Online entrusted witness system based on block chain technology

Abstract

The invention relates to the technical field of online entrusting and witnessing, in particular to an online entrusting and witnessing system based on a block chain technology. The system comprises a data acquisition module, a model construction module, a dynamic adjustment module and an evaluation rollback module. According to the invention, monitoring data corresponding to transaction frequency, security requirements and real-time requirements are collected in real time through the data acquisition module, and through the model construction module, a judgment matrix is constructed, a multi-index decision model is established, and an optimal consensus mechanism is selected. A corresponding consensus mechanism can be selected according to the transaction frequency, the security requirement and the real-time requirement of a consignor entrusted case, the verification process of witness data is accelerated, threshold division is performed on indexes through a dynamic adjustment module by using a quantile method, optimization solution is performed by using a genetic algorithm, an optimization result is analyzed, and a consensus result is obtained. The parameters of the consensus mechanism are dynamically adjusted, and the parameters of the corresponding consensus mechanism are dynamically adjusted by monitoring data changes in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of online entrustment witnessing, and in particular to an online entrustment witnessing system based on blockchain technology. Background Art

[0002] The online entrustment witness system is a service platform designed to use Internet technology to solve the problem that people cannot handle certain businesses in person due to time and space. It uses facial recognition, electronic signatures, visual processes, etc. to realize remote online handling of entrusted businesses, effectively reducing the time and space costs of entrusting and agent business handling, and alleviating the burden of people doing business. The online entrustment witness system based on blockchain technology, with the help of blockchain's distributed ledger, encryption technology, consensus mechanism and other core technologies, provides a decentralized, tamper-proof and traceable technical platform for online entrustment witnessing, making the entire process of entrustment witnessing more transparent, efficient and secure, reducing the trust cost, and improving the efficiency and credibility of business handling. Among them, the consensus mechanism can quickly verify and confirm the entrustment witness data, greatly improving the speed and efficiency of business processing.

[0003] At present, the traditional method of entrusted witnessing often requires the parties and the witnesses to be at the same time and place, which is greatly restricted by time and space. With the rapid development of blockchain technology, different consensus mechanisms can be selected to achieve rapid verification and confirmation of entrusted witness data. In order to be able to select the corresponding consensus mechanism according to the transaction frequency, security requirements and real-time requirements of the client's entrusted case, speed up the verification process of witness data, and at the same time, monitor data changes in real time, dynamically adjust the parameters of the corresponding consensus mechanism, and improve the speed of online entrusted witnessing while ensuring data security. Therefore, we propose an online entrusted witnessing system based on blockchain technology. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that when the blockchain witness entrustment system accepts a case entrusted by the client, the speed of online entrustment witnessing is too slow due to too many distributed nodes of the blockchain. In order to be able to select the corresponding consensus mechanism according to the transaction frequency, security requirements and real-time requirements of the client's entrusted case, speed up the verification process of the witness data, and at the same time, monitor data changes in real time and dynamically adjust the parameters of the corresponding consensus mechanism.

[0005] To achieve the above-mentioned purpose, the present invention provides an online delegation witness system based on blockchain technology, including a data collection module, a model building module, a dynamic adjustment module and an evaluation rollback module; The data collection module collects monitoring data corresponding to transaction frequency, security requirements and real-time requirements in real time by deploying monitoring tools in the business system. The transaction frequency is measured by the number of transactions per unit time. The security requirements are quantified through the risk assessment model. The real-time requirements are mainly measured by the transaction confirmation time, that is, the time interval from the transaction initiation to the transaction being confirmed and written into the blockchain. The evaluation indicator system is determined based on this, and the collected data is passed to the model construction module; The model building module uses the hierarchical analysis method to decompose the decision problem into elements of different levels, compares the relative importance of the elements of each level, builds a judgment matrix, calculates the weight of each indicator, and establishes a multi-indicator decision model by approximating the ideal solution sorting method. The collected indicator data and the determined indicator weights are substituted into the constructed decision model, and the calculation and solution are performed to select the optimal consensus mechanism; The dynamic adjustment module statistically analyzes the data of each indicator, divides the threshold of each indicator by using the quantile method, takes the consensus mechanism parameters selected by the model construction module as chromosomes, and each indicator as an evaluation indicator of the fitness function, and uses the genetic algorithm to optimize and solve, and dynamically adjusts the parameters of the consensus mechanism by analyzing the optimization results; Before optimizing and adjusting the consensus mechanism parameters, the evaluation and rollback module performs a technical risk assessment and a business risk assessment on the consensus mechanism parameters, and fully backs up all parameters to establish a rollback mechanism. When system anomalies occur and the business is seriously affected after the parameter adjustment, the parameters can be quickly rolled back to the state before the adjustment.

