A blockchain data change monitoring method and terminal based on digital fingerprints

Abstract

The application provides a blockchain data change monitoring method and terminal based on digital fingerprints, comprising: presetting different sizes of data sets, grouping the data sets and using different data encryption algorithms to confirm the running time, and determining the optimal data encryption algorithm; presetting the time interval of encryption calculation, encrypting the blockchain data at the time interval according to the optimal data encryption algorithm, generating a digital fingerprint, and comparing the generated digital fingerprint with the previously calculated digital fingerprint; if they are not the same, submitting a data change notification to an external system. The application can read the blockchain data that has undergone data changes in time and notify the external system of the data change in time, and the subsequent processing is performed by the external system, effectively realizing the timely monitoring of the blockchain data that has undergone data changes, so as to prevent the blockchain data from being tampered with.

Description

Technical Field

[0001] This invention relates to the fields of computer science and blockchain technology, and in particular to a blockchain data change monitoring method and terminal based on digital fingerprints. Background Technology

[0002] Currently, the only publicly disclosed technology based on blockchain and national cryptographic digital fingerprinting is system resource tamper-proofing. However, this method targets resource tamper-proofing and has a rollback capability. In certain data scenarios, this rollback capability is unnecessary, such as payment transaction data. Once a payment is completed, a forced rollback cannot be performed; instead, errors can only be corrected using a "red-ink reversal method." Therefore, a different monitoring method is needed. Furthermore, the aforementioned method operates on files, not data, which is a significant drawback. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a blockchain data change monitoring method and terminal based on digital fingerprints, so as to realize timely alarm for blockchain data that has been changed, so as to prevent blockchain data from being tampered with.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0005] A blockchain data change monitoring method based on digital fingerprints includes the following steps:

[0006] S1. Preset datasets of different sizes, group the datasets, and use different data encryption algorithms to verify the running time and determine the best data encryption algorithm;

[0007] S2. Set a preset time interval for encryption calculation, perform encryption calculation on blockchain data at the time interval according to the optimal data encryption algorithm, generate a digital fingerprint, and compare the generated digital fingerprint with the previously calculated digital fingerprint. If they are different, submit a data change notification to an external system.

[0008] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0009] A blockchain data change monitoring terminal based on digital fingerprints includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps described above in the blockchain data change monitoring based on digital fingerprints.

[0010] The beneficial effects of this invention are as follows: This invention provides a blockchain data change monitoring method and terminal based on digital fingerprints. It verifies and obtains the most time-efficient and effective data encryption algorithm by using a large dataset and different data encryption algorithms. Based on the obtained optimal data encryption algorithm, digital fingerprints are calculated on the blockchain data at preset time intervals. The current digital fingerprint of the blockchain data is compared with the previously calculated data fingerprint to promptly obtain chain data that has undergone data changes and notify external systems of the data change. The external system then performs timely follow-up processing, effectively achieving timely alerts for abnormal data changes in blockchain data to prevent blockchain data tampering. Furthermore, this method does not modify the original blockchain data of the system, therefore only read-only permissions are required. Attached Figure Description

[0011] Figure 1 This is an overall flowchart of a blockchain data change monitoring method based on digital fingerprints according to an embodiment of the present invention;

[0012] Figure 2 This is a flowchart illustrating the determination of the optimal data encryption algorithm in a blockchain data change monitoring method based on digital fingerprints, according to an embodiment of the present invention.

[0013] Figure 3 This is a flowchart illustrating the initialization of blockchain data in a blockchain data change monitoring method based on digital fingerprints, according to an embodiment of the present invention.

[0014] Figure 4 This is a flowchart illustrating the digital fingerprint comparison of blockchain data in a blockchain data change monitoring method based on digital fingerprints according to an embodiment of the present invention.

[0015] Figure 5 This is a schematic diagram of the structure of a blockchain data change monitoring terminal based on digital fingerprints according to an embodiment of the present invention.

[0016] Label Explanation:

[0017] 1. A blockchain data change monitoring terminal based on digital fingerprints; 2. Memory; 3. Processor. Detailed Implementation

[0018] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0019] Please refer to Figures 1 to 4 A blockchain data change monitoring method based on digital fingerprints, comprising the following steps:

[0020] S1. Preset datasets of different sizes, group the datasets, and use different data encryption algorithms to verify the running time and determine the best data encryption algorithm;

[0021] S2. Set a preset time interval for encryption calculation, perform encryption calculation on blockchain data at the time interval according to the optimal data encryption algorithm, generate a digital fingerprint, and compare the generated digital fingerprint with the previously calculated digital fingerprint. If they are different, submit a data change notification to an external system.

