Engineering information collection and storage method and system based on big data

Abstract

The present invention relates to the field of data processing technology, and in particular to a method and system for collecting and storing engineering information based on big data. The method comprises: obtaining a pore water pressure sequence and a pore water pressure curve, obtaining the main peak point of each cycle of the pore water pressure curve; calculating the curve distribution characteristic value of the cycle according to the main peak point in a cycle and the data points on the left and right sides of the main peak point; constructing a two-dimensional matrix according to the curve distribution characteristic value; traversing the two-dimensional matrix to obtain repeated data segments corresponding to traversal paths in different directions; calculating the degree of continuous similarity of each traversal path according to the repeated data segments corresponding to the traversal paths in each direction, and then obtaining the optimal path; sorting the data in the two-dimensional matrix according to the traversal order of the optimal path to obtain a first sequence; performing differentiation on the first sequence to obtain an optimized compression sequence, and compressing the optimized compression sequence. The present invention can improve the compression efficiency of pore water pressure data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for collecting and storing engineering information based on big data. Background Art

[0002] A large amount of engineering data will be generated in road construction projects. In addition to record-type information such as personnel, equipment, and construction progress, it also contains a large amount of engineering monitoring data, such as stress, strain, displacement, vibration, and other monitoring data. These data require high-precision collection, and due to the long time period of the project, each monitoring may generate hundreds of records per day, reaching hundreds of thousands within the project cycle, which will take up a lot of project terminal storage space. For the large amount of dynamically changing engineering monitoring data collected during the project, which requires long-term continuous observation, efficient collection and storage methods are urgently needed, such as pore water pressure monitoring data.

[0003] The existing technology can use the run-length encoding compression algorithm to compress the collected data for the purpose of long-term storage. This method has a good compression effect on multiple consecutive repeated data, but has a poor compression effect on data sets without consecutive repeated data, and the compression efficiency of engineering monitoring data is unstable. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method for collecting and storing engineering information based on big data. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides a method for collecting and storing engineering information based on big data, the method comprising: The pore water pressure data of fixed observation points are collected to obtain a pore water pressure sequence; a pore water pressure curve is obtained according to the pore water pressure sequence, and the period of the pore water pressure curve is obtained; Obtain the peak point with the largest value in the cycle of the pore water pressure curve, which is recorded as the main peak point; calculate the curve distribution characteristic value of the cycle based on the main peak point in a cycle and the data points on the left and right sides of the main peak point; Sort the periods in descending order according to the curve distribution eigenvalues, and construct a two-dimensional matrix based on the data in the sorted periods; Set traversal paths in different directions, traverse the two-dimensional matrix according to the traversal paths, and obtain repeated data segments corresponding to the traversal paths in different directions respectively; calculate the degree of continuous similarity of each traversal path according to the repeated data segments corresponding to the traversal paths in each direction; The traversal path with the largest continuous similarity is selected as the optimal path; the data in the two-dimensional matrix is ​​sorted according to the traversal order of the optimal path to obtain a first sequence; the first sequence is differentiated to obtain an optimized compression sequence, and the optimized compression sequence is compressed.

[0005] Preferably, obtaining a pore water pressure curve according to the pore water pressure sequence and obtaining a period of the pore water pressure curve comprises: The collected pore water pressure sequence is projected into the coordinate system, with the horizontal axis being time and the vertical axis being pore water pressure, to obtain the pore water pressure curve; the pore water pressure curve is converted from the time domain to the frequency domain using Fourier transform to obtain the maximum frequency in the spectrum, where the inverse of the maximum frequency is the period of the pore water pressure curve.

[0006] Preferably, the peak point with the largest value in the period of the pore water pressure curve is obtained, which is recorded as the main peak point, including: The AMPD peak search algorithm is used to obtain all peak points. In each cycle, the peak point with the maximum pore water pressure value in the cycle is obtained as the main peak point in the cycle.

[0007] Preferably, the curve distribution characteristic value of the period is calculated according to the main peak point in a period and the data points on the left and right sides of the main peak point, including: Normalize the value of the main peak point in a cycle to obtain the normalized peak value; obtain the slope between the data point adjacent to the left side of the main peak point in the cycle and the main peak point, recorded as the left slope, obtain the slope between the data point adjacent to the right side of the main peak point in the cycle and the main peak point, recorded as the right slope; normalize the absolute value of the difference between the left slope and the right slope to obtain the waveform sharpness; obtain the sum of the slopes between every two adjacent data points in the data points between the main peak point of the cycle and the first inflection point on the left, recorded as the left slope sum, obtain the sum of the slopes between every two adjacent data points in the data points between the main peak point of the cycle and the first inflection point on the right, recorded as the right slope sum; sum the left slope sum and the right slope sum and normalize them to obtain the continuous change characteristic value; sum the normalized peak value, waveform sharpness and continuous change characteristic value to obtain the curve distribution characteristic value of the cycle.

