CN120128190B - A trusted data storage method and system based on supply chain collaboration - Google Patents
A trusted data storage method and system based on supply chain collaborationInfo
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- CN120128190B CN120128190B CN202510621624.3A CN202510621624A CN120128190B CN 120128190 B CN120128190 B CN 120128190B CN 202510621624 A CN202510621624 A CN 202510621624A CN 120128190 B CN120128190 B CN 120128190B
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Abstract
The invention relates to the field of data processing, in particular to a trusted data storage method and system based on supply chain cooperation. The method comprises the steps of obtaining a trusted data sequence coordinated with a supply chain, obtaining classification boundary points of each value, determining the possibility that the classification boundary points of various values are probability switching points according to the distances between the classification boundary points of different values, screening out the probability switching points according to the probability of the probability switching points, obtaining the occurrence probability of each value in the trusted data sequence and the occurrence probability of each value between two adjacent probability switching points to be respectively recorded as integral probability and local probability, calculating the probability switching feasibility of each value according to the difference of the coding length under the integral probability and the coding length under the local probability, screening out the value of the probability to be switched according to the probability switching feasibility, switching the integral probability value of the probability to be switched into the local probability, and realizing coding compression based on the probability after switching. The storage capacity is effectively reduced.
Description
Technical Field
The invention relates to the field of data processing, in particular to a trusted data storage method and system based on supply chain cooperation.
Background
The chain includes a plurality of participating subjects, such as suppliers, manufacturers, distributors, retailers, and customers. To achieve efficient collaborative operation, a large amount of supply chain collaborative data is generated among all the participants. The data widely relates to a plurality of key fields such as product detailed information, real-time inventory level, order progress state, logistics transportation track and the like, and is an important basis for the collaborative management of a supply chain. Therefore, it is necessary to store these data.
With the continuous expansion of the scale of the supply chain and the continuous increase of the business complexity, the data volume generated in the coordination process of the supply chain shows explosive growth. Such a large amount of data presents a significant challenge to data storage. In this case, the data compression technique becomes one of the key means to solve the above-described problems.
The arithmetic coding algorithm is a commonly used data compression method, which implements data compression by dividing intervals according to the frequency of occurrence of data. The section division occupied by the data with high frequency is larger, the section division occupied by the data with low frequency is smaller, the length of the final section obtained by section division is relatively larger based on the section division occupied by the data with low frequency, and the length of the coded data obtained by section selection is shorter. The supply chain data comprises a plurality of types of data, and the data distribution difference of each part in the supply chain data sequence to be compressed is larger, so that the occurrence frequency difference of the same kind of value data of different parts is larger. Setting the interval division duty ratios of the same kind of values of different parts to be consistent tends to reduce the data compression effect. And different division duty ratios are set for each data, so that the data can be decoded only by recording the division duty ratios, and the memory capacity is increased when the division duty ratios are recorded. How to improve the compression effect by setting an appropriate division ratio to the data becomes an important point of study of the present invention.
Disclosure of Invention
In order to solve the problem of how to improve the compression effect by setting a proper division duty ratio for data, the invention provides a trusted data storage method and a trusted data storage system based on supply chain coordination.
In a first aspect, the present invention provides a trusted data storage method based on supply chain collaboration, which adopts the following technical scheme:
a trusted data storage method based on supply chain collaboration, comprising the steps of:
acquiring a trusted data sequence coordinated with a supply chain;
Acquiring a local area by taking any data in a trusted data sequence as a center, acquiring the data density of each value in the local area as the local density of each value under the data, carrying out clustering processing according to the local density of each value under all data, and acquiring a classification boundary point of each value;
Determining the possibility that various valued classification boundary points are probability switching points according to the distances between the different valued classification boundary points, wherein the probability of the probability switching points is inversely related to the distances between the different valued classification boundary points;
The probability switching feasibility of each value is calculated according to the coding length difference under the overall probability and the coding length difference under the local probability, and the probability switching feasibility is positively correlated with the coding length difference and negatively correlated with the length of the local probability value;
and screening out the value of the probability to be switched according to the probability switching feasibility, switching the whole probability value of the probability to be switched into the local probability, and realizing coding compression based on the probability after switching.
