Behavior data processing method, system and equipment based on block chain and storage medium

Abstract

The invention provides a behavior data processing method, system and device based on a block chain and a storage medium, and the method comprises the steps that a block chain network encrypts received initial behavior data to obtain system encrypted data, and the data structure of the initial behavior data is one of at least two preset data structures; in response to a received assessment instruction, calling a target algorithm rule through the first block chain node to calculate the first decrypted data to obtain performance data corresponding to the first decrypted data; the target algorithm rule is an algorithm rule matched with the data structure of the initial behavior data in at least two preset algorithm rules. According to the embodiment of the invention, maintenance and management of source data in various forms are facilitated, the complexity of algorithm rules can be reduced, and the data security is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a behavior data processing method, system, device and storage medium based on blockchain. Background Art

[0002] Performance data is obtained by quantifying and converting the behavior data of business personnel. In actual implementation, a data processing rule is usually deployed based on the type of data contained in the behavior data and the business attributes to process the behavior data to obtain performance data.

[0003] However, when the business system involves multiple forms of behavioral data, and the types of data contained in different forms of behavioral data are also different, it is necessary to deploy a data processing rule for each form of behavioral data. This will not only increase the complexity of the business system, but also face multiple data processing rules. The complexity of the calling mechanism is also high, which is not only inconvenient to maintain, but also has a greater security risk. Summary of the invention

[0004] This application proposes a behavior data processing method, system, device and storage medium based on blockchain, which is oriented to various forms of behavior data and various data processing rules, and can simplify the calling mechanism of the system and data processing rules, which is not only easy to maintain, but also helps to reduce security risks.

[0005] In the first aspect, the embodiment of the present application proposes a behavior data processing method based on blockchain, which is applied to a blockchain network and includes:

[0006] Encrypting the received initial behavior data to obtain system encrypted data, wherein the data structure of the initial behavior data is one of at least two preset data structures;

[0007] In response to the received assessment instruction, the first blockchain node calls the target algorithm rule to calculate the first decrypted data to obtain performance data corresponding to the first decrypted data; the target algorithm rule is an algorithm rule among at least two preset algorithm rules that matches the data structure of the initial behavior data, and the first decrypted data is obtained by decrypting the system encrypted data.

[0008] In an implementation of the first aspect, the system encrypted data is obtained by encrypting the system public key, and before responding to the received assessment instruction, the method further includes:

[0009] Decrypting the system encrypted data using the system private key to obtain second decrypted data;

[0010] Sending a confirmation request to a user corresponding to the second decrypted data, wherein the confirmation request is used to enable the user to confirm whether the second decrypted data is the initial behavior data;

[0011] Receive user encrypted data and store the user encrypted data in the first blockchain node, wherein the user encrypted data is obtained by encrypting the initial behavior data using the user private key when the user confirms that the second decrypted data is the initial behavior data.

[0012] In an implementation of the first aspect, after obtaining the performance data corresponding to the first decrypted data, the method further includes:

[0013] In response to the received verification instruction, obtaining, through the second blockchain node, a user identifier of the user corresponding to the verification instruction;

[0014] The second blockchain node transmits the user identifier to the first blockchain node;

[0015] The first blockchain node uses the user public key corresponding to the user identifier to decrypt the user encrypted data to obtain third decrypted data, and uses the system private key to decrypt the system encrypted data to obtain the first decrypted data, and transmits the first decrypted data and the third decrypted data to the second blockchain node;

[0016] The second blockchain node detects whether the first decrypted data is the same as the third decrypted data;

[0017] If the first decrypted data is identical to the third decrypted data, the verification result is determined to be verification passed.

[0018] In an implementation manner of the first aspect, the detecting whether the first decrypted data is the same as the third decrypted data includes:

[0019] Using the same algorithm to process the first decrypted data and the third decrypted data, respectively obtaining two calculation results;

[0020] If the two calculation results are the same, it is determined that the first decrypted data is the same as the third decrypted data.

[0021] In an implementation of the first aspect, the first blockchain node invoking a target algorithm rule to calculate the first decrypted data to obtain performance data corresponding to the first decrypted data includes:

[0022] Determining that the data structure of the first decrypted data is a target data structure;

[0023] Acquire multiple index fields included in the target data structure, each index field is used to indicate a data attribute of the first decrypted data, and the multiple index fields correspond to multiple algorithm factors in the target algorithm rule;

[0024] Acquire data corresponding to each index field from the first decrypted data;

[0025] Calculate the parameters of the algorithm factor corresponding to each index field according to the data corresponding to the corresponding index field;

[0026] The performance data is obtained by using various parameters to replace the corresponding algorithm factors in the target algorithm rules for calculation.

[0027] In an implementation of the first aspect, before encrypting the received initial behavior data to obtain system encrypted data, the method further includes:

[0028] Receive input user information through a third blockchain node;

[0029] Encrypting the user information to generate a user identifier according to a pre-deployed identity data encryption algorithm;

[0030] The user identification is transmitted to the first blockchain node.

[0031] In a second aspect, the embodiment of the present application provides a behavior data processing method based on blockchain, which is applied to a data server, including:

[0032] Acquire multiple data identifiers contained in source data, where the source data refers to recorded data corresponding to user behavior, and each data identifier is used to represent an attribute of the data of the data identifier;

[0033] Selecting a data structure matching the source data from at least two data structures according to a preset matching relationship, wherein the matching relationship includes a matching relationship between each data structure and at least one matching source data;

[0034] According to the conversion relationship between the data structure and the multiple data identifiers, the source data is converted into initial behavior data of the data structure;

[0035] The initial behavior data is transmitted to the blockchain network.

[0036] In an implementation of the second aspect, converting the source data into initial behavior data of the data structure according to the conversion relationship between the data structure and the multiple data identifiers includes at least one of the following:

[0037] Merging data of at least two data identifiers in the source data to obtain data corresponding to a first index field in the data structure, wherein the at least two data identifiers correspond to the first index field;

[0038] The data of at least one data identifier in the source data is calculated according to a preset rule to obtain data corresponding to a second index field in the data structure, wherein the at least one data identifier corresponds to the second index field.

[0039] In the third aspect, an embodiment of the present application provides a behavior data processing system, characterized in that the behavior data processing system includes a data server and a blockchain network, the data server is used to implement the method described in the second aspect above; the blockchain network is used to implement the method described in the first aspect above.

