A data product management system and method based on blockchain

Abstract

The present invention relates to the field of data product management, and specifically to a data product management system and method based on blockchain, comprising: a product value module, a hierarchical sampling module, a block fixing module, a data tracking module and a purchase update module, wherein the product value module is used to calculate the value of data products, the hierarchical sampling module is used to perform fuzzy sampling on data products to obtain sub-data blocks, the block fixing module is used to store sub-data blocks in blockchain, the data tracking module is used to publish updated data blocks in blockchain, and the purchase update module is used to give a purchaser a key for updating data blocks and extract data products of corresponding levels. The present application can help determine the value of data products, maintain the timeliness, integrity and consistency of data products, enable customers to obtain more personalized data product types, increase the purchase frequency of data products, and ensure the circulation and flexibility of data products.

Description

Technical Field

[0001] The present invention relates to the field of data product management, and specifically to a data product management system and method based on blockchain. Background Art

[0002] Data products are products that use data to solve problems or meet needs. They help users make decisions by collecting, processing, analyzing and displaying tracking data through data analysis, data mining and statistical techniques. Transactions for data products are generally conducted on the blockchain platform. Buyers and sellers provide data of equal value, or purchase virtual data for product transactions. Data product transactions help to fully utilize the value of data.

[0003] Buyers sometimes do not need accurate statistical data. Therefore, for some more detailed high-value data products, buyers are often unable to produce data of equal value, and are unwilling to pay high prices, which is not conducive to the circulation and utilization of data products. Due to the resource nature of blockchain, it is difficult for data product providers to solve the problem of tiered purchase of data products by releasing different versions of data products.

[0004] In addition, the value of data products is greatly affected by time and external environment. The data tracked by the product is actually constantly updated, but due to the characteristics of blockchain, the data cannot be changed once it is on the chain. The existing method is to re-release the data product after the data is updated, but this will take up additional blockchain resources. After the data product is traded, it is also necessary to frequently send key updates to customers, affecting the product trading experience. Summary of the invention

[0005] The purpose of the present invention is to provide a data product management system and method based on blockchain to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a data product management system based on blockchain, comprising: a product value module, a hierarchical sampling module, a block fixing module, a data tracking module and a purchase update module;

[0007] The product value module is used to retrieve the public data of the same label product in the blockchain after obtaining the data product, obtain the discrimination of the data product, calculate the data timeliness parameter according to the stability of the historical tracking data and the average failure time, and calculate the value of the data product according to the discrimination, data volume, data information entropy, timeliness parameter and preset label weight of the data product;

[0008] The hierarchical sampling module is used to calculate the resolution level of the data product according to the timeliness parameters and value of the data product, determine the sampling interval according to the resolution level, perform fuzzy sampling on the data product according to the sampling interval, obtain data of different sampling intervals, and encapsulate them into sub-data blocks respectively;

[0009] The block fixing module is used to store the sub-data blocks in the blockchain in descending order of resolution. After obtaining the hash address of the data block storage each time a data block is stored, the hash address is stored at the end of the next sub-data block until all sub-data blocks are stored;

[0010] The data tracking module is used to detect the total deviation value between the data product and the tracking data. When the total deviation value is higher than a threshold, an update data block is published in the blockchain, and the hash address of the last sub-data block is stored in the update block, and the blockchain keys of all sub-data blocks are stored;

[0011] The purchase update module is used to give the purchaser a key to update the data block after the purchaser applies for a purchase request, calculate the value of the data provided by the purchaser, determine the acquisition level of the data product based on the value of the provided data, delete the data block key with a resolution higher than the acquisition level from the update data block, extract the data of the corresponding resolution according to the number of sub-data blocks corresponding to the acquisition level, synthesize it into a data product of the corresponding resolution and provide it to the purchaser.

[0012] Furthermore, the product value module includes: distinguishing value units, timeliness units and value judgment units;

[0013] The distinguishing value unit is used to retrieve the same data and calculate the degree of distinction between the data product and the existing data;

[0014] The timeliness unit is used to calculate the stability of the data tracked by the data product within the validity period to obtain the timeliness parameter of the data product;

[0015] The value judgment unit is used to comprehensively evaluate the value of the data product according to the characteristics of the data product.

