CN117932577B - Internet data processing method and system - Google Patents
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Abstract
The application relates to the field of data processing, in particular to a method and a system for processing internet data, wherein the method comprises the following steps: according to the data analysis of the historical orders, calculating an abnormal coefficient value and a risk value of a single time period, and calculating a user malicious coefficient according to the abnormal coefficient value and the risk value; responding to the malicious coefficient of the user being larger than a set coefficient threshold value, and judging the user as a malicious user; and in response to the user malicious coefficient being smaller than or equal to the coefficient threshold, judging the user as a normal user, storing the order data of the normal user, authenticating the user through the intelligent contract, encrypting the order data of the normal user, and then carrying out uplink sharing. The intelligent contract management system reduces the possibility of intelligent contract evidence congestion and reduces the phenomenon of abnormal operation of an industrial Internet platform.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a method and a system for processing internet data.
Background
Industrial internet order data refers to various data sets related to orders generated on an industrial internet platform. Such data typically includes, but is not limited to, the quantity, type, status, time stamp, price, delivery date, payment terms, customer information, etc. Through the collection and analysis of the data, enterprises can better understand market demands, optimize inventory management, improve production efficiency and make more intelligent business decisions. The industrial internet platform generally adopts a centralized database to store order data of users, and the centralized database of the industrial internet platform has the security problems of malicious tampering of data, user information and the like.
The blockchain is a decentralised peer-to-peer network, and a decentralised distributed ledger database is commonly maintained by means of cryptography principles, time sequence data, consensus mechanisms, economic incentives and the like, so that the data can be verified, traced, difficult to tamper with and transparent. Blockchain technology, as a distributed, de-centralized solution, can provide a high degree of data security, is non-tamper-evident, and thus, can increase the security of data by storing order data onto the blockchain.
When the order is stored on the blockchain, because the data on the blockchain is public and transparent, in order to ensure the safety of user information, the order data is encrypted by using a user public key and then is uplink, and the uplink of the blockchain refers to the process of adding the data or transaction records to the blockchain. When order data is linked, the order data is firstly encrypted by public and private of an industrial Internet platform and then temporarily stored in an intelligent contract, and user authentication is carried out by utilizing the intelligent contract.
The low value order data for an anomalous user is typically referred to as those order information that contributes less to the business. Such orders may be categorized as low value orders because of their small amount, low profit margin, or little impact on the overall revenue and profit growth of the enterprise. However, when a large number of low-value order data of abnormal users are encountered, the intelligent contract is blocked, a large number of order data are stored, the intelligent contract system is paralyzed, and the industrial internet platform cannot normally operate.
Disclosure of Invention
In order to reduce the possibility of congestion caused by intelligent contract deposit and guarantee the normal operation of an intelligent contract system and an industrial internet platform, the application provides a processing method and a processing system of internet data. The following technical scheme is adopted:
in a first aspect, the present application provides a method for processing internet data, including the steps of:
according to the data analysis of the historical order, calculating an abnormal coefficient value of a single time period, wherein the calculation formula of the abnormal coefficient value is as follows: Wherein/> For/>Individual user number/>Abnormal coefficient value of time period,/>For/>Individual user number/>Number of order data in time period,/>For/>Individual user number/>Average value of time period order data price; determining an abnormal coefficient value greater than zero as a target value in a plurality of continuous time periods, calculating a first time interval between a current target value and a previous target value, calculating a second time interval between the current target value and a next target value, calculating an average value of the first time interval and the second time interval, and multiplying the average value with the current target value to obtain a sub-risk value; calculating a risk value, wherein the risk value is the average value of a plurality of sub-risk values; the user malicious coefficient is calculated, and the calculation formula of the user malicious coefficient is as follows: /(I)Wherein/>For/>User malicious coefficient of individual user,/>For/>The return rate of individual users after placing orders in the historical order data,For/>Risk values for anomaly coefficient values for individual users over a continuous time period; responding to the malicious coefficient of the user being larger than a set coefficient threshold value, and judging the user as a malicious user; and in response to the user malicious coefficient being smaller than or equal to the coefficient threshold, judging that the user is a normal user, storing the order data of the normal user, authenticating the user through an intelligent contract, encrypting the order data of the normal user, and then sharing the order data in a uplink manner.
