Construction project management cloud server communication method based on big data

Abstract

The present invention relates to the technical field of digital information transmission, and in particular to a construction project management cloud server communication method based on big data. The method comprises: obtaining a data sequence to be compressed corresponding to a construction project, determining sample data according to the difference between data in a window corresponding to each data; determining a cutting characterization value based on the difference between each data and the data on its left; determining an optimal characteristic value according to the difference between the cutting characterization value of each sample data and the adjacent data on its left, and then combining the original differential sequence and the cutting characterization value corresponding to the data sequence to be compressed to obtain a differential sequence to be analyzed; obtaining a first differential sequence according to the size of 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, performing differentiation on the first differential sequence to obtain a second differential sequence; encoding and transmitting the second differential sequence. The present invention improves the compression effect of monitoring data of construction projects.

Description

Technical Field

[0001] The present invention relates to the technical field of digital information transmission, and in particular to a construction project management cloud server communication method based on big data. Background Art

[0002] In the era of big data, people can obtain a large amount of data from industrial production, and improve production efficiency by optimizing data processing. At the same time, it also brings great data storage and transmission needs. In the field of construction project management, a large amount of monitoring data is usually stored in the cloud server, and a large amount of data transmission is required when using the data. When transmitting time series data such as audio, sensor data, etc. for engineering management, in order to speed up communication, data compression methods based on differential coding are often used for processing.

[0003] The traditional differential coding compression method uses the difference between the current data and the previous data as the compressed current data, thereby reducing the value range of the entire data segment to improve the similarity of the data, and further combines other compression methods such as Huffman compression to reduce the amount of data in the communication process, which is suitable for the communication transmission of various time series data or image data. Traditional differential coding can effectively improve the similarity of data when facing communication data with small changes and low change frequency, but when facing data with large and strong changes, traditional differential coding cannot improve the similarity of data, resulting in poor compression effect of monitoring data of construction projects, which ultimately leads to a suboptimal communication process. Summary of the invention

[0004] In order to solve the problem that the existing method has poor compression effect when compressing the monitoring data of the construction project, the purpose of the present invention is to provide a construction project management cloud server communication method based on big data, and the technical solution adopted is as follows:

[0005] The present invention provides a construction project management cloud server communication method based on big data, the method comprising the following steps:

[0006] Obtain the data sequence to be compressed corresponding to the construction project;

[0007] Taking each data in the data sequence to be compressed as the center, a window corresponding to each data is constructed; 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, the self-variation scale value of each data is obtained; based on all the self-variation scale values, the sample data is determined; based on the difference between each data in the data sequence to be compressed and the data on its left, the cutting characterization value of each data is determined; according to the difference between the cutting characterization value of each sample data and the adjacent data on its left and different preset characteristic values, the optimal characteristic value is determined;

[0008] Obtaining an original differential sequence corresponding to the data sequence to be compressed; obtaining a differential sequence to be analyzed based on the original differential sequence, the optimal eigenvalue and the cut characterization value; obtaining a first differential sequence according to the size relationship between the elements at each position in the original differential sequence and the elements at the corresponding position in the differential sequence to be analyzed and the cut characterization value; obtaining a second differential sequence based on the difference between adjacent elements in the first differential sequence;

[0009] The second differential sequence is encoded to obtain compressed construction project data, and the compressed construction project data is transmitted.

[0010] Preferably, obtaining the autovariation 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 includes:

[0011] For the ath data in the data sequence to be compressed:

[0012] Calculate the mean of all data in the window corresponding to the a-th data;

[0013] According to the difference between each data in the window corresponding to the a-th data and its adjacent previous data, the difference between the previous data of each data in the window corresponding to the a-th data and the mean, and the range of the data in the data sequence to be compressed, the self-variation scale value of the a-th data is obtained.

