data from unauthorized access and tampering. In industries with high risks and extremely high requirements for data accuracy, such as banking and finance, healthcare, and government agencies, it supports compliance audits, risk management, and data governance.

Among them, the bill data integrity verification system is a system specifically used to protect the integrity of bill data during processing and transmission. The system implements data verification protocols to prevent unauthorized modification of data during electronic bill exchange, financial transaction processing and archiving. It usually includes the use of digital signature technology to confirm that the data has not been tampered with, and the application of encryption measures to protect the privacy of data.

In the existing technology, although digital signatures and encryption technologies are used to ensure that data is not tampered with and privacy is protected in terms of integrity verification of bill data, it is difficult to effectively monitor the distribution characteristics of errors when errors occur during transmission, and it is also difficult to dynamically adjust data redundancy according to actual transmission conditions, resulting in an increased risk of data loss or damage in high-interference environments. In addition, the existing system is prone to inefficiency in the transmission of large amounts of data. In terms of data tampering detection, it is difficult to effectively intercept and respond to tampering in the early stages, which can easily lead to greater security risks.

Summary of the invention

The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a bill data integrity verification system and method.

In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A bill data integrity verification system comprises:

The data hash processing module constructs a hash value of the data content based on the acquired ticket data, calculates the time interval between each data item and its timestamp, associates the hash value and timestamp with a unique identifier, and generates a data integrity identifier;

The predictive error correction coding module inserts redundant data into the bill information based on the data integrity identifier, monitors the transmission errors in the data stream in real time, calculates the probability value of each character being lost during the transmission process, analyzes the uniformity and fluctuation range of the error distribution, adjusts the redundant data, and generates a dynamic error correction data stream;

The random audit verification module generates a random number sequence based on the dynamic error correction data stream, re-verifies the verification mark of the data block, compares the expected and current verification results, calculates the ratio of the data blocks with inconsistent verification results to the total data blocks, and forms a data integrity analysis result;

The tampering response processing module marks the data blocks with hash value differences as suspected tampering according to the data integrity analysis results, restricts access rights, updates the hash value and index information, and generates a security processing result.

As a further solution of the present invention, the step of obtaining the data integrity identifier is specifically as follows:

Based on the acquired bill data, the text and numbers on the bill are interpreted and the extracted data content is classified and sorted, including the date, amount, multiple key information of the payee and the unique identifier of each bill, to generate a data hash value;

Based on the data hash value, each data item and its timestamp are calculated to identify the time pattern of data input, and abnormal pattern monitoring of data input is performed to obtain data input pattern analysis results;

Based on the data input pattern analysis result, the hash value, timestamp and unique identifier of each data item are associated and stored in a data table, a bidirectional index relationship between the hash table and the data table is established, and a data integrity identifier is generated.

As a further solution of the present invention, the step of obtaining the probability value of each character being lost during transmission is specifically as follows:

According to the data integrity identifier, character frequency and position information during transmission are analyzed to determine the position in the data where errors are prone to occur, and redundant data is inserted to generate real-time monitoring information of transmission errors;

Based on the real-time monitoring information of the transmission errors, the number of transmission errors, the number of delays and the frequency of occurrence of each character are recorded, using the formula:

,

Calculate the probability of missing the i-th character , get the loss probability analysis result;

in, is the number of transmission errors of the i-th character, is the number of transmission delays for the ith character, is the frequency of occurrence of the i-th character, Indicates the total number of times character i is transmitted during the entire transmission process. are weight coefficients, which are used to adjust the influence ratio of the number of transmission errors, the number of delays and the frequency of character occurrence on the loss probability;

Based on the loss probability analysis result, the number of errors and the positions of characters in the transmission data packet are counted, and the distribution of errors in the transmission is analyzed in combination with the loss probability to generate a transmission error information analysis result.

As a further solution of the present invention, the step of obtaining the fluctuation range of the analysis error distribution is specifically:

Based on the transmission error information analysis result, the transmission characteristics of each character are analyzed using the formula:

,

Calculate the range of the error distribution , generate fluctuation range information;

in, Representing characters The time delay from the sender to the receiver during the transmission process, Indicates that the character The number of times it was resent. is the average probability of loss of all characters, is the total number of characters, which indicates the total number of characters that appear during the transmission;

Based on the fluctuation range information, the insertion of redundant data is readjusted, the change of the data stream is dynamically monitored, and adjustments are made to generate a dynamic error correction data stream.