[0040] In a fourth aspect, an embodiment of the present application provides a behavior data processing device based on blockchain, which is applied to a blockchain network, and the device includes:

[0041] An encryption module, used for encrypting the received initial behavior data to obtain system encrypted data, wherein the data structure of the initial behavior data is one of at least two preset data structures;

[0042] A calculation module is used to respond to the received assessment instruction, calculate the first decrypted data by calling the target algorithm rule through the first blockchain node, and obtain the performance data corresponding to the first decrypted data; the target algorithm rule is an algorithm rule that matches the data structure of the initial behavior data among at least two preset algorithm rules, and the first decrypted data is obtained by decrypting the system encrypted data.

[0043] In a fifth aspect, an embodiment of the present application provides a behavior data processing device based on blockchain, which is applied to a data server, and the device includes:

[0044] An acquisition module, used to acquire multiple data identifiers contained in source data, wherein the source data refers to recorded data corresponding to user behavior, and each data identifier is used to represent an attribute of the data of the data identifier;

[0045] A selection module, configured to select a data structure matching the source data from at least two data structures according to a preset matching relationship, wherein the matching relationship includes a matching relationship between each data structure and at least one matching source data;

[0046] A conversion module, configured to convert the source data into initial behavior data of the data structure according to a conversion relationship between the data structure and the plurality of data identifiers;

[0047] A transmission module is used to transmit the initial behavior data to the blockchain network.

[0048] An embodiment of the sixth aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect or the second aspect above.

[0049] The embodiment of the seventh aspect of the present application provides a computer-readable storage medium on which a computer program is stored, and the program is executed by a processor to implement the method described in the first aspect or the second aspect above.

[0050] The technical solution of the embodiment of the present application pre-deploys at least two data structures and at least two algorithmic rules. The at least two data structures and at least two algorithmic rules can correspond one to one, that is, each algorithmic rule calculates performance data for the data of the matching data structure. Based on this, after obtaining the source data of any form from the user, the source data can be converted into behavioral data for calculation according to the matching data structure, and the matching algorithmic rules can be called to calculate the performance data. It can be seen that by adopting this technical solution, by converting the source data of various forms into behavioral data of several structures, the structural form of the source data can be reduced, which is not only conducive to the maintenance and management of the source data of various forms, but also the deployment of algorithmic rules corresponding to the data structure can reduce the number of algorithmic rules and reduce the complexity of calling the algorithmic rules. In addition, the technical solution uses the blockchain network to maintain various algorithmic rules and the initial behavioral data of the user, and calculates the performance data based on the blockchain network, which can further reduce the complexity of the business system and improve the security of the data.

[0051] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative work.

[0053] Figure 1 A schematic diagram of the architecture of a behavior data processing system provided in an embodiment of the present application is shown;

[0054] Figure 2 A schematic diagram of a method flow of a behavior data processing method based on blockchain provided in an embodiment of the present application is shown;

[0055] Figure 3 A schematic diagram of a method flow of another method for processing behavior data based on blockchain provided in an embodiment of the present application is shown;

[0056] Figure 4 A data flow diagram of a behavior data processing method based on blockchain provided in an embodiment of the present application is shown;

[0057] Figure 5 A schematic diagram showing the composition of a blockchain-based behavior data processing device provided in an embodiment of the present application is shown;

[0058] Figure 6 A schematic diagram showing the composition of another blockchain-based behavior data processing device provided in an embodiment of the present application is shown;

[0059] Figure 7 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0060] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0061] It should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0062] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by technicians in the field to which this application belongs.

[0063] The embodiment of the present application relates to a data processing technology for processing a structured data through pre-deployed data processing rules to obtain statistical data. The technical solution of the embodiment of the present application can be applied to scenarios including performance data statistics.

[0064] Taking performance data statistics as an example, performance data statistics is to convert the collected behavior data of business personnel according to pre-deployed statistical rules to obtain the performance data of the business personnel. Among them, the pre-deployed statistical rules can be determined according to the structure and attributes of the behavior data and the business attributes of the business personnel. Based on this, as described in the background technology of this application, in scenarios with various forms of behavior data, it is necessary to deploy a data processing rule corresponding to different forms of behavior data, and in the process of performance data statistics, different data processing rules should be called for calculation for different behavior data. In this way, not only is the calling mechanism complicated and inconvenient to maintain, but the behavior data of business personnel are subject to security risks such as leakage and tampering.

[0065] In view of this, the embodiment of the present application provides a behavior data processing method based on blockchain, and at least two algorithm rules can be pre-deployed in the blockchain network. In combination with this setting, the technical solution of the embodiment of the present application converts the original data of the behavior of business personnel (hereinafter referred to as users) into behavior data for calculation according to the matching data structure, and transmits the behavior data to the blockchain network for maintenance. This is not only conducive to the maintenance and management of various forms of source data, but also can improve the security of data. Further, each data structure can be matched with an algorithm rule, so for the behavior data of any data structure, the blockchain network can call the matching algorithm rule to calculate the performance data. In other words, using the blockchain network to maintain various algorithm rules and their corresponding data structures not only isolates the algorithm rules from other data processing processes of the system, reduces the complexity of the business system, but also reduces the complexity of calling the algorithm rules and improves data security.

[0066] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0067] See also Figure 1 , Figure 1 The behavior data processing system related to the embodiment of the present application is illustrated, and the system can be used to execute the behavior data processing method based on blockchain of the embodiment of the present application. The behavior data processing system may include: a blockchain network 100 and a data server 200. Among them, the blockchain network 100 and the data server 200 support data interaction.

[0068] The data server 200 can be used to maintain at least two pre-configured data structures and conversion rules corresponding to the at least two data structures. The conversion rules may include data identifiers of source data that can be converted to each data structure, conversion systems between each data identifier of the source data and the matching data structure, conversion algorithms, etc. Further, the data server 200 can also be used to convert the source data into initial behavior data of the matching data structure according to the conversion rules after acquiring the source data, and transmit the converted initial behavior data to the blockchain network 100.

[0069] Among them, a database can be maintained in the behavior data processing system, and the database is used to store the source data of the user. The data server 200 can obtain the source data from the database. In some embodiments, the database can be deployed in the data server 200. In other embodiments, the database can be deployed independently of the data server 200. The embodiments of the present application are not limited to this.

[0070] It can be seen that this technical solution converts various forms of source data into a data structure with preset values, which is conducive to reducing the complexity of the data to be calculated. In addition, structuring the data before transmitting it to the blockchain network is conducive to reducing the amount of calculation of the blockchain network on the basis of improving data security.

[0071] The blockchain network 100 may include multiple distributed blockchain nodes, each of which may receive data from other devices and encrypt the received data and share it with other blockchain nodes in the blockchain network 100. Any of the other blockchain nodes may decrypt the encrypted data of the corresponding data during the use of the data, and then use the decrypted data.

