An integrated management method and system for multidimensional data

Abstract

The present invention relates to the field of data management technology, specifically to an integrated management method for multidimensional data, the method comprising the following steps: randomly selecting a detection area, and obtaining multidimensional monitoring data based on the detection area; constructing a feature information analysis model based on the multidimensional monitoring data, and obtaining key feature information of the detection area based on the feature information analysis model; establishing a feature information verification model based on the multidimensional monitoring data, obtaining the loss of key feature information based on the feature information verification model, and obtaining the best key feature information of the detection area; analyzing the detection area in combination with the best key feature information and big data information to obtain a multidimensional data analysis result of the detection area. The present invention utilizes a data model to perform feature analysis, verification, and integration on multidimensional monitoring data, thereby providing a more comprehensive, in-depth, and accurate integrated analysis result of multidimensional data, and providing strong support for data decision-making and management.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an integrated management method and system for multidimensional data. Background Art

[0002] With the rapid development of information technology, data has become an indispensable core resource for all walks of life. In the era of big data, the scale of data is getting larger and larger, the data dimensions are becoming more and more diverse, and the complexity is also growing. Existing data processing methods often only focus on a single type or a single dimension of data, and cannot effectively manage and systematically process multidimensional data.

[0003] When dealing with data from different sources, current multidimensional data management methods have diverse data formats, complex structures, and lack of unified specifications and standards, which can lead to data loss, errors, or garbled characters during data analysis. On the other hand, the query and analysis efficiency of multidimensional data will also face severe challenges, because the complexity and multidimensional nature of the data make the processing capabilities of traditional data management methods seriously insufficient, which affects the decision-making accuracy and operation efficiency of data management methods.

[0004] At present, a new integrated management method of multidimensional data is needed to achieve unified integration, efficient query and precise analysis of multidimensional data, so as to ensure the accuracy and completeness of multidimensional data. Only on this basis can we better utilize multidimensional data and provide more reliable and powerful support for subsequent decision-making, analysis and prediction. Summary of the invention

[0005] In view of the shortcomings of existing methods and the needs of practical applications, the present invention constructs a variety of data analysis models, performs feature analysis, inspection and efficient integration of data from different sources, so as to obtain comprehensive, accurate and in-depth multidimensional data integration results, improve the efficiency of complex data query and analysis, and use standard data management mechanisms to reduce the error probability of data management. On the one hand, the present invention provides an integrated management method for multidimensional data, which includes the following steps: randomly selecting a detection area, and obtaining multidimensional monitoring data based on the detection area; constructing a feature information analysis model based on the multidimensional monitoring data, and obtaining key feature information of the detection area based on the feature information analysis model; establishing a feature information verification model based on the multidimensional monitoring data, obtaining the loss of key feature information based on the feature information verification model, and obtaining the best key feature information of the detection area; combining the best key feature information and big data information to analyze the detection area to obtain the multidimensional data analysis results of the detection area. The present invention analyzes, extracts and inspects the multidimensional data of the detection area based on a mathematical model, provides more comprehensive, in-depth and accurate multidimensional data analysis results, helps to improve the accuracy and scientificity of the data analysis results, and improves the applicability of the integrated management method for multidimensional data.

[0006] Optionally, the multi-dimensional monitoring data obtained based on the detection area includes: monitoring and collecting the communication, navigation, and remote sensing data, sky data, ground data, and human activity data of the detection area based on the detection area; and obtaining the multi-dimensional monitoring data of the detection area by combining the communication, navigation, and remote sensing data, the sky data, the ground data, and the human activity data. The multi-dimensional monitoring data of the present invention mainly includes communication, navigation, and remote sensing data, sky data, ground data, and human activity data, thereby ensuring the richness and diversity of the data, and being able to more comprehensively reflect the actual situation of the detection area.

[0007] Optionally, the obtaining of key feature information of the detection area according to the feature information analysis model includes: obtaining feature data decoding information of the detection area according to the feature information analysis model; and obtaining key feature information of the detection area through the feature data decoding information. The present invention performs data decoding, standardization, normalization and other processing to obtain decoding information corresponding to feature data, and can convert multi-dimensional monitoring data into more interpretable and operable feature information, thereby ensuring the accuracy of key feature information.

[0008] Optionally, the feature information analysis model satisfies the following relationship:

[0009] f=J(B(X))

[0010] Wherein, f represents the decoding result of the feature information, J represents the decoding process of the feature information, B represents the encoding process of the feature information, and X represents the feature information obtained by encoding. The feature information analysis model of the present invention includes two operation modules, encoding and decoding, which can reasonably optimize and adjust the model to make the model more flexible and more practical.

[0011] Optionally, the loss of key feature information obtained based on the feature information verification model includes: obtaining a key feature loss coefficient of the detection area through the feature information verification model; and obtaining the loss of key feature information according to the key feature loss coefficient analysis. The present invention analyzes the loss of feature information based on the feature information verification model, which helps to improve data quality and reliability of analysis results, and further ensures the effective implementation of the integrated management method for multi-dimensional data.

[0012] Optionally, the feature information verification model satisfies the following relationship:

[0013]

[0014] Among them, loss represents the loss coefficient of key feature information, I represents the total number of key feature information groups, η represents the relevant probability coefficient of key feature information, S represents the total attention score of key feature information, T i The weight matrix represents the key feature information, and θ represents the bias constant of the weight matrix conversion. The present invention comprehensively considers the relevant probability coefficient of the key feature information, the attention score, the matrix bias constant and other related factors, so that the test model can more comprehensively and accurately reflect the loss status of the feature data.