[0006] As a further improvement of the technical solution, the model building module includes a weight allocation unit and a model building unit; The weight allocation unit converts the pairwise comparison results between the indicators into elements of the judgment matrix, uses a scaling method to represent the relative importance of the indicators, constructs the judgment matrix, and determines the weight of each indicator; The model building unit uses the approximate ideal solution sorting method to calculate the distance between each solution and the positive ideal solution and the negative ideal solution, calculates the closeness of each solution to the ideal solution, sorts and selects the solutions according to the closeness, and builds a multi-index decision model.

[0007] As a further improvement of the technical solution, the weight allocation unit calculates the consistency index according to the maximum eigenvalue of the judgment matrix, and performs a consistency check on the judgment matrix. The maximum eigenvalue formula of the judgment matrix is: ,in, is the maximum eigenvalue of the judgment matrix, is a matrix vector, is the number of vectors, For vector No. elements.

[0008] As a further improvement of the technical solution, the model building unit uses the Euclidean formula to calculate the distance between the positive ideal solution and the negative ideal solution, and calculates the closeness of each solution to the ideal solution.

[0009] As a further improvement of the technical solution, the dynamic adjustment module includes a threshold division unit and a parameter adjustment unit; The threshold division unit calculates the quantile of each indicator data, and divides the threshold according to the quantile value, where the upper quantile value is the upper limit and the lower quantile value is the lower limit; The parameter adjustment unit encodes the consensus mechanism parameters into chromosomes, randomly generates an initial population, designs a fitness function according to the indicators and optimization objectives after threshold division, performs comprehensive screening according to the fitness of individuals and whether the threshold requirements are met, and iteratively evolves the population using crossover and mutation operations.

[0010] As a further improvement of the technical solution, the parameter adjustment unit reduces the fitness of the individual through a penalty function for indicators exceeding the threshold range, so as to guide the genetic algorithm to search for a solution that meets the threshold requirement.

[0011] As a further improvement of the technical solution, the parameter adjustment unit performs weighted summation on each index to obtain a comprehensive fitness function value, and the formula of the comprehensive fitness function value is: ,in, is the comprehensive fitness function value, is the number of transactions per unit time, is the transaction frequency weight, The transaction confirmation time. is the real-time demand weight, For safety, is the safety requirement weight.

[0012] As a further improvement of the technical solution, the parameter adjustment unit sets the maximum number of iterations, the fitness value of the population converges to a threshold, and the optimal fitness value of the population for several consecutive generations does not change as termination conditions of the genetic algorithm.

[0013] As a further improvement of the present technical solution, the assessment rollback module performs quantitative analysis on the identified risks, sorts them according to the likelihood of occurrence and the degree of impact of the risks, and uses a risk matrix to divide the risks into three levels: high, medium, and low.

