CN117498873B - Intelligent processing system for vascular embolism spring assembly - Google Patents
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- 230000002792 vascular Effects 0.000 title claims abstract description 26
- 208000005189 Embolism Diseases 0.000 title claims abstract description 14
- 230000008707 rearrangement Effects 0.000 claims abstract description 93
- 230000006835 compression Effects 0.000 claims abstract description 41
- 238000007906 compression Methods 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 14
- 230000003073 embolic effect Effects 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 9
- 125000004122 cyclic group Chemical group 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000003860 storage Methods 0.000 abstract description 3
- 238000004519 manufacturing process Methods 0.000 description 7
- 230000000694 effects Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000003754 machining Methods 0.000 description 3
- 238000005452 bending Methods 0.000 description 2
- 238000005520 cutting process Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000000712 assembly Effects 0.000 description 1
- 238000000429 assembly Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention provides an intelligent processing system for a vascular embolism spring assembly, and relates to the technical field of electric digital data processing. The method comprises the following steps: the first acquisition module is used for acquiring a historical data sequence of the vascular embolism spring assembly; the second acquisition module is used for rearranging the historical data sequences to obtain one or more candidate rearranging sequences; the third acquisition module is used for encoding the candidate rearrangement sequence based on the target encoding length to obtain a corresponding encoding data length, wherein the target encoding length is determined by the candidate rearrangement sequence; and the fourth acquisition module is used for determining an optimal rearrangement sequence based on the length of the coded data, wherein the coding result of the optimal rearrangement sequence corresponding to the length of the coded data is the coding compression result of the historical data sequence. The compression efficiency of the historical data sequence can be improved, more historical data can be stored in a limited storage space, and the accuracy of predicting processing data is improved.
Description
The invention relates to the technical field of electric digital data processing, in particular to an intelligent processing system of a vascular embolism spring assembly.
Background
In the production process of the vascular embolism spring assembly, in order to ensure the stability and quality of the produced vascular embolism spring assembly, the processing parameters (such as cutting speed, feeding speed, cutter path and the like) of the equipment need to be accurately controlled; therefore, the actual processing data is predicted according to the historical processing data, and then the processing parameters of the equipment are adjusted according to the predicted processing data, so that the stability and the quality of the produced vascular embolism spring assembly are better.
The higher the accuracy of the predicted processing data is, the better the stability and quality of the produced vascular embolism spring assembly are; in order to improve the accuracy of the predicted machining data, more historical machining data is needed as the basis of the predicted machining data; however, the storage space of the production facility is limited, and the historical process data needs to be compressed in order to store more historical process data.
When the data is compressed by fixed-length coding or variable-length coding, the compression efficiency of the data is low, the compression effect is poor, and the prediction of the actual processing data based on the compressed data is not facilitated.
Disclosure of Invention
The invention aims to provide an intelligent processing system for a vascular embolism spring assembly, which adopts the following technical scheme:
an embodiment of the present invention provides an intelligent processing system for a vascular embolism spring assembly, comprising:
the first acquisition module is used for acquiring a historical data sequence of the vascular embolism spring assembly;
the second acquisition module is used for rearranging the historical data sequences to obtain one or more candidate rearranging sequences;
the third acquisition module is used for encoding the candidate rearrangement sequence based on a target encoding length to obtain a corresponding encoding data length, wherein the target encoding length is determined by the candidate rearrangement sequence;
and the fourth acquisition module is used for determining an optimal rearrangement sequence based on the length of the coded data, wherein the coding result of the optimal rearrangement sequence corresponding to the length of the coded data is the coding compression result of the historical data sequence.
In one embodiment of the present application, the encoding the candidate reordered sequence based on the target encoding length in the third acquisition module includes:
setting a preset category number node;
determining the next category data of the nodes with the preset category number from the candidate rearrangement sequence as a boundary data category;
dividing the candidate rearrangement sequence into one or more data intervals by taking the first appearance position of each boundary data type as a boundary;
and determining a target coding length corresponding to each data interval, and coding the corresponding data interval according to the target coding length.
In one embodiment of the present application, the preset number of types of nodes in the third obtaining module isWherein->，/>The number of categories for the historical data in the candidate reorder sequence; and the historical data with the same numerical value in the candidate rearrangement sequence are the same kind of data.