The invention considers that the occurrence probability of each valued data in the trusted data sequence is different in different areas, the compression effect is reduced by adopting unified probability to encode each area, so that the whole probability is replaced by local probability to control the encoding compression of the trusted data sequence, the compression effect is improved, further, the probability is used for subsequent decoding, the storage capacity is increased when the local probability is recorded, the number of the local probability is reduced, the probability switching points are obtained, one valued data between the two probability switching points utilizes the same local probability, the number of the local probability is reduced, the data storage capacity is reduced, and further, the storage capacity cannot be reduced when the local probability is switched according to the fact that the storage capacity increased by the local probability is larger than the reduced storage capacity of the local probability, so that the necessity of local probability switching is judged by calculating the probability switching feasibility, the probability value to be switched is screened, and the storage capacity is effectively saved.
Preferably, the acquiring the local area with any data in the trusted data sequence as a center includes:
And taking any data in the trusted data sequence as a center to acquire an area with a preset length and recording the area as a local area of the data.
Preferably, the acquiring the data density of each value in the local area is recorded as the local density of each value under the data, and includes:
and acquiring the data quantity of each value in the local area of the data, and dividing the data quantity of each value by the length of the local area to obtain the local density of each value under the data.
Preferably, the clustering process is performed according to the local density of each value under all data, and a classification boundary point of each value is obtained, including:
clustering all data in the trusted data sequence according to the local density of each value under each data to obtain a plurality of categories;
the same class is divided into a data segment, the trusted data sequence is divided into a plurality of data segments, and the last data in the previous data segment in two adjacent data segments is used as a classification boundary point to obtain a plurality of classification boundary points.
According to the clustering method, the clustering is carried out according to the density, the areas with different occurrence probabilities are divided, the areas with the same occurrence probability are divided into one area, and therefore the acquired local probability can represent the occurrence probability condition of the data in the area, and a basis is provided for the subsequent switching probability.
Preferably, the method for acquiring the probability of the probability switching point includes:
And clustering all the valued classification boundary points, and taking the reciprocal of the average value of the distances between each classification boundary point and other classification boundary points in the class as the probability switching point possibility.
The invention analyzes the distance between the classification boundary point and other classification boundary points in the category to characterize the condition of other classification boundary points in the category, thereby providing a basis for the subsequent screening of the classification boundary points.
Preferably, the screening the probability switching points according to the probability of the probability switching points includes:
And taking the classification boundary point with the highest probability of the probability switching point in one class as the probability switching point.
According to the invention, only one classification boundary point is selected in each category, so that the number of classification boundary points is reduced, the number of local probability records is reduced, and a basis is provided for saving storage.
Preferably, the calculating the probability switching feasibility of each value includes:
The method comprises the steps of obtaining the local quantity of each value between two adjacent probability switching points, recording the data quantity of each value as the local quantity of each value, taking the global probability of each value as a base number, taking the local quantity as an index, carrying out power calculation, taking the local probability of each value as the base number, taking the local quantity as the index, carrying out power calculation, taking the calculation result as the local probability of each value, taking the local probability of each value as the local probability of each value, selecting one piece of data with the shortest length in a range from 0 to L1, taking the selected length of the data as the coding length of each value under the global probability, selecting one piece of data with the shortest length in a range from 0 to L2, taking the selected length of the data as the coding length of each value under the local probability, taking the coding length of each value under the global probability and the coding length of the local probability under the local probability as the local probability to obtain the local probability switching feasibility of each value under the two adjacent probability switching points, and taking the local probability of each value under the two adjacent probability switching points as the average value.
Preferably, the screening the value of the probability to be switched according to the probability switching feasibility includes:
and taking the value with probability switching feasibility larger than a preset switching probability threshold value as the value of the probability to be switched.
