A method, system and device for uplinking multi-modal test data

Abstract

The present application relates to the technical field of data chaining, and specifically relates to a multi-modal test data chaining method, system and device. According to the change trend of data in the ascending representative sequence of indexes of all dimensions, a plurality of change representative positions are obtained. According to the distribution of the change representative positions, a plurality of sub-representative sequences of the ascending representative sequence are obtained. The performance test data of all dimensions in each sub-representative sequence is used to construct a corresponding hash bucket. According to the correlation of the performance test data of each same time position between corresponding dimensions in each hash bucket, the difference sensitivity of each hash bucket is obtained. The hash bucket is adjusted to obtain a new hash bucket. According to the index representative distribution of each dimension in the new different hash bucket, the chaining order of each new hash bucket is obtained, and the performance test data is chained. The present application adjusts the hash bucket by analyzing the accurate difference between the dimension data, thereby improving the effect of the test data chaining.

Description

Technical Field

[0001] This invention relates to the field of data on-chain technology, specifically to a method, system, and device for uploading multimodal experimental data to the blockchain. Background Technology

[0002] Multimodality refers to different forms of data or information. By putting multimodal experimental data on the blockchain, the data is encrypted and stored in the blockchain network using blockchain technology. Changing the data in any block will cause the hash value of the data block on the entire chain to point to the previous block to be incorrect. This protects the data from being illegally accessed and tampered with, and can significantly improve the security and credibility of the data. Therefore, putting multimodal experimental data on the blockchain is necessary.

[0003] Considering the high repetition and similarity of experimental data, existing technologies use Locality Sensitive Hashing (LSH) to divide the performance test data into hash buckets and build an index during the data uploading process. However, due to the high repetition and similarity of the performance test data, the traditional LSH algorithm fails to accurately identify the differences in data features and cannot accurately retrieve data with significant local differences, resulting in errors in hash bucket division and poor uploading performance. Summary of the Invention

[0004] To address the technical problem of inaccurate identification of data dissimilarity features, leading to errors in hash bucket partitioning and poor on-chain performance, this invention aims to provide a method, system, and device for on-chaining multimodal experimental data. The specific technical solution adopted is as follows:

[0005] This invention proposes a method for uploading multimodal experimental data to the blockchain, the method comprising:

[0006] Acquire multi-dimensional performance test data of the test facility at each time point, and construct a column vector in the sample space;

[0007] Based on the distribution of performance test data corresponding to different time positions in each dimension of the sample space, the index representativeness of each dimension is obtained; an ascending representative sequence of the index representativeness of all dimensions is obtained; based on the changing trend of the data in the ascending representative sequence, multiple changing representative positions are obtained; based on the distribution of the changing representative positions, multiple sub-representative sequences of the ascending representative sequence are obtained.

[0008] Construct corresponding hash buckets using performance test data for all dimensions in each sub-representation sequence. Obtain the difference sensitivity of each hash bucket based on the correlation between performance test data at the same time position between corresponding dimensions within each hash bucket. Adjust the hash buckets according to the difference sensitivity of different hash buckets to obtain new hash buckets.

[0009] Based on the representative distribution of the indexes for each dimension within the new hash buckets, the on-chain order of each new hash bucket is obtained, and the performance test data is then on-chained.

[0010] Furthermore, the method for obtaining the representativeness of the index includes:

[0011] Obtain the mean of the performance test data for each dimension of the sample space at all time points, and use it as the data mean;

[0012] Calculate the deviation of the performance test data at each time point in each dimension of the sample space from the data mean, and standardize it to obtain the standard deviation value.

[0013] The index representativeness of each dimension is obtained by power of the even-numbered standard deviation values ​​corresponding to different time positions in the sample space. The even-numbered standard deviation values ​​are positively correlated with the index representativeness.

[0014] Furthermore, the method for obtaining the position represented by the change includes:

[0015] Obtain the difference sequence of the ascending representative sequence; if the data in the difference sequence is greater than the adjacent data, the middle position of the two adjacent data in the ascending representative sequence corresponding to the data is taken as the change representative position.