[0022] As described above, the beneficial effects of this invention are as follows: by using a large dataset and verifying different data encryption algorithms, a data encryption algorithm with the least time consumption and best effect is obtained. Based on the obtained best data encryption algorithm, digital fingerprints are calculated on the blockchain data at preset time intervals. The current digital fingerprint of the blockchain data is compared with the previously calculated data fingerprint to promptly obtain chain data that has undergone data changes and promptly notify the external system of the data change. The external system then performs subsequent processing in a timely manner, effectively realizing timely alarms for blockchain data that has undergone abnormal data changes, thereby preventing the blockchain data from being tampered with. Furthermore, this method does not modify the original blockchain data of the system in any way, so only read-only permissions are required.

[0023] Further, step S1 specifically includes:

[0024] S11. Divide the pre-prepared datasets of different sizes into a preset number of sub-datasets, and calculate the feature sum of each sub-dataset under each dataset to obtain the feature values ​​of different fields, which are used as the feature values ​​of the dataset.

[0025] S12. For each of the sub-data sets under each dataset, different data encryption algorithms are used to encrypt and calculate the feature values ​​to obtain the running time;

[0026] S13. Compare the running time of each of the sub-datasets under each dataset, and introduce external evaluation metrics to obtain the running results;

[0027] S14. Based on the results, determine the optimal data encryption algorithm.

[0028] As described above, by further dividing multiple datasets of different sizes into a predetermined number of sub-datasets, and setting different feature values ​​for each field in each sub-dataset of each dataset, different encryption algorithms are then used to encrypt and calculate the feature values ​​for each sub-dataset of each size. The optimal data encryption algorithm is finally selected by combining the batch encryption data running time with external evaluation indicators, so as to ensure the efficiency and security of generating digital fingerprints for blockchain data using the optimal data encryption algorithm.

[0029] Furthermore, in step S2, the blockchain data is encrypted using the optimal data encryption algorithm, specifically as follows:

[0030] The blockchain data is sequentially divided into multiple individual data entries according to a preset field length, and each individual data entry is encrypted using the optimal data encryption algorithm. The preset field length is less than 10 fields.

[0031] As described above, the blockchain data is first segmented into individual data entries, each containing 10 fields, and then the subsequent digital fingerprint encryption calculation is performed to ensure computational efficiency and to quickly locate any changes made to the data in the blockchain.

[0032] Furthermore, the step between S1 and S2 also includes:

[0033] S20. During the initial encrypted computation, the blockchain data is initialized, specifically including:

[0034] Set up the Genesis module within the target system and generate a strategy file;

[0035] A certificate is generated for the target system using the optimal data encryption algorithm.

[0036] The blockchain data is encrypted using the best data encryption algorithm, and then the encrypted digest is encrypted using the certificate to generate an initial digital fingerprint. The blockchain data is divided into individual data items and encrypted sequentially to generate individual digital fingerprint feature values. The complete initial digital fingerprint is composed of each of the digital fingerprint feature values.

[0037] The Genesis module also generates administrator accounts and permissions for the target system.

[0038] As described above, a genesis module is set up for the target system to perform initial encryption calculations on the blockchain data, and the administrator account and administrator privileges corresponding to the target system are generated according to the determined best data encryption algorithm to ensure the security of the subsequent monitoring process of changed data in the blockchain.

[0039] Further, step S2 specifically includes:

[0040] S21. A preset time interval for encryption calculation is established. When the time interval is reached, the current digital fingerprint is generated according to the optimal data encryption algorithm.

[0041] S22. Compare the digital fingerprint feature value corresponding to each single data in the current digital fingerprint with the digital fingerprint feature value corresponding to each single data in the initial digital fingerprint. If there are differences in the digital fingerprint feature values, submit the single data corresponding to the changed digital fingerprint feature value and the data change notification to the external system according to the administrator account and permissions configured in the Genesis Module.

[0042] As described above, by encrypting and generating digital fingerprints for each individual piece of data in the blockchain, the blockchain data is gradually compared and changed to ensure that no data segment is missed during monitoring. At the same time, when a change in the digital fingerprint feature value of a single piece of data is detected, the corresponding location can be quickly located and the changed digital fingerprint feature value and its location can be reported to the external system through the pre-set genesis module, so that the external system can be notified to handle it in a timely manner.

