Hot data sharing platform based on block chain

Abstract

The invention discloses a hot data sharing platform based on a block chain, and the platform comprises a receiving module which is used for receiving first sharing request information sent by a plurality of data requester nodes; the first sharing request information comprises hot data requester identity information and request content information; the inquiry module is used for inquiring a data directory in a block chain hot data sharing platform based on the request content information and determining an attribute information set of shared hot data; the clustering module is used for clustering the attribute information set of the shared hot data and determining a clustering result; the generation module is used for packaging the hot data requester identity information and the clustering result to generate second sharing request information; the verification module is used for sending the second sharing request information to a data provider for verification; the sharing module is used for sending the complete hot data corresponding to the second sharing request information to the data requester for sharing when the verification is passed; and the hot data sharing efficiency and convenience are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data sharing, and in particular to a hot data sharing platform based on blockchain. Background Art

[0002] Blockchain is a distributed shared digital ledger supported by cryptography and stored in chronological order. Blockchain technology is favored for its many advantages such as decentralization, openness, transparency, and immutability. It has received widespread attention in many fields such as smart contracts, e-commerce, file storage, and data sharing. Among them, according to the frequency of data use, data can be divided into hot data, warm data, and cold data. Hot data refers to data that is frequently queried or updated. Cold data refers to data that is used very infrequently.

[0003] The existing method of hot data sharing is usually that the hot data provider publishes data description information to the blockchain, the hot data demander obtains the hot data description information published by the hot data provider from the blockchain, selects the target hot data, and publishes the hot data permission request to the blockchain. The hot data provider approves the data permission request published by the hot data demander according to specific rules. If the permission is approved, the access right is directly opened to the data demander. However, each data request requires the data provider to approve the permission, which is inefficient and has security risks in the sharing process.

[0004] Therefore, there is an urgent need for a blockchain-based hot data sharing platform to solve such problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems in the above-mentioned technology to a certain extent. To this end, the purpose of the present invention is to propose a hot data sharing platform based on blockchain, which improves the efficiency and convenience of hot data sharing and ensures the security and reliability of data sharing through blockchain technology.

[0006] To achieve the above objectives, the embodiment of the present invention proposes a hot data sharing platform based on blockchain, including:

[0007] A receiving module, configured to receive first sharing request information sent by a plurality of data requesting nodes; the first sharing request information includes the identity information of the hot data requesting party and the request content information;

[0008] A query module, used to query the data directory in the blockchain hot data sharing platform based on the request content information, and determine the attribute information set of the shared hot data;

[0009] A clustering module, used for clustering the attribute information set of the shared thermal data and determining a clustering result;

[0010] A generating module, used for packaging the hot data requester identity information and the clustering result to generate second sharing request information;

[0011] A verification module, used for sending the second sharing request information to the data provider for verification;

[0012] The sharing module is used to send the complete hot data corresponding to the second sharing request information to the data requester for sharing when the verification is passed.

[0013] Preferably, the query module includes:

[0014] A parsing submodule, configured to parse the first sharing request information to obtain first sharing parsing information;

[0015] The first acquisition submodule is used to:

[0016] Obtaining the data type corresponding to the first shared resolution information;

[0017] Based on the data type, a preset data type-smart contract data table is searched to determine the target smart contract data corresponding to the data type;

[0018] Obtain the data directory in the blockchain hot data sharing platform corresponding to the target smart contract data;

[0019] The query submodule is used to query the data directory based on the first shared resolution information to obtain a property information set of the shared hot data.

[0020] Preferably, the clustering module comprises:

[0021] The first determination submodule is used to:

[0022] Mapping the attribute information set of the shared thermal data to a pre-constructed vector space to obtain a plurality of data vectors;

[0023] Randomly select a data in the attribute information set of shared hot data as the target data;

[0024] Taking the target data as the center and the preset distance as the radius, determine the area to be clustered;

[0025] Obtain the sum of the data volumes corresponding to each data in the area to be clustered to obtain the total data volume;

[0026] Obtain the sum of the Euclidean distances between the data vectors corresponding to each data in the area to be clustered, and obtain the sum of the distances;

[0027] The ratio of the total amount of data to the total distance is taken as the density value of the target data;

[0028] Traverse all data in the attribute information set of shared hot data to obtain the density value corresponding to each data;

[0029] Compare the density value with a preset density threshold; use data with a density value greater than or equal to the preset density threshold as initial cluster centers to obtain a plurality of initial cluster centers;

[0030] Randomly select an initial cluster center as the target cluster center; calculate the sum of the Euclidean distances between the target cluster center and other initial cluster centers except the target cluster center to obtain the evaluation value corresponding to the target cluster center; traverse each initial cluster center to obtain several evaluation values;

[0031] Sort a number of evaluation values ​​in descending order to obtain an evaluation value sequence; calculate the difference between any two adjacent evaluation values ​​in the evaluation value sequence to obtain an evaluation value difference; compare the evaluation value difference with a preset evaluation value difference threshold, delete the smaller evaluation value of the two evaluation values ​​corresponding to the evaluation value difference being smaller than the preset evaluation value difference threshold, and obtain a target evaluation value sequence;

[0032] The cluster center corresponding to each evaluation value in the target evaluation value sequence is used as the target cluster center to obtain several target cluster centers;

[0033] The clustering submodule is used to cluster the attribute information set of the shared hot data based on the plurality of target cluster centers to obtain a clustering result.