In one embodiment of the present application, the third obtaining module further includes:
sequentially marking the data intervals of the candidate rearrangement sequences;
sequentially increasing fixed lengths from a preset initial target coding length according to the label size of the data interval, and determining the target coding length corresponding to each data interval; wherein, the preset initial target coding length is 1, and the fixed length is 1.
In an embodiment of the present application, the calculating of the corresponding encoded data length in the third obtaining module includes:
，
wherein,a coded data length representing a candidate reordered sequence; />Representing a target encoding length; />Representing a base 2 logarithmic function; />Representing a downward rounding function; />And->Respectively represent boundary data category->And boundary data category->The first occurrence position in the candidate rearrangement sequence; />Represent the firstThe first occurrence position of the seed history data in the candidate rearrangement sequence; l represents the length of the candidate reordered sequence.
In one embodiment of the present application, the rearranging the historical data sequence in the second obtaining module, to obtain one or more candidate rearranged sequences, includes:
processing the historical data sequence by using a BWT rearrangement algorithm to obtain a cyclic matrix;
each column in the circulant matrix is one of the candidate reordered sequences.
In one embodiment of the present application, the determining, in the fourth obtaining module, an optimal reordering sequence based on the encoded data length includes:
acquiring the coded data lengths of all the candidate rearrangement sequences;
and selecting the minimum value in the length of the coded data, wherein the candidate rearrangement sequence corresponding to the minimum value is the optimal rearrangement sequence.
In one embodiment of the present application, the fourth obtaining module further includes:
acquiring the first appearance position of each kind of data in the optimal rearrangement sequence;
and sorting all kinds of data from small to large according to the first appearance position corresponding to each kind of data to obtain a target sorting sequence, and storing the target sorting sequence.
In one embodiment of the present application, the fourth obtaining module further includes:
and acquiring all boundary data types in the optimal rearrangement sequence, and recording the first occurrence position of the boundary data types in the optimal rearrangement sequence to obtain target position data.
In one embodiment of the present application, the fourth obtaining module further includes:
and decoding the encoding compression result according to the target ordering sequence and the target position data.
The application has at least the following beneficial effects: according to the method, a plurality of candidate rearrangement sequences corresponding to the historical data sequences are obtained through rearrangement of the historical data sequences, more references are provided for compression of the historical data sequences, the target coding length corresponding to each candidate rearrangement sequence is determined according to the characteristics of the historical data included in the candidate rearrangement sequences, the corresponding coding data length is obtained through coding the candidate rearrangement sequences based on the target coding length, the problem that the compression efficiency is low due to single-use fixed-length coding or variable-length coding at present is avoided, the optimal rearrangement sequence and the coding compression result are determined according to the coding data length, the compression efficiency and the compression effect of the historical data sequences are improved, and further the accuracy of prediction processing data is improved.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a block diagram of an intelligent processing system for a vascular embolic spring assembly, provided by an embodiment of the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
An intelligent processing system for a vascular embolic spring assembly in accordance with embodiments of the present invention is described below with reference to the accompanying drawings.
Fig. 1 is a block diagram of a vascular embolism spring assembly intelligent processing system according to an embodiment of the present invention, as shown in fig. 1, the system includes the following modules:
a first acquisition module 100 is used to acquire a historical data sequence of the vascular embolic spring assembly.
In some implementations, when producing the vascular embolic spring assembly, appropriate production materials are selected according to different design specifications, the vascular embolic spring assembly is produced by setting processing parameters of equipment such as cutting speed, feeding speed, cutter path, bending angle, bending radius, heating temperature, heating time, cooling mode, stretching force, stretching direction and the like, and the size and performance of the produced vascular embolic spring assembly are measured by using appropriate measuring tools so as to ensure that the produced vascular embolic spring assembly meets quality standards and performance requirements.
That is, the specifications of the production materials, the processing parameters of the equipment, and the size and performance of the vascular embolic spring assembly during production of all batches of vascular embolic spring assemblies per day are collected as a historical data sequence.
A second obtaining module 200, configured to reorder the historical data sequence to obtain one or more candidate reordered sequences.
In some implementations, in order to improve the compression efficiency of the historical data sequence, a coding method combining fixed-length coding and variable-length coding may be used for compression, so that identical data in the historical data sequence needs to be arranged together as much as possible to improve the compression effect.
In some implementations, the historical data sequence may be processed using a BWT rearrangement algorithm to obtain a circulant matrix; each column in the circular matrix is a candidate reordered sequence.
That is, the historical data sequence is rearranged using a BWT rearrangement algorithm. It will be appreciated that the BWT algorithm is a data conversion algorithm that places similar characters in a string in adjacent locations to facilitate subsequent compression; the BTW rearrangement algorithm can construct all possible cyclic shift character strings and arrange the same data together as far as possible according to the dictionary sequence, and each column of the cyclic matrix obtained by the BTW algorithm is a possible arrangement mode after the historical data sequence is rearranged, that is, each column of the cyclic matrix is a candidate rearrangement sequence corresponding to the historical data sequence.
And a third obtaining module 300, configured to encode the candidate reordered sequence based on a target encoding length, to obtain a corresponding encoding data length, where the target encoding length is determined by the candidate reordered sequence.
It can be understood that, when encoding and compressing data, a fixed-length encoding method can be adopted to compress the data, so that the compression efficiency is improved, but as the variety of data increases, the length of the encoded data of the fixed-length encoding also increases, so that the final compression efficiency is low.
In some implementations, huffman coding may be used, where the compression efficiency of the historical data can be improved to some extent relative to fixed length coding by assigning short length codes to the historical data with large frequency, but huffman coding is a variable length coding, where each code is not used as the beginning of the other codes in order to ensure its decodability, which may result in only some of all codes of each length in the huffman coding being used, for example: huffman coding uses codes "1" to represent one type of history data, and then all codes beginning with "1" (e.g., 10,11,100,101,110,111, …) are not used any more, and if it is desired to assign codes to each type of history data, it is necessary to use codes beginning with "0" that are longer in length, which results in a low final compression efficiency.
As one example, assume that the historical data sequence is { C, D, a, E, a, B, Z, X, Y, a, C }, the historical data sequence length is 15, comprising a total of 8 types of historical data, a, B, C, D, E, X, Y, Z, respectively; if the fixed-length code is adopted to carry out code compression on the historical data sequence, the length of the obtained fixed-length code isThe encoded data length of the final encoding result is +.>The method comprises the steps of carrying out a first treatment on the surface of the If huffman coding is used to code the historical data sequence, the result of the 8 kinds of historical data after being respectively coded may be: 10. 01, 00, 1100, 1101, 11111, 1110 and 11110, the final encoded data length is +.>The compression efficiency is lower although the encoded data length is reduced compared to fixed length encoding.
In order to solve the problem of low compression efficiency, in the embodiment of the present application, a coding compression method combining fixed-length coding and non-fixed-length coding is used to process data to be compressed, for example, when local data of the data to be compressed is coded, the fixed-length coding may be used to perform analysis; when the whole data to be compressed is encoded, the length of the fixed-length encoding is continuously increased along with the increase of the variety and the number of the data to be compressed, and the variable-length encoding is performed on the whole, so that the compression efficiency is effectively improved.
Alternatively, a preset category number node may be set; determining the next type data of the nodes with the preset type number from the candidate rearrangement sequence as the boundary data type; dividing the candidate rearrangement sequence into one or more data intervals by taking the first appearance position of each boundary data type as a boundary; and determining a target coding length corresponding to each data interval, and coding the corresponding data interval according to the target coding length.
Specifically, embodiments of the present application may analyze the data types in the candidate reordered sequences to determine pairsThe target coding length of the candidate rearrangement sequence for compression, that is, a preset type number node can be set, and when the data type in the candidate rearrangement sequence exceeds the preset type number node, the target coding length is updated; it should be noted that, the preset number of types of nodes in the third obtaining module isWherein->，/>The number of categories for the historical data in the candidate reorder sequence; the historical data with the same value in the candidate rearrangement sequence are the same kind of data.
In some implementations, the data intervals of the candidate reordered sequence may also be numbered sequentially; sequentially increasing fixed lengths from a preset initial target coding length according to the label size of the data interval, and determining a target coding length corresponding to each data interval; the preset initial target coding length is 1, and the fixed length is 1.
Exemplary illustration, the preset category number node isDetermining that the preset category number nodes in the candidate rearrangement sequence are category 2 historical data, category 4 historical data, category 8 historical data and …; the corresponding boundary data types in the candidate rearrangement sequence are respectively 3 rd type historical data, 5 th type historical data, 9 th type historical data and the like; that is, the first appearance position of boundary data types such as 3 rd type historical data, 5 th type historical data, 9 th type historical data and the like is taken as a boundary, the candidate rearrangement sequence is divided into one or more data intervals, for example, the data before the first appearance position of the 3 rd type historical data is one data interval, the data before the first appearance position of the 3 rd type historical data-5 th type historical data is 2 nd data interval, and the 5 th type historical data-9 th type historical data is the first appearance positionThe data before the data are the 3 rd data interval, and the like, so as to obtain all the data intervals in the candidate rearrangement sequence.
