Data security emergency management analysis method and system based on big data

Abstract

The invention discloses a data security emergency management analysis method and system based on big data, and the key point of the technical scheme is that the method comprises the following steps: S1, collecting data from a plurality of heterogeneous data sources; s2, the collected data are preprocessed; s3, carrying out multi-modal data fusion on the processed data; s4, performing real-time analysis on the fused data by using a big data analysis algorithm to generate an emergency response suggestion; s5, generating emergency decision support based on an analysis result, and protecting data privacy through data encryption and desensitization; real-time analysis is carried out based on the big data technology, emergency response suggestions are generated, emergency resource allocation is optimized through a genetic algorithm or a particle swarm optimization algorithm, sensitive information is protected by adopting encryption and desensitization technologies in the aspect of data security, cross-department information sharing is achieved through a data open platform, and the security of the data open platform is improved. According to the method, the emergency management efficiency is improved, and the data security and privacy protection are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data security analysis, and in particular to a data security emergency management analysis method and system based on big data. Background Art

[0002] Emergency management refers to a series of measures taken by the government and relevant departments to protect the life, health and property safety of the public when an emergency occurs. With the frequent occurrence of natural disasters, accidents, disasters, public health emergencies and other emergencies, traditional emergency management methods have gradually exposed problems such as low efficiency, slow response, and inaccurate decision-making. There is an urgent need to improve emergency response capabilities through modern technical means.

[0003] As an effective means of processing massive amounts of information, big data technology has been widely used in many fields. In emergency management, big data can integrate information from different sources, such as sensor data, meteorological data, social media, historical event records, etc., to provide a basis for emergency decision-making. However, the current emergency management field still faces the following challenges:

[0004] 1. Emergency management data comes from various sources, including sensor data, social media, historical data, etc. Different data formats and quality differences make data fusion difficult, affecting real-time analysis and decision support. In addition, a large amount of data needs to be pre-processed such as denoising and missing value filling after collection. Traditional methods are inefficient and difficult to cope with the real-time processing needs of large-scale data.

[0005] 2. The existing emergency management system relies on manual decision-making or simple rule engines, lacks intelligent analysis and automated decision-making functions, resulting in untimely responses in complex emergency scenarios. The emergency management process involves a large amount of sensitive data, such as personal information, post-disaster resource allocation, etc. The existing system's technology in data encryption and privacy protection is not yet perfect, and is prone to the risk of data leakage;

[0006] To solve the above problems, we propose a data security emergency management analysis method and system based on big data. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides a data security emergency management analysis method and system based on big data to solve the problems raised in the background technology.

[0008] The above technical objectives of the present invention are achieved through the following technical solutions:

[0009] The data security emergency management analysis method based on big data includes the following steps:

[0010] S1: Collect data from multiple heterogeneous data sources;

[0011] S2: Preprocess the collected data, including denoising, missing value filling, format unification, etc.;

[0012] S3: Perform multimodal data fusion on the processed data to generate a unified data set;

[0013] S4: Use big data analysis algorithms to analyze the fused data in real time and generate emergency response recommendations;

[0014] S5: Generate emergency decision support based on the analysis results, and protect data privacy through data encryption and desensitization.

[0015] Preferably, step S1 includes collecting data from different types of data sources such as sensor data, social media data, historical emergency management data, meteorological data and government reports.

[0016] Preferably, step S2 includes the following operations:

[0017] Denoising: Use wavelet transform or other signal processing algorithms to denoise the data. The formula is as follows:

[0018] D cleaned = WaveletTransform(D raw )

[0019] Where D cleaned is the denoised data, D raw is the original data;

[0020] Missing value filling: Use mean interpolation or KNN interpolation to fill missing data values. The mean interpolation formula is as follows:

[0021]

[0022] Where D filled , is the data after filling, D i , is the i-th non-missing value, n is the number of non-missing values;

[0023] Format unification: Convert different data source formats into a unified format to facilitate subsequent processing and analysis.