[0072] It should be pointed out that the blockchain network 100 can be built based on at least one business system, so the above-mentioned multiple blockchain nodes can be distributed in the at least one business system. In view of this, if the blockchain network 100 can be built based on at least two business systems, some data of each business system may be data with security requirements for the business system. For any two blockchain nodes in the blockchain network 100, the permissions for sharing data between the two blockchain nodes can be set according to the business systems to which the two blockchain nodes belong. Exemplarily, if the two blockchain nodes belong to the same business system, more data can be shared between the two blockchain nodes; if the two blockchain nodes belong to two business systems respectively, the data that can be shared between the two blockchain nodes is relatively small.

[0073] For example, the blockchain network 100 can be built on the A system and the B system. The A system can be a system to be executed for performance data statistics, and the B system can be a performance data verification system, that is, the B system is a third-party system of the A system. In this example, the first blockchain node is, for example, a blockchain node in the A system, and the second blockchain node is, for example, a blockchain node in the B system. The first blockchain node can share data with greater relevance to the performance data verification to the second blockchain node, while data with less relevance to the performance data verification is not shared to the second blockchain node. For example, the first blockchain node can share the calculated performance data to the second blockchain node, while the user's initial behavior data and algorithm rules and other data may not be shared to the second blockchain node. For another example, the third blockchain node is, for example, also a blockchain node in the A system. Then, the first blockchain node can share the user's initial behavior data, algorithm rules and performance data to the third blockchain node.

[0074] In actual implementation scenarios, the blockchain network 100 can encrypt and decrypt data through public-private key pairs. The public-private key pair can be generated for the account during the account registration phase after the account is successfully registered. In combination with the characteristics of asymmetric encryption and decryption of public-private key pairs, for data that any blockchain node has decryption authority, the blockchain network 100 can use a private key to encrypt data, and any blockchain node can use the corresponding public key to encrypt. For data that a specific blockchain node has decryption authority, the public key can be used to decrypt the data, and in the scenario where decryption is performed through a certain blockchain node, the blockchain node can use the corresponding private key to decrypt.

[0075] For example, for the behavior data to be calculated for performance, any blockchain node in the blockchain network 100 has the computing authority and can use the system private key to encrypt the behavior data. Any blockchain node responds to the instruction and can use the system public key to decrypt the behavior data and calculate the performance data. For another example, for the behavior data used to verify the performance data, the blockchain node that triggers the verification of the user of the corresponding behavior data has the decryption authority and can use the user's public key to encrypt the behavior data. Only the blockchain node that receives the verification instruction can use the user's private key to decrypt the behavior data.

[0076] In this way, not only can the behavioral data of various algorithm rules and various data structures be managed based on the blockchain network, reducing maintenance costs and simplifying the data processing process, but also based on the encryption and decryption characteristics of the blockchain, different encryption and decryption methods can be used to perform corresponding security processing on the data according to different security levels.

[0077] It should be understood that Figure 1The data servers, blockchain nodes, etc. involved in the above can all be considered as logically equivalent functional components. In some embodiments, the data servers and blockchain nodes can be implemented as at least one device entity. For example, the data server and any blockchain node can be independent servers, including but not limited to computers, network hosts, single network servers, multiple network server sets, or cloud servers composed of multiple servers. Accordingly, the blockchain network can be a server network or server cluster composed of servers. This is not limited here.

[0078] It should be understood that Figure 1 The behavior data processing system shown is only an exemplary implementation of the present invention and does not constitute a limitation on the present invention. Figure 1 Compared with the implementation shown in the figure, the behavior data processing system of the embodiment of the present application may also include more or fewer components. For example, in other embodiments, the behavior data processing system may also include other servers, such as user servers; in still other embodiments, the behavior data processing system may not include a data server, but the functions of the above data server may be deployed in the blockchain implementation, etc. This is not limited here.

[0079] The following is a description of the blockchain-based behavior data processing method in an embodiment of the present application with reference to examples.

[0080] Figure 2 An exemplary blockchain-based behavior data processing method provided in an embodiment of the present application is shown. Figure 2 The illustrated blockchain-based behavior data processing method can be applied to a blockchain network, which can be as follows: Figure 1 A blockchain network 100 is shown.

[0081] The behavior data processing method based on blockchain described in the embodiment of the present application may include the following steps:

[0082] In step S201, the received initial behavior data is encrypted to obtain system encrypted data.

[0083] The initial behavior data may be behavior data processed by the data server, and the data structure of the initial behavior data is one of at least two pre-set data structures. The at least two data structures are pre-deployed in the data server.

[0084] The "data structure" involved in the embodiments of the present application may refer to the content of the data elements contained in the data, as well as the attribute information of each data element. The content of the data element may refer to what the data contains, such as the time of clocking in and out, date, etc.; the attribute information of the data element may refer to the information indicated by the data element (for example, the time of clocking in and out, whether to leave early, etc.), the field type of the data element (for example, string, integer, floating point, etc.).

[0085] Exemplarily, any data structure may include multiple index fields, each of which may represent a data element and may indicate the attributes of the data element. For example, a data structure includes index fields: statistical start time, statistical end time, and average working time, wherein the statistical start time indicates the starting time of the current performance data statistics, the statistical end time indicates the end time of the current performance data statistics, and the average working time indicates the average working time from the start time to the end time.

[0086] In some embodiments, for any two of the at least two data structures mentioned above, at least one of the index fields contained in the two data structures is different, and the number of index fields contained in the two data structures may also be different. Exemplarily, the first data structure may include, for example, the index fields: user identification, statistical start time, statistical end time, average working time, vacation time; the second data structure may include, for example, the index fields: user identification, statistical start time, statistical end time, assigned task volume, completed task volume, abnormal task volume. In this example, the first data structure contains 5 index fields, and the second data structure contains 6 index fields, and only 3 index fields are the same in the first data structure and the second data structure, and the other index fields are different.

[0087] In some embodiments, the initial behavior data may include data corresponding to each index field in the corresponding data structure. For example, the data structure of the initial behavior data is the second data structure, and the initial behavior data may include: user identification: xy001, statistical start time: August 1, 2023, statistical end time August 31, 2023, assigned task volume: 15, completed task volume: 12, abnormal task volume: 0.

[0088] In an embodiment of the present application, any blockchain node deployed in the blockchain network in system A (i.e., the system for executing performance data statistics) may have the authority to calculate performance data. Then, after the blockchain network receives the initial behavior data, it may use the system public key to encrypt the initial behavior data to obtain the system encrypted data of the initial behavior data.

[0089] Exemplarily, the system public key may be the public key in the public-private key pair configured by the blockchain network corresponding to the identifier of system A when the identifier of system A is registered in the blockchain network.