[0014] As a further improvement of the present technical solution, the evaluation rollback module sets the rollback trigger condition by monitoring the performance indicators and detecting the business anomaly mechanism. When the rollback condition is triggered, the rollback operation can be quickly executed and the state before the parameter adjustment can be automatically restored.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The online entrustment witness system based on blockchain technology collects monitoring data corresponding to transaction frequency, security requirements and real-time requirements in real time through the data collection module, and uses this to determine the evaluation index system. The model construction module uses the hierarchical analysis method to decompose the decision-making problem into elements of different levels, compare the relative importance of the elements at each level, build a judgment matrix, calculate the weight of each indicator, establish a multi-indicator decision model, and select the optimal consensus mechanism. It can select the corresponding consensus mechanism according to the transaction frequency, security requirements and real-time requirements of the client's entrusted case, and accelerate the verification process of witness data; Through the dynamic adjustment module, the data of each indicator is statistically analyzed, and the transaction frequency, security requirements and real-time performance of the entrusted case are divided into thresholds using the quantile method. The consensus mechanism parameters selected by the model construction module are used as chromosomes, and each indicator is used as an evaluation indicator of the fitness function. The genetic algorithm is used for optimization and solution. By analyzing the optimization results, the parameters of the consensus mechanism are dynamically adjusted to ensure that the dynamic adjustment module can adjust the parameters of the optimal consensus mechanism selected by the model construction module when the transaction frequency, security requirements and real-time performance of the entrusted case change, give full play to the performance of the consensus mechanism, further accelerate the process of data verification, and improve the speed of online entrusted witnessing; When the dynamic adjustment module adjusts the parameters of the consensus mechanism, the evaluation and rollback module is used to conduct technical risk assessment and business risk assessment on the parameter adjustment, and a complete backup of all parameters is performed to establish a rollback mechanism. When system abnormalities occur or the business is severely affected after the parameter adjustment, the parameters can be quickly rolled back to the state before the adjustment, and the parameter data of the consensus mechanism is protected. When an abnormality is encountered in the parameter adjustment, data verification can continue with the initial parameters of the consensus mechanism to ensure that data verification will not be interrupted and the normal progress of data verification is guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall process of the present invention; Figure 2 It is a schematic diagram of the overall detailed process of the present invention.

[0017] The meaning of each number in the figure is: 100. Data collection module; 200. Model building module; 210. Weight allocation unit; 220. Model building unit; 300. Dynamic adjustment module; 310. Threshold division unit; 320. Parameter adjustment unit; 400. Evaluation rollback module. DETAILED DESCRIPTION

[0018] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] At present, when the blockchain witness entrustment system accepts cases entrusted by clients, the online entrustment witnessing speed is too slow due to the excessive number of distributed nodes in the blockchain. In order to select the corresponding consensus mechanism according to the transaction frequency, security requirements and real-time requirements of the client's entrusted cases, speed up the verification process of witness data, and at the same time, monitor data changes in real time and dynamically adjust the parameters of the corresponding consensus mechanism.

[0020] Therefore, the present invention proposes to collect monitoring data corresponding to transaction frequency, security requirements and real-time requirements in real time through a data acquisition module, establish a multi-indicator decision-making model using a model construction module, select the optimal consensus mechanism, dynamically adjust relevant parameters of the consensus mechanism using a dynamic adjustment module, and ensure the normal operation of the system through an evaluation rollback module.