[0008] Preferably, the periods are sorted in descending order according to the curve distribution characteristic values, and a two-dimensional matrix is ​​constructed according to the data in the sorted periods, including: The periods are sorted from large to small according to the curve distribution eigenvalues, and a two-dimensional matrix is ​​constructed according to the sorted periods, where a row of data in the two-dimensional matrix is ​​a row of data in the period, the first row of the two-dimensional matrix is ​​the data in the period with the largest curve distribution eigenvalue, and so on, the last row of the two-dimensional matrix is ​​the data in the period with the smallest curve distribution eigenvalue.

[0009] Preferably, traversal paths in different directions are set, and the two-dimensional matrix is ​​traversed according to the traversal paths to obtain repeated data segments corresponding to the traversal paths in different directions, respectively, including: The traversal paths are horizontal and vertical traversal paths respectively; the starting point of the traversal is the first data in the upper left corner of the matrix. During the traversal process, if the values ​​of the adjacent previous data point and the next data point are the same, the two data points are marked as repeated elements, and the two repeated elements with the same values ​​are grouped into a repeated data segment. If the next data point is also a repeated element with the same value, it is included in the repeated data segment, and so on, to obtain the repeated data segments corresponding to the horizontal traversal path and the repeated data segments corresponding to the vertical traversal path respectively.

[0010] Preferably, the degree of continuity similarity of each traversal path is calculated respectively according to the repeated data segments corresponding to the traversal paths in each direction, including: The lengths of each data segment of the traversal path in one direction are squared and summed up to obtain a sum result, and the sum result is multiplied by the number of repeated data segments and normalized to obtain data repetition; the absolute value of the difference between every two adjacent data in the traversal path is obtained, and the absolute value of the difference is negatively correlated with an exponential function with a natural constant as the base to obtain data similarity; the sum of the data repetition and the data similarity is the continuous similarity degree of the traversal path in this direction.

[0011] Preferably, performing differentiation on the first sequence to obtain an optimized compressed sequence comprises: The first sequence is differentiated by subtracting the next data value from the previous data value to obtain a differential sequence of the first sequence, and the differential sequence is an optimized compressed sequence.

[0012] In a second aspect, the present invention also provides an engineering information collection and storage system based on big data, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of a method for collecting and storing engineering information based on big data are implemented.

[0013] The embodiments of the present invention have at least the following beneficial effects: the present invention divides the pore water pressure curve according to the period to obtain the curve distribution characteristic value of each period, and then sorts the periods in order from large to small according to the curve distribution characteristic value, and constructs a two-dimensional matrix according to the data in the sorted period. Compared with the prior art that directly uses run-length coding to compress the one-dimensional data sequence, the compression path selectivity is low, and the possibility of data repetition and continuity on the path is low. This scheme combines the periodic characteristics of pore water pressure monitoring data, and constructs a two-dimensional matrix form of a one-dimensional data sequence by analyzing the periodicity of the data, so as to obtain more path selection directions and improve the possibility of data repetition and continuity in the compression path; compared with the prior art that uses run-length coding to compress engineering monitoring data, the compression efficiency of run-length coding is unstable due to the uncertainty of the possible repetition relationship between adjacent data of the monitoring data. This scheme constructs a model according to the similar distribution characteristics of the data in the two-dimensional matrix, obtains the degree of continuous similarity and then obtains the optimal path, and further obtains a compression sequence with a high degree of repetition and continuity by differentiating the optimal path, so that the compression result of run-length coding is stable and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A method flow chart of a method for collecting and storing engineering information based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a method and system for collecting and storing engineering information based on big data proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] The following is a detailed description of a method and system for collecting and storing engineering information based on big data provided by the present invention in conjunction with the accompanying drawings.

[0019] Embodiment 1: The main application scenarios of the present invention are: This method combines the periodic characteristics of pore water pressure data and the data distribution characteristics in the two-dimensional matrix to reduce the defects of conventional run-length encoding compression data, such as small amount of continuous data repetition. When using the run-length encoding algorithm to collect and compress pore water pressure monitoring data in engineering information data, due to the uncertainty of data repetition degree, the run-length encoding compression efficiency is unstable and cannot be used as a long-term adaptive engineering project compression algorithm, and further improvement is needed.

[0020] See also Figure 1 , which shows a method flow chart of a method for collecting and storing engineering information based on big data provided by an embodiment of the present invention, the method comprising the following steps: Step S1, collecting pore water pressure data of fixed observation points to obtain a pore water pressure sequence; obtaining a pore water pressure curve according to the pore water pressure sequence, and obtaining the period of the pore water pressure curve.

[0021] Pore ​​water pressure observation is the most direct means to understand the consolidation state of the roadbed soil, and is the most effective method to control the interval time of strong compaction and fill loading. From the beginning of the roadbed laying of the road project to the end of the construction, the pore water pressure data of fixed observation points are collected. A steel string pore water pressure gauge is used, and a VW-1 vibrating wire reader is used to receive the pore pressure signal. Observations are carried out along with the progress of strong compaction and filling, and the time series data of pore water pressure is measured. After measuring and collecting the data of the day every day, the collected pore water pressure data constitutes a pore water pressure sequence, and the collected data is compressed and processed at the terminal.

[0022] During the construction of road projects, the pore water pressure will rise during strong compaction, and when the strong compaction ends, the pressure will gradually dissipate. From the regularity of this operation, it can be obtained that the change of pore water pressure has a certain periodicity. Further, the currently collected pore water pressure sequence is projected into the coordinate system, with the horizontal axis being time and the vertical axis being pore water pressure, to obtain the pore water pressure curve; further, the pore water pressure curve is converted from the time domain to the frequency domain using Fourier transform to obtain the maximum frequency in the spectrum, where the inverse of the maximum frequency is the period of the pore water pressure curve. The specific calculation formula is: , where T represents the period, Indicates the maximum frequency in the spectrum.

[0023] Due to the different strengths of each compaction, there may be pore water pressure curve cycles with high similarity and pore water pressure curve cycles with relatively low similarity. The pore water pressure curve is segmented according to the cycle to obtain multiple independent fluctuation cycles. Before each cycle, a mark indicating the order of the cycle in the original time series curve is stored (for restoration during decompression). The similarity between different cycles is judged based on the characteristic value of the curve distribution of each cycle.

[0024] Step S2, obtaining the peak point with the largest value in the cycle of the pore water pressure curve, recording it as the main peak point; calculating the curve distribution characteristic value of the cycle based on the main peak point in a cycle and the data points on the left and right sides of the main peak point.

[0025] Each cycle has a main peak point. Based on the curve distribution characteristic values ​​of the neighborhood of this main peak point, we can obtain similar features of different cycles that can be used as the basis for judging the degree of similarity. First, use the AMPD peak search algorithm to obtain all peak points. In each cycle, the peak point with the highest pore water pressure value in the cycle is obtained as the main peak point in the cycle. According to the peak value at the main peak point, the slope change and the change to the adjacent two sides, the curve distribution characteristic value of the neighborhood of the main peak of the cycle is obtained.

[0026] Furthermore, the curve distribution characteristic value of the cycle is calculated based on the main peak point in a cycle and the data points on the left and right sides of the main peak point. Specifically, the value of the main peak point in a cycle is normalized to obtain the normalized peak value; the slope between the data points adjacent to the left side of the main peak point in the cycle and the main peak point is obtained, which is recorded as the left slope, and the slope between the data points adjacent to the right side of the main peak point in the cycle and the main peak point is obtained, which is recorded as the right slope; the absolute value of the difference between the left slope and the right slope is normalized to obtain the waveform sharpness; the sum of the slopes between every two adjacent data points in the data points between the main peak point of the cycle and the first inflection point on the left is obtained, which is recorded as the left slope sum, and the sum of the slopes between every two adjacent data points in the data points between the main peak point of the cycle and the first inflection point on the right is obtained, which is recorded as the right slope sum; the left slope sum and the right slope sum are summed and normalized to obtain the continuous change characteristic value; the normalized peak value, the waveform sharpness and the continuous change characteristic value are summed to obtain the curve distribution characteristic value of the cycle.