[0016] Furthermore, the hierarchical sampling module includes: a sampling level unit and a classification and packaging unit;

[0017] The sampling level unit is used to calculate the number of data product levels and the sampling interval of each level of data products;

[0018] The classification and packaging unit is used to generate fuzzy sampling data of different levels according to the sampling interval, and encapsulate them into sub-data blocks respectively.

[0019] Furthermore, the block fixing module includes: a data uplink unit and an address merging unit;

[0020] The data uploading unit is used to upload the sub-data blocks to the blockchain in sequence and obtain the hash address of each sub-data block;

[0021] The address merging unit is used to merge the address of the previous sub-data block into the next sub-data block.

[0022] Further, the data tracking module includes: a data monitoring unit and an update publishing unit;

[0023] The data monitoring unit is used to monitor the fluctuation of the tracking data and pack the fluctuation data into data blocks;

[0024] The update publishing unit is used to add the address of the sub-data block after the data block of the fluctuating data and to add a data merging program.

[0025] Further, the purchase and update module includes: a transaction providing unit, a key downloading unit and a product merging unit;

[0026] The transaction providing unit is used to evaluate the value of the data provided by the purchaser and calculate the purchase level of the data product according to the value;

[0027] The key download unit is used to send the data block key of the purchaser's fluctuation data and activate the program after verifying the key;

[0028] The product merging unit is used to extract data blocks from corresponding addresses according to word data block addresses stored in the program, and merge them with the fluctuation data to output as data products.

[0029] A data product management method based on blockchain, comprising the following steps:

[0030] Step S1. Obtain the label of the data product, retrieve the public data of the product with the same label in the blockchain, calculate the distinction between the data product and the public data, and calculate the value of the data product based on the timeliness and data characteristics of the data product;

[0031] Step S2. Determine the number of data product grades based on the ratio of the data product value to the value of products with the same label, determine the data product sampling interval based on the number of grades and the total effective time of the data product, extract data from the data product according to the sampling interval, and obtain a sampled data block;

[0032] Step S3. Upload the sampled data blocks to the blockchain in ascending order of the sampling interval, and after obtaining the hash address of the preceding sampled data block, store the hash address at the end of the succeeding data block, until all the sampled data blocks are uploaded to the blockchain, and obtain the address of the sampled data block with the largest sampling interval;

[0033] Step S4. Monitor the tracking data of the data product. When the tracking data deviates from the inferred data of the data product by more than a threshold, pack the changed data into an update data block, store the address of the sampling data block with the largest sampling interval after the update data block, and set a jump counting program at the beginning of the update data block;

[0034] Step S5. After the customer provides the purchase data, the purchase level of the data product is judged according to the value of the purchase data, a jump key is generated according to the purchase level, and the address of the updated data block is provided. After extracting the updated data block, the jump counting program extracts the sampled data blocks in sequence according to the stored address until the number of jumps returns to zero, and synthesizes all the extracted data blocks to obtain the data product.

[0035] Further, step S1 includes:

[0036] Step S11. After labeling the data product according to the source of the data, retrieve the data with the same label as the data product, and record the retrieved data as a set {W1, W2, ..., Wk, ..., Wa}, where a represents the number of data with the same label, and Wk represents the kth data with the same label;

[0037] Step S12. Preset the initial value d of the sliding window parameter, generate a codeword sequence consisting of the 1st codeword to the dth codeword in Wk, search the codeword sequence in the data product, if the same codeword sequence exists in the data product, change the sliding window parameter to d+1, if the same codeword sequence does not exist in the data product, change the codeword sequence to consist of the 2nd codeword to the d+1th codeword in Wk;

[0038] Repeat step S12 until the elements in the symbol sequence are beyond the range of the data Wk, and output the size d0 of the sliding window parameter when stopping, and calculate the discrimination s0 of the data product relative to Wk, where s0=1-d0 / n, where n represents the symbol length of the data product;

[0039] Step S13. Execute step S12 for each data with the same label to obtain the discrimination degree of the data product relative to each data with the same label, and record the maximum value as the discrimination degree of the data product;

[0040] Step S14. According to the source of the data product, the maximum effective time T of the data product is obtained, and the value of the data product is calculated in combination with the data features. The data features include: the symbol length of the data product, the information entropy and the preset weight of each label. The specific calculation formula is as follows:

[0041]

[0042] Among them, Q represents the value of the data product, n represents the symbol length of the data product, r represents the information entropy of the data product, c represents the number of label types of the data product, and EC i represents the number of labels belonging to the i-th label type in the labels of the data product, EP i Represents the preset weight of the i-th label type, and e is the base of the natural logarithm.