[0021] The details are as follows: See also Figure 1 As shown, the present invention provides an online delegation witness system based on blockchain technology, including a data collection module 100, a model building module 200, a dynamic adjustment module 300 and an evaluation rollback module 400; The data collection module 100 collects monitoring data corresponding to transaction frequency, security requirements and real-time requirements in real time by deploying monitoring tools in the business system. The transaction frequency is measured by the number of transactions per unit time. The security requirements are quantified through the risk assessment model. The real-time requirements are mainly measured by the transaction confirmation time, that is, the time interval from the transaction initiation to the transaction being confirmed and written into the blockchain. The evaluation indicator system is determined based on this, and the collected data is passed to the model construction module 200; The model building module 200 uses the hierarchical analysis method to decompose the decision problem into elements of different levels, compares the relative importance of the elements of each level, builds a judgment matrix, calculates the weight of each indicator, and establishes a multi-indicator decision model by approximating the ideal solution sorting method. The collected indicator data and the determined indicator weights are substituted into the constructed decision model, and the calculation and solution are performed to select the optimal consensus mechanism; The dynamic adjustment module 300 statistically analyzes the data of each indicator, divides the threshold of each indicator by using the quantile method, takes the consensus mechanism parameters selected by the model construction module 200 as chromosomes, and each indicator as an evaluation indicator of the fitness function, and uses the genetic algorithm to optimize and solve, and dynamically adjusts the parameters of the consensus mechanism by analyzing the optimization results; Before optimizing and adjusting the consensus mechanism parameters, the evaluation and rollback module 400 performs a technical risk assessment and a business risk assessment on the consensus mechanism parameters, and fully backs up all parameters and establishes a rollback mechanism. When system abnormalities or business is seriously affected after parameter adjustment, the parameters can be quickly rolled back to the state before adjustment to ensure the normal operation of the system.

[0022] like Figure 2 As shown, the model building module 200 includes a weight allocation unit 210 and a model building unit 220; The weight allocation unit 210 converts the pairwise comparison results between the indicators into elements of a judgment matrix, uses a scaling method to represent the relative importance of the indicators, constructs a judgment matrix, and determines the weight of each indicator; The model building unit 220 uses the approximate ideal solution sorting method to calculate the distance between each solution and the positive ideal solution and the negative ideal solution, calculates the closeness of each solution to the ideal solution, sorts and selects the solutions according to the closeness, and builds a multi-index decision model with the core goal of improving the overall performance of the blockchain system. It is used as the highest layer of the hierarchy, i.e., the target layer. According to the characteristics of business needs and influencing factors, the target layer is further decomposed into different middle-layer indicators. The middle-layer indicators may include transaction processing speed, system throughput, and scalability. Each middle-layer indicator is refined to determine specific bottom-layer indicators. The bottom-layer indicators include average transaction confirmation time and number of transactions per second. Through face-to-face interviews, e-mails, online surveys, etc., questionnaires are distributed to experts or survey subjects or interviews are conducted to collect their opinions on the relative importance of each indicator. The expert scores or questionnaire survey results are sorted and summarized, and the pairwise comparison results of each indicator are converted into elements of the judgment matrix. The 1-9 scale method is used to indicate the relative importance of indicators, where 1 indicates that the two indicators are equally important, 9 indicates that one indicator is extremely more important than another indicator, and the numbers in between indicate different degrees of importance; According to the sorted results, construct a judgment matrix ,in Indicators Relative to the indicator The importance of the judgment matrix has the following properties: ;

[0023] In order to calculate the consistency index according to the maximum eigenvalue of the judgment matrix, the weight allocation unit 210 calculates the consistency index according to the maximum eigenvalue of the judgment matrix, and performs a consistency check on the judgment matrix. The maximum eigenvalue formula of the judgment matrix is: ,in, is the maximum eigenvalue of the judgment matrix, is a matrix vector, is the number of vectors, For vector No. An element indicator.

[0024] Calculate the maximum eigenvalue according to the maximum eigenvalue formula of the judgment matrix , substituting it into the consistency formula: ,in, is the consistency indicator, is the maximum eigenvalue of the matrix, is the number of vectors.

[0025] By finding the corresponding average random consistency index , and finally calculate the consistency ratio ,when When , the judgment matrix is ​​considered to have satisfactory consistency and the calculated weights are reasonable and effective; otherwise, the judgment matrix needs to be readjusted until it passes the consistency test.

[0026] In order to obtain the optimal solution, the model building unit 220 uses the Euclidean formula to calculate the distance between the positive ideal solution and the negative ideal solution, and calculates the closeness of each solution to the ideal solution; The vector normalization method is used to standardize the dimensions and magnitudes of each indicator. The formula for standardization is: ,in, is the standardized matrix element, For the matrix Elements of For the number.