Optionally, pre-classifying the malicious coefficient value to obtain an initial threshold value, counting data of the intelligent contract when authentication is carried out, and adjusting the initial threshold value to be the coefficient threshold value.
Optionally, the pre-classifying the malicious coefficient value to obtain an initial threshold value includes the steps of: according to a clustering algorithm, users are divided into at least three types, one type of users with the maximum malicious coefficient value is divided into malicious users, and the other at least two types of users are divided into non-malicious users; sorting the malicious coefficient values according to the value from large to small, and calculating the minimum value of the distance value between the average value of the malicious coefficient values of the first user and each malicious coefficient value of the second user as a first minimum value; calculating the minimum value of the distance value between the average value of the malicious coefficient values of the second user and each malicious coefficient value of the first user as a second minimum value; and calculating the average value of the first minimum value and the second minimum value as the initial threshold value.
Optionally, the data of the statistical intelligent contract when authentication is performed, and the initial threshold is adjusted to be the coefficient threshold, including the steps of: calculating the intelligent contract authentication passing rate of each user, wherein the calculation formula is as follows: Wherein/> Authentication pass rate for intelligent contracts of users,/>Accumulating the number of passes for authentication of a user,/>Accumulating the number of times of failed authentication for the user; and in response to the intelligent contract authentication passing rate being smaller than or equal to a preset passing rate threshold, updating the initial threshold to obtain the coefficient threshold.
Optionally, the user authentication through the intelligent contract includes the steps of: acquiring order data of a normal user; encrypting order data of a normal user by using a fixed public key of a preset industrial Internet platform; generating an information abstract of the order data by utilizing the data signature algorithm at a preset user side, and generating a user public key for each user by utilizing an asymmetric encryption algorithm; the information abstract, the user public key and the acquired user information are used as a query request to be sent to an industrial Internet platform; and sending the information abstract and the user information to an intelligent contract, and authenticating through the intelligent contract.
In a second aspect, the present application provides an internet data processing system, including: a processor and a memory storing computer program instructions which, when executed by the processor, implement a method of processing internet data according to the above.
The application has the following technical effects:
By analyzing malicious users, malicious users in the users are extracted, the intelligent contract certificate storing pressure and the authentication pressure are reduced, the stable operation of the industrial Internet platform is ensured when the order data certificate storing and sharing is carried out, and the user certificate storing cost is reduced.
And when the industrial internet platform user is authenticated, the user is authenticated through the intelligent contract and encrypted by using the user private key, so that the privacy security of the user is further improved.
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The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present application will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. In the drawings, several embodiments of the application are illustrated by way of example and not by way of limitation, and like or corresponding reference numerals refer to like or corresponding parts.
Fig. 1 is a method flowchart of a processing method of internet data according to an embodiment of the present application.
Fig. 2 is a flowchart of a method of step S5 in a method of processing internet data according to an embodiment of the present application.
Fig. 3 is a flowchart of a method of step S7 in a method of processing internet data according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that when the terms "first," "second," and the like are used in the claims, the specification and the drawings of the present application, they are used merely for distinguishing between different objects and not for describing a particular sequential order. The terms "comprises" and "comprising" when used in the specification and claims of the present application are taken to specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Smart contract (Smart contract) is a computer protocol that propagates, validates, or executes contracts in an informative manner. Smart contracts allow trusted transactions to be made without third parties, which transactions are traceable and irreversible.
The embodiment of the application discloses a processing method of internet data, which reduces the possibility of intelligent contract evidence storage congestion and ensures the normal operation of an intelligent contract system and an industrial internet platform through effectively screening users. Referring to fig. 1, the method includes steps S1 to S7, specifically as follows:
S1: an anomaly coefficient value for a single time period is calculated based on a data analysis of the historical orders.
After a single order data is obtained by a preset management center of the industrial internet platform, in order to encrypt the order data of a user, a pair of fixed public keys and fixed private keys are randomly generated, and after the fixed public keys and the fixed private keys are generated, the fixed public keys and the fixed private keys are not changed.
Before the order data is encrypted by the public key, the user needs to be subjected to abnormal analysis, so that the order data is prevented from being malicious order data generated by malicious users.
Acquisition of the firstHistorical order data for individual users, if/>More low-value order data exist in the order data of each user, and the user is possibly a malicious user.