[0043] Further, step S2 includes:

[0044] Step S21. Calculate the ratio of the value of the data product to each element in the set {W1, W2, ..., Wk, ..., Wa}, record the average of all the ratios as the average value of the data product, and record the minimum ratio as the minimum value of the data product;

[0045] Determine the number of grades U of the data product, where U=UPS(D1 / D2), where UPS is a round-up function, D1 represents the average value of the data product, and D2 represents the minimum value of the data product;

[0046] Step S22: Determine the minimum sampling interval tc of the data product, where tc=T / U 2 , the data products are sampled according to the integer multiples of the minimum sampling interval, and the sampling results are encapsulated into data blocks that can be connected to the blockchain, and the sampling data block set {G1, G2, …, GU} is obtained, where GU represents the sampling data block composed of the sampling results of the data product when the sampling interval is U·tc.

[0047] Further, step S3 includes:

[0048] Step S31, starting from the sampled data block G1 with the smallest sampling interval, G1 is connected to the blockchain, and the hash address A1 of G1 in the blockchain is obtained. A1 is added as a data field to the sampled data block G2, and G2 is connected to the blockchain, and so on, until all sampled data blocks are connected to the blockchain, and the hash address AU of GU in the blockchain is obtained;

[0049] Step S32. Check whether the tracking data of the data product is updated. If not, set the update data block to empty data and go to step S43. If there is an update, go to step S41.

[0050] Further, step S4 includes:

[0051] Step S41. After the data product is stored in the blockchain, the tracking data of the data product is continuously monitored, and the deviation between the tracking data and the data product is calculated:

[0052]

[0053] Among them, a represents the deviation between the tracking data and the data product, t0 represents the moment when the data product is deposited into the blockchain, t1 represents the current moment, F(t) represents the variation function of the tracking data over time, and H(t) represents the variation function of the predicted data of the data product over time;

[0054] Step S42. When it is detected that a < a0, it is determined that there is a deviation in the tracking data, where a0 is a preset deviation threshold, and all data within the time period of a < a0 is packaged into an update data block;

[0055] Step S43. Insert a jump counting program after the update data block. The jump counting program can extract data in the blockchain according to the address in the data block and stop reading the blockchain address after the number of reads of the blockchain address reaches a preset number of times;

[0056] Place the hash address AU of GU in the blockchain into the initial address of the jump counting program, and connect the update data block to the blockchain to obtain the address AG of the update data block.

[0057] Further, step S5 includes:

[0058] Step S51. When a customer purchases a data product, judge the value of the exchange data provided by the customer, divide it by the minimum value of the data product and round down to obtain the purchase level of the customer;

[0059] Step S52. Send the address AG of the update data block to the customer, and generate a key according to the customer's purchase level. After the customer extracts the update data block, input the key into the jump counting program;

[0060] The jump counting program uses the customer's purchase level as the number of jumps, extracts the sampling data block of the corresponding level in the blockchain, and after merging the sampling data block with the update data block, sends it to the customer as the data product finally purchased by the customer.

[0061] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0062] 1. The present invention can calculate the discrimination and timeliness parameters of the data product, and calculate the value of the data product according to the discrimination, data volume, data information entropy, timeliness parameter and preset label weight of the data product, which can help determine whether the data product meets the user's needs, whether it can continuously provide value for the user, and ensure the continuous development and value-added ability of the data product.

[0063] 2. The present invention can determine the sampling interval according to the resolution level, chain the sub-data blocks of different sampling intervals in sequence, and after obtaining the hash address of the previous data block, store it at the end of the next sub-data block. Through flexible grading on data blocks, customers can choose the appropriate data product level according to their own needs and budget, obtain more personalized data product types, increase the purchase frequency of data products, and ensure the liquidity and flexibility of data products.