[0027] Calculated according to the formula , the Euclidean distance formula is used to calculate the distance to the positive ideal solution and the distance to the negative ideal solution respectively, and the formula is: ,in, For closeness, is the distance to the positive ideal solution, is the distance to the negative ideal solution.

[0028] The dynamic adjustment module 300 includes a threshold division unit 310 and a parameter adjustment unit 320; The threshold division unit 310 calculates the quantile of each indicator data, and divides the threshold according to the value of the quantile, with the upper quantile value being the upper limit and the lower quantile value being the lower limit; The parameter adjustment unit 320 encodes the consensus mechanism parameters into chromosomes, randomly generates an initial population, designs a fitness function according to the indicators and optimization objectives after the threshold division, performs comprehensive screening according to the fitness of the individuals and whether the threshold requirements are met, and iteratively evolves the population using crossover and mutation operations; Therefore, we comprehensively analyze business requirements and system characteristics, determine key evaluation indicators related to the consensus mechanism, such as system throughput, transaction confirmation time, security, energy consumption, and node participation, conduct statistical analysis on the collected data, understand the value range, distribution law, mean, variance, etc. of each indicator, provide a basis for threshold division, and calculate the quantiles of each indicator. For example, the upper quartile of the throughput indicator is used as the threshold for high throughput, and the lower quartile is used as the threshold for low throughput. The various parameters of the consensus mechanism are binary-encoded and converted into the chromosome representation in the genetic algorithm. For example, for a simple consensus mechanism, the block time parameter range is [1,10] seconds, and the block size parameter range is [1MB,10MB]. If binary encoding is used, each parameter can be represented by a binary string of a certain length, such as the block time is represented by 4 bits of binary and the block size is represented by 5 bits of binary. Then a chromosome can be represented as a string consisting of these two binary strings.

[0029] In order to reduce the fitness of the index beyond the threshold range, the parameter adjustment unit 320 reduces the fitness of the individual through a penalty function for the index beyond the threshold range, so as to guide the genetic algorithm to search for a solution that meets the threshold requirement; An exponential function is used for punishment, so that individuals exceeding the threshold are punished more severely. The penalty formula is: ,in, As an indicator, is the exponential parameter, is the index coefficient, is the maximum threshold of the indicator.

[0030] The penalty function is combined with the fitness function to obtain the final evaluation function, and the weight is dynamically adjusted according to the importance of each indicator in the optimization goal. , , If the system focuses more on improving throughput at a certain stage, it can be appropriately increased If the security requirement is more stringent, increase The value of The size of the penalty coefficient directly affects the intensity of the penalty for exceeding the threshold indicator. In the early stage of the genetic algorithm, a smaller penalty coefficient can be set so that the algorithm can search in a wider solution space; as the iteration proceeds, the penalty coefficient is gradually increased to guide the algorithm to converge to the area that meets the threshold requirements.

[0031] In order to obtain the comprehensive fitness function, the parameter adjustment unit 320 performs weighted summation on each index to obtain the comprehensive fitness function value, and the formula of the comprehensive fitness function value is: ,in, is the comprehensive fitness function value, is the number of transactions per unit time, is the transaction frequency weight, The transaction confirmation time. is the real-time demand weight, For safety, is the safety requirement weight.