[0043] Furthermore, after step S22, the method further includes:

[0044] If the comparison result shows that the digital fingerprint feature values ​​are different, then the current digital fingerprint with different digital fingerprint feature values ​​will be used as the initial digital fingerprint for the next comparison of digital fingerprint feature values.

[0045] As described above, a digital fingerprint is calculated every time a time interval has elapsed. If there are differences in the comparison results during the comparison process, the current digital fingerprint is used as the initial digital fingerprint for comparison with the current digital fingerprint calculated in the next time time interval. This eliminates the need to re-initialize the blockchain data calculation and processing, effectively improving the efficiency of data change monitoring.

[0046] Furthermore, the time interval for the preset encryption calculation in step S21 is specifically as follows:

[0047] S21a. Suppose that the data source of the blockchain data is a sample set G = (t, X) = (ti, Xi1, Xi2, ..., Xij) with a time element column, where t is a date element, i = 1, 2, ..., j, i represents the i-th single data in the blockchain data, and j is the total number of single data.

[0048] Based on the date-type characteristic of t, the above sample set is divided into the following sample subsets, totaling 38 subsets:

[0049] G1: Represents the previous day, which can be denoted as T-2;

[0050] G2: indicates the day before yesterday, which can be denoted as T-3;

[0051] G3: Indicates the month M, that is, from the 1st of this month to the end of this month;

[0052] G4(M-1), G5(M-2)...G38(M-35) represent the 35 months preceding month M;

[0053] Based on the sample subset, the time interval is determined to be every other day, every other month, or every 36 months;

[0054] Yesterday can be denoted as G0(T-1).

[0055] Furthermore, S21 also includes:

[0056] Perceptual calculations are performed on each of the aforementioned sample subsets based on the time interval, specifically as follows:

[0057] S21a. For Gi, during the initialization of blockchain data, the digital fingerprint feature value of each single data gij (gij∈Gi) is calculated, and the initial feature value of the single data is recorded as kij0.

[0058] S21b. Every other day, calculate the digital fingerprint feature values ​​of yesterday's G0(T-1), and use the digital fingerprint feature values ​​of yesterday's G1(T-2) as the initial feature values.

[0059] Every month, that is, after the end of the month, the digital fingerprint feature values ​​of G3(M) for the current month are calculated, and the digital fingerprint feature values ​​of G4(M-1) for the previous month are used as the initial feature values.

[0060] Every 36 months, the digital fingerprint feature values ​​of G3 to G38 from the previous 36 months are calculated, and the digital fingerprint feature values ​​from the previous 36 months are used as the initial feature values.

[0061] As described above, the blockchain data is divided into 38 different sample subsets based on the grouping strategy of time intervals of one day, one month, and 36 months. Subsequently, different encryption calculation strategies can be selected for different sample subsets at different time intervals. Based on the perception calculation under different time intervals, the blockchain data can be quickly obtained to determine whether data changes have occurred, thereby improving the monitoring efficiency of data changes.

[0062] Further, step S22 specifically includes:

[0063] S22a. After each time interval, for each Gi included in the calculation, compare kij and kij0 one by one. When the existing digital fingerprint feature value kij of a certain single data is not equal to the corresponding digital fingerprint feature value kij0 in the initial digital fingerprint, it indicates that the single data has been changed.

[0064] S22b. When the kij of a single data entry changes, the kij of that single data entry is submitted to the external system according to the administrator account and permissions configured in the Genesis module, and the data change notification is reported.

[0065] As described above, by comparing the digital fingerprint calculation results of individual data entries one by one to see if the digital fingerprint feature values ​​have changed, the changed individual data entries can be quickly located and obtained, effectively improving the monitoring efficiency of data changes.

[0066] Please refer to Figure 5 A blockchain data change monitoring terminal based on digital fingerprints includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the aforementioned blockchain data change monitoring based on digital fingerprints.

[0067] As described above, the beneficial effects of this invention are as follows: Based on the same technical concept, a blockchain data change monitoring terminal based on digital fingerprints is provided. Through a large dataset and different data encryption algorithms, the system verifies and obtains the data encryption algorithm with the least time consumption and best effect. Based on the obtained best data encryption algorithm, digital fingerprints are calculated on the blockchain data at preset time intervals. The current digital fingerprint of the blockchain data is compared with the previously calculated data fingerprint to promptly obtain chain data that has undergone data changes and to promptly notify the external system of the data change. The external system then performs subsequent processing in a timely manner, effectively achieving timely alarms for blockchain data that has undergone abnormal data changes, thus preventing the blockchain data from being tampered with. Furthermore, this method does not modify the original blockchain data of the system, therefore only read-only permissions are required.