[0027] The specific calculation formula of the curve distribution characteristic value is: , in, represents the characteristic value of the curve distribution in the tth period; norm represents the normalization operation; It represents the slope between the main peak point in the tth period and the adjacent data point on the left, which is recorded as the left slope; It represents the slope between the main peak point in the tth period and the adjacent data point on the right, which is recorded as the right slope; It represents the number of slopes between every two adjacent data points between the main peak point of the tth cycle and the first inflection point on the left; Represents the i-th slope of the slope between every two adjacent data points between the main peak point of the t-th cycle and the first inflection point on the left; It represents the number of slopes between every two adjacent data points between the main peak point of the tth cycle and the first inflection point on the right; Represents the jth slope among the slopes between every two adjacent data points between the main peak point of the tth cycle and the first inflection point on the right.

[0028] Indicates the sharpness of the waveform in the tth cycle, and They represent the left slope and the right slope and respectively. By summing these two slopes, we can obtain the eigenvalue representing the continuous change of the main peak point of the tth cycle to the adjacent two sides. Norm is the magnitude of the linear normalized unified three-part eigenvalue.

[0029] Thus, the curve distribution characteristic value of each period is obtained. The curve distribution characteristic value extracts the distribution characteristics at the main peak in the period, and can relatively accurately characterize the similarity between the curves. The more similar the curve distribution characteristic values ​​of the curves are, that is, the smaller the difference between the curve distribution characteristic values ​​of the two periods is, the more obvious the similar characteristics of the corresponding two period curves are.

[0030] Step S3, sorting the periods in descending order according to the curve distribution characteristic values, and constructing a two-dimensional matrix according to the data in the sorted periods.

[0031] Furthermore, the periods are sorted from large to small according to the curve distribution eigenvalues, and a two-dimensional matrix is ​​constructed according to the sorted periods after sorting, where the data of one row of the two-dimensional matrix is ​​a row of data within the period, and the data is filled starting from the first row of the matrix according to the sorted period, that is, the first row of the two-dimensional matrix is ​​the data within the period with the largest curve distribution eigenvalue, and the second row, the third row, and the last row of the two-dimensional matrix are obtained in turn according to the size of the curve distribution eigenvalue. Thus, a two-dimensional matrix with m rows and n columns is obtained, in which the period similarity between rows is the highest, and the possibility of similarity between the longitudinal adjacent data between rows can be maximized. Where n represents the number of pore water pressure data within the period, and m represents the number of periods.

[0032] Therefore, compared with the one-dimensional sequence, more compression paths are possible in the two-dimensional matrix. Here, a continuous similarity model is constructed according to the repeatability and similarity of data points on different paths to evaluate the compression paths in different directions.

[0033] Step S4, setting traversal paths in different directions, traversing the two-dimensional matrix according to the traversal paths, and obtaining repeated data segments corresponding to the traversal paths in different directions respectively; and calculating the degree of continuity similarity of each traversal path according to the repeated data segments corresponding to the traversal paths in each direction respectively.

[0034] Furthermore, traversal paths in different directions are set. The traversal paths selected here are horizontal and vertical traversal paths respectively. The starting point of the traversal is the first data in the upper left corner of the matrix. During the traversal process, if the adjacent previous data point has the same value as the next data point, the two data points are marked as repeated elements, and the two repeated elements with the same value are grouped into a repeated data segment. If the next data point is also a repeated element with the same value, it is included in the repeated data segment. In this way, the repeated data segment corresponding to the horizontal traversal path and the repeated data segment corresponding to the vertical traversal path can be obtained.

[0035] It should be noted that when traversing according to the horizontal traversal path, if the value of the last data point of a row is the same as the first data point of the next row, the first data point of the next row is still included in the repeated data segment where the last data point of a row is located. The same is true when traversing according to the vertical traversal path. The more repeated data segments there are in the traversal path and the longer each repeated data segment is, the more continuous repeated adjacent data there are in the path in this direction, and the higher the compression efficiency is in the run-length coding compression process.

[0036] Furthermore, the degree of continuous similarity of each traversal path is calculated according to the repeated data segments corresponding to the traversal paths in each direction. Specifically, the lengths of each data segment of the traversal path in one direction are squared and summed to obtain the sum result, and the sum result is multiplied by the number of repeated data segments and normalized to obtain the data repetition degree; the absolute value of the difference between every two adjacent data in the traversal path is obtained, and the absolute value of the difference is negatively correlated with the exponential function with the natural constant as the base to obtain the data similarity; the sum of the data repetition degree and the data similarity is the degree of continuous similarity of the traversal path in this direction. The specific calculation formula is: , Among them, R represents the degree of continuous similarity corresponding to the traversal path in one direction; tanh represents the hyperbolic tangent function, which is used for direct proportional normalization; Indicates the number of repeated data segments corresponding to the traversal path in this direction; Indicates the length of the i-th repeated data segment in the repeated data segment corresponding to the traversal path in this direction. Because the run-length encoding has a higher compression efficiency for repeated and continuous data with longer lengths, The length of the repeated data segment itself is used as the weight, and the repetition degree obtained is higher for the repeated data segments with longer continuous lengths. For example, if there are 3 repeated data segments with a length of 2 and 2 repeated data segments with a length of 3, the repetition degree of the 2 repeated data segments with a length of 3 is higher; exp() represents an exponential function with a natural constant as the base; and The jth data and j+1th data in the traversal path in this direction, is the absolute value of the difference between the jth data point and the next data point, indicating the difference of the point. It is the overall data point difference in the traversal path. The smaller the cumulative difference is, the higher the similarity of the data in the traversal path is. An exponential function with a natural constant as the base is used for inverse proportional normalization. and They are data duplication and data similarity respectively.