[0032] According to the needs and goals of the blockchain system, select appropriate indicators as evaluation indicators of the fitness function. These indicators may include system throughput, transaction confirmation time, security, energy consumption, etc. For example, in a blockchain system that pursues high transaction processing efficiency, system throughput and transaction confirmation time may be more critical evaluation indicators. The selected evaluation indicators are integrated to construct a fitness function. The design of the fitness function should be able to accurately reflect the quality of the individual, that is, the quality of the consensus mechanism parameter combination represented by the chromosome. A common method is to perform weighted summation or multiplication of each indicator to obtain a comprehensive fitness value; According to the fitness value of the individual, a roulette wheel selection strategy is used to select the parent individual from the current population for genetic operation. The roulette wheel selection determines the probability of being selected according to the proportion of the individual fitness to the total fitness. The higher the fitness, the greater the probability of being selected. Perform a crossover operation on the selected parent individuals to generate new offspring individuals. The crossover operation simulates the gene exchange process in biological genetics, and increases the diversity of the population by exchanging some gene fragments of the parent chromosomes. Using single-point crossover, a crossover point is randomly selected, and the part of the parent chromosome after the crossover point is exchanged to obtain two new offspring chromosomes; The offspring individuals after crossover are mutated to change the values ​​of certain genes on the chromosome with a certain probability. The mutation operation can introduce new gene combinations to prevent the genetic algorithm from falling into a local optimal solution. The mutation probability is usually set to a small value, generally between 0.001 and 0.1. For example, in binary coding, the mutation operation can be to change a gene on a chromosome from 0 to 1 or from 1 to 0; in real number coding, the mutation operation can be to add or subtract a random number to a parameter value.

[0033] The offspring individuals generated by genetic operations are added to the current population to form a new population, and then operations such as selection, crossover, and mutation are repeated to continuously iterate and update the population until the termination conditions are met.

[0034] In order to better determine the termination condition of the genetic algorithm, the parameter adjustment unit 320 sets the maximum number of iterations, the fitness value of the population converges to a threshold, and the optimal fitness value of the population for several consecutive generations does not change as the termination condition of the genetic algorithm; By setting multiple termination conditions, when the genetic algorithm meets the termination conditions, the optimal individual obtained is the optimized consensus mechanism parameter combination. Analyze the optimization results, including the values ​​of each parameter and the corresponding evaluation index values, understand the performance of the optimized consensus mechanism in different aspects, apply the optimized consensus mechanism parameters to the actual blockchain system for testing and verification, and observe the actual operation effect of the system. According to the actual situation, it may be necessary to further fine-tune or adjust the parameters to ensure the stability and performance of the system to the best state.

[0035] In order to better quantify the identified risks, the assessment and rollback module 400 quantifies the identified risks, sorts them according to the likelihood of occurrence and impact, and divides the risks into three levels: high, medium, and low using a risk matrix; Draw a two-dimensional matrix with the possibility of risk occurrence as the horizontal axis and the risk impact as the vertical axis. The matrix is ​​usually divided into 9 cells, forming a 3×3 table. According to the combination of the possibility of risk occurrence and the impact, the risk is divided into three levels: high, medium and low. Generally speaking, the high-risk area refers to the area with high possibility and high impact, usually located in the upper right corner of the matrix; the medium-risk area refers to the area where one of the possibility or impact is high and the other is medium; the low-risk area is the area with low possibility and low impact, located in the lower left corner of the matrix; During the execution of a project or business, risks should be reassessed regularly, and the assessment results of the possibility of risk occurrence and the degree of impact, as well as the risk level classification, should be adjusted according to actual conditions. Based on the results of the risk assessment, the implementation of preventive measures should be adjusted in a timely manner, and measures that are not effective should be optimized or replaced to ensure that risks are always under control.

[0036] In order to ensure that the system can automatically restore to the state before the parameter adjustment when an abnormality is detected, the evaluation rollback module 400 sets the rollback triggering condition by monitoring the performance indicators and detecting the business abnormality mechanism, and can quickly perform the rollback operation when the rollback condition is triggered, and automatically restore to the state before the parameter adjustment; Real-time monitoring of the key performance indicators of the system, such as system response time, throughput, error rate, etc. Once these indicators exceed the preset normal range and are determined to be caused by parameter adjustment, immediately trigger the rollback operation, establish a business anomaly detection mechanism, and promptly discover situations where the business is seriously affected by the analysis of transaction data, user feedback, etc. For example, when abnormal situations such as a sharp increase in transaction failure rate and a sharp increase in the number of user complaints occur, trigger the rollback and write an automated rollback script that can quickly execute the rollback operation when the rollback condition is triggered. The rollback script should be able to automatically restore to the state before the parameter adjustment, including the restoration of configuration files and the rollback of the database. Before the actual rollback operation, the rollback script and process are strictly tested and verified to ensure the reliability and effectiveness of the rollback operation. By simulating the rollback scenario, check whether the system can be smoothly restored to the state before the adjustment and whether all functions are normal.