As a further solution of the present invention, the step of obtaining the comparison expected and current verification results is specifically as follows:

Based on the dynamic error correction data stream, a random number sequence is generated, the position of the data block to be audited is determined, and the position information of the data block to be audited is obtained;

Based on the position information of the data block to be audited, the verification identifiers of the receiving end and the sending end are compared, and the verification identifier of each data block is re-verified to obtain a verification identifier verification result;

Based on the verification result of the verification mark, the formula is adopted:

,

Calculate the data block checksum inconsistency ratio , generate difference ratio information of data block verification;

in, Indicates the number of inconsistent data blocks found after verification. is the audit frequency, is the transmission delay, is the data block size, is the total number of audit data blocks.

As a further solution of the present invention, the step of obtaining the number of occurrences of the statistical verification difference is specifically:

Based on the difference ratio information of the data block verification, the formula is adopted:

,

Calculate the total number of checksum differences , generate integrated verification difference analysis results;

in, Indicates the number of data blocks in the entire audit process. It is The number of checksums for each data block, It is The number of differences between data blocks indicates the The number of differences detected during the verification process for each data block, express and The number of data blocks for both series;

Based on the integrated verification difference analysis result, the original verification mark and the verification mark after transmission are compared, and the difference of each data block is analyzed step by step to generate a data integrity analysis result.

As a further solution of the present invention, the step of obtaining the updated hash value and index information is specifically as follows:

Based on the result of the data integrity analysis, the hash value of each data block is reallocated and compared with the original value, the data blocks with inconsistent hash values are identified, and the data blocks are marked as suspected tampering, and a list of suspected tampering data blocks is generated;

Based on the suspected tampered data block list, the formula is used:

,

Calculating the quantified severity of data tampering , generating a severity indicator of data tampering;

in, Used to measure the proportion of suspected tampered data blocks, is the number of data blocks marked as suspected of tampering, is the total number of data blocks;

Based on the severity index of the data tampering, the access rights of the suspected tampered data blocks are restricted, the unique identifier and timestamp of each data block are recorded, and the hash value and index information are updated to generate a security processing result.

A method for verifying the integrity of bill data, which is performed based on the above-mentioned bill data integrity verification system, comprises the following steps:

S1: Based on the acquired ticket data, read the content and associate the timestamp for each data item, input the combination of the content and timestamp of each data item into the hash process, and generate a unique hash value for each combination;

S2: according to the unique hash value of each combination, associating the hash value with the corresponding timestamp and unique identifier to form a data integrity identifier;

S3: Based on the data integrity identifier, redundant data is added to the ticket information, and the data stream is monitored in real time, errors in data transmission and the probability of loss of each character are recorded and analyzed, and a dynamic error correction data stream is generated;

S4: Based on the dynamic error correction data stream, a random number sequence is generated to select a data block that needs to be re-verified, a verification mark is checked on the selected data block, and the expected and current performance of the verification result are compared to form a data integrity analysis result;

S5: Based on the data integrity analysis result, identify and mark the data blocks with hash value differences, perform access restrictions on these data blocks, update the hash value and index information, and generate a security processing result.

Compared with the prior art, the advantages and positive effects of the present invention are:

In the present invention, inserting redundant data and monitoring errors in transmission in real time helps to improve the accuracy and stability of data transmission. The analysis of the distribution and fluctuation range of errors enables the system to dynamically adjust redundant data according to the actual transmission environment, reducing the probability of data loss and ensuring reliable transmission of data in complex environments. The verification mechanism is generated by a random number sequence to ensure continuous verification of the data stream, accurately detect error blocks in data transmission, and enhance the flexibility and efficiency of data verification. The access restriction and security processing of suspected tampered data blocks further enhance the data's responsiveness when tampered with, preventing damage to the system and users caused by malicious tampering of data. The security of bill data in each link of transmission, storage and processing has been comprehensively improved.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a system flow chart of the present invention;

FIG2 is a flow chart of steps for obtaining a data integrity identifier according to the present invention;

FIG3 is a flow chart of the steps for calculating the probability value of each character being lost during transmission according to the present invention;

