Information sharing method and system based on block chain

Abstract

The invention discloses an information sharing method and system based on a block chain, and the method comprises the steps: collecting user demands and time sequence information data, and carrying out the preprocessing of the user demands; obtaining an information sharing type according to the query permission of the time sequence information data, customizing a sharing strategy according to the user demand and the information sharing type, and performing association matching on the user demand and the time sequence information data to obtain first query data; correcting the first query data according to the sharing strategy to obtain second query data, and constructing a time sequence information data information sharing model according to the first query data and the second query data; and optimizing the time sequence information data information sharing model, inputting a to-be-queried request into the optimized time sequence information data information sharing model, and outputting a sharing result. The method not only can improve the precision of information sharing based on the block chain, but also can be directly applied to an information sharing system based on the block chain.

Description

Technical Field

[0001] The present invention relates to the field of information sharing, and in particular to a blockchain-based information sharing method and system. Background Art

[0002] With the rapid development of Internet technology, information sharing has become an indispensable part of modern society. However, traditional information sharing methods often face challenges in data security, privacy protection, and data consistency. As a distributed ledger technology, time series information data technology provides a new approach to solving these problems with its decentralized, tamper-proof, and transparent characteristics.

[0003] At present, the application of time series information data technology in the field of information sharing is still in the exploratory stage. Although there have been some attempts to use time series information data for data sharing systems, most of these systems focus on the basic storage function of time series information data, while ignoring issues such as query efficiency, authority control, and data accuracy in the information sharing process. Especially when dealing with a large number of user needs, how to quickly and accurately retrieve the required information from the time series information data and intelligently match it according to the user's authority and data sharing strategy is a technical problem that needs to be solved in the current field of time series information data sharing. Summary of the invention

[0004] The purpose of the present invention is to provide an information sharing method based on blockchain.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions: The present invention comprises the following steps: Collecting user needs and time series information data, and preprocessing the user needs; Obtaining an information sharing type according to the query authority of the time series information data, customizing a sharing strategy according to the user requirements and the information sharing type, and performing correlation matching between the user requirements and the time series information data to obtain first query data; Correcting the first query data according to the sharing strategy to obtain second query data, and constructing a time series information data sharing model according to the first query data and the second query data; The time series information data sharing model is optimized, the query request is input into the optimized time series information data sharing model, and the sharing result is output.

[0006] Furthermore, the method for customizing the sharing strategy according to the user needs and the information sharing type includes: Extract keywords and semantic information of user needs, and set recommended information browsing according to user identity permissions; When the user's identity permission allows browsing, the correlation between user needs and time series information is calculated based on the bidirectional long short-term memory network: , , The cth user demand vector is , the cth time series information data vector is , transposed to T, the cth bias is , the number of semantic information vectors of user needs is , the amount of time series data is , the importance of the cth semantic information is , the word vector embedding matrix is , user demand vector and timing information data information vector The correlation is , the cth keyword of user demand is , the cth keyword of the time series data is , the number of keywords required by users is , the number of keywords in the time series data is ; The relevance is sorted in descending order, and the first five are output as personalized blockchain shared information; when the user identity authority does not allow browsing, the relevance of the semantic information and the remaining time series information data is calculated, and the remaining time series information data with a relevance greater than 0.517 is output as personalized blockchain shared information.

[0007] Furthermore, the method of associating and matching the user demand and the time series information data to obtain the first query data includes: Input user requirements into the association matching model based on time series information data and calculate the time step output of user requirements: , The forward LSTM network is , the reverse LSTM network is , the j-th time step output of the user demand is , the word vector corresponding to the jth word in user demand is , the weight parameter of the jth word is , the bias parameter of the time step is , the forward hidden vector at the s-1th moment is , the reverse hidden vector at the s-1th moment is ; Concatenate all time step outputs to obtain the sentence matrix of user needs, calculate the attention weight of the word vector and the sentence feature vector of user needs: , , The attention weight of the jth word vector is , time step output h attention weight The linear layer parameters are , transposed to T, the activation function is , the output sentence matrix is , the sentence concatenation attention weight of user demand is , the sentence feature vector of user demand is ; Linearly concatenate the attention weights according to the order of words and calculate the similarity between the sentence feature vector and the keyword: , The jth keyword of the time series information data is , keywords The output sentence feature vector is , sentence feature vector With keywords The similarity is , the number of words required by users is ; Keywords with similarity greater than 0.6135 are used as candidate keywords, and the time series information data corresponding to the candidate keywords are used as candidate information; Calculate the information feature vector of user needs: , The information feature vector of user demand is , the number of keywords in the candidate information is , the mean of the relative distance between user needs and candidate keywords in time series is ; The time series information data whose information characteristic vector is greater than 0.774 is output as the first query data.

