Multivariate data information resource exchanging and sharing method and system

Abstract

The invention provides a multivariate data information resource exchange sharing method and system, and the method comprises the steps: obtaining business information resources, and carrying out the fusion integration of the business information resources according to business demands, and obtaining shared information resources; and pushing the shared information resource according to a preset pushing strategy. The invention provides a multivariate data information resource exchanging and sharing method and system, which can effectively improve the utilization rate of information resources.

Description

Technical Field

[0001] The present invention relates to the technical field of data information processing, and in particular to a method and system for exchanging and sharing multiple data information resources. Background Art

[0002] Information resource exchange and sharing refers to the exchange and sharing of information and information products between information systems at different levels and departments. It is to share information, a resource that is becoming increasingly important in the Internet era, with others in order to achieve more reasonable resource allocation and save costs. Specifically, information resource exchange and sharing is based on information system technology and transmission technology, which enables information resources to be exchanged and shared between information systems at different levels and departments, which can improve the utilization rate of information resources and avoid repeated waste in information collection, storage and management.

[0003] In the process of information resource exchange and sharing, information resources usually have characteristics such as multi-source, heterogeneous and massive, which can easily cause the problem of "information islands" and is not conducive to the efficient use of information resources across applications, dimensions and levels. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a method and system for exchanging and sharing multi-data information resources, which can effectively improve the utilization rate of information resources.

[0005] The first object of the present invention is to provide a method for exchanging and sharing multiple data information resources, comprising obtaining business information resources, and further comprising the following steps:

[0006] According to business requirements, the business information resources are integrated to obtain shared information resources;

[0007] According to a preset push strategy, the shared information resource is pushed.

[0008] Preferably, the business information resource is data generated by various terminal devices.

[0009] In any of the above solutions, preferably, the fusing and integrating the business information resources comprises the following sub-steps:

[0010] Verify the authenticity of the business information resources;

[0011] Completely reconstructing the business information resources;

[0012] The information resources are merged and reorganized according to the semantics of the information resources.

[0013] In any of the above solutions, preferably, the authenticity identification of the business information resource includes:

[0014] Performing tensor expansion on the business information resource to obtain an expansion matrix;

[0015] Performing eigenvalue decomposition on the unfolded matrix to obtain eigenvalues;

[0016] Performing correlation analysis based on the characteristic values ​​to detect the authenticity of the data;

[0017] The business information resource is tensor filled to obtain a restored business information resource.

[0018] In any of the above solutions, preferably, performing a first complete reconstruction of the business information resource comprises the following sub-steps:

[0019] Calculating the fractal dimension of the business information resource;

[0020] Acquire a fractal learning model, and extract potential relationships between business information resources from the fractal dimension through the fractal learning model;

[0021] Performing initial clustering according to the fractal dimension to generate cluster members;

[0022] Clustering the cluster members again by using a voting method to generate a clustering result;

[0023] The business information resources are integrated according to the potential relationship between the business information resources and / or the clustering result to obtain the business information resources after the first complete reconstruction.

[0024] In any of the above schemes, it is preferred that the fractal dimension is an estimate of the degree of freedom of distribution of the data set. For a data set with an embedding dimension of n, it is embedded in an n-dimensional grid with each cell side length of γ (γ∈(γ1,γ2)), where (γ1,γ2) is the range of variation of the side length of the data set with fractal characteristics. The number of data points falling into the i-th cell is calculated and recorded as Fractal dimension D q The definition is as follows:

[0025]

[0026] Among them, q is a parameter that affects the calculation of the fractal quantile, and D2 is the probability that the distance between two randomly selected points is less than an eigenvalue.

[0027] In any of the above solutions, preferably, performing a second completeness reconstruction on the business information resources after the first completeness reconstruction further includes the following sub-steps:

[0028] Construct a Gaussian mixture model of business information resources;

[0029] The covariance crossover algorithm is used to estimate the Gaussian mixture model and obtain the business information resources after the second completeness reconstruction.

[0030] In any of the above solutions, it is preferred that the observation model is set as: X=Hx*+n, where X is the observation value, x* is the true value, H is the degradation matrix, and n is the noise;

[0031] Known fusion source (x1, x2, ···, x N ) are N random vectors, and the covariance crossover algorithm is used for estimation:

[0032]

[0033] in, and

[0034] In any of the above schemes, preferably, the weight ω i By minimizing tr(P 00 )Solve:

[0035]

[0036] Among them, p ii To find the weight ω i The matrix involved.

[0037] In any of the above solutions, preferably, performing a third completeness reconstruction on the business information resource after the second completeness reconstruction further includes the following sub-steps:

[0038] Processing the business information resource using a support vector machine to obtain a support vector machine output result;

[0039] The business information resources are integrated according to the output results by using the basic probability distribution function in the DS evidence theory to obtain the business information resources after the third completeness reconstruction.

[0040] In any of the above solutions, preferably, the fourth completeness reconstruction of the business information resources after the third completeness reconstruction further includes the following sub-steps:

[0041] Constructing a relationship matrix, a constraint matrix and an objective function according to the business information resources;

[0042] Perform matrix decomposition on the relationship matrix, and regularize the matrix decomposition process through the constraint matrix, iterate the matrix decomposition k times, when k=k max Stop the iteration when the objective function is reduced, and get the decomposition matrix, where k max is the maximum number of iterations;

[0043] The business information resources are integrated according to the decomposition matrix to obtain the business information resources after final completeness reconstruction.