Determining target coding lengths of different data intervals, and coding the data intervals according to the target coding lengths, namely coding the 1 st data interval (data before the first occurrence position of the 3 rd type of historical data) by using fixed-length coding with the target coding length of 1 before the 3 rd type of historical data in the candidate rearrangement sequence; before the 5 th historical data appears, the historical data of the 2 nd data interval is encoded by using fixed-length encoding with the target encoding length of 2, namely, the historical data before the first appearance position of the 3 rd kind data-5 th kind data in the candidate rearrangement sequence is encoded by using fixed-length encoding with the target encoding length of 2; and so on, in the firstBefore the occurrence of the species history data, the target coding length is +.>Fixed length coding of (2) for front->Coding the species history data, i.e. using fixed length coding with a target coding length k, for the +.>Species history data appear later to +.>And encoding the historical data before the occurrence of the historical data, and the like until the encoding of all the historical data in the candidate rearrangement sequence is completed, so as to obtain encoded data corresponding to the candidate rearrangement sequence.
As an example, assuming that the candidate rearrangement sequence is { C, D, a, E, a, B, Z, X, Y, a, C }, when the candidate rearrangement sequence is encoded, it is determined that the boundary data types are the 3 rd type history data and the 5 th type history data, respectively; the first appearance position of the 3 rd historical data A is the 4 th bit, so that the data before the 4 th bit is encoded by using a fixed-length code with the target encoding length of 1 to obtain the codes of the data with the first 3 bits as 0,0 and 1; further, the first occurrence position of the 5 th historical data B in the candidate rearrangement sequence is the 7 th bit, so that the historical data before the 4 th bit and the 7 th bit are encoded by adopting fixed-length encoding with the target encoding length of 2, and the encoding of the historical data before the 4 th bit and the 7 th bit is 10,11,10; further, since the 9 th historical data does not exist in the candidate rearrangement sequence, the historical data after the 7 th bit is encoded by using the fixed-length encoding with the target encoding length of 3, and the encoding of the historical data after the 7 th bit is 100,100,100,101,110,111,010,010,000 respectively, so that the encoding of all the historical data in the candidate rearrangement sequence is completed, the encoding result is {0,0,1,10,11,10,100,100,100,101,110,111,010,010,000}, and the encoding data length can be determined to be 36 according to the encoding result.
As an example, assuming that another candidate rearrangement sequence is { a, C, D, a, E, a, B, Z, X, Y }, the candidate rearrangement sequence is encoded according to the above-described method of combining fixed-length encoding and variable-length encoding, and the obtained encoding result is {0,0,1,1,1,10,00,11,00,100,100,100,101,110,111}, thereby determining that the encoded data length at this time is 31.
For ease of understanding, the candidate reordered sequence { C, C, D, A, E, A, B, B, B, Z, X, Y, A, A, C } is noted as a first candidate reordered sequence, the other candidate reordered sequence { A, A, C, C, C, D, A, E, A, B, B, Z, X, Y } is noted as a second candidate reordered sequence, the first candidate reordered sequence being longer in length than the encoded data of the second candidate reordered sequence because the 3 rd historical data in the second candidate reordered sequence occurs at the 6 th position, the 3 rd historical data in the first candidate rearrangement sequence appears at the 4 th bit, so that the first candidate rearrangement sequence is encoded by using a fixed-length code with the length of 2 from the 4 th bit, thereby leading the encoded data length of the first candidate rearrangement sequence to be larger than the encoded data length of the second candidate rearrangement sequence, that is, the same data in the historical data sequences are arranged together as much as possible, and the compression efficiency can be effectively improved.
Further, the encoded data length of each candidate reordered sequence is obtained, and the effect that each candidate reordered sequence can be compressed is reflected by the encoded data length. Alternatively, the calculation of the corresponding encoded data length after encoding each candidate reordered sequence may be:
，
wherein,representing the encoded data length; />Representing a target encoding length; />Representing a base 2 logarithmic function; />Representing a downward rounding function; n represents the number of types of history data in the candidate rearrangement sequence; />Andrespectively represent boundary data category->And boundary data category->The first position of occurrence in the candidate rearrangement sequence, i.e./th>Historical data and->The first occurrence of the seed history data in the candidate reorder sequence; />Indicate->The first occurrence of the seed history data in the candidate reorder sequence; l represents the length of the candidate reordered sequence.