FIG4 is a flow chart of steps for obtaining the fluctuation range of error distribution analysis according to the present invention;

FIG5 is a flow chart of the steps of obtaining the comparison between the expected and current verification results of the present invention;

FIG6 is a flow chart of the steps of obtaining the number of occurrences of statistical verification differences according to the present invention;

FIG. 7 is a flow chart of the steps for updating the hash value and the index information of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

Please refer to FIG1 , the present invention provides a technical solution: a bill data integrity verification system includes:

The data hash processing module extracts the data content and unique identifier based on the acquired ticket data, constructs the hash value of the data content, calculates the time interval between each data item and its timestamp to prevent abnormal data input patterns, associates the hash value and timestamp with the unique identifier, stores the data content and user signature in the data table, and generates a data integrity identifier;

The predictive error correction coding module uses the character frequency and position information in the transmission process based on the data integrity identifier, inserts redundant data into the ticket information, monitors the transmission errors in the data stream in real time, calculates the probability value of each character being lost in the transmission process, counts the number and position of transmission errors, analyzes the fluctuation range of error distribution, adjusts redundant data according to the error distribution characteristics, and generates a dynamic error correction data stream;

The random audit verification module generates a random number sequence based on the dynamic error correction data stream to determine the location of the data block to be audited, re-verifies the verification mark of the data block, compares the expected and current verification results, calculates the ratio of data blocks with inconsistent verification results to the total data blocks, to assist in determining the location of the data block that needs to be tampered with, counts the number of occurrences of verification differences, and the information of the difference data blocks, and forms the data integrity analysis results;

The tampering response processing module marks the data blocks with hash value differences as suspected tampering based on the data integrity analysis results, quantifies the severity of the data tampering, restricts access rights, records the unique identifier and timestamp of the data block, updates the hash value and index information, and generates a security processing result;

The data integrity identification includes the associated data hash value, the combination of data content and user signature, timestamp information, and the unique identifier associated with it. The dynamic error correction data stream includes the added redundant data type, error correction parameters automatically adjusted according to the error distribution, and error statistics used for monitoring. The data integrity analysis results include the recalculated data block hash value, the data block information with inconsistent hash values, and the location and content details of these data blocks. The security processing results include the identification information of the marked suspected tampered data blocks, the access restriction measures taken, and the updated hash value and index information.

Please refer to Figure 2, the steps for obtaining the data integrity identifier are as follows:

Based on the acquired bill data, the text and numbers on the bill are interpreted and the extracted data content is classified and sorted, including the date, amount, multiple key information of the payee and the unique identifier of each bill, to generate a data hash value;

The process of acquiring bill data involves extracting digital information from various bills after scanning. This process usually includes OCR (optical character recognition) technology to automatically interpret the text and numbers on the bill, which is then classified and organized by the data extraction module. The extracted data content includes but is not limited to key information such as date, amount, payee, and unique identifiers for each bill, such as serial numbers or specific QR code information. These unique identifiers are assigned by the internal system when the bill is generated to ensure the traceability of each bill in subsequent processing. The extracted data content and unique identifiers are used to construct the hash value of the bill data. The hash value is calculated using a specific hash function, such as SHA-256, to ensure that any slight data change will result in a significant change in the hash value, so that any unauthorized modification of the data can be effectively detected during the data verification phase.

Based on the data hash value, each data item and its timestamp are calculated to identify the time pattern of data input, and abnormal pattern monitoring of data input is performed to obtain data input pattern analysis results;

For calculation of each data item and its timestamp, the formula is: ,in, is the time interval between the ith data item and the previous data item, is the timestamp of the ith data item, is the timestamp of the i-1th data item;

like and ,but Minutes. Usually obtained by synchronizing the server time or using the Network Time Protocol (NTP). By statistically analyzing the time intervals between consecutive ticket inputs, the regularity or abnormality of the input time can be found. For example, if It is significantly lower than other time periods, which may indicate that there is batch processing of bills during this period. The results show that the calculation and analysis of time intervals can effectively monitor and identify preliminary abnormal patterns of data input.