[0016] Furthermore, the method for obtaining the sub-representative sequence includes:

[0017] The ascending representative sequence is divided based on the changing representative position to obtain multiple sub-representative sequences.

[0018] Furthermore, the method for obtaining the difference sensitivity includes:

[0019] For any hash bucket, obtain the cryptographic hash value of the performance test data for each dimension at each time point;

[0020] For each time position, if there are identical elements in the same order of the cryptographic hash values ​​across all dimensions, output a two-dimensional value of 1; otherwise, output a two-dimensional value of 0.

[0021] The difference sensitivity of each hash bucket is obtained based on the number of two-dimensional values ​​1 and 0 at all time points, as well as the number of dimensions within each hash bucket. The number of two-dimensional values ​​1 and the number of dimensions are positively correlated with the difference sensitivity, while the number of two-dimensional values ​​0 is negatively correlated with the difference sensitivity.

[0022] Furthermore, the method for obtaining the new hash bucket includes:

[0023] Select hash buckets whose difference sensitivity is less than a preset sensitivity threshold, merge the corresponding hash buckets to form a new hash bucket;

[0024] For hash buckets with a difference sensitivity greater than or equal to a preset sensitivity threshold, the corresponding hash bucket will be used as the new hash bucket.

[0025] Furthermore, the method for obtaining the chain order includes:

[0026] For any new hash bucket, obtain the mean of the index representativeness of all dimensions as the average representativeness;

[0027] Select the dimension that is closest to the average representativeness among all dimensions and use it as the typical dimension; sort all hash buckets in ascending order according to the index representativeness of the typical dimension, and use the corresponding order as the chaining order of each new hash bucket.

[0028] Furthermore, the method for obtaining the difference sequence includes:

[0029] Perform first-order difference processing on the ascending representative sequence to obtain the difference sequence.

[0030] This invention proposes a system for uploading multimodal experimental data to the blockchain, the system comprising:

[0031] Test data acquisition module: Acquires multi-dimensional performance test data of the test facility at each time point, forming a column vector in the sample space;

[0032] Representativeness analysis module: Based on the distribution of performance test data at different time points corresponding to each dimension in the sample space, obtain the index representativeness of each dimension; obtain the ascending representative sequence of the index representativeness of all dimensions; based on the changing trend of the data in the ascending representative sequence, obtain multiple changing representative positions; based on the distribution of the changing representative positions, obtain multiple sub-representative sequences of the ascending representative sequence.

[0033] Hash bucket optimization module: Construct corresponding hash buckets based on the performance test data of all dimensions in each sub-representation sequence. Obtain the difference sensitivity of each hash bucket based on the correlation of the performance test data of the corresponding dimensions at each time position within each hash bucket. Adjust the hash buckets according to the difference sensitivity of the new hash buckets to obtain new hash buckets.

[0034] Data on-chain processing: Based on the representative distribution of the indexes of each dimension in the new different hash buckets, obtain the on-chain order of each new hash bucket, and put the performance test data on the chain.

[0035] The present invention also proposes a system for uploading multimodal experimental data to the blockchain, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the methods for uploading multimodal experimental data to the blockchain.

[0036] The present invention has the following beneficial effects:

[0037] This invention considers that performance test data of different dimensions at different time positions may have different distribution characteristics. Based on the distribution of performance test data for each dimension at different time positions in the sample space, the index representativeness of each dimension is obtained, quantifying the influence and distinguishability of each dimension in the overall data. Based on the changing trend of data in the ascending representative sequence of all dimensions' index representativeness, multiple change representative positions are obtained, reflecting the dynamic characteristics and potential patterns of the data, and identifying key change points in the data. Based on the distribution of change representative positions, multiple sub-representative sequences of the ascending representative sequence are obtained, making the data within each sub-representative sequence have higher similarity and consistency, facilitating more efficient data storage and retrieval in hash buckets. A corresponding hash bucket is constructed using the performance test data of all dimensions in each sub-representative sequence. Based on the correlation of performance test data at each time position between corresponding dimensions within each hash bucket, the difference sensitivity of each hash bucket is obtained, reflecting the degree of similarity and difference of the data within the hash bucket. The hash buckets are adjusted to obtain new hash buckets. Based on the index representativeness distribution of each dimension within the new different hash buckets, the on-chain order of each new hash bucket is obtained, and the performance test data is on-chain, reducing dependency conflicts and verification complexity between data, and improving on-chain efficiency. This invention improves the effectiveness of uploading experimental data to the blockchain by analyzing the accurate differences between dimensional data and adjusting the hash bucket. Attached Figure Description

