Trusted data sharing method and system based on supply chain collaboration

Abstract

The invention relates to the field of data processing, in particular to a trusted data sharing method and system based on supply chain collaboration. The method comprises the following steps: acquiring a supply chain collaborative data sequence; obtaining the constitutive property of each value in the supply chain collaborative data sequence; a first-layer classification number is calculated according to the constitutive property of all the values in the supply chain collaborative data sequence, all the values in the supply chain collaborative data sequence are clustered based on the first-layer classification number, and a data set formed by all the values in each category is marked as a first-layer data set; taking the accumulated sum of the data quantity of all the values in the first-layer data set as an occurrence frequency, taking the first-layer data set as a coding object, carrying out coding processing on the first-layer data set based on the occurrence frequency, and taking a code of the first-layer data set as a first-layer code of an inner value of the first-layer data set; and completing coding of each layer of the supply chain collaborative data sequence, and storing a coding result into a shared space. And the decoding efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular, 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 prioritize obtaining the overall data change trend, such as the fluctuation of inventory turnover rate and regional demand distribution. Traditional compression algorithms cannot achieve "prioritized decoding and presentation of key rule data", and need to wait for the decompression of all data to be completed before analysis, making it difficult to meet the real-time decision-making requirements. For example, when dealing with raw material price fluctuations, enterprises need to quickly obtain the overall change trend of inventory data at each node to adjust procurement strategies, while traditional methods have a lag 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 rule 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, existing data sharing methods do not perform hierarchical coding according to the rule characteristics of supply chain data, resulting in insufficient decoding timeliness of overall change trend data and being unable to meet the "quick data insight" requirements of intelligent supply chains. Developing a hierarchical coding method based on the ability to describe data rules and achieving efficient compression and prioritized decoding of rule 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 prioritized decoding of rule 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: A trusted data sharing method based on supply chain collaboration includes the steps of: Obtain a supply chain collaboration data sequence; Obtain the structure of each value in the supply chain collaboration data sequence, where the structure characterizes the ability of each value's data 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, and 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. 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; 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 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, take the first-layer data set with the number of value types greater than 1 as the data set to be analyzed, perform clustering on the data set to be analyzed to obtain the second-layer data set, perform encoding on 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.

[0007] According to the present invention, different encoding layers are set for the data of different values according to the ability of each value's data to describe 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, thus 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, the occurrence frequency is calculated by analyzing the data quantities of all values in each layer of the data set, so that the data with more occurrences has a shorter encoding, improving the compression effect.

[0008] Preferably, the method for obtaining the structure of each value includes: Perform multiple rounds of 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 sequences as the peak distance; obtain the size and number of rounds of the filter used for each layer of reference data sequences, perform power calculation with the size of the filter as the base and the number of rounds as the exponent 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 mean value of the comprehensive peak distances of all data of each value as the exponent, and perform power calculation with the natural number as the base to obtain the structure of each value.

[0009] The present invention filters and downsamples the supply chain collaborative data sequence to extract the peak points of the variation rules at different levels, thereby providing a basis for subsequent analysis of the structure of each value; further, when calculating the structure of the data of each value, the peak points generally have a strong ability to describe the variation rules, so the peak distance is introduced to accurately reflect the ability of the data of each value to describe the variation rules; further, when calculating the structure of the data of 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 the detailed variation information, and the stronger the ability to extract the information describing the overall variation rules, so the structure weight is introduced to evaluate the ability of each peak point to describe the overall variation rules, thereby providing a basis for accurately calculating the structure of the data of each value subsequently.

[0010] Preferably, the method for obtaining the peak points includes: 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.

[0011] The way of the present invention to obtain peak points by extracting extreme points is relatively simple and has high implementation efficiency.

[0012] Preferably, calculating the first-layer classification quantity according to the structures of all values in the supply chain collaborative data sequence includes: 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.

[0013] The present invention sets the classification quantity according to the structure of each value, so as to control the ability to quickly split individual data for the data with strong structure, thereby providing a basis for realizing that the data with strong structure has fewer coding layers.

[0014] Preferably, clustering all values in the supply chain collaborative 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 collaborative data sequence.