[0014] Preferably, the following formula is used to calculate the self-variation scale value of the ath data:

[0015] ;

[0016] in, Indicates the independent variation scale value of the a-th data. Indicates the number of data in the window corresponding to the a-th data. Indicates the wth data in the window corresponding to the ath data, Indicates the w-1th data in the window corresponding to the ath data. Indicates the range of the data in the data sequence to be compressed. represents the mean of all data in the window corresponding to the a-th data, mod() represents the remainder function, Indicates the absolute value sign.

[0017] Preferably, the determining of sample data based on all the independent variation scale values ​​includes:

[0018] Sort all the self-variation scale values ​​in order from large to small to obtain the self-variation scale value sequence;

[0019] The data corresponding to the first preset number of self-variation scale values ​​in the self-variation scale value sequence are determined as sample data.

[0020] Preferably, the step of determining the cutting characterization value of each data based on the difference between each data in the to-be-compressed data sequence and the data on its left side comprises:

[0021] For the ath data in the data sequence to be compressed:

[0022] If the a-th data in the to-be-compressed data sequence is greater than the a-1-th data, then traverse forward from the a-th data in the to-be-compressed data sequence, and take the absolute value of the difference between the first element greater than the a-th data and the a-th data as the cutting representation value of the a-th data;

[0023] If the a-th data in the to-be-compressed data sequence is smaller than the a-1-th data, then traverse forward from the a-th data in the to-be-compressed data sequence, and take the opposite of the absolute value of the difference 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;

[0024] If the ath data in the to-be-compressed data sequence is equal to the a-1th data or no qualifying element is found in the forward traversal under the first two size relationships, the constant 0 is used as the cutting representation value of the ath data.

[0025] Preferably, determining the optimal eigenvalue according to the difference between the cutting characterization value of each sample data and the adjacent data on its left side and different preset eigenvalues ​​includes:

[0026] For the cth preset characteristic value: for the bth sample data, the difference between the cutting characterization value of the bth sample data and the adjacent data on its left is recorded as the first difference; the product of the cth preset characteristic value and the cutting characterization value of the bth sample data is recorded as the reference index corresponding to the cth preset characteristic value; the absolute value of the difference between the first difference and the reference index corresponding to the cth preset characteristic value is determined as the evaluation value of the bth sample data; the sum of the evaluation values ​​of all sample data is used as the evaluation index of the cth preset characteristic value;

[0027] The preset eigenvalue corresponding to the minimum evaluation index is determined as the optimal eigenvalue.

[0028] Preferably, obtaining the differential sequence to be analyzed based on the original differential sequence, the optimal eigenvalue and the cutting characterization value comprises:

[0029] The product of each element in the original difference sequence and the cutting representation value is recorded as the first product corresponding to each element in the original difference sequence;

[0030] The first products corresponding to all elements in the original difference sequence constitute the difference sequence to be analyzed.

[0031] Preferably, the first differential sequence is obtained according to the size relationship between the elements at each position in the original differential sequence and the elements at the corresponding position in the differential sequence to be analyzed and the cutting characterization value, including:

[0032] If the ath element in the original difference sequence is greater than or equal to the ath element in the difference sequence to be analyzed, the ath element in the original difference sequence is used as the ath element in the first difference sequence;

[0033] If the ath element in the original differential sequence is less than the ath element at the corresponding position in the differential sequence to be analyzed, when the cut characterization value of the ath element in the differential sequence to be analyzed is the absolute value of the difference between the first element greater than the ath data and the ath data, then the product of the constant 3 and the original differential value corresponding to the ath element in the compressed data sequence is recorded as the second product, and the second product is used as the ath element in the first differential sequence; when the cut characterization value of the ath element in the differential sequence to be analyzed is the opposite number of the absolute value of the difference between the first element less than the ath data and the ath data, then the sum of the constant 1 and the second product is used as the ath element in the first differential sequence; when the cut characterization value of the ath element in the differential sequence to be analyzed is 0, then the sum of the constant 2 and the second product is used as the ath element in the first differential sequence;

[0034] A first difference sequence is obtained based on all elements in the first difference sequence.

[0035] Preferably, obtaining the second difference sequence based on the difference between adjacent elements in the first difference sequence includes:

[0036] Calculate the difference between the latter element and the former element of every two adjacent elements in the first difference sequence respectively, as the difference corresponding to the corresponding two adjacent elements in the first difference sequence;

[0037] The difference between the first element in the first difference sequence and all the adjacent elements in the first difference sequence constitutes the second difference sequence.

[0038] Preferably, the obtaining of the original differential sequence corresponding to the data sequence to be compressed includes:

[0039] Calculate the difference between the latter element and the former element of every two adjacent elements in the data sequence to be compressed as the difference between the two adjacent elements;

[0040] The differences between the first element in the data sequence to be compressed and all the adjacent elements in the data sequence to be compressed constitute an original differential sequence corresponding to the data sequence to be compressed.

[0041] The present invention has at least the following beneficial effects:

[0042] The present invention samples the data sequence to be compressed corresponding to the construction project by constructing a window, determines the self-variation scale value of each data according to the difference change of adjacent values ​​in the window, and characterizes the drastic degree of change of elements at different positions in the data sequence to be compressed. The larger the value, the more drastic the change of the elements in the window compared with other elements in the data sequence to be compressed, and the worse the effect after traditional differential encoding. The data segment with poor effect is used as the area for calculating the excellent eigenvalue, and the subsequent analysis effect is affected by a small amount of data, which speeds up the calculation speed of the entire algorithm while ensuring that the eigenvalue finally determined is the optimal eigenvalue; further based on the original differential sequence, the optimal eigenvalue and the cutting corresponding to the data sequence to be compressed The characterization value obtains the differential sequence to be analyzed, and according to the size relationship between the elements at each position in the original differential sequence and the elements at the corresponding position in the differential sequence to be analyzed and the cutting characterization value, a first differential sequence is obtained, and the first differential sequence is differentially processed again to obtain a second differential sequence, and then the second differential sequence is encoded and transmitted. The method provided by the present invention performs differential encoding on the data in the compressed data sequence, which further improves the data similarity compared to the traditional differential encoding, and can also well improve the data similarity effect when facing data with large and drastic changes, thereby improving the compression effect of the monitoring data of the construction project, reducing the data transmission overhead in the communication process, and achieving better communication effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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.

[0044] Figure 1 A flowchart of a construction project management cloud server communication method based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, a construction project management cloud server communication method based on big data proposed by the present invention is described in detail below in combination with the accompanying drawings and preferred embodiments.

[0046] 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.

[0047] The following is a detailed description of a specific solution of a construction project management cloud server communication method based on big data provided by the present invention in conjunction with the accompanying drawings.

[0048] Construction project management cloud server communication method embodiment based on big data:

[0049] The specific scenario targeted by this embodiment is: since the amount of construction project monitoring data is large, it is necessary to encode, compress and transmit the construction project monitoring data. This embodiment will collect the monitoring data of the construction project, build a data sequence to be compressed based on the collected monitoring data, analyze the data features in the data sequence to be compressed, and then realize the encoding, compression and transmission of the construction project monitoring data.

[0050] This embodiment proposes a construction project management cloud server communication method based on big data, such as Figure 1 As shown, the construction project management cloud server communication method based on big data in this embodiment includes the following steps:

[0051] Step S1, obtaining a data sequence to be compressed corresponding to the construction project.

[0052] This embodiment first collects monitoring data in the construction project. It should be noted that the monitoring data can be sensor data or image data, etc. The sensor data is collected using the corresponding sensor, and the image data is collected using a camera. If the monitoring data is sensor data, all the collected monitoring data are sorted in chronological order to obtain the data sequence to be compressed corresponding to the construction project; if the monitoring data is image data, the grayscale values ​​of all pixels in the image are sorted from left to right and from top to bottom according to the grayscale value of each pixel in the image to obtain the data sequence to be compressed corresponding to the construction project. This embodiment does not limit the type of monitoring data, and the implementer can adjust it according to the specific situation.