[0034] Preferably, it also includes: an evaluation module, used to evaluate the clustering result, and send the clustering result to the generation module after the evaluation result is qualified;

[0035] The evaluation module comprises:

[0036] The first evaluation submodule is used to:

[0037] Clustering the attribute information set of the shared hot data based on a plurality of target cluster centers to obtain a clustering result, wherein the clustering result includes a plurality of cluster sets;

[0038] Randomly select two cluster sets as the first cluster set and the second cluster set;

[0039] Evaluate the first cluster set and the second cluster set based on a preset evaluation algorithm to obtain a cluster evaluation value;

[0040] An abnormal marking submodule, used for comparing the cluster evaluation value with a preset evaluation threshold, and marking the first cluster set and the second cluster set corresponding to the cluster evaluation value less than the preset evaluation threshold as abnormal;

[0041] Traverse all cluster sets and count the number of abnormal markers in each cluster set;

[0042] The second determination submodule is used to determine that the clustering result is qualified when it is determined that the maximum value of the number of abnormal marks is less than or equal to a preset mark threshold;

[0043] The sending submodule is used to send the qualified clustering results to the generating module.

[0044] Preferably, the preset evaluation algorithm includes:

[0045]

[0046] Among them, D i，j represents the clustering evaluation value corresponding to the i-th cluster set and the j-th cluster set; H i represents the total number of attribute data in the i-th cluster set; || || represents the norm; Z i，p Represents the characteristic value of the pth attribute data in the i-th cluster set; Represents the mean of the characteristic values ​​between the attribute data in the i-th cluster set; H j Represents the total number of attribute data in the jth cluster set; V j，q Represents the characteristic value of the qth attribute data in the jth cluster set; Represents the mean of the characteristic values ​​between each attribute data in the jth cluster set; Represents the mean of the Euclidean distances between the cluster centers in the i-th cluster set and the cluster centers in the j-th cluster set.

[0047] Preferably, the shared module includes:

[0048] The second acquisition submodule is used to:

[0049] Obtain several data transmission channels between the blockchain hot data sharing platform and the data requester;

[0050] Select any data transmission channel as the first data transmission channel;

[0051] Acquire status information of each transmission node in the first data transmission channel;

[0052] Based on the status information of each transmission node and a preset transmission evaluation algorithm, the first data transmission channel is evaluated to obtain a first data transmission channel evaluation value;

[0053] Comparing the first data transmission channel evaluation value with a preset transmission evaluation threshold;

[0054] When it is determined that the evaluation value of the first data transmission channel is greater than or equal to a preset transmission evaluation threshold, the first data transmission channel is used as a target data transmission channel to obtain a plurality of target data transmission channels;

[0055] Calculating the ratios between the evaluation values ​​of the plurality of target data transmission channels as the data volume allocation weights of the target data transmission channels;

[0056] Obtaining the total data volume of the complete hot data corresponding to the second sharing request information;

[0057] Allocate the total data volume value based on the data volume allocation weight to obtain a target transmission data volume corresponding to each target data transmission channel;

[0058] Based on a preset time algorithm, the target data transmission channel is estimated to complete the transmission of the target transmission data volume;

[0059] Traverse each target data transmission channel to obtain a time estimate for each target data transmission channel;

[0060] The sharing submodule is used to calculate the total value of the time estimate values ​​of all target data transmission channels and compare it with the preset time transmission threshold. If the total value of the time estimate values ​​is less than or equal to the preset time transmission threshold, the complete hot data corresponding to the second sharing request information is transmitted based on the target transmission channel to complete the sharing; if the total value of the time estimate values ​​is greater than the preset time transmission threshold, the backup transmission channel information is obtained for evaluation until the total value of the time estimate values ​​is less than or equal to the preset time transmission threshold.

[0061] Preferably, the preset node evaluation algorithm includes:

[0062]

[0063] Among them, k a represents the evaluation value of the ath data transmission channel; α and β are preset proportional coefficients, and α+β=1; N a represents the total number of transmission nodes in the a-th data transmission channel; S a，i Indicates the amount of data currently being processed by the i-th transmission node in the a-th data transmission channel; Indicates the maximum amount of data carried by the i-th transmission node in the a-th data transmission channel; It is represented by the expected amount of data carried by the i-th transmission node in the a-th data transmission channel when the working stability is the highest; P a，irepresents the performance index of the i-th transmission node in the a-th data transmission channel; A represents the CPU usage rate of the server in the current blockchain hot data sharing platform; B represents the memory usage rate of the server in the current blockchain hot data sharing platform; Indicates the service rate of the server in the current blockchain hot data sharing platform.