The invention screens out partial values to carry out probability switching, thereby ensuring that the switching probability can reduce the storage capacity instead of increasing the storage capacity.
Preferably, the switching the overall probability value of the probability to be switched to the local probability, and implementing the encoding compression based on the probability after switching includes:
The method comprises the steps of marking the value of the required switching probability as the value to be switched, replacing the whole probability of the data to be switched in the trusted data sequence by using the corresponding local probability, subtracting the accumulated sum of the local probabilities of all the values to be switched from 1 to obtain the adjusted residual probability, subtracting the accumulated sum of the whole probabilities of all the values to be switched from 1 to obtain the residual probability before adjustment, multiplying the whole probability of the data to be switched by the ratio of the residual probability after adjustment to the residual probability before adjustment to obtain the adjustment probability of the data to be switched, replacing the whole probability of the data to be switched by using the adjustment probability, controlling the interval division of arithmetic coding based on the replaced probability, and coding and compressing the trusted data sequence.
In a second aspect, the present invention provides a trusted data storage system based on supply chain collaboration, which adopts the following technical scheme:
A supply chain collaboration-based trusted data storage system includes a processor and a memory storing computer program instructions that when executed by the processor implement a supply chain collaboration-based trusted data storage method as described above.
By adopting the technical scheme, the trusted data storage method based on the cooperation of the supply chain generates the computer program, and the computer program is stored in the memory to be loaded and executed by the processor, so that the terminal equipment is manufactured according to the memory and the processor, and the use is convenient.
The invention has the following technical effects:
According to the method, the occurrence probability of each valued data in the trusted data sequence in different areas is considered to be different, and the areas are encoded by adopting the unified probability, so that the compression effect is reduced, and the local probability is used for replacing the overall probability to control the encoding compression of the trusted data sequence, so that the compression effect is improved;
Further, considering that the probability can be used for subsequent decoding, and recording local probability can increase the storage capacity, so that the number of the local probabilities is reduced, and the probability switching points are obtained, so that one valued data between the two probability switching points all use the same local probability, thereby reducing the number of the local probabilities and reducing the data storage capacity;
Further, considering that the storage amount of which the local probability is increased is more than the reduced storage amount of the local probability is switched, the local probability cannot be switched, and therefore the necessity of local probability switching is judged by calculating probability switching feasibility, probability values needing switching are screened out, and the storage amount is effectively saved.
Drawings
FIG. 1 is a flow chart of a method of trusted data storage based on supply chain collaboration in accordance with an embodiment of the present invention.
Detailed Description
The embodiment of the invention discloses a trusted data storage method based on supply chain cooperation, which referring to fig. 1, comprises the following steps of S1-S6:
s1, acquiring a trusted data sequence coordinated with a supply chain.
Specifically, the trusted data of the coordination of the supply chain is obtained, and the trusted data of the coordination of the supply chain may be product detailed information data, real-time inventory level data, order progress status data, logistics transportation track data, or other data, which is not limited in this embodiment. The sequence of all trusted data in the supply chain collaboration is denoted as a trusted data sequence.
S2, taking any data in the trusted data sequence as a center to obtain a local area, obtaining the data density of each value in the local area as the local density of each value under the data, carrying out clustering processing according to the local density of each value under all data, and obtaining the classification boundary point of each value.
It should be noted that, in the conventional arithmetic coding algorithm, the occurrence frequency of each value is counted according to all data, and the section division duty ratio of each value data is set according to the occurrence frequency of each value to perform the coding compression processing. When decoding is performed, the data is decoded by acquiring the section division duty ratio of each value by using the frequency of occurrence of the data of each value. Thus, to facilitate subsequent decoding, the interval division duty ratio of each valued data is recorded. Since the data of each part of the trusted data sequence has large differences, the frequency of occurrence of each part of data has large differences. If the section division duty ratio of the data is set with the actual frequency of occurrence of each piece of data, it can achieve a good effect. However, this also causes a difference in the division ratio of each data of different portions, and thus requires more memory space to record the division ratio of each data. In order to increase the compression, it is necessary to balance it.