[0053] At this point, the data sequence to be compressed corresponding to the construction project is obtained.

[0054] Step S2, respectively constructing a window corresponding to each data in the data sequence to be compressed as the center; obtaining the autovariance scale value of each data according to the difference between the data in the window corresponding to each data and the extreme value of the data in the data sequence to be compressed; determining the sample data based on all the autovariance scale values; determining the cutting characterization value of each data based on the difference between each data in the data sequence to be compressed and the data on its left; determining the optimal eigenvalue according to the difference between the cutting characterization value of each sample data and the adjacent data on its left and different preset eigenvalues.

[0055] When compressing the data sequence to be compressed corresponding to the construction project, this embodiment determines the high-frequency data area through the overall change characteristics of the data. The high-frequency data area is a data segment with large and intensive changes in the data, and it is also a data segment that is difficult to make similar through calculation when using the differential coding algorithm for processing.

[0056] A window of preset length is constructed with each data in the data sequence to be compressed as the center, as the window corresponding to each data. In this embodiment, the preset length is 7. In specific applications, the implementer can set it according to the specific situation.

[0057] Considering that the difference between data and its adjacent previous data can reflect the similarity between data, the smaller the similarity, the higher the degree of attention should be given in subsequent analysis. Based on this, this embodiment will determine the self-variation scale value of each data according to the difference between the data in the window corresponding to each data and the extreme difference of the data in the data sequence to be compressed, and the self-variation scale value is used to characterize the degree of attention of the data.

[0058] For the ath data in the data sequence to be compressed:

[0059] Calculate the mean of all data in the window corresponding to the a-th data; calculate the self-variation scale value of the a-th data according to the difference between each data in the window corresponding to the a-th data and its adjacent previous data, the difference between the previous data of each data in the window corresponding to the a-th data and the mean, and the range of the data in the data sequence to be compressed. The specific calculation formula for the self-variation scale value of the a-th data is:

[0060] ;

[0061] in, Indicates the independent variation scale value of the a-th data. Indicates the number of data in the window corresponding to the a-th data. Indicates the wth data in the window corresponding to the ath data, Indicates the w-1th data in the window corresponding to the ath data. Indicates the range of the data in the data sequence to be compressed. represents the mean of all data in the window corresponding to the a-th data, mod() represents the remainder function, Indicates the absolute value sign.

[0062] When the difference between the wth data and the previous data adjacent to it in the window corresponding to the ath data is greater, the effect of calculating the data similarity of the communication data through the traditional differential coding is worse, the more difficult it is to calculate the data similarity of this segment of data through the traditional differential algorithm, the greater the weight of the importance of the data should be, that is, the greater the value of the self-variation scale; the introduction of the extreme difference of the data in the compressed data sequence is to normalize the difference between each data in the window corresponding to the ath data and the previous data adjacent to it. This embodiment adopts the method of data divisor cutting, and the divisor size of the divisor cutting is approximately the difference between the previous data and the mean of all elements in the window, and the remainder can approximately represent the optimal number of cutting times of the data; the larger the remainder, the more drastic and frequent the changes in the monitoring data in the data segment, the more times the cutting operation needs to be performed to eliminate the data difference, the more attention should be paid, and the larger the value of the self-variation scale of the data.

[0063] By adopting the above 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, the greater the weight should be when performing cutting simulation. All self-variation scale values ​​are sorted in order from large to small to obtain a self-variation scale value sequence; the data corresponding to the first preset number of self-variation scale values ​​in the self-variation scale value sequence are determined as sample data. It should be noted that: as other implementation methods, all self-variation scale values ​​can also be sorted in order from small to large to obtain a self-variation scale value sequence; the data corresponding to the last preset number of self-variation scale values ​​in the self-variation scale value sequence are determined as sample data, that is, the data with larger self-variation scale values ​​are used as sample data. In this embodiment, the preset number is 5% of the total number of data in the data sequence to be compressed. In specific applications, the implementer can set it according to specific circumstances.