[0038] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating a method for uploading multimodal experimental data to an online platform, as provided in one embodiment of the present invention;

[0040] Figure 2 A flowchart illustrating a method for obtaining index representativeness according to an embodiment of the present invention;

[0041] Figure 3 A flowchart illustrating a method for obtaining difference sensitivity according to an embodiment of the present invention;

[0042] Figure 4 This is a structural block diagram of a multimodal test data uplink system provided in one embodiment of the present invention. Detailed Implementation

[0043] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method, system, and device for uploading multimodal test data according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0045] The following description, in conjunction with the accompanying drawings, details the specific scheme of the multimodal test data uploading method, system, and equipment provided by the present invention.

[0046] Please see Figure 1 The diagram illustrates a flowchart of a method for uploading multimodal experimental data to the blockchain according to an embodiment of the present invention. The specific method includes:

[0047] Step S1: Obtain multi-dimensional performance test data of the test facility at each time point and construct a column vector in the sample space.

[0048] In embodiments of this invention, during the on-chain process of various types of test data, to avoid high facility similarity and the need for relatively uniform standards, the distribution of data with high repetition and many identical attributes may affect the analysis of data differences. Therefore, it is necessary to perform feature differentiation processing on the performance test data across multiple dimensions. First, for each location, time-aligned multi-dimensional sensors are deployed to acquire multi-dimensional performance test data of the test facility at each time point, forming a column vector in the sample space. Each dimension corresponds to a type of performance test data.

[0049] It should be noted that, in one embodiment of the present invention, the test facility is a highway infrastructure, which can acquire multi-dimensional performance test data such as test pressure data in the mechanical performance test, settlement depth data in the roadbed settlement test, temperature and humidity status inside the highway structure layer in the durability performance test, physical performance data, and traffic flow data.

[0050] It should be noted that in the embodiments of the present invention, due to factors such as erroneous measurement and equipment failure during the test, the accuracy of the results may be poor. Therefore, the data needs to be preprocessed, including: the implementers can obtain the range of performance parameters of the test facility based on human experience and relevant professional knowledge, and remove the unqualified data corresponding to the performance parameter range; the nearest neighbor interpolation method is used to complete the data according to the observation time sequence to obtain the complete performance test data corresponding to each time position; the specific nearest neighbor interpolation method is a technical means well known to those skilled in the art, and will not be described in detail here.

[0051] It should be noted that, in order to facilitate subsequent data processing and avoid differences in units and magnitudes between data, the multi-dimensional performance test data are standardized to eliminate the influence of dimensions in data calculations.

[0052] Step S2: Based on the distribution of performance test data at different time points corresponding to each dimension in the sample space, obtain the index representativeness of each dimension; obtain the ascending representative sequence of the index representativeness of all dimensions; based on the changing trend of the data in the ascending representative sequence, obtain multiple changing representative positions; based on the distribution of the changing representative positions, obtain multiple sub-representative sequences of the ascending representative sequence.

[0053] Because the materials and textures of the test facilities vary, performance test data for the same dimension at different times and locations may exhibit different distribution characteristics, reflecting the dynamic changes within the test facilities. The more significant the differences between performance test data for each dimension, the more obvious the performance of the test facility in that dimension, and the higher the index representativeness. Therefore, the index representativeness of each dimension is obtained based on the distribution of performance test data for each dimension at different times and locations in the sample space.

[0054] Preferably, in one embodiment of the present invention, the method for obtaining the representativeness of the index is described in [reference needed]. Figure 2 It illustrates a flowchart of a method for obtaining index representativeness, including:

[0055] Step S201: Obtain the mean of the performance test data for each dimension of the sample space at all time points, and use it as the data mean.