Based on the data input pattern analysis results, the hash value, timestamp and unique identifier of each data item are associated and stored in the data table, a bidirectional index relationship between the hash table and the data table is established, and a data integrity identifier is generated;

Associate hash values and timestamps with unique identifiers. The association step includes matching and storing the hash value of each data item with its corresponding timestamp and unique identifier to ensure the integrity and traceability of each data item in the data table. The associated data is managed through a database using database software such as MySQL or Oracle. These data are then used to generate an overall data integrity identifier, which is generated through a specific algorithm such as a Merkle tree structure. Each node represents the hash value of a single data item, which is aggregated layer by layer to ultimately form a root hash value. The root hash value serves as an identifier of data integrity, and it combines the hash values of all data items. Any change in data will result in a change in the root hash value, thereby providing a verification mechanism when data is accessed or exchanged.

Please refer to FIG3 , the specific steps for calculating the probability value of each character being lost during transmission are:

According to the data integrity mark, the character frequency and position information in the transmission process are analyzed to determine the position in the data where errors are prone to occur, and redundant data is inserted to generate real-time monitoring information of transmission errors;

First, it is necessary to analyze the frequency and position information of characters in the transmission process. The character frequency is obtained by scanning the transmitted data stream character by character and counting the number of times the characters appear. By analyzing the various characters in the bill information, the system can track the frequency and distribution of each character in the transmission process in real time. At the same time, the position information of the characters is achieved by marking each character with a unique serial number. The generation of the serial number is based on the order of data packet transmission, and the position of the character is recorded by appending it to each character. By inserting redundant data, the system can automatically identify the positions in the transmission that are most prone to errors based on the frequency and position information, and insert additional redundant data at these positions. By real-time monitoring of errors in the transmission process, the system checks each transmitted character to ensure that the character data that is not lost can be accurately identified and processed after the data packet arrives at the receiving end.

Based on the real-time monitoring information of transmission errors, the number of transmission errors, delays and their frequency of occurrence for each character are recorded, using the formula:

,

Calculate the probability of missing the i-th character , get the loss probability analysis result;

in, Indicates the probability that character i is lost during transmission. is the number of transmission errors of the i-th character, obtained through the verification mechanism at the receiving end, is the number of transmission delays of the i-th character, obtained by real-time monitoring of the transmission delay log. is the frequency of occurrence of the i-th character, which is the number of times character i appears in the data stream divided by the total number of characters, Indicates the total number of times character i is transmitted during the entire transmission process, obtained through the transmission statistics of the receiving end. Are weight coefficients, which are used to adjust the proportion of the number of transmission errors, number of delays and frequency of character occurrence in the loss probability. The weight setting is based on the analysis of the actual data transmission process. The reasonable range is determined by analyzing historical data to ensure the flexibility and accuracy of the system.

If character A has 100 transmission errors, is delayed 50 times, and is transmitted 1000 times, the frequency of character A is for , set the weight coefficient to , , , then the probability of losing character A is:

,

The results show that the loss probability of character A is 0.08, indicating that it may be lost about 8 times out of 1000 transmissions. The final calculated result can help identify the transmission risk of character A and adjust the redundant data insertion strategy in the system based on this probability to reduce the possibility of character A loss and ensure the integrity of the bill data.

Based on the loss probability analysis results, the number of errors and character positions in the transmitted data packets are counted, and the distribution of errors in the transmission is analyzed in combination with the loss probability to generate the transmission error information analysis results;

By analyzing the data packet transmission log, the system first records the unique serial number of each data packet and its transmission timestamp. The number of transmission errors is counted by checking whether the character data fed back by the receiving end matches the original data. If inconsistent characters are found, the system will mark them as erroneous characters and record the occurrence position and transmission period of the character. In this way, the system can locate the exact position and number of each character error. In addition, the system will analyze the frequency and distribution of errors during the transmission process, and combined with the calculation results of the aforementioned loss probability, further aggregate statistics on the areas and time periods of transmission errors. Through these aggregate analyses, the areas and time periods where errors frequently occur during transmission are identified, and the insertion strategy of redundant data is adjusted to ensure that the error rate of these areas and time periods is reduced in future transmissions.

