CN117857648A - Big data-based construction engineering management cloud server communication method - Google Patents
Big data-based construction engineering management cloud server communication method Download PDFInfo
- Publication number
- CN117857648A CN117857648A CN202410240818.4A CN202410240818A CN117857648A CN 117857648 A CN117857648 A CN 117857648A CN 202410240818 A CN202410240818 A CN 202410240818A CN 117857648 A CN117857648 A CN 117857648A
- Authority
- CN
- China
- Prior art keywords
- data
- sequence
- value
- compressed
- differential
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000010276 construction Methods 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 43
- 230000006854 communication Effects 0.000 title claims abstract description 36
- 238000004891 communication Methods 0.000 title claims abstract description 30
- 238000005520 cutting process Methods 0.000 claims abstract description 52
- 238000012512 characterization method Methods 0.000 claims abstract description 35
- 238000011156 evaluation Methods 0.000 claims description 14
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 18
- 238000012544 monitoring process Methods 0.000 abstract description 17
- 230000005540 biological transmission Effects 0.000 abstract description 11
- 238000007906 compression Methods 0.000 abstract description 10
- 230000006835 compression Effects 0.000 abstract description 10
- 230000008859 change Effects 0.000 description 11
- 238000007726 management method Methods 0.000 description 10
- 238000004422 calculation algorithm Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013144 data compression Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000006837 decompression Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Landscapes
- Compression, Expansion, Code Conversion, And Decoders (AREA)
Abstract
The invention relates to the technical field of digital information transmission, in particular to a construction engineering management cloud server communication method based on big data. The method comprises the following steps: acquiring a data sequence to be compressed corresponding to a construction project, and determining sample data according to the difference between the data in the windows corresponding to each data; determining a cut characterization value based on the difference of each data from the data to the left thereof; determining an optimal characteristic value according to the difference between the cutting characteristic value of each sample data and the cutting characteristic value of the adjacent data on the left side of each sample data, and further obtaining a differential sequence to be analyzed by combining an original differential sequence corresponding to the data sequence to be compressed and the cutting characteristic value; according to the sizes of each position element in the original differential sequence, the corresponding position element in the differential sequence to be analyzed and the cutting characterization value, a first differential sequence is obtained, and the first differential sequence is subjected to differential to obtain a second differential sequence; the second differential sequence is encoded and transmitted. The invention improves the compression effect of the monitoring data of the building engineering.
Description
Technical Field
The invention relates to the technical field of digital information transmission, in particular to a construction engineering management cloud server communication method based on big data.
Background
In the big data age, people can acquire a large amount of data from industrial production, and the improvement of production efficiency is achieved by optimizing data processing and generating, and meanwhile, great data storage and transmission requirements are brought. In the field of construction engineering management, a large amount of monitoring data generated is generally stored in a cloud server, and a large amount of data transmission is required when the data is used. In order to increase communication speed when time series data such as audio and sensor data managed by engineering are transmitted, a data compression method based on differential coding is often used for processing.
The traditional differential coding compression method takes the difference value between the current data and the previous data as the compressed current data, so as to reduce the value range of the whole data to improve the similarity of the data, and further combines other compression methods such as Huffman compression to reduce the data quantity in the communication process, thereby being applicable to the communication transmission of various time sequence data or image data. The traditional differential coding can better promote data similarity when facing communication data with small data change and low change frequency, but can not promote data similarity when facing data with large data change and strong change, so that the compression effect of monitoring data of building engineering is poor, and finally, the communication process is not ideal.
Disclosure of Invention
In order to solve the problem of poor compression effect of the existing method when the monitoring data of the building engineering are compressed, the invention aims to provide a building engineering management cloud server communication method based on big data, and the adopted technical scheme is as follows:
the invention provides a construction engineering management cloud server communication method based on big data, which comprises the following steps:
acquiring a data sequence to be compressed corresponding to a construction project;
respectively taking each data in the data sequence to be compressed as a center, and constructing a window corresponding to each data; obtaining a self-variation scale value of each data according to the difference between the data in the window corresponding to each data and the extremely poor data in the data sequence to be compressed; determining sample data based on all of the self-variance scale values; determining a cutting characterization value of each data based on the difference between each data in the data sequence to be compressed and the data on the left side of the data sequence; determining an optimal characteristic value according to the difference between the cutting characteristic values of each sample data and the adjacent data on the left side and different preset characteristic values;
acquiring an original differential sequence corresponding to a data sequence to be compressed; obtaining a differential sequence to be analyzed based on the original differential sequence, the optimal characteristic value and the cutting characterization value; obtaining a first differential sequence according to the size relation between each position element in the original differential sequence and the corresponding position element in the differential sequence to be analyzed and the cutting characterization value; obtaining a second differential sequence based on differences between adjacent elements in the first differential sequence;
and encoding the second differential sequence to obtain compressed construction engineering data, and transmitting the compressed construction engineering data.