It should be further noted that, since the data in the trusted data sequence has a region similarity, the frequency of occurrence of the data in the region is similar. The same interval split duty cycle can be used within the region. Firstly, the region division is required to be carried out according to the similarity of data distribution in the trusted data sequence.
And S20, taking any data in the trusted data sequence as a center to acquire a local area.
Preferably, as an example, the acquiring the local area with any data in the trusted data sequence as a center includes:
And taking any data in the trusted data sequence as a center to acquire an area with a preset length and recording the area as a local area of the data.
And S21, acquiring the data density of each value in the local area and recording the data density as the local density of each value under the data.
Preferably, as an example, obtaining the data density of each value in the local area is recorded as the local density of each value under the data, including:
and acquiring the data quantity of each value in the local area of the data, and dividing the data quantity of each value by the length of the local area to obtain the local density of each value under the data.
S22, clustering is carried out according to the local density of each value under all data, and classification boundary points of each value are obtained.
Preferably, as an example, clustering is performed according to the local density of each value under all data, and classification boundary points of each value are obtained, including:
clustering all data in the trusted data sequence according to the local density of each value under each data to obtain a plurality of categories;
the same class is divided into a data segment, the trusted data sequence is divided into a plurality of data segments, and the last data in the previous data segment in two adjacent data segments is used as a classification boundary point to obtain a plurality of classification boundary points.
It can be understood that the classification boundary points are region division points with different distribution of one value, wherein the frequency of occurrence of the data with one value is similar in each region between the two classification boundary points. The same interval division duty ratio can be set for one of the valued data between the two classification boundary points.
And S3, determining the probability of the classification boundary points with various values as probability switching points according to the distances between the classification boundary points with different values, wherein the probability of the probability switching points is inversely related to the distances between the classification boundary points with different values, and screening out the probability switching points according to the probability of the probability switching points.
It should be noted that, since the classification boundary points obtained by the data with different values may be different, if the classification boundary points are reserved for each value, a large number of classification boundary points exist, and thus the number of obtained segments is large. Since the segmented areas all require recording a segment split duty, they will record a large number of segment split duty, and thus they will require a large amount of memory space to record the segment split duty. To reduce the number of segmented regions, the partial classification boundary point reservations need to be screened.
It should be further noted that if one classification boundary point is not coincident with other classification boundary points, but its distance is shorter, it may completely retain only one classification boundary point and remove the remaining classification boundary points. Thus, some classification boundary points may be screened based on the distance between classification boundary points.
S30, determining the probability that the classification boundary points with various values are probability switching points according to the distances among the classification boundary points with different values.
Preferably, as an example, determining the probability that the classification boundary points of various values are probability switching points according to the distances between the classification boundary points of different values includes:
And clustering all the valued classification boundary points, and taking the reciprocal of the average value of the distances between each classification boundary point and other classification boundary points in the class as the probability switching point possibility.
And S31, screening out the probability switching points according to the probability of the probability switching points.
Preferably, as an example, screening out the probability switching points according to the probability of the probability switching points includes:
And taking the classification boundary point with the highest probability of the probability switching point in one class as the probability switching point.
It can be appreciated that by the method, a classification boundary point can be screened out from a plurality of classification boundary points which are relatively close to each other for reservation, so that the problem of reserving too many classification boundary points is effectively prevented, and unnecessary data storage quantity is reduced.
And S4, acquiring the occurrence probability of each value in the trusted data sequence and the occurrence probability of each value between two adjacent probability switching points, respectively marking the occurrence probability as integral probability and local probability, and calculating the probability switching feasibility of each value according to the coding length difference under the integral probability and the coding length difference under the local probability, wherein the probability switching feasibility is positively correlated with the coding length difference and negatively correlated with the length of the local probability value.