Please refer to FIG4 , the steps for obtaining the fluctuation range of the analysis error distribution are specifically as follows:

Based on the results of the transmission error information analysis, the transmission characteristics of each character are analyzed using the formula:

,

Calculate the range of the error distribution , generate fluctuation range information;

in, It is used to measure the uneven distribution of errors of each character during transmission. A high value means that the transmission error distribution is uneven, and a low value means that the error distribution is uniform. Representing characters The time delay from sending to receiving end during the transmission process is obtained through the network delay log. A long delay time may indicate a problem with the transmission path or network. Indicates that the character The number of retransmissions is obtained through the feedback records of the receiving end. A high number of retransmissions usually means that the network is unstable or there are many transmission errors. is the average loss probability of all characters, through the formula: calculate, It is the total number of characters, which indicates the total number of different characters that appear during transmission, recorded by the transmission network.

If 4 types of characters are transmitted in the data stream, the probability of loss is , , , , the transmission delay is , , , , the number of retransmissions is , , , , then the average loss probability is:

,

Plug into the formula:

,

,

The results show that the fluctuation range of the error distribution is 13.6377. A large fluctuation range indicates that some characters have large errors, delays, or retransmission times in transmission, while other characters are relatively normal. This result is used to determine whether the system needs to adjust the insertion strategy of redundant data in a specific area. Generally speaking, if the fluctuation range exceeds 10, the system will adjust the redundant data distribution in the high error area to reduce transmission errors.

Based on the fluctuation range information, the insertion of redundant data is readjusted, the changes of data stream are dynamically monitored, and adjustments are made to generate dynamic error correction data stream;

By analyzing the loss probability, delay and number of retransmissions of each character during the transmission process, these data are obtained through error reports and delay logs fed back by the receiving end. After analyzing these data, the system comprehensively calculates the transmission characteristics of each character, especially for characters with large fluctuations. The system will focus on the transmission path, delay time and number of retransmissions of these characters. By analyzing the distribution and transmission behavior of the characters, the system can identify which areas have abnormal fluctuations in character transmission. Then, by inserting more redundant data in these high-fluctuation areas, the system ensures the integrity of the data in transmission, including dynamic monitoring and adjustment of data streams. The adjustment of redundant data is based on the error probability calculation results of each character, allocating more redundant data to characters with large fluctuations, and reducing the allocation of redundant data in areas with stable transmission. The system generates a dynamic error correction data stream through this adjustment strategy, ensuring the stability and accuracy of data in different transmission environments.

Please refer to FIG5 , the specific steps for obtaining the comparison between the expected and current verification results are as follows:

Based on the dynamic error correction data stream, a random number sequence is generated to determine the location of the data block to be audited, and the location information of the data block to be audited is obtained;

The system's random number generation method relies on the transmission order and timestamp information of the data stream. By extracting and processing this information, the generated data sequence can correspond to a specific data block in the data stream, ensuring randomness during the audit process while avoiding repeated audits of data blocks that have already been checked. The specific operations include extracting information about each data block in the data stream, mapping these data blocks to locations in the storage unit using the generated random number sequence, and constructing an audit data block list to ensure extensive coverage of the audit process and random distribution of data blocks, thereby determining the location of the data blocks to be audited.

Based on the location information of the data block to be audited, the verification identifiers of the receiving end and the sending end are compared, and the verification identifier of each data block is re-verified to obtain the verification identifier verification result;

After determining the data block to be audited, the checksum of each data block is re-verified. The specific process includes comparing the checksum of the receiving end with the checksum of the original record of the sending end. The system extracts the checksum value of the data block recorded in the transmission log and calls the corresponding checksum algorithm such as SHA-256 to recalculate the hash value of the data block to ensure the integrity of the data during transmission. The system calculates the checksum for each data block one by one, and verifies whether the data block has been tampered with or lost during transmission by comparing the checksum value, ensuring the integrity and accuracy of the checksum of each audited data block. The system obtains the checksum result by comparing the checksum of the original data block with the checksum of the current data block, ensuring that the checksums of all data blocks are correct during the re-verification process.