[0064] 3. The present invention can publish updated data blocks in the blockchain and store the hash address of the last sub-data block. When the purchaser provides the purchase data, the data of corresponding resolutions are extracted in sequence, and data products of corresponding value are synthesized and provided to the purchaser, which can maintain the timeliness, integrity and consistency of the data and make the data products more competitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] 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:

[0066] Figure 1 It is a structural schematic diagram of a data product management system based on blockchain in the present invention;

[0067] Figure 2 It is a schematic diagram of the steps of a data product management method based on blockchain of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] See also Figure 1 ,The present invention provides a technical solution: a data product management system based on blockchain, including: a product value module, a hierarchical sampling module, a block fixing module, a data tracking module and a purchase update module;

[0070] The product value module is used to retrieve the public data of the same label product in the blockchain after obtaining the data product, obtain the discrimination of the data product, calculate the data timeliness parameter according to the stability of the historical tracking data and the average failure time, and calculate the value of the data product according to the discrimination, data volume, data information entropy, timeliness parameter and preset label weight of the data product;

[0071] The product value module includes: a distinguishing value unit, a timeliness unit and a value judgment unit;

[0072] The distinguishing value unit is used to retrieve the same data and calculate the degree of distinction between the data product and the existing data;

[0073] The timeliness unit is used to calculate the stability of the data tracked by the data product within the validity period to obtain the timeliness parameter of the data product;

[0074] The value judgment unit is used to comprehensively evaluate the value of the data product according to the characteristics of the data product.

[0075] The hierarchical sampling module is used to calculate the resolution level of the data product according to the timeliness parameters and value of the data product, determine the sampling interval according to the resolution level, perform fuzzy sampling on the data product according to the sampling interval, obtain data of different sampling intervals, and encapsulate them into sub-data blocks respectively;

[0076] The hierarchical sampling module includes: a sampling level unit and a classification and packaging unit;

[0077] The sampling level unit is used to calculate the number of data product levels and the sampling interval of each level of data products;

[0078] The classification and packaging unit is used to generate fuzzy sampling data of different levels according to the sampling interval, and encapsulate them into sub-data blocks respectively.

[0079] The block fixing module is used to store the sub-data blocks in the blockchain in descending order of resolution. After obtaining the hash address of the data block storage each time a data block is stored, the hash address is stored at the end of the next sub-data block until all sub-data blocks are stored;

[0080] The block fixing module includes: a data uplink unit and an address merging unit;

[0081] The data uploading unit is used to upload the sub-data blocks to the blockchain in sequence and obtain the hash address of each sub-data block;

[0082] The address merging unit is used to merge the address of the previous sub-data block into the next sub-data block.

[0083] The data tracking module is used to detect the total deviation value between the data product and the tracking data. When the total deviation value is higher than a threshold, an update data block is published in the blockchain, and the hash address of the last sub-data block is stored in the update block, and the blockchain keys of all sub-data blocks are stored;

[0084] The data tracking module includes: a data monitoring unit and an update publishing unit;

[0085] The data monitoring unit is used to monitor the fluctuation of the tracking data and pack the fluctuation data into data blocks;

[0086] The update publishing unit is used to add the address of the sub-data block after the data block of the fluctuating data and to add a data merging program.

[0087] The purchase update module is used to give the purchaser a key to update the data block after the purchaser applies for a purchase request, calculate the value of the data provided by the purchaser, determine the acquisition level of the data product based on the value of the provided data, delete the data block key with a resolution higher than the acquisition level from the update data block, extract the data of the corresponding resolution according to the number of sub-data blocks corresponding to the acquisition level, synthesize it into a data product of the corresponding resolution and provide it to the purchaser.

[0088] The purchase and update module includes: a transaction providing unit, a key downloading unit and a product merging unit;

[0089] The transaction providing unit is used to evaluate the value of the data provided by the purchaser and calculate the purchase level of the data product according to the value;

[0090] The key download unit is used to send the data block key of the purchaser's fluctuation data and activate the program after verifying the key;

[0091] The product merging unit is used to extract data blocks from corresponding addresses according to word data block addresses stored in the program, and merge them with the fluctuation data to output as data products.