It will be appreciated that in the first placeBefore the occurrence of the species history data, the former is encoded with a fixed length of length k>Coding the species history data, i.e. using fixed-length coding with a target code length k, for the first in the candidate reordered sequenceSpecies history data to->The history data before the occurrence of the seed history data is encoded, i.e. the +.A fixed-length encoding with a target encoding length of k is used>The historical data is encoded, and the length of the encoding result is +.>The method comprises the steps of carrying out a first treatment on the surface of the In the present embodiment, for the remainder of the candidate reordered sequencesHistory data, a target coding length of +.>Is encoded by fixed length encoding, the length of the encoded encoding result is +.>。
The fourth obtaining module 400 is configured to determine an optimal reordering sequence based on the encoded data length, where the encoded result of the optimal reordering sequence corresponding to the encoded data length is the encoded compression result of the historical data sequence.
In some implementations, the encoded data lengths of all candidate reordered sequences are obtained; and selecting the minimum value in the length of the coded data, wherein the candidate rearrangement sequence corresponding to the minimum value is the optimal rearrangement sequence.
It will be appreciated that after determining the coding length corresponding to each candidate reordered sequence, the smaller the coding length, the better the compression efficiency, so that the candidate reordered sequence with the smallest coding length is selected as the optimal reordered sequence, and the column number of the optimal reordered sequence, that is, the column number of the candidate reordered sequence corresponding to the optimal reordered sequence in the cyclic matrix, is recorded. As an example, assume that the historical data sequence is { C, D, a, E, a, B, Z, X, Y, a, of all the candidate rearrangement sequences corresponding to C, the 13 th candidate rearrangement sequence { A, A, C, C, C, D, A, E, A, B, B, B, Z, X, Y } is the smallest in coding length, the optimal rearrangement sequence of the historical data sequence is { A, A, C, C, D, A, E, A, B, B, B, Z, X, Y }, and the column number of the optimal rearrangement sequence is 13.
It can be understood that after the optimal rearrangement sequence of the historical data sequence is determined, the encoding result corresponding to the optimal rearrangement sequence is the encoding compression result corresponding to the historical data sequence.
Further, in order to be able to facilitate decoding, a position where each kind of data first appears in the optimal rearrangement sequence may be acquired; and sorting all kinds of data from small to large according to the first appearance position corresponding to each kind of data to obtain a target sorting sequence, and storing the target sorting sequence. And acquiring all boundary data types in the optimal rearrangement sequence, and recording the first occurrence position of the boundary data types in the optimal rearrangement sequence to obtain target position data. And decoding the encoding compression result according to the target ordering sequence and the target position data. That is, the first occurrence position of the history data of each category in the optimal rearrangement sequence is obtained, the sorting is performed from small to large according to the first occurrence position, and the first occurrence position is simultaneously used for sortingMarking the first occurrence of the species history data in the optimal rearrangement sequence, wherein +_s>。
For example, assuming that the historical data sequence is { C, C, D, A, E, A, B, B, B, Z, X, Y, A, A, C }, the optimal rearrangement sequence corresponding thereto is { A, A, C, C, C, D, A, E, A, B, B, B, Z, X, Y }, in which there are 8 types of historical data, A, C, D, E, B, Z, X, Y are present, the historical data are ordered from small to large according to the position in which each of the historical data first appears in the optimal rearrangement sequence, in that order A, C, D, E, B, Z, X, Y, and the first one of them is markedSpecies history data, namely D and B, the ordered target ordering sequence is { A, C, D, E, B, Z, X, Y }, and the recorded target position data is A, C, D (6), E, B (10), Z, X, Y; and storing the target position data so as to decode the coding compression result according to the target ordering sequence and the target position data and restore the original data.