[0024] Preferably, step S3 is performed in the following manner:

[0025] Weighted average method: According to the weight of each data source, the data from different data sources are integrated. The formula is as follows:

[0026]

[0027] Where D i is the processed data of the ith data source, w iis the weight of the data source, and n is the number of data sources;

[0028] Bayesian method: Through the Bayesian reasoning method, the probability information of multiple data sources is combined and integrated. The formula is as follows:

[0029]

[0030] Among them, P(D) is the posterior probability of the fused data, P(D i |θ) is the conditional probability of the ith data source under the given model parameter θ.

[0031] Preferably, the step S4 adopts the following analysis algorithm:

[0032] Machine learning algorithms: including decision trees, support vector machines (SVMs), random forests, etc., for pattern recognition and classification;

[0033] Deep learning algorithms: including convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), which are used for prediction and anomaly detection of time series data.

[0034] Preferably, step S5 includes intelligent decision support based on real-time data and historical data, wherein the decision support adopts the following manner;

[0035] Genetic algorithm: used to optimize the allocation of emergency resources, the formula is as follows:

[0036]

[0037] Among them, C k is the cost of the kth resource, R k is the demand for the kth resource, and m is the number of resource types;

[0038] Particle Swarm Optimization (PSO): It is used to optimize resource allocation in multi-objective optimization problems to ensure efficient allocation of resources.

[0039] Preferably, the step S5 further includes:

[0040] Data encryption: Use symmetric encryption algorithms or asymmetric encryption algorithms to encrypt data to ensure the security of data during transmission;

[0041] Data desensitization: Desensitize sensitive data through de-identification or group encryption methods to protect user privacy and sensitive information and ensure that user personal information is not leaked during data sharing and access.

[0042] The present invention also provides a data security emergency management analysis system based on big data, the system comprising:

[0043] Data collection module: used to collect data from multiple heterogeneous data sources, including sensor data, social media data, historical emergency management data, meteorological data, and government report data;

[0044] Data processing module: used to pre-process the collected data, including denoising, missing value filling and format unification. The denoising adopts wavelet transform and the missing value filling adopts mean interpolation method;

[0045] Data fusion and analysis module: used to fuse data from different platforms and formats, and use machine learning and deep learning algorithms to perform real-time data analysis to generate emergency response recommendations;

[0046] Decision support module: Generate emergency response suggestions based on analysis results and support automated decision making. The automated decision support system uses genetic algorithms or particle swarm optimization algorithms to optimize emergency resource allocation;

[0047] Privacy protection module: used to implement data encryption, desensitization and other technologies to ensure data privacy protection and security;

[0048] Data open platform module: used to provide data sharing and access services, and ensure the security and controllability of data

[0049] Preferably, the data fusion and analysis module adopts a deep learning algorithm, including a convolutional neural network (CNN) or a long short-term memory network (LSTM), to extract key features in emergency management and predict the trend of emergency events.

[0050] Preferably, the privacy protection module includes a multi-level security authentication mechanism to ensure access control of data in the platform, prevent leakage of sensitive data, and ensure the security of user privacy.

[0051] In summary, the present invention mainly has the following beneficial effects:

[0052] 1. The present invention uses multimodal data fusion technology to efficiently integrate data from different data sources and perform unified processing to improve data utilization efficiency. Combined with big data analysis and machine learning algorithms, it can analyze data in real time, generate accurate emergency response recommendations, support intelligent decision-making and automated response, thereby improving emergency management efficiency, reducing resource waste and optimizing resource allocation.

[0053] 2. Introduce deep learning and optimization algorithms (such as genetic algorithms and particle swarm optimization algorithms) to automatically adjust emergency resource allocation, reduce manual intervention, and improve the level of automation of emergency response. At the same time, through data encryption and desensitization processing, ensure the security and privacy protection of sensitive data and prevent data leakage.

[0054] 3. Through the data open platform, cross-departmental and cross-field data sharing can be realized, information barriers can be broken, collaboration efficiency can be improved, and the timely flow of emergency information can be ensured. Combining historical data and real-time analysis, the present invention can accurately predict emergencies, provide scientific and reasonable emergency response plans, and improve the timeliness and accuracy of emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart diagram of the method of the present invention;

[0056] Figure 2 It is a system module connection diagram of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0058] The following examples are used to illustrate the present invention, but they cannot be used to limit the scope of protection of the present invention. The conditions in the examples can be further adjusted according to specific conditions. Simple improvements to the method of the present invention under the premise of the concept of the present invention belong to the scope of protection claimed in the present invention.