[0092] like Figure 2 As shown, a data product management method based on blockchain includes the following steps:

[0093] Step S1. Obtain the label of the data product, retrieve the public data of the product with the same label in the blockchain, calculate the distinction between the data product and the public data, and calculate the value of the data product based on the timeliness and data characteristics of the data product;

[0094] Step S1 includes:

[0095] Step S11. After labeling the data product according to the source of the data, retrieve the data with the same label as the data product, and record the retrieved data as a set {W1, W2, ..., Wk, ..., Wa}, where a represents the number of data with the same label, and Wk represents the kth data with the same label;

[0096] Step S12. Preset the initial value d of the sliding window parameter, generate a codeword sequence consisting of the 1st codeword to the dth codeword in Wk, search the codeword sequence in the data product, if the same codeword sequence exists in the data product, change the sliding window parameter to d+1, if the same codeword sequence does not exist in the data product, change the codeword sequence to consist of the 2nd codeword to the d+1th codeword in Wk;

[0097] Repeat step S12 until the elements in the symbol sequence are beyond the range of the data Wk, and output the size d0 of the sliding window parameter when stopping, and calculate the discrimination s0 of the data product relative to Wk, where s0=1-d0 / n, where n represents the symbol length of the data product;

[0098] Step S13. Execute step S12 for each data with the same label to obtain the discrimination degree of the data product relative to each data with the same label, and record the maximum value as the discrimination degree of the data product;

[0099] Step S14. According to the source of the data product, the maximum effective time T of the data product is obtained, and the value of the data product is calculated in combination with the data features. The data features include: the symbol length of the data product, the information entropy and the preset weight of each label. The specific calculation formula is as follows:

[0100]

[0101] Among them, Q represents the value of the data product, n represents the symbol length of the data product, r represents the information entropy of the data product, c represents the number of label types of the data product, and EC i represents the number of labels belonging to the i-th label type in the labels of the data product, EP i Represents the preset weight of the i-th label type, and e is the base of the natural logarithm.

[0102] Step S2. Determine the number of data product grades based on the ratio of the data product value to the value of products with the same label, determine the data product sampling interval based on the number of grades and the total effective time of the data product, extract data from the data product according to the sampling interval, and obtain a sampled data block;

[0103] Step S2 includes:

[0104] Step S21. Calculate the ratio of the value of the data product to each element in the set {W1, W2, ..., Wk, ..., Wa}, record the average of all the ratios as the average value of the data product, and record the minimum ratio as the minimum value of the data product;

[0105] Determine the number of grades U of the data product, where U=UPS(D1 / D2), where UPS is a round-up function, D1 represents the average value of the data product, and D2 represents the minimum value of the data product;

[0106] Step S22: Determine the minimum sampling interval tc of the data product, where tc=T / U 2 , the data products are sampled according to the integer multiples of the minimum sampling interval, and the sampling results are encapsulated into data blocks that can be connected to the blockchain, and the sampling data block set {G1, G2, …, GU} is obtained, where GU represents the sampling data block composed of the sampling results of the data product when the sampling interval is U·tc.

[0107] Step S3. Upload the sampled data blocks to the blockchain in ascending order of the sampling interval, and after obtaining the hash address of the preceding sampled data block, store the hash address at the end of the succeeding data block, until all the sampled data blocks are uploaded to the blockchain, and obtain the address of the sampled data block with the largest sampling interval;

[0108] Step S3 includes:

[0109] Step S31, starting from the sampled data block G1 with the smallest sampling interval, G1 is connected to the blockchain, and the hash address A1 of G1 in the blockchain is obtained. A1 is added as a data field to the sampled data block G2, and G2 is connected to the blockchain, and so on, until all sampled data blocks are connected to the blockchain, and the hash address AU of GU in the blockchain is obtained;

[0110] Step S32. Check whether the tracking data of the data product is updated. If not, set the update data block to empty data and go to step S43. If there is an update, go to step S41.