[0090] It should be noted that the blockchain node that receives the initial behavior data and encrypts the initial behavior data can be any blockchain node that the blockchain network can deploy in system A. After encrypting the initial behavior data to obtain system encrypted data, the blockchain node can share the system encrypted data with all other blockchain nodes in system A.

[0091] In step S202, in response to the received assessment instruction, the first blockchain node calls the target algorithm rule to calculate the first decrypted data to obtain performance data corresponding to the first decrypted data.

[0092] Among them, in the embodiment of the present application, at least two algorithm rules can be preset, and the at least two algorithm rules correspond to at least two data structures one by one, that is, each algorithm rule can be used to calculate performance data for the behavior data of the corresponding data structure. Then, the target algorithm rule is the algorithm rule that matches the data structure of the initial behavior data among the at least two algorithm rules.

[0093] Exemplarily, the at least two algorithm rules include, for example, a first algorithm rule and a second algorithm rule. The first algorithm rule can, for example, calculate the behavior data of the first data structure to obtain the performance data of the behavior data of the first data structure; the second algorithm rule can, for example, calculate the behavior data of the second data structure to obtain the performance data of the behavior data of the second data structure. In conjunction with the above example, the data structure of the initial behavior data is, for example, the second data structure, and correspondingly, the data structure of the first decrypted data is also the second data structure, and the target algorithm rule can be the second algorithm rule.

[0094] In some embodiments, the at least two algorithmic rules, and the correspondence between the at least two algorithmic rules and the at least two data structures, can be pre-deployed in all blockchain nodes in system A. After the blockchain network receives the assessment instruction through the first blockchain node, the first blockchain node can use the system private key to decrypt the system encrypted data to obtain the first decrypted data. Furthermore, for example, based on the fact that the first decrypted data contains specific data, the data structure of the first decrypted data can be determined to be the target data structure. After that, based on the correspondence between the algorithmic rules and the data structure, the algorithmic rules corresponding to the target data structure are determined to obtain the target algorithmic rules, and the target algorithmic rules are called to calculate the first decrypted data to obtain the performance data corresponding to the first decrypted data.

[0095] In some embodiments, the target algorithm rule may include multiple algorithm factors, and the multiple algorithm factors may correspond to multiple index fields of the target data structure, that is, the target algorithm rule is an algorithm rule for calculating the data of multiple index fields of the target data structure. In view of this, after determining the target data structure and the target algorithm rule, the data corresponding to each index field can be obtained from the first decrypted data, and the parameters of the algorithm factor corresponding to the corresponding index field can be calculated based on the data corresponding to each index field, and the corresponding algorithm factor in the target algorithm rule can be replaced with each parameter for calculation to obtain the corresponding performance data.

[0096] In actual implementation scenarios, for some algorithm factors, an algorithm factor may correspond to an index field. In this scenario, the data corresponding to the index field in the first decrypted data or another quantized parameter converted from the data is the parameter of the corresponding algorithm factor. For other algorithm factors, an algorithm factor may correspond to at least two index fields. In this scenario, the data corresponding to the at least two index fields in the first decrypted data may be used to perform calculations according to preset rules, and the parameters obtained by calculation are the parameters of the corresponding algorithm factor.

[0097] For example, a target algorithm rule satisfies: K = ax + by + cz - dq, where K refers to performance data, a, b, c, d refer to algorithm coefficients, and x, y, z, q refer to algorithm factors. For example, x represents the working time, which can correspond to the two index fields of the statistical start time and the statistical end time. The specific parameters of x can be obtained by subtracting the statistical start time from the statistical end time; y represents the algorithm factor corresponding to the assigned task volume, which can be converted according to the specific value corresponding to the assigned task volume;

[0098] z represents the algorithm factor corresponding to the completed task volume, which can be converted according to the specific value corresponding to the completed task volume; q represents the algorithm factor corresponding to the abnormal task volume, which can be the specific value corresponding to the abnormal task volume.

[0099] The conversion method for the above-mentioned assigned task amount and completed task amount can be flexibly set according to the task form. In one example, the assigned task amount and completed task amount can be set according to the duration required for the task. For example, for a task expected to take three months, the task amount is determined according to the degree of completion corresponding to the statistical period; in another example, the assigned task amount and completed task amount can use the specific number of tasks as the task amount. This is not limited here.

[0100] It can be seen that by adopting this implementation method, by deploying at least two data structures and at least two algorithmic rules corresponding to each other, when the user source data is complex and diverse in form, it is not necessary to deploy algorithmic rules for each form of source data, but the source data with complex and diverse forms is converted into data of at least two data structures, and then, the matching algorithmic rules are selected from at least two algorithmic rules to calculate the performance data. In this way, on the one hand, the complexity of the behavior data to be calculated is reduced; on the other hand, the algorithmic rules that should be deployed and maintained are reduced. In addition, the blockchain network is used to maintain various algorithmic rules and the user's initial behavior data, and the performance data is calculated based on the blockchain network. On the basis of further reducing the complexity of the business system, it can also improve the security of the data.

[0101] Based on the above-mentioned embodiment of calculating the performance data, in some implementation scenarios, there may be a risk that the first decrypted data used to calculate the performance data may have been tampered with, and therefore is not the accurate behavior data of the corresponding user. For example, there may be other technical interventions, and the system private key may be obtained, and then the system encrypted data may be tampered with. In view of this, based on the above-mentioned embodiment, the embodiment of the present application also provides a method for data verification.

[0102] In order to support the reliability of the data used in the verification process, after the above step S201 and before the above step S202, the blockchain network can also use the system private key to decrypt the system encrypted data to obtain the second decrypted data, and then send a confirmation request to the user corresponding to the second decrypted data, which is used to make the user confirm whether the second decrypted data is the initial behavior data, that is, to confirm the accuracy of the second decrypted data. Further, if the second decrypted data is the initial behavior data (that is, the second decrypted data is accurate data), the blockchain network can receive the user encrypted data, and can store the user encrypted data in the first blockchain node.

[0103] Exemplarily, in order to ensure the security of the user encrypted data, the user encrypted data may be obtained by the user encrypting the second decrypted data (ie, the initial behavior data) using the user's private key.

[0104] In some embodiments, after receiving the user encrypted data, the first blockchain node may store the user encrypted data and the system encrypted data of the initial behavior data at the same address, so that in the stage of verifying the data, all the required data can be read from one address without accessing multiple addresses. Then, the confirmation request sent to the user may include the storage address corresponding to the second decrypted data, which may be, for example, a block height in the first blockchain node.