[0037] In summary, the working principle of this solution is as follows: The online entrustment witness system based on blockchain technology collects monitoring data corresponding to transaction frequency, security requirements and real-time requirements in real time through the data acquisition module 100, and determines the evaluation index system based on this. The model construction module 200 uses the hierarchical analysis method to decompose the decision-making problem into elements of different levels, compares the relative importance of the elements at each level, constructs a judgment matrix, calculates the weight of each indicator, establishes a multi-indicator decision-making model, selects the optimal consensus mechanism, and realizes the ability to select the corresponding consensus mechanism according to the transaction frequency, security requirements and real-time requirements of the client's entrusted case, speeds up the verification process of the witness data, and statistically analyzes the data of each indicator through the dynamic adjustment module 300. The transaction frequency, security requirements and real-time requirements of the entrusted case are used as indicators for threshold division using the quantile method. The consensus mechanism parameters selected by the model construction module 200 are used as chromosomes, and each indicator is used as an evaluation indicator of the fitness function. The genetic algorithm is used for optimization. Optimized solution is obtained by analyzing the optimization results and dynamically adjusting the parameters of the consensus mechanism to ensure that the dynamic adjustment module 300 can adjust the parameters of the optimal consensus mechanism selected by the model building module 200 when the transaction frequency, security requirements and real-time performance of the entrusted case change, give full play to the performance of the consensus mechanism, further accelerate the process of data verification, and improve the speed of online entrusted witnessing. When the dynamic adjustment module 300 adjusts the parameters of the consensus mechanism, the evaluation and rollback module 400 is used to perform technical risk assessment and business risk assessment on the parameter adjustment, and a complete backup of all parameters is performed to establish a rollback mechanism. When a system abnormality occurs after the parameter adjustment or the business is seriously affected, the parameters can be quickly rolled back to the state before the adjustment, and the parameter data of the consensus mechanism is protected to ensure that when the parameter adjustment encounters an abnormality, the data verification can continue with the initial parameters of the consensus mechanism to ensure that the data verification will not be interrupted and the data verification is carried out normally.

[0038] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An online entrustment witness system based on blockchain technology, characterized by: It includes a data collection module (100), a model building module (200), a dynamic adjustment module (300) and an evaluation rollback module (400); The data collection module (100) collects monitoring data corresponding to transaction frequency, security requirements and real-time requirements in real time by deploying monitoring tools in the business system. The transaction frequency is measured by the number of transactions per unit time. The security requirements are quantified by the risk assessment model. The real-time requirements are mainly measured by the transaction confirmation time, that is, the time interval from the initiation of the transaction to the confirmation of the transaction and its writing into the blockchain. The evaluation index system is determined based on this, and the collected data is transmitted to the model construction module (200); The model building module (200) uses the hierarchical analysis method to decompose the decision problem into elements of different levels, compares the relative importance of the elements of each level, builds a judgment matrix, calculates the weight of each indicator, establishes a multi-indicator decision model by approximating the ideal solution sorting method, substitutes the collected indicator data and the determined indicator weights into the constructed decision model, performs calculation and solution, and selects the optimal consensus mechanism; The dynamic adjustment module (300) statistically analyzes the data of each indicator, divides the threshold of each indicator by using the quantile method, uses the consensus mechanism parameters selected by the model construction module (200) as chromosomes, and each indicator as an evaluation indicator of the fitness function, uses a genetic algorithm to optimize and solve, and dynamically adjusts the parameters of the consensus mechanism by analyzing the optimization results; Before optimizing and adjusting the consensus mechanism parameters, the evaluation and rollback module (400) performs a technical risk assessment and a business risk assessment on the consensus mechanism parameters, and fully backs up all parameters to establish a rollback mechanism. When a system anomaly occurs after the parameter adjustment and the business is seriously affected, the parameters can be quickly rolled back to the state before the adjustment.