[0015] Preferably, clustering the dataset to be analyzed to obtain the second-layer dataset includes: Calculate the second-layer classification quantity according to the structures of all values in the dataset to be analyzed. The second-layer classification quantity is positively correlated with the mean value of the structures of all 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 values in the dataset to be analyzed. Denote the dataset composed of all values in each category as the second-layer dataset.

[0016] Preferably, calculating the number of second-layer classifications based on the structure of all values in the dataset to be analyzed includes: After multiplying the mean value of the structure of all values in the dataset to be analyzed by a preset adjustment coefficient, perform ceiling processing to obtain the number of second-layer classifications.

[0017] Preferably, encoding the second-layer dataset includes: Taking the second-layer dataset as the encoding object, taking the number of data of all values in the second-layer dataset as the occurrence frequency of the second-layer dataset, and performing encoding processing on the second-layer dataset based on the occurrence frequency.

[0018] 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, thereby providing a basis for improving the decoding efficiency of some data subsequently.

[0019] Preferably, storing the encoding result in the shared space includes: Obtain all-layer encodings of each value as the overall encoding of the data of each 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.

[0020] In a second aspect, the present invention provides a trusted data sharing system based on supply chain collaboration, adopting the following technical solution: 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.

[0021] 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, thereby manufacturing a terminal device according to the memory and the processor for convenient use.

[0022] The present invention has the following technical effects: The present invention sets different encoding layers for the data of different values according to the description ability of the data of each value for the variation law, so that the data with strong variation law description ability has fewer encoding layers, so that the data with strong variation law description ability can be decoded faster, thereby providing a basis for reconstructing the information of the overall variation law of the sequence and presenting the overall variation law information of the sequence to people faster; Furthermore, when performing hierarchical encoding, calculate the occurrence frequency by analyzing the number of data of all values in each layer of dataset, so that the data with more occurrences has a shorter encoding, improving the compression effect. Description of the Drawings

[0023] Figure 1 It is a flowchart of the method in an embodiment of the present invention for a trusted data sharing method based on supply chain collaboration. Detailed Implementation Manner

[0024] An embodiment of the present invention discloses a trusted data sharing method based on supply chain collaboration. Refer to Figure 1 , including steps S1 - S4: S1: Obtain the supply chain collaboration data sequence.

[0025] Specifically, obtain each type of supply chain collaboration data at each moment, and arrange all types of supply chain collaboration data at all moments in time sequence to obtain the 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.

[0026] S2: Obtain the structure of each value in the supply chain collaboration data sequence, where the structure characterizes the ability of the data 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, 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 its internal values.

[0027] It should be noted that the supply chain collaboration data sequence stores time - series data, and time - series data generally has a certain variation law. Therefore, each data in the supply chain collaboration data sequence can be fitted out through the variation law. When decompressing, decompressing all data requires a large amount of decompression time. If only part of the data is decompressed and other data is fitted out using the information of part of the data, this will present the supply chain collaboration information to people faster and save people's reading waiting time. For more accurate data, decoding can be performed during the reading process. Thus, people's reading efficiency is improved.

[0028] It should be further noted that some data in the supply chain collaborative data sequence have a stronger ability to describe the variation law. For example, peak data. If these data are decoded first and used, other data can be relatively accurately fitted. At this time, the presented data is relatively accurate, improving the accuracy of people reading 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 law. First, analyze the ability of each data in the supply chain collaborative data sequence to describe the law.

[0029] S20: Obtain the structure of each value in the supply chain collaborative data sequence.

[0030] It should be noted that the data near the law change point (extreme point) in the supply chain collaborative data sequence has a stronger ability to describe the law. Therefore, the ability of each data to describe the law can be analyzed according to the distance of each data in the supply chain collaborative data sequence from the law change point. At the same time, due to some small fluctuations, although these fluctuations also have law changes, the ability of the data at these small fluctuation points to describe the law is weaker than that of the fluctuations with a larger degree of fluctuation. Therefore, when analyzing the ability of data to describe the law using the distance from the extreme point, it is also necessary to evaluate the fluctuation situation at the extreme point. In this embodiment, the structure is used to reflect the ability of data to describe the law.

[0031] Preferably, as an example, obtaining the structure of each value in the supply chain collaborative data sequence includes: Perform multiple rounds of filtering and downsampling processing on the supply chain collaborative data sequence in turn to obtain several layers of reference data sequences; record the distance between each data with each value in the supply chain collaborative data sequence and the nearest peak point in each layer of reference data sequences as the peak distance; obtain the size and number of rounds of the filter used for each layer of reference data sequences, 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 with each value under all layers of reference data sequences to obtain the comprehensive peak distance; use the opposite number of the mean value of the comprehensive peak distances of all data with each value as the exponent, and use the natural number as the base number for power calculation to obtain the structure of each value.

[0032] It can be understood that the later the layer number, the greater the degree of removal of the detailed fluctuations, and what remains are mostly larger fluctuations. Therefore, the ability of the extreme points in the reference data sequences with later layer numbers to describe the law is stronger. Therefore, 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 the small fluctuations, the structure of the data is calculated by introducing the size of the filter. The peak distance reflects the distance of the data from the extreme point. The greater the distance from the extreme point, the worse the ability of the data to describe the law. Therefore, the structure of the data is calculated by introducing the peak distance.

[0033] It should be added that the method for obtaining the peak points includes: Obtain the extreme points in each layer of reference data sequences, and use the extreme points as the peak points in each layer of reference data sequences.

[0034] S21: Calculate the first-layer classification quantity according to the structure of all values in the supply chain collaboration data sequence, 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.

[0035] 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 a single data for decoding. Therefore, the traditional Huffman coding algorithm cannot achieve the decoding of the data to be decoded. In order to quickly decode the data to be decoded, this embodiment proposes a hierarchical coding algorithm. For the data 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.

[0036] 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 rule description ability needs to be quickly decoded. Therefore, the number of coding layers of the data needs to be set according to the data rule description.

[0037] Preferably, as an example, calculating the first-layer classification quantity according to the structure 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, including: Multiply the mean value of the structure of all values in the supply chain collaboration data sequence by a preset adjustment coefficient and then perform ceiling processing to obtain the first-layer classification quantity.

[0038] Set the clustering quantity of the clustering algorithm based on the first-layer classification quantity, use the clustering algorithm to perform clustering processing on all values in the supply chain collaboration data sequence, and denote the data set composed of all values in each category as the first-layer data set.

[0039] It can be understood that in this embodiment, the classification quantity is set through the structure. The larger the classification quantity, the faster the data in the data set is split into single data. And each time a classification is performed, a layer of coding is set. Therefore, the faster the data is split into single data, the fewer the coding layers required. And it can also be decoded faster.

[0040] S22: Accumulate the sum of the data quantities of all types of values in the first-layer dataset as the occurrence frequency. Take the first-layer dataset as the encoding object, perform encoding processing on the first-layer dataset based on the occurrence frequency, and use the encoding of the first-layer dataset as the first-layer encoding for the values therein.

[0041] Preferably, as an example, accumulating the sum of the data quantities of all types of values in the first-layer dataset as the occurrence frequency, taking the first-layer dataset as the encoding object, performing encoding processing on the first-layer dataset based on the occurrence frequency, and using the encoding of the first-layer dataset as the first-layer encoding for the values therein includes: Accumulate the sum of the data quantities of all types of values in the first-layer dataset as the occurrence frequency, take the first-layer dataset as the encoding object, perform encoding processing on the first-layer dataset using the Huffman coding algorithm based on the occurrence frequency, and use the encoding of the first-layer dataset as the first-layer encoding for the values therein.

[0042] It can be understood that the larger the sum of the data quantities of all types of values in the first-layer dataset, the more data uses the encoding corresponding to this dataset. Therefore, all values in the first-layer dataset S3: In response to the number of value types in the dataset being greater than 1, take the first-layer dataset with the number of value types greater than 1 as the dataset to be analyzed, perform clustering on the dataset to be analyzed to obtain the 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.

[0043] It should be noted that in order to ensure that each value has a unique encoding, encoding needs to be performed until there is only one type of value. Therefore, in this embodiment, the encoding termination is controlled by the number of types in the dataset.

[0044] 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.

[0045] S30: In response to the number of value types in the dataset being greater than 1, take the first-layer dataset with the number of value types greater than 1 as the dataset to be analyzed, perform clustering on the dataset to be analyzed to obtain the 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.

[0046] Preferably, as an example, in response to the number of value types in the dataset being greater than 1, the first-layer dataset with the number of value types greater than 1 is used as the dataset to be analyzed. The dataset to be analyzed is clustered to obtain the second-layer dataset, and the second-layer dataset is encoded. The encoding of the second-layer dataset is used as the second-layer encoding of the values therein, including: In response to the number of value types in the dataset being greater than 1, the first-layer dataset with the number of value types greater than 1 is used as the dataset to be analyzed.

[0047] Multiply the structural mean of all value types in the dataset to be analyzed by a preset adjustment coefficient and 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 perform clustering processing on all value types in the dataset to be analyzed. The dataset composed of all value types in each category is recorded as the second-layer dataset.

[0048] Use the second-layer dataset as the encoding object, and use 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. Based on the occurrence frequency, use the Huffman coding algorithm to perform encoding processing on the second-layer dataset.

[0049] It can be understood that if the number of value types in the dataset is greater than 1, it means that the data has not been segmented into individual data yet. At this time, the data of various value types do not have specific encodings, so further classification is required. The cumulative sum of the data quantities of all value types in the second-layer dataset reflects the number of data using the encoding of the second-layer dataset. The larger this value is, the more data uses the encoding of the second-layer dataset, and thus the shorter the length of this encoding. Encoding the second-layer dataset using the Huffman coding algorithm 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.

[0050] S31: 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.

[0051] Preferably, as an example, 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, including: 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.

[0052] For any layer, if any data in the supply chain collaboration data sequence does not have a code in that layer, then use an empty code as the code of that data in that layer, and connect the codes of all data in all supply chain collaboration data sequences in that layer and store them together; in the same way, store the codes of the supply chain collaboration data sequence in each layer. Record the correspondence between all possible values of the supply chain collaboration data sequence and the data sets in each layer, and store the Huffman trees obtained from the data sets in each layer.

[0053] Specifically, if there is only one type of data in the data set, then use that value as the coding object, use the number of occurrences of that value in the supply chain data sequence as the occurrence frequency, and perform coding based on the occurrence frequency.

[0054] It should be noted that for the convenience of explanation, the method of setting the empty code will be illustrated by an example below. Suppose there are 4 codes in this layer, namely 00, 01, 100, and 101. By observation, it can be found that the code 11 does not exist among these 4 codes, so the empty code can be 11.

[0055] S4: Perform decompression processing on the code.

[0056] Preferably, as an example, performing decompression processing on the code includes: Divide the data sets according to the correspondence between all possible values and the data sets in each layer to obtain the data sets in each layer.

[0057] Starting from the first layer, obtain the codes of each data in the supply chain data sequence in each layer, and based on the mapping relationship between the codes and the data sets in the Huffman trees of the data sets in each layer, decode the data sets to which each data belongs. And so on, until the corresponding code is the empty code, the decoding ends. Obtain the data corresponding to the code of the layer above the empty code to achieve data decoding.

[0058] It should be noted that since the data with strong rule description ability has fewer coding layers, these data can be read out quickly. For the convenience of people to quickly read the overall information of the supply chain data, the linear interpolation method can be used to estimate other undecoded data. It is convenient for people to read initially. Subsequently, when all the data is decoded, a more accurate supply chain data sequence will be presented for people to read.

[0059] 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 small, and the number of coding types in each data set is relatively small. Therefore, the efficiency of decoding each data set is relatively high, which also provides a basis for decoding some data faster.

[0060] An embodiment of the present invention also discloses a trusted data sharing system based on supply chain collaboration, which includes 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.

[0061] The above system further includes a communication bus, a communication interface, and other components well-known to those skilled in the art. Their settings and functions are known in the art, so they will not be described in detail here.

[0062] In the present invention, the foregoing 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 device. 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 part of the device or accessible or connectable to the device.

[0063] The above are all preferred embodiments of the present invention. The protection scope of the present invention is not limited accordingly. 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: Includes steps: Obtain supply chain collaborative data series; Obtain the structure of each value in the supply chain collaborative data sequence, the structure characterizes the ability of each value to describe the change information of the supply chain collaborative data sequence; calculate the first-level classification quantity according to the structure of all values ​​in the supply chain collaborative data sequence, the first-level classification quantity is positively correlated with the structural mean of all values ​​in the supply chain collaborative data sequence, cluster all values ​​in the supply chain collaborative data sequence based on the first-level classification quantity, and record the data set consisting of all values ​​in each category as the first-level data set; The cumulative sum of the number of data of all kinds of values ​​in the first-layer data set is taken as the frequency of occurrence, the first-layer data set is taken as the encoding object, the first-layer data set is encoded based on the frequency of occurrence, and the encoding of the first-layer data set is taken as the first-layer encoding of the values ​​therein; In response to the fact that the number of value types in the data set is greater than 1, a first-layer data set whose number of value types is greater than 1 is used as a data set to be analyzed, the data set to be analyzed is clustered to obtain a second-layer data set, the second-layer data set is encoded, and the encoding of the second-layer data set is used as a second-layer encoding of the values ​​therein; In response to the fact that there is no data set in which the number of value types is greater than 1, the encoding of each data in the supply chain collaborative data sequence is completed, and the encoding result is stored in the shared space.

2. According to claim 1, a trusted data sharing method based on supply chain collaboration is characterized in that: The structural acquisition method of each value includes: The supply chain collaborative data sequence is subjected to multiple rounds of filtering and downsampling in sequence to obtain several layers of reference data sequences; the distance between each data of each value in the supply chain collaborative data sequence and the nearest peak point in each layer of the reference data sequence is obtained and recorded as the peak distance; the size of the filter used for each layer of the reference data sequence and the number of rounds are obtained, and the filter size is taken as the base and the number of rounds is taken as the exponent to perform power calculation to obtain the structural weight; the structural weight is taken as the weight, and the peak distances of each data of each value in all layers of the reference data sequence are weightedly summed to obtain the comprehensive peak distance; the opposite of the mean of the comprehensive peak distances of all data of each value is taken as the exponent and the natural number is taken as the base to perform power calculation to obtain the structurality 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: The extreme value points are obtained in the reference data sequence of each layer, and the extreme value points are used as the peak points in the reference data sequence of each layer.

4. The trusted data sharing method based on supply chain collaboration according to claim 1 is characterized in that: The first-level classification quantity is calculated based on the structure of all values ​​in the supply chain collaborative data sequence, including: The structural mean of all values ​​in the supply chain collaboration data sequence is multiplied by the preset adjustment coefficient and then rounded up to obtain the first-level classification quantity.

5. The trusted data sharing method based on supply chain collaboration according to claim 1 is characterized in that: The clustering of all values ​​in the supply chain collaborative data sequence based on the first-level classification quantity includes: The number of clusters of the clustering algorithm is set based on the number of first-level classifications, and the clustering algorithm is used to cluster all values ​​in the supply chain collaborative data sequence.

6. A trusted data sharing method based on supply chain collaboration according to claim 4, characterized in that: The step of clustering the data set to be analyzed to obtain the second layer of data set includes: The number of second-layer classifications is calculated according to the structure of all the values ​​in the data set to be analyzed. The number of second-layer classifications is positively correlated with the structural mean of all the values ​​in the data set to be analyzed. The number of clusters of the clustering algorithm is set based on the number of second-layer classifications. The clustering algorithm is used to cluster all the values ​​in the data set to be analyzed, and the data set composed of all the values ​​in each category is recorded as the second-layer data set.

7. A trusted data sharing method based on supply chain collaboration according to claim 6, characterized in that: The second-level classification quantity is calculated based on the structure of all values ​​in the data set to be analyzed, including: The structural mean of all values ​​in the data set to be analyzed is multiplied by the preset adjustment coefficient and then rounded up to obtain the number of second-level classifications.

8. The trusted data sharing method based on supply chain collaboration according to claim 1 is characterized in that: The encoding of the second layer data set includes: The second layer data set is taken as the encoding object, the number of data of all kinds of values ​​in the second layer data set is taken as the occurrence frequency of the second layer data set, and the second layer data set is encoded based on the occurrence frequency.

9. The trusted data sharing method based on supply chain collaboration according to claim 1 is characterized in that: The step of storing the encoding result in a shared space includes: For any layer, if any data in the supply chain collaborative data sequence does not have a code at that layer, the empty code will be used as the code of the data at that layer, and the codes of all data in all supply chain collaborative data sequences at that layer will be connected together and stored; completing the storage of each layer of the code of the supply chain collaborative data sequence.

10. A trusted data sharing system based on supply chain collaboration, characterized in that: include: A processor and a memory, wherein 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 any one of claims 1-9 is implemented.

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