[0105] Further, after the above step S202, the user corresponding to the initial behavior data can verify the obtained performance data through a third-party system (such as the aforementioned B system). In response to the received verification instruction, the blockchain network can obtain the user identification of the user corresponding to the verification instruction through the second blockchain node. Since the second blockchain node is a blockchain node of a third-party system, the second blockchain node can only store performance data, but not various types of data related to the behavior data. The second blockchain node can transmit the user identification to the first blockchain node (hereinafter still taking the first blockchain node as an example) or any other blockchain node in the A system. The first blockchain node can obtain the user encrypted data corresponding to the user identification, and the system encrypted data corresponding to the performance data. Furthermore, the first blockchain node uses the user public key of the user to decrypt the user encrypted data to obtain the third decrypted data, and uses the system private key to decrypt the system encrypted data to obtain the first decrypted data, and transmits the first decrypted data and the third decrypted data to the second blockchain node. The second blockchain node detects whether the first decrypted data is the same as the third decrypted data. If the first decrypted data is the same as the third decrypted data, it can be considered that the first decrypted data and the third decrypted data are both the aforementioned initial behavior data, and the first decrypted data has not been tampered with. It can be determined that the verification result is verification passed.

[0106] In some embodiments, the second blockchain node may use the same algorithm to process the first decrypted data and the third decrypted data to obtain two calculation results respectively. If the two calculation results are the same, it can be determined that the first decrypted data is the same as the third decrypted data. For example, the first decrypted data and the third decrypted data may be hashed to obtain two calculation results respectively.

[0107] In this way, there is no need to analyze the data type of each index field in the first decrypted data and the third decrypted data and then compare them. Not only is the processing method simple and the verification efficiency high, but the error rate of the verification result is also low.

[0108] The user identification involved in the above embodiment may be stored in the blockchain network before the above step S201. Exemplarily, the blockchain network may also receive input user information through a third blockchain node. Afterwards, the user information may be encrypted according to a pre-deployed identity data encryption algorithm to generate a user identification. Furthermore, the third blockchain node may share the user identification with other blockchain nodes in the A system, including the first blockchain node.

[0109] Exemplarily, the user information may include user account information and fingerprint information received by the user server. Furthermore, the user account information may be encrypted according to a pre-deployed identity data encryption algorithm to generate a digital identity identifier as a user identifier, and a hash mapping relationship between the user identifier and the fingerprint information may be established. The pre-deployed identity data encryption algorithm may be, for example, an RSA encryption algorithm or a national encryption algorithm.

[0110] In summary, the technical solution of the embodiment of the present application can reduce the structural form of the source data by converting source data of various forms into behavioral data of several structures, which is conducive to the maintenance and management of source data of various forms. Furthermore, by setting a one-to-one correspondence between at least two data structures and at least two algorithm rules, each algorithm rule calculates performance data for data of matching data structures, which can reduce the number of algorithm rules and reduce the complexity of calling algorithm rules. The present technical solution uses a blockchain network to maintain various algorithm rules and users' initial behavioral data, and calculates performance data based on the blockchain network. On the basis of further reducing the complexity of the business system, it can also improve the security of the data. In addition, the present technical solution also provides a verification mechanism for performance data, and the present technical solution verifies the performance data by verifying the behavioral data used to calculate the performance data, which is conducive to identifying whether the behavioral data has been tampered with and ensuring the reliability of the data.

[0111] and Figure 2 Corresponding to the illustrated embodiment, the embodiment of the present application also provides another behavior data processing method based on blockchain, which can be applied to a data server. The data server can be as follows Figure 1 A data server 200 is shown.

[0112] like Figure 3 As shown, the method on the data server side may include the following steps:

[0113] Step S301, obtaining multiple data identifiers contained in the source data.

[0114] The source data refers to the recorded data corresponding to the user's behavior over a period of time, for example, the time when the user punches in every day within a period of time, the operation log of the user's office system, the number of tasks accepted by the user, the progress information of each task, etc. Each type of data in the source data can be indicated by a data identifier.

[0115] It should be noted that the source data involved in the embodiments of the present application may include multiple source data, and the forms of the multiple source data may be different. The form of the source data here may also refer to the structure of the source data, and the difference in the form of any two source data may refer to the number of data identifiers contained in the two source data and at least one of the one or more data identifiers being different.

[0116] For example, source data 1 includes the following data identifiers: user account, clock-in time, earliest clock-in time, latest clock-in time, and office system operation log; source data 2 includes the following data identifiers: user account, earliest clock-in time, latest clock-in time, newly created task number, abnormal task number, promoted task number, and the latest progress information of each task. Obviously, source data 1 and source data 2 are source data in two data forms.

[0117] It should be understood that the above examples of source data are only illustrative descriptions and do not limit the source data involved in the embodiments of the present application. In actual implementation scenarios, the source data can be in more forms, and each form of source data can include more or fewer data identifiers compared to the above examples. This is not limited here.

[0118] Step S302: selecting a data structure matching the source data from at least two data structures according to a preset matching relationship.

[0119] It should be noted that the data server can maintain a matching relationship between source data and data structures, and the matching relationship can be preset. The matching relationship includes a matching relationship between each data structure and at least one matching source data. Each source data can be converted into initial behavior data of a data structure matching the source data.

[0120] Step S303: converting the source data into initial behavior data of the data structure according to the conversion relationship between the data structure and the multiple data identifiers.

[0121] Furthermore, for the matching source data and data structure, there is a conversion relationship between each data identifier of the source data and the index field of the data structure, and the source data can be converted into initial behavior data of the data structure according to the corresponding conversion relationship.

[0122] The conversion relationship between different data identifiers in the source data and the corresponding index fields may be different. For example, for a source data, the data of the first data identifier and the data of the second data identifier in the source data are combined to obtain the data of the corresponding index field; the data of the third data identifier in the source data is calculated to obtain the data of the index field corresponding to the third data identifier.

[0123] Based on this, the data server converts the source data into the initial behavior data of the data structure according to the conversion relationship between the data structure and the multiple data identifiers, which may include at least one of the following:

[0124] Merging data of at least two data identifiers in the source data to obtain data corresponding to a first index field in the data structure, wherein the at least two data identifiers correspond to the first index field;

[0125] The data of the at least one data identifier is calculated according to a preset rule to obtain data corresponding to the second index field in the data structure. The at least one data identifier corresponds to the second index field.

