Information processing method, device, electronic device and storage medium based on data link

Abstract

The present invention provides a data link-based information processing method, device, electronic device, and storage medium. The method includes: determining the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to activity information; determining a data link that matches the target object; when the activity information of the target object changes, determining the relationship between different asset feature vectors in the data link; and determining an information processing result that matches the data link based on the relationship between different asset feature vectors in the data link. Thus, all the activity information of the target object is represented through the data link, ensuring the target object's activity information and achieving efficient processing of the activity information. At the same time, the data link is used to achieve a holistic analysis of the processing of various activity information in social activities, and the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information are used to enhance the macro-analysis of information processing.

Description

Technical Field

[0001] The present invention relates to information processing technology in a data link, and in particular to an information processing method, device, electronic device and storage medium based on a data link. Background Art

[0002] In the traditional activity information processing process, activity information management is managed by centralized storage of paper files. Even if electronic activity information is being promoted, data analysis is generally established using digital tools, methods and systems. It is impossible to establish information processing for different target objects, making the information processing process for the target objects too single and unable to achieve a holistic analysis of the various activity information processing in social activities. Summary of the Invention

[0003] In view of this, an embodiment of the present invention provides a data link-based information processing method, device, electronic device and storage medium, which can store all user activity information through the data link for representation, thereby achieving efficient processing of activity information.

[0004] The technical solution of the embodiment of the present invention is achieved as follows:

[0005] An embodiment of the present invention provides a data link-based information processing method, the method comprising:

[0006] Get the target object's activity information;

[0007] Based on the activity information of the target object, determining a cognitive asset feature vector, an event asset feature vector, and a tool asset feature vector corresponding to the activity information;

[0008] Determining a data chain that matches the target object based on the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector corresponding to the activity information;

[0009] When the activity information of the target object changes, determining the relationship between different asset feature vectors in the data chain;

[0010] Based on the relationship between different asset feature vectors in the data chain, an information processing result matching the data chain is determined.

[0011] An embodiment of the present invention further provides an information processing device based on a data link, the device comprising:

[0012] Information transmission module, used to obtain activity information of target objects;

[0013] An information processing module, configured to determine, based on the activity information of the target object, a cognitive asset feature vector, an event asset feature vector, and a tool asset feature vector corresponding to the activity information;

[0014] The information processing module is configured to determine a data link that matches the target object based on the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector corresponding to the activity information;

[0015] The information processing module is configured to determine the relationship between different asset feature vectors in the data chain when the activity information of the target object changes;

[0016] The information processing module is used to determine an information processing result that matches the data chain based on the relationship between different asset feature vectors in the data chain.

[0017] In the above scheme,

[0018] The information processing module is configured to determine a cognitive asset feature vector corresponding to the activity information based on changes in cognitive ability parameters in the activity information of the target object;

[0019] The information processing module is used to extract the interaction records of different element events in the activity information of the target object and determine the cognitive asset feature vector corresponding to the activity information;

[0020] The information processing module is configured to obtain tool information used in the execution process of the activity information of the target object, and determine a tool asset feature vector corresponding to the activity information based on the tool information.

[0021] In the above scheme,

[0022] The information processing module is used to determine the environmental characteristics of the target object;

[0023] In response to the environmental characteristics, the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector corresponding to the activity information are randomly combined to determine particle state parameters that match the target object.

[0024] In the above scheme,

[0025] The information processing module is used to, when the environmental characteristics of the target object change,

[0026] Determine the spatiotemporal changes in the particle state parameters matching the target object based on the change characteristics of the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector;

[0027] Based on the spatiotemporal changes of the particle state parameters, the wave state parameters that match the target object are determined.

[0028] In the above scheme,

[0029] The information processing module is used to determine the first wave particle property parameter corresponding to the target object according to the grouping vector of the particle state parameter and the wave state parameter.

[0030] In the above scheme,

[0031] The information processing module is configured to determine the number of different target objects participating in the activity information based on the activity information of the target objects;

[0032] Based on the number of different target objects, the living space parameters are determined.

[0033] In the above scheme,

[0034] The information processing module is used to determine the number of target objects participating in different dimensions of the living space parameters according to the living space parameters;

[0035] Determining corresponding data units based on the number of target objects of different dimensions in the living space parameter and the activity information;

[0036] When the activity information of the target object changes, the flow information of the data in the data unit is determined according to the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector corresponding to the activity information;

[0037] Based on the flow information of the data in the data unit, a data link matching the target object and the environmental parameters of the data link are determined.

[0038] In the above scheme,

[0039] The information processing module is configured to adjust the corresponding first-wave granularity parameters of the target object based on the cognitive asset feature vectors to determine second-wave granularity parameters when the relationship between different asset feature vectors in the data chain is an internal relationship pattern.

[0040] In the above scheme,

[0041] The information processing module is configured to adjust the corresponding first-wave granularity parameters of the target object based on the cognitive asset feature vector and the event asset feature vector to determine third-wave granularity parameters when the relationship between different asset feature vectors in the data chain is a collaborative relationship mode.

[0042] In the above scheme,

[0043] The information processing module is configured to adjust the target object relative to the corresponding first-wave granularity parameter based on the event asset feature vector to determine a fourth-wave granularity parameter when the relationship between different asset feature vectors in the data link is a cooperative relationship mode.

[0044] In the above scheme,

[0045] The information processing module is used to adjust the corresponding first-wave granularity parameters of the target object based on the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector to determine the fifth-wave granularity parameters when the relationship between different asset feature vectors in the data chain is a cooperative relationship model.

[0046] In the above scheme,

[0047] The information processing module is used to send the target object identification, the activity information matching the target object and the information processing result matching the data chain to the blockchain network so that

[0048] The nodes of the blockchain network fill the target object identifier, the activity information matching the target object, and the information processing results matching the data chain into a new block, and when a consensus is reached on the new block, the new block is appended to the end of the blockchain.

[0049] An embodiment of the present invention further provides an electronic device, comprising:

[0050] a memory for storing executable instructions;

[0051] The processor is used to implement the preceding data link-based information processing method when running the executable instructions stored in the memory.

[0052] An embodiment of the present invention further provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, implement the aforementioned data link-based information processing method.

[0053] The embodiments of the present invention have the following beneficial effects:

[0054] The present invention obtains the activity information of the target object; based on the activity information of the target object, determines the cognitive asset feature vector, event asset feature vector and tool asset feature vector corresponding to the activity information; determines the data chain that matches the target object according to the cognitive asset feature vector, event asset feature vector and tool asset feature vector corresponding to the activity information; when the activity information of the target object changes, determines the relationship between different asset feature vectors in the data chain; based on the relationship between different asset feature vectors in the data chain, determines the information processing result that matches the data chain. In this way, the activity information of the target object is fully represented by the data chain, ensuring the target object's activity information, realizing efficient processing of the activity information, and at the same time realizing an overall analysis of the processing of various activity information in social activities through the data chain, and utilizing the cognitive asset feature vector, event asset feature vector and tool asset feature vector corresponding to the activity information to enhance the macro-analysis of information processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A schematic diagram of an environment in which a data link-based information processing method is used according to an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of the structure of a data link-based information processing device provided in an embodiment of the present invention;

[0057] Figure 3 An optional flowchart of the data link-based information processing method provided in an embodiment of the present invention;

[0058] Figure 4 Schematic diagram of forming living space parameters in an embodiment of the present invention;

[0059] Figure 5 An optional flowchart of the data link-based information processing method provided in an embodiment of the present invention;

[0060] Figure 6 To obtain information processing results matching the data link in the embodiment of the present invention;

[0061] Figure 7 A schematic diagram of the working process of the data analysis system model in an embodiment of the present invention;

[0062] Figure 8 1 is a schematic diagram of the architecture of a target object determination device 100 provided in an embodiment of the present invention;

[0063] Figure 9 2 is a schematic diagram of the structure of a blockchain in a blockchain network 200 provided in an embodiment of the present invention;

[0064] Figure 102 is a functional architecture diagram of the blockchain network 200 provided in an embodiment of the present invention;

[0065] Figure 11 Schematic diagram of event asset features in the data chain-based information processing method according to an embodiment of the present invention;

[0066] Figure 12 Schematic diagram of tool asset features in the data chain-based information processing method according to an embodiment of the present invention;

[0067] Figure 13 Schematic diagram of the cognitive asset features in the data chain-based information processing method according to an embodiment of the present invention;

[0068] Figure 14 Schematic diagram of the cognitive asset features in the data chain-based information processing method according to an embodiment of the present invention;

[0069] Figure 15 The present invention provides an optional flowchart of a data link-based information processing method. DETAILED DESCRIPTION

[0070] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0071] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0072] The relevant data collection and processing in the embodiments of this application should be strictly in accordance with the requirements of relevant laws and regulations when applied in examples, and the informed consent or separate consent of the personal information subject should be obtained. Subsequent data use and processing should be carried out within the scope of authorization of laws and regulations and the personal information subject.