[0015] Optionally, the loss of key feature information obtained based on the feature information verification model and obtaining the best key feature information of the detection area includes: optimizing the key feature information of the detection area according to the key feature loss coefficient; and obtaining the best key feature information of the detection area based on the optimized key feature information. The present invention optimizes the key feature information of the detection area through the feature loss coefficient, reduces the degree of data loss during transmission, processing or analysis, and helps to ensure the reliability of subsequent analysis results.

[0016] Optionally, the combination of the best key feature information and the big data information to analyze the detection area to obtain a multidimensional data analysis result of the detection area includes: integrating the above-mentioned best key feature information and obtaining a key feature set of the detection area; comparing the key feature set of the detection area with the big data information and obtaining a comparative analysis result of the detection area; and obtaining a multidimensional data analysis result of the detection area based on the comparative analysis result and the big data information. The present invention integrates the key feature information to obtain the core and key information of the detection area, thereby obtaining a comprehensive and accurate analysis result.

[0017] Optionally, the detection area key feature set satisfies the following relationship:

[0018]

[0019] Where I represents the integration result of key features, μ represents the data collection ratio of the detection area, q represents the signal strength of the monitoring data, L represents the central location information of the detection area, L1 represents the longitude of the center of the detection area, L2 represents the latitude of the center of the detection area, t represents the time corresponding to the monitoring data, t1 represents the monitoring time corresponding to the first key feature, t2 represents the monitoring time corresponding to the second key feature, t3 represents the monitoring time corresponding to the third key feature, t n represents the monitoring time corresponding to the nth key feature, λ1 represents the proportion coefficient of the first key feature, f1 represents the first key feature, λ2 represents the proportion coefficient of the second key feature, f2 represents the second key feature, λ n Indicates the proportion of the nth key feature, f n The present invention integrates data collection conditions, center location information, time and key feature information, so that the key feature set can fully reflect all aspects of the detection area and provide an information basis for the integrated management method of multi-dimensional data.

[0020] In the second aspect, in order to efficiently execute the integrated management method of multidimensional data provided by the present invention, the present invention also provides an integrated management system for multidimensional data, including a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the integrated management method for multidimensional data as described in the first aspect of the present invention. The integrated management system for multidimensional data of the present invention has a compact structure and stable performance, and can stably execute the integrated management method for multidimensional data provided by the present invention, thereby improving the overall applicability and practical application capability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the integrated management method of multi-dimensional data of the present invention;

[0022] Figure 2 It is a schematic diagram of extracting key feature data for integrated management of multi-dimensional data of the present invention;

[0023] Figure 3 A schematic diagram of a key characteristic data set for integrated management of multi-dimensional data of the present invention;

[0024] Figure 4 This is a structural diagram of the integrated management system for multidimensional data of the present invention. DETAILED DESCRIPTION

[0025] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0026] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0027] See also Figure 1 In order to perform feature analysis, inspection and integration of multi-dimensional monitoring data, provide more comprehensive, in-depth and accurate multi-dimensional data analysis results, and provide strong support for the decision-making and planning of subsequent detection areas. The present invention provides an integrated management method for multi-dimensional data, which includes the following steps:

[0028] S1. Randomly select a detection area and obtain corresponding multi-dimensional monitoring data based on the above detection area. The specific implementation steps and contents are as follows:

[0029] Based on the monitoring of the detection area and the collection of monitoring data, the monitoring data of the above detection area includes but is not limited to the communication, navigation and remote sensing data, sky data, ground data and human activity data of the detection area.

[0030] In order to ensure that the detection area is representative within the entire monitoring range, rather than being limited to a specific area. In this embodiment, the detection area is selected by random sampling, which helps to more comprehensively understand the actual situation of the monitoring area and reduce the errors and influences caused by subjective judgment or specific needs. On the other hand, the random selection method has good flexibility and can adapt to different monitoring data requirements by adjusting the sample size and sampling method, and can efficiently collect multidimensional monitoring data under limited resources. At the same time, it saves manpower, material resources and financial resources to obtain multidimensional monitoring information with utilization value. In addition, the same area can be repeatedly monitored at different time points or in different situations to ensure the credibility and accuracy of the multidimensional monitoring data of the detection area, thereby improving the practical application prospects of the multidimensional data monitoring method.

[0031] Furthermore, in this implementation case, the communication, navigation and remote sensing data include but are not limited to data information obtained through communication technology, navigation systems and remote sensing technology. The above data mainly comes from on-orbit satellites, UAV flight platforms, and ground communication and navigation base stations. Based on this, all-round and multi-level data monitoring of the monitoring area is carried out, ensuring the comprehensiveness and feasibility of the communication, navigation and remote sensing data in the implementation example.

[0032] Sky data refers to a data set covering various meteorological and environmental parameters inside and above the atmosphere. The above data can be collected through advanced meteorological satellites, ground radar systems, aircraft and other technologies. In the embodiment, sky data includes but is not limited to multiple key indicators such as wind speed, wind direction, temperature, humidity, air pressure, precipitation intensity and air quality in the detection area. Based on this, the weather conditions, climate change and environmental quality of the detection area can be roughly understood, which is conducive to the unified management and scientific analysis of the detection area data.