Preferably, the obtaining the self-variation scale value of each data according to the difference between the data in the window corresponding to each data and the extremely poor data in the data sequence to be compressed includes:
for the a-th data in the data sequence to be compressed:
calculating the average value of all data in the window corresponding to the a-th data;
and obtaining a self-variation scale value of the data according to the difference between each data in the window corresponding to the data a and the adjacent previous data, the difference between the previous data of each data in the window corresponding to the data a and the average value, and the extremely poor data in the data sequence to be compressed.
Preferably, the self-variance scale value of the a-th data is calculated using the following formula:
；
wherein,a self-variation scale value representing the a-th data, < >>Represents the number of data in the window corresponding to the a-th data,/or->Represents the w-th data in the window corresponding to the a-th data,>represents the w-1 th data in the window corresponding to the a-th data,/th data>Representing the extreme difference of the data in the data sequence to be compressed, < >>Representing the mean value of all data within the window to which the a-th data corresponds, mod () represents the remainder function,/>Representing taking absolute value symbols.
Preferably, the determining sample data based on all the self-variance scale values includes:
sequencing all the self-variation scale values according to the sequence from large to small to obtain a self-variation scale value sequence;
and determining data corresponding to the front preset number of self-variation scale values in the self-variation scale value sequence as sample data.
Preferably, the determining the cut characterization value of each data based on the difference between each data in the data sequence to be compressed and the data on the left side thereof includes:
for the a-th data in the data sequence to be compressed:
if the a-th data in the data sequence to be compressed is larger than the a-1-th data, traversing the a-th data forward in the data sequence to be compressed, and taking the absolute value of the difference value between the element which is larger than the a-th data and the a-th data as the cutting representation value of the a-th data;
if the a-th data in the data sequence to be compressed is smaller than the a-1-th data, traversing the a-th data forward in the data sequence to be compressed, and taking the opposite number of the absolute value of the difference value between the first element smaller than the a-th data and the a-th data as the cutting representation value of the a-th data;
if the a-th data in the data sequence to be compressed is equal to the a-1-th data or the first two size relations, traversing forwards and not finding out the element meeting the condition, and taking the constant 0 as the cutting representation value of the a-th data.
Preferably, the determining the optimal characteristic value according to the difference between the cut characteristic value of each sample data and the adjacent data on the left side and the different preset characteristic values includes:
for the c-th preset feature value: for the b sample data, recording the difference between the cut characterization value of the b sample data and the adjacent data on the left side of the b sample data as a first difference; respectively marking the product of the c-th preset characteristic value and the cutting characteristic value of the b-th sample data as a reference index corresponding to the c-th preset characteristic value; determining the absolute value of the difference between the first difference and a reference index corresponding to the c-th preset characteristic value as an evaluation value of the b-th sample data; taking the sum of the evaluation values of all sample data as an evaluation index of a c-th preset characteristic value;
and determining the preset characteristic value corresponding to the minimum evaluation index as an optimal characteristic value.
Preferably, the obtaining the differential sequence to be analyzed based on the original differential sequence, the optimal eigenvalue and the cut characterization value includes:
respectively marking the product of each element in the original differential sequence and the cutting characterization value as a first product corresponding to each element in the original differential sequence;
the first products corresponding to all elements in the original differential sequence form the differential sequence to be analyzed.
Preferably, according to the size relation between each position element in the original differential sequence and the corresponding position element in the differential sequence to be analyzed and the cutting characterization value, a first differential sequence is obtained, including:
if the a-th element in the original differential sequence is larger than or equal to the a-th corresponding position element in the differential sequence to be analyzed, taking the a-th element in the original differential sequence as the a-th element in the first differential sequence;
if the a-th element in the original differential sequence is smaller than the a-th corresponding position element in the differential sequence to be analyzed, when the cutting representation value of the a-th element in the differential sequence to be analyzed is the absolute value of the difference value between the a-th data element and the a-th data, marking the product of the constant 3 and the original differential value corresponding to the a-th element in the data sequence to be compressed as a second product, and taking the second product as the a-th element in the first differential sequence; when the cutting characterization value of the a-th element in the differential sequence to be analyzed is the first number which is smaller than the opposite number of the absolute value of the difference value of the a-th data and the a-th data, taking the sum of a constant 1 and the second product as the a-th element in the first differential sequence; when the cutting characterization value of the a-th element in the differential sequence to be analyzed is 0, taking the sum of a constant 2 and the second product as the a-th element in the first differential sequence;
the first differential sequence is obtained based on all elements in the first differential sequence.
Preferably, the obtaining a second differential sequence based on the difference between adjacent elements in the first differential sequence includes:
respectively calculating the difference value of the last element and the previous element in every two adjacent elements in the first differential sequence, and taking the difference value as the difference value corresponding to the two adjacent elements in the first differential sequence;
the first element in the first differential sequence and the differences corresponding to all adjacent elements in the first differential sequence form a second differential sequence.