[0126] Taking the conversion of source data 1 of a user for a period of time into the aforementioned first data structure as an example, the source data includes, for example, data identifiers: user account, punch-in time, earliest punch-in time, latest punch-in time, and operation log of the office system; the first data structure includes index fields: user identifier, statistical start time, statistical end time, average working time, and vacation time. Among them, the user account can be used as the user identifier. The statistical start time and statistical end time can be preset. For example, the statistical start time is August 1, 2023, and the statistical end time is August 31, 2023. Then, through the earliest punch-in time and the latest punch-in time of the user, the effective working time and vacation time of the user from the statistical start time to the statistical end time of the user can be calculated. For example, the period when the earliest punch-in time is earlier than 9:00 and the latest punch-in time is later than 18:00 is taken as the effective working time; the effective working time of the user from August 1, 2023 to August 31, 2023 is 20 days, and the vacation time is 3 days. Furthermore, the average working hours can be calculated based on the earliest clock-in time and the latest clock-in time of the 20 days. The average working hours is, for example, 8.5 hours.

[0127] It should be understood that the above source data converted into the data of the second data structure is only a schematic description and does not constitute a limitation on the embodiments of the present application. In the actual implementation scenario, the conversion method can be flexibly set according to the nature and quantity of the data identifier and index field. It is not limited here.

[0128] Step S304: transmitting the initial behavior data to the blockchain network.

[0129] The data server can communicate with the blockchain network through any blockchain node of system A through the blockchain network. After converting the initial behavior data, the data server can transmit the initial behavior data to the blockchain network through the blockchain node so that the blockchain network can execute Figure 2 All or part of the embodiments in the illustrative method are not described in detail here.

[0130] It can be seen that by adopting this implementation method, by converting source data of various forms into behavioral data of several structures, the structural forms of the source data can be reduced, which is conducive to the maintenance and management of source data of various forms.

[0131] The following is an introduction to the technical solutions of the embodiments of the present application in chronological order in conjunction with an exemplary behavior data processing system.

[0132] See also Figure 4 , Figure 4 A behavior data processing method based on blockchain provided in an embodiment of the present application is shown. The behavior data processing system used in the method may include: a business system 41, a third-party verification system 42 and a blockchain network 43. The blockchain network 43 may be deployed across the business system 41 and the third-party verification system 42. The business system 41 includes a data server 411 and a user server 412. The blockchain network 43 may include blockchain nodes 431 to 434, wherein the blockchain nodes 431 to 433 are deployed in the business system 41, and the blockchain node 434 is deployed in the third-party verification system 42.

[0133] It should be understood that Figure 4 This is only a schematic illustration of the embodiment of the present application. In actual implementation, the blockchain network may include Figure 4 More blockchain nodes.

[0134] In some implementation scenarios, various users can input information or instructions to the business system 41 or the third-party verification system 42 through the client page to trigger some data processing processes involved in the embodiments of the present application. Various users can include, for example, users to be assessed and assessing users.

[0135] Exemplarily, at least two data structures, matching relationships between the at least two data structures and source data, conversion algorithms for converting each source data into a matching data structure, etc. are pre-stored in the data server 411. In each blockchain node from blockchain node 431 to blockchain node 433, information such as an identity data encryption algorithm for user information, an encryption algorithm for behavior data, at least two algorithm rules for calculating performance data, and a corresponding relationship between the at least two algorithm rules and at least two data structures can be pre-deployed. Data verification rules can be pre-deployed in blockchain node 434.

[0136] The following describes the technical solution of the embodiment of the present application in chronological order according to the three stages of data preprocessing, performance data processing and data verification.

[0137] Data preprocessing stage:

[0138] In the data preprocessing stage, the user server 412 can transmit the identity information of various users to the blockchain network 43, and the data server 411 can structure the source data of each user to be assessed into initial behavior data and then transmit it to the blockchain network 43. The blockchain network 43 can encrypt and store the identity information of various users and the structured initial behavior data, and so on.

[0139] Exemplarily, the user server 412 may receive account information and fingerprint information input by various users through the client's page, and then, for example, transmit the account information and fingerprint information to the blockchain node 431 .

[0140] Blockchain node 431 can encrypt the user account information according to the identity data encryption algorithm to generate a digital identity as the user's user ID, and can establish a hash mapping relationship between the user ID and the fingerprint information. Afterwards, blockchain node 431 can share the user ID and the hash mapping relationship between the user ID and the fingerprint information with blockchain node 432 and blockchain node 433.

[0141] Furthermore, the blockchain node 431 can also assign a public key and a private key to the corresponding user, and send the assigned public key and private key, as well as the hash mapping relationship between the user identification and the fingerprint information to the client of the corresponding user.

[0142] Exemplarily, if the user account information is determined to be the account information of a user to be assessed based on the permissions of the user account information, a user public key and a user private key can be assigned to the user; if the user account information is determined to be the account information of a user to be assessed based on the permissions of the user account information, a system public key and a system private key can be assigned to the user.

[0143] In the data preprocessing stage, the data server 411 obtains source data from a database, for example, and converts the obtained source data into initial behavior data, and then, for example, the initial behavior data can be transmitted to the blockchain node 432.

[0144] It should be noted that the data server 411 can perform conversion operations on multiple source data, and, for source data in different data formats, convert the corresponding source data into initial behavior data of a matching data structure. For an embodiment of the data server 411 converting source data into initial behavior data, see Figure 3 The description of the corresponding embodiments will not be repeated here.

[0145] Blockchain node 432 can encrypt the initial behavior data using the system public key according to the encryption algorithm of the behavior data to obtain the system encrypted data of the initial behavior data. Afterwards, blockchain node 432 can share the system encrypted data with blockchain node 431 and blockchain node 433.

[0146] Furthermore, before calculating the performance data, at least one of blockchain node 432, blockchain node 431 and blockchain node 433 may also use the system private key to decrypt the system encrypted data, and then send the decrypted data (such as the aforementioned second decrypted data), the block height of the system encrypted data, etc. to the user client corresponding to the data, so that the user can confirm whether the decrypted data is the user's initial behavior data.

[0147] If the decrypted data is the user's initial behavior data, the user can use the user's public key to encrypt the confirmed data to obtain the user's encrypted data of the initial behavior data, and send the user's encrypted data to the corresponding block connection point. The corresponding block connection point stores the user's encrypted data and the system's encrypted data together at the address indicated by the block height.

[0148] Performance data processing stage:

[0149] After completing the above data preprocessing, within the preset assessment time period, the blockchain node 433, for example, receives the assessment instruction input by the assessment user through the client. Afterwards, the blockchain node 433 can read the stored system encrypted data, decrypt the system encrypted data using the system private key, and obtain the decrypted data for performance calculation (such as the aforementioned first decrypted data). Further, the blockchain node 433 can, for example, determine the data structure of the decrypted data, and select the target algorithm rule corresponding to the data structure of the decrypted data, and use the target algorithm rule to calculate the decrypted data to obtain the performance data of the user corresponding to the decrypted data.

