A Trusted Data Sharing Method and System Based on Supply Chain Collaboration

Abstract

The present invention relates to the field of data processing, and particularly to a trusted data sharing method and system based on supply chain collaboration. The method includes the steps of: obtaining a supply chain collaboration data sequence; obtaining the structure of each value in the supply chain collaboration data sequence; calculating the first-layer classification quantity according to the structures of all values in the supply chain collaboration data sequence, clustering all values in the supply chain collaboration data sequence based on the first-layer classification quantity, and denoting the data set composed of all values in each category as the first-layer data set; taking the cumulative sum of the data quantities of all values in the first-layer data set as the occurrence frequency, taking the first-layer data set as the encoding object, performing encoding processing on the first-layer data set based on the occurrence frequency, and using the encoding of the first-layer data set as the first-layer encoding of the values therein; completing the encoding of each layer of the supply chain collaboration data sequence, and storing the encoding result in the shared space. The decoding efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and particularly to a trusted data sharing method and system based on supply chain collaboration. Background Art

[0002] With the increase in the number of supply chain node enterprises, the demand for sharing massive business data has grown exponentially, thus requiring a large amount of shared space to store this massive data. For example, in the automotive supply chain, tens of millions of material requirement data need to be exchanged daily between the vehicle manufacturer and hundreds of parts suppliers, and the uncompressed original data occupies a large amount of storage resources in the shared space.

[0003] Existing data sharing technologies mainly adopt general compression algorithms such as Huffman coding, but these algorithms expose significant limitations in the supply chain scenario: supply chain decision-makers often need to obtain the overall data change trend preferentially, such as the fluctuation of inventory turnover rate and regional demand distribution. Traditional compression algorithms cannot achieve "prior decoding and presentation of key regular data", and need to wait for the decompression of all data to be completed before analysis, which is difficult to meet the real-time decision-making requirements. For example, when dealing with the fluctuation of raw material prices, enterprises need to quickly obtain the overall change trend of inventory data at each node to adjust the procurement strategy, but the traditional method lags behind in trend analysis due to low decoding efficiency, and may miss the best response opportunity.

[0004] The development of intelligent supply chains poses a "hierarchical response" requirement for data sharing: it is necessary to not only meet the compressed storage of massive data but also achieve the rapid decoding and trend presentation of key regular data. Existing technologies lack a differential processing mechanism for the ability to describe data rules and cannot balance between compression effect and decoding priority. In summary, the existing data sharing methods do not perform hierarchical coding according to the rule characteristics of supply chain data, resulting in insufficient decoding timeliness of the overall change trend data and being unable to meet the "rapid data insight" requirement of intelligent supply chains. Developing a hierarchical coding method based on the ability to describe data rules to achieve efficient compression and prior decoding of regular data is of great significance for improving supply chain collaboration efficiency and reducing data sharing costs. Summary of the Invention

[0005] To solve the problem of how to achieve efficient compression and prior decoding of regular data, the present invention provides a trusted data sharing method and system based on supply chain collaboration.

[0006] In a first aspect, the present invention provides a trusted data sharing method based on supply chain collaboration, adopting the following technical solutions:

[0007] A trusted data sharing method based on supply chain collaboration includes the steps of:

[0008] Obtain a supply chain collaboration data sequence;

[0009] Obtain the structure of each value in the supply chain collaboration data sequence, where the structure characterizes the ability of each value to describe the change information of the supply chain collaboration data sequence; calculate the first-layer classification quantity according to the structures of all values in the supply chain collaboration data sequence, where the first-layer classification quantity is positively correlated with the average value of the structures of all values in the supply chain collaboration data sequence, and cluster all values in the supply chain collaboration data sequence based on the first-layer classification quantity, and record the data set composed of all values in each category as the first-layer data set; accumulate the sum of the data quantities of all values in the first-layer data set as the occurrence frequency, use the first-layer data set as the encoding object, perform encoding processing on the first-layer data set based on the occurrence frequency, and use the encoding of the first-layer data set as the first-layer encoding of the values therein; in response to the number of value types in the data set being greater than 1, use the first-layer data set with the number of value types greater than 1 as the data set to be analyzed, cluster the data set to be analyzed to obtain the second-layer data set, encode the second-layer data set, and use the encoding of the second-layer data set as the second-layer encoding of the values therein; in response to the number of value types in the data set not being greater than 1, the encoding of each data in the supply chain collaboration data sequence is completed, and store the encoding result in the shared space.