Preferably, the obtaining the original differential sequence corresponding to the data sequence to be compressed includes:
respectively calculating the difference value between the last element and the previous element in every two adjacent elements in the data sequence to be compressed, and taking the difference value as the difference value corresponding to the two adjacent elements;
the first element in the data sequence to be compressed and the differences corresponding to all adjacent elements in the data sequence to be compressed form an original differential sequence corresponding to the data sequence to be compressed.
The invention has at least the following beneficial effects:
according to the method, the data sequence to be compressed corresponding to the construction engineering is sampled through the construction window, the self-variation scale value of each data is determined according to the difference change condition of adjacent numerical values in the window, the greater the value of the self-variation scale value is used for representing the intensity of element changes at different positions in the data sequence to be compressed, the more intense the element changes in the window compared with other element numerical values in the data sequence to be compressed are, the worse the effect is when the data segment with the poor effect is used as a region for calculating the optimal characteristic value, the subsequent analysis effect is influenced by less data quantity, the calculation speed of the whole algorithm is accelerated, and the finally determined characteristic value is ensured to be the optimal characteristic value; the method provided by the invention is used for carrying out differential encoding on data in the data sequence to be compressed, compared with the traditional differential encoding, the data similarity is further improved, the data similarity effect can be well improved when the data with large variation and strong variation is faced, the compression effect of monitoring data of construction engineering is improved, the data transmission cost in the communication process is reduced, and better communication effect is achieved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a communication method of a construction engineering management cloud server based on big data provided by an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description is given to a construction engineering management cloud server communication method based on big data according to the present invention by referring to the accompanying drawings and the preferred embodiments.
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.
The invention provides a concrete scheme of a cloud server communication method based on big data construction engineering management, which is specifically described below with reference to the accompanying drawings.
Big data-based construction engineering management cloud server communication method embodiment:
the specific scene aimed at by this embodiment is: because the data volume of the construction engineering monitoring data is large, the construction engineering monitoring data needs to be encoded, compressed and transmitted, the embodiment acquires the construction engineering monitoring data, constructs a data sequence to be compressed based on the acquired monitoring data, analyzes the data characteristics in the data sequence to be compressed, and further realizes encoding, compression and transmission of the construction engineering monitoring data.
The embodiment provides a big data-based construction engineering management cloud server communication method, as shown in fig. 1, which includes the following steps:
step S1, a data sequence to be compressed corresponding to a construction project is obtained.
The embodiment firstly collects monitoring data in construction engineering, and the following needs to be described: the monitoring data can be sensor data, image data and the like, the sensor data is collected by a corresponding sensor, the image data is collected by a camera, and if the monitoring data is the sensor data, all the collected monitoring data are sequenced according to time sequence to obtain a data sequence to be compressed corresponding to construction engineering; and if the monitoring data are image data, sequencing the gray values of all the pixel points in the image according to the gray value of each pixel point in the image and the sequence from left to right and from top to bottom to obtain a data sequence to be compressed corresponding to the construction engineering. The present embodiment does not limit the kind of the monitoring data, and the practitioner can adjust according to the specific situation.
So far, the data sequence to be compressed corresponding to the construction engineering is obtained.
Step S2, respectively taking each data in the data sequence to be compressed as a center, and constructing a window corresponding to each data; obtaining a self-variation scale value of each data according to the difference between the data in the window corresponding to each data and the extremely poor data in the data sequence to be compressed; determining sample data based on all of the self-variance scale values; determining a cutting characterization value of each data based on the difference between each data in the data sequence to be compressed and the data on the left side of the data sequence; and determining an optimal characteristic value according to the difference between the cutting characteristic value of each sample data and the adjacent data on the left side and different preset characteristic values.
In the embodiment, when the data sequence to be compressed corresponding to the construction engineering is compressed, the high-frequency data area is determined according to the change characteristics of the whole data, wherein the high-frequency data area is a data segment with large change and strong change in the data, and is also a data segment which is difficult to make the data have similarity through calculation when the differential coding algorithm is used for processing.
And respectively taking each data in the data sequence to be compressed as a center, constructing a window with a preset length, wherein the preset length is 7 in the embodiment as a window corresponding to each data, and setting by an implementer according to specific conditions in specific application.
Considering that the difference between data and the last data adjacent thereto can reflect the similarity between data, the smaller the similarity, the higher the degree of attention should be given in the subsequent analysis. Based on this, the present embodiment will determine a self-variance scale value of each data according to the difference between the data in the window corresponding to each data and the range of the data in the data sequence to be compressed, where the self-variance scale value is used to characterize the attention degree of the data.