[0073] Before further explaining the embodiments of the present invention in detail, the nouns and terms involved in the embodiments of the present invention are explained. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.

[0074] 1) Transaction, equivalent to the computer term "transaction," includes operations that need to be submitted to the blockchain network for execution, and does not refer solely to transactions in a business context. Given the conventional use of the term "transaction" in blockchain technology, the embodiments of the present invention follow this convention.

[0075] For example, the Deploy transaction is used to install a specified smart contract to a node in the blockchain network and prepare it to be called; the Invoke transaction is used to append transaction records to the blockchain by calling a smart contract and perform operations on the blockchain's state database, including update operations (including adding, deleting, and modifying key-value pairs in the state database) and query operations (i.e., querying key-value pairs in the state database).

[0076] 2) Blockchain is an encrypted, chain-like transaction storage structure formed by blocks.

[0077] For example, the header of each block can include the hash value of all transactions in the block, as well as the hash value of all transactions in the previous block, thereby achieving tamper-proof and anti-forgery of transactions in the block based on the hash value; newly generated transactions are filled into the block and, after consensus among nodes in the blockchain network, will be appended to the end of the blockchain to form a chain-like growth.

[0078] 3) Blockchain Network: A collection of nodes that incorporate new blocks into the blockchain through consensus.

[0079] 4) Ledger is a general term for the blockchain (also known as ledger data) and the state database synchronized with the blockchain.

[0080] Among them, the blockchain records transactions in the form of files in the file system; the state database records transactions in the blockchain in the form of different types of key (Key) value (Value) pairs to support fast queries on transactions in the blockchain.

[0081] 5) Smart Contracts, also known as chaincode or application code, are programs deployed on nodes in a blockchain network. Nodes execute smart contracts invoked in received transactions to update or query key-value pairs in the ledger database.

[0082] 6) Consensus is a process in a blockchain network used to reach agreement on transactions in a block among multiple nodes involved. The agreed-upon block will be appended to the end of the blockchain. Mechanisms for achieving consensus include Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof-of-Stake (DPoS), and Proof of Elapsed Time (PoET).

[0083] 7) User, used to represent an individual, legal person or organization (such as multiple governments, enterprises, organizational departments, or possibly a combination of the above) who needs to submit relevant materials to the blockchain for a business that requires the participation of multiple government units, enterprises and organizations.

[0084] 8) Target objects are used to represent government agencies, enterprises (such as banks, when users apply for bank accounts when starting a company), and organizations (such as accounting firms) that participate in the business. Of course, they can also be independent individuals participating in the activity. Different target objects constitute the organization in the activity.

[0085] 9) In response, it is used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed can be real-time or have a set delay. Unless otherwise specified, there is no restriction on the order of execution of the multiple operations executed.

[0086] Figure 1 The following is a schematic diagram of the use environment of the information processing method based on the data link shown in the embodiment of the present invention; Figure 1 The data link-based information processing method shown is described using the environment, see Figure 1 When the target object is a government organization, the corresponding activity information is government information. A client of the government application process is set on the terminal (including terminal 10-1 and terminal 10-2). The user can process the government information through the set client and display the developed government application process to the government department; the terminal is connected to the server 200 through the network 300. The network 300 can be a wide area network or a local area network, or a combination of the two, and a wireless link is used to realize data transmission.

[0087] As an example, the server 200 is configured to deploy a corresponding data link-based information processing device to implement a data link-based information processing method to obtain the target object's activity information; based on the target object's activity information, determine the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information; determine a data link that matches the target object based on the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information; when the target object's activity information changes, determine the relationship between different asset feature vectors in the data link; and based on the relationship between different asset feature vectors in the data link, determine an information processing result that matches the data link. The information processing result is used to characterize the activity processing effect of the target object's activity information in different data processing dimensions. Taking government information processing as an example, by detecting identity card information, tax certificate information, legal person certificate information, and corresponding government documents that match different target objects through data links, the target object's tax status in the tax certificate information processing dimension can be accurately obtained. In addition, the relationship between different asset feature vectors can be used to approve the target object's business in the government document processing dimension. Taking economic information as an example, when trading the target user's physical assets through the data chain-based information processing method of this application, different operations can be performed in multiple asset categories. By utilizing the relationship between different asset feature vectors in the data chain, when the trading platform has access to multiple asset categories, it is possible to combine existing algorithmic systems by operating multiple assets simultaneously in a single strategy. For example, a company can buy stocks and cover them with options, or it can integrate news and various real-time data channels into the trading system to facilitate the execution of economic activities in different data processing dimensions.

[0088] The structure of the information processing device based on the data link of the embodiment of the present invention is described in detail below. The information processing device based on the data link can be implemented in various forms, such as a dedicated terminal with an information processing function based on the data link, or a server with an information processing function based on the data link, such as the preceding Figure 1 Server 200 in. Figure 2 The schematic diagram of the structure of the information processing device based on the data link provided in the embodiment of the present invention can be understood as follows: Figure 2 Only the exemplary structure of the information processing device based on the data link is shown, not all structures, and can be implemented as needed. Figure 2 Partial or complete structure shown.

[0089] The data link-based information processing device provided in the embodiment of the present invention includes: at least one processor 201, a memory 202, a user interface 203 and at least one network interface 204. The various components in the data link-based information processing device are coupled together via a bus system 205. It can be understood that the bus system 205 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 205 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, the bus system 205 is not described in detail. Figure 2 Various buses are labeled as bus system 205.

[0090] The user interface 203 may include a display, a keyboard, a mouse, a trackball, a click wheel, keys, buttons, a touch pad or a touch screen.

[0091] It is understood that the memory 202 can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The memory 202 in the embodiment of the present invention can store data to support the operation of the terminal (such as 10-1). Examples of such data include: any computer program used to operate on the terminal (such as 10-1), such as an operating system and an application program. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program can include various application programs.

[0092] In some embodiments, the data link-based information processing device provided by the embodiments of the present invention can be implemented in a combination of software and hardware. As an example, the question-answering model training device provided by the embodiments of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the data link-based information processing method provided by the embodiments of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0093] As an example of an information processing device based on a data link provided in an embodiment of the present invention being implemented by a combination of software and hardware, the information processing device based on a data link provided in an embodiment of the present invention can be directly embodied as a combination of software modules executed by a processor 201. The software module can be located in a storage medium, and the storage medium is located in the memory 202. The processor 201 reads the executable instructions included in the software module in the memory 202, and combines with the necessary hardware (for example, including the processor 201 and other components connected to the bus 205) to complete the information processing method based on a data link provided in an embodiment of the present invention.

[0094] As an example, the processor 201 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., where the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0095] As an example of a hardware implementation of the data link-based information processing device provided in an embodiment of the present invention, the device provided in an embodiment of the present invention can be directly executed by a processor 201 in the form of a hardware decoding processor, for example, one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), or other electronic components to implement the data link-based information processing method provided in an embodiment of the present invention.