[0044] In any of the above solutions, it is preferred that different types of data sources e and f have a dimension of n e , n f , the relationship between them is expressed by the sparse matrix R ef Indicates that the dimension of the same type of data source e is n e , the relationship between data sources is represented by the constraint matrix Θ e Indicates that the relationship matrix R is decomposed by the triple penalty matrix decomposition method. ef Decompose, and regularize the decomposition process through the constraint matrix, that is, Construct the objective function. The optimization objective function of the matrix decomposition is the sum of the relation matrix reconstruction error and the constraint matrix reconstruction trace:

[0045]

[0046] Among them, G is a potential variable matrix, G e is some potential feature matrix, S ef is a weight or relationship matrix between data sources e and f during the conversion process, is the transposed matrix of some potential feature, t is the number of iterations, maxt e is the objective function, tr is the trace of the matrix, G T is the transformation matrix, Θ (t) is the relationship matrix after iteration t times.

[0047] In any of the above solutions, preferably, the merging and reorganizing the information resources according to the semantics of the information resources comprises the following sub-steps:

[0048] Define a unified semantic description specification and establish a global information resource directory based on the semantic description specification;

[0049] The information resources are merged and reorganized according to the global information resource directory.

[0050] The second object of the present invention is to provide a multi-data information resource exchange and sharing system, including an information resource pre-exchange module and an information resource integration module.

[0051] The information resource pre-exchange module is used to obtain business information resources.

[0052] The information resource front-end exchange module is located between the information resource integration module and the business application system.

[0053] The information resource integration module is used to integrate the business information resources according to business requirements to obtain shared information resources.

[0054] The system uses the method described in the first purpose to realize the exchange and sharing of multi-dimensional data information resources.

[0055] Preferably, the information resource front-end exchange module 401 includes: an adapter management unit, an exchange control unit, a data extraction unit, a data push unit and a data exchange security control unit.

[0056] In any of the above schemes, it is preferred that the information resource integration module includes a task monitoring unit, a data processing component management unit, an integrated task scheduling unit, a data integration processing unit, an integrated task scheduling unit, an integrated processing unit, an inter-station data push unit and an inter-station transmission security control unit.

[0057] In any of the above schemes, it is preferred that the multi-data information resource exchange and sharing system also includes: a platform management module 403, a data management module 404, an application integration support module 405, an operation and maintenance monitoring module 406, a data sharing service module 407 and a public information service module 408.

[0058] In any of the above solutions, it is preferred that the platform management module is used to perform integrated management of the information resource directory system, system operation mode, access rules and system configuration.

[0059] In any of the above schemes, it is preferred that the data management module is used to centrally store and manage shared information resources, including: a distributed file storage engine unit, a distributed database engine unit, a data organization and management unit, a data storage security control unit, a data disaster recovery backup unit, a public basic database, a public business database and a special service database.

[0060] In any of the above solutions, preferably, the application integration support module is used to provide basic function call support.

[0061] In any of the above schemes, it is preferred that the operation and maintenance monitoring module is used to monitor the operating status, record usage logs, manage computing resources, storage resources, network resources and application resources, including: a platform resource management unit, a platform operating status monitoring unit and a data usage log management unit.

[0062] In any of the above solutions, preferably, the data sharing service module is used to provide information resource sharing services in the form of application access control, including: a subject directory service unit, a data query service unit, a file service unit and an application access control service unit.

[0063] In any of the above solutions, preferably, the public information service module is used to provide information resource sharing services in the form of special applications, including: a public service portal unit, a special management unit, a special information organization unit and a special information display unit.

[0064] The present invention proposes a method and system for exchanging and sharing multi-dimensional data information resources, which can integrate the business information resources according to business needs, so as to standardize and analyze data from different sources, different specifications, and different qualities, integrate information resources and explore the knowledge contained in information resources to obtain integrated shared information resources, realize efficient use of information resources across applications, multiple dimensions, and multiple levels, and promote information resource optimization and maximum resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 The flowchart is a preferred embodiment of the method for exchanging and sharing multiple data information resources according to the present invention.

[0066] Figure 2 The figure is a flow chart of an embodiment of authenticity identification of information resources according to the multi-data information resource exchange and sharing method of the present invention.

[0067] Figure 3 The figure is a flow chart of an embodiment of information resource fusion based on support vector machine and DS evidence theory according to the multivariate data information resource exchange and sharing method of the present invention.

[0068] Figure 4 It is a schematic diagram of a preferred embodiment of a multi-data information resource exchange and sharing system according to the present invention.

[0069] Figure 5 The figure is a schematic diagram of the structure of a computer device according to an embodiment of the method for exchanging and sharing multiple data information resources of the present invention. DETAILED DESCRIPTION

[0070] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0071] Embodiment 1

[0072] This embodiment provides an information resource exchange and sharing method that can be executed by a server / terminal device, such as Figure 1 As shown, step 100 is to obtain business information resources.