[0056] In order to comprehensively reflect the overall situation of the performance test data of each dimension in the sample space, the performance test data of each dimension at all time positions is quantified by averaging to obtain the overall level of the performance test data of each dimension, which is convenient for subsequent comparison.

[0057] Step S202: Calculate the deviation of the performance test data at each time point in each dimension of the sample space from the data mean, and standardize it to obtain the standard deviation value.

[0058] It should be noted that, in one embodiment of the present invention, the standardization of the deviation of the performance test data at each time point relative to the data mean can be achieved by calculating the deviation and the standard deviation of the performance test data at all time points for each dimension. The closer the standard deviation is to 0, the closer the performance test data is to the data mean, and the more concentrated the data distribution.

[0059] Step S203: Obtain the index representativeness of each dimension based on the even power of the standard deviation value corresponding to different time positions in the sample space. The even power of the standard deviation value is positively correlated with the index representativeness.

[0060] Among them, positive correlation means that the dependent variable increases as the independent variable increases, and the dependent variable decreases as the independent variable decreases. That is, the larger the even power of the standard deviation value, the greater the deviation between the performance test data at each time point and the data mean, the more uneven the data distribution, the more likely abnormal situations will occur, and the greater the representativeness of the index.

[0061] In one embodiment of the present invention, the representative formula for the index is expressed as:

[0062]

[0063] Among them, Y d p represents the representativeness of the index of the d-th dimension in the sample space; d,i This represents the performance test data at the i-th time point corresponding to the d-th dimension in the sample space. This represents the mean of the performance test data at all time points corresponding to the d-th dimension in the sample space, i.e., the data mean. n represents the standard deviation of the performance test data corresponding to all time points in the d-th dimension of the sample space; d This represents the number of time positions corresponding to the d-th dimension in the sample space.

[0064] In the representative formula of the index, This represents the ratio between the deviation of the performance test data at the i-th time position corresponding to the d-th dimension in the calculation sample space relative to the data mean and the standard deviation, i.e., standardizing the deviation to obtain the standard deviation value; This represents the summation of the fourth power of the standard deviation values ​​corresponding to all time positions in the d-th dimension of the sample space. The larger the summation value, the greater the difference between the performance test data and the data mean, the more uneven the data distribution, the greater the data discrimination, and the greater the index representativeness.

[0065] By sorting the representativeness of all dimensions' indexes in ascending order from smallest to largest, we can gain a comprehensive understanding of the impact of each dimension on facility performance and reflect the changing patterns and characteristics of facility performance across different dimensions. We can obtain an ascending sequence of representative indexes for all dimensions and, based on the changing trends of the data in the ascending sequence, obtain multiple representative positions of change.

[0066] Preferably, in one embodiment of the present invention, the method for obtaining the change representing the position includes:

[0067] Obtain the difference sequence of the ascending representative sequence; if the data in the difference sequence is greater than the adjacent data, the middle position of the two adjacent data in the ascending representative sequence corresponding to the data is taken as the change representative position.

[0068] Difference sequences can remove non-stationary trends in ascending representative sequences, making it easier to observe and analyze the patterns and characteristics of changes in the sequence. They can highlight the changing parts of the sequence, i.e., the parts that are different from the trend of the sequence. It should be noted that, in the embodiments of the present invention, the ascending representative sequence is subjected to first-order difference processing to obtain the difference sequence. The difference processing is a well-known technique to those skilled in the art and will not be described in detail here.

[0069] Since the change representative position is usually the inflection point or important turning point of the data change trend, it reflects the significant change in the data performance characteristics between dimensions. Dividing the ascending representative sequence into sub-representative sequences by the distribution of the change representative position can highlight the similarity of changes between different dimensions. Therefore, based on the distribution of the change representative position, multiple sub-representative sequences of the ascending representative sequence can be obtained.

[0070] Preferably, in one embodiment of the present invention, the method for obtaining the sub-representative sequence includes:

[0071] The ascending representative sequence is divided based on the changing representative position to obtain multiple sub-representative sequences. The sub-representative sequences illustrate the concentration of performance test data from different dimensions within the test facility in terms of facility performance.