[0096] The memory 202 in the embodiment of the present invention is used to store various types of data to support the operation of the data link-based information processing device. Examples of such data include any executable instructions for operating on the data link-based information processing device, such as executable instructions. A program implementing the data link-based information processing method of the embodiment of the present invention may be included in the executable instructions.

[0097] In other embodiments, the data link-based information processing device provided by the embodiments of the present invention may be implemented in software. Figure 2The data link-based information processing device stored in the memory 202 is shown. The device may be software in the form of a program or plug-in, and may include a series of modules. An example of a program stored in the memory 202 may include a data link-based information processing device. The data link-based information processing device includes the following software modules: an information transmission module 2081 and an information processing module 2082. When the software modules in the data link-based information processing device are read into the RAM and executed by the processor 201, the data link-based information processing method provided by the embodiment of the present invention is implemented. The functions of the various software modules in the data link-based information processing device are described below, including:

[0098] Information transmission module 2081, used to obtain activity information of the target object;

[0099] An information processing module 2082 is configured to determine, based on the activity information of the target object, a cognitive asset feature vector, an event asset feature vector, and a tool asset feature vector corresponding to the activity information;

[0100] The information processing module 2082 is configured to determine a data link that matches the target object based on the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information;

[0101] The information processing module 2082 is configured to determine the relationship between different asset feature vectors in the data chain when the activity information of the target object changes;

[0102] The information processing module 2082 is used to determine an information processing result that matches the data chain based on the relationship between different asset feature vectors in the data chain.

[0103] according to Figure 2 In one aspect of the electronic device shown, the present application further provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of the computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to execute the various embodiments and combinations of embodiments provided in the various optional implementations of the above-mentioned data link-based information processing method.

[0104] Combine Figure 2 The information processing device based on the data link shown illustrates the information processing method provided by the embodiment of the present invention, see Figure 3 , Figure 3 An optional flow chart of the information processing method based on the data link provided in the embodiment of the present invention is provided. It can be understood that: Figure 3The steps shown can be executed by various electronic devices running data link-based information processing devices, such as dedicated terminals, servers or server clusters with data link-based information processing functions. The data link involved in this application conforms to the wave-particle duality characteristics of people as independent units; the data link is composed of cognitive assets, event assets and tool assets, and the three assets have their own characteristic vectors in different situations; the data link of this application can simulate the four basic relationships of internal, collaborative, cooperative and service in human social organizations, and ultimately form an organizational governance system that supports people (organization refers to various forms of morphological units formed or agreed upon between people in the social system due to non-family relationships), thereby realizing data processing in different dimensions. The dedicated terminal with a data link-based information processing device can be the preamble Figure 2 The electronic device with information processing device in the embodiment shown. Figure 3 The steps shown are explained.

[0105] The following is for Figure 3 The steps shown are explained.

[0106] Step 301: The information processing device based on the data link obtains the activity information of the target object.

[0107] Among them, the target objects in this application can be either natural persons participating in various types of social activities or organizations participating in various types of social activities. For example, when the activity information is government information, the target objects participating in the government information processing include but are not limited to government organizations. The processed government information may include identity card information, tax certificate information, legal person certificate information, and corresponding government documents (document collections) awaiting approval that match the target objects.

[0108] Step 302: The data link-based information processing device determines the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information based on the activity information of the target object.

[0109] In some embodiments of the present invention, based on the activity information of the target object, determining the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information can be achieved by:

[0110] Based on changes in cognitive ability parameters within the target subject's activity information, a cognitive asset feature vector corresponding to the activity information is determined; different element-event interaction records within the target subject's activity information are extracted to determine the cognitive asset feature vector corresponding to the activity information; and tool information used during the execution of the target subject's activity information is obtained, and based on the tool information, a tool asset feature vector corresponding to the activity information is determined. Since the target subject may utilize different processing tools when participating in an activity, the data processing generated during the activity may form different element-event interaction records; the data processing results generated during the activity may cause changes in the target subject's cognitive ability parameters, and these changes in cognitive ability parameters may be inherited and used when the target subject participates in other activities, thereby enhancing the target subject's ability to participate in the activity. For example, in government information processing, the target user's tax information may be checked. In the initial tax information check, the target subject's matching ID card information, tax certificate information, legal person certificate information, and corresponding government documents may be used. In all subsequent tax information checks, the target subject's matching ID card information, tax certificate information, and legal person certificate information may be inherited and used to obtain inspection results across different data processing dimensions.

[0111] The following uses activity information as government information as an example for explanation. The target object involved in government information processing is a government organization. The process of determining the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information is explained. Since government information is used to represent the information of the target user in the government processing process at different corresponding stages (for example, changes in legal person information), the target user generates a wide variety of government information and a large amount of government information during the change of government information. Therefore, when processing the cognitive record of the target object, first, based on the changes in the cognitive ability parameters in the target object's activity information, the cognitive asset feature vector corresponding to the activity information is determined. The cognitive asset feature vector i can be expressed in the following form. The cognitive asset feature vector i can include each change in the cognitive ability parameter:

[0112]

[0113] Among them, α, β, and γ are used to represent the cognitive ability parameters generated when the target object participates in the activity.

[0114] Similarly, taking the social economy and corporate economic information as an example, when trading the target user's physical assets through the data chain-based information processing method of this application, different operations can be performed in multiple asset categories. By utilizing the relationship between different asset feature vectors in the data chain, when the trading platform has access to multiple asset categories, it is possible to combine existing algorithmic systems by operating multiple assets simultaneously in a single strategy. For example, a company can buy stocks and cover them with options, or it can integrate news and various real-time data channels into the trading system to facilitate the execution of economic activities in different data processing dimensions.

[0115] When the target object participates in different activities, different element events can be generated. For example, in the process of government affairs processing, the query and confirmation of government affairs information, and the adjustment and punishment of government affairs information participants can interact with each other. For example, by querying government affairs information, it is determined that adjustments or punishments are made to government affairs information participants. The records of events that occur in the interaction between various elements can be represented as event asset feature vectors e, which can be expressed as:

[0116]

[0117] Among them, λ, μ, and ν are used to represent different element events generated when the target object participates in the activity.

[0118] When the target object participates in different activities, it can use different information processing tools, such as information query tools, business processing tools, and authority recording tools. For example, in the process of government affairs processing, the query and confirmation of government affairs information, and the adjustment and punishment of government affairs information participants can also use the tax collection processing network and the industrial and commercial information query network as tools for participating in activities. When the target object processes other activities, it can still use various types of tools. Therefore, based on the tool information, the tool asset feature vector j corresponding to the activity information can be expressed as:

[0119]

[0120] Among them, K / M / N can represent the different tools used by the target objects when participating in the activities.

[0121] Step 303: The data link-based information processing device determines a data link that matches the target object according to the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information.

[0122] In some embodiments of the present invention, in order to realize the process of representing the target object's participation in different activities through a data link, the environmental characteristics of the target object can be determined; in response to the environmental characteristics, the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information are randomly combined to determine the particle state parameters that match the target object, wherein the particle state parameters can be expressed as,

[0123]

[0124] The particle state parameter is represented by T, which is used to represent the random grouping of cognitive assets i, event assets e, and instrument assets j. h represents the environment surrounding the target object. In a steady-state environment, the target object is involved in the series range of the characteristic vectors of the three types of assets i, e, and j, and the characteristic vector is evaluated through the series of the three types of assets i, e, and j.