Specifically, obtain the firstIndividual user at/>And if the abnormal coefficient value of the order data in each time period is a low-value order in a plurality of continuous time periods, the abnormal coefficient value of the order data in the time period is considered to be higher.
First, theThe calculation formula of the abnormal coefficient value of the order data in the time period is as follows:
wherein, For/>Individual user number/>Abnormal coefficient value of time period,/>For/>Individual user number/>Number of order data in time period,/>For/>Individual user number/>Average value of time period order data prices.
The lower the value of (2) >, the moreThe lower the order value of the order data over a period of time, the more/>, the adoptionAnd/>Multiplication is due to/>The representative average price does not contain quantity information, and if a user occasionally places a small number of low-value orders as normal operation, when placing a large number of low-value order data, the user may be abnormal operation. Therefore,/>The lower the value of/>The larger the value should be, the negative correlation mapping is performed using an exp (-x) function.
S2: and in a plurality of continuous time periods, determining an abnormal coefficient value larger than zero as a target value, calculating a first time interval between the current target value and a previous target value, calculating a second time interval between the current target value and a next target value, calculating the average value of the first time interval and the second time interval, and multiplying the average value with the current target value to obtain a sub-risk value.
S3: and calculating a risk value, wherein the risk value is the average value of a plurality of sub-risk values.
The method for acquiring the risk value of the abnormal coefficient value comprises the following steps:
Obtain the first hour Sequence of outlier values/>, of individual users over a continuous time periodOne hour is a super parameter that can be adjusted by the practitioner according to the specific implementation scenario.
Because the normal user order has interval, malicious users can continuously make low-value order, so that the method can obtainThe value of the middle value is larger than 0, the value is recorded as a target value, a first time interval between the current target value and the previous target value is calculated, the longer the average value of the first time interval is, the lower the frequency is indicated, the lower the risk is, and if the average value of the first time interval is smaller, the higher the frequency is indicated, and the higher the risk is.
And calculating a second time interval between the current target value and the next target value, wherein the longer the average value of the second time interval is, the lower the frequency is indicated, the lower the risk is, and if the average value of the second time interval is smaller, the higher the frequency is indicated, and the higher the risk is.
And calculating the average value of the first time interval and the second time interval and multiplying the average value by the current target value to obtain the sub-risk value. And calculating the average value of all the sub-risk values to obtain the risk value of the abnormal coefficient value.
S4: and calculating the user malicious coefficient.
If a user keeps a large number of low-value orders for a long time, the user is a malicious user, the resource consumption of the industrial Internet platform management center is carried out through the large number of low-value orders, the abnormal coefficients of the user in different time periods are required to be analyzed, the malicious coefficients of the user are obtained, and the calculation formula of the malicious coefficients of the user is as follows:
wherein, For/>User malicious coefficients of individual users. /(I)The larger the value of (c) represents the larger the risk factor, the greater the probability that the user may be a malicious user.
For/>The return rate of individual users after ordering in the historical order data, if the/>The higher the order rejection rate of the individual user, the/>The individual users are malicious users. The order return rate can be obtained by counting the user historical order quantity and the order return accumulated quantity, and the order return accumulated quantity is compared with the user historical order quantity to obtain/>，/>The larger the value of (1), the higher the user return rate, the greater the likelihood that the current user is considered to be a malicious user, and the greater the likelihood that the current user is considered to be a malicious userIs large.
For/>Risk values for anomaly coefficient values for individual users over a continuous period of time. The application adopts minutes to divide time periods, one minute is a time period, and if the continuous time period has high anomaly coefficient value, the method is the/>The individual user may be a malicious user, th/>Individual users are consuming resources of the industrial internet platform.
S5: pre-classifying malicious coefficient values to obtain initial thresholds, counting data of the intelligent contract during authentication, and adjusting the initial thresholds to coefficient thresholds.
Authentication refers to the process of data verification and validation using smart contracts. Through certification authentication, the intelligent contract can provide higher credibility and security for data use and sharing.
Specifically, after the user malicious coefficient is obtained, when the user malicious coefficient is larger than a certain threshold, the current user is considered to be a malicious user, and the order data of the malicious user is not subjected to verification processing. When the user malicious coefficient is smaller than a certain threshold value, the current user is considered to be a normal user, the order data of the normal user is subjected to verification processing, user information authentication is performed through an intelligent contract, the accuracy of the user information is further ensured, and the order data is encrypted and then shared in a uplink manner.