[0072] For example, if there exists an ascending representative sequence (1, 2, 5, 6, 8, 9) and a difference sequence (1, 2, 1, 2, 1), where the 2nd and 4th data in the difference sequence are greater than their adjacent data, the corresponding data in the ascending representative sequence are 2 and 5, 6 and 8. The middle position of the two sets of data is the change representative position, and the resulting sub-representative sequences are (1, 2), (5, 6), and (8, 9).

[0073] Step S3: Construct corresponding hash buckets using the performance test data of all dimensions in each sub-representation sequence. Obtain the difference sensitivity of each hash bucket based on the correlation of the performance test data of each dimension at the same time position within each hash bucket. Adjust the hash buckets according to the difference sensitivity of different hash buckets to obtain new hash buckets.

[0074] Each sub-representative sequence contains dimensions with relatively small changing trends. Constructing corresponding hash buckets using the performance test data of all dimensions in each sub-representative sequence helps to centrally manage and analyze the performance test data. Correlation can be used to assess whether there is a correlation between the performance test data of different dimensions at the same time position and the degree of correlation. The higher the degree of correlation, the more likely the performance data of other dimensions will change when the performance test data of one dimension changes, indicating a high sensitivity to differences between dimensions. Therefore, the difference sensitivity of each hash bucket is obtained based on the correlation of the performance test data of the corresponding dimensions at each time position within each hash bucket.

[0075] Preferably, in one embodiment of the present invention, the method for obtaining the difference sensitivity is described in [reference needed]. Figure 3 It illustrates a flowchart of a method for obtaining difference sensitivity, including:

[0076] Step S301: For any hash bucket, obtain the cryptographic hash value of the performance test data for each dimension at each time point.

[0077] It should be noted that, in one embodiment of the present invention, the performance test data is hashed using the SHA-512 algorithm to obtain the corresponding encrypted hash value; wherein, the SHA-512 algorithm is a secure hash algorithm that generates a 512-bit hash value, that is, the encrypted hash value is a binary number with a length of 512 bits; the specific SHA-512 algorithm is a technical means well known to those skilled in the art, and will not be described in detail here.

[0078] Step S302: For each time position, if there are identical elements in the same order of the encrypted hash values ​​across all dimensions, output the two-dimensional value 1; otherwise, output the two-dimensional value 0.

[0079] By comparing the values ​​of cryptographic hashes across dimensions, we can analyze the similarity between data, indicating that there is some correlation or common characteristics between the data in these dimensions.

[0080] For example, if the dimension is 3, the encrypted hash values ​​of the data in different dimensions at each time point are 3acdcd0a3894f0fcb837, 3ardcd0a3724f0d8b837, and 3aedcd0a3894f06cq837, respectively. The corresponding output result is 1101111100111000111.

[0081] Step S303: Based on the number of two-dimensional values ​​1 and 0 at all time points, and the number of dimensions in each hash bucket, obtain the difference sensitivity of each hash bucket. The number of two-dimensional values ​​1 and the number of dimensions are positively correlated with the difference sensitivity, while the number of two-dimensional values ​​0 is negatively correlated with the difference sensitivity.

[0082] In this context, a positive correlation indicates that the dependent variable increases as the independent variable increases, and decreases as the independent variable decreases. That is, the greater the number of 1s in the two-dimensional model, the greater the number of dimensions, indicating greater similarity and smaller differences between the data across multiple dimensions, resulting in higher sensitivity to data with discrepancies. Conversely, a negative correlation indicates that the dependent variable decreases as the independent variable increases, and increases as the independent variable decreases. That is, the greater the number of 0s in the two-dimensional model, the greater the differences between the dimensions, resulting in lower sensitivity to data with discrepancies.