[0068] This invention provides a blockchain data change monitoring method and terminal based on digital fingerprints, which is mainly used for real-time monitoring of changes in on-chain data in blockchain. The following is a detailed description with reference to the embodiments.

[0069] Please refer to Figure 1 Embodiment 1 of the present invention is as follows:

[0070] In a well-designed target system, blockchain data modification issues should not occur. For example, in a hospital information system environment, when medical information to be uploaded to the blockchain changes, the reasons usually fall into the following categories:

[0071] (A) There was a flaw in the modification of the target system's program. The revision of the blockchain data did not adopt the principle of "red ink reversal" but directly modified the historical data;

[0072] (B) Unauthorized alteration refers to the modification of historical data without authorization;

[0073] (C) Directive modification refers to modifying historical data under the authorization of the unit.

[0074] Situations A and B described above must be prevented. However, once these two situations occur, no change information is proactively recorded. Without a proactive mechanism, external systems struggle to detect problems in a timely manner. Often, discrepancies are only discovered when historical data is compared against the target system, or when data inconsistencies arise between the target and external systems under similar circumstances. Once discovered, locating and restoring the altered data is extremely costly.

[0075] Situation (C) is acceptable. If this occurs, the external system should be notified proactively to make the corresponding changes.

[0076] This embodiment proposes a blockchain data change monitoring method based on digital fingerprints for the blockchain data change scenarios A and B described above. Figure 1 As shown, the steps include:

[0077] S1. Preset datasets of different sizes, group the datasets, and use different data encryption algorithms to verify the running time and determine the best data encryption algorithm;

[0078] S2. Preset the time interval for encryption calculation, perform encryption calculation on blockchain data at time intervals according to the best data encryption algorithm, generate digital fingerprints, and compare the generated digital fingerprints with the previously calculated digital fingerprints. If they are different, submit a data change notification to the external system.

[0079] In this embodiment, a data encryption algorithm with the shortest processing time and best performance is verified using a large dataset and different data encryption algorithms. Based on the obtained best data encryption algorithm, digital fingerprints are calculated on the blockchain data at preset time intervals. The current digital fingerprint of the blockchain data is compared with the previously calculated data fingerprint to promptly obtain chain data that has undergone data changes and notify the external system of the data change. The external system then performs subsequent processing in a timely manner, effectively realizing timely alarms for blockchain data that has undergone abnormal data changes, thereby preventing the blockchain data from being tampered with. Furthermore, this method does not modify the original blockchain data of the system, so only read-only permissions are required.

[0080] Please refer to Figure 2 Embodiment two of the present invention is as follows:

[0081] A blockchain data change monitoring method based on digital fingerprints, building upon the above-described embodiment one, in this embodiment, as follows: Figure 2 As shown, step S1 specifically involves:

[0082] S11. Divide the pre-prepared datasets of different sizes into a preset number of sub-datasets, and calculate the feature sum of each sub-dataset under each dataset to obtain the feature values ​​of different fields, which are used as the feature values ​​of the dataset.

[0083] In this embodiment, datasets of 10,000 records, 20,000 records, 50,000 records, 100,000 records, and 200,000 records are prepared in advance. Each of these five datasets is divided into four sub-datasets, and feature values ​​are set for each of the four sub-datasets, namely, sub-datasets containing feature values ​​of three fields, four fields, five fields, and six fields.

[0084] S12. For each subset of the dataset, different data encryption algorithms are used to encrypt and calculate the feature values ​​to obtain the running time.

[0085] In this embodiment, four subsets of data are obtained, and four different data encryption algorithms are used: MD5 algorithm, simple key-value hash algorithm, DEC algorithm and national standard SM3 algorithm, to perform encryption calculations on the feature values ​​in the four subsets of data respectively.

[0086] S13. Compare the running time of each subset of each dataset, and introduce external evaluation metrics to obtain the running results, which are as follows:

[0087] For simple key-value hashing algorithms, the time consumption is the least, but it can only be used on specific platforms and is limited in cross-platform compatibility;

[0088] The DEC algorithm can be accepted if the number of computation records is controlled, but its security is poor.

[0089] The MD5 algorithm is applicable to different development platforms, but it is simple and easily tampered with.

[0090] The SM3 algorithm, a national cryptographic standard, is applicable to different development platforms, is secure, not easily tampered with, and can be used across platforms.

[0091] Therefore, in this embodiment:

[0092] S14. Based on the results, the national standard SM3 algorithm is determined to be the best data encryption algorithm.

[0093] In this embodiment, multiple datasets of different sizes are further divided into a preset number of sub-datasets, and different feature values ​​are set for each sub-dataset under each dataset. Then, different encryption algorithms are used to encrypt and calculate the feature values ​​for multiple sub-datasets of each size. The optimal data encryption algorithm is finally selected by combining the batch encryption data running time with external evaluation indicators to ensure the efficiency and security of generating digital fingerprints for blockchain data using the optimal data encryption algorithm.

[0094] Please refer to Figure 3 and Figure 4 Embodiment 3 of the present invention is as follows:

[0095] A blockchain data change monitoring method based on digital fingerprints, building upon the above-described Embodiment 1 or Embodiment 2, in this embodiment, step S2 involves encrypting the blockchain data using the optimal data encryption algorithm, specifically as follows:

[0096] The blockchain data is sequentially divided into multiple individual data entries based on a preset field length, and each individual data entry is encrypted using the best data encryption algorithm. The preset field length is less than 10 fields.

[0097] In this embodiment, the blockchain data is first segmented into individual data entries, each containing 10 fields, and then the subsequent digital fingerprint encryption calculation is performed to ensure computational efficiency and to quickly locate any changes made to the data in the blockchain.

[0098] The steps between S1 and S2 also include:

[0099] S20. During the initial encrypted computation, the blockchain data is initialized, specifically including:

[0100] Set up the Genesis module within the target system and generate a strategy file;

[0101] Generate certificates for the target system using the best data encryption algorithm;

[0102] The best data encryption algorithm is used to generate an encrypted digest of the blockchain data, and then the encrypted digest is encrypted with a certificate to generate an initial digital fingerprint. The blockchain data is divided into individual data and encrypted sequentially to generate individual digital fingerprint feature values. The complete initial digital fingerprint is composed of each digital fingerprint feature value.

[0103] The Genesis module also generates administrator accounts and permissions for the target system.

[0104] The blockchain data initialization process is as follows: Figure 3 As shown.

[0105] In this embodiment, a genesis module is set up for the target system to perform initial encryption calculations on the blockchain data, and an administrator account and administrator permissions corresponding to the target system are generated according to the determined optimal data encryption algorithm to ensure the security of the subsequent monitoring process of changed data in the blockchain.

[0106] In this embodiment, such as Figure 4 As shown, step S2 specifically involves:

[0107] S21. Preset the time interval for encryption calculation. When the time interval is reached, generate the current digital fingerprint according to the best data encryption algorithm.

[0108] S22. Compare the digital fingerprint feature value corresponding to each individual data in the current digital fingerprint with the digital fingerprint feature value corresponding to each individual data in the initial digital fingerprint. If there are differences in the digital fingerprint feature value, submit the individual data corresponding to the changed digital fingerprint feature value and report the data change notification to the external system according to the administrator account and permissions configured in the Genesis module.

[0109] This means that by encrypting and generating digital fingerprints for each individual piece of data in the blockchain, the blockchain data is gradually compared and changed to ensure that no data segment is missed during monitoring. At the same time, when a change in the digital fingerprint feature value of a single piece of data is detected, the corresponding location can be quickly located and the changed digital fingerprint feature value and its location can be reported to an external system through a pre-set genesis module, so that the external system can be notified to handle it in a timely manner.

[0110] In this embodiment, step S22 is followed by:

[0111] If the comparison results show that the digital fingerprint feature values ​​are different, the current digital fingerprint with the different feature values ​​will be used as the initial digital fingerprint for the next comparison. That is, a digital fingerprint is calculated every time a time interval has elapsed. When a difference is found during the comparison process, the current digital fingerprint is used as the initial digital fingerprint for the next time interval comparison, without needing to re-initialize the blockchain data calculation, effectively improving the efficiency of data change monitoring.

[0112] The time interval for the preset encryption calculation in step S21 is as follows:

[0113] S21a. Suppose that the data source of the blockchain data is a sample set G = (t, X) = (ti, Xi1, Xi2, ..., Xij) with a time element column, where t is a date element, i = 1, 2, ..., j, i represents the i-th single data in the blockchain data, and j is the total number of single data;

[0114] Based on the date-type characteristic of t, the above sample set is divided into the following sample subsets, totaling 38 subsets:

[0115] G1: Represents the previous day, which can be denoted as T-2;

[0116] G2: indicates the day before yesterday, which can be denoted as T-3;

[0117] G3: Indicates the month M, that is, from the 1st of this month to the end of this month;

[0118] G4(M-1), G5(M-2)...G38(M-35) represent the 35 months preceding month M;

[0119] Based on the sample subset, determine whether the time interval is every other day, every other month, or every 36 months;

[0120] Yesterday can be denoted as G0(T-1).