[0111] Step S4. Monitor the tracking data of the data product. When the tracking data deviates from the inferred data of the data product by more than a threshold, pack the changed data into an update data block, store the address of the sampling data block with the largest sampling interval after the update data block, and set a jump counting program at the beginning of the update data block;

[0112] Step S4 includes:

[0113] Step S41. After the data product is stored in the blockchain, the tracking data of the data product is continuously monitored, and the deviation between the tracking data and the data product is calculated:

[0114]

[0115] Wherein, a represents the deviation between the tracking data and the data product, t0 represents the moment when the data product is stored in the blockchain, t1 represents the current moment, F(t) represents the variation function of the tracking data over time, and H(t) represents the variation function of the predicted data of the data product over time;

[0116] Step S42. When it is detected that a < a0, it is determined that there is a deviation in the tracking data, where a0 is a preset deviation threshold, and all data within the time period of a < a0 is packaged into an update data block;

[0117] Step S43. Insert a jump counting program after the update data block. The jump counting program can extract data in the blockchain according to the address in the data block and stop reading the blockchain address after the number of reads of the blockchain address reaches a preset number of times;

[0118] Place the hash address AU of GU in the blockchain into the initial address of the jump counting program, and connect the update data block to the blockchain to obtain the address AG of the update data block.

[0119] Step S5. After the customer provides purchase data, judge the purchase level of the data product according to the value of the purchase data, generate a jump key according to the purchase level, and provide the address of the update data block. After extracting the update data block, the jump counting program sequentially extracts sampling data blocks according to the stored address until the number of jumps reaches zero, and synthesizes all the extracted data blocks to obtain the data product.

[0120] Step S5 includes:

[0121] Step S51. When the customer purchases a data product, judge the value of the exchange data provided by the customer, divide it by the minimum value of the data product and round down to obtain the customer's purchase level;

[0122] Step S52. Send the address AG of the update data block to the customer, and generate a key according to the customer's purchase level. After the customer extracts the update data block, input the key into the jump counting program;

[0123] The jump counting program uses the customer's purchase level as the number of jumps, extracts sampling data blocks of the corresponding level in the blockchain, merges the sampling data blocks with the update data block, and sends it to the customer as the data product finally purchased by the customer.

[0124] Example: According to the data characteristics of the data product, it is calculated that the value of the data product is 20, and the values of the same-label products are 40, 5, 10, 20, and 2 respectively. Then the minimum value of the data product is 0.5, the average value is 3.5, the number of grading levels is 7, and the effective duration of the data product is 50 days. Then the data is sampled and segmented at sampling intervals of 1 - 7 days respectively to obtain 7 sampling data packets, and the addresses of the data packets with a sampling duration of 7 days are obtained after connecting them to the blockchain in sequence;

[0125] When a customer purchases a data product, he submits data with a value of 10. Then the customer's jump count is determined to be 3, and the sampled data with a sampling interval of 4 days is extracted, combined with the updated data to generate a data product and send it to the customer.

[0126] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device.

[0127] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data product management method based on blockchain, characterized in that: The method comprises the following steps: Step S1. Obtain the label of the data product, retrieve the public data of the product with the same label in the blockchain, calculate the distinction between the data product and the public data, and calculate the value of the data product based on the timeliness and data characteristics of the data product; Step S2. Determine the number of data product grades based on the ratio of the data product value to the value of products with the same label, determine the data product sampling interval based on the number of grades and the total effective time of the data product, extract data from the data product according to the sampling interval, and obtain a sampled data block; Step S3. Upload the sampled data blocks to the blockchain in ascending order of the sampling interval, and after obtaining the hash address of the preceding sampled data block, store the hash address at the end of the succeeding data block, until all the sampled data blocks are uploaded to the blockchain, and obtain the address of the sampled data block with the largest sampling interval; Step S4. Monitor the tracking data of the data product. When the tracking data deviates from the inferred data of the data product by more than a threshold, pack the changed data into an update data block, store the address of the sampling data block with the largest sampling interval after the update data block, and set a jump counting program at the beginning of the update data block; Step S5. After the customer provides the purchase data, the purchase level of the data product is judged according to the value of the purchase data, a jump key is generated according to the purchase level, and the address of the updated data block is provided. After extracting the updated data block, the jump counting program extracts the sampled data blocks in sequence according to the stored address until the number of jumps returns to zero, and synthesizes all the extracted data blocks to obtain the data product.