[0083] In one embodiment of the present invention, the formula for difference sensitivity is expressed as:

[0084]

[0085] Among them, M T Indicates the difference sensitivity of the T-th hash bucket; n T,1 This represents the number of 1s corresponding to the two-dimensional value in the T-th hash bucket; n T,0 This represents the number of 0 values ​​corresponding to the two-dimensional value in the T-th hash bucket; n T,d This represents the number of corresponding dimensions in the T-th hash bucket; max(n) T′,d ) represents the maximum number of dimensions in all hash buckets except the T-th hash bucket; sigmoid() represents the logistic function.

[0086] In the formula for difference sensitivity, n T,0 +0.01 for n T,0 Adding 0.01 is to prevent the formula from being meaningless when the value is 0; The larger the number of 1s in the T-th hash bucket, the smaller the number of 0s in the two-dimensional hash bucket, the smaller the ratio, the more similar the data between dimensions, the more sensitive to differences, and the greater the difference sensitivity. This represents the ratio between the number of corresponding dimensions in the T-th hash bucket and the maximum number of dimensions in all other hash buckets except the T-th hash bucket. The larger the ratio, the larger the number of corresponding dimensions in the T-th hash bucket, indicating that there are more correlations between data in different dimensions. If there are differences, more dimensions will be affected, and the sensitivity to differences will be greater.

[0087] By adjusting hash buckets based on their difference sensitivity, computational load is reduced, allowing data dimensions that fully represent the actual situation during experiments to occupy a major portion of the on-chain contract. New hash buckets are obtained by adjusting the hash buckets according to their difference sensitivity.

[0088] Preferably, in one embodiment of the present invention, the method for obtaining the new hash bucket includes:

[0089] Select hash buckets whose difference sensitivity is less than a preset sensitivity threshold, merge the corresponding hash buckets to form a new hash bucket;

[0090] For hash buckets with a difference sensitivity greater than or equal to a preset sensitivity threshold, the corresponding hash bucket will be used as the new hash bucket.

[0091] It should be noted that, in one embodiment of the present invention, the preset sensitivity threshold is set to 0.1; in other embodiments of the present invention, the preset sensitivity threshold may be set according to specific circumstances, and will not be limited or elaborated here.

[0092] Step S4: Based on the representative distribution of the indexes of each dimension in the new different hash buckets, obtain the on-chain order of each new hash bucket and put the performance test data on the chain.

[0093] During the data uploading process, dependencies may exist between data items. Determining the uploading order according to the representative distribution of the index ensures that dependent items are uploaded before the items they depend on, avoiding data inconsistency issues and facilitating subsequent data retrieval. Therefore, based on the representative distribution of the index for each dimension within the new hash buckets, the uploading order for each new hash bucket is obtained, and performance test data is uploaded to the blockchain.

[0094] Preferably, in one embodiment of the present invention, the method for obtaining the chain order includes:

[0095] For any new hash bucket, obtain the mean of the index representativeness of all dimensions as the average representativeness; select the dimension that is closest to the average representativeness among all dimensions as the typical dimension; sort all hash buckets in ascending order according to the index representativeness of the typical dimension, and the corresponding order is the chaining order of each new hash bucket.

[0096] It should be noted that, in another embodiment of the present invention, after obtaining the on-chain order of each new hash bucket, the data in each new hash bucket is stored in a chain list in sequence; a smart contract defining the data processing logic and rules is constructed, and the results after chain list storage are passed to the contract by calling the pre-deployed smart contract. If a transaction containing detailed information of the contract call is generated after executing the contract, it needs to be verified and confirmed by nodes in the blockchain network; if it conforms to the consensus mechanism and rules of the blockchain, the block containing the transaction data will be added to the blockchain, realizing the on-chain data. The introduction of smart contracts increases the transparency and traceability of data, making the data processing process more fair and open.

[0097] In summary, this invention obtains the index representativeness of each dimension based on the distribution of performance test data at different time points in the sample space; obtains multiple change representative positions based on the changing trends of data in the ascending representative sequence of all dimensions' index representativeness; obtains multiple sub-representative sequences of the ascending representative sequence based on the distribution of change representative positions; constructs corresponding hash buckets using the performance test data of all dimensions in each sub-representative sequence; obtains the difference sensitivity of each hash bucket based on the correlation of performance test data at the same time point between corresponding dimensions within each hash bucket; adjusts the hash buckets to obtain new hash buckets; and obtains the on-chain order of each new hash bucket based on the index representativeness distribution of each dimension within the new different hash buckets, and then on-chains the performance test data. This invention improves the effectiveness of on-chaining test data by analyzing the accurate differences between dimensional data.