2. According to claim 1, the online entrusted witness system based on blockchain technology is characterized in that: The model building module (200) includes a weight allocation unit (210) and a model building unit (220); The weight allocation unit (210) converts the pairwise comparison results between the indicators into elements of a judgment matrix, uses a scaling method to represent the relative importance of the indicators, constructs a judgment matrix, and determines the weight of each indicator; The model building unit (220) uses the approximate ideal solution ranking method to calculate the distance between each solution and the positive ideal solution and the negative ideal solution, calculates the closeness of each solution to the ideal solution, sorts and selects the solutions according to the closeness, and builds a multi-index decision model.

3. The online entrusted witness system based on blockchain technology according to claim 2 is characterized in that: The weight allocation unit (210) calculates the consistency index according to the maximum eigenvalue of the judgment matrix and performs a consistency check on the judgment matrix. The maximum eigenvalue formula of the judgment matrix is: ,in, is the maximum eigenvalue of the judgment matrix, is a matrix vector, is the number of vectors, For vector No. elements.

4. The online entrusted witness system based on blockchain technology according to claim 2 is characterized in that: The model building unit (220) uses the Euclidean formula to calculate the distance between the positive ideal solution and the negative ideal solution, and calculates the degree of closeness between each solution and the ideal solution.

5. The online entrusted witness system based on blockchain technology according to claim 1 is characterized in that: The dynamic adjustment module (300) comprises a threshold division unit (310) and a parameter adjustment unit (320); The threshold division unit (310) calculates the quantile of each indicator data, and divides the threshold according to the quantile value, wherein the upper quantile value is the upper limit and the lower quantile value is the lower limit; The parameter adjustment unit (320) encodes the consensus mechanism parameters into chromosomes, randomly generates an initial population, designs a fitness function according to the indicators and optimization objectives after the threshold division, performs comprehensive screening according to the fitness of individuals and whether the threshold requirements are met, and iteratively evolves the population using crossover and mutation operations.

6. The online entrusted witness system based on blockchain technology according to claim 5 is characterized by: The parameter adjustment unit (320) reduces the fitness of the individual through a penalty function for indicators exceeding a threshold range, so as to guide the genetic algorithm to search for a solution that meets the threshold requirement.

7. The online entrusted witness system based on blockchain technology according to claim 5 is characterized by: The parameter adjustment unit (320) performs weighted summation on each index to obtain a comprehensive fitness function value, the formula of which is: ,in, is the comprehensive fitness function value, is the number of transactions per unit time, is the transaction frequency weight, The transaction confirmation time. is the real-time demand weight, For safety, is the safety requirement weight.

8. The online entrusted witness system based on blockchain technology according to claim 7 is characterized in that: The parameter adjustment unit (320) sets the maximum number of iterations, the fitness value of the population converges to a threshold, and the optimal fitness value of the population for several consecutive generations does not change as termination conditions of the genetic algorithm.

9. The online entrusted witness system based on blockchain technology according to claim 1 is characterized in that: The assessment rollback module (400) performs quantitative analysis on the identified risks, sorts the risks according to the likelihood of occurrence and the degree of impact, and uses a risk matrix to classify the risks into three levels: high, medium, and low.

10. The online entrusted witness system based on blockchain technology according to claim 1 is characterized in that: The evaluation rollback module (400) monitors performance indicators, detects business anomaly mechanisms, sets rollback trigger conditions, and can quickly execute a rollback operation when the rollback conditions are triggered, automatically restoring to the state before parameter adjustment.

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