[0008] Furthermore, the method for obtaining the second query data includes: The first query data that does not satisfy the sharing strategy is eliminated to obtain the second query data.

[0009] Furthermore, a method for constructing a time series information data sharing model according to the first query data and the second query data includes: The objective function of the time series information sharing model is constructed based on the second query data and the first query data. The expression is: , Among them The objective function of this test is , the first weight is , the second weight is , No. The second query data for the test is , No. The first query data of the test , No. The actual shared results of this test are , No. The predicted shared results of the test are ; The time series information data sharing model includes timestamp incremental update algorithm, neural network matching classification algorithm, and consensus algorithm; The neural network matching classification algorithm automatically extracts key information by learning the feature representation and pattern of the input data, and obtains classified data by matching and classifying according to the similarity of the key information; The timestamp incremental update algorithm records the maximum timestamp of each completed reading, and only obtains the newly added classification data in the source table during subsequent readings, and writes the classification data to the blockchain to obtain updated data; The consensus algorithm establishes a trust relationship between distributed nodes that have no trust in each other through voting by special nodes based on updated data, and achieves common recognition and information sharing of the blockchain status according to the objective function.

[0010] Furthermore, the method for optimizing the time series information data sharing model includes: Introducing particle population, taking particles as candidate solutions in the problem search space, and taking the inverse of information sharing error as the fitness function; Calculate the particle fitness, take the particle with the largest fitness as the optimal solution, and calculate the particle vector: , The random number between 0 and 1 is , the bth particle vector is , the upper limit of the bth particle vector is , the lower limit of the bth particle vector is ; Calculate the position of the particle: , , The position of the bth particle at the t+1th iteration is , the b-th particle vector of the t-th iteration is , the movement step length is , the optimal solution for the tth iteration is , the normal random value is p, and the spiral constant is , the constant coefficient is , the random walk coefficient of the tth iteration is , the random numbers from 0 to 1 are , , , , 0 to The random value is , a random number from 0 to 2 is , the gamma function is ; Update the particle position to obtain the predation position, the expression is: , , The predation position of the bth particle in the t+1th iteration is , the position of the bth particle at the tth iteration is , the step size adjustment parameter is , the current number of iterations is t, and the maximum number of iterations is , a random number from 0 to 1 is ; Adaptive weights are used to update the prey position to obtain the adapted position, which is expressed as: , , The adaptive weight of the tth iteration is , the adaptation position of the bth particle in the t+1th iteration is , the predation position of the bth particle in the tth iteration is , a random number from 0 to 1 is ; Iterate continuously until the maximum number of iterations is reached, otherwise update the particles and search for the optimal solution again.

[0011] In the second aspect, a blockchain-based information sharing system includes: Data collection module: used to collect user demand and time series information data, and pre-process the user demand; A customized matching module: used for obtaining an information sharing type according to the time series information data, customizing a sharing strategy according to the user requirements and the information sharing type, and performing an associative matching between the user requirements and the time series information data to obtain first query data; A correction and construction module: used for correcting the first query data according to the sharing strategy to obtain second query data, and constructing a time series information data sharing model according to the first query data and the second query data; Optimization output module: used to optimize the time series information data sharing model, input the query request into the optimized time series information data sharing model, and output the sharing result.