[0059] Example 1

[0060] refer to Figure 1 The present invention provides a data security emergency management analysis method based on big data, comprising the following steps:

[0061] S1: Collect data from multiple heterogeneous data sources;

[0062] S2: Preprocess the collected data, including denoising, missing value filling, format unification, etc.;

[0063] S3: Perform multimodal data fusion on the processed data to generate a unified data set;

[0064] S4: Use big data analysis algorithms to analyze the fused data in real time and generate emergency response recommendations;

[0065] S5: Generate emergency decision support based on the analysis results, and protect data privacy through data encryption and desensitization.

[0066] System architecture and data collection

[0067] First, the data acquisition module is used to obtain data from multiple heterogeneous data sources, including sensor data, social media data, meteorological data, historical emergency management data, and government reports. Sensor data can collect real-time environmental monitoring data such as temperature, humidity, and air quality through embedded devices; social media data can understand the spread of events by capturing real-time public information from platforms such as Weibo and WeChat; meteorological data obtains real-time meteorological information through the API interface of the National Meteorological Department; historical emergency management data comes from previous emergency event handling information released by government departments; and government reports are policy and decision-making documents obtained through public channels.

[0068] Data preprocessing

[0069] After data collection, different types of data are preprocessed, including denoising, missing value filling and format unification, including the following operations:

[0070] Denoising: Use wavelet transform or other signal processing algorithms to denoise the data. The formula is as follows:

[0071] D cleaned = WaveletTransform(D raw )

[0072] Where D cleaned is the denoised data, D raw is the original data;

[0073] Missing value filling: Use mean interpolation or KNN interpolation to fill missing data values. The mean interpolation formula is as follows:

[0074]

[0075] Where D filled , is the data after filling, D i , is the i-th non-missing value, n is the number of non-missing values;

[0076] Format unification: Convert different data source formats into a unified format to facilitate subsequent processing and analysis.

[0077] Data Fusion

[0078] The processed data will be fused through the data fusion and analysis module, where the weighted average method and Bayesian method are used in the following way:

[0079] Weighted average method: According to the weight of each data source, the data from different data sources are integrated. The formula is as follows:

[0080]

[0081] Where Di is the processed data of the ith data source, w i is the weight of the data source, and n is the number of data sources;

[0082] Bayesian method: Through the Bayesian reasoning method, the probability information of multiple data sources is combined and integrated. The formula is as follows:

[0083]

[0084] Among them, P(D) is the posterior probability of the fused data, P(D i |θ) is the conditional probability of the ith data source under the given model parameter θ.

[0085] Data analysis and emergency response recommendation generation

[0086] The following analysis algorithm was used:

[0087] Machine learning algorithms: including decision trees, support vector machines (SVMs), random forests, etc., for pattern recognition and classification;

[0088] Deep learning algorithms: including convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), which are used for prediction and anomaly detection of time series data.

[0089] Emergency decision support and resource optimization

[0090] Based on the analysis results, the system generates emergency decision support and uses genetic algorithms to optimize emergency resource allocation, where the decision support adopts the following methods;

[0091] Genetic algorithm: used to optimize the allocation of emergency resources, the formula is as follows:

[0092]

[0093] Among them, C k is the cost of the kth resource, R k is the demand for the kth resource, and m is the number of resource types;

[0094] Particle Swarm Optimization (PSO): It is used to optimize resource allocation in multi-objective optimization problems to ensure efficient allocation of resources.

[0095] Data privacy protection

[0096] Through the privacy protection module, the collected sensitive data is encrypted using a symmetric encryption algorithm to ensure the security of the data during storage and transmission. At the same time, sensitive data is desensitized, such as de-identifying the user's identity information, to ensure that sensitive information will not be leaked during data sharing, including:

[0097] Data encryption: Use symmetric encryption algorithms or asymmetric encryption algorithms to encrypt data to ensure the security of data during transmission;

[0098] Data desensitization: Desensitize sensitive data through de-identification or group encryption methods to protect user privacy and sensitive information and ensure that user personal information is not leaked during data sharing and access.

[0099] Cross-departmental data sharing and collaboration

[0100] All processed data and analysis results will be shared through the data open platform module to ensure the flow of information across departments and fields. Governments at all levels and relevant departments can obtain relevant data on emergency response in a timely manner through the platform, further strengthen collaboration and improve the efficiency of emergency response.

[0101] Emergency Response Execution

[0102] During the emergency response execution phase, the system will automatically dispatch relevant resources based on the optimized emergency resource allocation recommendations. For example, based on real-time meteorological data, earthquake prediction data, etc., the system can automatically deploy rescue teams and medical resources to ensure that rescue operations are carried out in the shortest time possible.

[0103] Example 2

[0104] Reference Figure 2 As shown, the present invention provides a data security emergency management analysis system based on big data, including the following modules:

[0105] Data collection module: used to collect data from multiple heterogeneous data sources, including sensor data, social media data, historical emergency management data, meteorological data, and government report data;

[0106] Data processing module: used to pre-process the collected data, including denoising, missing value filling and format unification. The denoising adopts wavelet transform and the missing value filling adopts mean interpolation method;

[0107] Data fusion and analysis module: used to fuse data from different platforms and formats, and use machine learning and deep learning algorithms to perform real-time data analysis to generate emergency response recommendations;

[0108] Decision support module: Generate emergency response suggestions based on analysis results and support automated decision making. The automated decision support system uses genetic algorithms or particle swarm optimization algorithms to optimize emergency resource allocation;

[0109] Privacy protection module: used to implement data encryption, desensitization and other technologies to ensure data privacy protection and security;

[0110] Data open platform module: used to provide data sharing and access services, and ensure the security and controllability of data.

[0111] Data processing and analysis process

[0112] The system collects data from multiple data sources such as sensors, social media, and historical data, and performs unified preprocessing, fusion, and analysis to ultimately generate accurate emergency response recommendations. For example, when the system detects a possible emergency, it can quickly generate emergency response recommendations and provide reasonable resource allocation plans based on historical and real-time data.

[0113] Optimization algorithms and intelligent decision making

[0114] Through genetic algorithms and particle swarm optimization algorithms, the system can make intelligent decisions based on real-time and historical data, automatically optimize the allocation of emergency resources, reduce manual intervention, and improve the degree of automation of emergency response. The system can also adjust decisions in real time to respond to dynamic changes in emergencies.

[0115] Security and Privacy

[0116] During the process of data collection, transmission and storage, all sensitive information is encrypted and desensitized to ensure the security and privacy of the data.

[0117] Working principle: Please refer to Figure 1-Figure 2 As shown, the present invention is based on big data technology. By collecting and preprocessing data in real time from multiple heterogeneous data sources (such as denoising, filling missing values, and unifying formats), a multimodal data fusion method (such as weighted average method or Bayesian method) is used to integrate the data into a unified data set. Then, machine learning and deep learning algorithms are used for real-time analysis to generate emergency response recommendations, and emergency resource allocation is optimized through genetic algorithms or particle swarm optimization algorithms. In terms of data security, encryption and desensitization technologies are used to protect sensitive information, and cross-departmental information sharing is achieved through a data open platform to improve the coordination and timeliness of emergency response. This method improves emergency management efficiency, optimizes resource allocation, and ensures data security and privacy protection.

[0118] Although the embodiments of the present invention have been shown and described, it is understood by those skilled in the art that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the usual meanings understood by persons with ordinary skills in the field to which the present invention belongs, and the words "include" or "comprise" and the like used in the present invention mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents.

[0119] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data security emergency management analysis method based on big data, characterized in that: The following steps are involved: S1: Collect data from multiple heterogeneous data sources; S2: Preprocess the collected data, including denoising, missing value filling, format unification, etc.; S3: Perform multimodal data fusion on the processed data to generate a unified data set; S4: Use big data analysis algorithms to analyze the fused data in real time and generate emergency response recommendations; S5: Generate emergency decision support based on the analysis results, and protect data privacy through data encryption and desensitization.

2. The data security emergency management analysis method based on big data according to claim 1 is characterized in that: The step S1 includes collecting data from different types of data sources such as sensor data, social media data, historical emergency management data, meteorological data, and government reports.

3. The data security emergency management analysis method based on big data according to claim 1 is characterized in that: Described step S2 comprises the following operations: Denoising: Use wavelet transform or other signal processing algorithms to denoise the data. The formula is as follows: D cleaned =WaveletTransform(D raw ) Where D cleaned is the denoised data, D raw is the original data; Missing value filling: Use mean interpolation or KNN interpolation to fill missing data values. The mean interpolation formula is as follows: Where D filled , is the data after filling, D i , is the i-th non-missing value, n is the number of non-missing values; Format unification: Convert different data source formats into a unified format to facilitate subsequent processing and analysis.

4. The data security emergency management analysis method based on big data according to claim 1 is characterized in that: The step S3 is implemented in the following manner: Weighted average method: According to the weight of each data source, the data from different data sources are integrated. The formula is as follows: Where D i is the processed data of the ith data source, w i is the weight of the data source, and n is the number of data sources; Bayesian method: Through the Bayesian reasoning method, the probability information of multiple data sources is combined and integrated. The formula is as follows: Among them, P(D) is the posterior probability of the fused data, P(D i |θ) is the conditional probability of the ith data source under the given model parameter θ.

5. The data security emergency management analysis method based on big data according to claim 1 is characterized in that: The step S4 adopts the following analysis algorithm: Machine learning algorithms: including decision trees, support vector machines (SVMs), random forests, etc., for pattern recognition and classification; Deep learning algorithms: including convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), which are used for prediction and anomaly detection of time series data.

6. The data security emergency management analysis method based on big data according to claim 1 is characterized in that: The step S5 includes intelligent decision support based on real-time data and historical data, wherein the decision support adopts the following method; Genetic algorithm: used to optimize the allocation of emergency resources, the formula is as follows: Among them, C k is the cost of the kth resource, R k is the demand for the kth resource, and m is the number of resource types; Particle Swarm Optimization (PSO): It is used to optimize resource allocation in multi-objective optimization problems to ensure efficient allocation of resources.

7. The data security emergency management analysis method based on big data according to claim 1 is characterized in that: The step S5 further comprises: Data encryption: Use symmetric encryption algorithms or asymmetric encryption algorithms to encrypt data to ensure the security of data during transmission; Data desensitization: Desensitize sensitive data through de-identification or group encryption methods to protect user privacy and sensitive information and ensure that user personal information is not leaked during data sharing and access.

8. Data security emergency management analysis system based on big data, characterized by: The system comprises: Data collection module: used to collect data from multiple heterogeneous data sources, including sensor data, social media data, historical emergency management data, meteorological data, and government report data; Data processing module: used to pre-process the collected data, including denoising, missing value filling and format unification. The denoising adopts wavelet transform and the missing value filling adopts mean interpolation method; Data fusion and analysis module: used to fuse data from different platforms and formats, and use machine learning and deep learning algorithms to perform real-time data analysis to generate emergency response recommendations; Decision support module: Generate emergency response suggestions based on analysis results and support automated decision making. The automated decision support system uses genetic algorithms or particle swarm optimization algorithms to optimize emergency resource allocation; Privacy protection module: used to implement data encryption, desensitization and other technologies to ensure data privacy protection and security; Data open platform module: used to provide data sharing and access services, and ensure the security and controllability of data.

9. The data security emergency management analysis system based on big data according to claim 8 is characterized in that: The data fusion and analysis module adopts deep learning algorithms, including convolutional neural networks (CNN) or long short-term memory networks (LSTM), to extract key features in emergency management and predict the trend of emergency events.

10. The data security emergency management analysis system based on big data according to claim 8 is characterized in that: The privacy protection module includes a multi-level security authentication mechanism to ensure access control of data in the platform, prevent sensitive data leakage, and ensure the security of user privacy.

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