Based on the verification result of the verification mark, the formula is used:

,

Calculate the data block checksum inconsistency ratio , generate difference ratio information of data block verification;

in, Indicates the proportion of data blocks that need further review, ranging from 0 to 1, used to measure the inconsistency of the checksum of the data block. Indicates the number of inconsistent data blocks found after verification. The method of obtaining it is to compare the hash value of the data block. It is the audit frequency, which is obtained by setting the audit strategy. When the frequency is higher, problems can be found earlier, which reflects the intensity of the audit. It is the transmission delay, which means the delay caused by network or system reasons during data transmission. The delay time is collected by the monitoring system. The greater the delay, the greater the risk of verification inconsistency. It is the data block size, indicating the size difference of the data block. Larger data blocks are more likely to have inconsistent verification during transmission. The acquisition method is the actual size of the data block. It is the total number of audit data blocks, indicating the number of all data blocks selected in this audit.

If 100 data blocks are selected during the audit process, and 5 of them have inconsistent checksums, the audit frequency is , the transmission delay is seconds, the data block size difference is , the calculation formula is:

,

The results show that the verification inconsistency ratio is 0.08, indicating that among the 100 data blocks, 8% of the data blocks have inconsistent verification results. The system needs to further analyze these data blocks to determine possible errors or tampering during the transmission process.

Please refer to FIG6 , the specific steps of obtaining the number of occurrences of statistical verification differences are:

Based on the difference ratio information of data block verification, the formula is:

,

Calculate the total number of checksum differences , generate integrated verification difference analysis results;

in, Indicates the total number of inconsistencies in all checks counted by the system during the audit process. This result can help the system accurately measure the differences between data blocks and identify possible errors or tampering during the transmission process. Indicates the number of data blocks in the entire audit process. When the system is auditing, it will randomly generate audit data blocks from all data, extract and count this number. It is The number of times the system verifies each data block is obtained by recording multiple verifications of each data block. It is The number of differences between data blocks indicates the The number of differences detected during the verification process for each data block. The differences are calculated by comparing the expected verification mark with the actual verification mark. express and The number of data blocks for both series.

Setting parameters , audited data blocks, and the number of checksum differences for each data block is , , , , then calculate the total number of verification differences as follows:

,

The results show that the total number of checksum differences in the system is 14, which means that there are 14 checksum differences in 100 data blocks. These statistical results provide the system with a basis for in-depth analysis of the difference data blocks, so as to further confirm whether these differences are caused by tampering or transmission errors, and take corresponding remedial measures.

Based on the integrated verification difference analysis results, the original verification mark and the verification mark after transmission are compared, and the difference of each data block is gradually analyzed to generate the data integrity analysis results;

First, all data blocks involving verification differences are extracted from the transmission log, and the relevant information of each data block is recorded, including the time when the difference occurred, the transmission path, the size and location of the data block, etc. These data are obtained through the log file generated during the transmission process or the real-time monitoring system. The system calls the verification record of each data block in turn, and compares the verification mark of the difference data block with the original data block. By comparing the verification results of each data block, the system can identify which data blocks have inconsistent conditions during transmission. Then, these difference data blocks are further analyzed. By counting the frequency of occurrence of differences, combined with the transmission path and time period, the system can determine whether these differences are regular or appear in a concentrated manner under certain transmission conditions. In addition, the system will integrate and associate the relevant information of each difference data block, and combine the statistical results with other audit data to ensure the integrity of data transmission. After this operation, the data integrity analysis results are generated, and further processing suggestions are made for the difference data blocks.

Please refer to FIG. 7 , the specific steps for obtaining updated hash values and index information are as follows:

Based on the results of data integrity analysis, the hash value of each data block is reallocated and compared with the original value, data blocks with inconsistent hash values are identified and marked as suspected tampering, and a list of suspected tampered data blocks is generated;

The hash value of each data block is regenerated through the hash algorithm and compared with the original value. The system uses this comparison process to identify data blocks with inconsistent hash values. These data blocks are then marked as suspected tampering objects. The hash calculation and comparison process is recorded in detail in the system's audit log for subsequent tracking and analysis. Through these steps, all data blocks suspected of being tampered with can be successfully marked.

Based on the list of suspected tampered data blocks, the formula is used:

,

Calculating the quantified severity of data tampering , generating a severity indicator of data tampering;

in, Used to measure the proportion of suspected tampered data blocks, is the number of data blocks marked as suspected of tampering, is the total number of data blocks.

like , , ,but:

,

The results show that the severity of data tampering is 1%, indicating that among all the data blocks reviewed, 1% of the data blocks are detected as suspected tampering, which is a key metric for decision making. Generally, any data tampering greater than 0.5% is detected as suspected tampering. Values above 0.001 should be considered an indicator of concern as they may indicate that the system's security controls are vulnerable or have been bypassed.