[0098] Based on the same application concept as the method for uploading multimodal experimental data to the blockchain provided in this embodiment, this embodiment also proposes a system for uploading multimodal experimental data to the blockchain, such as... Figure 4 As shown, the system includes: an experimental data acquisition module 401, a representativeness analysis module 402, a hash bucket optimization module 403, and a data on-chain processing module 404.

[0099] Test data acquisition module 401: Acquires multi-dimensional performance test data of the test facility at each time point, forming a column vector in the sample space;

[0100] Representativeness analysis module 402: Based on the distribution of performance test data at different time positions corresponding to each dimension in the sample space, obtain the index representativeness of each dimension; obtain the ascending representative sequence of the index representativeness of all dimensions; obtain multiple change representative positions based on the changing trend of the data in the ascending representative sequence; obtain multiple sub-representative sequences of the ascending representative sequence based on the distribution of the change representative positions.

[0101] Hash bucket optimization module 403: Constructs corresponding hash buckets based on the performance test data of all dimensions in each sub-representation sequence; obtains the difference sensitivity of each hash bucket based on the correlation of the performance test data of each dimension at the same time position within each hash bucket; adjusts the hash buckets according to the difference sensitivity of different hash buckets to obtain new hash buckets;

[0102] Data on-chain processing 404: Based on the representative distribution of the indexes of each dimension in the new different hash buckets, obtain the on-chain order of each new hash bucket, and put the performance test data on the chain.

[0103] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above.

[0104] It should be understood that the system provided in this embodiment is used to execute the above-described method for uploading multimodal test data to the blockchain, and therefore has the same beneficial effects as the methods adopted, run, or implemented by the application it stores.

[0105] The present invention also proposes a device for uploading multimodal test data, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the steps of a method for uploading multimodal test data.

[0106] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for uploading multimodal experimental data to an online blockchain, characterized in that, The method includes: Acquire multi-dimensional performance test data of the test facility at each time point, forming a column vector in the sample space; Based on the distribution of performance test data at different time points corresponding to each dimension in the sample space, the index representativeness of each dimension is obtained; an ascending representative sequence of the index representativeness of all dimensions is obtained; based on the changing trend of the data in the ascending representative sequence, multiple changing representative positions are obtained; based on the distribution of the changing representative positions, multiple sub-representative sequences of the ascending representative sequence are obtained. Construct corresponding hash buckets using performance test data for all dimensions in each sub-representation sequence. Obtain the difference sensitivity of each hash bucket based on the correlation between performance test data at the same time position between corresponding dimensions within each hash bucket. Adjust the hash buckets according to the difference sensitivity of different hash buckets to obtain new hash buckets. Based on the representative distribution of the indexes of each dimension within the new hash buckets, the on-chain order of each new hash bucket is obtained, and the performance test data is on-chained. The methods for obtaining representativeness of the index include: Obtain the mean of the performance test data for each dimension of the sample space at all time points, and use it as the data mean; Calculate the deviation of the performance test data at each time point in each dimension of the sample space from the data mean, and standardize it to obtain the standard deviation value. The index representativeness of each dimension is obtained by power of the even number of times the standard deviation value corresponds to the position of each dimension in the sample space. The even number of times the standard deviation value corresponds to the index representativeness is positively correlated. The method for obtaining the difference sensitivity includes: For any hash bucket, obtain the cryptographic hash value of the performance test data for each dimension at each time point; For each time position, if there are identical elements in the same order of the cryptographic hash values ​​across all dimensions, output a two-dimensional value of 1; otherwise, output a two-dimensional value of 0. The difference sensitivity of each hash bucket is obtained based on the number of two-dimensional values ​​1 and 0 at all time points, as well as the number of dimensions within each hash bucket. The number of two-dimensional values ​​1 and the number of dimensions are positively correlated with the difference sensitivity, while the number of two-dimensional values ​​0 is negatively correlated with the difference sensitivity.