Although providing different section division ratios for each section reduces the encoding length, the recording section division ratio requires a storage space. If the memory amount of the recording section division ratio is larger than the code length reduction amount caused by the adjustment section division ratio, the adjustment section division ratio is described and the compression amount is increased, so that the adjustment section division ratio is not needed at this time. Therefore, the integrated analysis of the reduced encoding length caused by the adjustment of the section division duty ratio and the storage amount of the recorded section division duty ratio is needed to determine whether to adjust the section division duty ratio.
S40, acquiring the occurrence probability of each value in the trusted data sequence and the occurrence probability of each value between two adjacent probability switching points, and respectively recording the occurrence probability and the local probability as the whole probability.
Preferably, as an example, obtaining the occurrence probability of each value in the trusted data sequence and the occurrence probability of each value between two adjacent probability switching points are respectively recorded as an overall probability and a local probability, including:
Obtaining the occurrence frequency of each valued data in the trusted data sequence, dividing the occurrence frequency of each valued data by the length of the trusted data sequence to obtain the occurrence probability of each valued data, and recording the occurrence probability as the overall probability of each valued data;
The occurrence frequency of each valued data is obtained in the part of the trusted data sequence between two adjacent probability switching points, the occurrence frequency of each valued data is divided by the length of the part between the two adjacent probability switching points to obtain the occurrence probability of each valued data, and the occurrence probability is recorded as the local probability of each valued.
S41, calculating probability switching feasibility of each value according to the coding length difference under the overall probability and the coding length difference under the local probability.
Preferably, as an example, calculating probability switching feasibility of each value according to a data length after section division under overall probability and a data length difference after section division under partial probability includes:
The method comprises the steps of obtaining the local quantity of each value between two adjacent probability switching points, recording the data quantity of each value as the local quantity of each value, taking the global probability of each value as a base number, taking the local quantity as an index, carrying out power calculation, taking the local probability of each value as the base number, taking the local quantity as the index, carrying out power calculation, taking the calculation result as the local probability of each value, taking the local probability of each value as the local probability of each value, selecting one piece of data with the shortest length in a range from 0 to L1, taking the selected length of the data as the coding length of each value under the global probability, selecting one piece of data with the shortest length in a range from 0 to L2, taking the selected length of the data as the coding length of each value under the local probability, taking the coding length of each value under the global probability and the coding length of the local probability under the local probability as the local probability to obtain the local probability switching feasibility of each value under the two adjacent probability switching points, and taking the local probability of each value under the two adjacent probability switching points as the average value.
It can be understood that the coding length under the overall probability reflects the coding length obtained by coding only the overall probability of the data with one value between two adjacent probability switching points, and the coding length under the local probability reflects the coding length obtained by coding only the local probability of the data with one value between two adjacent probability switching points. The probability switching probability reflects the situation where the memory saved by the adjustment section division duty ratio is larger than the memory recorded by the adjustment section division duty ratio, which is larger, which means that the adjustment section division duty ratio can save more memory by performing encoding compression, and therefore the adjustment section division duty ratio is more necessary.
It should be noted that, the method for obtaining encoding compression is based on a basic principle technology of an arithmetic coding algorithm, and the arithmetic coding algorithm is a prior art and will not be described herein. Arithmetic coding algorithms typically use probabilities as interval-dividing duty cycles, and thus adjust probabilities, i.e., adjust interval-dividing duty cycles.
And S5, screening out the value of the probability to be switched according to the probability switching feasibility, switching the whole probability value of the probability to be switched into the local probability, and realizing coding compression based on the probability after switching.
Preferably, as an example, the selecting the value of the probability to be switched according to the probability switching feasibility, switching the whole probability value of the probability to be switched into the local probability, and implementing coding compression based on the probability after switching, including:
and taking the value with probability switching feasibility larger than a preset switching probability threshold value as the value of the probability to be switched.