Based on the severity index of data tampering, restrict access rights to suspected tampered data blocks, record the unique identifier and timestamp of each data block, and update the hash value and index information to generate a security processing result;

After a data block is marked as suspected of tampering, the system further restricts access rights to the data block by setting an access control list (ACL) to prohibit unauthorized access and records the unique identifier and timestamp of each data block to ensure that the source and modification time of each data block can be accurately tracked. In addition, the system also updates the hash value and index information of the marked data block to enhance the protection of data integrity. All these operations are recorded in the system log for subsequent security audits and risk assessments.

A method for verifying the integrity of bill data, which is performed based on the above-mentioned bill data integrity verification system, comprises the following steps:

S1: Based on the acquired ticket data, read the content and associate the timestamp for each data item, input the combination of the content and timestamp of each data item into the hash process, and generate a unique hash value for each combination;

S2: Based on the unique hash value of each combination, the hash value is associated with the corresponding timestamp and unique identifier to form a data integrity identifier;

S3: Based on the data integrity identifier, redundant data is added to the ticket information, and the data stream is monitored in real time, recording and analyzing the errors in data transmission and the loss probability of each character, and generating a dynamic error correction data stream;

S4: Based on the dynamic error correction data stream, a random number sequence is generated to select the data block that needs to be re-verified, the verification mark is checked on the selected data block, and the expected and current performance of the verification result are compared to form a data integrity analysis result;

S5: Based on the data integrity analysis results, identify and mark data blocks with hash value differences, perform access restrictions on these data blocks, update hash values and index information, and generate security processing results.

The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims (8)

1. A ticket data integrity verification system, the system comprising:

The data hash processing module constructs hash values of data contents based on the acquired bill data, calculates the time interval between each data item and the time stamp thereof, associates the hash values, the time stamp and the unique identifier, and generates a data integrity identifier;

The prediction error correction coding module inserts redundant data into bill information based on the data integrity mark, monitors transmission errors in the data stream in real time, calculates a probability value of each character lost in the transmission process, analyzes the uniformity degree and fluctuation range of error distribution, adjusts the redundant data and generates a dynamic error correction data stream;

The random audit verification module generates a random number sequence according to the dynamic error correction data stream, re-verifies the verification identification of the data block, compares the expected and current verification results, calculates the proportion of the data block with inconsistent verification results and the total data block, counts the occurrence times of verification differences and information of the difference data block, and forms a data integrity analysis result;

And marking the data blocks with hash value differences as suspected tampering by the tamper response processing module according to the data integrity analysis result, limiting access rights, updating the hash value and index information, and generating a security processing result.

2. The ticket data integrity verification system of claim 1, wherein the step of obtaining the data integrity identifier comprises:

based on the acquired bill data, generating a data hash value by reading characters and numbers on the bill and classifying and sorting the extracted data content, wherein the data hash value comprises a date, an amount of money, a plurality of key information of a payee and a unique identifier of each bill;

Based on the data hash value, calculating each data item and a time stamp thereof, identifying a time mode of data input, and monitoring an abnormal mode of data input to obtain a data input mode analysis result;

Based on the data input mode analysis result, associating the hash value, the time stamp and the unique identifier of each data item, storing the data items into a data table, establishing a bidirectional index relation between the hash table and the data table, and generating a data integrity identifier.

3. The ticket data integrity verification system of claim 2, wherein the step of calculating a probability value for each character lost during transmission comprises:

analyzing character frequency and position information in the transmission process according to the data integrity identification, determining the position where errors easily occur in the data, inserting redundant data, and generating real-time monitoring information of the transmission errors;

Based on the real-time monitoring information of the transmission errors, recording the transmission error times, delay times and occurrence frequency of each character, and adopting the formula:

，

Calculating the loss probability of the ith character Obtaining a loss probability analysis result;

Wherein, Is the number of transmission errors for the i-th character,Is the number of transmission delays of the ith character,Is the frequency of occurrence of the i-th character,Representing the total number of times character i is transmitted throughout the transmission,The weight coefficient is used for adjusting the influence proportion of the transmission error times, the delay times and the character occurrence frequency in the loss probability respectively;

Based on the loss probability analysis result, counting the number of errors and the occurrence positions of characters in the transmission data packet, and generating a transmission error information analysis result by combining the distribution situation of errors in the loss probability analysis transmission.