[0033] Ground data includes, but is not limited to, various key information about the surface and underground environment of the detection area, which can be collected through ground monitoring facilities and technical means. In the embodiment, the monitoring facilities include, but are not limited to, soil monitoring stations, hydrological monitoring stations, earthquake monitoring stations, and ground sensor networks. Based on this, data on soil properties, hydrological conditions, geological structures, and ground dynamic conditions in the detection area can be obtained. The above data are of great significance for ground environmental conditions, assessment of natural resources, prediction of geological disasters, and guidance of urban planning.

[0034] Human activity data refers to data related to human behavior and activities, such as socioeconomic conditions, population distribution and dynamics, traffic operation conditions, etc. It mainly comes from diversified channels such as population census, traffic flow monitoring, social media analysis, and mobile phone positioning data. Human activity data has broad application value in urban planning, traffic management, market research, and social policy formulation.

[0035] Multi-dimensional monitoring data provides a comprehensive monitoring perspective of the detection area, and then obtains relevant information from the sky to the ground and all levels of human activities in the monitoring area, which helps to more deeply understand the complexity and diversity of the detection area and more accurately assess the actual situation of the detection area. On the other hand, through continuous monitoring and data analysis, we can more effectively respond to environmental problems, optimize resource allocation, and promote the sustainable development of the detection area.

[0036] Furthermore, the specific content and collection method of the multidimensional monitoring data in this embodiment is merely an optional condition of the present invention. In one or some other embodiments, the collection method of the multidimensional monitoring data can be changed according to the actual situation of the detection area and the data collection requirements to meet the actual needs of different detection areas, which has practical application value for promoting the development of environmental protection, urban planning, traffic management and other fields.

[0037] S2. Construct a feature information analysis model based on the multi-dimensional monitoring data of the detection area, and obtain key feature information of the detection area based on the feature information analysis model. The specific implementation steps and contents are as follows;

[0038] In this embodiment, the multidimensional monitoring data is first preprocessed to ensure the quality of the relevant data and the accuracy of the analysis results. The above preprocessing includes but is not limited to data cleaning: identifying and removing invalid data, erroneous data and duplicate data to ensure the reliability and consistency of the data; data formatting: converting data in different formats into a standard format to facilitate subsequent data processing and analysis; data standardization and normalization: converting data to the same dimension or range through specific mathematical transformations to eliminate dimensional differences between multidimensional data and improve the accuracy of the analysis results; missing value processing: for missing values ​​in the data, appropriate filling or interpolation methods are used to process them to reduce the impact of data incompleteness on the analysis results. The preprocessing method based on the above method makes the multidimensional monitoring data more standardized, clear and reliable, and further provides a data foundation for subsequent data management.

[0039] Then, by integrating and extracting key features of multi-dimensional monitoring data, the most representative model parameters can be extracted from multi-source data, providing a scientific basis for model construction and optimization. Data integration intelligently integrates data from different sources, formats and types, and then builds a comprehensive and unified multi-source data set. In this process, the temporal and spatial matching of data of different dimensions is further analyzed to ensure that different data information can be accurately aligned. Based on this, more comprehensive and richer multi-source data information can be obtained, providing a basis for subsequent data analysis and scientific management.

[0040] On the other hand, the feature extraction of multidimensional monitoring data is mainly based on the specific characteristics of the detection area and the analysis objectives, that is, the key parameter information is screened out from the multidimensional monitoring data. The above features include but are not limited to the statistics of feature data, trend analysis, periodicity, and correlation between different data points. In order to extract features more effectively, advanced technologies such as data mining and machine learning can be used to mine feature patterns and correlations from multidimensional monitoring data, providing strong support for subsequent model construction and optimization.

[0041] Next, the feature information analysis model will be constructed and selected. Based on the integration results of multi-dimensional monitoring data and feature extraction information, and combined with feature attributes and specific analysis objectives, a suitable model framework will be selected. The above model framework includes but is not limited to regression models, classification models, clustering models, and time series analysis. In order to further ensure the effectiveness of the selected model, advanced technologies such as cross-validation and model evaluation can also be used to comprehensively compare the initially established feature information analysis models. Based on the comparative analysis results, the feature analysis model with the best performance can be selected to provide strong support for the final data interpretation and management.

[0042] In this embodiment, the above feature information analysis model satisfies the following relationship:

[0043] f=J(B(X))

[0044] Among them, f represents the decoding result of the feature information, J represents the decoding process of the feature information, B represents the encoding process of the feature information, and X represents the feature information obtained by encoding.

[0045] The decoding result of feature information refers to the interpretation and understanding of feature information based on the model. The above decoding results include but are not limited to the description, classification, prediction or correlation analysis of key features, which is conducive to a deeper understanding of the potential laws and changing trends in multidimensional monitoring data.

[0046] In an optional embodiment, environmental conditions are monitored through multi-dimensional monitoring data, and the decoding results of characteristic information include but are not limited to key characteristic information such as identifying the source of specific pollutants, predicting future pollution trends, and evaluating the degree of environmental pollution; atmospheric conditions are monitored through multi-dimensional monitoring data, and the decoding results may involve identifying potential factors of atmospheric pollution, predicting changing trends in carbon emissions, and evaluating the effectiveness of atmospheric governance, thereby providing valuable reference information for decision makers, helping to better understand and utilize multi-dimensional monitoring data information, and supporting them to make more scientific and reasonable data management plans.