But when a certain threshold value is selected, the user is selected byThe threshold setting is performed, but there is a problem that the threshold setting is not good, and the normal user is judged as a malicious user or the malicious user is judged as the normal user.
In order to ensure that the selection of the threshold value does not affect the accuracy of the subsequent intelligent contracts, the initial threshold value is obtained by pre-classifying the malicious coefficient value according to the user, and then the initial threshold value is adjusted by counting the data when the authentication is carried out according to the intelligent contract, so that the waste of operating resources of the industrial Internet platform is reduced when the malicious order is attacked, the validity of the authentication is ensured, and the normal use of the user is ensured. Specifically, referring to fig. 2, step S5 includes steps S50-S55, specifically as follows:
s50: according to a clustering algorithm, users are classified into at least three types, one type of users with the largest malicious coefficient value is classified into malicious users, and the other at least two types of users are classified into non-malicious users.
The method comprises the steps of selecting users to be initially classified into five types, selecting users in the type with the largest malicious coefficient value in the five types as malicious users, and marking the order data of the malicious users as abnormal order data.
The five types of super parameters can be adjusted by an implementer according to specific implementation scenes, the method selects the five types of super parameters instead of the two types, malicious users are few users relative to all users, if the malicious users are classified into the two types, a large number of normal users can be classified into the malicious users, the classification is finer as the classification is more, and the malicious users can be classified into the normal users as the classification is too fine.
The clustering algorithm selects a k-means algorithm, sets k=5, classifies malicious coefficient values of all users to obtain five classification results, selects the classification result with the maximum malicious coefficient value as the classification result of the malicious user, selects the classification result selection process of the maximum malicious coefficient value as the malicious coefficient value of the user in each classification, and selects the category with the maximum mean value as the malicious user category.
S51: sorting the malicious coefficient values according to the value from large to small, and calculating the minimum value of the distance value between the average value of the malicious coefficient values of the first user and each malicious coefficient value of the second user as the first minimum value.
S52: and calculating the minimum value of the distance value between the average value of the malicious coefficient values of the second user and each malicious coefficient value of the first user as a second minimum value.
S53: and calculating the average value of the first minimum value and the second minimum value as an initial threshold value.
S54: and calculating the intelligent contract authentication passing rate of each user.
The calculation formula is as follows:
wherein, Authentication pass rate for intelligent contracts of users, recorded as/>Value/>The number of passes is accumulated for authentication of the user,The number of failed times is accumulated for authentication of the user. /(I)The larger the value, the greater the likelihood that the user belongs to a normal user,/>The smaller the value, the greater the likelihood that the user belongs to a malicious user.
S55: and in response to the intelligent contract authentication passing rate being smaller than or equal to a preset passing rate threshold, updating the initial threshold to obtain a coefficient threshold.
Acquiring all users with malicious coefficient values equal to an initial thresholdAverage of the values, noted as/>Value/>The ideal state of the value is 1, but because the consumption habits of users are different, the risk values of users are different when classifying the users, so that the risk values of the users are not the sameThe value cannot be guaranteed to be 1, and the expected passing rate is set to be/>, taking the performance of the intelligent contract into consideration=0.8，/>Can be adjusted by the implementer according to the specific real-time scenario.
When (when)When the method is applied, the industrial Internet platform can stably run so as to ensure smooth encryption and certification and sharing of order data.
When (when)The value +./>When the malicious coefficient value is updated, the initial threshold value of the malicious coefficient value is adjusted, and the specific mode is as follows:
When (when) Value >/>The initial threshold is incremented by 0.1 at each update.
When (when)Value ofThe initial threshold is decremented by 0.1 each time.
After the updating of the initial threshold is completed, coefficient thresholds required by different moments are obtained so as to ensure that the industrial Internet platform can smoothly store and share order data, and 0.1 is a super parameter which can be adjusted by an implementer according to a specific implementation scene. The adjustment is carried out 1 time every 1 hour, the time interval of 1 hour is super parameter, and the adjustment can be carried out by an implementer according to specific implementation scenes.
S6: and judging the user as a malicious user in response to the malicious user coefficient being larger than a set coefficient threshold.