[0150] Among them, the blockchain node 433 responds to the assessment instruction and executes the data processing process, see Figure 2 The description of the corresponding embodiments will not be repeated here.

[0151] Furthermore, blockchain node 433 can share the calculated performance data with blockchain node 431, blockchain node 432 and blockchain node 434.

[0152] Data verification phase:

[0153] In the embodiment of the present application, any user can verify the accuracy of his / her performance data in the third-party verification system 42. Correspondingly, after the blockchain node 434 in the third-party verification system 42 receives the user's verification instruction, it can read the user's user ID from the verification instruction and determine the user's performance data based on the user ID. Afterwards, the blockchain node 434 can transmit the user ID and performance data to any blockchain node (e.g., blockchain node 433) among the blockchain nodes 431, 432, and 433.

[0154] Blockchain node 433 can find the user encrypted data of the user according to the user ID, and determine and calculate the system encrypted data of the performance data according to the performance data. Further, blockchain node 433 uses the system private key to decrypt the system encrypted data to obtain the behavior data to be verified; the user can use the user public key to decrypt the user encrypted data to obtain the reference data for verification, and transmit the behavior data to be verified and the reference data to blockchain node 434.

[0155] The blockchain node 434 performs hash operations on the reference data and the behavior data to be verified, and obtains two operation results respectively. If the two operation results are the same, it is considered that the verification is passed; if the two operation results are different, it can be considered that the behavior data to be verified has been tampered with, and the performance data of the user is inaccurate.

[0156] In summary, it can be seen that the technical solution of the embodiment of the present application can reduce the structural form of the source data by converting source data of various forms into behavioral data of several structures, which is conducive to the maintenance and management of source data of various forms. Furthermore, by setting a one-to-one correspondence between at least two data structures and at least two algorithm rules, each algorithm rule calculates performance data for data of matching data structures, which can reduce the number of algorithm rules and the complexity of calling algorithm rules. The present technical solution uses a blockchain network to maintain various algorithm rules and users' initial behavioral data, and calculates performance data based on the blockchain network. On the basis of further reducing the complexity of the business system, it can also improve the security of the data. In addition, the present technical solution also provides a verification mechanism for performance data, and the present technical solution verifies the performance data by verifying the behavioral data used to calculate the performance data, which is conducive to identifying whether the behavioral data has been tampered with and ensuring the reliability of the data.

[0157] Corresponding to the implementation of the above blockchain-based behavior data processing method, the embodiment of the present application also provides a blockchain-based behavior data processing device.

[0158] like Figure 5As shown, the embodiment of the present application provides a behavior data processing device based on blockchain, which can be applied to Figure 1 In the blockchain network, the method for processing behavior data based on blockchain at the blockchain network end described in any of the above embodiments is used to execute. The behavior data processing device based on blockchain may include: an encryption module 51 and a calculation module 52.

[0159] Among them, the encryption module 51 is used to encrypt the received initial behavior data to obtain system encrypted data, and the data structure of the initial behavior data is one of at least two preset data structures; the calculation module 52 is used to respond to the received assessment instruction, and calculate the first decrypted data through the first blockchain node by calling the target algorithm rule to obtain the performance data corresponding to the first decrypted data; the target algorithm rule is an algorithm rule that matches the data structure of the initial behavior data among the at least two preset algorithm rules, and the first decrypted data is obtained by decrypting the system encrypted data.

[0160] Similarly, corresponding Figure 1 The data server described above, the embodiment of the present application also provides a behavior data processing device based on blockchain. Figure 6 As shown, the behavior data processing device based on blockchain applied to the data server may include: an acquisition module 61, a selection module 62, a conversion module 63 and a transmission module 64.

[0161] Among them, the acquisition module 61 is used to obtain multiple data identifiers contained in the source data, and the source data refers to the recorded data corresponding to the user's behavior, and each data identifier is used to characterize the attributes of the data of the data identifier; the selection module 62 is used to select a data structure matching the source data from at least two data structures according to a preset matching relationship, and the matching relationship includes a matching relationship between each data structure and at least one matching source data; the conversion module 63 is used to convert the source data into the initial behavior data of the data structure according to the conversion relationship between the data structure and the multiple data identifiers; the transmission module 64 is used to transmit the initial behavior data to the blockchain network.

[0162] For other functions of the above modules, please refer to part or all of the implementation methods of the above-mentioned blockchain-based behavior data processing method, which will not be described in detail here.

[0163] The blockchain-based behavior data processing device provided in the above-mentioned embodiment of the present application and the blockchain-based behavior data processing method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0164] Figure 5and Figure 6 It is a software module divided from the perspective of logical functions. In implementation, the functions of these software modules can be integrated into hardware entities for implementation.

[0165] Based on this, the present application also provides an electronic device that can be used as Figure 1 The blockchain network or data server in the system can execute the above-mentioned blockchain-based behavior data processing method. Figure 7 , which shows a schematic diagram of an electronic device provided by some embodiments of the present application. Figure 7 As shown, the electronic device 7 includes: a processor 700, a memory 701, a bus 702 and a communication interface 703, wherein the processor 700, the communication interface 703 and the memory 701 are connected via the bus 702; the memory 701 stores a computer program that can be run on the processor 700, and when the processor 700 runs the computer program, the blockchain-based behavior data processing method provided in any of the aforementioned embodiments of the present application is executed.

[0166] The memory 701 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 703 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0167] The bus 702 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 701 is used to store programs. After receiving the execution instruction, the processor 700 executes the program. Figure 2 The blockchain-based behavior data processing method disclosed in any of the illustrated embodiments may be applied to the processor 700 or implemented by the processor 700.

[0168] The processor 700 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 700. The above processor 700 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 701, and the processor 700 reads the information in the memory 701 and completes the steps of the above method in combination with its hardware.

[0169] The electronic device provided in the embodiment of the present application and the behavior data processing method based on blockchain provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented therein.

[0170] Corresponding to the blockchain-based behavior data processing method provided in the aforementioned embodiments, the embodiments of the present application also provide a computer-readable storage medium on which a computer program (i.e., program item) is stored. When the computer program is run by a processor, it will execute the blockchain-based behavior data processing method provided in any of the aforementioned embodiments.

[0171] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0172] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the blockchain-based behavior data processing method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0173] It should be noted that:

[0174] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.

[0175] Similarly, it should be understood that in order to streamline the present application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following schematic diagram: the claimed application requires more features than the features clearly stated in each claim. More specifically, as reflected in the claims below, the inventive aspects are less than all the features of the single embodiment disclosed above. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present application.