[0073] Business information resources are usually data generated by business application systems. Business application systems can be various terminal devices, such as mobile phones, computers, monitoring equipment, etc. The diversification of business application systems often leads to the diversification of the formats of the data generated by business application systems. It can be seen that business information resources are usually a kind of multi-heterogeneous data.

[0074] The acquisition of business information resources is realized by the information resource front-end exchange module. The information resource front-end exchange module is deployed in the network environment where the information source is located and is responsible for connecting with the business application system. The information resource front-end exchange module integrates various information resources from various data sources in an orderly manner, completes the extraction of original information resources, and pushes them to the information resource integration module after data exchange, cleaning, and integration.

[0075] Step 200: The business information resources are integrated according to business requirements to obtain shared information resources.

[0076] Fusion integration is to standardize and analyze data from different sources, specifications, and qualities, integrate information resources, and explore the knowledge contained in information resources to obtain highly complete shared information resources and achieve efficient use of information resources. For example, customer information may be scattered in different business units. In order to obtain an overall portrait of the customer, it is necessary to integrate the relevant data into one platform for fusion integration.

[0077] In the application scenarios of cross-organizational collaboration and decision-making assistance, since the decision-making models, business models, and processes are quite flexible and comprehensive to a large extent, "single-domain" information services are far from meeting the needs. It is necessary to refine comprehensive information through data fusion based on public basic databases and public business databases to better provide information resource services on demand for cross-organizational collaboration in various departments. Based on the above analysis, information resource integration can complete the overall integration and sharing of information resources in accordance with a unified information resource catalog, exchange strategy, and integration strategy.

[0078] The fusion and integration of the business information resources can be realized by the information resource integration module. The information resource integration module receives various information resources from each information resource front-end exchange module, and processes the data from each business application system through integration, processing and other methods according to the dynamically configured various comprehensive subject business needs, builds the business database required by the business application, enriches the public service database, enhances the value of data, and realizes the transformation of data into information. The information resource integration module also pushes the shared information resources to each information resource front-end exchange module according to the information push strategy.

[0079] The integration of the business information resources includes at least one of step 210, step 220 and step 240. Step 210, step 220 and step 240 are independent to a certain extent, and one or more of them can be selected as needed to realize the integration of business information resources during specific implementation.

[0080] Step 210: Authenticity identification of the business information resource is performed.

[0081] When the business information resources are integrated, the authenticity of the business information data can be identified, which is conducive to filling in missing data and improving the performance of data analysis.

[0082] In view of the multidimensional attributes of multivariate data, tensor representation methods and high-order decomposition techniques (high-order singular value decomposition or PARAFAC decomposition) are used to conduct a more reasonable and scientific analysis of multivariate data, including: a generalized tensor expansion method that can significantly improve the system's recognition ability; a tensor filling method for estimating missing data.

[0083] like Figure 2 As shown, the authenticity identification of the business information resource includes steps 211 to 214.

[0084] Step 211: Perform tensor expansion on the business information resource to obtain an expansion matrix. That is, the expansion matrix is ​​obtained by converting the business information resource into a matrix form.

[0085] Step 212: Perform eigenvalue decomposition on the expansion matrix to obtain eigenvalues. For example, perform eigenvalue decomposition on the γth mode expansion matrix of the tensor to obtain the sample eigenvalues ​​in the γth mode.

[0086] Step 213: Perform correlation analysis based on the eigenvalues ​​to detect the authenticity of the data. The tensor structure of multidimensional data can significantly improve the recognition ability of the detection method, thereby enabling low-rank recovery of data with a higher subspace rank. The authenticity of the data can be judged based on the correlation of the eigenvalues, for example, data with a correlation higher than a threshold is determined as real data, and data with a correlation higher than a threshold is retained, thereby completing the authenticity identification of the business information resource.

[0087] Steps 211 to 213 are to perform generalized tensor expansion on the business information resources, using the n ) pattern, utilizing the tensor structure of multidimensional data, can significantly improve recognition capabilities and facilitate authenticity identification of the business information resources.

[0088] Step 214: Perform tensor filling on the business information resource to obtain the restored business information resource.

[0089] During the data acquisition process, data loss may occur due to data record absence and severe noise interference in terms of region, time, and industry, thus affecting data integrity. To restore important data for subsequent data processing and feature extraction, matrix filling methods can be used to estimate the missing data, which helps solve the problem of data loss. Moreover, during the matrix filling process of matrix data, the use of the intrinsic tensor structure of massive data can improve the estimation performance.

[0090] Keyi can directly perform tensor filling on the business information resource to complete the missing data in the business information resource. It can also first identify the authenticity of the business information resource, eliminate false data and retain true data in the business information resource, and then perform tensor filling on the business information resource after eliminating false data and retaining true data to complete the missing data in the business information resource, thereby completing the integration of the business information resource.

[0091] To enhance the robust characteristics against impulse noise or outliers, this embodiment adopts a tensor filling method based on the high-dimensional structure of multivariate data, and improves the robustness of the tensor filling theory through the tensor trace norm and the lp (0 < p < 1) norm. In addition, to improve the convergence speed of the algorithm and reduce the computational complexity, relaxation means are used to reasonably transform the objective function, and the iterative reweighting method is used for fast solution.

[0092] Step 220: Recreate the completeness of the business information resource.