The method comprises the steps of marking the value of the required switching probability as the value to be switched, replacing the whole probability of the data to be switched in the trusted data sequence by using the corresponding local probability, subtracting the accumulated sum of the local probabilities of all the values to be switched from 1 to obtain the adjusted residual probability, subtracting the accumulated sum of the whole probabilities of all the values to be switched from 1 to obtain the residual probability before adjustment, multiplying the whole probability of the data to be switched by the ratio of the residual probability after adjustment to the residual probability before adjustment to obtain the adjustment probability of the data to be switched, replacing the whole probability of the data to be switched by using the adjustment probability, controlling the interval division of arithmetic coding based on the replaced probability, and coding and compressing the trusted data sequence.
And acquiring ordinal numbers of the probability switching points in the trusted data sequence and local probabilities between every two probability switching points, and storing the acquired ordinal numbers, the overall probabilities, the local probabilities and the coded and compressed coding sequence.
In order to facilitate subsequent decompression, a segmenter is inserted between ordinal numbers, local probabilities, and the code sequence at the time of storage. And marking the value to be switched.
S6, decompression processing is carried out.
Preferably, as an example, the decompression process is performed, including:
and splitting the probability switching point ordinal number and the local probability according to the segmenter, acquiring the probability switching point based on the probability switching point ordinal number, and replacing the overall probability of the value to be switched between every two probability switching points by using the corresponding local probability.
The method comprises the steps of obtaining the residual probability after adjustment by subtracting the accumulated sum of the local probabilities of all values to be switched from 1, obtaining the residual probability before adjustment by subtracting the accumulated sum of the overall probabilities of all values to be switched from 1, obtaining the adjustment probability of the residual value data by multiplying the overall probability of the residual value data by the ratio of the residual probability after adjustment to the residual probability before adjustment, and replacing the overall probability of the residual value data by the adjustment probability.
And (5) controlling interval division of arithmetic coding by using the replaced probability to realize decompression of the coding sequence.
The embodiment of the invention also discloses a trusted data storage system based on supply chain coordination, which comprises a processor and a memory, wherein the memory stores computer program instructions, and the trusted data storage method based on supply chain coordination is realized when the computer program instructions are executed by the processor.
The above system further comprises other components well known to those skilled in the art, such as a communication bus and a communication interface, the arrangement and function of which are known in the art and therefore are not described in detail herein.
In the context of this patent, the foregoing memory may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, the computer readable storage medium may be any suitable magnetic or magneto-optical storage medium, such as, for example, resistance change memory, dynamic random access memory, static random access memory, enhanced dynamic random access memory, high bandwidth memory, hybrid storage cube, etc., or any other medium that can be used to store the desired information and that can be accessed by an application, a module, or both. Any such computer storage media may be part of, or accessible by, or connectable to, the device.
While various embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Many modifications, changes, and substitutions will now occur to those skilled in the art without departing from the spirit and scope of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention.
The above embodiments are not intended to limit the scope of the invention, so that the equivalent changes of the structure, shape and principle of the invention are covered by the scope of the invention.
Claims (10)
1. A trusted data storage method based on supply chain collaboration, comprising the steps of:
acquiring a trusted data sequence coordinated with a supply chain;
Acquiring a local area by taking any data in a trusted data sequence as a center, acquiring the data density of each value in the local area as the local density of each value under the data, carrying out clustering processing according to the local density of each value under all data, and acquiring a classification boundary point of each value;
Determining the possibility that various valued classification boundary points are probability switching points according to the distances between the different valued classification boundary points, wherein the probability of the probability switching points is inversely related to the distances between the different valued classification boundary points;
The probability switching feasibility of each value is calculated according to the coding length difference under the overall probability and the coding length difference under the local probability, and the probability switching feasibility is positively correlated with the coding length difference and negatively correlated with the length of the local probability value;
and screening out the value of the probability to be switched according to the probability switching feasibility, switching the whole probability value of the probability to be switched into the local probability, and realizing coding compression based on the probability after switching.