[0047] The decoding process of feature information refers to converting feature information from a machine-readable format into a simple and easy-to-understand form, from which useful data information can be quickly extracted. The above process deeply interprets and analyzes the model output results, which is conducive to converting abstract data and statistical results into information with practical significance and business value. In this embodiment, the decoding process mainly includes the steps of understanding model output, data visualization, result interpretation, business interpretation, etc., which is conducive to the trend prediction of subsequent data, risk factor identification, optimization suggestion proposal, etc., and helps to realize the integrated management method of multi-dimensional data.

[0048] The encoding process of feature information refers to converting the feature information in multidimensional data into a digital representation that can be understood and processed by the machine learning model. The above process involves cleaning, converting and formatting the multidimensional data in order to extract features that are useful for model training. The encoding process of feature information mainly includes steps such as data cleaning, feature extraction, feature conversion, feature selection and feature construction. Converting multidimensional data into a digital representation that can be understood and processed by the machine learning model provides an information basis for subsequent model training and analysis, which is of great significance for improving model performance and analysis results.

[0049] The feature information obtained by encoding refers to the relevant information extracted from the multidimensional data and converted into a digital representation after feature encoding, and the above information is the basis for machine learning model training and analysis. In the embodiment, the feature information obtained by encoding can be a set of numerical values ​​or vectors, which can describe the attributes or characteristics of one or some aspects of the multidimensional data. The feature information can be extracted based on the statistics, structure, relationship or pattern of the original data, thereby accurately reflecting the inherent laws and attribute characteristics of the relevant data.

[0050] The feature information obtained by the above encoding satisfies the following relationship:

[0051]

[0052] Among them, X represents the feature information obtained by encoding, n represents the number of groups of feature information, m represents the total number of groups of feature information, S nrepresents the attention score of the nth group of data, x n Indicates the nth group of data. The number of groups of feature information refers to the number of combinations of features, variables, and attributes in different detection area data sets.

[0053] In an optional embodiment, the feature information obtained by encoding in the image recognition task mainly includes the pixel value, texture feature, shape feature, etc. of the image; the feature information obtained by encoding in the natural language processing task mainly includes the word frequency, word vector, grammatical structure, etc. of the text. Simulation training of the machine learning model based on the above feature information is conducive to providing the key information required for model learning and prediction, and then extracting more effective and usable feature information, thereby improving the performance and accuracy of the feature model.

[0054] Furthermore, in an optional embodiment, the characteristic information analysis model is systematically trained and optimized. First, 30% of the integrated multidimensional monitoring data is randomly selected as a training data set, and the model is preliminarily trained using the training data set, and the simulated output results of the model are obtained. The relevant parameters of the model are adjusted according to the above simulated output results to further improve the prediction ability and analysis performance of the characteristic information analysis model. Then, the remaining multidimensional monitoring data is randomly selected as a verification data set, and the results of the model are compared and verified, which can improve the practicality and generalization ability of the model, thereby ensuring the stability and reliability of the model. Finally, the performance of the model is evaluated, where the evaluation indicators include but are not limited to the accuracy, recall rate and F1 score of the output results. The above indicators can more accurately measure the actual performance of the model in the analysis of multidimensional monitoring data, which is conducive to building a characteristic information analysis model with excellent performance, and provide a decision-making basis for subsequent data analysis and intelligent management.

[0055] Obtaining feature data decoding information of the detection area according to the feature information analysis model, and obtaining key feature information of the detection area through the feature data decoding information, the specific implementation content of which is as follows;

[0056] Obtaining the feature data decoding information of the detection area based on the feature information analysis model and further extracting the key feature information of the detection area through the decoding information is a continuous dynamic analysis process.

[0057] First, the output results are obtained from the trained feature information analysis model, including but not limited to the feature values, probabilities or classification results of important parameters in the monitoring area. Then the key feature data output by the model is decoded and converted from the internal representation of the model into a simple and understandable form, which will involve operations such as converting values ​​into actual physical quantities and converting probabilities into risk levels.

[0058] After decoding the characteristic data of the monitoring area, key characteristic information is identified from it. The above key characteristics should be the most representative of the detection area, the most relevant to the prediction target, or the most influential on the decision. In an optional embodiment, the key characteristic information includes but is not limited to the statistical analysis results of key parameters, feature importance evaluation, expert analysis and other information. The extracted and interpreted key characteristic information is further presented in an easy-to-understand manner, including but not limited to charts, reports, dashboards, etc. For details, please refer to Figure 2 , where X represents the initial data input into the detection area, UF represents the unstructured file data of the detection area, AS represents the application system data of the detection area, DT represents the format file data of the detection area, OD represents other databases of the detection area, T1 represents the first attribute of the feature, T2 represents the second attribute of the feature, T3 represents the third attribute of the feature, and IDS represents the key feature data of the detection area. In order to ensure the accuracy and efficiency of the monitoring area information, different information sources are integrated to extract information related to the key features of the detection area. Through unified processing and analysis, the standardization and consistency of the key feature information of the monitoring area are ensured, which is conducive to the actual implementation of the multi-dimensional data integrated management method.

[0059] In this embodiment, the key feature information of the detection area is obtained from the feature information analysis model and presented in a visual form, which not only improves the utilization and analysis efficiency of the relevant multidimensional data, but also helps to quickly grasp the main characteristics and change trends of the detection area, thereby improving the practicality and actual utilization value of the multidimensional data.

[0060] Furthermore, in the present embodiment, the key feature information extraction method for different detection areas is only an optional condition of the present invention. In one or some other embodiments, the key feature information extraction method and the corresponding analysis model can be adjusted according to the actual situation and detection requirements. Different detection areas have different characteristics, data structures and business needs. Adjusting the key feature information extraction method and analysis model can better adapt to the needs of different detection areas, making the integrated management method of multi-dimensional data scalable.