[0125] In some embodiments of the present invention, in order to realize the process of representing the target object's participation in different activities through a data link, it is also possible to determine the spatiotemporal changes in the particle state parameters that match the target object based on the change characteristics of the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector when the environmental characteristics of the target object change; based on the spatiotemporal changes in the particle state parameters, determine the wave state parameters that match the target object, wherein the change in the environmental characteristics of the target object refers to the process in which the steady-state environment where the target object is located changes due to certain external factors, causing the environment to seek a steady state again, that is, after experiencing the wave state, the environment will return to the particle state. The particle state is stateless, and the wave state has the individual associated attributes of the target object. Therefore, the calculation process of the wave state parameters can be expressed as:

[0126]

[0127] in, Represents the wave state parameter, which refers to the fluctuations in the target object's activity caused by the arbitrary influence of the three assets i, e, and j. Because particle states are stateless, wave states cause changes in the particle state's spatiotemporal domain. Therefore, by determining the wave state parameters that match the target object based on the spatiotemporal changes in the particle state parameters, we can better determine the changes in different eigenvectors during the target object's participation in the activity.

[0128] Since the number of activities in which the target object participates is large and the information interaction process is complex, the environment of the target object changes repeatedly between the steady state and the changing state due to external driving. Therefore, the first wave granularity parameter corresponding to the target object can be determined according to the grouping vector of the particle state parameter and the wave state parameter, wherein the first wave granularity parameter H can be expressed as:

[0129]

[0130] When the target object performs an activity, the number of different target objects participating in the activity information can also be determined based on the activity information of the target object; based on the number of different target objects, the living space parameter can be determined, referring to Figure 4 , Figure 4 This is a schematic diagram of the formation of living space parameters in an embodiment of the present invention. When executing an activity, the target objects participating in the activity include: executors, responsible persons, and managers. The three constitute a ternary structure in the organization. Figure 4 The spatial parameter E shown can be expressed as:

[0131]

[0132] The spatial parameter E represents the total set of events grouped for all target objects within a specific timeframe, and measures the influence of each event within the target object group, i.e., r / R. Here, r represents the target object generating the event, and R is the total number of target objects generating the event.

[0133] In some embodiments of the present invention, in the process of determining a data chain that matches the target object, a data unit may be first determined. When data in the data unit begins to flow and generate flow information, the data unit can form a corresponding data chain due to the event flow. Specifically, the number of target objects participating in different dimensions of the living space parameters may be determined based on the living space parameters; based on the number of target objects in different dimensions of the living space parameters and the activity information, the corresponding data unit is determined. The data unit D may be expressed as:

[0134]

[0135] Among them, K represents the total number of events surrounding an individual in the living space within a certain effective time, which is a subset of R; k represents the event person. It represents three types of event cognitive roles: executor, person in charge, and manager. The cognitive asset grouping vector represents the three types of roles, that is, each type of cognitive role will have a cognitive impact on the event.

[0136] When the activity information of the target object changes, the flow information of the data in the data unit can be determined based on the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information. Based on the flow information of the data in the data unit, the data chain matching the target object and the environmental parameters of the data chain are determined. The data chain L marks all the characteristics of events that may have an impact on the results within a period of time. The data chain L can be expressed as:

[0137]

[0138] Different cognitive subjects can be applied to data units during the event flow, namely Figure 4 The cognitive influence of different “executors, responsible persons, and managers” related to the event is shown, thus realizing the flow of events in the social system. In this process, the superposition of the cognitive influence of different cognitive subjects around the data unit can be expressed as Refers to the grouping of cognitive subjects. After experiencing the flow of data volume, the event set also conforms to the wave-particle principle and returns to the particle steady state, forming an environment. That is, the environment parameter Ek of the data chain can be expressed as:

[0139]

[0140] Furthermore, due to the large number and types of activities in which the target object participates, categories such as tool assets can be added during the construction of the data chain to form a hybrid data chain that spans time periods and fields, thereby expanding the applicable scenarios of the data chain-based information processing method provided in this application. The hybrid data chain L can be expressed as:

[0141]

[0142] Step 304: When the activity information of the target object changes, the information processing device based on the data link determines the relationship between different asset feature vectors in the data link.

[0143] Step 305: The information processing device based on the data link determines an information processing result that matches the data link based on the relationship between the different asset feature vectors in the data link.

[0144] Since the relationship between different asset feature vectors in the data chain is different when the target object's activity information changes, the information processing results that match the data chain must be classified. Figure 5 , Figure 5 An optional flow chart of the information processing method based on the data link provided in the embodiment of the present invention is provided. It can be understood that: Figure 5 The steps shown can be executed by various electronic devices running a data link-based information processing device, such as a dedicated terminal, server, or server cluster with a data link-based information processing function, wherein the dedicated terminal with a data link-based information processing device can be the preceding Figure 2 The electronic device with information processing device in the embodiment shown. Figure 5 The steps shown are explained.

[0145] Step 501: When the relationship between different asset feature vectors in the data chain is an internal relationship model, based on the cognitive asset feature vector, the target object is adjusted relative to the corresponding first-wave granularity parameter to determine the second-wave granularity parameter.

[0146] When the target object performs different activities, the information processing results matching the data chain can be obtained by using the relationship between different asset feature vectors in the data chain through the data chain matching the target object. Figure 6 , Figure 6 In the embodiments of the present invention, information processing results matching the data chain are obtained. After the target object participates in different activities and forms a corresponding data chain, the resulting data chain can be used to conduct thematic analysis processing for scenario fusion. For example, this may include scenario indicator definition processing, fusion calculation configuration processing, and diagram element configuration processing. Furthermore, the event assets, cognitive assets, and tool assets included in the data chain can express the human-centric operating model of the organizational ecosystem. This expression allows the characteristics of the organizational ecosystem formed by the target object to be captured and mined. These characteristics can also be continuously and reliably recorded through the data chain as the basis for information processing. This data chain recording is self-iterable, that is, by capturing and updating the feature vectors of event assets, cognitive assets, and tool assets at different times and within different scopes, the continuity of the data chain is achieved. Data analysis based on the data chain can perform system analysis, business analysis, and content analysis. System analysis includes diagram element configuration, customized calculation logic, and customized tracking support. At the same time, different service processes can be executed through the information processing results that match the data link, such as: general collection and access services, general computing services, and general view services, and further general operation services can be completed by receiving user feedback and interaction.

[0147] In some embodiments of the present invention, when the relationship between different asset feature vectors in the data chain is an internal relationship model, because the event asset e and the instrument asset j in the internal relationship are homogeneous, the second wave granularity relationship Hi can be expressed as:

[0148] H i =(T|T t,j )

[0149] Then the internal relationship is: E K *H iIn the following, activity information is taken as government information, and the target object involved in government information processing is a government organization. The process of determining the cognitive asset feature vector, event asset feature vector, and tool asset feature vector corresponding to the activity information is explained. Since government information is used to represent the information of the target user in the government processing process at different corresponding stages (for example, changes in legal person information occur), the target user generates a wide range of government information types and a large amount of government information during the change of government information. Therefore, when processing the cognitive records of the target object, when the relationship between different asset feature vectors in the data chain is an internal relationship model, the target object can use the same event asset and tool asset respectively, such as jointly using the tax collection processing network and the industrial and commercial information query network as tools for participating in the activity, but the cognitive assets generated are different (for example, adjustments or penalties are made to government information participants). Therefore, the corresponding first-wave granularity parameters of the target object can be adjusted through the cognitive asset feature vector, so that the first-wave granularity parameters are more consistent with the change trend of the cognitive asset feature vector.

[0150] Step 502: When the relationship between different asset feature vectors in the data chain is a collaborative relationship mode, the target object is adjusted relative to the corresponding first-wave granularity parameters based on the cognitive asset feature vector and the event asset feature vector to determine the third-wave granularity parameters.

[0151] Among them, when the relationship between different asset feature vectors in the data chain is a collaborative relationship model, in the organizational system composed of the target object, the relationship between organizational entities with the same attributes can become a collaborative relationship, and its tool assets j are homogeneous, then the third wave granularity parameter H i，j Expressed as:

[0152]

[0153] The synergistic relationship is expressed as: E K *H i，j , making the changes in the third wave of granularity parameters consistent with the changing trends of cognitive asset characteristic vectors and event asset characteristic vectors.