For the a-th data in the data sequence to be compressed:
calculating the average value of all data in the window corresponding to the a-th data; and calculating the self-variation scale value of the data a according to the difference between each data in the window corresponding to the data a and the adjacent previous data, the difference between the previous data of each data in the window corresponding to the data a and the average value, and the extremely poor data in the data sequence to be compressed. The specific calculation formula of the self-variation scale value of the a data is as follows:
；
wherein,a self-variation scale value representing the a-th data, < >>Represents the number of data in the window corresponding to the a-th data,/or->Represents the w-th data in the window corresponding to the a-th data,>represents the w-1 th data in the window corresponding to the a-th data,/th data>Representing the extreme difference of the data in the data sequence to be compressed, < >>Representing the mean value of all data within the window to which the a-th data corresponds, mod () represents the remainder function,/>Representing taking absolute value symbols.
When the difference between the w-th data and the adjacent last data in the window corresponding to the a-th data is larger, the effect of calculating the data similarity of the communication data through the traditional differential coding is worse, the data similarity of the segment of data is harder to calculate through the traditional differential algorithm, and the importance degree weight of the data is larger, namely the self-variation scale value is larger; the extreme difference of the data in the data sequence to be compressed is introduced to normalize the difference between each data and the immediately preceding data within the window corresponding to the a-th data. In the embodiment, a data divisor cutting method is adopted, the divisor size of divisor cutting is similar to the difference between the previous data and the average value of all elements in a window, and the remainder can be approximately represented as the optimal cutting frequency of the data; the larger the remainder, the more intense and frequent the monitored data within the data segment, the more often the cutting operation is required to eliminate the data variability, the more important should be the self-variation scale value of the data.
By adopting the method, the self-variation scale value of each data in the data sequence to be compressed can be obtained, the larger the self-variation scale value is, the larger the weight is when the cutting simulation is carried out, and all the self-variation scale values are sequenced according to the sequence from large to small to obtain the self-variation scale value sequence; and determining data corresponding to the front preset number of self-variation scale values in the self-variation scale value sequence as sample data. It should be noted that: as other embodiments, all the self-variance scale values may be sorted in order from small to large to obtain a self-variance scale value sequence; and determining the data corresponding to the rear preset number of self-variation scale values in the self-variation scale value sequence as sample data, namely, taking the data with larger self-variation scale values as the sample data. In this embodiment, the preset number is 5% of the total number of data in the data sequence to be compressed, and in a specific application, an implementer may set according to a specific situation.
Next, the present embodiment will determine a cut characterization value of each data based on the difference between each data in the data sequence to be compressed and the data to the left thereof. Specifically, for the a-th data in the data sequence to be compressed: if the a-th data in the data sequence to be compressed is larger than the a-1-th data, traversing the a-th data forward in the data sequence to be compressed, and taking the absolute value of the difference value between the element which is larger than the a-th data and the a-th data as the cutting representation value of the a-th data. If the a-th data in the data sequence to be compressed is smaller than the a-1-th data, traversing the a-th data forward in the data sequence to be compressed, and taking the opposite number of the absolute value of the difference value of the first element smaller than the a-th data and the a-th data as the cutting representation value of the a-th data. If the a-th data in the data sequence to be compressed is equal to the a-1-th data or the elements meeting the conditions are not found by forward traversal under the relation of the first two sizes, taking the constant 0 as the cutting representation value of the a-th data. By analogy to the above method, a cut characterization value for each data in the data sequence to be compressed can be obtained.
In this embodiment, a plurality of preset feature values are set, where the preset feature values are as followsAll integers in the interval, wherein M is the maximum preset characteristic value; the M is specifically obtained by the following steps: for data sequence to be compressedIs set in the database of the computer system: and calculating the absolute value of the difference between the data and the adjacent previous data, and taking the upward rounding value of the ratio between the absolute value of the difference and the cutting characterization value of the data as the maximum preset characteristic value M.
For the c-th preset feature value: for the b sample data, recording the difference between the cut characterization value of the b sample data and the adjacent data on the left side of the b sample data as a first difference; respectively marking the product of the c-th preset characteristic value and the cutting characteristic value of the b-th sample data as a reference index corresponding to the c-th preset characteristic value; determining the absolute value of the difference between the first difference and a reference index corresponding to the c-th preset characteristic value as an evaluation value of the b-th sample data; and taking the sum of the evaluation values of all the sample data as an evaluation index of the c-th preset characteristic value. By adopting the method, the evaluation index of each preset characteristic value can be obtained. The smaller the evaluation index is, the better the cutting effect after the simulation cutting of the sampling data is represented, so that the preset characteristic value corresponding to the minimum evaluation index is determined as the optimal characteristic value in the embodiment.
So far, the method provided by the embodiment is adopted to determine the optimal characteristic value.
S3, acquiring an original differential sequence corresponding to a data sequence to be compressed; obtaining a differential sequence to be analyzed based on the original differential sequence, the optimal characteristic value and the cutting characterization value; obtaining a first differential sequence according to the size relation between each position element in the original differential sequence and the corresponding position element in the differential sequence to be analyzed and the cutting characterization value; a second differential sequence is obtained based on differences between adjacent elements in the first differential sequence.