2. A blockchain-based data product management method according to claim 1, characterized in that: Step S1 includes: Step S11. After labeling the data product according to the source of the data, retrieve the data with the same label as the data product, and record the retrieved data as a set {W1, W2, ..., Wk, ..., Wa}, where a represents the number of data with the same label, and Wk represents the kth data with the same label; Step S12. Preset the initial value d of the sliding window parameter, generate a codeword sequence consisting of the 1st codeword to the dth codeword in Wk, search the codeword sequence in the data product, if the same codeword sequence exists in the data product, change the sliding window parameter to d+1, if the same codeword sequence does not exist in the data product, change the codeword sequence to consist of the 2nd codeword to the d+1th codeword in Wk; Repeat step S12 until the elements in the symbol sequence are beyond the range of the data Wk, and output the size d0 of the sliding window parameter when stopping, and calculate the discrimination s0 of the data product relative to Wk, where s0=1-d0 / n, where n represents the symbol length of the data product; Step S13. Execute step S12 for each data with the same label to obtain the discrimination degree of the data product relative to each data with the same label, and record the maximum value as the discrimination degree of the data product; Step S14. According to the source of the data product, the maximum effective time T of the data product is obtained, and the value of the data product is calculated in combination with the data features. The data features include: the symbol length of the data product, the information entropy and the preset weight of each label. The specific calculation formula is as follows: Among them, Q represents the value of the data product, n represents the symbol length of the data product, r represents the information entropy of the data product, c represents the number of label types of the data product, and EC i represents the number of labels belonging to the i-th label type in the labels of the data product, EP i Represents the preset weight of the i-th label type, and e is the base of the natural logarithm.

3. A blockchain-based data product management method according to claim 2, characterized in that: Step S2 includes: Step S21. Calculate the value ratio of the data product to each element in the set {W1, W2, …, Wk, …, Wa}, record the average value of all the ratios as the average value of the data product, and record the smallest ratio as the minimum value of the data product; Determine the number of levels U of the data product, where U = UPS(D1 / D2), UPS is the ceiling function, D1 represents the average value of the data product, and D2 represents the minimum value of the data product; Step S22: Determine the minimum sampling interval tc of the data product, where tc=T / U 2 , sample the data products according to the integer multiples of the minimum sampling interval, and encapsulate the sampling results into data blocks that can be connected to the blockchain, and obtain the sampling data block set {G1, G2, …, GU}, where GU represents the sampling data block composed of the sampling results of the data product when the sampling interval is U·tc; Step S3 includes: Step S31. Starting from the sampling data block G1 with the smallest sampling interval, connect G1 to the blockchain and obtain the hash address A1 of G1 in the blockchain. Add A1 as a data field after the sampling data block G2, connect G2 to the blockchain, and so on until all sampling data blocks are connected to the blockchain and obtain the hash address AU of GU in the blockchain; Step S32. Detect whether there is an update to the tracking data of the data product. If there is no update, set the update data block as an empty data block and go to Step S43. If there is an update, go to Step S41.

4. A blockchain-based data product management method according to claim 3, characterized in that: Step S4 includes: Step S41. Continuously monitor the tracking data of the data product after the data product is deposited into the blockchain and calculate the deviation between the tracking data and the data product: where a represents the deviation between the tracking data and the data product, t0 represents the moment when the data product is deposited into the blockchain, t1 represents the current moment, F(t) represents the function of the tracking data changing with time, and H(t) represents the function of the predicted data of the data product changing with time; Step S42. When it is detected that a < a0, determine that there is a deviation in the tracking data, where a0 is a preset deviation threshold, and package all the data within the time period of a < a0 into an update data block; Insert a jump counting program after the update data block. The jump counting program can extract data from the blockchain according to the addresses in the data block and stop reading the blockchain addresses after the number of reads of the blockchain addresses reaches a preset number; Place the hash address AU of GU in the blockchain into the initial address of the jump counting program, and connect the update data block to the blockchain to obtain the address AG of the update data block.