[0121] S21 also includes:

[0122] Perceptual calculations are performed on each sample subset based on time intervals, specifically as follows:

[0123] S21a. For Gi, during the initialization of blockchain data, calculate the digital fingerprint feature value of each individual data gij (gij∈Gi), and record the initial feature value of each individual data as kij0;

[0124] S21b. Every other day, calculate the digital fingerprint feature values ​​of yesterday's G0(T-1), and use the digital fingerprint feature values ​​of yesterday's G1(T-2) as the initial feature values. As time goes by, as long as the perception calculation is uninterrupted, there is no need to re-perform the initialization blockchain data calculation process in step S20.

[0125] Every month, after the end of the month, the digital fingerprint feature values ​​of G3(M) for the current month are calculated, and the digital fingerprint feature values ​​of G4(M-1) for the previous month are used as the initial feature values. Similarly, as time goes by, as long as the perception calculation is uninterrupted, there is no need to re-perform the initialization blockchain data calculation process in step S20.

[0126] Every 36 months, the digital fingerprint feature values ​​of G3 to G38 from the previous 36 months are calculated, and the digital fingerprint feature values ​​of the next 36 months before that are used as the initial feature values. Similarly, as time goes by, as long as the perception calculation is uninterrupted, there is no need to re-perform the initialization blockchain data calculation process in step S20.

[0127] This involves dividing blockchain data into 38 different sample subsets based on time intervals of one day, one month, and 36 months. Subsequently, different encryption calculation strategies can be selected for different sample subsets at different time intervals. Based on the perception calculation under different time intervals, it is possible to quickly obtain whether the blockchain data has changed, thereby improving the efficiency of data change monitoring.

[0128] Then step S22 is as follows:

[0129] S22a. After each time interval, compare kij and kij0 for each Gi included in the calculation. When the existing digital fingerprint feature value kij of a certain single data is not equal to the corresponding digital fingerprint feature value kij0 in the initial digital fingerprint, it indicates that the single data has been changed.

[0130] S22b. When the kij of a single data entry is changed, the kij of this single data entry is submitted to the external system according to the administrator account and permissions configured in the Genesis module, and a data change notification is reported.

[0131] This involves comparing the digital fingerprint calculation results of individual data entries one by one to check whether the digital fingerprint feature values ​​have changed, so as to quickly locate and obtain the individual data entries that have changed, effectively improving the monitoring efficiency of data changes.

[0132] In this embodiment, the results of perception calculations are listed under the conditions of every day, every month, and every 36 months. Under the assumption that the daily increment is uniformly distributed and only three months are accumulated over multiple consecutive months, let f be the sample size of G1 and G2, that is, the average amount of one day. Then the sample size of G3 is 30f, and the sample size of G4 to G15 is 90f.

[0133] Then, under the criteria of sensory computing, calculations are performed according to the sensory computing periodic table in Appendix 1:

[0134] Appendix 1:

[0135] date Q1 Q2 <![CDATA[Q3]]> <![CDATA[Q4]]> <![CDATA[Q5]]> <![CDATA[Q6]]> <![CDATA[Q7]]> <![CDATA[Q8]]> <![CDATA[Q9]]> <![CDATA[Q 10 ]]> <![CDATA[Q 11 ]]> <![CDATA[Q 12 ]]> <![CDATA[Q 13 ]]> <![CDATA[Q 14 ]]> <![CDATA[Q 15 ]]> <![CDATA[Q 16 ]]> <![CDATA[Q 17 ]]> <![CDATA[Q 18 ]]> <![CDATA[Q 19 ]]> T f(T-1)f(T-2) f(T-3) 30f 30f 30f T+1 f(T)f(T-1) f(T-2) 30f 30f 30f T+2 f(T+1)f(T) f(T-1) 30f 30f 30f T+3 f(T+2)(T+1) f(T) 30f 30f 30f T+4 f(T+3)(T+2) f(T+1) 30f 30f 30f T+5 f(T+4)(T+3) f(T+2) 30f 30f 30f T+6 f(T+5)(T+4) f(T+3) 30f T+7 f(T+6)(T+5) f(T+4) T+8 f(T+7)(T+6) f(T+5) T+9 f(T+8)(T+7) f(T+6) 30f T+10 f(T+9)(T+8) f(T+7) T+11 f(T+10)(T+9) f(T+8) T+12 f(T+11)(T+10) f(T+9) 30f T+13 f(T+12)(T+11) f(T+10) T+14 f(T+13)(T+12) f(T+11)