[0176] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0177] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A behavior data processing method based on blockchain, It is characterized in that Applied to blockchain networks, including: Encrypting the received initial behavior data to obtain system encrypted data, wherein the data structure of the initial behavior data is one of at least two preset data structures; In response to the received assessment instruction, the first blockchain node calls the target algorithm rule to calculate the first decrypted data to obtain performance data corresponding to the first decrypted data; the target algorithm rule is an algorithm rule among at least two preset algorithm rules that matches the data structure of the initial behavior data, and the first decrypted data is obtained by decrypting the system encrypted data.

2. The method according to claim 1, It is characterized in that The system encrypted data is obtained by encrypting the system public key, and before responding to the received assessment instruction, it also includes: Decrypting the system encrypted data using the system private key to obtain second decrypted data; Sending a confirmation request to a user corresponding to the second decrypted data, wherein the confirmation request is used to enable the user to confirm whether the second decrypted data is the initial behavior data; Receive user encrypted data and store the user encrypted data in the first blockchain node, wherein the user encrypted data is obtained by encrypting the initial behavior data using the user private key when the user confirms that the second decrypted data is the initial behavior data.

3. The method according to claim 2, It is characterized in that After obtaining the performance data corresponding to the first decrypted data, the method further includes: In response to the received verification instruction, obtaining, through the second blockchain node, a user identifier of the user corresponding to the verification instruction; The second blockchain node transmits the user identifier to the first blockchain node; The first blockchain node uses the user private key corresponding to the user identifier to decrypt the user encrypted data to obtain third decrypted data, and uses the system public key to decrypt the system encrypted data to obtain the first decrypted data, and transmits the first decrypted data and the third decrypted data to the second blockchain node; The second blockchain node detects whether the first decrypted data is the same as the third decrypted data; If the first decrypted data is identical to the third decrypted data, the verification result is determined to be verification passed.

4. The method according to claim 3, It is characterized in that The detecting whether the first decrypted data is the same as the third decrypted data comprises: Using the same algorithm to process the first decrypted data and the third decrypted data, respectively obtaining two calculation results; If the two calculation results are the same, it is determined that the first decrypted data is the same as the third decrypted data.

5. The method according to claim 1, It is characterized in that The first blockchain node calls the target algorithm rule to calculate the first decrypted data to obtain performance data corresponding to the first decrypted data, including: Determining that the data structure of the first decrypted data is a target data structure; Acquire multiple index fields included in the target data structure, each index field is used to indicate a data attribute of the first decrypted data, and the multiple index fields correspond to multiple algorithm factors in the target algorithm rule; Acquire data corresponding to each index field from the first decrypted data; Calculate the parameters of the algorithm factor corresponding to each index field according to the data corresponding to the corresponding index field; The performance data is obtained by using various parameters to replace the corresponding algorithm factors in the target algorithm rules for calculation.

6. The method according to claim 1, It is characterized in that Before encrypting the received initial behavior data to obtain system encrypted data, the method further includes: Receive input user information through a third blockchain node; Encrypting the user information to generate a user identifier according to a pre-deployed identity data encryption algorithm; The user identification is transmitted to the first blockchain node.

7. A behavior data processing method based on blockchain, It is characterized in that Applied to a data server, the method comprises: Acquire multiple data identifiers contained in source data, where the source data refers to recorded data corresponding to user behavior, and each data identifier is used to represent an attribute of the data of the data identifier; Selecting a data structure matching the source data from at least two data structures according to a preset matching relationship, wherein the matching relationship includes a matching relationship between each data structure and at least one matching source data; According to the conversion relationship between the data structure and the multiple data identifiers, the source data is converted into initial behavior data of the data structure; The initial behavior data is transmitted to the blockchain network.

8. The method according to claim 7, It is characterized in that The converting the source data into the initial behavior data of the data structure according to the conversion relationship between the data structure and the multiple data identifiers includes at least one of the following: Merging data of at least two data identifiers in the source data to obtain data corresponding to a first index field in the data structure, wherein the at least two data identifiers correspond to the first index field; The data of at least one data identifier in the source data is calculated according to a preset rule to obtain data corresponding to a second index field in the data structure, wherein the at least one data identifier corresponds to the second index field.

9. A behavioral data processing system, It is characterized in that The behavior data processing system includes a data server and a blockchain network, wherein: The data server is used to obtain multiple data identifiers contained in source data, where the source data refers to recorded data corresponding to the user's behavior, and each data identifier is used to characterize the attributes of the data of the data identifier; select a data structure matching the source data from at least two data structures according to a preset matching relationship, where the matching relationship includes a matching relationship between each data structure and at least one matching source data; convert the source data into initial behavior data of the data structure according to the conversion relationship between the data structure and the multiple data identifiers; and transmit the initial behavior data to the blockchain network; The blockchain network is used to encrypt the initial behavior data to obtain system encrypted data, and the data structure of the initial behavior data is one of the at least two data structures; in response to the received assessment instruction, the first blockchain node calls the target algorithm rule to calculate the first decrypted data to obtain the performance data corresponding to the first decrypted data; the target algorithm rule is an algorithm rule that matches the data structure of the initial behavior data among at least two preset algorithm rules, and the first decrypted data is obtained by decrypting the system encrypted data.

10. A behavior data processing device based on blockchain, It is characterized in that Applied to a blockchain network, the device comprises: An encryption module, used for encrypting the received initial behavior data to obtain system encrypted data, wherein the data structure of the initial behavior data is one of at least two preset data structures; A calculation module is used to respond to the received assessment instruction, calculate the first decrypted data by calling the target algorithm rule through the first blockchain node, and obtain the performance data corresponding to the first decrypted data; the target algorithm rule is an algorithm rule that matches the data structure of the initial behavior data among at least two preset algorithm rules, and the first decrypted data is obtained by decrypting the system encrypted data.

11. A behavior data processing device based on blockchain, It is characterized in that Applied to a data server, the device comprises: An acquisition module, used to acquire multiple data identifiers contained in source data, wherein the source data refers to recorded data corresponding to user behavior, and each data identifier is used to represent an attribute of the data of the data identifier; A selection module, configured to select a data structure matching the source data from at least two data structures according to a preset matching relationship, wherein the matching relationship includes a matching relationship between each data structure and at least one matching source data; A conversion module, configured to convert the source data into initial behavior data of the data structure according to a conversion relationship between the data structure and the plurality of data identifiers; A transmission module is used to transmit the initial behavior data to the blockchain network.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that The processor runs the computer program to implement the method according to any one of claims 1 to 8.

13. A computer-readable storage medium having a computer program stored thereon, It is characterized in that The program is executed by a processor to implement the method according to any one of claims 1 to 8.

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