[0093] Since the business information resource comes from the databases of different systems and platforms and the data of data monitoring points, it often has the characteristics of defectiveness and incompleteness and cannot directly support the construction of the underlying data resource directory system. Therefore, this embodiment solves the problem of data defectiveness by mining the correlation between multi-source data, realizes the recreation of data completeness, and constructs a complete basic data resource library.

[0094] The key methods adopted for the recreation of data completeness include: using machine learning methods based on fractal theory and the Fractral Cluster (FC) algorithm to explore the potential relationships of multi-source data; using methods such as covariance intersection algorithm, Support Vector Machine (SVM), Dempster-Shafer (D-S) evidence theory, and matrix decomposition for data fusion to overcome the problems of data uncertainty and loss, thereby providing scientific and objective decision support for data integration.

[0095] The complete reconstruction of the business information resource includes steps 221 to 225.

[0096] Step 221: Calculate the fractal dimension of the business information resource. For example, the fractal dimension of the business information resource is calculated using a box method, a random walk method, a frequency domain method, or the like.

[0097] Step 222: Acquire a fractal learning model, and extract potential relationships between business information resources from the fractal dimension using the fractal learning model.

[0098] By calculating the fractal dimension of business information resources, extracting relevant rules and establishing corresponding machine learning models, a fractal learning process is formed to provide guidance for future behaviors; and the feedback information of the behavior can be used to correct and update the rules or models, thereby continuously optimizing the performance of the machine learning model.

[0099] Among them, the fractal dimension is an estimate of the degree of freedom of the distribution of a data set, reflecting the distribution characteristics of data in multidimensional space and its ability to fill space, and plays a very important role in fractal theory. For a data set with an embedding dimension of n, it is embedded in an n-dimensional grid with a cell side length of γ (γ∈(γ1,γ2)), where (γ1,γ2) is the range of variation of the side length of the data set with fractal characteristics. Calculate the number of data points that fall into the i-th cell, denoted as Fractal dimension D q The definition is as follows:

[0100]

[0101] Different values ​​of q can be used to calculate different fractal dimension values, which describe the characteristics of the data set from different perspectives. When q = 0, D0 is calculated to be the Hausdorff fractal dimension; when q is limited to 1, D1 is calculated to be the information dimension; when q = 2, D2 is calculated to be the correlation dimension. D2 represents the probability that the distance between two randomly selected points is less than a characteristic value, reflecting the distribution characteristics of the data.

[0102] Step 221 and step 222 are steps of a machine learning method based on fractal theory. The fractal idea is applied to the study of machine learning methods, the self-similarity of the data resource management system is used as the basic basis for reasoning and learning, the self-similarity of the system is quantitatively described with the help of fractal dimension, and a learning method that uses the self-similarity of the whole and the part to understand things is proposed, which can extract hidden information from high-dimensional and massive data.

[0103] Step 223: Perform initial clustering according to the fractal dimension to generate cluster members. For example, perform initial clustering using a clustering algorithm based on the fractal dimension to generate cluster members. The clustering algorithm may be a K-means clustering algorithm, a network-based clustering algorithm, or a model-based method.

[0104] Step 224: cluster the cluster members again using voting method to generate cluster results. Clustering the cluster members again using voting method etc. can maximize the mutual information of the existing cluster results and obtain a better result than a single clustering algorithm.

[0105] Step 225: The business information resources are integrated according to the potential relationship between the business information resources and / or the clustering results to obtain the business information resources after complete reconstruction. In the integration process, the business information resources with potential relationship and / or the same clustering category are integrated to complete the complete reconstruction of the business information resources.

[0106] In order to overcome the impact of poor-quality clustering and clustering inconsistency on the subsequent data fusion step, a selective clustering algorithm based on fractal dimension can be used, and incremental clustering can be used to discover clusters of arbitrary shapes. Mutual information is used to calculate weights and select high-quality clusters to achieve cluster fusion.

[0107] Step 223, step 224 and step 225 are steps of clustering fusion algorithm based on fractal dimension. By applying fractal method to cluster analysis of multi-source data and statistical data, the robustness of data fusion can be enhanced. Specifically, by using clustering algorithm based on fractal dimension, clustering is performed based on the self-similarity difference between data rather than the distance. It is not restricted by any data clustering shape and can handle the situation of uneven density inside the data set. At the same time, with the addition of data points, the algorithm can dynamically describe the characteristics of the data set, which is suitable for data stream clustering, and can effectively overcome the problems of uneven density inside the cluster and non-adjacent clusters, as well as the typical characteristics of real-time monitoring data with data streams. Data stream clustering can filter out random noise in the data and increase the accuracy and reliability of the data.

[0108] The data stream clustering method based on the non-stationary characteristics of monitoring data is combined with the idea of ​​data density clustering to realize real-time processing of monitoring data streams and improve the data stream clustering efficiency and clustering accuracy. It is suitable for clustering data of arbitrary distribution shapes.

[0109] The complete reconstruction of the business information resources also includes: step 226 and step 227.

[0110] Step 226: construct a Gaussian mixture model of business information resources. Using a Gaussian mixture model to represent the state of business information resources is conducive to better describing the state of business information resources. Specifically, a sampling-based Gaussian mixture model solving algorithm is used to solve the mathematical closed solution of the Gaussian mixture model, and the approximate algorithm is used to speed up the solution and ensure the accuracy of the approximation, so as to obtain a fusion method of multiple Gaussian mixture models in a probabilistic framework.