[0064] Preferably, the preset time algorithm includes:

[0065]

[0066] Among them, t a represents the estimated time value of the target data transmission channel a; t1 represents the preset expected duration; k a represents the evaluation value of the a-th target data transmission channel; N a represents the total number of transmission nodes in the a-th data transmission channel; Q a，i represents the amount of data allocated when the i-th transmission node in the a-th target data transmission channel processes the target data volume; F a，i represents the total amount of data processed by the i-th transmission node in the a-th target data transmission channel; θ a，i It represents the working efficiency of the i-th transmission node in the a-th target data transmission channel.

[0067] Preferably, it also includes: an update module, which is used to periodically obtain hot data of each data provider node in the blockchain, and update the data directory in the blockchain hot data sharing platform based on the hot data.

[0068] Preferably, the update module includes:

[0069] The third acquisition submodule is used to:

[0070] Periodically obtain the hot data of each data provider node in the blockchain to obtain the data to be updated;

[0071] Obtain the data type of the data to be updated, query the preset data type-smart contract data table based on the data type, and determine the target smart contract data corresponding to the data type information; obtain the target data directory in the blockchain hot data sharing platform corresponding to the target smart contract data;

[0072] The attribute extraction submodule is used to extract the attributes of the data to be updated to obtain target attribute data;

[0073] The update submodule is used to update the target data directory in the blockchain hot data sharing platform based on the target attribute data.

[0074] The present invention discloses a hot data sharing platform based on blockchain. By receiving sharing request information sent by a data requester node, the data directory in the blockchain hot data sharing platform is queried through the sharing request information, the attribute information set of the shared hot data is determined, the attribute information set is clustered, and packaged with identity information, batch verification of data request information is realized, and the hot data is shared after the verification is passed; by receiving and processing data requests, the relevant information is quickly queried and clustered, the required data can be found and shared more efficiently; the request information and data are verified to ensure that the shared data is accurate and reliable, and the propagation of erroneous data is reduced; the verification link of the data provider can increase the control of data sharing and prevent unauthorized access and abuse; the clustering module helps to classify and manage hot data and improve the rationality of data organization and utilization; a convenient sharing path is provided for multiple data requesters, which is conducive to the rapid circulation and utilization of data between different nodes and maximizes the value of data.

[0075] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0076] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0078] Figure 1 is a block diagram of a hot data sharing platform based on blockchain according to an embodiment of the present invention;

[0079] Figure 2 is a block diagram of a query module according to an embodiment of the present invention;

[0080] Figure 3 is a block diagram of a clustering module according to one embodiment of the present invention. DETAILED DESCRIPTION

[0081] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0082] Example 1

[0083] like Figure 1 As shown, a heat data sharing platform based on blockchain includes:

[0084] A receiving module, configured to receive first sharing request information sent by a plurality of data requesting nodes; the first sharing request information includes the identity information of the hot data requesting party and the request content information;

[0085] A query module, used to query the data directory in the blockchain hot data sharing platform based on the request content information, and determine the attribute information set of the shared hot data;

[0086] A clustering module, used for clustering the attribute information set of the shared thermal data and determining a clustering result;

[0087] A generating module, used for packaging the hot data requester identity information and the clustering result to generate second sharing request information;

[0088] A verification module, used for sending the second sharing request information to the data provider for verification;

[0089] The sharing module is used to send the complete hot data corresponding to the second sharing request information to the data requester for sharing when the verification is passed.

[0090] In this embodiment, the attribute information set of shared hot data includes but is not limited to data name, data category, data range, data permission, data storage node and other information.

[0091] In this embodiment, the identity information includes but is not limited to user ID, name, organization name, contact information, etc.

[0092] In this embodiment, the second sharing request information is sent to the data provider for verification, which includes identity authentication and verification of data content in the clustering results, checking whether the user ID exists in the database, whether the timestamp is valid, etc.

[0093] The beneficial effects of the above technical solution are: by receiving the sharing request information sent by the data requester node, querying the data directory in the blockchain hot data sharing platform through the sharing request information, determining the attribute information set of the shared hot data, clustering the attribute information set, and packaging it with the identity information, batch verification of the data request information is realized, and the hot data is shared after the verification is passed; by receiving and processing data requests, quickly querying and clustering related information, it is possible to find the required data more efficiently and share it; verifying the request information and data to ensure that the shared data is accurate and reliable, and reducing the spread of erroneous data; the verification link of the data provider can increase the control of data sharing and prevent unauthorized access and abuse; the clustering module helps to classify and manage hot data and improve the rationality of data organization and utilization; it provides a convenient sharing path for multiple data requesters, which is conducive to the rapid circulation and utilization of data between different nodes, and maximizes the value of data.

[0094] Example 2

[0095] like Figure 2 As shown, the query module includes:

[0096] A parsing submodule, configured to parse the first sharing request information to obtain first sharing parsing information;

[0097] The first acquisition submodule is used to:

[0098] Obtaining the data type corresponding to the first shared resolution information;

[0099] Based on the data type, a preset data type-smart contract data table is searched to determine the target smart contract data corresponding to the data type;

[0100] Obtain the data directory in the blockchain hot data sharing platform corresponding to the target smart contract data;

[0101] The query submodule is used to query the data directory based on the first shared resolution information to obtain a property information set of the shared hot data.