5. A blockchain-based data product management method according to claim 4, characterized in that: Step S5 includes: Step S51. When a customer purchases a data product, judge the value of the exchange data provided by the customer, divide it by the minimum value of the data product and round down to obtain the purchase level of the customer; Step S52. Send the address AG of the update data block to the customer, generate a key according to the customer's purchase level. After the customer extracts the update data block, input the key into the jump counting program; The jump counting program uses the customer's purchase level as the number of jumps, extracts the sampling data block of the corresponding level in the blockchain, and merges the sampling data block with the update data block and sends it to the customer as the data product finally purchased by the customer.

6. A data product management system based on blockchain, characterized in that: The system includes the following modules: product value module, hierarchical sampling module, block fixing module, data tracking module, and purchase update module; The product value module is used to retrieve the public data of the same label product in the blockchain after obtaining the data product, obtain the discrimination of the data product, calculate the data timeliness parameter according to the stability of the historical tracking data and the average failure time, and calculate the value of the data product according to the discrimination, data volume, data information entropy, timeliness parameter and preset label weight of the data product; The hierarchical sampling module is used to calculate the resolution level of the data product according to the timeliness parameters and value of the data product, determine the sampling interval according to the resolution level, perform fuzzy sampling on the data product according to the sampling interval, obtain data of different sampling intervals, and encapsulate them into sub-data blocks respectively; The block fixing module is used to store the sub-data blocks in the blockchain in descending order of resolution. After obtaining the hash address of the data block storage each time a data block is stored, the hash address is stored at the end of the next sub-data block until all sub-data blocks are stored; The data tracking module is used to detect the total deviation value between the data product and the tracking data. When the total deviation value is higher than a threshold, an update data block is published in the blockchain, and the hash address of the last sub-data block is stored in the update block, and the blockchain keys of all sub-data blocks are stored; The purchase update module is used to give the purchaser a key to update the data block after the purchaser applies for a purchase request, calculate the value of the data provided by the purchaser, determine the acquisition level of the data product based on the value of the provided data, delete the data block key with a resolution higher than the acquisition level from the update data block, extract the data of the corresponding resolution according to the number of sub-data blocks corresponding to the acquisition level, synthesize it into a data product of the corresponding resolution and provide it to the purchaser.

7. A data product management system based on blockchain according to claim 6, characterized in that: The product value module includes: a distinguishing value unit, a timeliness unit and a value judgment unit; The distinguishing value unit is used to retrieve the same data and calculate the degree of distinction between the data product and the existing data; The timeliness unit is used to calculate the stability of the data tracked by the data product within the validity period to obtain the timeliness parameter of the data product; The value judgment unit is used to comprehensively evaluate the value of the data product according to the characteristics of the data product.

8. A data product management system based on blockchain according to claim 7, characterized in that: The hierarchical sampling module includes: a sampling level unit and a classification and packaging unit; The sampling level unit is used to calculate the number of data product levels and the sampling interval of each level of data products; The classification and packaging unit is used to generate different levels of fuzzy sampling data according to the sampling interval, and encapsulate them into sub-data blocks respectively; The block fixing module includes: a data uplink unit and an address merging unit; The data uploading unit is used to upload the sub-data blocks to the blockchain in sequence and obtain the hash address of each sub-data block; The address merging unit is used to merge the address of the previous sub-data block into the next sub-data block.

9. A data product management system based on blockchain according to claim 8, characterized in that: The data tracking module includes: a data monitoring unit and an update publishing unit; The data monitoring unit is used to monitor the fluctuation of the tracking data and pack the fluctuation data into data blocks; The update publishing unit is used to add the address of the sub-data block after the data block of the fluctuating data and to add a data merging program.

10. A data product management system based on blockchain according to claim 9, characterized in that: The purchase and update module includes: a transaction providing unit, a key downloading unit and a product merging unit; The transaction providing unit is used to evaluate the value of the data provided by the purchaser and calculate the purchase level of the data product according to the value; The key download unit is used to send the data block key of the purchaser's fluctuation data and activate the program after verifying the key; The product merging unit is used to extract data blocks from corresponding addresses according to word data block addresses stored in the program, and merge them with the fluctuation data to output as data products.

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