[0154] Step 503: When the relationship between different asset feature vectors in the data chain is a cooperative relationship mode, the target object is adjusted relative to the corresponding first-wave granularity parameter based on the event asset feature vector to determine the fourth-wave granularity parameter.

[0155] Among them, the cooperative relationship is a relationship that occurs when the organizational subject establishes a capability expansion relationship with other organizations outside the organizational system due to capability reasons. In this relationship, the cognitive assets and tool assets established due to capability supply and demand are homogeneous, so the fourth wave granularity parameter H e It can be expressed as:

[0156] H e =(T|T t,e )

[0157] The cooperative relationship is expressed as: E K *H e .

[0158] Step 504: When the relationship between different asset feature vectors in the data chain is a cooperative relationship model, the target object is adjusted relative to the corresponding first-wave granularity parameters based on the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector to determine the fifth-wave granularity parameters.

[0159] Among them, the service relationship refers to the relationship between the main body of the organization and other organizations. This relationship is a non-coupling relationship. The fifth wave granularity parameter Hi, e, j can be expressed as:

[0160]

[0161] The service relationship is: E K *H i,e,j .

[0162] Step 505: Based on the information processing results matched by the data chain, determine the organizational governance data analysis system model based on the data chain.

[0163] Among them, reference Figure 7 , Figure 7 Schematic diagram of the working process of the data analysis system model in an embodiment of the present invention. Specifically, the organizational governance data analysis system model G can be expressed as:

[0164]

[0165] like Figure 7 As shown in the figure, in the working process of the data analysis system model, d represents the data chain of the digital system dimension, b represents the data chain of the business-driven dimension, and a represents the data chain of the content-driven dimension. Because different target objects constitute an organization, the participation process of any organizational entity can be abstracted into three levels: data system, business-driven, and content-driven. All other organizational behaviors are any combination of these three basic dimensions. For example: Figure 7First of all, any organization has relatively independent strategic decision-making activities, which are divided into four dimensions: digital system, business, content, and strategy. After the decision is executed, the organizational activities are reflected in the performance of technology empowerment, which is carried by both the system and the business. In the specific process of organizational activities, organizational roles generate event behavior activities, which have the expression elements of cognitive asset feature vectors, event asset feature vectors, and tool asset feature vectors. Then, based on the data analysis system model, with the help of the activity behavior events occurring on the digital carriers of the system and business, through the data chain of the digital system dimension, the data chain of the business-driven dimension, and the data chain of the content-driven dimension, it is possible to achieve systematic monitoring of organizational activity behavior, conduct analysis and draw conclusions to evaluate the sustainable ecological characteristics of organizational decision-making and technology empowerment, thereby achieving overall cognitive monitoring of the organization. In addition, the single dimension of the digital system can be systematically monitored through the data chain of the physical system operation characteristics represented by the digital system dimension.

[0166] In general, when the target object performs different activities, the information processing results matching the data chain can be obtained by using the relationship between different asset feature vectors in the data chain through the data chain matching the target object. Figure 6 , Figure 6 In the embodiments of the present invention, information processing results matching a data chain are obtained. In this case, target objects participate in different activities, forming corresponding data chains. These chains can then be used for analysis and processing. The event assets, cognitive assets, and tool assets included in the data chain can express the human-centric operational model of the organizational ecosystem. This expression allows the capture and exploration of the characteristics of the organizational ecosystem formed by the target objects. These characteristics can then be continuously and reliably recorded through the data chain, serving as the basis for information processing. This data chain recording is self-iterable, namely, by capturing and updating the feature vectors of event assets, cognitive assets, and tool assets at different times and within different scopes, the continuity of the data chain is achieved. Data chain-based data analysis can be performed to perform system analysis, business analysis, content analysis, and more advanced scenario-integrated thematic analysis. Figure 6 This outlines the product architecture, encompassing universal collection and access services, universal computing services, universal view services, and universal operations services, encompassing the entire operational analysis and governance process. Building upon this foundation, data chain analysis models for system analysis, business analysis, and content analysis can be modularly overlaid, as well as scenario-based integrated analysis models for these three.

[0167] The embodiments of the present invention may be implemented in conjunction with cloud technology. Cloud technology refers to a hosting technology that unifies hardware, software, and network resources within a wide area network or local area network to enable data computing, storage, processing, and sharing. It can also be understood as a general term for network technology, information technology, integration technology, management platform technology, and application technology based on cloud computing business models. Backend services of technical network systems, such as video websites, image websites, and more portal websites, require a large amount of computing and storage resources. Therefore, cloud technology needs to be supported by cloud computing.

[0168] It should be noted that cloud computing is a computing model that distributes computing tasks across a resource pool consisting of a large number of computers, enabling various application systems to obtain computing power, storage space, and information services as needed. The network that provides these resources is called the "cloud." To users, the resources in the "cloud" appear to be infinitely scalable and can be accessed at any time, used on demand, and expanded at any time, with a pay-per-use fee. As a provider of cloud computing's basic capabilities, a cloud computing resource pool platform, often referred to as Infrastructure as a Service (IaaS), is established. Various types of virtual resources are deployed within the resource pool for external customers to choose from. The cloud computing resource pool primarily includes computing devices (which can be virtualized machines, including operating systems), storage devices, and network devices.

[0169] Combined with the preamble Figure 1 As shown, the data link-based information processing method provided in the embodiment of the present invention can be implemented by corresponding cloud devices. For example, terminals (including terminal 10-1 and terminal 10-2) are connected to server 200 located in the cloud via network 300. Network 300 can be a wide area network or a local area network, or a combination of the two. It is worth noting that server 200 can be a physical device or a virtualized device.

[0170] In some embodiments of the present invention, the data link-based information processing method further includes:

[0171] Receive data synchronization requests from other nodes in the blockchain network; verify the permissions of the other nodes in response to the data synchronization requests; when the permissions of the other nodes are verified, control data synchronization between the current node and the other nodes to enable the other nodes to obtain the target object identifier, the data link of the target object, and the information processing results based on the data link.

[0172] In some embodiments of the present invention, the data link-based information processing method further includes:

[0173] In response to a query request, the query request is parsed to obtain a corresponding object identifier; based on the object identifier, permission information within a target block in the blockchain network is obtained; the matching of the permission information and the object identifier is verified; when the permission information matches the object identifier, the corresponding target object identifier, the data link of the target object, and the information processing result based on the data link are obtained in the blockchain network; in response to the query instruction, the obtained corresponding target object identifier, the data link of the target object, and the information processing result based on the data link are pushed to the corresponding government information client, so that the government information client obtains the corresponding target object identifier, the data link of the target object, and the information processing result based on the data link stored in the blockchain network, and realizes data migration between different terminals (government information clients) of the same user's data link.

[0174] See also Figure 8 , Figure 8 It is a schematic diagram of the architecture of the target object determination device 100 provided in an embodiment of the present invention, including a blockchain network 200 (consensus nodes 210-1 to consensus nodes 210-3 are shown as an example), an authentication center 300, a business entity 400 and a business entity 500, which are described below respectively.

[0175] The blockchain network 200 is flexible and diverse, and can be, for example, a public blockchain, a private blockchain, or a consortium blockchain. For example, with a public blockchain, any business entity's electronic device, such as a user terminal or server, can access the blockchain network 200 without authorization. For example, with a consortium blockchain, a business entity can access the blockchain network 200 with authorization, and its electronic devices (such as terminals / servers) become client nodes in the blockchain network 200.

[0176] In some embodiments, client nodes may only serve as observers of blockchain network 200, providing support for business entities to initiate transactions (e.g., storing data on-chain or querying on-chain data). Client nodes may implement the functions of consensus node 210 of blockchain network 200, such as sorting, consensus services, and ledger functions, by default or selectively (e.g., depending on the specific business needs of the business entity). This allows the business entity's data and business processing logic to be migrated to blockchain network 200 to the greatest extent possible, ensuring the trustworthiness and traceability of data and business processing processes through blockchain network 200.