[0111] Step 227: Use the covariance crossover algorithm to estimate the Gaussian mixture model to obtain the business information resource after complete reconstruction. The covariance crossover fusion (CI fusion) method can achieve the fusion of business information resources without calculating the cross-covariance.

[0112] Step 226 and step 227 are steps of the fusion algorithm based on covariance crossover. In view of the uncertainty problem of massive and multi-source business information resources, the estimation theory can be used to achieve data fusion, that is, the fusion is regarded as an estimation problem. Assume that the observation model is:

[0113] X=Hx*+n

[0114] Where X is the observed value, x* is the true value, H is the degradation matrix, and n is the noise.

[0115] Known fusion source (x1, x2, ···, x N ) are N random vectors. In order to obtain a reliable fusion result x0, the covariance crossover algorithm is proposed for estimation:

[0116]

[0117] in, and

[0118] The covariance crossover algorithm gives P ij (i≠j) is a consistent estimate when the source is unknown, so the minimum accuracy of the fusion estimate is not lower than the minimum accuracy of any fusion source estimate. i is controllable and can be achieved by minimizing tr(P 00 )Solve:

[0119]

[0120] The covariance crossover algorithm can integrate business information resources very well and obtain complete reconstructed business information resources.

[0121] like Figure 3 As shown, the complete reconstruction of the business information resources also includes step 228 and step 229.

[0122] Step 228: Use a support vector machine (SVM) to process the business information resources and obtain the output result of the support vector machine. The support vector machine method is based on the VC dimension theory and the structural risk minimization principle of statistical learning theory, and seeks the best compromise between the complexity and learning ability of the model according to limited sample information, in order to obtain the best output result.

[0123] Step 229: Adopt the basic probability distribution function in DS evidence theory to fuse the business information resources according to the output results to obtain the business information resources after completeness reconstruction.

[0124] This implementation provides a soft decision output method. Since the output of SVM (f1, f2, ···, f n ) is a relative distance, which contains the relative support information of the category. Therefore, a probability output model is established based on the lower limit of SVM accuracy, and the basic probability assignment (BPA) function in DS evidence theory is used as the fusion output data.

[0125] Step 228 and step 229 are steps of the fusion method based on support vector machine and DS evidence theory. As some business data and statistical information have small sample, nonlinear and multi-dimensional characteristics, the fusion of this type of data using SVM and DS evidence theory can improve the accuracy of data fusion.

[0126] The complete reconstruction of the business information resource also includes steps 230 to 232. The relationships between various types of heterogeneous data sets are complex and unknown, which brings great difficulties to data fusion. Based on this, in view of the problem that there are missing relationships between various target objects, multi-level relationships, multi-type data, and state-type data in multi-source data fusion, a matrix decomposition method is used to fuse multiple heterogeneous data sets.

[0127] Step 231: construct a relationship matrix, a constraint matrix and an objective function based on the business information resources. For example, different types of data sources i and j, with a dimension of n i , n j , the relationship between them is expressed by the sparse matrix R ij Indicates that the dimension of the same type of data source i is n i , the relationship between data sources is represented by the constraint matrix Θ i Indicates that the relationship matrix R is decomposed by the triple penalty matrix decomposition method. ij Decompose, and regularize the decomposition process through the constraint matrix, that is, Construct the objective function. The optimization objective function of the matrix decomposition is the sum of the relation matrix reconstruction error and the constraint matrix reconstruction trace:

[0128]

[0129] Among them, R ij is a sparse matrix, Θ i is the constraint matrix.

[0130] Step 232: perform matrix decomposition on the relationship matrix, and regularize the matrix decomposition process by using the constraint matrix, iterate the matrix decomposition multiple times to reduce the objective function, and obtain a decomposition matrix. The objective function value is minimized or less than a preset threshold value by iterative calculation, that is, alternately updating matrices G and S until convergence, and obtaining a final decomposition matrix.

[0131] Step 233: The business information resources are integrated according to the decomposition matrix to obtain the business information resources after complete reconstruction. The data fusion method based on matrix decomposition can well integrate multivariate, heterogeneous and complex state data, and adopts a constrained matrix decomposition algorithm to realize the integration of multiple heterogeneous data sources in the middle layer (between the data layer and the decision layer). By decomposing the relationship matrix composed of multiple relationships and multiple types of objects, and constructing an objective function related to minimizing approximate errors and relationship constraints, the decomposition form of the multivariate data matrix is ​​calculated, and the relationship between business information resources is detected from the factors of the matrix decomposition, and then the business information resources are integrated.

[0132] Steps 230 to 232 are steps of a heterogeneous data fusion method based on matrix decomposition, which uniformly represents multiple heterogeneous data sets through a single model, converts them into structured prediction problems and solves them using an inference algorithm.

[0133] In response to the real-time requirements of multi-source data fusion and the mixing of static and dynamic data, an approximate fast algorithm for matrix decomposition in data stream scenarios is established. The static data matrix pre-decomposition is combined with the approximate fast decomposition of dynamic data, which is conducive to solving the problem of real-time computing in data processing.