[0037] In this way, the degree of continuous similarity of traversal paths in different directions is obtained, and then subsequent analysis is carried out.

[0038] Step S5, selecting the traversal path with the greatest degree of continuous similarity as the optimal path; sorting the data in the two-dimensional matrix according to the traversal order of the optimal path to obtain a first sequence; performing differentiation on the first sequence to obtain an optimized compressed sequence, and compressing the optimized compressed sequence.

[0039] In step S4, the degree of continuous similarity of each traversal path is obtained. Since the greater the degree of continuous similarity, the higher the compression efficiency, the traversal path with the largest degree of continuous similarity is selected as the optimal path, and the data in the two-dimensional matrix is ​​sorted according to the traversal order of the optimal path to obtain the first sequence. That is, if the horizontal traversal path is the optimal path, the data in each row is connected end to end to obtain the first sequence; if the vertical traversal path is the optimal path, the data in each column is connected end to end to obtain the first sequence.

[0040] Furthermore, the first sequence is differentiated, and the differential method is to subtract the next data value from the previous data value to obtain the differential sequence of the first sequence, and the differential sequence is an optimized compression sequence. In the sequence after differentiation, pore water pressure data with a high degree of similarity may become repeated data, thereby improving the efficiency of run-length encoding.

[0041] Finally, the optimized compression sequence is compressed using the run-length encoding algorithm. The final compressed data is obtained and stored in the database of the engineering terminal. When decompressing the data, the data is first reverse-differentiated to obtain the data sequence before the difference, and the data sequence is reversely restored to a two-dimensional matrix in the direction of the traversal path, and the periodic curve of each row is restored to the correct position of the original curve according to the mark at the beginning of each row, and the decompression is completed to obtain the original curve time series data.

[0042] Embodiment 2: This embodiment provides an engineering information collection and storage system based on big data, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of a method for collecting and storing engineering information based on big data are implemented. Since Embodiment 1 has already described a method for collecting and storing engineering information based on big data in detail, it will not be described in detail here.

[0043] In summary, this scheme proposes a method and system for engineering information collection and storage based on big data. For the pore water pressure monitoring data in the engineering information data with periodic characteristics, combined with the run-length encoding algorithm compression, by analyzing the periodic distribution characteristics of the data, evaluate and construct a relatively optimal compression path, so that the data in the compression path contains more repeated continuous data, and obtain a stable and efficient run-length encoding compression effect, thereby improving the collection and storage efficiency of engineering information.

[0044] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0045] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for collecting and storing engineering information based on big data, characterized in that: The method includes: The pore water pressure data of fixed observation points are collected to obtain a pore water pressure sequence; a pore water pressure curve is obtained according to the pore water pressure sequence, and the period of the pore water pressure curve is obtained; Obtain the peak point with the largest value in the cycle of the pore water pressure curve, which is recorded as the main peak point; calculate the curve distribution characteristic value of the cycle based on the main peak point in a cycle and the data points on the left and right sides of the main peak point; Sort the periods in descending order according to the curve distribution eigenvalues, and construct a two-dimensional matrix based on the data in the sorted periods; Set traversal paths in different directions, traverse the two-dimensional matrix according to the traversal paths, and obtain repeated data segments corresponding to the traversal paths in different directions respectively; calculate the degree of continuous similarity of each traversal path according to the repeated data segments corresponding to the traversal paths in each direction; The traversal path with the largest continuous similarity is selected as the optimal path; the data in the two-dimensional matrix is ​​sorted according to the traversal order of the optimal path to obtain a first sequence; the first sequence is differentiated to obtain an optimized compression sequence, and the optimized compression sequence is compressed.