[0177] The consensus nodes in the blockchain network 200 receive data from different business entities (e.g. Figure 1 4 and 500) of the client nodes (eg, Figure 1The client node 410 belonging to the business entity 400 and the client node 510 belonging to the system 500 of the electronic device) submit transactions, execute transactions to update the ledger or query the ledger, and various intermediate results or final results of the transaction can be returned to the client node of the business entity for display.

[0178] For example, the client node 410 / 510 can subscribe to events of interest in the blockchain network 200, such as transactions occurring in a specific organization / channel in the blockchain network 200, and the consensus node 210 pushes the corresponding transaction notification to the client node 410 / 510, thereby triggering the corresponding business logic in the client node 410 / 510.

[0179] The following uses the example of multiple business entities accessing the blockchain network to manage the results of target object determination to illustrate an exemplary application of the blockchain network.

[0180] See also Figure 8 The management process involves multiple business entities, such as business entity 400, which can be an artificial intelligence-based target object determination device, and business entity 500, which can be a display system with a target object determination function. They register with the authentication center 300 to obtain their respective digital certificates. The digital certificate includes the business entity's public key and the digital signature signed by the authentication center 300 on the business entity's public key and identity information. It is used to attach it to the transaction together with the business entity's digital signature for the transaction and is sent to the blockchain network for the blockchain network to extract the digital certificate and signature from the transaction to verify the authenticity of the message (i.e., whether it has been tampered with) and the identity information of the business entity sending the message. The blockchain network will verify the identity, such as whether it has the authority to initiate the transaction. Clients running on electronic devices (such as terminals or servers) under the jurisdiction of the business entity can request access to the blockchain network 200 and become client nodes.

[0181] The client node 410 of the business entity 400 is used to obtain resource transaction data corresponding to different objects; determine the level information of the different objects based on the resource transaction data, and determine the basic objects among the different objects based on the level information; determine the difference feature vectors matching the different objects based on the resource transaction data; determine the association relationship network between the different objects based on the difference feature vectors matching the different objects; determine the clustering result of the association relationship network between the different objects in response to the basic object; determine the target object matching the basic object among the different objects based on the clustering result of the association relationship network between the different objects and the corresponding level information, and send the target object identifier, the data chain of the target object and the information processing result based on the data chain to the blockchain network 200.

[0182] The target object identifier, the target object's data chain, and the information processing result based on the data chain are sent to the blockchain network 200. Business logic can be pre-set on the client node 410. When the corresponding target object determination result is formed, the client node 410 automatically sends the target object identifier, the target object's data chain, and the information processing result based on the data chain to the blockchain network 200. Alternatively, a business person of the business entity 400 can log in to the client node 410, manually package the target object identifier, the target object's data chain, and the information processing result based on the data chain, and send it to the blockchain network 200. During the sending process, the client node 410 generates a transaction corresponding to the update operation based on the target object identifier, the target object's data chain, and the information processing result based on the data chain. The transaction specifies the smart contract to be called to implement the update operation and the parameters to be passed to the smart contract. The transaction also carries the digital certificate of the client node 410 and a signed digital signature (for example, obtained by encrypting the transaction digest using the private key in the digital certificate of the client node 410), and broadcasts the transaction to the consensus node 210 in the blockchain network 200.

[0183] When consensus node 210 in blockchain network 200 receives a transaction, it verifies the digital certificate and digital signature carried in the transaction. If verification is successful, it then determines whether business entity 400 has transaction authority based on the identity of the business entity 400 carried in the transaction. Either of these verifications will result in transaction failure. After successful verification, node 210 signs its own digital signature (e.g., by encrypting the transaction digest using the private key of node 210-1) and continues to broadcast the transaction within blockchain network 200.

[0184] After receiving a successfully verified transaction, the consensus node 210 in the blockchain network 200 inserts the transaction into a new block and broadcasts it. When the consensus node 210 in the blockchain network 200 broadcasts a new block, it performs a consensus process on the new block. If the consensus is successful, the new block is appended to the end of the blockchain stored in the network, and the state database is updated based on the transaction results. The transaction in the new block is executed: For transactions that submit updates to the target object identifier, the target object's data chain, and the information processing results based on the data chain, a key-value pair including the target object identifier, the target object's data chain, and the information processing results based on the data chain is added to the state database.

[0185] The business personnel of the business entity 500 logs in to the client node 510 and inputs the target object determination result or the target object query request. The client node 510 generates a transaction corresponding to the update operation / query operation based on the target object determination result or the target object query request. The transaction specifies the smart contract that needs to be called to implement the update operation / query operation and the parameters passed to the smart contract. The transaction also carries the digital certificate of the client node 510 and the signed digital signature (for example, the transaction summary is encrypted using the private key in the digital certificate of the client node 510), and broadcasts the transaction to the consensus node 210 in the blockchain network 200.

[0186] The consensus node 210 in the blockchain network 200 receives the transaction, verifies the transaction, fills the block and reaches consensus, then appends the filled new block to the end of the blockchain stored in itself, updates the status database according to the transaction result, and executes the transaction in the new block: for the transaction submitted to update a target object identifier, the data chain of the target object and the information processing result based on the data chain, the key-value pair corresponding to the target object determination result in the status database is updated according to the manual identification result; for the transaction submitted to query the determination result of a target object, the key-value pair corresponding to the target object determination result is queried from the status database, and the transaction result is returned.

[0187] It is worth noting that in Figure 8 The example illustrates the process of directly uploading the target object identifier, the target object data chain, and the information processing results based on the data chain to the blockchain. However, in other embodiments, when the target object determination result has a large amount of data, the client node 410 may upload the hash of the target object determination result and the hash of the corresponding target object determination result to the blockchain in pairs, and store the original target object determination result and the corresponding target object determination result in a distributed file system or database. After the client node 510 obtains the target object determination result and the corresponding target object determination result from the distributed file system or database, it may verify them in conjunction with the corresponding hash in the blockchain network 200, thereby reducing the workload of the uploading operation.

[0188] As an example of blockchain, see Figure 9 , Figure 9 This is a schematic diagram of the structure of the blockchain in the blockchain network 200 provided by an embodiment of the present invention. The header of each block can include the hash values ​​of all transactions in the block as well as the hash values ​​of all transactions in the previous block. The records of newly generated transactions are filled into the block and, after consensus among the nodes in the blockchain network, are appended to the end of the blockchain to form a chain growth. The chain structure between blocks based on hash values ​​ensures that transactions in the block are tamper-proof and anti-forgery.

[0189] The following describes an exemplary functional architecture of the blockchain network provided by an embodiment of the present invention. Figure 10 , Figure 10 2 is a functional architecture diagram of a blockchain network 200 provided in an embodiment of the present invention, including an application layer 201, a consensus layer 202, a network layer 203, a data layer 204, and a resource layer 205, which are described below respectively.

[0190] The resource layer 205 encapsulates the computing resources, storage resources, and communication resources of each node 210 in the blockchain network 200.

[0191] The data layer 204 encapsulates various data structures that implement the ledger, including the blockchain implemented as files in the file system, the key-value state database, and the existence proof (such as the hash tree of transactions in the block).

[0192] The network layer 203 encapsulates the functions of point-to-point (P2P) network protocol, data transmission mechanism and data verification mechanism, access authentication mechanism and business subject identity management.

[0193] Among them, the P2P network protocol realizes the communication between the nodes 210 in the blockchain network 200, the data propagation mechanism ensures the propagation of transactions in the blockchain network 200, and the data verification mechanism is used to realize the reliability of data transmission between nodes 210 based on cryptographic methods (such as digital certificates, digital signatures, public / private key pairs); the access authentication mechanism is used to authenticate the identity of the business subject joining the blockchain network 200 according to the actual business scenario, and grant the business subject the right to access the blockchain network 200 when the authentication is passed; the business subject identity management is used to store the identity of the business subject allowed to access the blockchain network 200, and the authority (such as the type of transaction that can be initiated).