[0134] Step 240: Merge and reorganize the information resources according to the semantics of the information resources. By performing semantic analysis on the information resources, it is possible to achieve orderly integration and organic fusion of multi-source heterogeneous data according to the semantics of the information resources, and to form semantically consistent public basic data services, public business data services, and data services required for other business services. Specifically, the semantic-based information exchange and sharing method includes steps 241 and 242.

[0135] Step 241: Define a unified semantic description specification, and establish a global information resource directory based on the semantic description specification. To solve the problem of structural matching between multi-source heterogeneous data, define a unified semantic description specification, define the exchange and sharing data structure based on it, and establish a global information resource directory.

[0136] Step 242: Merge and reorganize information resources according to the global information resource directory. Merge and reorganize original information resources from different application systems according to the unified semantics defined in the global information resource directory, realize the organic integration of multi-source heterogeneous information resources at the semantic level, and then realize information exchange and sharing among different networks.

[0137] This embodiment aims at service quality business data analysis and data decision-making, ensuring that the business meaning of quality data is not distorted, confused, or repeated during the process of collection, processing, sharing and exchange. On this basis, the basic capabilities of data services are formed to provide effective data support for data analysis.

[0138] Execute step 300 and push the shared information resource according to a preset push strategy.

[0139] After the shared information resources are generated, they need to be pushed to the user to assist the user in making decisions. The preset push strategy can be a push strategy set by the user according to the needs, which can realize the push of the corresponding shared information resources according to the user's requirements.

[0140] The push of the shared information resources is implemented by the information resource front-end exchange module. The information resource front-end exchange module receives the shared information resources pushed by the information resource integration system and pushes them to the demand business unit.

[0141] In the information resource exchange and sharing method of this embodiment, the business information resources can be integrated and fused according to business needs, so that data from different sources, different specifications, and different qualities can be standardized and analyzed and processed, and information resources can be integrated and the knowledge contained in the information resources can be explored to obtain integrated shared information resources, thereby achieving efficient use of information resources across applications, multiple dimensions, and multiple levels, and promoting information resource optimization and maximization of resource utilization.

[0142] Step 300 is to push the shared information resources to the corresponding business application system to complete the sharing and exchange of information resources. The preset push strategy can be set according to the needs and levels of the department, so as to provide the department with corresponding shared information resources, which is conducive to the efficient use of information resources.

[0143] This embodiment uses a combination of multiple data storage, transmission, fusion and analysis methods to achieve resource sharing of multi-source, heterogeneous and massive quality data. Based on the unified and standardized business meaning, the knowledge contained in information resources is explored, and various information resources such as business information, inherent data and status data are collected, organically integrated and fused in real time, breaking down various regional and systemic barriers, and establishing a complete information resource exchange database to solve the common needs of different industries, departments and regions for basic data resources, avoid the "information island" problem caused by the difference in information identification, and achieve efficient use of information resources across applications, multiple dimensions and multiple levels, promote information resource optimization and maximize resource utilization, and support relevant decisions in information management.

[0144] Embodiment 2

[0145] like Figure 4 As shown, this embodiment provides an information resource exchange and sharing system 40, including: an information resource pre-exchange module 401 and an information resource integration module 402.

[0146] The information resource pre-exchange module 401 is used to obtain business information resources.

[0147] The information resource pre-exchange module 401 is located between the information resource integration module 402 and the business application system, and is used to implement information resource exchange between the information resource integration module 402 and the business application system, including obtaining business information resources from the business application system. The information resource pre-exchange module 401 isolates the information resource integration module 402 from the business application system to ensure the independence of the application unit business information base and the business application system.

[0148] The information resource front-end exchange module 401 includes: an adapter management unit, an exchange control unit, a data extraction unit, a data push unit and a data exchange security control unit.

[0149] The information resource integration module 402 is used to integrate the business information resources according to business needs to obtain shared information resources. The information resource integration module 402 is used to complete the overall integration and sharing of information resources according to a unified information resource directory, exchange strategy and integration strategy. The information resource integration module 402 includes: a task monitoring unit, a data processing component management unit, an integrated task scheduling unit, a data integration processing unit, an integrated task scheduling unit, an integrated processing unit, an inter-station data push unit, an inter-station transmission security control unit, etc.

[0150] The information resource pre-exchange module 401 is also used to push the shared information resources according to a preset push strategy. The information resource pre-exchange module 401 can push the shared information to the business application system to realize the sharing and exchange of information resources.

[0151] In this embodiment, the information resource exchange system can realize information exchange and sharing among different networks through the information resource pre-exchange module 401 and the information resource integration module 402.

[0152] In one implementation manner, the information resource exchange and sharing system 40 further includes: a platform management module 403 , a data management module 404 , an application integration support module 405 , an operation and maintenance monitoring module 406 , a data sharing service module 407 and a public information service module 408 .

[0153] The platform management module 403 is used for integrated management of the information resource directory system, system operation mode, access rules and system configuration.

[0154] The platform management module 403 integrates the management of the information resource directory system, operation mode, access rules and system configuration of the entire data platform, ensures that the quality data information resource exchange prototype system operates efficiently, stably, safely and orderly, and gives full play to the supporting role of the data center for the business system. The platform management module 403 includes: a subject directory management unit, a subject metadata management unit, a business application management unit, a shared service management unit, an exchange strategy management unit, an integration strategy management unit, a system configuration management unit, etc.