2. The method for collecting and storing engineering information based on big data according to claim 1 is characterized in that: The step of obtaining a pore water pressure curve according to the pore water pressure sequence and obtaining the period of the pore water pressure curve comprises: The collected pore water pressure sequence is projected into the coordinate system, with the horizontal axis being time and the vertical axis being pore water pressure, to obtain the pore water pressure curve; the pore water pressure curve is converted from the time domain to the frequency domain using Fourier transform to obtain the maximum frequency in the spectrum, where the inverse of the maximum frequency is the period of the pore water pressure curve.

3. The method for collecting and storing engineering information based on big data according to claim 1 is characterized in that: The peak point with the largest value in the cycle of the pore water pressure curve is recorded as the main peak point, including: The AMPD peak search algorithm is used to obtain all peak points. In each cycle, the peak point with the maximum pore water pressure value in the cycle is obtained as the main peak point in the cycle.

4. The method for collecting and storing engineering information based on big data according to claim 1 is characterized in that: The calculation of the curve distribution characteristic value of the period according to the main peak point in a period and the data points on the left and right sides of the main peak point includes: Normalize the value of the main peak point in a cycle to obtain the normalized peak value; obtain the slope between the data point adjacent to the left side of the main peak point in the cycle and the main peak point, recorded as the left slope, obtain the slope between the data point adjacent to the right side of the main peak point in the cycle and the main peak point, recorded as the right slope; normalize the absolute value of the difference between the left slope and the right slope to obtain the waveform sharpness; obtain the sum of the slopes between every two adjacent data points in the data points between the main peak point of the cycle and the first inflection point on the left, recorded as the left slope sum, obtain the sum of the slopes between every two adjacent data points in the data points between the main peak point of the cycle and the first inflection point on the right, recorded as the right slope sum; sum the left slope sum and the right slope sum and normalize them to obtain the continuous change characteristic value; sum the normalized peak value, waveform sharpness and continuous change characteristic value to obtain the curve distribution characteristic value of the cycle.

5. The method for collecting and storing engineering information based on big data according to claim 1 is characterized in that: The steps of sorting the periods in descending order according to the curve distribution characteristic values ​​and constructing a two-dimensional matrix according to the data in the sorted periods include: The periods are sorted from large to small according to the curve distribution eigenvalues, and a two-dimensional matrix is ​​constructed according to the sorted periods, where a row of data in the two-dimensional matrix is ​​a row of data in the period, the first row of the two-dimensional matrix is ​​the data in the period with the largest curve distribution eigenvalue, and so on, the last row of the two-dimensional matrix is ​​the data in the period with the smallest curve distribution eigenvalue.

6. The method for collecting and storing engineering information based on big data according to claim 1 is characterized in that: The step of setting traversal paths in different directions, traversing the two-dimensional matrix according to the traversal paths, and obtaining repeated data segments corresponding to the traversal paths in different directions respectively includes: The traversal paths are horizontal and vertical traversal paths respectively; the starting point of the traversal is the first data in the upper left corner of the matrix. During the traversal process, if the values ​​of the adjacent previous data point and the next data point are the same, the two data points are marked as repeated elements, and the two repeated elements with the same values ​​are grouped into a repeated data segment. If the next data point is also a repeated element with the same value, it is included in the repeated data segment, and so on, to obtain the repeated data segments corresponding to the horizontal traversal path and the repeated data segments corresponding to the vertical traversal path respectively.

7. The method for collecting and storing engineering information based on big data according to claim 1 is characterized in that: The step of calculating the degree of continuity similarity of each traversal path according to the repeated data segments corresponding to the traversal paths in each direction includes: The lengths of each data segment of the traversal path in one direction are squared and summed up to obtain a sum result, and the sum result is multiplied by the number of repeated data segments and normalized to obtain data repetition; the absolute value of the difference between every two adjacent data in the traversal path is obtained, and the absolute value of the difference is negatively correlated with an exponential function with a natural constant as the base to obtain data similarity; the sum of the data repetition and the data similarity is the continuous similarity degree of the traversal path in this direction.

8. The method for collecting and storing engineering information based on big data according to claim 1 is characterized in that: The step of performing differentiation on the first sequence to obtain an optimized compressed sequence comprises: The first sequence is differentiated by subtracting the next data value from the previous data value to obtain a differential sequence of the first sequence, and the differential sequence is an optimized compressed sequence.

9. An engineering information collection and storage system based on big data, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by the processor, the steps of the engineering information collection and storage method based on big data as described in any one of claims 1 to 8 are implemented.

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