[0136] date Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33 Q34 Q35 Q36 Q37 Q38 Workload T 93f T+1 93f T+2 93f T+3 93f T+4 93f T+5 93f T+6 30f 30f 93f T+7 30f 30f 30f 93f T+8 30f 30f 30f 93f T+9 30f 30f 93f T+10 30f 30f 30f 93f T+11 30f 30f 30f 93f T+12 30f 30f 30f 93f T+13 3f T+14 3f

[0137] Compared to 36 months per day (36 * 30f = 1080f), the computational cost of grouping the sample set is only 93f / 1080f = 8.61% of the former.

[0138] With f = 200,000, the daily calculation time for the Fash value is estimated to be about 4 minutes according to the perception calculation cycle table, where K1 = K2, K3 = K4 = K5 = 30K1, K6 = K7 = K8 = K9 = K10 = 60K1.

[0139] Here, K1 to K10 represent the first to tenth years. For example, a pregnant woman's pregnancy and childbirth cycle is about one year; a surgical patient's readmission usually occurs within one year; medical insurance patients' settlement cycle is one year; a child's health care cycle is from birth to age 6, but most outpatient visits occur before age 2; if an outpatient does not return for three years after their initial visit, the probability of them returning is still very small. In other words, a silent (inactive) outpatient record is highly likely to be a long-term silent record.

[0140] Most business systems, due to the growth of data sources, have limitations on the storage and retrieval of data from those sources. For example, data from three years ago is moved out of the business system and stored in a dedicated location for later retrieval, rather than being accessed online. For most data analyses, data models are significantly less sensitive to data from five years ago; therefore, comparative analyses generally focus on data from the most recent three years.

[0141] Based on the above scenarios, we can consider K=3, meaning the data source change awareness period is 3 years, or 36 months. We believe that awareness and monitoring within this range meets the general requirements. Data older than 3 years is no longer within the awareness range, and the business system should have corresponding protection measures.

[0142] Therefore, the final result obtained by combining the information can be:

[0143] If the data source for yesterday or the day before is changed, the change will be detected the following day.

[0144] If the data source for the current month is changed, the change will be detected within 4 days.

[0145] If a data source is changed within the last 36 months, the change will be detected within 12 days.

[0146] Please refer to Figure 5 Embodiment five of the present invention is as follows:

[0147] A blockchain data change monitoring terminal 1 based on digital fingerprints, such as Figure 5 As shown, it includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it implements the steps of a blockchain data change monitoring method based on digital fingerprints according to any one of the embodiments of the first to third embodiments described above.

[0148] In summary, the present invention provides a blockchain data change monitoring method and terminal based on digital fingerprints, which can monitor changes to blockchain data in a target system in a timely manner without modifying any data in the original target system or touching the internal structure of the target system. It only requires providing the system's blockchain data source and read-only permissions for the corresponding view. By introducing data encryption algorithms and calculation strategies for individual data, digital fingerprints are generated for the blockchain data. By comparing the digital fingerprints of two individual data points calculated within a preset time interval, the changed on-chain data can be identified in a timely manner and notified to the external system for subsequent processing. This effectively achieves timely alerts for changed blockchain data and also effectively prevents the tampering of important blockchain data.

[0149] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A blockchain data change monitoring method based on digital fingerprints, characterized in that, Including the following steps: S1. Preset datasets of different sizes, group the datasets, and use different data encryption algorithms to verify the running time and determine the best data encryption algorithm; S2. Preset the time interval for encryption calculation, perform encryption calculation on the blockchain data at the time interval according to the optimal data encryption algorithm, generate a digital fingerprint, and compare the generated digital fingerprint with the previously calculated digital fingerprint. If they are different, submit a data change notification to the external system. The step between S1 and S2 also includes: S20. During the initial encrypted computation, the blockchain data is initialized, specifically including: Set up the Genesis module within the target system and generate a strategy file; A certificate is generated for the target system using the optimal data encryption algorithm. The blockchain data is encrypted using the best data encryption algorithm, and then the encrypted digest is encrypted using the certificate to generate an initial digital fingerprint. The blockchain data is divided into individual data items and encrypted sequentially to generate individual digital fingerprint feature values. The complete initial digital fingerprint is composed of each of the digital fingerprint feature values. The Genesis Module also generates administrator accounts and permissions for the target system; Step S2 specifically involves: S21. A preset time interval for encryption calculation is established. When the time interval is reached, the blockchain data is sequentially divided into multiple single data entries according to the preset field length. Each single data entry is encrypted and calculated according to the optimal data encryption algorithm to generate the current digital fingerprint. The preset field length is less than 10 fields. S22. Compare the digital fingerprint feature value corresponding to each single data in the current digital fingerprint with the digital fingerprint feature value corresponding to each single data in the initial digital fingerprint. If there are differences in the digital fingerprint feature values, submit the single data corresponding to the changed digital fingerprint feature value and the data change notification to the external system according to the administrator account and permissions configured in the Genesis Module.

2. The blockchain data change monitoring method based on digital fingerprints according to claim 1, characterized in that, Step S1 specifically involves: S11. Divide the pre-prepared datasets of different sizes into a preset number of sub-datasets, and calculate the feature sum of each sub-dataset under each dataset to obtain the feature values ​​of different fields, which are used as the feature values ​​of the dataset. S12. For each of the sub-data sets under each dataset, different data encryption algorithms are used to encrypt and calculate the feature values ​​to obtain the running time; S13. Compare the running time of each of the sub-datasets under each dataset, and introduce external evaluation metrics to obtain the running results; S14. Based on the results, determine the optimal data encryption algorithm.

3. The blockchain data change monitoring method based on digital fingerprints according to claim 1, characterized in that, Following step S22, the following is also included: If the comparison result shows that the digital fingerprint feature values ​​are different, then the current digital fingerprint with different digital fingerprint feature values ​​will be used as the initial digital fingerprint for the next comparison of digital fingerprint feature values.

4. The blockchain data change monitoring method based on digital fingerprints according to claim 1, characterized in that, The time interval for the preset encryption calculation in step S21 is specifically as follows: S21a. Suppose that the data source of the blockchain data is a sample set G=(t,X)=(ti,Xi1,Xi2,…,Xij) with a time element column, where t is a date element, i=1,2,…,j, i represents the i-th single data in the blockchain data, and j is the total number of single data. Based on the date-type characteristic of t, the above sample set is divided into the following sample subsets, totaling 38 subsets: G1: Represents the previous day, which can be denoted as T-2; G2: indicates the day before yesterday, which can be denoted as T-3; G3: Indicates the month M, that is, from the 1st of this month to the end of this month; G4(M-1), G5(M-2)...G38(M-35) represent the 35 months preceding month M; Based on the sample subset, the time interval is determined to be every other day, every other month, or every 36 months; Yesterday can be denoted as G0(T-1).

5. The blockchain data change monitoring method based on digital fingerprints according to claim 4, characterized in that, S21 also includes: Perceptual calculations are performed on each of the aforementioned sample subsets based on the time interval, specifically as follows: S21a. For Gi, during the initialization of blockchain data, the digital fingerprint feature value of each single data gij (gij∈Gi) is calculated, and the initial feature value of the single data is recorded as kij0. S21b. Every other day, calculate the digital fingerprint feature values ​​of yesterday's G0(T-1), and use the digital fingerprint feature values ​​of yesterday's G1(T-2) as the initial feature values. Every month, that is, after the end of the month, the digital fingerprint feature values ​​of G3(M) for the current month are calculated, and the digital fingerprint feature values ​​of G4(M-1) for the previous month are used as the initial feature values. Every 36 months, the digital fingerprint feature values ​​of G3 to G38 from the previous 36 months are calculated, and the digital fingerprint feature values ​​from the previous 36 months are used as the initial feature values.

6. The blockchain data change monitoring method based on digital fingerprints according to claim 5, characterized in that, Step S22 specifically involves: S22a. After each time interval, for each Gi included in the calculation, compare kij and kij0 one by one. When the existing digital fingerprint feature value kij of a certain single data is not equal to the corresponding digital fingerprint feature value kij0 in the initial digital fingerprint, it indicates that the single data has been changed. S22b. When the kij of a single data entry changes, the kij of that single data entry is submitted to the external system according to the administrator account and permissions configured in the Genesis module, and the data change notification is reported.

7. A blockchain data change monitoring terminal based on digital fingerprints, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps in a blockchain data change monitoring system based on digital fingerprints as described in any one of claims 1 to 6.

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