[0155] The data management module 404 is used to centrally store and manage shared information resources.

[0156] The information resource exchange system will be the hub for resource public information transmission, exchange, sharing and storage, and will quickly process large amounts of structured and unstructured data with high concurrency requirements. In order to meet the needs of data exchange and sharing, the data management module 404 in the information resource exchange system adopts distributed data storage methods such as HADOOP, which can realize the centralized storage management of all quality resource perception data and business information by using the parallel computing capabilities of data distributed cluster technology (MapReduce, YARN, etc.), the high-speed processing capabilities of real-time memory database technology (SPARK, etc.), and the data storage capabilities of distributed file storage technology (HDFS, etc.), and realize high-performance exchange of information resources, high-speed computing and processing, massive storage, and reliable services under high concurrency.

[0157] The data management module 404 includes: a distributed file storage engine unit, a distributed database engine unit, a data organization and management unit, a data storage security control unit, a data disaster recovery backup unit, a public basic database, a public business database, a special service database, etc.

[0158] The application integration support module 405 is used to provide basic function call support. The application integration support module 405 provides basic function call support for various applications of the comprehensive business platform and the decision support platform. According to the service-oriented system architecture concept, the relevant public functions in environmental protection applications such as organizational management, user management, unified authentication, and authority management are integrated into the support platform in the form of public components or public services, and relevant API interfaces are provided to reduce the coupling between systems, which is convenient for system expansion and deployment. The application integration support module 405 includes: organizational management unit, unit user management unit, unified identity authentication unit, unified authority management unit, etc.

[0159] The operation and maintenance monitoring module 406 is used to monitor the operation status, record the usage log, and manage computing resources, storage resources, network resources, and application resources. The operation and maintenance monitoring module 406 monitors the operation of the system. By deeply mining the working status of the system, it uniformly manages the computing resources, storage resources, network resources, and application resources of the data platform, monitors the operation status of the platform in real time, and records the platform data usage log, so as to realize the overall, active, centralized, and transparent management of the data platform. The operation and maintenance monitoring module 406 is oriented to the application requirements such as real-time data monitoring, data processing, data analysis, and comprehensive presentation, supports the whole-process management of task planning, task preparation, process monitoring, effect presentation, and retrospective analysis, and provides real-time monitoring, processing, integration, management, and analysis of various types of data; it supports unified monitoring of multiple regions, cross-platforms, and various types of equipment, ensuring real-time monitoring of data without affecting the task, collecting important parameters, and quickly storing, analyzing, and processing. The operation and maintenance monitoring module 406 has the characteristics of stability, security, real-time, and portability.

[0160] The operation and maintenance monitoring module 406 includes: a platform resource management unit, a platform operation status monitoring unit, a data usage log management unit, etc.

[0161] The data sharing service module 407 is used to provide information resource sharing services in the form of application access control.

[0162] The data sharing service module 407 ensures the legitimacy of users (business application systems) using the data platform through application access control, and specifically provides unified information resource sharing (access) services to legitimate application systems through subscription push, integrated data services, etc. The data sharing service module 407 provides a variety of information resource sharing services including directory services, data services, and file services, realizes business collaboration, public data opening, and provides users with data integration application services.

[0163] The data sharing service module 407 includes: a subject directory service unit, a data query service unit, a file service unit, an application access control service unit, and the like.

[0164] The public information service module 408 is used to provide information resource sharing services in the form of special applications. The public information service module 408 is used to provide integrated services and data perspective services covering information resources in all business fields in the form of special applications, such as providing public service portals, special management, special information organization and special information display and other public information services.

[0165] The public information service module 408 includes: a public service portal unit, a topic management unit, a topic information organization unit and a topic information display unit.

[0166] The information resource exchange and sharing system 40 of this embodiment is composed of a platform management module 403, an information resource front exchange module 401, an information resource integration module 402, a data management module 404, a data sharing service module 407, a public information service module 408, an application integration support module 405, and an operation and maintenance monitoring module 406. Among them, the application integration support module 405 and the operation and maintenance monitoring module 406 are basic supporting modules that provide the integration, safe operation, and unified management capabilities of the entire quality resource application module; the information resource front exchange module 401 and the information resource integration module 402 are key background modules for completing information resource integration. The information resource front exchange module 401 is deployed in the network environment where the information source is located, and is responsible for docking with the business application module, completing the extraction of original information resources, and pushing shared information resources to the business module. The information resource integration module 402 is responsible for the integration and distribution of business information and perception data, and completing the integration and processing of multiple information subject data across business fields. The data sharing service module 407 is responsible for providing resource services for various quality resource applications; the public information service module 408 provides integrated services covering information resources in all business fields.

[0167] Embodiment 3

[0168] The present application also provides a computer device, such as Figure 5 What is shown is a basic structural block diagram of the computer device of this embodiment.

[0169] The computer device 50 includes a memory 501, a processor 502, and a network interface 503 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 50 with components 501-503, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), digital processors (DSP), embedded devices, etc.