[0012] In a third aspect, an embodiment of the present application further provides an electronic device, including: A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0013] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0014] The beneficial effects of the present invention are: The present invention is a blockchain-based information sharing method and system. Compared with the prior art, the present invention has the following technical effects: The present invention can improve the accuracy of information sharing of blockchain through preprocessing, obtaining information sharing type, customizing sharing strategy, association matching, data correction, model construction and model optimization steps, thereby improving the accuracy of information sharing of blockchain, optimizing the information sharing of blockchain, greatly saving resources, and improving work efficiency. It can realize automatic sharing of information on blockchain, perform data correction and customize sharing strategy for information sharing of blockchain in real time, which is of great significance to information sharing of blockchain, can adapt to the information sharing of blockchains of different standards and the information sharing needs of different blockchains, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flowchart of a method for sharing information based on blockchain in the present invention; Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0016] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0017] The present invention provides a blockchain-based information sharing method and system, comprising the following steps: like Figure 1 As shown, in this embodiment, the following steps are included: Collecting user needs and time series information data, and preprocessing the user needs; In the actual evaluation, the blockchain production data update in the XXX area is taken as the research object; the user demand of ordinary worker A is: to query the yield rate and production line capacity of a certain production link in the past week; The time series information data includes the yield rate, production capacity, loss, and remaining material quantity of production link 1 and production link 2; Obtaining an information sharing type according to the query authority of the time series information data, customizing a sharing strategy according to the user requirements and the information sharing type, and performing correlation matching between the user requirements and the time series information data to obtain first query data; In the actual evaluation, the information sharing types are divided into public time series information data, private time series information data and alliance time series information data; the authority level of production link 1 is public, and the authority level of production link 2 is internal; The first query data is the yield rate and production capacity of production link 1 and production link 2; Correcting the first query data according to the sharing strategy to obtain second query data, and constructing a time series information data sharing model according to the first query data and the second query data; In the actual evaluation, the second query data is the yield rate and production capacity of production link 1; The time series information data sharing model is optimized, the query request is input into the optimized time series information data sharing model, and the sharing result is output.

[0018] In this embodiment, the method for customizing the sharing strategy according to the user needs and the information sharing type includes: Extract keywords and semantic information of user needs, and set recommended information browsing according to user identity permissions; When the user's identity permission allows browsing, the correlation between user needs and time series information is calculated based on the bidirectional long short-term memory network: , , The cth user demand vector is , the cth time series information data vector is , transposed to T, the cth bias is , the number of semantic information vectors of user needs is , the amount of time series data is , the importance of the cth semantic information is , the word vector embedding matrix is , user demand vector and timing information data information vector The correlation is , the cth keyword of user demand is , the cth keyword of the time series data is , the number of keywords required by users is , the number of keywords in the time series data is ; The relevance is sorted in descending order, and the first five are output as personalized blockchain shared information; when the user identity authority does not allow browsing, the relevance of the semantic information and the remaining time series information data is calculated, and the remaining time series information data with a relevance greater than 0.517 is output as personalized blockchain shared information.

[0019] In this embodiment, the method for obtaining first query data by associating and matching the user demand and the time series information data includes: Input user requirements into the association matching model based on time series information data and calculate the time step output of user requirements: , The forward LSTM network is , the reverse LSTM network is , the j-th time step output of the user demand is , the word vector corresponding to the jth word in user demand is , the weight parameter of the jth word is , the bias parameter of the time step is , the forward hidden vector at the s-1th moment is , the reverse hidden vector at the s-1th moment is ; Concatenate all time step outputs to obtain the sentence matrix of user needs, calculate the attention weight of the word vector and the sentence feature vector of user needs: , , The attention weight of the jth word vector is , time step output h attention weight The linear layer parameters are , transposed to T, the activation function is , the output sentence matrix is , the sentence concatenation attention weight of user demand is , the sentence feature vector of user demand is ; Linearly concatenate the attention weights according to the order of words and calculate the similarity between the sentence feature vector and the keyword: , The jth keyword of the time series information data is , keywords The output sentence feature vector is , sentence feature vector With keywords The similarity is , the number of words required by users is ; Keywords with similarity greater than 0.6135 are used as candidate keywords, and the time series information data corresponding to the candidate keywords are used as candidate information; Calculate the information feature vector of user needs: , The information feature vector of user demand is , the number of keywords in the candidate information is , the mean of the relative distance between user needs and candidate keywords in time series is ; The time series information data whose information characteristic vector is greater than 0.774 is output as the first query data.

[0020] In this embodiment, the method for obtaining the second query data includes: The first query data that does not satisfy the sharing strategy is eliminated to obtain the second query data.

[0021] In this embodiment, the method for constructing a time series information data sharing model according to the first query data and the second query data includes: The objective function of the time series information sharing model is constructed based on the second query data and the first query data. The expression is: , Among them The objective function of this test is , the first weight is , the second weight is , No. The second query data for the test is , No. The first query data of the test , No. The actual shared results of this test are , No. The predicted shared results of the test are ; The time series information data sharing model includes timestamp incremental update algorithm, neural network matching classification algorithm, and consensus algorithm; The neural network matching classification algorithm automatically extracts key information by learning the feature representation and pattern of the input data, and obtains classified data by matching and classifying according to the similarity of the key information; The timestamp incremental update algorithm records the maximum timestamp of each completed reading, and only obtains the newly added classification data in the source table during subsequent readings, and writes the classification data to the blockchain to obtain updated data; The consensus algorithm establishes a trust relationship between distributed nodes that have no trust in each other through voting by special nodes based on updated data, and achieves common recognition and information sharing of the blockchain status according to the objective function.

[0022] In this embodiment, the method for optimizing the time series information data sharing model includes: Introducing particle population, taking particles as candidate solutions in the problem search space, and taking the inverse of information sharing error as the fitness function; Calculate the particle fitness, take the particle with the largest fitness as the optimal solution, and calculate the particle vector: , The random number between 0 and 1 is , the bth particle vector is , the upper limit of the bth particle vector is , the lower limit of the bth particle vector is ; Calculate the position of the particle: , , The position of the bth particle at the t+1th iteration is , the b-th particle vector of the t-th iteration is , the movement step length is , the optimal solution for the tth iteration is , the normal random value is p, and the spiral constant is , the constant coefficient is , the random walk coefficient of the tth iteration is , the random numbers from 0 to 1 are , , , , 0 to The random value is , a random number from 0 to 2 is , the gamma function is ; Update the particle position to obtain the predation position, the expression is: , , The predation position of the bth particle in the t+1th iteration is , the position of the bth particle at the tth iteration is , the step size adjustment parameter is , the current number of iterations is t, and the maximum number of iterations is , a random number from 0 to 1 is ; Adaptive weights are used to update the prey position to obtain the adapted position, which is expressed as: , , The adaptive weight of the tth iteration is , the adaptation position of the bth particle in the t+1th iteration is , the predation position of the bth particle in the tth iteration is , a random number from 0 to 1 is ; Iterate continuously until the maximum number of iterations is reached, otherwise update the particles and search for the optimal solution again.

[0023] In the second aspect, a blockchain-based information sharing system includes: Data collection module: used to collect user demand and time series information data, and pre-process the user demand; A customized matching module: used for obtaining an information sharing type according to the time series information data, customizing a sharing strategy according to the user requirements and the information sharing type, and performing an associative matching between the user requirements and the time series information data to obtain first query data; A correction and construction module: used for correcting the first query data according to the sharing strategy to obtain second query data, and constructing a time series information data sharing model according to the first query data and the second query data; Optimization output module: used to optimize the time series information data sharing model, input the query request into the optimized time series information data sharing model, and output the sharing result.

[0024] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0025] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0026] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0027] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a blockchain-based information sharing device at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the aforementioned blockchain-based information sharing methods.

[0028] The above application Figure 1The blockchain-based information sharing method disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in software form. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0029] The electronic device may also perform Figure 1 A blockchain-based information sharing method is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0030] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, any one of the aforementioned blockchain-based information sharing methods is executed.

[0031] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0032] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0033] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0034] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0035] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0036] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0037] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0038] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0039] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0040] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A blockchain-based information sharing method, characterized in that: The following steps are involved: Collecting user needs and time series information data, and preprocessing the user needs; Obtaining an information sharing type according to the query authority of the time series information data, customizing a sharing strategy according to the user requirements and the information sharing type, and performing correlation matching between the user requirements and the time series information data to obtain first query data; Correcting the first query data according to the sharing strategy to obtain second query data, and constructing a time series information data sharing model according to the first query data and the second query data; The time series information data sharing model is optimized, the query request is input into the optimized time series information data sharing model, and the sharing result is output.

2. According to claim 1, a blockchain-based information sharing method is characterized in that: The method for customizing a sharing strategy according to the user requirements and the information sharing type includes: Extract keywords and semantic information of user needs, and set recommended information browsing according to user identity permissions; When the user's identity permission allows browsing, the correlation between user needs and time series information is calculated based on the bidirectional long short-term memory network: ， ， The cth user demand vector is , the cth time series information data vector is , transposed to T, the cth bias is , the number of semantic information vectors of user needs is , the amount of time series data is , the importance of the cth semantic information is , the word vector embedding matrix is , user demand vector and timing information data information vector The correlation is , the cth keyword of user demand is , the cth keyword of the time series data is , the number of keywords required by users is , the number of keywords in the time series data is ; The relevance is sorted in descending order, and the first five are output as personalized blockchain shared information; when the user identity authority does not allow browsing, the relevance of the semantic information and the remaining time series information data is calculated, and the remaining time series information data with a relevance greater than 0.517 is output as personalized blockchain shared information.

3. According to the information sharing method based on blockchain in claim 1, it is characterized in that: The method for obtaining first query data by associating and matching the user demand and the time series information data includes: Input user requirements into the association matching model based on time series information data and calculate the time step output of user requirements: ， The forward LSTM network is , the reverse LSTM network is , the j-th time step output of the user demand is , the word vector corresponding to the jth word in user demand is , the weight parameter of the jth word is , the bias parameter of the time step is , the forward hidden vector at the s-1th moment is , the reverse hidden vector at the s-1th moment is ; Concatenate all time step outputs to obtain the sentence matrix of user needs, calculate the attention weight of the word vector and the sentence feature vector of user needs: ， ， The attention weight of the jth word vector is , time step output h attention weight The linear layer parameters are , transposed to T, the activation function is , the output sentence matrix is , the sentence concatenation attention weight of user demand is , the sentence feature vector of user demand is ; Linearly concatenate the attention weights according to the order of words and calculate the similarity between the sentence feature vector and the keyword: ， The jth keyword of the time series information data is , keywords The output sentence feature vector is , sentence feature vector With keywords The similarity is , the number of words required by users is ; Keywords with similarity greater than 0.6135 are used as candidate keywords, and the time series information data corresponding to the candidate keywords are used as candidate information; Calculate the information feature vector of user needs: ， The information feature vector of user demand is , the number of keywords in the candidate information is , the mean of the relative distance between user needs and candidate keywords in time series is ; The time series information data whose information characteristic vector is greater than 0.774 is output as the first query data.

4. According to claim 1, a blockchain-based information sharing method is characterized in that: The method for constructing a time series information data sharing model according to the first query data and the second query data includes: The objective function of the time series information sharing model is constructed based on the second query data and the first query data. The expression is: ， Among them The objective function of this test is , the first weight is , the second weight is , No. The second query data for the test is , No. The first query data of the test , No. The actual shared results of this test are , No. The predicted shared results of the test are ; The time series information data sharing model includes timestamp incremental update algorithm, neural network matching classification algorithm, and consensus algorithm; The neural network matching classification algorithm automatically extracts key information by learning the feature representation and pattern of the input data, and obtains classified data by matching and classifying according to the similarity of the key information; The timestamp incremental update algorithm records the maximum timestamp of each completed reading, and only obtains the newly added classification data in the source table during subsequent readings, and writes the classification data to the blockchain to obtain updated data; The consensus algorithm establishes a trust relationship between distributed nodes that have no trust in each other through voting by special nodes based on updated data, and achieves common recognition and information sharing of the blockchain status according to the objective function.

5. According to claim 1, a blockchain-based information sharing method is characterized in that: The method for optimizing the time series information data sharing model comprises: Introducing particle population, taking particles as candidate solutions in the problem search space, and taking the inverse of information sharing error as the fitness function; Calculate the particle fitness, take the particle with the largest fitness as the optimal solution, and calculate the particle vector: ， The random number between 0 and 1 is , the bth particle vector is , the upper limit of the bth particle vector is , the lower limit of the bth particle vector is ; Calculate the position of the particle: ， ， The position of the bth particle at the t+1th iteration is , the bth particle vector of the tth iteration is , the movement step length is , the optimal solution for the tth iteration is , the normal random value is p, and the spiral constant is , the constant coefficient is , the random walk coefficient of the tth iteration is , the random numbers from 0 to 1 are , , , , 0 to The random value is , a random number from 0 to 2 is , the gamma function is ; Update the particle position to obtain the predation position, the expression is: ， ， The predation position of the bth particle in the t+1th iteration is , the position of the bth particle at the tth iteration is , the step size adjustment parameter is , the current number of iterations is t, and the maximum number of iterations is , a random number from 0 to 1 is ; Adaptive weights are used to update the prey position to obtain the adapted position, which is expressed as: ， ， The adaptive weight of the tth iteration is , the adaptation position of the bth particle in the t+1th iteration is , the predation position of the bth particle in the tth iteration is , a random number from 0 to 1 is ; Iterate continuously until the maximum number of iterations is reached, otherwise update the particles and search for the optimal solution again.

6. A blockchain-based information sharing system for executing the method described in any one of claims 1 to 5, characterized in that: include: Data collection module: used to collect user needs and time series information data, and pre-process the user needs; Customized matching module: used for obtaining the information sharing type according to the time series information data, customizing the sharing strategy according to the user demand and the information sharing type, and performing correlation matching between the user demand and the time series information data to obtain the first query data; A correction and construction module: used for correcting the first query data according to the sharing strategy to obtain second query data, and constructing a time series information data sharing model according to the first query data and the second query data; Optimization output module: used to optimize the time series information data sharing model, input the query request into the optimized time series information data sharing model, and output the sharing result.

7. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 5.

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