[0061] S3. Establish a feature information verification model based on the multi-dimensional monitoring data of the detection area, obtain the loss of key feature information based on the feature information verification model, and obtain the best key feature information of the detection area. The specific implementation steps and contents are as follows:

[0062] First, the key feature loss coefficient of the detection area is obtained through the feature information verification model of the detection area. The specific implementation content is as follows:

[0063] In order to more deeply quantify and evaluate the degree of loss of key feature information in the detection area, the embodiment uses pre-processed multi-dimensional monitoring data, and the above data includes multiple information sources such as sensor readings, images, text descriptions, etc., and extracts the proportion, total number and influence coefficient related to the key feature information. Based on this, a feature information verification model is constructed. The feature information verification model of the embodiment is intended to evaluate the degree of loss that may occur in the transmission, processing or storage of key feature information, thereby ensuring the feasibility of key data.

[0064] The above feature information verification model satisfies the following relationship:

[0065]

[0066] Among them, loss represents the loss coefficient of key feature information, I represents the total number of key feature information groups, η represents the relevant probability coefficient of key feature information, S represents the total attention score of key feature information, T i represents the weight matrix of key feature information, and θ represents the bias constant for weight matrix conversion.

[0067] The loss coefficient of key characteristic information is a quantitative indicator that can measure the loss of key characteristic information during data transmission, processing or storage. The above coefficient can measure the integrity and accuracy of characteristic information, and directly reflects the degree of reduction or degradation of key characteristic information relative to the initial state in a specific link or the entire process. For application scenarios such as atmospheric monitoring, environmental assessment, and pollution analysis, the loss coefficient of key characteristic information is an important indicator for evaluating system performance and data reliability.

[0068] The total number of key feature information groups refers to the total number or total set of key feature information extracted from multi-dimensional monitoring data. The above key feature information is extracted from a specific detection area and represents the key attributes, states or behavior characteristics of the detection area. The total number of key feature information groups is a quantitative indicator that can be used to describe the quantity or scale of extracted key feature information, which helps to understand the complexity of feature data, select appropriate attribute features, evaluate computing resource requirements, and evaluate data quality.

[0069] The correlation probability coefficient of key feature information refers to a statistic that measures the correlation between key feature information. In data analysis, when there is a certain correlation or trend between two or more key feature information, the correlation probability coefficient can be used to quantify the degree of mutual correlation. When testing the feature information model, understanding the correlation between features is conducive to selecting the most representative features and avoiding feature redundancy or overfitting.

[0070] The total attention score of key feature information can reflect the relative importance and influence of different key feature information on decision-making, analysis or processing in a specific regional environment. The total attention score usually involves multiple factors, including but not limited to the weight, frequency, significance, relevance, etc. of feature information, and can be replaced and adjusted according to the specific application scenarios and needs of the detection area, thereby ensuring the practicality of the integrated management method of multi-dimensional data.

[0071] In this embodiment, the attention scores of different key feature information of the detection area satisfy the following relationship:

[0072] S n =softmax(x n , C))

[0073] Among them, S n represents the attention score of the nth group of data, softmax represents the attention function of the data, x n represents the nth group of data, and C represents the correlation of feature information.

[0074] When the softmax function is used to represent the attention function of data, it specifically means that in the deep learning model, the model will assign different weights to different parts of the input data. Based on this, the attention weights can be converted into a valid probability distribution, so as to better understand and explain the importance of the model to the input data when generating the output.

[0075] The correlation of feature information refers to the degree of association between different features. It can be used to verify whether there is a linear or nonlinear relationship between different features, as well as the strength and direction of different relationships, which helps to subsequently extract information of different key features.

[0076] The weight matrix of key feature information can be used to represent and quantify the importance or influence of each key feature information in the multidimensional data space. In an optional embodiment, the matrix is ​​usually an array or table, in which the rows represent data samples and the columns represent key feature information. In the embodiment, each element in the weight matrix represents the weight or correlation between the corresponding sample and the feature. In addition, the size and direction of the weight can reflect the importance, contribution direction or influence of the key feature on the sample in the detection area. When constructing a feature information verification model, the weight matrix is ​​used to represent the relationship between the input feature and the output target, and then verify the degree of loss or loss of key feature information.

[0077] The bias constant of the weight matrix conversion refers to a constant term introduced to adjust or optimize the conversion result when multidimensional data or key features are converted through the weight matrix. The above bias constant is usually used together with the weight matrix to adjust the output or decision boundary of the model, which is conducive to translating the output or decision boundary of the model to better fit the data or adapt to the needs of specific tasks, thereby helping the feature information test model to better analyze the distribution and fluctuation trend of multidimensional data, improve the generalization ability and practical performance of the model, and thus optimize the prediction or test effect of the model.

[0078] Furthermore, in the present embodiment, the method for establishing the feature information verification model is only an optional condition of the present invention. In one or some other embodiments, the model can be adjusted and optimized according to the multidimensional data situation and the monitoring conditions. The feature information verification model can be adjusted according to the specific situation to ensure that the model better adapts to the changes in the detection environment, thereby improving the accuracy and effectiveness of the model output results.