[0064] Next, this embodiment will determine the cutting characterization value of each data based on the difference between each data in the data sequence to be compressed and the data on its left. 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 greater than the a-1-th data, then traverse forward from the a-th data in the data sequence to be compressed, and take the absolute value of the difference between the first element greater than the a-th data and the a-th data as the cutting characterization value of the a-th data. If the a-th data in the data sequence to be compressed is less than the a-1-th data, then traverse forward from the a-th data in the data sequence to be compressed, and take the opposite of the absolute value of the difference between the first element less than the a-th data and the a-th data as the cutting characterization 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 no qualified element is found in the forward traversal under the first two size relationships, then the constant 0 is taken as the cutting characterization value of the a-th data. By analogy with the above method, the cutting characterization value of each data in the data sequence to be compressed can be obtained.

[0065] In this embodiment, multiple preset characteristic values ​​are set. The preset characteristic values ​​are All integers in the interval, where M is the largest preset eigenvalue; the specific process of obtaining M is: for any data in the data sequence to be compressed: calculate the absolute value of the difference between the data and its adjacent previous data, and round up the ratio of the absolute value of the difference to the cutting characterization value of the data as the largest preset eigenvalue M.

[0066] For the cth preset eigenvalue: for the bth sample data, the difference between the cutting characterization value of the bth sample data and the adjacent data on its left is recorded as the first difference; the product of the cth preset eigenvalue and the cutting characterization value of the bth sample data is recorded as the reference index corresponding to the cth preset eigenvalue; the absolute value of the difference between the first difference and the reference index corresponding to the cth preset eigenvalue is determined as the evaluation value of the bth sample data; the sum of the evaluation values ​​of all sample data is taken as the evaluation index of the cth preset eigenvalue. By adopting the above method, the evaluation index of each preset eigenvalue can be obtained. The smaller the evaluation index is, the better the cutting effect of the sampled data after simulated cutting is. Therefore, in this embodiment, the preset eigenvalue corresponding to the minimum evaluation index is determined as the optimal eigenvalue.

[0067] So far, the optimal eigenvalue has been determined using the method provided in this embodiment.

[0068] Step S3, obtaining the original differential sequence corresponding to the data sequence to be compressed; obtaining the differential sequence to be analyzed based on the original differential sequence, the optimal eigenvalue and the cutting characterization value; obtaining the first differential sequence according to the size relationship between the elements at each position in the original differential sequence and the elements at the corresponding position in the differential sequence to be analyzed and the cutting characterization value; obtaining the second differential sequence based on the difference between adjacent elements in the first differential sequence.

[0069] In this embodiment, the difference between the latter element and the former element of every two adjacent elements in the data sequence to be compressed is calculated as the difference corresponding to the two adjacent elements; the difference corresponding to the first element in the data sequence to be compressed and all the adjacent elements in the data sequence to be compressed constitutes the original differential sequence corresponding to the data sequence to be compressed. The product of each element in the original differential sequence and the cutting characterization value is respectively recorded as the first product corresponding to each element in the original differential sequence; the first products corresponding to all elements in the original differential sequence constitute the differential sequence to be analyzed.

[0070] When the element in the original differential sequence is greater than the element at the same position in the differential sequence to be analyzed, it means that the difference between the data and its previous adjacent data is greater. In order to ensure the data compression effect, the traditional differential encoding algorithm should not be used. Based on this, if the ath element in the original differential sequence is greater than or equal to the ath corresponding position element in the differential sequence to be analyzed, the ath element in the original differential sequence is used as the ath element in the first differential sequence. If the ath element in the original differential sequence is less than the ath corresponding position element in the differential sequence to be analyzed, when the cut characterization value of the ath element in the differential sequence to be analyzed is the absolute value of the difference between the first element greater than the ath data and the ath data, then the product of the constant 3 and the original differential value corresponding to the ath element in the compressed data sequence is recorded as the second product, and the second product is used as the ath element in the first differential sequence; when the cut characterization value of the ath element in the differential sequence to be analyzed is the opposite number of the absolute value of the difference between the first element less than the ath data and the ath data, then the sum of the constant 1 and the second product is used as the ath element in the first differential sequence; when the cut characterization value of the ath element in the differential sequence to be analyzed is 0, then the sum of the constant 2 and the second product is used as the ath element in the first differential sequence. By using the above method, all elements in the first differential sequence can be obtained, and all elements in the first differential sequence constitute the first differential sequence.

[0071] At this point, this embodiment has completed the first differential processing of all data in the data sequence to be compressed corresponding to the construction project, and mapped adjacent data with large numerical differences using the above method to reduce their numerical differences, further increase the similarity of the data, improve the subsequent compression quality, and reduce the transmission overhead during the communication process.

[0072] This embodiment then respectively calculates the difference between the latter element and the former element of every two adjacent elements in the first differential sequence as the difference corresponding to the corresponding two adjacent elements in the first differential sequence; the difference corresponding to the first element in the first differential sequence and all the adjacent elements in the first differential sequence constitutes 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, to obtain a second differential sequence, further increase the similarity between data, and achieve better transmission effect during communication.

[0073] Step S4, encoding the second differential sequence to obtain compressed construction project data, and transmitting the compressed construction project data.

[0074] The Huffman coding method is used to encode the data in the second differential sequence to obtain compressed construction engineering data. The compressed construction engineering data has a smaller data volume and a faster transmission speed when it is transmitted. The Huffman coding method is a prior art and will not be described in detail here.

[0075] The compressed construction engineering data is transmitted. After the compressed construction engineering data is received at the cloud server communication receiving end, the compressed construction engineering data is used as input, and the Huffman coding decoding method is used to output the second differential sequence; the second differential sequence is used as input, and the differential coding decoding method is used to output the first differential sequence. Then, the data sequence to be compressed is obtained according to the first differential sequence.

[0076] The specific process of obtaining the data sequence to be compressed according to the first differential sequence is:

[0077] (1) Calculate the solution difference discriminant value corresponding to each element in the first difference sequence:

[0078] ;

[0079] in, is the uth element in the first difference sequence, is the solution difference discriminant value corresponding to the u-th element in the first difference sequence, and mod() represents the remainder function.

[0080] Decompress the data sequence:

[0081] ;

[0082] in, is the first element in the data sequence to be compressed, is the u-1th element in the data sequence to be compressed, is the u-1th element in the data sequence to be compressed, is the first element in the first difference sequence, is the uth element in the first difference sequence, is the u-2th element in the first difference sequence, To solve the first difference sequence in the process of giving the first starting item element of the data sequence to be compressed, is the tag value of the u-1th element in the data sequence to be compressed, is the solution difference discriminant value corresponding to the u-th element in the first difference sequence.

[0083] The process of obtaining the mark value of the u-1th element in the data sequence to be compressed is: when the cutting representation value of the u-1th element in the differential sequence to be analyzed is the absolute value of the difference between the first element greater than the u-1th data and the u-1th data, the mark value of the u-1th element is 1; when the cutting representation value of the u-1th element in the differential sequence to be analyzed is the opposite of the absolute value of the difference between the first element less than the u-1th data and the u-1th data, the mark value of the u-1th element is 1; when the cutting representation value of the u-1th element in the differential sequence to be analyzed is 0, the mark value of the u-1th element is 0.

[0084] This embodiment obtains each element in the data sequence to be compressed by inverse operation of the first differential process, that is, obtains all the original monitoring data of the construction project, completes the decompression at the cloud server communication receiving end, reduces the data transmission overhead in the communication process, and achieves better communication effect.

[0085] In this embodiment, a window is constructed to sample the data sequence to be compressed corresponding to the construction project, and the self-variation scale value of each data is determined by the difference change of adjacent values ​​in the window, which represents the drastic degree of change of elements at different positions in the data sequence to be compressed. The larger the value, the more drastic the change of the elements in the window compared with other elements in the data sequence to be compressed, and the worse the effect after traditional differential encoding. The data segment with poor effect is used as the area for calculating the excellent eigenvalue, which affects the subsequent analysis effect with less data, speeds up the calculation speed of the entire algorithm, and ensures that the eigenvalue finally determined is the optimal eigenvalue; further based on the original differential sequence, the optimal eigenvalue and the cutting corresponding to the data sequence to be compressed The characterization value obtains the differential sequence to be analyzed, and according to the size relationship between the elements at each position in the original differential sequence and the elements at the corresponding position in the differential sequence to be analyzed and the cutting characterization value, a first differential sequence is obtained, and the first differential sequence is differentially processed again to obtain a second differential sequence, and then the second differential sequence is encoded and transmitted. The method provided in this embodiment performs differential encoding on the data in the compressed data sequence, which further improves the data similarity compared to the traditional differential encoding. It can also well improve the data similarity effect when facing data with large and drastic changes, thereby improving the compression effect of the monitoring data of the construction project, reducing the data transmission overhead during the communication process, and achieving better communication effect.

[0086] It should be noted that 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 principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A construction project management cloud server communication method based on big data, characterized in that: The method comprises the following steps: Obtain the data sequence to be compressed corresponding to the construction project; Taking each data in the data sequence to be compressed as the center, a window corresponding to each data is constructed; 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, the self-variation scale value of each data is obtained; based on all the self-variation scale values, the sample data is determined; based on the difference between each data in the data sequence to be compressed and the data on its left, the cutting characterization value of each data is determined; according to the difference between the cutting characterization value of each sample data and the adjacent data on its left and different preset characteristic values, the optimal characteristic value is determined; Obtaining an original differential sequence corresponding to the data sequence to be compressed; obtaining a differential sequence to be analyzed based on the original differential sequence, the optimal eigenvalue and the cut characterization value; obtaining a first differential sequence based on the size relationship between the elements at each position in the original differential sequence and the elements at the corresponding position in the differential sequence to be analyzed and the cut characterization value; obtaining a second differential sequence based on the difference between adjacent elements in the first differential sequence; encoding the second differential sequence to obtain compressed construction project data, and transmitting the compressed construction project data; The method of obtaining the autovariation 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 includes: For the ath data in the data sequence to be compressed: Calculate the mean of all data in the window corresponding to the a-th data; According to the difference between each data in the window corresponding to the a-th data and its adjacent previous data, the difference between the previous data of each data in the window corresponding to the a-th data and the mean, and the range of the data in the data sequence to be compressed, the autovariation scale value of the a-th data is obtained; The following formula is used to calculate the independent variation scale value of the ath data: ； in, Indicates the independent variation scale value of the a-th data. Indicates the number of data in the window corresponding to the a-th data. Indicates the wth data in the window corresponding to the ath data. Indicates the w-1th data in the window corresponding to the ath data. Indicates the range of the data in the data sequence to be compressed. represents the mean of all data in the window corresponding to the a-th data, mod() represents the remainder function, Indicates the absolute value sign; The step of determining the cutting characterization value of each data based on the difference between each data in the to-be-compressed data sequence and the data on its left side comprises: For the ath data in the data sequence to be compressed: If the a-th data in the to-be-compressed data sequence is greater than the a-1-th data, then traverse forward from the a-th data in the to-be-compressed data sequence, and take the absolute value of the difference between the first element greater 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 to-be-compressed data sequence is smaller than the a-1-th data, then traverse forward from the a-th data in the to-be-compressed data sequence, and take the opposite of the absolute value of the difference 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 ath data in the to-be-compressed data sequence is equal to the a-1th data or no qualifying element is found in the forward traversal under the first two size relationships, the constant 0 is used as the cutting representation value of the ath data; The step of determining the optimal eigenvalue according to the difference between the cut characterization value of each sample data and the adjacent data on its left side and different preset eigenvalues ​​includes: For the cth preset characteristic value: for the bth sample data, the difference between the cutting characterization value of the bth sample data and the adjacent data on its left is recorded as the first difference; the product of the cth preset characteristic value and the cutting characterization value of the bth sample data is recorded as the reference index corresponding to the cth preset characteristic value; the absolute value of the difference between the first difference and the reference index corresponding to the cth preset characteristic value is determined as the evaluation value of the bth sample data; the sum of the evaluation values ​​of all sample data is used as the evaluation index of the cth preset characteristic value; The preset eigenvalue corresponding to the minimum evaluation index is determined as the optimal eigenvalue; According to the size relationship 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 ath element in the original difference sequence is greater than or equal to the ath element in the difference sequence to be analyzed, the ath element in the original difference sequence is used as the ath element in the first difference sequence; If the ath element in the original differential sequence is less than the ath element at the corresponding position in the differential sequence to be analyzed, when the cut characterization value of the ath element in the differential sequence to be analyzed is the absolute value of the difference between the first element greater than the ath data and the ath data, then the product of the constant 3 and the original differential value corresponding to the ath element in the compressed data sequence is recorded as the second product, and the second product is used as the ath element in the first differential sequence; when the cut characterization value of the ath element in the differential sequence to be analyzed is the opposite number of the absolute value of the difference between the first element less than the ath data and the ath data, then the sum of the constant 1 and the second product is used as the ath element in the first differential sequence; when the cut characterization value of the ath element in the differential sequence to be analyzed is 0, then the sum of the constant 2 and the second product is used as the ath element in the first differential sequence; A first difference sequence is obtained based on all elements in the first difference sequence.

2. The construction project management cloud server communication method based on big data according to claim 1 is characterized in that: The method of determining sample data based on all the independent variation scale values ​​includes: Sort all the self-variation scale values ​​in order from large to small to obtain the self-variation scale value sequence; The data corresponding to the first preset number of self-variation scale values ​​in the self-variation scale value sequence are determined as sample data.

3. The construction project management cloud server communication method based on big data according to claim 1 is characterized in that: The step of obtaining a differential sequence to be analyzed based on the original differential sequence, the optimal eigenvalue and the cutting characterization value comprises: The product of each element in the original difference sequence and the cutting representation value is recorded as the first product corresponding to each element in the original difference sequence; The first products corresponding to all elements in the original difference sequence constitute the difference sequence to be analyzed.

4. The construction project management cloud server communication method based on big data according to claim 1 is characterized in that: The obtaining a second difference sequence based on the difference between adjacent elements in the first difference sequence comprises: Calculate the difference between the latter element and the former element of every two adjacent elements in the first difference sequence respectively, as the difference corresponding to the corresponding two adjacent elements in the first difference sequence; The difference between the first element in the first difference sequence and all the adjacent elements in the first difference sequence constitutes the second difference sequence.

5. The construction project management cloud server communication method based on big data according to claim 1 is characterized in that: The obtaining of the original differential sequence corresponding to the data sequence to be compressed includes: Calculate the difference between the latter element and the former element of every two adjacent elements in the data sequence to be compressed as the difference between the two adjacent elements; The differences between the first element in the data sequence to be compressed and all the adjacent elements in the data sequence to be compressed constitute an original differential sequence corresponding to the data sequence to be compressed.

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