2. The method for uploading multimodal experimental data to the blockchain according to claim 1, characterized in that, The method for obtaining the position represented by the change includes: Obtain the difference sequence of the ascending representative sequence; if the data in the difference sequence is greater than the adjacent data, the middle position of the two adjacent data in the ascending representative sequence corresponding to the data is taken as the change representative position.

3. The method for uploading multimodal experimental data to the blockchain according to claim 1, characterized in that, The method for obtaining the sub-representative sequence includes: The ascending representative sequence is divided based on the changing representative position to obtain multiple sub-representative sequences.

4. The method for uploading multimodal experimental data to the blockchain according to claim 1, characterized in that, The method for obtaining the new hash bucket includes: Select hash buckets whose difference sensitivity is less than a preset sensitivity threshold, merge the corresponding hash buckets to form a new hash bucket; For hash buckets with a difference sensitivity greater than or equal to a preset sensitivity threshold, the corresponding hash bucket will be used as the new hash bucket.

5. The method for uploading multimodal test data to the blockchain according to claim 1, characterized in that, The method for obtaining the chain order includes: For any new hash bucket, obtain the mean of the index representativeness of all dimensions as the average representativeness; Select the dimension that is closest to the average representativeness among all dimensions and use it as the typical dimension; sort all hash buckets in ascending order according to the index representativeness of the typical dimension, and use the corresponding order as the chaining order of each new hash bucket.

6. The method for uploading multimodal test data to the blockchain according to claim 2, characterized in that, The method for obtaining the difference sequence includes: Perform first-order difference processing on the ascending representative sequence to obtain the difference sequence.

7. A system for uploading multimodal experimental data to the blockchain, characterized in that, The system includes: Test data acquisition module: Acquires multi-dimensional performance test data of the test facility at each time point, forming a column vector in the sample space; Representativeness analysis module: Based on the distribution of performance test data at different time points corresponding to each dimension in the sample space, obtain the index representativeness of each dimension; obtain the ascending representative sequence of the index representativeness of all dimensions; based on the changing trend of the data in the ascending representative sequence, obtain multiple changing representative positions; based on the distribution of the changing representative positions, obtain multiple sub-representative sequences of the ascending representative sequence. The methods for obtaining representativeness of the index include: Obtain the mean of the performance test data for each dimension of the sample space at all time points, and use it as the data mean; Calculate the deviation of the performance test data at each time point in each dimension of the sample space from the data mean, and standardize it to obtain the standard deviation value. The index representativeness of each dimension is obtained by power of the even number of times the standard deviation value corresponds to the position of each dimension in the sample space. The even number of times the standard deviation value corresponds to the index representativeness is positively correlated. Hash bucket optimization module: Construct corresponding hash buckets based on the performance test data of all dimensions in each sub-representation sequence. Obtain the difference sensitivity of each hash bucket based on the correlation of the performance test data of each dimension at the same time position within each hash bucket. Adjust the hash buckets according to the difference sensitivity of different hash buckets to obtain new hash buckets. The method for obtaining the difference sensitivity includes: For any hash bucket, obtain the cryptographic hash value of the performance test data for each dimension at each time point; For each time position, if there are identical elements in the same order of the cryptographic hash values ​​across all dimensions, output a two-dimensional value of 1; otherwise, output a two-dimensional value of 0. Based on the number of two-dimensional values ​​1 and 0 at all time points, and the number of dimensions in each hash bucket, the difference sensitivity of each hash bucket is obtained. The number of two-dimensional values ​​1 and the number of dimensions are positively correlated with the difference sensitivity, while the number of two-dimensional values ​​0 is negatively correlated with the difference sensitivity. Data on-chain processing: Based on the representative distribution of the indexes of each dimension in the new different hash buckets, obtain the on-chain order of each new hash bucket, and put the performance test data on the chain.

8. A device for uploading multimodal experimental data, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for uploading multimodal test data as described in any one of claims 1 to 6.

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