[0079] Then, according to the above key feature loss coefficient analysis, the loss of key feature information is obtained, and its specific implementation content is as follows:

[0080] Collecting and analyzing the loss coefficients of key feature information in the detection area not only analyzes the degree of loss of different features, but also compares the difference in loss between different features. In an optional embodiment, collecting and analyzing the loss coefficients of key feature information in the detection area can not only evaluate the severity of loss of different features, but also analyze the difference in loss coefficients between different features. If more than 80% of the feature data are abnormal data, it means that the sensor in the detection area may have failed, the data transmission path is incorrect, or the data processing process is deviated. On the other hand, if only less than 80% or the loss coefficient of individual features is abnormal, it means that the decoding result of the relevant feature data is incorrect, the translation model fails, or there is a problem with the data collection device. Based on this, detailed inspections can be performed on different feature data to ensure the accuracy and feature integrity of different feature data.

[0081] In addition, the loss trends of key features can be analyzed to predict the possibility of loss of feature data in the future, which helps to take data prevention measures in a timely manner and reduce the risk of loss of feature data. The analysis of loss trends can also find out whether there are periodic or seasonal loss patterns in certain feature data, so as to better manage and optimize the data collection and processing processes.

[0082] Finally, the key feature information of the detection area is optimized according to the key feature loss coefficient, and the best key feature information of the detection area is obtained based on the optimized key feature information. The specific implementation content is as follows:

[0083] After comprehensively evaluating and analyzing the loss coefficients of the key feature information of the detection area, different feature information is optimized based on different feature loss coefficients. In an optional embodiment, the optimization process will comprehensively consider the loss degree, loss difference and possible data problems between different features, and use advanced algorithms and technical means to screen, calibrate and enhance the key feature information to minimize the data loss degree and improve the accuracy of the information, and obtain the best key feature information of the detection area after optimization.

[0084] The optimal key feature information is more accurate, complete and reliable, and can better reflect the actual situation of the detection area. Subsequent decision-making, analysis and prediction based on the optimized key feature information can improve the actual value of multidimensional data and provide strong support for practical applications.

[0085] Furthermore, in the present embodiment, the method for obtaining the best key feature information is only an optional condition of the present invention. In one or some other embodiments, the information acquisition method can be adjusted according to the feature attributes and actual conditions. The key feature information in different detection areas and different application scenarios has different characteristics and requirements. By adjusting the information acquisition method, it can better adapt to the actual application scenarios and ensure the accurate acquisition of the best key feature information.

[0086] S4. Analyze the detection area by combining the best key feature information and big data information to obtain the multi-dimensional data analysis results of the detection area. The specific implementation steps and contents are as follows:

[0087] In this embodiment, the above-mentioned best key feature information is first integrated to obtain a key feature set of the detection area. The integration process includes but is not limited to uniformly processing the best key feature information from different sources and in different formats to form a unified and complete key feature set.

[0088] The key feature set of the detection area mainly contains important and verified feature information in the detection area, providing a comprehensive and accurate data basis for subsequent decision-making, analysis and prediction. By integrating the best key feature information, we can better understand and grasp the actual situation of the detection area. In addition, the integrated key feature set helps to improve the consistency and comparability of data. Since feature information from different sources may have differences and conflicts, these differences and conflicts can be eliminated through integration processing to ensure the accuracy and reliability of feature data, so that the detection area can be compared and analyzed more accurately and consistently at different times, locations and scenarios. For details, see Figure 3, where X represents the initial data input into the detection area, UF represents the unstructured file data of the detection area, AS represents the application system data of the detection area, DT represents the format file data of the detection area, OD represents other databases of the detection area, and T1 ’ Indicates the first attribute corresponding to different features, T2 ’ Indicates the second attribute corresponding to different features, T3 ’ It represents the third attribute corresponding to different features, I1 represents the first key feature data of the detection area, I2 represents the second key feature data of the detection area, I3 represents the third key feature data of the detection area, and I represents the key feature set of the detection area.

[0089] The above detection area key feature set satisfies the following relationship:

[0090]

[0091] Where I represents the integration result of key features, μ represents the data collection ratio of the detection area, q represents the signal strength of the monitoring data, L represents the central location information of the detection area, L1 represents the longitude of the center of the detection area, L2 represents the latitude of the center of the detection area, t represents the time corresponding to the monitoring data, t1 represents the monitoring time corresponding to the first key feature, t2 represents the monitoring time corresponding to the second key feature, t3 represents the monitoring time corresponding to the third key feature, t n represents the monitoring time corresponding to the nth key feature, λ1 represents the proportion coefficient of the first key feature, f1 represents the first key feature, λ2 represents the proportion coefficient of the second key feature, f2 represents the second key feature, λ n Indicates the proportion of the nth key feature, f n Represents the nth key feature.

[0092] The integrated key feature result in this embodiment is a feature set, which mainly includes key features related to the detection area. The above features come from different data sources, sensors and analysis methods, and are integrated into a feature set through integration processing, providing comprehensive, consistent and comparable key feature information, providing important data support for subsequent regional data decision-making, analysis and prediction.

[0093] The data collection ratio of the detection area usually refers to the ratio of the amount of data collected to the total amount of data in the detection area or an important part of the total amount of data when collecting data in the detection area. The above ratio can reflect the coverage, accuracy and importance of data collection. In practical applications, the data collection ratio of the detection area will be affected by many factors, such as the accuracy and quantity of data collection equipment, the choice of collection method, sampling frequency, etc. Understanding the data collection ratio of the detection area is of great significance for evaluating data quality, formulating data collection strategies, and conducting data analysis and decision-making. By analyzing and optimizing the data collection ratio, the representativeness, reliability and accuracy of the data can be improved, thereby better supporting decision-making and practice in related fields.

[0094] The signal strength of the monitoring data usually refers to the power strength of the received wireless signal. The signal strength reflects the signal power level received by the wireless communication device and is one of the important indicators for evaluating the quality of wireless communication. In wireless communication, the signal strength is affected by many factors, such as distance, obstacles, interference, etc. The stronger the signal strength, the better the communication quality and the higher the data transmission rate. Therefore, the signal strength of the monitoring data can help evaluate the performance and reliability of wireless communication equipment, as well as optimize the layout and configuration of the wireless communication network. In specific applications, appropriate signal strength monitoring methods and indicators can be selected according to actual conditions and needs.

[0095] The central location information of the detection area usually refers to the coordinates of the geographical or spatial center point within the detection area determined by a variety of methods. The above-mentioned center point can be the geometric center, centroid, population center of gravity or other representative points of the area. The central location information of the detection area can be obtained through the geographic information system (GIS) to understand the spatial distribution and characteristics of the detection area, providing important reference for related decision-making. On the other hand, the central location information of the detection area will be affected by many factors, including but not limited to terrain, landform, population distribution, etc. When determining the central location information, it is necessary to comprehensively consider various factors and adopt appropriate methods for position calculation and correction.

[0096] The time corresponding to the monitoring data refers to the specific time point or time period when the monitoring data is recorded. Time information is very important for understanding and analyzing monitoring data, and can help determine the data collection time and the time change trend.

[0097] The above monitoring data corresponding to time needs to satisfy the following relationship:

[0098] t=(t1,t2,t3…t n )

[0099] Among them, t represents the time corresponding to the monitoring data, t1 represents the monitoring time corresponding to the first key feature, t2 represents the monitoring time corresponding to the second key feature, t3 represents the monitoring time corresponding to the third key feature, and t n The monitoring time corresponding to the nth key feature is represented. The recording method of the monitoring time is different in different monitoring areas or databases. In an optional embodiment, the time information of environmental monitoring mainly includes the specific date, time, season or year, etc.; the time information in atmospheric data monitoring mainly includes atmospheric information data of time periods such as hours, days, weeks or months. Understanding the time corresponding to the monitoring data is helpful to evaluate the timeliness and reliability of the data, as well as the changes of the data over time, which is of great significance for predicting future trends, formulating relevant policies or conducting scientific research.

[0100] In this embodiment, different key features of the detection area correspond to different monitoring times, which specifically refers to different collection time points and time periods set for different key features or parameters during the monitoring process. In different monitoring fields and application scenarios, key features mainly include temperature, humidity, pressure, flow, concentration, noise, vibration and other physical or chemical quantities. For each key feature, a different monitoring time is set according to its characteristics and importance. In an optional embodiment, during the environmental monitoring process, for key features such as the air quality index (AQI) or water quality indicators, continuous real-time monitoring is required to obtain the latest data and information, while for other key features, such as soil moisture or noise level, only regular monitoring is required, such as once a day, week or month.

[0101] In addition, monitoring time will be affected by other factors, including but not limited to the performance of the monitoring equipment, the investment of manpower and material resources, and the monitoring budget. In actual operation, it is necessary to flexibly adjust the monitoring time according to the specific situation to ensure the validity and reliability of the monitoring data. Reasonable monitoring time arrangements can better understand the changing trends and laws of key characteristics and provide a scientific basis for data management and analysis.

[0102] The proportion coefficient of different key features refers to the weight or proportion of different key features in the overall evaluation when conducting data analysis or evaluation. The above-mentioned proportion coefficient reflects the important proportion of different key features in the evaluation, and it is necessary to comprehensively consider the influence of different key features on the overall analysis results of the data.

[0103] Then, the key feature set of the detection area is compared with the historical big data information database, and the comparative analysis results of the detection area are obtained. The specific implementation content is as follows:

[0104] When comparing and analyzing the key feature set of the detection area with historical big data, in addition to using statistical methods and machine learning models to quantify the differences, similarities, and significance between the detection area and historical data, other methods can also be used to conduct in-depth analysis.

[0105] The distribution differences and similarities between the key features of the detection area and the historical data can be compared to observe whether there is a significant offset or change between the two; the changing trend of the key features of the detection area over time can be analyzed and compared with the long-term trend of the historical data to quickly identify anomalies or turning points; more advanced data mining techniques can also be introduced, including but not limited to association rule mining techniques, to find the association rules between the key features of the detection area and the historical data, and reveal the potential relationship between different feature data.

[0106] Furthermore, expert knowledge and domain-specific analysis information of the detection area can be introduced, and domain experts can participate in the data analysis process to provide professional insights and reasonable explanations to enhance the credibility and practicality of the analysis results. Different analysis methods and evaluation indicators can be customized according to the characteristics and actual needs of the field to which the detection area belongs, which can more accurately evaluate the correlation between the key features of the detection area and historical big data information.

[0107] Through the above comparative analysis methods and techniques, we can more comprehensively and deeply analyze the differences, similarities and significance between the key feature set of the detection area and the historical big data, and provide more accurate and powerful support for relevant decisions in the detection area.

[0108] Finally, based on the comparative analysis results and big data information, the multidimensional data analysis results of the detection area are obtained. The specific implementation contents are as follows:

[0109] In this embodiment, multivariate statistical analysis methods can be applied, including but not limited to principal component analysis, cluster analysis, etc., to perform multi-dimensional analysis on the comparative analysis results of the detection area and identify relevant feature information contained in the multi-dimensional data. Machine learning algorithms, such as random forests and neural networks, can also be combined to predict and classify the comparative analysis results of the detection area to reveal future trends and potential problems in the detection area.