4. A ticket data integrity verification system as claimed in claim 3 wherein said step of obtaining a fluctuation range of said analysis error distribution is specifically:

based on the analysis result of the transmission error information, the transmission characteristics of each character are analyzed, and the formula is adopted:

，

Calculating fluctuation range of error distribution Generating fluctuation range information;

Wherein, Representing charactersThe time delay from the sending to the receiving end during transmission,Representing characters due to transmission errorsThe number of times that it is retransmitted,Is the average probability of loss of all the characters,Is the total number of characters, representing the total number of characters that occur during transmission;

And readjusting the insertion of redundant data based on the fluctuation range information, dynamically monitoring the change of the data stream, and adjusting to generate a dynamic error correction data stream.

5. The ticket data integrity verification system of claim 4 wherein said step of comparing expected and current verification results comprises the steps of:

generating a random number sequence based on the dynamic error correction data stream, and determining the position of the data block to be audited to obtain the position information of the data block to be audited;

Based on the position information of the data block to be audited, comparing the verification identifications of the receiving end and the sending end, and re-verifying the verification identifications of each data block to obtain a verification result of the verification identifications;

based on the verification result of the verification mark, adopting the formula:

，

Calculating data block verification inconsistency ratio Generating difference proportion information of data block verification;

Wherein, Indicating the number of data blocks found to be inconsistent by the verification,Is the frequency of the audit,Is the transmission delay of the packet,Is the size of the data block and,Is the total audit data block number.

6. The ticket data integrity verification system of claim 5, wherein the step of obtaining the number of occurrences of the statistical verification difference comprises:

based on the difference proportion information of the data block verification, adopting the formula:

，

calculating the total check difference times Generating an integrated verification difference analysis result;

Wherein, Representing the number of data blocks throughout the audit process,Is the firstThe number of checks of the individual data blocks,Is the firstThe number of differences of the data blocks indicates the firstThe number of differences detected by the data blocks during the verification process,Representation ofAndThe number of data blocks of the two series;

and based on the integrated check difference analysis result, comparing the original check mark with the transmitted check mark, and gradually analyzing the difference condition of each data block to generate a data integrity analysis result.

7. The ticket data integrity verification system of claim 6, wherein the step of obtaining updated hash value and index information comprises:

Based on the result of the data integrity analysis, reassigning the hash value of each data block and comparing the hash value with the original value, identifying the data block with inconsistent hash value, marking the data block as suspected tampering, and generating a suspected tampered data block list;

based on the suspected tampered data block list, adopting the formula:

，

calculating quantization severity of data tampering Generating a severity index of data tampering;

Wherein, For measuring the proportion of suspected tampered data blocks,Is the number of data blocks marked as suspected tampering,Is the total number of data blocks;

And limiting the access authority of suspected tampered data blocks based on the severity index of the data tampering, recording the unique identifier and the time stamp of each data block, updating the hash value and the index information, and generating a security processing result.

8. A ticket data integrity verification method, characterized in that the ticket data integrity verification system according to any one of claims 1-7 is executed, comprising the steps of:

Based on the acquired bill data, carrying out content reading and time stamp association on each data item, inputting the combination of the content of each data item and the time stamp into hash processing, and generating a unique hash value of each combination;

according to the unique hash value of each combination, associating the hash value with the corresponding time stamp and the unique identifier to form a data integrity identifier;

based on the data integrity mark, adding redundant data in bill information, simultaneously monitoring data flow in real time, recording and analyzing errors in data transmission and loss probability of each character, and generating dynamic error correction data flow;

based on the dynamic error correction data stream, generating a random number sequence, which is used for selecting a data block to be re-verified, checking a check mark on the selected data block, and comparing the expected and current performances of the check result to form a data integrity analysis result;

Based on the data integrity analysis result, identifying and marking data blocks with hash value differences, performing access restriction on the data blocks, updating hash values and index information, and generating a security processing result.