In summary, the embodiment of the present invention includes a first acquiring module 100, a second acquiring module 200, a third acquiring module 300, and a fourth acquiring module 400, where the first acquiring module 100 is configured to acquire a historical data sequence, and the second acquiring module 200 is configured to reorder the historical data sequence to obtain a plurality of candidate reordered sequences; the third obtaining module 300 is configured to encode the candidate reordered sequence with different target encoding lengths to obtain corresponding encoded data lengths; the fourth obtaining module 400 is configured to determine an optimal rearrangement sequence according to the length of the encoded data and an encoding compression result of the historical data sequence, and perform compression by using an encoding compression method combining fixed-length encoding and non-fixed-length encoding to obtain an encoding compression result of the optimal rearrangement sequence with the shortest encoded data length, and arrange the same historical data together as far as possible as the encoding compression result of the historical data sequence, so that new types of the historical data appear at a later position in the historical data sequence as far as possible, improve the compression efficiency of the historical data sequence, and achieve good compression of the historical data sequence, so that the intelligent processing condition of the vascular embolic spring assembly can be analyzed in time based on the compressed historical encoding data, and the compression efficiency of the historical data sequence can be improved, so that more historical data can be stored in a limited storage space of production equipment, the accuracy of the predicted processing data is higher, and the stability and quality of the production of the vascular embolic spring assembly are improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. An intelligent processing system for a vascular embolic spring assembly, comprising:
the first acquisition module is used for acquiring a historical data sequence of the vascular embolism spring assembly;
the second acquisition module is used for rearranging the historical data sequences to obtain one or more candidate rearranging sequences;
the third acquisition module is used for encoding the candidate rearrangement sequence based on a target encoding length to obtain a corresponding encoding data length, wherein the target encoding length is determined by the candidate rearrangement sequence;
a fourth obtaining module, configured to determine an optimal rearrangement sequence based on the encoded data length, where a coding result of the optimal rearrangement sequence corresponding to the encoded data length is a coding compression result of the historical data sequence;
the third obtaining module encodes the candidate rearrangement sequence based on a target encoding length, including:
setting a preset category number node;
determining the next category data of the nodes with the preset category number from the candidate rearrangement sequence as a boundary data category;
dividing the candidate rearrangement sequence into one or more data intervals by taking the first appearance position of each boundary data type as a boundary;
and determining a target coding length corresponding to each data interval, and coding the corresponding data interval according to the target coding length.
2. The system of claim 1, wherein the predetermined category number node in the third acquisition module is 2 k Wherein, the method comprises the steps of, wherein,n is the number of types of history data in the candidate rearrangement sequence; and the historical data with the same numerical value in the candidate rearrangement sequence are the same kind of data.
3. The system of claim 2, wherein the third acquisition module further comprises:
sequentially marking the data intervals of the candidate rearrangement sequences;
sequentially increasing fixed lengths from a preset initial target coding length according to the label size of the data interval, and determining the target coding length corresponding to each data interval; wherein, the preset initial target coding length is 1, and the fixed length is 1.
4. A system according to claim 3, wherein the third obtaining module obtains a calculation of a corresponding encoded data length, comprising:
wherein B represents the encoded data length of the candidate reordered sequence; k represents a target encoding length; log of 2 () Representing a base 2 logarithmic function;representing a downward rounding function; w (2) k-1 +1) and w (2) k +1) represents the boundary data category 2 respectively k-1 +1 and boundary data category 2 k +1 first occurrence position in the candidate rearrangement sequence; />Indicate->The first occurrence position of the seed history data in the candidate rearrangement sequence; l represents the length of the candidate reordered sequence.
5. The system of claim 1, wherein the rearranging the historical data sequence in the second acquisition module results in one or more candidate rearranged sequences comprising:
processing the historical data sequence by using a BWT rearrangement algorithm to obtain a cyclic matrix;
each column in the circulant matrix is one of the candidate reordered sequences.
6. The system of claim 5, wherein the fourth acquisition module determining an optimal reordering sequence based on the encoded data length comprises:
acquiring the coded data lengths of all the candidate rearrangement sequences;
and selecting the minimum value in the length of the coded data, wherein the candidate rearrangement sequence corresponding to the minimum value is the optimal rearrangement sequence.
7. The system of claim 4, wherein the fourth acquisition module further comprises:
acquiring the first appearance position of each kind of data in the optimal rearrangement sequence;
and sorting all kinds of data from small to large according to the first appearance position corresponding to each kind of data to obtain a target sorting sequence, and storing the target sorting sequence.
8. The system of claim 7, wherein the fourth acquisition module further comprises:
and acquiring all boundary data types in the optimal rearrangement sequence, and recording the first occurrence position of the boundary data types in the optimal rearrangement sequence to obtain target position data.
9. The system of claim 8, wherein the fourth acquisition module further comprises:
and decoding the encoding compression result according to the target ordering sequence and the target position data.
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