Geographical science information intelligent management method and system based on big data analysis

Abstract

The present invention discloses a method and system for intelligent management of geographic science information based on big data analysis, specifically relating to the technical field of intelligent management of geographic science information. By acquiring and quantifying image compression distortion information, format conversion information, and external attack pressure information, an image compression distortion coefficient, a format conversion anomaly coefficient, and an external attack pressure coefficient are generated respectively. This method can comprehensively cover the potential sources of hidden dangers in the access link of high-resolution image data. By constructing an image data access hidden danger assessment model, an image data access hidden danger assessment index is generated, which quantitatively characterizes the overall risk level of potential hidden dangers in the image data access process, thereby achieving real-time assessment of hidden dangers in complex data access environments. After the potential hidden dangers are identified, combined with the real-time data access scale, an intelligent judgment is made as to whether there are high-risk hidden dangers in the high-resolution image data access process, thereby achieving early warning and accurate response, and avoiding fault escalation.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology of geographic science information, and more specifically, to a method and system for intelligent management of geographic science information based on big data analysis. Background Art

[0002] In recent years, with the development of remote sensing technology, satellite imagery, and geographic information systems (GIS), the application of high-resolution imagery data in the field of geographic science has become increasingly widespread. In particular, in key areas such as urban planning, disaster warning, precision agricultural management, and ecological protection, high-resolution imagery data provides rich spatial information, offering important support to decision makers. However, with the surge in data volume, the storage and management of high-resolution imagery data face enormous technical challenges. First, this type of data typically has high resolution and long time spans, and its storage requirements far exceed the processing capabilities of traditional data management systems, resulting in a significant increase in storage costs. Second, due to the dynamic nature of data and the high-frequency access requirements, traditional storage architectures often fail to provide sufficient flexibility and efficiency. This is especially true when large-scale imagery data needs to be queried and processed in real time, which can lead to performance bottlenecks.

[0003] With the development of big data technology, intelligent geographic information management systems based on big data analysis have gradually become an important approach to solving the challenges of high-resolution image data management. However, in the storage and access process of high-resolution image data, potential hidden dangers and failure risks are often overlooked, resulting in the system's inability to respond promptly when problems arise, which in turn affects data availability and decision accuracy. For example, if data access failures or delays are not discovered in a timely manner, critical data may not be available in real time, resulting in missed opportunities for optimal decision-making and even serious waste of resources and decision-making errors. To address these issues, an intelligent, big data-driven geographic information management method is urgently needed that can monitor anomalies in the data access process in real time, automatically identify potential failures, and respond before failures occur to ensure data stability and availability. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method and system for intelligent management of geographic science information based on big data analysis to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The intelligent management method of geographic science information based on big data analysis includes the following steps:

[0007] Step S1, obtaining image compression distortion information during the high-resolution image data access process, obtaining an image compression distortion coefficient based on the image compression distortion information, and evaluating the impact of the abnormal degree of image compression distortion on the high-resolution image data access;

[0008] Step S2, obtaining format conversion information of the high-resolution image data access process, obtaining a format conversion abnormality coefficient based on the format conversion information, and evaluating the impact of abnormality levels of different high-resolution image data format conversions on the high-resolution image data access;

[0009] Step S3, obtaining external attack pressure information during the high-resolution image data access process, obtaining an external attack pressure coefficient based on the external attack pressure information, and evaluating the impact of the external attack pressure during the data access process on the high-resolution image data access;

[0010] Step S4: constructing an image data access hidden danger assessment model based on the image compression distortion coefficient, the format conversion anomaly coefficient, and the external attack pressure coefficient, generating an image data access hidden danger assessment index, and responding to and identifying potential hidden dangers in the high-resolution image data access process;

[0011] Step S5: When there are potential hidden dangers in the high-resolution image data access process, an early warning time for high-risk hidden dangers in the high-resolution image data access process is determined in combination with the current data access scale.

[0012] In a preferred embodiment, by acquiring image compression distortion information during the high-resolution image data access process, analyzing the image compression distortion during the high-resolution image data access process, and obtaining an image compression distortion coefficient, the degree of influence of the abnormal degree of image compression distortion on the high-resolution image data access is measured;

[0013] The logic for obtaining the image compression distortion coefficient is as follows:

[0014] Get the width, height, and color depth of the high-resolution image before compression, and calculate the size of the high-resolution image before compression. The expression is as follows Where DA1 represents the size of the high-resolution image before compression, Kd represents the width of the high-resolution image, Gd represents the height of the high-resolution image, and Ys represents the color depth of the high-resolution image;

[0015] Calculate the image compression ratio YSB, the expression is as follows Where DA2 represents the compressed size of the high-resolution image;

[0016] Calculate the image peak signal-to-noise ratio YFZ, the expression is as follows Where MSE represents the mean square error of the high-resolution image before and after compression, and the calculation expression is as follows Where Yq(x i ) represents the pixel x of the high-resolution image before compression i The value of Yh(x i ) represents the pixel x after high-resolution image compression i , i={1,2,...,I}, I is a positive integer;

[0017] