Biological scientific research data collaborative analysis system based on cloud platform

Abstract

The invention discloses a biological scientific research data collaborative analysis system based on a cloud platform, and relates to the technical field of data analysis, the system comprises a management center, the management center is connected with a basic acquisition module, a platform processing module, an automatic analysis module and a service support module; performing data conversion on the biological scientific research data to obtain a biological scientific research signal, and performing dimension conversion on the biological scientific research signal to obtain a biological wave variation matrix; carrying out digital state replacement and digital diagram conversion on the biological wave change matrix by updating the prediction matrix to obtain a biologic fluctuation diagram, and setting a mobile grabbing shaft to carry out feature capture on the biologic fluctuation diagram to obtain an identity feature segment; performing type distinguishing on the identity feature segments according to the position node intervals to obtain biological identity type libraries, and performing screening combination on the biological identity type libraries to obtain biological type sequences; the cloud platform is used for efficient data processing, the collaboration experience is optimized, and the data security is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a biological research data collaborative analysis system based on a cloud platform. Background Art

[0002] Cloud platform, also known as cloud computing platform, is a service platform based on cloud computing technology. It provides a series of computing resources, storage resources, network resources, applications and services. Users can access and use these resources on demand through the Internet.

[0003] Collaborative analysis of biological research data refers to the process in which multiple researchers or teams work together to integrate, analyze and mine biological research data in the process of biomedical research in order to discover biological laws, analyze biological phenomena and promote scientific research progress. With the development of science and technology, the amount of biological data has increased dramatically, the data types are diverse, and the analysis process is complex. Biological research often requires cross-disciplinary and cross-regional teamwork, and has high requirements for the real-time and accuracy of data sharing and analysis. However, existing biological data analysis tools and platforms have limitations in processing big data, supporting remote collaboration, and ensuring data security. For this reason, a collaborative analysis system for biological research data based on a cloud platform is now provided to improve data processing efficiency and enhance scientific research collaboration by collaboratively analyzing biological research data. Summary of the invention

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A biological research data collaborative analysis system based on a cloud platform, comprising a management center, wherein the management center is connected to a basic acquisition module, a platform processing module, an automated analysis module, and a service support module;

[0006] The process of collecting biological research data by the basic collection module includes:

[0007] Obtain cloud platform information and build a collaborative scientific research cloud platform based on the cloud platform information;

[0008] Set up a virtual collection terminal and build a transmission link between the virtual collection terminal and the collaborative scientific research cloud platform;

[0009] Through the virtual collection terminal, data is collected from multiple source technology terminals to obtain biological research data, and the obtained biological research data is uploaded to the collaborative research cloud platform.

[0010] The process of constructing the initial prediction element array based on the predicted wave distance includes:

[0011] Performing data conversion on the obtained biological research data to obtain biological research signals, extracting attributes from the biological research data to obtain attribute sets, and setting wave response coefficients according to the attribute sets;

[0012] The obtained wave response coefficient is locally adjusted to obtain the wave variation parameter, the wave response coefficient is dimensionally predicted according to the wave variation parameter to obtain the predicted wave distance, and the initial prediction element array is set according to the predicted wave distance.

[0013] The process of obtaining the bio-wave transformation includes:

[0014] The wave variation sequence is obtained according to the obtained predicted wave distance, and the wave response coefficient is divided into layers through the wave variation sequence to obtain the predicted wave variation segment;

[0015] The predicted wave variation segments are uploaded to the biological research signal based on the order of layer division, and the biological research signal is captured layer by layer through the predicted wave variation segments to obtain the biological research wave variation segments;

[0016] Uploading the obtained biological research wave variation segment to the initial predicted element array, supplementing the initial predicted element array with elements according to the obtained biological research wave variation segment, and obtaining the biological wave variation array;

[0017] The predicted wave segments are assembled according to the obtained predicted wave distances to obtain an updated prediction array.

[0018] The process of obtaining identity feature segments includes:

[0019] The updated prediction matrix is ​​averaged to obtain an expected mean value, and the updated prediction matrix is ​​statistically analyzed based on the expected mean value to obtain an expected prediction value.

[0020] According to the expected mean value and the expected predicted value, the biological wave array is digitally changed to obtain the update wave array, and the obtained update wave array is digitally converted to a graph to obtain the biological wave graph;

[0021] Setting a mobile grabbing axis, uploading the mobile grabbing axis to the bio-science wave map, capturing features of the bio-science wave map through the mobile grabbing axis, and obtaining identity feature segments;

[0022] The bio-science fluctuation graph is interval-marked according to the identity feature segments to obtain the position node intervals.

[0023] The process of replacing the number state of the biowave array according to the expected mean and expected predicted value includes:

[0024] Perform mean division on each column of the biological wave array to obtain the biological column mean;

[0025] According to the obtained biological column mean, expected mean and expected predicted value, the biological wave array is permuted to obtain the permuted wave band value;

[0026] The obtained replacement band value is used to replace the element at the original position of the bio-wave change array to obtain the replacement wave change segment, until all elements in the bio-wave change array are replaced by the replacement wave change segment, and the bio-wave change array after element replacement is marked as the updated wave change array.

[0027] The process of distinguishing the types of identity feature segments according to the location node interval includes:

[0028] Select an identity feature segment of the bio-science wave diagram as the comparison identity segment, obtain the position node interval corresponding to the comparison identity segment, record it as the comparison node interval, identify the biological wave variation curve segment corresponding to the comparison node interval in the remaining bio-science wave diagrams, record it as the matching wave variation segment;

[0029] Perform similarity matching on the obtained comparison identity segment and the matching wave variation segment to obtain the matching wave variation segment, construct a biological identity type library, and upload the comparison identity segment and the matching wave variation segment corresponding to the comparison identity segment to the biological identity type library;

[0030] Select any one of the remaining identity feature segments as the comparison identity segment, and repeat the process of obtaining the biometric identity type library corresponding to the comparison identity segment until all identity feature segments have corresponding biometric identity type libraries.

[0031] The process of screening and combining biometric identity type libraries includes:

[0032] Performing time screening on the biometric identity type library to obtain a starting identity segment, and marking the biometric identity type library corresponding to the starting identity segment as a starting type library;

[0033] Performing time matching on the remaining biometric identity type libraries according to the starting identity segment to obtain a continued identity segment, obtaining a biometric identity type library corresponding to the continued identity segment, recorded as a continued type library, removing the starting type library and the continued type library, performing time matching on the remaining biometric identity type libraries, and repeating the process of obtaining the continued identity segment until all biometric identity type libraries are time matched;

[0034] The initial identity segment and the continued identity segment are combined in a time matching order to obtain a biological data sequence, and the biological data sequence is replaced accordingly according to the biological identity type library to obtain a biological type sequence.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] Transform the biological research data to obtain biological research signals, extract features from the biological research signals, and perform graphic conversion to obtain a curve fluctuation graph, perform feature recognition on the curve fluctuation graph, and obtain identity feature segments corresponding to the biological research data; using identity feature segments to represent biological research data is conducive to efficient processing of large-scale biological research data, and converting the collected biological research data into data forms is conducive to ensuring data security and protecting user data from being leaked;

[0037] By analyzing the identity feature segments in the cloud platform, the same type of identity feature segments are obtained, and a biological identity type library is constructed. The biological identity type library is then screened and analyzed to obtain the action sequence and interaction relationship of different types of biological research data, which is conducive to the integration and cross-analysis of biological research data, using the cloud platform for efficient data processing, and optimizing the collaborative experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be 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.

[0039] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] like Figure 1 As shown, a biological research data collaborative analysis system based on a cloud platform includes a management center, and the management center is connected to a basic acquisition module, a platform processing module, an automated analysis module, and a service support module;

[0042] The basic acquisition module is used to collect biological research data, and the specific process includes:

[0043] Obtain cloud platform information and build a collaborative scientific research cloud platform based on the cloud platform information;

[0044] Further, the cloud platform information represents construction information formed according to the existing process of building a cloud platform, that is, cloud platform information, and a cloud platform for processing biological research data and analyzing biological research data is constructed according to the obtained cloud platform information, that is, a collaborative research cloud platform;

[0045] Setting up a virtual collection terminal and building a transmission link between the virtual collection terminal and the collaborative scientific research cloud platform, wherein the transmission link is used to upload the data information generated by the virtual collection terminal to the collaborative scientific research cloud platform;

[0046] Collect data from multiple source technology terminals through a virtual collection terminal to obtain biological research data, including genomic data, proteomic data, metabolomic data, clinical data, experimental data, literature data, and multi-omics data;

[0047] Furthermore, the multi-source technology end refers to technology companies, scientific researchers and group organizations that generate biological research data;

[0048] Upload the obtained biological research data to the collaborative research cloud platform.

[0049] The platform processing module is used to perform data conversion on biological research data, obtain biological research signals, perform dimension conversion on biological research signals, and obtain biological wave transformation. The specific process includes:

[0050] Perform data exchange on the biological research data obtained to obtain biological research signals;

[0051] Further, the data conversion means converting the obtained biological research data into a signal form. According to the genomic data, proteomic data, metabolomic data, clinical data, experimental data, literature data, and multi-omics data included in the biological research data, the biological research signals include genomic signals, proteomic signals, metabolomic signals, clinical signals, experimental signals, literature signals, and multi-omics signals;

[0052] Extracting attributes of the obtained biological research signal to obtain an attribute set, wherein the attribute set includes frequency characteristics, smoothness, and stationarity;

[0053] The wave response coefficient is set according to the obtained attribute set, and the expression form of the wave response coefficient is a function form, which is determined according to the wavelet function, including but not limited to Haar wavelet, Symlet wavelet, Coiflet wavelet, wherein the characteristics of the biological research signal are determined according to the frequency characteristics, smoothness and stability included in the attribute set, and the type of wavelet function is determined according to the signal characteristics. For example, for a signal requiring high time resolution, a wavelet function Haar wavelet with a short branch set is selected;

[0054] The obtained wave response coefficients are locally adjusted to obtain wave-variant parameters;

[0055] The local adjustment means performing a scaling and translation transformation on the wave response coefficient in the time dimension and the frequency dimension, and performing statistics on the scales of the scaling and translation transformation to obtain the wave variation parameters;

[0056] According to the obtained wave variation parameters, the wave response coefficient is dimensionally predicted to obtain the predicted wave distance, and the obtained predicted wave distance is marked as y;

[0057] The dimensional prediction means that the distance between two adjacent wave variation parameters in the wave response coefficient is counted to obtain the predicted wave distance, and the obtained distance is rounded down to an integer, that is, the obtained predicted wave distance is a positive integer, for example, if the distance is 12.6, the predicted wave distance is recorded as 12;

[0058] An initial prediction element matrix is ​​set according to the obtained prediction wave distance, wherein the set initial prediction element matrix is ​​expressed in a matrix form, and is a matrix with equal rows and columns, satisfying that the number of diagonal elements of the initial prediction element matrix is ​​equal to the prediction wave distance, that is, the number of rows of the initial prediction element matrix is ​​equal to y, and the number of columns of the initial prediction element matrix is ​​also equal to y; for example, if the prediction wave distance y=12, then there are 12 elements in the diagonal position of the initial prediction element matrix, that is, the initial prediction element matrix is ​​a matrix with 12 rows and 12 columns;

[0059] The wave-variable sequence is obtained according to the obtained predicted wave distance, and the obtained wave-variable sequence is marked as b, where b=3y-2;

[0060] The wave response coefficients are divided into layers according to the obtained wave variation layers to obtain the predicted wave variation segments;

[0061] Furthermore, the sequence division means that the wave response coefficient is equally divided according to the number of wave-variable sequences to obtain predicted wave-variable segments of equal length, that is, the number of predicted wave-variable segments is equal to the wave-variable sequences;

[0062] The predicted wave variation segments are uploaded to the biological research signal based on the hierarchical division order, and the biological research signal is captured layer by layer through the predicted wave variation segments to obtain the biological research wave variation segments;

[0063] It should be further explained that, in the specific implementation process, the layer-by-layer capture process includes:

[0064] The first predicted wave variation segment is uploaded to the biological research signal in the order of hierarchical division, and the biological research signal is hierarchically extracted by the predicted wave variation segment to obtain a hierarchical biological wave variation segment, wherein the hierarchical extraction means convolving the predicted wave variation segment with the corresponding element of the biological research signal;

[0065] Performing discrete Fourier transformation on the obtained hierarchical biological wave variation segments to obtain biological scientific research wave variation segments;

[0066] Based on the order of hierarchical division, the next predicted wave segment is uploaded to the biological research signal, and the process of obtaining the biological research wave segment is repeated until all the predicted wave segments are hierarchically extracted from the biological research signal, and the obtained biological research wave segments are counted according to the order of hierarchical division;

[0067] Based on the hierarchical division order, the obtained biological research wave variation segments are uploaded to the initial prediction element array, and the initial prediction element array is supplemented with elements according to the obtained biological research wave variation segments to obtain the biological wave variation array, and the elements in the biological wave variation array are marked as , y=1, 2, 3, ..., u1, u1 is a positive integer, "yy" represents the element in the yth row and yth column, for example, "y" is 55, which is , represents the element at the fifth row and fifth column;

[0068] Furthermore, the element supplement means that the elements at the first row, first column and diagonal position of the initial prediction element array are first retained, and the elements at the remaining positions are replaced with zero, and the biological research wave variable segments are uploaded to the corresponding positions in the order of the first row, the second row, the third row, ..., the yth row according to the order of the hierarchical division, that is, the first biological research wave variable segment is uploaded to the first row and first column position of the initial prediction element array, the second biological research wave variable segment is uploaded to the first row and second column position of the initial prediction element array, and the third biological research wave variable segment is uploaded to the initial prediction element array. The first row and third column of the array, ..., the y+1th biological research wave variation segment is uploaded to the second row and first column of the initial prediction element array, the y+2th biological research wave variation segment is uploaded to the second row and second column of the initial prediction element array, the y+3th biological research wave variation segment is uploaded to the third row and first column of the initial prediction element array, the y+4th biological research wave variation segment is uploaded to the third row and third column of the initial prediction element array... until the elements in the first row, first column and diagonal position of the initial prediction element array are completed to obtain the biological wave variation array;

[0069] According to the obtained predicted wave distance, the predicted wave variation segments are assembled to obtain an updated prediction array;

[0070] Further, the set construction means constructing a matrix of m rows and m columns according to the number of predicted wave intervals, and uploading the predicted wave change segments to the constructed matrix in sequence according to the order of layer division to obtain an updated prediction matrix, wherein m*m≥y, if there are still vacant positions after uploading y predicted wave change segments to the updated prediction matrix, the vacant position elements are replaced with zero, wherein m=1, 2, 3, ..., u2, u2 is a positive integer;

[0071] Mark the internal elements of the update prediction matrix as , where "mm" represents the element in the mth row and mth column. For example, if "mm" is 15, then , which represents the element at the first row and fifth column.

[0072] The automated analysis module is used to perform digital state replacement and digital-image conversion on the biological wave variation array by updating the prediction array, obtain the biological wave diagram, set the mobile grabbing axis to capture the characteristics of the biological wave diagram, and obtain the identity feature segment. The specific process includes:

[0073] The obtained updated prediction matrix is ​​element-averaged to obtain the expected mean, and the obtained expected mean is marked as Q, where: ;

[0074] According to the obtained expected mean, the updated prediction matrix is ​​expected to be statistically analyzed to obtain the expected prediction value, which is marked as W, where: , P represents the total value of the elements of the updated prediction matrix, that is, the determinant operation of the updated prediction matrix is ​​performed, and the value of the determinant obtained is the total value of the elements, that is, P;

[0075] According to the obtained expected mean value and expected predicted value, the biological wave array is numerically replaced to obtain an updated wave array;

[0076] It should be further explained that, in the specific implementation process, the process of digital state replacement includes:

[0077] Perform mean division on each column of the biological wave array to obtain the mean of each column, which is recorded as the biological column mean. The mean division means calculating the mean of the elements of each column;

[0078] According to the obtained biological column mean, expected mean and expected predicted value, the biological wave array is permuted to obtain the permuted wave band value;

[0079] The column permutation means calculating the permutation band value of the element at the corresponding position according to the arrangement order of the elements of the bio-wave transformation array, and marking the obtained permutation band value as ,in, , represents the biological column mean of the yth column, that is, The biological column mean corresponding to that column, β is the equilibrium factor, when W=P, ,when hour, , when W=P and hour, ;

[0080] The obtained replacement band value is used to replace the element at the original position to obtain the replacement band, and the corresponding replacement band value is replaced for all elements in the biological wave array. All the obtained elements are replacement bands, and the biological wave array after element replacement is marked as an updated wave array;

[0081] Associating the obtained update wave array with the corresponding biological research data;

[0082] Performing digital-to-graphic conversion on the obtained update wave array to obtain a bio-science wave map;

[0083] Furthermore, the digital-to-graphic conversion represents the construction of a two-dimensional rectangular coordinate system, the horizontal axis represents time, the vertical axis represents the elements in the update wave array, and the update wave array is converted into a curve, recorded as a biological wave curve, and the obtained biological wave curve is marked as , n represents the intersection of the point on the curve and the horizontal axis, that is, the time node number, i represents the number of the update wave change array, which is generated by the update wave change array according to the biological wave change curve, i also represents the number of the biological wave change curve, i=1, 2, 3, ..., u3, u3 is a positive integer; each point on the curve represents an element in the update wave change array, and the obtained biological wave change curve is uploaded to the two-dimensional rectangular coordinate system to obtain the biological wave diagram;

[0084] A movable grabbing axis is provided, wherein the movable grabbing axis is two parallel lines parallel to the horizontal axis of the bio-science wave diagram, can freely move up and down in the bio-science wave diagram, and is parallel to the horizontal axis during the movement, wherein the movable grabbing axis includes a grabbing top axis and a grabbing bottom axis, and the grabbing top axis is located above the grabbing bottom axis;

[0085] The obtained moving grasping axis is uploaded to the bio-science wave map, and the bio-science wave map is featured captured by the moving grasping axis to obtain the identity feature segment;

[0086] It should be further explained that, in the specific implementation process, the feature capture process includes:

[0087] Move the obtained grabbing top axis and grabbing bottom axis of the moving grabbing axis to the highest point of the biological wave change curve in the bio-science wave diagram, keep the grabbing top axis stationary, and translate the grabbing bottom axis downward until the intersection of the grabbing bottom axis and the biological wave change curve meets the discontinuity interval, then stop moving, and mark the biological wave change curve corresponding to the discontinuity interval as an identity feature segment, wherein the discontinuity interval represents a time period of a predetermined fixed length, a time segment represented on the horizontal axis, that is, a time segment between the left and right intersection points of the grabbing bottom axis and the biological wave change curve reaches the distance of the discontinuity interval, then stop moving the grabbing bottom axis, and mark the left and right intersection points of the grabbing bottom axis and the biological wave change curve as the first node and the second node respectively, and mark the obtained first node as , mark the obtained second node as "in" represents the nth time node of the ith bio-wave variation curve, "i-(n+e)" represents the time node after the nth time node of the ith bio-wave variation curve with the length of the discontinuity interval, and e represents the length of the discontinuity interval;

[0088] The bio-science fluctuation graph is marked according to the obtained identity feature segment to obtain the position node interval, which represents the intersection of the discontinuous interval corresponding to the identity feature segment, that is, to It represents the position node interval, and further represents the corresponding position of the identity feature segment in the bio-wave variation curve.

[0089] The service support module is used to distinguish the types of identity feature segments according to the location node interval, obtain a biometric identity type library, screen and combine the biometric identity type library, and obtain a biometric type sequence. The specific process includes:

[0090] Differentiate the types of the obtained identity feature segments based on the position node intervals to obtain a biometric identity type library;

[0091] Furthermore, the type differentiation process includes:

[0092] Select any identity feature segment of the bio-science wave diagram as the comparison identity segment, obtain the position node interval corresponding to the comparison identity segment, record it as the comparison node interval, identify the biological wave variation curve segment corresponding to the comparison node interval in the remaining bio-science wave diagrams, record it as the matching wave variation segment;

[0093] The obtained comparison identity segment is similarly matched with the matching wave variation segment to obtain the matching wave variation segment, the obtained matching wave variation segment and the comparison identity segment are classified as the same type of biological research data, and a biological identity type library is constructed, and the comparison identity segment and the matching wave variation segment corresponding to the comparison identity segment are uploaded to the biological identity type library, that is, one biological identity type library represents one type of biological research data, including the identity feature segments corresponding to all biological research data of the type;

[0094] In particular, the similar matching means that within the comparison node interval, the curve coincidence of the comparison identity segment and other matching wave variation segments is compared. If there are other matching wave variation segments whose coincidence with the comparison identity segment reaches the coincidence threshold, they are recorded as matching wave variation segments, wherein the coincidence threshold represents the threshold of the degree of coincidence of two curve segments. In this embodiment, the curve coincidence is determined by the smoothness and curvature of the curve.

[0095] Select any one of the remaining identity feature segments as the comparison identity segment, and repeat the process of obtaining the biological identity type library corresponding to the comparison identity segment until all identity feature segments are classified;

[0096] The obtained biometric identity type library is labeled , k represents the number of biological research data type, k=1, 2, 3, ..., u4, u4 is a positive integer;

[0097] Performing time screening on the obtained biometric identity type library to obtain a starting identity segment, and marking a position node interval corresponding to the starting identity segment as a starting node interval;

[0098] Furthermore, the time screening means screening the position node intervals corresponding to the identity feature segments in all the obtained biological identity type libraries in a time order, obtaining the position node interval closest to the origin of the bio-science wave graph, that is, the first node closest to the origin, recorded as the starting node interval, and marking the identity feature segment corresponding to the starting node interval as the starting identity segment;

[0099] Marking the biometric identity type library corresponding to the starting identity segment as the starting type library, performing time matching on the remaining biometric identity type libraries according to the obtained starting identity segment to obtain the continued identity segment, wherein the time matching is a matching of the remaining biometric identity type libraries after removing the starting type library;

[0100] Further, the time matching means obtaining the second node of the starting node interval corresponding to the starting identity segment, performing difference statistics on the obtained second node and the first node of the position node interval of all identity feature segments in the remaining biometric identity type library to obtain a comparison difference, and the difference statistics means subtracting the value of the second node of the starting node interval on the bio-wave variation curve from the value of the first node of the position node interval of all identity feature segments in the remaining biometric identity type library on the bio-wave variation curve, and calculating the absolute value of the operation result to obtain a positive comparison difference;

[0101] The obtained comparison differences are sorted in order from small to large, and the identity feature segment corresponding to the comparison difference ranked first is recorded as the continued identity segment;

[0102] Obtain a biometric identity type library corresponding to the continuation identity segment, recorded as the continuation type library;

[0103] After removing the starting type library and the continuation type library, time matching is performed on the remaining biometric identity type libraries to obtain a second continuation identity segment, and the process of time matching is repeated on the second continuation identity segment until all biometric identity type libraries are matched, wherein time matching can only be performed after the biometric identity type library corresponding to the current identity feature segment and the identity feature segment before the first node of the identity segment is removed;

[0104] The obtained starting identity segment and the continued identity segment are combined in the order of time matching to obtain a biological data sequence, that is, after time matching, a sequence of identity feature segments is obtained in the order of the first node of the position node interval, indicating the order of action relationship of different types of biological research data;

[0105] By making corresponding replacements on the biological data sequence according to the biological identity type library, a biological type sequence is obtained. That is, each identity feature segment in the biological data sequence is replaced with the corresponding biological identity type library. In this way, the chronological relationship between different biological research data can be observed and the interactive relationship between different types can be obtained.

[0106] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A cloud-based biological research data collaborative analysis system, including a management center, characterized in that: The management center is connected to a basic acquisition module, a platform processing module, an automated analysis module, and a service support module; The basic acquisition module is used to collect biological research data; The platform processing module is used to perform data conversion on biological research data, obtain biological research signals, set wave reaction coefficients according to biological research signals and perform dimensional prediction, obtain predicted wave distances, construct an initial prediction element array according to the predicted wave distances, perform hierarchical division of the wave reaction coefficients, obtain predicted wave variation segments and form an updated prediction array, capture biological research signals layer by layer through the predicted wave variation segments and supplement elements to the initial prediction element array to obtain a biological wave variation array; The automated analysis module is used to perform digital state replacement on the biological wave array by updating the prediction array to obtain the updated wave array, perform digital-to-image conversion on the updated wave array to obtain the biological wave map, set a mobile capture axis to perform feature capture on the biological wave map to obtain the identity feature segment; The service support module is used to distinguish the types of identity feature segments according to the location node interval, obtain a biological identity type library, screen and combine the biological identity type library, and obtain a biological type sequence.

2. A cloud platform-based biological research data collaborative analysis system according to claim 1, characterized in that: The process of collecting biological research data by the basic collection module includes: Obtain cloud platform information and build a collaborative scientific research cloud platform based on the cloud platform information; Set up a virtual collection terminal and build a transmission link between the virtual collection terminal and the collaborative scientific research cloud platform; Through the virtual collection terminal, data is collected from multiple source technology terminals to obtain biological research data, and the obtained biological research data is uploaded to the collaborative research cloud platform.

3. A cloud platform-based biological research data collaborative analysis system according to claim 2, characterized in that: The process of constructing the initial prediction element array based on the predicted wave distance includes: Performing data conversion on the obtained biological research data to obtain biological research signals, extracting attributes from the biological research data to obtain attribute sets, and setting wave response coefficients according to the attribute sets; The obtained wave response coefficient is locally adjusted to obtain the wave variation parameter, the wave response coefficient is dimensionally predicted according to the wave variation parameter to obtain the predicted wave distance, and the initial prediction element array is set according to the predicted wave distance.

4. A cloud platform-based biological research data collaborative analysis system according to claim 3, characterized in that: The process of obtaining the bio-wave transformation includes: The wave variation sequence is obtained according to the obtained predicted wave distance, and the wave response coefficient is divided into layers through the wave variation sequence to obtain the predicted wave variation segment; The predicted wave variation segments are uploaded to the biological research signal based on the order of layer division, and the biological research signal is captured layer by layer through the predicted wave variation segments to obtain the biological research wave variation segments; Uploading the obtained biological research wave variation segment to the initial predicted element array, supplementing the initial predicted element array with elements according to the obtained biological research wave variation segment, and obtaining the biological wave variation array; The predicted wave segments are assembled according to the obtained predicted wave distances to obtain an updated prediction array.

5. A cloud platform-based biological research data collaborative analysis system according to claim 4, characterized in that: The process of obtaining identity feature segments includes: The updated prediction matrix is ​​averaged to obtain an expected mean value, and the updated prediction matrix is ​​expected to be statistically analyzed based on the expected mean value to obtain an expected prediction value. According to the expected mean value and the expected predicted value, the biological wave array is digitally changed to obtain the update wave array, and the obtained update wave array is digitally converted to a graph to obtain the biological wave graph; Setting a mobile grabbing axis, uploading the mobile grabbing axis to the bio-science wave map, capturing features of the bio-science wave map through the mobile grabbing axis, and obtaining identity feature segments; The bio-science fluctuation graph is interval-marked according to the identity feature segments to obtain the position node intervals.

6. A cloud platform-based biological research data collaborative analysis system according to claim 5, characterized in that: The process of replacing the number state of the biowave array according to the expected mean and expected predicted value includes: Perform mean division on each column of the biological wave array to obtain the biological column mean; According to the obtained biological column mean, expected mean and expected predicted value, the biological wave array is permuted to obtain the permuted wave band value; The obtained replacement band value is used to replace the element at the original position of the bio-wave change array to obtain the replacement wave change segment, until all elements in the bio-wave change array are replaced by the replacement wave change segment, and the bio-wave change array after element replacement is marked as the updated wave change array.

7. A cloud platform-based biological research data collaborative analysis system according to claim 6, characterized in that: The process of distinguishing the types of identity feature segments according to the location node interval includes: Select an identity feature segment of the bio-science wave diagram as the comparison identity segment, obtain the position node interval corresponding to the comparison identity segment, record it as the comparison node interval, identify the biological wave variation curve segment corresponding to the comparison node interval in the remaining bio-science wave diagrams, record it as the matching wave variation segment; Perform similarity matching on the obtained comparison identity segment and the matching wave variation segment to obtain the matching wave variation segment, construct a biological identity type library, and upload the comparison identity segment and the matching wave variation segment corresponding to the comparison identity segment to the biological identity type library; Select any one of the remaining identity feature segments as the comparison identity segment, and repeat the process of obtaining the biometric identity type library corresponding to the comparison identity segment until all identity feature segments have corresponding biometric identity type libraries.

8. The cloud-based biological research data collaborative analysis system according to claim 7, characterized in that: The process of screening and combining biometric identity type libraries includes: Performing time screening on the biometric identity type library to obtain a starting identity segment, and marking the biometric identity type library corresponding to the starting identity segment as a starting type library; Performing time matching on the remaining biometric identity type libraries according to the starting identity segment to obtain a continued identity segment, obtaining a biometric identity type library corresponding to the continued identity segment, recorded as a continued type library, removing the starting type library and the continued type library, performing time matching on the remaining biometric identity type libraries, and repeating the process of obtaining the continued identity segment until all biometric identity type libraries are time matched; The initial identity segment and the continued identity segment are combined in a time matching order to obtain a biological data sequence, and the biological data sequence is replaced accordingly according to the biological identity type library to obtain a biological type sequence.

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