[0194] The consensus layer 202 encapsulates the mechanism by which nodes 210 in the blockchain network 200 reach consensus on blocks (i.e., the consensus mechanism), as well as transaction management and ledger management functions. Consensus mechanisms include consensus algorithms such as POS, POW, and DPOS, and support pluggable consensus algorithms.

[0195] Transaction management is used to verify the digital signature carried in the transaction received by the verification node 210, verify the identity information of the business subject, and determine whether it has the authority to conduct the transaction based on the identity information (read relevant information from the business subject identity management); for business subjects that have obtained authorization to access the blockchain network 200, they all have digital certificates issued by the certification center. The business subject uses the private key in its own digital certificate to sign the submitted transaction, thereby declaring its legal identity.

[0196] Ledger management is used to maintain the blockchain and state database. Consensus-reached blocks are appended to the end of the blockchain. Transactions in the consensus-reached blocks are executed. When the transaction includes an update operation, the key-value pairs in the state database are updated. When the transaction includes a query operation, the key-value pairs in the state database are queried and the query results are returned to the client node of the business entity. Multiple query operations on the state database are supported, including: querying blocks based on block vector numbers (e.g., transaction hash values); querying blocks based on block hash values; querying blocks based on transaction vector numbers; querying transactions based on transaction vector numbers; querying business entity account data based on their account numbers (vector numbers); and querying the blockchain in a channel based on the channel name.

[0197] The application layer 201 encapsulates various services that can be implemented by the blockchain network, including transaction traceability, evidence storage, and verification.

[0198] Therefore, when the same user uses the data chain application process to process information through different terminals, the cloud server can push the target object identifier, the data chain matching the target object, and the information processing results matching the data chain stored in the blockchain network to the terminal in a timely manner, thereby speeding up the information processing efficiency, reducing waiting time, and improving the user experience.

[0199] In order to more accurately introduce the information processing method based on data link provided by the present application, the information processing method based on data link provided by the present invention is described below by taking government information processing as an example, wherein: Figure 11 This is a schematic diagram of the event asset features in the data chain-based information processing method in an embodiment of the present invention. In this embodiment, event assets refer to the capture of various events driven by the behavior of C-end users using government services, including information such as usage time, user, usage duration, used functions, and used services; Figure 11Ultimately, the current status, trends and problems of government organizations in the development of government services can be evaluated from a holistic perspective, including but not limited to the ability of government organizations to provide services to the public, their internal business collaboration capabilities, their own ecological construction and cooperation capabilities, and their own internal driving force for digital transformation. Specifically, in the event asset feature vector, it can be further classified according to different dimensions, including: user asset feature vector, participation asset feature vector, public asset feature vector, business asset feature vector, content asset feature vector, and activation asset feature vector. Specifically: the user asset feature vector can be determined by the number of people involved in government information processing, number of times, activity, retention rate, churn rate, return rate, channel, equipment, distribution and user stickiness; the participation asset feature vector can be determined by the number of participations, participation channels, and participation portraits; the public asset feature vector can be determined by the number of perceptions, perception channels, and perception portraits; the business asset feature vector can be determined by the number of times people participate in government information processing, activity, activity trends, business stickiness, business user portraits and other information; the content asset feature vector can be determined by activity trends, business stickiness, business user portraits, time content portraits and other information; the activation asset feature vector can be determined by channels, feedback, points, recommendations and other features.

[0200] Figure 12 This is a schematic diagram of the tool asset features in the data chain-based information processing method in an embodiment of the present invention. The administrative department can process the corresponding government information through different tool asset sub-features in the tool asset features. Among them, the tool asset features include: content standardization feature vectors, value feature vectors, asset feature vectors, exchange standard feature vectors, supply and demand feature vectors, and other feature vectors. Specifically, the content standardization feature vector includes: natural person information, legal person information, investment project information, service item information, application information, material information and corresponding acceptance information involved in government information. Referring to Table 1, taking the approval item processing result information as an example, through the information processing shown in Table 1, the tool asset feature vector can be obtained according to the approval items of different dimensions.

[0201] Table 1

[0202]

[0203]

[0204] refer to Figure 13 and Figure 14 , Figure 13 Schematic diagram of the cognitive asset features in the data chain-based information processing method according to an embodiment of the present invention. Figure 14 Schematic diagram of cognitive asset features in the information processing method based on data chain in an embodiment of the present invention. Administrative departments provide different government information services to users through different cognitive asset features, such as Figure 13 As shown in Figure 2, the first cognitive tool asset characteristics include: application ecosystem maturity characteristic vector, online service maturity characteristic vector, online processing depth characteristic vector, service mode completeness characteristic vector, service item coverage characteristic vector and service guide accuracy characteristic vector. Referring to Table 2, taking the online service maturity characteristic vector as an example, the online service maturity characteristic vector is composed of parameters of different dimensions, including: user usage parameters, service timeliness parameters, special service parameters, service popularity parameters and service extension parameters.

[0205] Table 2

[0206]

[0207]

[0208] like Figure 13 As shown, for the units that undertake government information, the second cognitive tool asset characteristics include: digital asset feature vector, digital dependency feature vector, process dependency feature vector, process dependency feature vector, communication characteristic feature vector and intelligent analysis feature vector. Referring to Table 3, taking the digital asset feature vector as an example, it can be measured through the calculation specifications of different dimensions included in the service guide release situation B2 and standardization degree B3 shown in Table 3.

[0209] Table 3

[0210]

[0211]

[0212] pass Figures 11 to 14 After obtaining the cognitive asset feature vector, event asset feature vector, and tool asset feature vector in the process of government information processing, the government information can be processed by the information processing method of the data chain provided in this application. Figure 15 An optional flow chart of the information processing method based on the data link provided in the embodiment of the present invention is as follows: Figure 15 As shown, the target objects involved in government information processing include but are not limited to government organizations. The government information processed may include identity card information, tax certificate information, legal person certificate information, and corresponding government documents (file collection) to be approved that match the target objects. The specific steps include:

[0213] Step 1501: The government organization node obtains information on government activities through the government information network.

[0214] Step 1502: The government organization node determines the cognitive asset feature vector, event asset feature vector, and tool asset feature vector of the government activity according to the information of the government activity and the processing specification.

[0215] Step 1503: The government organization node processes the cognitive asset feature vector, event asset feature vector, and tool asset feature vector of the government activity to obtain a government information data chain.

[0216] Step 1504: The government organization node receives government information processing requests from different target user nodes.

[0217] Step 1505: The government organization node obtains the government information processing result based on the cognitive asset feature vector, event asset feature vector and tool asset feature in the government information data chain.

[0218] Step 1506: The government organization node transmits the government information processing results to the target user node.

[0219] The present invention has the following beneficial technical effects:

[0220] By obtaining the activity information of the target object; based on the activity information of the target object, determining the cognitive asset feature vector, event asset feature vector and tool asset feature vector corresponding to the activity information; determining the data chain that matches the target object based on the cognitive asset feature vector, event asset feature vector and tool asset feature vector corresponding to the activity information; when the activity information of the target object changes, determining the relationship between different asset feature vectors in the data chain; based on the relationship between different asset feature vectors in the data chain, determining the information processing result that matches the data chain. In this way, the activity information of the target object is fully represented through the data chain, ensuring the target object's activity information, realizing efficient processing of the activity information, and at the same time realizing the overall analysis of the various activity information processing in social activities through the data chain, and utilizing the cognitive asset feature vector, event asset feature vector and tool asset feature vector corresponding to the activity information to enhance the macro analysis of information processing.

[0221] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data link-based information processing method, characterized in that: The method comprises: Get the target object's activity information; determining a cognitive asset feature vector corresponding to the activity information based on a change in a cognitive ability parameter in the activity information of the target object; Extracting interaction records of different element events in the activity information of the target object, and determining a cognitive asset feature vector corresponding to the activity information; Acquire tool information used in the execution process of the activity information of the target object, and determine a tool asset feature vector corresponding to the activity information based on the tool information; Determining a data chain that matches the target object based on the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector corresponding to the activity information; When the activity information of the target object changes, determining the relationship between different asset feature vectors in the data chain; Based on the relationship between different asset feature vectors in the data chain, an information processing result matching the data chain is determined, wherein the information processing result is used to characterize the activity processing effect of the activity information of the target object in different data processing dimensions.

2. The method according to claim 1, characterized in that The method further comprises: determining environmental characteristics of the target object; In response to the environmental characteristics, the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector corresponding to the activity information are randomly combined to determine particle state parameters that match the target object.

3. The method according to claim 2, characterized in that The method further comprises: When the environmental characteristics of the target object change, Determine the spatiotemporal changes in the particle state parameters matching the target object based on the change characteristics of the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector; Based on the spatiotemporal changes of the particle state parameters, the wave state parameters that match the target object are determined.

4. The method according to claim 3, characterized in that The method further comprises: According to the grouping vectors of the particle state parameters and the wave state parameters, the first wave particle property parameter corresponding to the target object is determined.

5. The method according to claim 1, wherein The method further comprises: Determining, based on the activity information of the target object, the number of different target objects participating in the activity information; Based on the number of different target objects, the living space parameters are determined.

6. The method according to claim 5, characterized in that The determining of a data chain matching the target object according to the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector corresponding to the activity information includes: Determining, based on the living space parameters, the number of target objects participating in different dimensions of the living space parameters; Determining corresponding data units based on the number of target objects of different dimensions in the living space parameter and the activity information; When the activity information of the target object changes, the flow information of the data in the data unit is determined according to the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector corresponding to the activity information; Based on the flow information of the data in the data unit, a data link matching the target object and the environmental parameters of the data link are determined.

7. The method according to claim 1, characterized in that The determining of an information processing result matching the data chain based on a relationship between different asset feature vectors in the data chain includes: When the relationship between different asset feature vectors in the data chain is an internal relationship pattern, the target object is adjusted relative to the corresponding first-wave granularity parameter based on the cognitive asset feature vector to determine the second-wave granularity parameter.

8. The method according to claim 1, characterized in that The determining of an information processing result matching the data chain based on a relationship between different asset feature vectors in the data chain includes: When the relationship between different asset feature vectors in the data chain is a collaborative relationship mode, the target object is adjusted relative to the corresponding first-wave granularity parameters based on the cognitive asset feature vector and the event asset feature vector to determine the third-wave granularity parameters.

9. The method according to claim 1, characterized in that The determining of an information processing result matching the data chain based on a relationship between different asset feature vectors in the data chain includes: When the relationship between different asset feature vectors in the data chain is a cooperative relationship mode, the target object is adjusted relative to the corresponding first-wave granularity parameter based on the event asset feature vector to determine the fourth-wave granularity parameter.

10. The method according to claim 1, characterized in that The determining of an information processing result matching the data chain based on a relationship between different asset feature vectors in the data chain includes: When the relationship between different asset feature vectors in the data chain is a cooperative relationship model, the target object is adjusted relative to the corresponding first-wave granularity parameters based on the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector to determine the fifth-wave granularity parameters.

11. The method according to any one of claims 1 to 10, characterized in that: The method further comprises: The target object identification, the activity information matching the target object and the information processing results matching the data chain are sent to the blockchain network so that The nodes of the blockchain network fill the target object identifier, the data chain matching the target object, and the information processing results matching the data chain into a new block, and when a consensus is reached on the new block, the new block is appended to the end of the blockchain.

12. An information processing device based on a data link, characterized in that: The device comprises: Information transmission module, used to obtain activity information of target objects; An information processing module is configured to determine a cognitive asset feature vector corresponding to the activity information based on changes in cognitive ability parameters in the activity information of the target object; extract interaction records of different element events in the activity information of the target object to determine a cognitive asset feature vector corresponding to the activity information; obtain tool information used in the execution of the activity information of the target object, and determine a tool asset feature vector corresponding to the activity information based on the tool information; The information processing module is configured to determine a data link that matches the target object based on the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector corresponding to the activity information; The information processing module is configured to determine the relationship between different asset feature vectors in the data chain when the activity information of the target object changes; The information processing module is used to determine an information processing result that matches the data chain based on the relationship between different asset feature vectors in the data chain.

13. The device according to claim 12, characterized in that The information processing module is further used to determine the environmental characteristics of the target object; in response to the environmental characteristics, randomly combine the cognitive asset feature vector, event asset feature vector and tool asset feature vector corresponding to the activity information to determine the particle state parameters that match the target object.

14. The device according to claim 13, characterized in that The information processing module is also used to determine the spatiotemporal changes in the particle state parameters matching the target object based on the change characteristics of the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector when the environmental characteristics of the target object change; and determine the wave state parameters matching the target object based on the spatiotemporal changes in the particle state parameters.

15. The device according to claim 14, characterized in that The information processing module is further configured to determine the first wave particle property parameter corresponding to the target object based on the grouping vector of the particle state parameter and the wave state parameter.

16. The device according to claim 12, characterized in that The information processing module is further configured to determine the number of different target objects participating in the activity information based on the activity information of the target object; and determine a living space parameter based on the number of different target objects.

17. The device according to claim 16, characterized in that The information processing module is further used to determine, based on the living space parameters, the number of target objects participating in different dimensions of the living space parameters; determine the corresponding data unit based on the number of target objects in different dimensions of the living space parameters and the activity information; when the activity information of the target object changes, determine the flow information of the data in the data unit based on the cognitive asset feature vector, event asset feature vector and tool asset feature vector corresponding to the activity information; based on the flow information of the data in the data unit, determine the data chain matching the target object and the environmental parameters of the data chain.

18. The device according to claim 12, characterized in that The information processing module is further configured to adjust the corresponding first-wave granularity parameters of the target object based on the cognitive asset feature vectors to determine second-wave granularity parameters when the relationship between different asset feature vectors in the data chain is an internal relationship pattern.

19. The device according to claim 12, characterized in that The information processing module is further configured to adjust the corresponding first-wave granularity parameters of the target object based on the cognitive asset feature vector and the event asset feature vector to determine third-wave granularity parameters when the relationship between different asset feature vectors in the data chain is a collaborative relationship mode.

20. The device according to claim 12, characterized in that The information processing module is further configured to adjust the target object relative to the corresponding first-wave granularity parameter based on the event asset feature vector to determine a fourth-wave granularity parameter when the relationship between different asset feature vectors in the data link is a cooperative relationship mode.

21. The device according to claim 12, characterized in that The information processing module is further configured to adjust the corresponding first-wave granularity parameters of the target object based on the cognitive asset feature vector, the event asset feature vector, and the tool asset feature vector to determine the fifth-wave granularity parameters when the relationship between different asset feature vectors in the data chain is a cooperative relationship model.

22. The device according to any one of claims 12 to 21, characterized in that The information processing module is further used to send the target object identifier, the activity information matching the target object, and the information processing results matching the data chain to the blockchain network, so that the nodes of the blockchain network fill the target object identifier, the activity information matching the target object, and the information processing results matching the data chain into a new block, and when consensus is reached on the new block, the new block is appended to the end of the blockchain.

23. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; A processor, configured to implement the data link-based information processing method according to any one of claims 1 to 11 when running the executable instructions stored in the memory.

24. A computer-readable storage medium storing executable instructions, characterized in that: When the executable instructions are executed by the processor, the data link-based information processing method according to any one of claims 1 to 11 is implemented.

25. A computer program product storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the data link-based information processing method according to any one of claims 1 to 11 is implemented.

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