[0102] In this embodiment, smart contracts are used to manage data directories, execute query logic, and verify permissions.

[0103] The working principle and beneficial effect of the above technical solution are as follows: the parsing submodule parses the first sharing request information to obtain the first sharing parsing information. Then, the first acquisition submodule plays a role, first obtains the data type corresponding to the first sharing parsing information, and then queries the preset data type-smart contract data table based on this data type, and then determines the target smart contract data corresponding to the data type. At the same time, the data directory of the target smart contract data in the blockchain hot data sharing platform is also obtained. Finally, the query submodule queries the data directory according to the first sharing parsing information to obtain the attribute information set of the shared hot data; through the clear association between the data type and the smart contract data table, the relevant data can be quickly located and obtained, reducing the query time; based on the parsing information and the specific data directory, the query is ensured to obtain the shared hot data attribute information that is highly related to the request, which improves the accuracy and pertinence of the data; the design of this module helps to better organize and manage the data in the blockchain hot data sharing platform, and promote the effective sharing and utilization of data in different scenarios; it can be flexibly configured and expanded according to different data types and requirements, and adapt to various business scenarios and changes; through standardized processes and smart contract constraints, the security and stability of data can be better maintained during data query and sharing.

[0104] Example 3

[0105] like Figure 3 As shown, the clustering module includes:

[0106] The first determination submodule is used to:

[0107] Mapping the attribute information set of the shared thermal data to a pre-constructed vector space to obtain a plurality of data vectors;

[0108] Randomly select a data in the attribute information set of shared hot data as the target data;

[0109] Taking the target data as the center and the preset distance as the radius, determine the area to be clustered;

[0110] Obtain the sum of the data volumes corresponding to each data in the area to be clustered to obtain the total data volume;

[0111] Obtain the sum of the Euclidean distances between the data vectors corresponding to each data in the area to be clustered, and obtain the sum of the distances;

[0112] The ratio of the total amount of data to the total distance is taken as the density value of the target data;

[0113] Traverse all data in the attribute information set of shared hot data to obtain the density value corresponding to each data;

[0114] Compare the density value with a preset density threshold; use data with a density value greater than or equal to the preset density threshold as initial cluster centers to obtain a plurality of initial cluster centers;

[0115] Randomly select an initial cluster center as the target cluster center; calculate the sum of the Euclidean distances between the target cluster center and other initial cluster centers except the target cluster center to obtain the evaluation value corresponding to the target cluster center; traverse each initial cluster center to obtain several evaluation values;

[0116] Sort a number of evaluation values ​​in descending order to obtain an evaluation value sequence; calculate the difference between any two adjacent evaluation values ​​in the evaluation value sequence to obtain an evaluation value difference; compare the evaluation value difference with a preset evaluation value difference threshold, delete the smaller evaluation value of the two evaluation values ​​corresponding to the evaluation value difference being smaller than the preset evaluation value difference threshold, and obtain a target evaluation value sequence;

[0117] The cluster center corresponding to each evaluation value in the target evaluation value sequence is used as the target cluster center to obtain several target cluster centers;

[0118] The clustering submodule is used to cluster the attribute information set of the shared hot data based on the plurality of target cluster centers to obtain a clustering result.

[0119] The beneficial effects of the above technical scheme are: by determining the initial cluster center and the target cluster center through complex calculations and analysis, the data can be classified more accurately and the quality and accuracy of clustering can be improved; multiple factors such as data volume and distance are taken into consideration, so that the scheme can adapt to shared hot data with different characteristics and distributions, and has strong adaptability; by sorting and processing the evaluation values, more suitable cluster centers are screened out, reducing the uncertainty and error of clustering; a large amount of shared hot data is effectively organized and classified, which is convenient for subsequent data processing and analysis, and improves overall efficiency; the clustering results help to better understand and explore the potential patterns and relationships in the shared hot data, and provide strong support for further data analysis; the preset thresholds and other parameters can be flexibly adjusted according to actual needs and data characteristics to achieve the best clustering effect.

[0120] Example 4

[0121] It also includes: an evaluation module, which is used to evaluate the clustering result and send the clustering result to the generation module after the evaluation result is qualified;

[0122] The evaluation module comprises:

[0123] The first evaluation submodule is used to:

[0124] Clustering the attribute information set of the shared hot data based on a plurality of target cluster centers to obtain a clustering result, wherein the clustering result includes a plurality of cluster sets;

[0125] Randomly select two cluster sets as the first cluster set and the second cluster set;

[0126] Evaluate the first cluster set and the second cluster set based on a preset evaluation algorithm to obtain a cluster evaluation value;

[0127] An abnormal marking submodule, used for comparing the cluster evaluation value with a preset evaluation threshold, and marking the first cluster set and the second cluster set corresponding to the cluster evaluation value less than the preset evaluation threshold as abnormal;

[0128] Traverse all cluster sets and count the number of abnormal markers in each cluster set;

[0129] The second determination submodule is used to determine that the clustering result is qualified when it is determined that the maximum value of the number of abnormal marks is less than or equal to a preset mark threshold;

[0130] The sending submodule is used to send the qualified clustering results to the generating module.