2. The method for storing trusted data based on supply chain coordination as claimed in claim 1, wherein said obtaining a local area with any data in the trusted data sequence as a center comprises:
And taking any data in the trusted data sequence as a center to acquire an area with a preset length and recording the area as a local area of the data.
3. The method for trusted data storage based on supply chain coordination of claim 1, wherein said obtaining the data density of each value in said local area is noted as the local density of each value under the data, comprising:
and acquiring the data quantity of each value in the local area of the data, and dividing the data quantity of each value by the length of the local area to obtain the local density of each value under the data.
4. The method for storing trusted data based on supply chain cooperation according to claim 1, wherein the clustering process is performed according to the local density of each value under all data, and the classification boundary point of each value is obtained, comprising:
clustering all data in the trusted data sequence according to the local density of each value under each data to obtain a plurality of categories;
the same class is divided into a data segment, the trusted data sequence is divided into a plurality of data segments, and the last data in the previous data segment in two adjacent data segments is used as a classification boundary point to obtain a plurality of classification boundary points.
5. The method for trusted data storage based on supply chain coordination of claim 1, wherein the method for obtaining the probability of a probability switching point comprises the following steps:
And clustering all the valued classification boundary points, and taking the reciprocal of the average value of the distances between each classification boundary point and other classification boundary points in the class as the probability switching point possibility.
6. The method for trusted data storage based on supply chain coordination of claim 5, wherein said screening out probabilistic switching points based on their likelihood comprises:
And taking the classification boundary point with the highest probability of the probability switching point in one class as the probability switching point.
7. The method for trusted data storage based on supply chain coordination of claim 1, wherein said calculating probability switching feasibility for each value comprises:
The method comprises the steps of obtaining the local quantity of each value between two adjacent probability switching points, recording the data quantity of each value as the local quantity of each value, taking the global probability of each value as a base number, taking the local quantity as an index, carrying out power calculation, taking the local probability of each value as the base number, taking the local quantity as the index, carrying out power calculation, taking the calculation result as the local probability of each value, taking the local probability of each value as the local probability of each value, selecting one piece of data with the shortest length in a range from 0 to L1, taking the selected length of the data as the coding length of each value under the global probability, selecting one piece of data with the shortest length in a range from 0 to L2, taking the selected length of the data as the coding length of each value under the local probability, taking the coding length of each value under the global probability and the coding length of the local probability under the local probability as the local probability to obtain the local probability switching feasibility of each value under the two adjacent probability switching points, and taking the local probability of each value under the two adjacent probability switching points as the average value.
8. The method for storing trusted data based on supply chain coordination according to claim 7, wherein said selecting a value of a required switching probability according to the probability switching feasibility comprises:
and taking the value with probability switching feasibility larger than a preset switching probability threshold value as the value of the probability to be switched.
9. The method for storing trusted data based on supply chain cooperation according to claim 1, wherein said switching the overall probability value of the probability to be switched to the local probability, and implementing the encoding compression based on the probability after switching, comprises:
The method comprises the steps of marking the value of the required switching probability as the value to be switched, replacing the whole probability of the data to be switched in the trusted data sequence by using the corresponding local probability, subtracting the accumulated sum of the local probabilities of all the values to be switched from 1 to obtain the adjusted residual probability, subtracting the accumulated sum of the whole probabilities of all the values to be switched from 1 to obtain the residual probability before adjustment, multiplying the whole probability of the data to be switched by the ratio of the residual probability after adjustment to the residual probability before adjustment to obtain the adjustment probability of the data to be switched, replacing the whole probability of the data to be switched by using the adjustment probability, controlling the interval division of arithmetic coding based on the replaced probability, and coding and compressing the trusted data sequence.
10. A supply chain collaboration based trusted data storage system comprising a processor and a memory, the memory storing computer program instructions that when executed by the processor implement a supply chain collaboration based trusted data storage method as claimed in any one of claims 1 to 9.
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