S7: and in response to the user malicious coefficient being smaller than or equal to the coefficient threshold, judging the user as a normal user, storing the order data of the normal user, authenticating the user through the intelligent contract, encrypting the order data of the normal user, and then carrying out uplink sharing. Referring to fig. 3, step S7 includes steps S70 to S74, specifically as follows:
s70: order data of a normal user is acquired.
S71: and encrypting order data of the normal user by using a fixed public key of the industrial Internet platform.
S72: and generating a message abstract of order data at a preset user side by using a data signature algorithm, and generating a user public key for each user by using an asymmetric encryption algorithm.
S73: and sending the information abstract, the user public key and the acquired user information to an industrial Internet platform as a query request.
S74: and sending the information abstract and the user information to the intelligent contract, and authenticating through the intelligent contract.
Specifically, a certain malicious user generally exists in normal users, authentication passing rate is counted when the user is authenticated through an intelligent contract, if the authentication passing rate is large, the current initial threshold value is proper, if the authentication passing rate is low, the current initial threshold value is overlarge, and if the authentication passing rate is extremely large, the current initial threshold value is small.
When authenticating a normal user, in order to ensure the authenticity of the user, after the industrial internet platform obtains the order data of the normal user, the fixed public key of the industrial internet platform is used for encrypting the order data of the normal user, and meanwhile, a data signature algorithm is used for generating an information abstract of the order, the data signature algorithm selects MD5, an implementation can be adjusted according to a specific implementation scene, an RSA algorithm is used as an exemplary method for encrypting the order data, the RSA algorithm is an asymmetric encryption algorithm, and the encryption algorithm can be adjusted according to the specific implementation scene by the implementation.
After the intelligent contract is completed, the industrial Internet platform is required to return a certification-storing response value to indicate successful certification, at the user end of a normal user, the order data information abstract generated by the same digital signature algorithm is utilized by the order information of the normal user, a user public key and a user private key are generated for each user by utilizing an RSA algorithm, and the order data information abstract, the user public key and the user information of the normal user are used as a query request to be sent to the industrial Internet platform.
The industrial internet platform sends the message abstract and the user information to the intelligent contract, and the intelligent contract authenticates the user.
Authentication is performed through the intelligent contract, because the intelligent contract is non-tamper-proof, the accuracy of each authentication is guaranteed, and the privacy security of a user is prevented after the industrial Internet platform data is maliciously modified.
If the user passes the intelligent contract authentication, the authentication passing times of the current user is increased by 1, and the authentication accumulated passing times of the single user can be obtained. After the user is authenticated by the smart contract, the industrial Internet platform may decrypt the encrypted order data encrypted with the fixed public key.
After the order data is decrypted, the order data is encrypted by using the user public key, so that the security of the order data of the user can be ensured after the fixed private key is revealed, and the risk of the order data disclosure is greatly reduced because each user private key is kept secret.
The application selects IPFS (INTERPLANETARY FILE SYSTEM, interstellar file system) distributed accounting to store the order data encrypted by the user public key, returns an encrypted data storage address to an industrial Internet platform, and the industrial Internet platform sends the encrypted storage address to a user section, so that the order data of the user only has decryption capability, and the user does not need to send own user private key in the whole scheme, thereby ensuring the safety of user information, and the order data of the user has the characteristics of disclosure and non-falsification on a blockchain, so that the user can permanently inquire the order data of the user and can share the encrypted order information by himself.
And for the user with failed authentication, adding 1 to the authentication failing times of the current user, and acquiring the authentication cumulative failing times of the single user. And because the authentication fails, the current order data is unsafe data, the order data cannot be linked, and the platform stores and manages the encrypted order data.
While various embodiments of the present application have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Many modifications, changes, and substitutions will now occur to those skilled in the art without departing from the spirit and scope of the application. It should be understood that various alternatives to the embodiments of the application described herein may be employed in practicing the application.
The embodiment of the application also discloses an internet data processing system, which comprises a processor and a memory, wherein the memory stores computer program instructions, and the computer program instructions realize the internet data processing method according to the application when being executed by the processor.
The above system further comprises other components well known to those skilled in the art, such as a communication bus and a communication interface, the arrangement and function of which are known in the art and therefore are not described in detail herein.