[0131] The beneficial effects of the above technical solution are: by evaluating the clustering results, it is possible to promptly discover possible abnormal or undesirable clustering situations, thereby ensuring that the final output clustering results have high quality and reliability; by comparing and evaluating different clustering sets, it is possible to accurately find out the parts that may not meet the requirements, so as to make targeted improvements or adjustments; after strict evaluation and screening, the clustering results sent to the generation module are more credible and usable, providing a more solid foundation for subsequent applications; avoiding potential problems or errors caused by low-quality clustering results entering subsequent processes, and improving the stability and robustness of the entire system; with the help of quantitative indicators such as clustering evaluation values ​​and evaluation thresholds, the evaluation process is made more objective and scientific, reducing the influence of subjective factors; through statistics and judgment of abnormal markers, it is possible to promptly discover and control possible risks, and ensure the normal operation of the system and the accuracy of data processing.

[0132] Example 5

[0133] The preset evaluation algorithm includes:

[0134]

[0135] Among them, D i , j represents the clustering evaluation value corresponding to the i-th cluster set and the j-th cluster set; H i represents the total number of attribute data in the i-th cluster set; || || represents the norm; Z i，pRepresents the characteristic value of the pth attribute data in the i-th cluster set; Represents the mean of the characteristic values ​​between the attribute data in the i-th cluster set; H j Represents the total number of attribute data in the jth cluster set; V j，q Represents the characteristic value of the qth attribute data in the jth cluster set; Represents the mean of the characteristic values ​​between each attribute data in the jth cluster set; Represents the mean of the Euclidean distances between the cluster centers in the i-th cluster set and the cluster centers in the j-th cluster set.

[0136] The beneficial effects of the above technical solution are: it comprehensively considers multiple factors such as the number of attribute data in the cluster set, the difference in eigenvalues, the mean eigenvalues ​​between attribute data, and the mean distance between cluster centers, so that the evaluation is more comprehensive and detailed; through the calculation of these indicators, the similarity or difference between two cluster sets can be measured more accurately, thereby obtaining a reliable clustering evaluation value; it reflects the characteristics of the cluster set itself and the relationship with other cluster sets, which helps to deeply understand the rationality and accuracy of the clustering results; it can be applied to attribute data of different types and characteristics, and has strong versatility and adaptability; the results obtained based on this evaluation algorithm can provide a clear direction and basis for further optimizing the clustering process or adjusting parameters.

[0137] Example 6

[0138] Shared modules, including:

[0139] The second acquisition submodule is used to:

[0140] Obtain several data transmission channels between the blockchain hot data sharing platform and the data requester;

[0141] Select any data transmission channel as the first data transmission channel;

[0142] Acquire status information of each transmission node in the first data transmission channel;

[0143] Based on the status information of each transmission node and a preset transmission evaluation algorithm, the first data transmission channel is evaluated to obtain a first data transmission channel evaluation value;

[0144] Comparing the first data transmission channel evaluation value with a preset transmission evaluation threshold;

[0145] When it is determined that the evaluation value of the first data transmission channel is greater than or equal to a preset transmission evaluation threshold, the first data transmission channel is used as a target data transmission channel to obtain a plurality of target data transmission channels;

[0146] Calculating the ratios between the evaluation values ​​of the plurality of target data transmission channels as the data volume allocation weights of the target data transmission channels;

[0147] Obtaining the total data volume of the complete hot data corresponding to the second sharing request information;

[0148] Allocate the total data volume value based on the data volume allocation weight to obtain a target transmission data volume corresponding to each target data transmission channel;

[0149] Based on a preset time algorithm, the target data transmission channel is estimated to complete the transmission of the target transmission data volume;

[0150] Traverse each target data transmission channel to obtain a time estimate for each target data transmission channel;

[0151] The sharing submodule is used to calculate the total value of the time estimate values ​​of all target data transmission channels and compare it with the preset time transmission threshold. If the total value of the time estimate values ​​is less than or equal to the preset time transmission threshold, the complete hot data corresponding to the second sharing request information is transmitted based on the target transmission channel to complete the sharing; if the total value of the time estimate values ​​is greater than the preset time transmission threshold, the backup transmission channel information is obtained for evaluation until the total value of the time estimate values ​​is less than or equal to the preset time transmission threshold.

[0152] In this embodiment, several data transmission channels between the blockchain hot data sharing platform and the data requester are first obtained, and the data transmission channels are first screened by a preset algorithm by obtaining the status information of the nodes in the data transmission channels, and the data transmission channels that meet the requirements are used as target data transmission channels; after determining the target data transmission channel, the total data volume of the complete hot data is allocated, and the evaluation value of the target data transmission channel is used as the allocation weight, which helps to make a reasonable allocation based on the current status of the data transmission channel; after the data volume of each data transmission channel is allocated, the transmission time needs to be estimated; the transmission time is estimated based on a preset time algorithm; when it is determined that the total value of the time estimation value of all target data transmission channels is within the preset time transmission threshold range, the complete hot data corresponding to the second sharing request information is transmitted based on the target data transmission channel to complete the sharing, and the second screening of the target data transmission channel is completed; if the total value of the time estimation value is greater than the preset time transmission threshold, the backup transmission channel information is obtained for evaluation until the total value of the time estimation value is less than or equal to the preset time transmission threshold.

[0153] The beneficial effects of the above technical solution are: by evaluating the data transmission channel and selecting the appropriate target channel, as well as reasonably allocating the data volume and estimating the transmission time, the efficiency and speed of data transmission can be improved; the quality of the transmission channel is ensured, and only channels that meet the evaluation threshold are selected to ensure the stability and reliability of data transmission; the data volume is allocated according to the evaluation values ​​of different channels, and the flexible allocation of resources is realized, making the transmission more reasonable. The estimation of the transmission time allows users to understand the transmission situation in advance, which is convenient for arrangement and planning; when the total value of the time estimation value does not meet the requirements, the backup channel can be obtained in time for evaluation, which enhances the adaptability and fault tolerance of the system; overall, the efficiency and effect of blockchain hot data sharing are improved, and the sharing experience is optimized.

[0154] Example 7

[0155] Preset node evaluation algorithms, including:

[0156]

[0157] Among them, k a represents the evaluation value of the ath data transmission channel; α and β are preset proportional coefficients, and α+β=1; N a represents the total number of transmission nodes in the a-th data transmission channel; S a，i Indicates the amount of data currently being processed by the i-th transmission node in the a-th data transmission channel; Indicates the maximum amount of data carried by the i-th transmission node in the a-th data transmission channel; It is represented by the expected amount of data carried by the i-th transmission node in the a-th data transmission channel when the working stability is the highest; P a，i represents the performance index of the i-th transmission node in the a-th data transmission channel; A represents the CPU usage rate of the server in the current blockchain hot data sharing platform; B represents the memory usage rate of the server in the current blockchain hot data sharing platform; Indicates the service rate of the server in the current blockchain hot data sharing platform.

[0158] In this embodiment, when the utilization rate and memory utilization rate are high, it may affect the service rate. If the CPU undertakes too many computing tasks or the memory is occupied by a large amount of memory, it may cause the processing speed of requests to slow down, which in turn affects the service rate, making the service response time longer and the efficiency lower; conversely, a higher service rate demand, that is, a large number of concurrent service requests, may cause CPU and memory resource tension, thereby pushing up CPU utilization and memory utilization. At the same time, these three interact with each other. If the server configuration is unreasonable, such as insufficient CPU performance or insufficient memory capacity, it may be difficult to cope with high service rate requirements, further resulting in high CPU utilization and memory utilization, affecting the performance and stability of the entire system. Together, they reflect the resource utilization and service provision status of the server when processing tasks related to the blockchain hot data sharing platform.

[0159] The beneficial effects of the above technical solution are: combining the data processing volume, maximum load capacity, expected load capacity, performance index of the transmission node, and multiple factors such as the CPU usage rate, memory usage rate and service rate of the server, comprehensively evaluating the data transmission channel, making the evaluation more accurate and comprehensive; considering the impact of the real-time status of the server on data transmission, it can be dynamically adjusted and evaluated according to the actual situation of the current system to improve the adaptability of the algorithm; through the detailed evaluation of each channel, resources can be better allocated to ensure the efficiency and reliability of data transmission. It is helpful to discover potential transmission bottlenecks and problem nodes, so as to take measures in advance to ensure the quality and stability of data transmission; overall, it is conducive to improving the data transmission performance and service quality of the blockchain hot data sharing platform.

[0160] Example 8

[0161] Preset time algorithms, including:

[0162]

[0163] Among them, t a represents the estimated time value of the target data transmission channel a; t1 represents the preset expected duration; k a represents the evaluation value of the a-th target data transmission channel; N a represents the total number of transmission nodes in the a-th data transmission channel; Q a，i represents the amount of data allocated when the i-th transmission node in the a-th target data transmission channel processes the target data volume; F a，i represents the total amount of data processed by the i-th transmission node in the a-th target data transmission channel; θ a，i It represents the working efficiency of the i-th transmission node in the a-th target data transmission channel.

[0164] The beneficial effects of the above technical solution are: by comprehensively considering factors such as the evaluation value of the target data transmission channel, the relevant data volume and work efficiency of each transmission node, it is possible to more accurately estimate the time required for each channel to complete data transmission; it is helpful to reasonably arrange and adjust the distribution of data on different channels based on time estimation, so as to better optimize resource utilization and improve transmission efficiency; it can calculate the time according to the specific status and performance of each transmission node, has strong adaptability, and can cope with a variety of different transmission scenarios and conditions; it provides a reliable time basis for the formulation of data transmission strategies, so as to make more reasonable transmission decisions.

[0165] Example 9

[0166] It also includes: an update module, which is used to periodically obtain hot data of each data provider node in the blockchain, and update the data directory in the blockchain hot data sharing platform based on the hot data.

[0167] Example 10

[0168] The update module comprises:

[0169] The third acquisition submodule is used to:

[0170] Periodically obtain the hot data of each data provider node in the blockchain to obtain the data to be updated;

[0171] Obtain the data type of the data to be updated, query the preset data type-smart contract data table based on the data type, and determine the target smart contract data corresponding to the data type information; obtain the target data directory in the blockchain hot data sharing platform corresponding to the target smart contract data;

[0172] The attribute extraction submodule is used to extract the attributes of the data to be updated to obtain target attribute data;

[0173] The update submodule is used to update the target data directory in the blockchain hot data sharing platform based on the target attribute data.

[0174] The beneficial effects of the above technical solution are: periodic acquisition and updating ensure the real-time and freshness of blockchain hot data, so that the data is always kept up to date; through the association of data types and smart contract data tables, the corresponding target data directory can be accurately found for updating, ensuring the accuracy and pertinence of the update; clear processes and division of labor improve the efficiency of update operations and reduce unnecessary waste of time and resources; it helps to maintain the consistency of data in the blockchain hot data sharing platform and avoid data confusion and errors; it can adapt to different types of data update needs and has good versatility and flexibility; timely updating of the target data directory ensures the quality and availability of data and provides users with more reliable data services.

[0175] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A hot data sharing platform based on blockchain, characterized in that: include: A receiving module, configured to receive first sharing request information sent by a plurality of data requesting nodes; The first sharing request information includes the identity information of the hot data requester and the request content information; A query module, used to query the data directory in the blockchain hot data sharing platform based on the request content information, and determine the attribute information set of the shared hot data; A clustering module, used for clustering the attribute information set of the shared thermal data and determining a clustering result; A generating module, used for packaging the hot data requester identity information and the clustering result to generate second sharing request information; A verification module, used for sending the second sharing request information to the data provider for verification; The sharing module is used to send the complete hot data corresponding to the second sharing request information to the data requester for sharing when the verification is passed.

2. The blockchain-based heat data sharing platform according to claim 1, characterized in that: Query modules, including: A parsing submodule, configured to parse the first sharing request information to obtain first sharing parsing information; The first acquisition submodule is used to: Obtaining the data type corresponding to the first shared resolution information; Based on the data type, a preset data type-smart contract data table is searched to determine the target smart contract data corresponding to the data type; Obtain the data directory in the blockchain hot data sharing platform corresponding to the target smart contract data; The query submodule is used to query the data directory based on the first shared resolution information to obtain a property information set of the shared hot data.

3. The blockchain-based heat data sharing platform according to claim 1, characterized in that: Clustering modules, including: The first determination submodule is used to: Mapping the attribute information set of the shared thermal data to a pre-constructed vector space to obtain a plurality of data vectors; Randomly select a data in the attribute information set of shared hot data as the target data; Taking the target data as the center and the preset distance as the radius, determine the area to be clustered; Obtain the sum of the data volumes corresponding to each data in the area to be clustered to obtain the total data volume; Obtain the sum of the Euclidean distances between the data vectors corresponding to each data in the area to be clustered, and obtain the sum of the distances; The ratio of the total amount of data to the total distance is taken as the density value of the target data; Traverse all data in the attribute information set of shared hot data to obtain the density value corresponding to each data; Compare the density value with a preset density threshold; use data with a density value greater than or equal to the preset density threshold as initial cluster centers to obtain a plurality of initial cluster centers; Randomly select an initial cluster center as the target cluster center; calculate the sum of the Euclidean distances between the target cluster center and other initial cluster centers except the target cluster center to obtain the evaluation value corresponding to the target cluster center; traverse each initial cluster center to obtain several evaluation values; Sort a number of evaluation values ​​in descending order to obtain an evaluation value sequence; calculate the difference between any two adjacent evaluation values ​​in the evaluation value sequence to obtain an evaluation value difference; compare the evaluation value difference with a preset evaluation value difference threshold, delete the smaller evaluation value of the two evaluation values ​​corresponding to the evaluation value difference being smaller than the preset evaluation value difference threshold, and obtain a target evaluation value sequence; The cluster center corresponding to each evaluation value in the target evaluation value sequence is used as the target cluster center to obtain several target cluster centers; The clustering submodule is used to cluster the attribute information set of the shared hot data based on the plurality of target cluster centers to obtain a clustering result.

4. The blockchain-based heat data sharing platform as claimed in claim 3, characterized in that: Also includes: An evaluation module is used to evaluate the clustering result and send the clustering result to a generation module if the evaluation result is qualified; The evaluation module comprises: The first evaluation submodule is used to: Clustering the attribute information set of the shared hot data based on a plurality of target cluster centers to obtain a clustering result, wherein the clustering result includes a plurality of cluster sets; Randomly select two cluster sets as the first cluster set and the second cluster set; Evaluate the first cluster set and the second cluster set based on a preset evaluation algorithm to obtain a cluster evaluation value; An abnormal marking submodule, used for comparing the cluster evaluation value with a preset evaluation threshold, and marking the first cluster set and the second cluster set corresponding to the cluster evaluation value less than the preset evaluation threshold as abnormal; Traverse all cluster sets and count the number of abnormal markers in each cluster set; The second determination submodule is used to determine that the clustering result is qualified when it is determined that the maximum value of the number of abnormal marks is less than or equal to a preset mark threshold; The sending submodule is used to send the qualified clustering results to the generating module.

5. The blockchain-based heat data sharing platform according to claim 4, characterized in that: The preset evaluation algorithm includes: Among them, D i , j represents the clustering evaluation value corresponding to the i-th cluster set and the j-th cluster set; H i represents the total number of attribute data in the i-th cluster set; |||| represents the norm; Z i，p Represents the characteristic value of the pth attribute data in the i-th cluster set; Represents the mean of the characteristic values ​​between the attribute data in the i-th cluster set; H j Represents the total number of attribute data in the jth cluster set; V j，q Represents the characteristic value of the qth attribute data in the jth cluster set; Represents the mean of the characteristic values ​​between each attribute data in the jth cluster set; Represents the mean of the Euclidean distances between the cluster centers in the i-th cluster set and the cluster centers in the j-th cluster set.

6. The blockchain-based heat data sharing platform according to claim 1, characterized in that: Shared modules, including: The second acquisition submodule is used to: Obtain several data transmission channels between the blockchain hot data sharing platform and the data requester; Select any data transmission channel as the first data transmission channel; Acquire status information of each transmission node in the first data transmission channel; Based on the status information of each transmission node and a preset transmission evaluation algorithm, the first data transmission channel is evaluated to obtain a first data transmission channel evaluation value; Comparing the first data transmission channel evaluation value with a preset transmission evaluation threshold; When it is determined that the evaluation value of the first data transmission channel is greater than or equal to a preset transmission evaluation threshold, the first data transmission channel is used as a target data transmission channel to obtain a plurality of target data transmission channels; Calculating the ratios between the evaluation values ​​of the plurality of target data transmission channels as the data volume allocation weights of the target data transmission channels; Obtaining the total data volume of the complete hot data corresponding to the second sharing request information; Allocate the total data volume value based on the data volume allocation weight to obtain a target transmission data volume corresponding to each target data transmission channel; Based on a preset time algorithm, the target data transmission channel is estimated to complete the transmission of the target transmission data volume; Traverse each target data transmission channel to obtain a time estimate for each target data transmission channel; The sharing submodule is used to calculate the total value of the time estimate values ​​of all target data transmission channels and compare it with the preset time transmission threshold. If the total value of the time estimate values ​​is less than or equal to the preset time transmission threshold, the complete hot data corresponding to the second sharing request information is transmitted based on the target transmission channel to complete the sharing; if the total value of the time estimate values ​​is greater than the preset time transmission threshold, the backup transmission channel information is obtained for evaluation until the total value of the time estimate values ​​is less than or equal to the preset time transmission threshold.

7. The blockchain-based heat data sharing platform according to claim 6, characterized in that: Preset node evaluation algorithms, including: Among them, k a represents the evaluation value of the ath data transmission channel; α and β are preset proportional coefficients, and α+β=1; N a represents the total number of transmission nodes in the a-th data transmission channel; S a，i Indicates the amount of data currently being processed by the i-th transmission node in the a-th data transmission channel; Indicates the maximum amount of data carried by the i-th transmission node in the a-th data transmission channel; It is represented by the expected amount of data carried by the i-th transmission node in the a-th data transmission channel when the working stability is the highest; P a，i represents the performance index of the i-th transmission node in the a-th data transmission channel; A represents the CPU usage rate of the server in the current blockchain hot data sharing platform; B represents the memory usage rate of the server in the current blockchain hot data sharing platform; Indicates the service rate of the server in the current blockchain hot data sharing platform.

8. The blockchain-based heat data sharing platform according to claim 6, characterized in that: Preset time algorithms, including: Among them, t a represents the estimated time value of the target data transmission channel a; t1 represents the preset expected duration; k a represents the evaluation value of the a-th target data transmission channel; N a represents the total number of transmission nodes in the a-th data transmission channel; Q a，i represents the amount of data allocated when the i-th transmission node in the a-th target data transmission channel processes the target data volume; F a，i represents the total amount of data processed by the i-th transmission node in the a-th target data transmission channel; θ a，i It represents the working efficiency of the i-th transmission node in the a-th target data transmission channel.

9. The blockchain-based heat data sharing platform according to claim 1, characterized in that: Also includes: The update module is used to periodically obtain the hot data of each data provider node in the blockchain, and update the data directory in the blockchain hot data sharing platform based on the hot data.

10. The blockchain-based heat data sharing platform according to claim 9, characterized in that: The update module comprises: The third acquisition submodule is used to: Periodically obtain the hot data of each data provider node in the blockchain to obtain the data to be updated; Obtain the data type of the data to be updated, query the preset data type-smart contract data table based on the data type, and determine the target smart contract data corresponding to the data type information; obtain the target data directory in the blockchain hot data sharing platform corresponding to the target smart contract data; The attribute extraction submodule is used to extract the attributes of the data to be updated to obtain target attribute data; The update submodule is used to update the target data directory in the blockchain hot data sharing platform based on the target attribute data.

Images