[0110] Furthermore, the comparative analysis results can be visualized and interpreted in depth. Data visualization tools, including but not limited to heat maps, scatter plots, time series diagrams, etc. corresponding to the detection area, can be used to display the multidimensional data analysis results in an intuitive way. On the other hand, combined with professional knowledge and field experience of different feature information, the visualization results can be interpreted and analyzed to provide decision-making support for the planning, management, and monitoring of the detection area, and then formulate targeted strategies and suggestions to solve existing problems, optimize resource allocation, and improve the overall regional competitiveness.

[0111] In this embodiment, a continuous data monitoring mechanism will also be established to regularly update multidimensional data sets and analysis results. Existing strategies will be adjusted and optimized based on new data and analysis results to maintain the effectiveness and adaptability of decisions, provide a comprehensive and scientific basis for relevant decisions and actions in different areas of the detection area, and promote the value of big data in practical applications.

[0112] Furthermore, the implementation content of the detection area comparison results in the present embodiment is only an optional condition of the present embodiment. In one or some other embodiments, the detection area comparison method can be optimized according to the implementation situation and actual needs, which can not only improve the accuracy and reliability of the analysis results, enhance the effectiveness and practicality of decision-making, but also promote technological innovation and progress, and promote sustainable development.

[0113] See also Figure 4 In an optional embodiment, in order to efficiently execute the integrated management method of multidimensional data provided by the present invention, the present invention also provides an integrated management system for multidimensional data, the system comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, the processor is configured to call the program instructions, and execute the specific steps of the embodiment related to the integrated management method for multidimensional data provided by the present invention. The integrated management system for multidimensional data of the present invention has a complete structure, is objective and stable, and can efficiently execute the integrated management method for multidimensional data of the present invention, thereby improving the overall applicability and practical application capability of the present invention.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An integrated management method for multidimensional data, characterized in that: The steps include: Randomly selecting a detection area, and obtaining multi-dimensional monitoring data based on the detection area; Constructing a characteristic information analysis model based on the multi-dimensional monitoring data, and obtaining key characteristic information of the detection area based on the characteristic information analysis model; Establishing a feature information verification model according to the multi-dimensional monitoring data, obtaining the loss of key feature information based on the feature information verification model, and obtaining the best key feature information of the detection area; Analyze the detection area in combination with the optimal key feature information and the big data information to obtain a multi-dimensional data integrated analysis result of the detection area; The key feature information of the detection area obtained according to the feature information analysis model includes: Obtaining feature data decoding information of the detection area according to the feature information analysis model; Obtaining key feature information of the detection area through the feature data decoding information; The feature information analysis model satisfies the following relationship: ， in, Indicates the decoding result of feature information, Represents the decoding process of feature information, Represents the encoding process of feature information, Represents the feature information obtained by encoding; The loss of key feature information obtained based on the feature information verification model includes: Obtaining the key feature loss coefficient of the detection area through the feature information verification model; Analyzing the loss of key feature information based on the key feature loss coefficient; The feature information verification model satisfies the following relationship: ， in, represents the loss coefficient of key feature information, Indicates the total number of groups of key feature information, Represents the relevant probability coefficient of key feature information, Represents the total attention score of key feature information, The weight matrix representing the key feature information, The bias constant representing the weight matrix conversion; the combination of the optimal key feature information and the big data information to analyze the detection area to obtain the multi-dimensional data integrated analysis result of the detection area includes: Integrate the above optimal key feature information and obtain a key feature set of the detection area; Compare the key feature set of the detection area with the big data information, and obtain a comparative analysis result of the detection area; Obtaining a multi-dimensional data integrated analysis result of the detection area based on the comparative analysis result and the big data information; The key feature set of the detection area satisfies the following relationship: ， in, represents the integration result of key features, Indicates the proportion of data collection in the detection area, Indicates the signal strength of the monitoring data. Indicates the center position information of the detection area. Indicates the longitude of the center of the detection area, Indicates the latitude of the center of the detection area, Indicates the time corresponding to the monitoring data, Indicates the monitoring time corresponding to the first key feature, Indicates the monitoring time corresponding to the second key feature, Indicates the monitoring time corresponding to the third key feature, Indicates The monitoring time corresponding to the key features, Indicates the proportion coefficient of the first key feature, represents the first key feature, Represents the proportion coefficient of the second key feature, represents the second key feature, Indicates The proportion of key features, Indicates Key features: The obtaining of multi-dimensional monitoring data based on the detection area comprises: Based on the detection area, the communication, navigation and telemetry data, sky data, ground data and human activity data of the detection area are monitored and collected; The multi-dimensional monitoring data of the detection area is obtained by combining the communication, navigation and remote sensing data, the sky data, the ground data and the human activity data.

2. The integrated management method of multidimensional data according to claim 1, characterized in that: The loss of key feature information obtained based on the feature information verification model and obtaining the best key feature information of the detection area include: Optimizing key feature information of the detection area according to the key feature loss coefficient; The best key feature information of the detection area is obtained based on the optimized key feature information.

3. An integrated management system for multidimensional data, characterized in that: The system includes a processor, an input device, an output device and a memory, wherein the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the integrated management method of multidimensional data as described in any one of claims 1-2.

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