[0170] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0171] The memory 501 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 501 can be an internal storage unit of the computer device 5, such as a hard disk or memory of the computer device 50. In other embodiments, the memory 501 can also be an external storage device of the computer device 50, such as a plug-in hard disk equipped on the computer device 6, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 501 can also include both the internal storage unit of the computer device 50 and its external storage device. In this embodiment, the memory 501 is generally used to store the operating system and various application software installed on the computer device 5, such as computer-readable instructions of the information resource exchange and sharing method, etc. In addition, the memory 501 can also be used to temporarily store various types of data that have been output or are to be output.

[0172] The processor 502 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 502 is generally used to control the overall operation of the computer device 50. In this embodiment, the processor 502 is used to run the computer-readable instructions stored in the memory 501 or process data, such as computer-readable instructions for running the information resource exchange and sharing method.

[0173] The network interface 503 may include a wireless network interface or a wired network interface. The network interface 503 is generally used to establish a communication connection between the computer device 50 and other electronic devices.

[0174] Embodiment 4

[0175] The present application also provides a computer-readable storage medium, which stores computer-readable instructions. The computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the information resource exchange and sharing method as described above.

[0176] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0177] In order to better understand the present invention, the above is described in detail in conjunction with the specific embodiments of the present invention, but it is not intended to limit the present invention. Any simple modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention. Each embodiment in this specification focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referenced to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

Claims

1. A method for exchanging and sharing multiple data information resources, comprising obtaining business information resources, characterized in that: The following steps are also included: According to business requirements, the business information resources are integrated to obtain shared information resources; According to a preset push strategy, the shared information resource is pushed.

2. The method for exchanging and sharing multiple data information resources as claimed in claim 1, characterized in that: The step of integrating the business information resources comprises the following sub-steps: Verify the authenticity of the business information resources; Completely reconstructing the business information resources; The information resources are merged and reorganized according to the semantics of the information resources.

3. The method for exchanging and sharing multiple data information resources as claimed in claim 2, characterized in that: The authenticity identification of the business information resource includes: Performing tensor expansion on the business information resource to obtain an expansion matrix; Performing eigenvalue decomposition on the unfolded matrix to obtain eigenvalues; Performing correlation analysis based on the characteristic values ​​to detect the authenticity of the data; Perform tensor filling on the business information resource to obtain the restored business information resource.

4. The method for exchanging and sharing multiple data information resources as claimed in claim 3, characterized in that: The first complete reconstruction of the business information resource includes the following sub-steps: Calculating the fractal dimension of the business information resource; Acquire a fractal learning model, and extract potential relationships between business information resources from the fractal dimension through the fractal learning model; Performing initial clustering according to the fractal dimension to generate cluster members; Clustering the cluster members again by using a voting method to generate a clustering result; The business information resources are integrated according to the potential relationship between the business information resources and / or the clustering result to obtain the business information resources after the first complete reconstruction.

5. The method for exchanging and sharing multiple data information resources as claimed in claim 4, characterized in that: The fractal dimension is an estimate of the degree of freedom of the distribution of a data set. For a data set with an embedding dimension of n, it is embedded in an n-dimensional grid with each cell having a side length of γ (γ∈(γ1,γ2)), where (γ1,γ2) is the range of variation of the side length of the data set with fractal characteristics. The number of data points falling into the i-th cell is calculated and recorded as Fractal dimension D q The definition is as follows: Among them, q is a parameter that affects the calculation of the fractal quantile, and D2 is the probability that the distance between two randomly selected points is less than an eigenvalue.

6. The method for exchanging and sharing multiple data information resources as claimed in claim 5, characterized in that: The business information resource after the first completeness reconstruction is subjected to completeness reconstruction, and further comprises the following sub-steps: Construct a Gaussian mixture model of business information resources; The covariance crossover algorithm is used to estimate the Gaussian mixture model and obtain the business information resources after the second completeness reconstruction.

7. The method for exchanging and sharing multiple data information resources as claimed in claim 6, characterized in that: The observation model is set as: X = Hx* + n, where X is the observed value, x* is the true value, H is the degradation matrix, and n is the noise; Known fusion source (x1, x2, ···, x N ) are N random vectors, and the covariance crossover algorithm is used for estimation: Among them, P ij =E[x i x j T ], and 8. The method for exchanging and sharing multiple data information resources as claimed in claim 7, characterized in that: Weight ω i By minimizing tr(P 00 )Solve: Among them, p ii To find the weight ω i The matrix involved.

9. The method for exchanging and sharing multiple data information resources as claimed in claim 8, characterized in that: The completeness reconstruction of the business information resources after the second completeness reconstruction also includes the following sub-steps: Processing the business information resource using a support vector machine to obtain a support vector machine output result; The business information resources are integrated according to the output results by using the basic probability distribution function in the DS evidence theory to obtain the business information resources after the third completeness reconstruction.

10. A multi-data information resource exchange and sharing system, comprising an information resource pre-exchange module, characterized in that: It also includes information resource integration modules. The information resource pre-exchange module is used to obtain business information resources. The information resource front-end exchange module is located between the information resource integration module and the business application system. The information resource integration module is used to integrate the business information resources according to business requirements to obtain shared information resources. The system adopts the method as claimed in claim 1 to realize the exchange and sharing of multi-dimensional data information resources.

Images