In the context of this patent, the foregoing memory may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium may be any suitable magnetic or magneto-optical storage medium, such as, for example, resistance change memory RRAM (Resistive Random Access Memory), dynamic random access memory DRAM (Dynamic Random Access Memory), static random access memory SRAM (Static Random Access Memory), enhanced dynamic random access memory EDRAM (ENHANCED DYNAMIC Random Access Memory), high bandwidth memory HBM (High Bandwidth Memory), hybrid storage cube HMC (Hybrid Memory Cube), etc., or any other medium that may be used to store the desired information and that may be accessed by an application, a module, or both. Any such computer storage media may be part of, or accessible by, or connectable to, the device.
While various embodiments of the present application have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Many modifications, changes, and substitutions will now occur to those skilled in the art without departing from the spirit and scope of the application. It should be understood that various alternatives to the embodiments of the application described herein may be employed in practicing the application.
The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (3)
1. The internet data processing method is characterized by comprising the following steps:
according to the data analysis of the historical order, calculating an abnormal coefficient value of a single time period, wherein the calculation formula of the abnormal coefficient value is as follows:
wherein, For/>Individual user number/>Abnormal coefficient value of time period,/>For/>Individual user number/>Number of order data in time period,/>For/>Individual user number/>Average value of time period order data price;
Determining an abnormal coefficient value greater than zero as a target value in a plurality of continuous time periods, calculating a first time interval between a current target value and a previous target value, calculating a second time interval between the current target value and a next target value, calculating an average value of the first time interval and the second time interval, and multiplying the average value with the current target value to obtain a sub-risk value;
Calculating a risk value, wherein the risk value is the average value of a plurality of sub-risk values;
the user malicious coefficient is calculated, and the calculation formula of the user malicious coefficient is as follows:
wherein, For/>User malicious coefficient of individual user,/>For/>Return rate of individual users after ordering in historical order data,/>For/>Risk values for anomaly coefficient values for individual users over a continuous time period;
responding to the malicious coefficient of the user being larger than a set coefficient threshold value, and judging the user as a malicious user;
Responding to the user malicious coefficient smaller than or equal to the coefficient threshold value, judging that the user is a normal user, storing the order data of the normal user, authenticating the user through an intelligent contract, encrypting the order data of the normal user, and then sharing the encrypted order data in a uplink manner;
Pre-classifying malicious coefficient values to obtain initial thresholds, counting data of the intelligent contracts when authentication is carried out, and adjusting the initial thresholds to the coefficient thresholds;
The pre-classifying the malicious coefficient value to obtain an initial threshold value comprises the following steps:
according to a clustering algorithm, users are divided into at least three types, one type of users with the maximum malicious coefficient value is divided into malicious users, and the other at least two types of users are divided into non-malicious users;
sorting the malicious coefficient values according to the value from large to small, and calculating the minimum value of the distance value between the average value of the malicious coefficient values of the first user and each malicious coefficient value of the second user as a first minimum value;
Calculating the minimum value of the distance value between the average value of the malicious coefficient values of the second user and each malicious coefficient value of the first user as a second minimum value;
calculating the average value of the first minimum value and the second minimum value as the initial threshold value;
the statistical intelligent contract data when carrying out certification authentication, adjusts an initial threshold value to be the coefficient threshold value, and comprises the following steps:
calculating the intelligent contract authentication passing rate of each user, wherein the calculation formula is as follows:
wherein, Authentication pass rate for intelligent contracts of users,/>Accumulating the number of passes for authentication of a user,/>Accumulating the number of times of failed authentication for the user;
and in response to the intelligent contract authentication passing rate being smaller than or equal to a preset passing rate threshold, updating the initial threshold to obtain the coefficient threshold.
2. The method for processing internet data according to claim 1, wherein said user authentication by smart contract comprises the steps of:
acquiring order data of a normal user;
Encrypting order data of a normal user by using a fixed public key of a preset industrial Internet platform;
Generating a message abstract of the order data by utilizing a data signature algorithm at a preset user side, and generating a user public key for each user by utilizing an asymmetric encryption algorithm;
the information abstract, the user public key and the acquired user information are used as a query request to be sent to an industrial Internet platform;
and sending the information abstract and the user information to an intelligent contract, and authenticating through the intelligent contract.
3. An internet data processing system, comprising: a processor and a memory storing computer program instructions which, when executed by the processor, implement the method of processing internet data according to claim 1 or 2.
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| CN202410338285.3A CN117932577B (en) | 2024-03-25 | Internet data processing method and system |
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