Firstly, respectively calculating the difference value of the last element and the previous element in every two adjacent elements in a data sequence to be compressed, and taking the difference value as the corresponding difference value of the two adjacent elements; the first element in the data sequence to be compressed and the differences corresponding to all adjacent elements in the data sequence to be compressed form an original differential sequence corresponding to the data sequence to be compressed. Respectively marking the product of each element in the original differential sequence and the cutting characterization value as a first product corresponding to each element in the original differential sequence; the first products corresponding to all elements in the original differential sequence form the differential sequence to be analyzed.
When the elements in the original differential sequence are larger than the elements in the same position in the differential sequence to be analyzed, the larger the difference between the data and the previous adjacent data is, the more the traditional differential coding algorithm is not needed to be adopted in order to ensure the compression effect of the data. Based on the above, if the a-th element in the original differential sequence is greater than or equal to the a-th corresponding position element in the differential sequence to be analyzed, the a-th element in the original differential sequence is used as the a-th element in the first differential sequence. If the a-th element in the original differential sequence is smaller than the a-th corresponding position element in the differential sequence to be analyzed, when the cutting representation value of the a-th element in the differential sequence to be analyzed is the absolute value of the difference value between the a-th data element and the a-th data, marking the product of the constant 3 and the original differential value corresponding to the a-th element in the data sequence to be compressed as a second product, and taking the second product as the a-th element in the first differential sequence; when the cutting characterization value of the a-th element in the differential sequence to be analyzed is the first number which is smaller than the opposite number of the absolute value of the difference value of the a-th data and the a-th data, taking the sum of a constant 1 and the second product as the a-th element in the first differential sequence; when the cut characterization value of the a-th element in the differential sequence to be analyzed is 0, taking the sum of the constant 2 and the second product as the a-th element in the first differential sequence. By adopting the method, all elements in the first differential sequence can be obtained, and all elements in the first differential sequence form the first differential sequence.
The first differential processing of all data in the data sequence to be compressed corresponding to the construction engineering is completed, and the data with larger adjacent numerical value difference is mapped by the method, so that the numerical value difference is reduced, the similarity of the data is further increased, the next compression quality is improved, and the transmission cost in the communication process is reduced.
The embodiment calculates the difference value between the last element and the previous element in every two adjacent elements in the first differential sequence, and uses the difference value as the difference value corresponding to the two adjacent elements in the first differential sequence; the first element in the first differential sequence and the differences corresponding to all adjacent elements in the first differential sequence form a second differential sequence, the first element in the second differential sequence is the first element in the first differential sequence, the second element in the second differential sequence is the difference between the second element in the first differential sequence and the first element in the first differential sequence, the third element in the second differential sequence is the difference between the second element in the third differential sequence and the second element in the first differential sequence, and so on, the second differential sequence is obtained, the similarity between data is further increased, and the transmission effect in the communication process is better.
And S4, encoding the second differential sequence to obtain compressed construction engineering data, and transmitting the compressed construction engineering data.
And the Huffman coding method is adopted to code the data in the second differential sequence, so as to obtain the compressed construction engineering data, the data volume of the compressed construction engineering data is smaller, and the transmission speed is faster when the compressed construction engineering data is transmitted in a communication way. The huffman coding method is the prior art and will not be described in detail here.
Transmitting the compressed construction engineering data, receiving the compressed construction engineering data at a communication receiving end of the cloud server, taking the compressed construction engineering data as input, adopting a Huffman coding decoding method, and outputting as a second differential sequence; the second differential sequence is taken as an input, differential encoding is adopted for decoding, and the output is taken as a first differential sequence. And obtaining a data sequence to be compressed according to the first differential sequence.
The specific process of obtaining the data sequence to be compressed according to the first differential sequence is as follows:
(1) Calculating a solution difference discrimination value corresponding to each element in the first difference sequence:
；
wherein,for the u-th element in the first differential sequence, and (2)>For the solution difference discrimination value corresponding to the u-th element in the first difference sequence, mod () represents a remainder function.
And (3) solving a data sequence to be compressed:
；
wherein,for the 1 st element in the data sequence to be compressed, -, is>For the (u-1) th element in the data sequence to be compressed,/o>For the (u-1) th element in the data sequence to be compressed,/o>For element 1 in the first differential sequence, -, is->For the u-th element in the first differential sequence, and (2)>For the (u-2) th element in the first differential sequence,/o>For the first start item element when the data sequence to be compressed is given in the process of solving the first differential sequence,/-, is given>For the tag value of the (u-1) th element in the data sequence to be compressed,/for the tag value of the (u-1) th element in the data sequence to be compressed>And the difference discrimination value is a solution difference discrimination value corresponding to the u-th element in the first difference sequence.
The tag value of the (u-1) th element in the data sequence to be compressed is obtained by the following steps: when the cutting characterization value of the (u-1) th element in the differential sequence to be analyzed is the absolute value of the difference value between the element larger than the (u-1) th data and the (u-1) th data, the marking value of the (u-1) th element is 1; when the cutting characterization value of the (u-1) th element in the differential sequence to be analyzed is the first number which is smaller than the opposite number of the absolute value of the difference value between the (u-1) th element and the (u-1) th data, the mark value of the (u-1) th element is 1; when the cut characterization value of the u-1 element in the differential sequence to be analyzed is 0, the mark value of the u-1 element is 0.
According to the method, each element in the data sequence to be compressed is obtained through inverse operation of the first differential process, namely all original monitoring data of the construction project are obtained, decompression of the cloud server communication receiving end is completed, data transmission cost in the communication process is smaller, and a better communication effect is achieved.
According to the embodiment, the data sequence to be compressed corresponding to the construction engineering is sampled through the construction window, the self-variation scale value of each data is determined according to the difference change condition of adjacent numerical values in the window, the greater the value of the self-variation scale value is used for representing the intensity of element change at different positions in the data sequence to be compressed, the more intense the element change in the window is compared with other element numerical values in the data sequence to be compressed, the worse the effect is when the data segment with the poor effect is used as a region for calculating the optimal characteristic value, the subsequent analysis effect is influenced by less data quantity, the calculation speed of the whole algorithm is accelerated, and the finally determined characteristic value is ensured to be the optimal characteristic value; the method further comprises the steps of obtaining a differential sequence to be analyzed based on an original differential sequence corresponding to the data sequence to be compressed, an optimal characteristic value and a cutting characteristic value, obtaining a first differential sequence according to the size relation between each position element in the original differential sequence and the corresponding position element in the differential sequence to be analyzed and the cutting characteristic value, performing differential processing on the first differential sequence again to obtain a second differential sequence, further encoding and transmitting the second differential sequence, and performing differential encoding on data in the data sequence to be compressed.
It should be noted that: 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 (10)
1. The construction engineering management cloud server communication method based on big data is characterized by comprising the following steps of:
acquiring a data sequence to be compressed corresponding to a construction project;
respectively taking each data in the data sequence to be compressed as a center, and constructing a window corresponding to each data; obtaining a self-variation scale value of each data according to the difference between the data in the window corresponding to each data and the extremely poor data in the data sequence to be compressed; determining sample data based on all of the self-variance scale values; determining a cutting characterization value of each data based on the difference between each data in the data sequence to be compressed and the data on the left side of the data sequence; determining an optimal characteristic value according to the difference between the cutting characteristic values of each sample data and the adjacent data on the left side and different preset characteristic values;
acquiring an original differential sequence corresponding to a data sequence to be compressed; obtaining a differential sequence to be analyzed based on the original differential sequence, the optimal characteristic value and the cutting characterization value; obtaining a first differential sequence according to the size relation between each position element in the original differential sequence and the corresponding position element in the differential sequence to be analyzed and the cutting characterization value; obtaining a second differential sequence based on differences between adjacent elements in the first differential sequence;
and encoding the second differential sequence to obtain compressed construction engineering data, and transmitting the compressed construction engineering data.
2. The cloud server communication method for big data-based construction engineering management according to claim 1, wherein the obtaining the self-variation scale value of each data according to the difference between the data in the window corresponding to each data and the extremely poor data in the data sequence to be compressed comprises:
for the a-th data in the data sequence to be compressed:
calculating the average value of all data in the window corresponding to the a-th data;
and obtaining a self-variation scale value of the data according to the difference between each data in the window corresponding to the data a and the adjacent previous data, the difference between the previous data of each data in the window corresponding to the data a and the average value, and the extremely poor data in the data sequence to be compressed.
3. The big data-based construction engineering management cloud server communication method according to claim 2, wherein the self-variance scale value of the a-th data is calculated by adopting the following formula:
；
wherein,a self-variation scale value representing the a-th data, < >>Indicating the amount of data in the window corresponding to the a-th data,represents the w-th data in the window corresponding to the a-th data,>represents the w-1 th data in the window corresponding to the a-th data,/th data>Representing the extreme difference of the data in the data sequence to be compressed, < >>Representing the mean value of all data within the window to which the a-th data corresponds, mod () represents the remainder function,/>Representing taking absolute value symbols.
4. The big data based construction engineering management cloud server communication method according to claim 1, wherein the determining sample data based on all the self-variance scale values includes:
sequencing all the self-variation scale values according to the sequence from large to small to obtain a self-variation scale value sequence;
and determining data corresponding to the front preset number of self-variation scale values in the self-variation scale value sequence as sample data.
5. The big data-based construction engineering management cloud server communication method according to claim 1, wherein determining the cut characterization value of each data based on the difference between each data and the data on the left side thereof in the data sequence to be compressed comprises:
for the a-th data in the data sequence to be compressed:
if the a-th data in the data sequence to be compressed is larger than the a-1-th data, traversing the a-th data forward in the data sequence to be compressed, and taking the absolute value of the difference value between the element which is larger than the a-th data and the a-th data as the cutting representation value of the a-th data;
if the a-th data in the data sequence to be compressed is smaller than the a-1-th data, traversing the a-th data forward in the data sequence to be compressed, and taking the opposite number of the absolute value of the difference value between the first element smaller than the a-th data and the a-th data as the cutting representation value of the a-th data;
if the a-th data in the data sequence to be compressed is equal to the a-1-th data or the first two size relations, traversing forwards and not finding out the element meeting the condition, and taking the constant 0 as the cutting representation value of the a-th data.
6. The big data-based construction engineering management cloud server communication method according to claim 1, wherein the determining the optimal feature value according to the difference between the cut characterization value of each sample data and the left adjacent data thereof and the different preset feature value comprises:
for the c-th preset feature value: for the b sample data, recording the difference between the cut characterization value of the b sample data and the adjacent data on the left side of the b sample data as a first difference; respectively marking the product of the c-th preset characteristic value and the cutting characteristic value of the b-th sample data as a reference index corresponding to the c-th preset characteristic value; determining the absolute value of the difference between the first difference and a reference index corresponding to the c-th preset characteristic value as an evaluation value of the b-th sample data; taking the sum of the evaluation values of all sample data as an evaluation index of a c-th preset characteristic value;
and determining the preset characteristic value corresponding to the minimum evaluation index as an optimal characteristic value.
7. The big data-based construction engineering management cloud server communication method according to claim 1, wherein the obtaining the differential sequence to be analyzed based on the original differential sequence, the optimal feature value and the cut feature value comprises:
respectively marking the product of each element in the original differential sequence and the cutting characterization value as a first product corresponding to each element in the original differential sequence;
the first products corresponding to all elements in the original differential sequence form the differential sequence to be analyzed.
8. The big data-based construction engineering management cloud server communication method according to claim 5, wherein obtaining the first differential sequence according to the size relation between each position element in the original differential sequence and the corresponding position element in the differential sequence to be analyzed and the cut characterization value comprises:
if the a-th element in the original differential sequence is larger than or equal to the a-th corresponding position element in the differential sequence to be analyzed, taking the a-th element in the original differential sequence as the a-th element in the first differential sequence;
if the a-th element in the original differential sequence is smaller than the a-th corresponding position element in the differential sequence to be analyzed, when the cutting representation value of the a-th element in the differential sequence to be analyzed is the absolute value of the difference value between the a-th data element and the a-th data, marking the product of the constant 3 and the original differential value corresponding to the a-th element in the data sequence to be compressed as a second product, and taking the second product as the a-th element in the first differential sequence; when the cutting characterization value of the a-th element in the differential sequence to be analyzed is the first number which is smaller than the opposite number of the absolute value of the difference value of the a-th data and the a-th data, taking the sum of a constant 1 and the second product as the a-th element in the first differential sequence; when the cutting characterization value of the a-th element in the differential sequence to be analyzed is 0, taking the sum of a constant 2 and the second product as the a-th element in the first differential sequence;
the first differential sequence is obtained based on all elements in the first differential sequence.
9. The big data based construction engineering management cloud server communication method according to claim 1, wherein the obtaining a second differential sequence based on differences between adjacent elements in the first differential sequence includes:
respectively calculating the difference value of the last element and the previous element in every two adjacent elements in the first differential sequence, and taking the difference value as the difference value corresponding to the two adjacent elements in the first differential sequence;
the first element in the first differential sequence and the differences corresponding to all adjacent elements in the first differential sequence form a second differential sequence.