[0010] The present invention sets different encoding layers for the data of different values according to the description ability of each value to the change law, so that the data with strong change law description ability has fewer encoding layers, so that the data with strong change law description ability can be decoded faster, thereby providing a basis for reconstructing the information of the overall change law of the sequence and presenting the information of the overall change law of the sequence to people faster; further, when performing hierarchical encoding, calculate the occurrence frequency by analyzing the data quantities of all values in each layer of data set, so that the data with more occurrences has a shorter encoding, improving the compression effect.

[0011] Preferably, the method for obtaining the structure of each value includes:

[0012] Perform multiple rounds of filtering and downsampling processing on the supply chain collaboration data sequence in sequence to obtain several layers of reference data sequences; obtain the distance between each data of each value in the supply chain collaboration data sequence and the nearest peak point in each layer of reference data sequence as the peak distance; obtain the size and number of rounds of the filter used for each layer of reference data sequence, use the size of the filter as the base number, and use the number of rounds as the exponent for power calculation to obtain the structure weight; use the structure weight as the weight, and perform weighted summation on the peak distances of each data of each value under all layers of reference data sequences to obtain the comprehensive peak distance; use the opposite number of the average value of the comprehensive peak distances of all data of each value as the exponent, and use the natural number as the base number for power calculation to obtain the structure of each value.

[0013] The present invention filters and downsamples the supply chain collaborative data sequence to extract the peak points of the variation laws at different levels, thereby providing a basis for subsequent analysis of the structure of each value; further, when calculating the structure of the data for each value, the peak points are generally points with strong variation law description ability, so the peak distance is introduced to accurately reflect the variation law description ability of the data for each value; further, when calculating the structure of the data for each value, considering that the higher the number of filtering rounds and the larger the size of the filter, the greater the intensity of removing detailed variation information, and the stronger the ability to extract information describing the overall variation law, so the structure weight is introduced to evaluate the ability of each peak point to describe the overall variation law, thereby providing a basis for accurately calculating the structure of the data for each value subsequently.

[0014] Preferably, the method for obtaining the peak points includes:

[0015] Obtain the extreme points in each layer of reference data sequence, and use the extreme points as the peak points in each layer of reference data sequence.

[0016] The way of the present invention to obtain peak points by extracting extreme points is relatively simple and has high implementation efficiency.

[0017] Preferably, calculating the first-layer classification quantity according to the structures of all values in the supply chain collaborative data sequence includes:

[0018] Multiply the mean value of the structures of all values in the supply chain collaborative data sequence by a preset adjustment coefficient and then perform ceiling processing to obtain the first-layer classification quantity.

[0019] The present invention sets the classification quantity according to the structure of each value, so as to control the strong-structured data to be quickly segmented into individual data, thereby providing a basis for the strong-structured data to have fewer coding layers.

[0020] Preferably, clustering all values in the supply chain collaborative data sequence based on the first-layer classification quantity includes:

[0021] Set the clustering quantity of the clustering algorithm based on the first-layer classification quantity, and use the clustering algorithm to perform clustering processing on all values in the supply chain collaborative data sequence.

[0022] Preferably, clustering the data set to be analyzed to obtain the second-layer data set includes:

[0023] Calculate the number of second-layer classifications based on the structure of all types of values in the dataset to be analyzed. The number of second-layer classifications is positively correlated with the structural mean of all types of values in the dataset to be analyzed. Set the number of clusters of the clustering algorithm based on the number of second-layer classifications, and use the clustering algorithm to cluster all types of values in the dataset to be analyzed. Denote the dataset composed of all types of values in each category as the second-layer dataset.

[0024] Preferably, the calculating the number of second-layer classifications based on the structure of all types of values in the dataset to be analyzed includes:

[0025] Multiply the mean of the structure of all types of values in the dataset to be analyzed by a preset adjustment coefficient and then perform rounding up to obtain the number of second-layer classifications.

[0026] Preferably, the encoding the second-layer dataset includes:

[0027] Take the second-layer dataset as the encoding object, take the number of data of all types of values in the second-layer dataset as the occurrence frequency of the second-layer dataset, and perform encoding processing on the second-layer dataset based on the occurrence frequency.

[0028] The present invention takes into account that compared with all the data in the supply chain collaboration data sequence, the number of datasets in each layer is small. Taking each layer of dataset as the encoding object can reduce the encoded type data, thus providing a basis for improving the decoding efficiency of some data subsequently.

[0029] Preferably, the storing the encoding result in the shared space includes:

[0030] Obtain all-layer encodings of each type of value as the overall encoding of the data of each type of value in the supply chain collaboration data sequence, and store the overall encodings of all the data in the supply chain collaboration data sequence in the shared space.

[0031] In a second aspect, the present invention provides a trusted data sharing system based on supply chain collaboration, adopting the following technical solution:

[0032] A trusted data sharing system based on supply chain collaboration includes: a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned trusted data sharing method based on supply chain collaboration is implemented.

[0033] By adopting the above technical solution, generate a computer program for the above-mentioned trusted data sharing method based on supply chain collaboration and store it in the memory to be loaded and executed by the processor, so as to manufacture a terminal device according to the memory and the processor, which is convenient to use.

[0034] The present invention has the following technical effects:

[0035] The present invention sets different coding layers for data with different values according to the description ability of the variation law of each value of the data, so that the data with strong variation law description ability has fewer coding layers, so that the data with strong variation law description ability can be decoded faster, thus providing a basis for reconstructing the information of the overall variation law of the sequence and presenting the information of the overall variation law of the sequence to people faster;

[0036] Further, when performing hierarchical coding, the occurrence frequency is calculated by analyzing the number of data with all values in each layer of the dataset, so that the data with more occurrences has a shorter code, improving the compression effect. Brief Description of the Drawings

[0037] Figure 1 is a flowchart of the method in a method for sharing trusted data based on supply chain collaboration according to an embodiment of the present invention. Detailed Embodiment

[0038] An embodiment of the present invention discloses a method for sharing trusted data based on supply chain collaboration, referring to Figure 1 , including steps S1 - S4:

[0039] S1: Obtain a supply chain collaboration data sequence.

[0040] Specifically, obtain each type of supply chain collaboration data at each moment, and arrange each type of supply chain collaboration data at all moments in chronological order to obtain a supply chain collaboration data sequence. The types of supply chain collaboration data include but are not limited to the following aspects: product detailed information data, real-time inventory level data, order progress status data, logistics transportation track data, and can also be other types of data, which are not specifically limited in this embodiment.

[0041] S2: Obtain the structure of each value in the supply chain collaboration data sequence, where the structure characterizes the description ability of the data with each value for the variation information of the supply chain collaboration data sequence; calculate the first-layer classification quantity according to the structures of all values in the supply chain collaboration data sequence, where the first-layer classification quantity is positively correlated with the mean value of the structures of all values in the supply chain collaboration data sequence, perform clustering on all values in the supply chain collaboration data sequence based on the first-layer classification quantity, and denote the dataset composed of all values in each category as the first-layer dataset; accumulate the sum of the number of data with all values in the first-layer dataset as the occurrence frequency, use the first-layer dataset as the coding object, perform coding processing on the first-layer dataset based on the occurrence frequency, and use the code of the first-layer dataset as the first-layer code of the values therein.

[0042] It should be noted that the supply chain collaborative data sequence stores time-series data, which generally has certain variation rules. Therefore, each data in the supply chain collaborative data sequence can be fitted through the variation rules. When decompressing, decompressing all data requires a large amount of decompression time. If only some data is decompressed and other data is fitted using the information of the partial data, this will present the supply chain collaborative information to people faster, saving people's reading waiting time. For more accurate data, decoding can be performed during the reading process, thereby improving people's reading efficiency.

[0043] It should be further noted that some data in the supply chain collaborative data sequence have a strong ability to describe the variation rules. For example, peak data. If these data are decoded first and other data can be relatively accurately fitted using these data, the data presented to people at this time is relatively accurate, improving the accuracy of people's reading of the data. Therefore, it is necessary to determine the decoding order of each data according to the ability of each data in the supply chain collaborative data sequence to describe the rules. First, analyze the ability of each data in the supply chain collaborative data sequence to describe the rules.

[0044] S20: Obtain the structure of each value in the supply chain collaborative data sequence.

[0045] It should be noted that the data near the rule change points (extreme value points) in the supply chain collaborative data sequence has a strong ability to describe the rules. Therefore, the ability of each data to describe the rules can be analyzed according to the distance of each data in the supply chain collaborative data sequence from the rule change points. At the same time, due to some small fluctuations, although these fluctuations also have rule changes, the ability of the data at these small fluctuation points to describe the rules is weaker than that of the data at the fluctuations with a larger degree of fluctuation. Therefore, when analyzing the ability of data to describe the rules using the distance from the extreme value points, it is also necessary to evaluate the fluctuation situation at the extreme value points. In this embodiment, the structure is used to reflect the ability of data to describe the rules.

[0046] Preferably, as an example, obtaining the structure of each value in the supply chain collaborative data sequence includes:

[0047] Perform multi-round filtering and downsampling processing on the supply chain collaboration data sequence in turn to obtain several layers of reference data sequences; obtain the distances between each data of each value in the supply chain collaboration data sequence and the nearest peak points in each layer of reference data sequences, which are denoted as peak distances; obtain the size of the filter and the number of rounds used for each layer of reference data sequences, take the size of the filter as the base number, and take the number of rounds as the exponent for power calculation to obtain the structural weight; use the structural weight as the weight, and perform weighted summation on the peak distances of each data of each value under all layers of reference data sequences to obtain the comprehensive peak distance; take the opposite number of the average value of the comprehensive peak distances of all data of each value as the exponent, and take the natural number as the base number for power calculation to obtain the structure of each value.

[0048] It can be understood that the later the layer, the greater the degree of removal of detailed fluctuations, and what remains are mostly large fluctuations. Therefore, the ability to describe the law at the extreme points in the reference data sequence of the later layer is stronger. Thus, the structure of the data is calculated by introducing the layer number of the reference data sequence. At the same time, since the larger the size of the filter, the greater the intensity of removing small fluctuations, the structure of the data is calculated by introducing the size of the filter. The peak distance reflects the distance between the data and the extreme point. The greater the distance from the extreme point, the worse the ability to describe the data law. Therefore, the structure of the data is calculated by introducing the peak distance.

[0049] It should be added that the method for obtaining the peak points includes:

[0050] Obtain the extreme points in each layer of reference data sequences, and take the extreme points as the peak points in each layer of reference data sequences.

[0051] S21: Calculate the classification quantity of the first layer according to the structures of all values in the supply chain collaboration data sequence, perform clustering on all values in the supply chain collaboration data sequence based on the classification quantity of the first layer, and denote the data set composed of all values in each category as the data set of the first layer.

[0052] It should be noted that the traditional Huffman coding algorithm determines the decoding order of data according to the arrangement order of the data. This algorithm cannot randomly extract some or single data for decoding. Therefore, it is impossible to use the traditional Huffman coding algorithm to decode the data that wants to be decoded. In order to quickly decode the data that wants to be decoded, this embodiment proposes a hierarchical coding algorithm. For the data that wants to be quickly decoded, the number of coding layers set is less, and for the data that does not need to be quickly decoded, the number of coding layers set is more, so as to achieve efficient decoding control of the data.

[0053] It should be further noted that in order to quickly present the overall information of the supply chain collaboration data to people, the data with strong law description ability needs to be quickly decoded. Therefore, the number of coding layers of the data needs to be set according to the data law description.

[0054] Preferably, as an example, calculate the number of first-layer classifications based on the structure of all the values in the supply chain collaboration data sequence, cluster all the values in the supply chain collaboration data sequence based on the number of first-layer classifications, and denote the data set composed of all the values in each category as the first-layer data set, including:

[0055] Multiply the structural mean of all the values in the supply chain collaboration data sequence by a preset adjustment coefficient and then round up to obtain the number of first-layer classifications.

[0056] Set the number of clusters of the clustering algorithm based on the number of first-layer classifications, and use the clustering algorithm to cluster all the values in the supply chain collaboration data sequence. Denote the data set composed of all the values in each category as the first-layer data set.

[0057] It can be understood that in this embodiment, the number of classifications is set based on the structure. The larger the number of classifications, the faster the data in the data set is split into individual data. And each time a classification is performed, a layer of encoding is set. Therefore, the faster the data is split into individual data, the fewer the number of encoding layers required. It can also be decoded faster.

[0058] S22: Take the sum of the data quantities of all the values in the first-layer data set as the occurrence frequency, use the first-layer data set as the encoding object, and perform encoding processing on the first-layer data set based on the occurrence frequency. Use the encoding of the first-layer data set as the first-layer encoding of the values therein.

[0059] Preferably, as an example, take the sum of the data quantities of all the values in the first-layer data set as the occurrence frequency, use the first-layer data set as the encoding object, and perform encoding processing on the first-layer data set based on the occurrence frequency. Use the encoding of the first-layer data set as the first-layer encoding of the values therein, including:

[0060] Take the sum of the data quantities of all the values in the first-layer data set as the occurrence frequency, use the first-layer data set as the encoding object, and perform encoding processing on the first-layer data set based on the occurrence frequency using the Huffman coding algorithm. Use the encoding of the first-layer data set as the first-layer encoding of the values therein.

[0061] It can be understood that the larger the data quantity of all the values in the first-layer data set, the more data uses the encoding corresponding to this data set. Therefore, all the values in the first-layer data set

[0062] S3: In response to the number of value types in the dataset being greater than 1, use the first-layer dataset with the number of value types greater than 1 as the dataset to be analyzed. Cluster the dataset to be analyzed to obtain a second-layer dataset, encode the second-layer dataset, and use the encoding of the second-layer dataset as the second-layer encoding for the values therein. In response to the number of value types in the dataset not being greater than 1, the encoding of each data in the supply chain collaboration data sequence is completed, and the encoding result is stored in the shared space.

[0063] It should be noted that, in order to give each value a unique encoding, the encoding needs to be carried out until there is a single value type. Therefore, in this embodiment, the encoding termination is controlled by the number of value types in the dataset.

[0064] It should be further noted that, in order to shorten the encoding length, data with a high occurrence frequency should have a shorter encoding, and data with a low occurrence frequency should have a longer encoding. Therefore, the encoding length needs to be controlled according to the data occurrence frequency.

[0065] S30: In response to the number of value types in the dataset being greater than 1, use the first-layer dataset with the number of value types greater than 1 as the dataset to be analyzed. Cluster the dataset to be analyzed to obtain a second-layer dataset, encode the second-layer dataset, and use the encoding of the second-layer dataset as the second-layer encoding for the values therein.

[0066] Preferably, as an example, in response to the number of value types in the dataset being greater than 1, using the first-layer dataset with the number of value types greater than 1 as the dataset to be analyzed, clustering the dataset to be analyzed to obtain a second-layer dataset, encoding the second-layer dataset, and using the encoding of the second-layer dataset as the second-layer encoding for the values therein includes:

[0067] In response to the number of value types in the dataset being greater than 1, use the first-layer dataset with the number of value types greater than 1 as the dataset to be analyzed.

[0068] Multiply the structural mean of all value types in the dataset to be analyzed by a preset adjustment coefficient, then round up to obtain the second-layer classification quantity. Set the clustering quantity of the clustering algorithm based on the second-layer classification quantity, and use the clustering algorithm to cluster all value types in the dataset to be analyzed. Denote the dataset composed of all value types in each category as the second-layer dataset.

[0069] Use the second-layer dataset as the encoding object, take the cumulative sum of the data quantities of all value types in the second-layer dataset as the occurrence frequency of the second-layer dataset, and encode the second-layer dataset based on the occurrence frequency using the Huffman coding algorithm.

[0070] It can be understood that when the number of value types in the data set is greater than 1, it indicates that the data has not been segmented into individual data yet. At this time, there is no specific encoding for the data of various values, so classification needs to be continued. The cumulative sum of the data quantities of all value types in the second-layer data set reflects the number of data using the encoding of this second-layer data set. The larger this value is, the more data uses the encoding of this second-layer data set, and thus the shorter the length of this encoding. Using the Huffman coding algorithm to encode the second-layer data set based on the occurrence frequency can achieve setting shorter encodings for data with high occurrence frequencies and longer encodings for data with low occurrence frequencies.

[0071] S31: In response to the non-existence of a data set with a value type number greater than 1 and the completion of the encoding of each data in the supply chain collaboration data sequence, store the encoding result in the shared space.

[0072] Preferably, as an example, in response to the non-existence of a data set with a value type number greater than 1 and the completion of the encoding of each data in the supply chain collaboration data sequence, storing the encoding result in the shared space includes:

[0073] In response to the non-existence of a data set with a value type number greater than 1, the encoding of each data in the supply chain collaboration data sequence is completed.

[0074] For any layer, if any data in the supply chain collaboration data sequence does not have an encoding in this layer, use the empty encoding as the encoding of this data in this layer, and connect the encodings of all data in all supply chain collaboration data sequences in this layer and store them together; in the same way, store the encodings of the supply chain collaboration data sequence in each layer. Record the corresponding relationship between all value types of the supply chain collaboration data sequence and the data set in each layer, and store the Huffman tree obtained from the data set in each layer.

[0075] Specifically, if there is only one type of value data in the data set, then use this value as the encoding object, use the number of occurrences of this value in the supply chain data sequence as the occurrence frequency, and perform encoding based on the occurrence frequency.

[0076] It should be noted that for the convenience of explanation, the method of setting the empty encoding will be exemplified below. Assume that there are 4 encodings in this layer, namely 00, 01, 100, and 101. By observation, it can be found that the encoding 11 does not exist among these 4 encodings, so the empty encoding can be 11.

[0077] S4: Perform decompression processing on the encoding.

[0078] Preferably, as an example, performing decompression processing on the encoding includes:

[0079] Divide the data set according to the corresponding relationship between all value types and the data set in each layer to obtain the data set in each layer.

[0080] Starting from the first layer, obtain the encoding of each data in the supply chain data sequence at each layer. Based on the mapping relationship between the encoding and the data set in the Huffman tree of each layer's data set, decode the data set to which each data belongs. And so on, until the corresponding encoding is an empty encoding, the decoding ends. Obtain the data corresponding to the encoding of the previous layer of the empty encoding to implement data decoding.

[0081] It should be noted that since the data with strong regularity description ability has fewer encoding layers, these data can be read out quickly. To facilitate people to quickly read the overall information of the supply chain data, the linear interpolation method can be used to estimate other undecoded data. This is convenient for people's preliminary reading. Subsequently, when all the data is decoded, a more accurate supply chain data sequence will be presented for people to read.

[0082] It can be understood that compared with all the data in the supply chain collaboration data sequence, the number of data sets in each layer is smaller, and the number of encoding types in each layer's data set is relatively small. Therefore, the efficiency of decoding each layer's data set is relatively high, which also provides a basis for decoding some data faster.

[0083] The embodiment of the present invention also discloses a trusted data sharing system based on supply chain collaboration, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a trusted data sharing method based on supply chain collaboration according to the present invention is implemented.

[0084] The above system also includes other components well-known to those skilled in the art such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be elaborated here.

[0085] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high-bandwidth memory, a hybrid storage cube, etc., or any other medium that can be used to store the required information and can be accessed by an application program, a module, or both. Any such computer storage medium can be a part of the device or accessible or connectable to the device.

[0086] The above are all the preferred embodiments of the present invention. The protection scope of the present invention is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A trusted data sharing method based on supply chain collaboration, characterized in that Including the steps: Obtain the supply chain collaboration data sequence; Obtain the structure of each value in the supply chain collaboration data sequence, where the structure characterizes the description ability of each value for the change information of the supply chain collaboration data sequence; calculate the first-layer classification quantity according to the structures of all values in the supply chain collaboration data sequence, where the first-layer classification quantity is positively correlated with the mean value of the structures of all values in the supply chain collaboration data sequence, and cluster all values in the supply chain collaboration data sequence based on the first-layer classification quantity, and denote the data set composed of all values in each category as the first-layer data set; Take the sum of the data quantities of all values in the first-layer data set as the occurrence frequency, take the first-layer data set as the encoding object, perform encoding processing on the first-layer data set based on the occurrence frequency, and take the encoding of the first-layer data set as the first-layer encoding of the values therein; In response to the number of value types in the data set being greater than 1, take the first-layer data set with the number of value types greater than 1 as the data set to be analyzed, cluster the data set to be analyzed to obtain the second-layer data set, encode the second-layer data set, and take the encoding of the second-layer data set as the second-layer encoding of the values therein; In response to the number of value types in the data set not being greater than 1, the encoding of each data in the supply chain collaboration data sequence is completed, and the encoding result is stored in the shared space.

2. The method for sharing trusted data based on supply chain collaboration according to claim 1, wherein The method for obtaining the structure of each value includes: Perform multi-round filtering and downsampling processing on the supply chain collaboration data sequence in turn to obtain several layers of reference data sequences; obtain the distance between each data of each value in the supply chain collaboration data sequence and the nearest peak point in each layer of reference data sequence as the peak distance; obtain the size and number of rounds of the filter used for each layer of reference data sequence, use the size of the filter as the base number and the number of rounds as the exponent for power calculation to obtain the structure weight; use the structure weight as the weight, and perform weighted summation on the peak distances of each data of each value under all layers of reference data sequences to obtain the comprehensive peak distance; take the opposite number of the mean value of the comprehensive peak distances of all data of each value as the exponent and the natural number as the base number for power calculation to obtain the structure of each value.

3. A trusted data sharing method based on supply chain collaboration according to claim 2, characterized in that The method for obtaining the peak point includes: Obtain the extreme points in each layer of reference data sequence, and take the extreme points as the peak points in each layer of reference data sequence.

4. A trusted data sharing method based on supply chain collaboration according to claim 1, characterized in that, The calculation of the first-layer classification quantity according to the structures of all values in the supply chain collaboration data sequence includes: Multiply the mean value of the structures of all values in the supply chain collaboration data sequence by a preset adjustment coefficient and then perform rounding up to obtain the first-layer classification quantity.

5. A trusted data sharing method based on supply chain collaboration according to claim 1, characterized in that, The clustering of all values in the supply chain collaboration data sequence based on the first-layer classification quantity includes: Set the clustering quantity of the clustering algorithm based on the first-layer classification quantity, and use the clustering algorithm to perform clustering processing on all values in the supply chain collaboration data sequence.

6. A trusted data sharing method based on supply chain collaboration according to claim 4, characterized in that The clustering of the data set to be analyzed to obtain the second-layer data set includes: Calculate the second - layer classification quantity based on the structure of all kinds of values in the dataset to be analyzed. The second - layer classification quantity is positively correlated with the structural mean value of all kinds of values in the dataset to be analyzed. Set the clustering quantity of the clustering algorithm based on the second - layer classification quantity, and use the clustering algorithm to perform clustering processing on all kinds of values in the dataset to be analyzed. Denote the dataset composed of all kinds of values in each category as the second - layer dataset.

7. A trusted data sharing method based on supply chain collaboration according to claim 6, characterized in that, The calculation of the second - layer classification quantity based on the structure of all kinds of values in the dataset to be analyzed includes: Round up the product of the structural mean value of all kinds of values in the dataset to be analyzed and a preset adjustment coefficient to obtain the second - layer classification quantity.

8. A trusted data sharing method based on supply chain collaboration according to claim 1, characterized in that, The encoding of the second - layer dataset includes: Take the second - layer dataset as the encoding object, take the data quantity of all kinds of values in the second - layer dataset as the occurrence frequency of the second - layer dataset, and perform encoding processing on the second - layer dataset based on the occurrence frequency.

9. A trusted data sharing method based on supply chain collaboration according to claim 1, characterized in that, The storing of the encoding result in the shared space includes: For any layer, if any data in the supply - chain collaboration data sequence does not have an encoding in this layer, use the empty encoding as the encoding of this data in this layer, and connect the encodings of all data in all supply - chain collaboration data sequences in this layer and store them together; complete the storage of the encoding of each layer of the supply - chain collaboration data sequence.

10. A trusted data sharing system based on supply chain collaboration, characterized in that, Includes: A processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, a trusted data sharing method based on supply - chain collaboration according to any one of claims 1 - 9 is implemented.

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