10. The big data-based construction engineering management cloud server communication method according to claim 1, wherein the obtaining the original differential sequence corresponding to the data sequence to be compressed comprises:
respectively calculating the difference value between the last element and the previous element in every two adjacent elements in the data sequence to be compressed, and taking the difference value as the difference value corresponding to the two adjacent elements;
the first element in the data sequence to be compressed and the differences corresponding to all adjacent elements in the data sequence to be compressed form an original differential sequence corresponding to the data sequence to be compressed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410240818.4A CN117857648A (en) | 2024-03-04 | 2024-03-04 | Big data-based construction engineering management cloud server communication method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410240818.4A CN117857648A (en) | 2024-03-04 | 2024-03-04 | Big data-based construction engineering management cloud server communication method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117857648A true CN117857648A (en) | 2024-04-09 |
Family
ID=90540235
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410240818.4A Pending CN117857648A (en) | 2024-03-04 | 2024-03-04 | Big data-based construction engineering management cloud server communication method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117857648A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1689335A (en) * | 2002-10-07 | 2005-10-26 | 皇家飞利浦电子股份有限公司 | Efficient motion-vector prediction for unconstrained and lifting-based motion compensated temporal filtering |
| US20100259631A1 (en) * | 2007-10-26 | 2010-10-14 | Fujifilm Corporation | Data compression apparatus, data compression program and image-taking apparatus |
| CN115827577A (en) * | 2022-09-27 | 2023-03-21 | 天津大学 | Cloud collaborative self-adaptive dividing and treating method for high-frequency data compression and reconstruction of intelligent electric meter |
| CN117150217A (en) * | 2023-11-01 | 2023-12-01 | 苏州爱雄斯通信技术有限公司 | Data compression processing method based on big data |
| CN117155407A (en) * | 2023-10-31 | 2023-12-01 | 博洛尼智能科技（青岛）有限公司 | Intelligent mirror cabinet disinfection log data optimal storage method |
| CN117373600A (en) * | 2023-12-04 | 2024-01-09 | 邦盛高科特种车辆（天津）有限公司 | Medical detection vehicle data optimal storage method |
-
2024
- 2024-03-04 CN CN202410240818.4A patent/CN117857648A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1689335A (en) * | 2002-10-07 | 2005-10-26 | 皇家飞利浦电子股份有限公司 | Efficient motion-vector prediction for unconstrained and lifting-based motion compensated temporal filtering |
| US20100259631A1 (en) * | 2007-10-26 | 2010-10-14 | Fujifilm Corporation | Data compression apparatus, data compression program and image-taking apparatus |
| CN115827577A (en) * | 2022-09-27 | 2023-03-21 | 天津大学 | Cloud collaborative self-adaptive dividing and treating method for high-frequency data compression and reconstruction of intelligent electric meter |
| CN117155407A (en) * | 2023-10-31 | 2023-12-01 | 博洛尼智能科技（青岛）有限公司 | Intelligent mirror cabinet disinfection log data optimal storage method |
| CN117150217A (en) * | 2023-11-01 | 2023-12-01 | 苏州爱雄斯通信技术有限公司 | Data compression processing method based on big data |
| CN117373600A (en) * | 2023-12-04 | 2024-01-09 | 邦盛高科特种车辆（天津）有限公司 | Medical detection vehicle data optimal storage method |
Non-Patent Citations (2)
| Title |
|---|
| 梁捷;蒋雯倩;李金瑾;: "基于动态字典和差分编码的计量数据压缩研究", 信息技术, no. 10, 22 October 2020 (2020-10-22) * |
| 郑域: "面向大规模天文观测数据的压缩算法设计与实现研究", 《中国优秀硕士学位论文全文数据库·基础科学辑》, 15 January 2022 (2022-01-15) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP3017380B2 (en) | Data compression method and apparatus, and data decompression method and apparatus | |
| TWI426774B (en) | A method for classifying an uncompressed image respective to jpeg compression history, an apparatus for classifying an image respective to whether the image has undergone jpeg compression and an image classification method | |
| CN100380975C (en) | Method for generating hashes from a compressed multimedia content | |
| CN116388767B (en) | Security management method for software development data | |
| US6697529B2 (en) | Data compression method and recording medium with data compression program recorded therein | |
| CN115063326B (en) | Infrared night vision image efficient communication method based on image compression | |
| CN115459782A (en) | Industrial Internet of things high-frequency data compression method based on time sequence segmentation and clustering | |
| CN113328755A (en) | Compressed data transmission method facing edge calculation | |
| CN114745553A (en) | Image data storage method based on big data | |
| US6594375B1 (en) | Image processing apparatus, image processing method, and storage medium | |
| CN113362915B (en) | Material performance prediction method and system based on multi-modal learning | |
| CN117376430B (en) | Industrial data rapid transmission method and system based on DCS | |
| CN117857648A (en) | Big data-based construction engineering management cloud server communication method | |
| JP2011244101A (en) | Image processing apparatus and image processing method | |
| CN115695564B (en) | Efficient transmission method of Internet of things data | |
| CN116233479B (en) | Live broadcast information content auditing system and method based on data processing | |
| CN112153388A (en) | Image compression method, device and related equipment | |
| CN116567269A (en) | Spectrum monitoring data compression method based on signal-to-noise separation | |
| CN116256025A (en) | Aeration data monitoring system of ultra-filtration water device | |
| CN109698703B (en) | Gene sequencing data decompression method, system and computer readable medium | |
| CN112861135B (en) | Malicious code detection method based on attention mechanism | |
| CN115190314A (en) | Method for adjusting video recording coding parameters and related equipment | |
| de Sousa Filho et al. | Entropy estimation in bidimensional sequences | |
| CN117373100B (en) | Face recognition method and system based on differential quantization local binary pattern | |
| CN117979009B (en) | Smart community information management method and system based on cloud computing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |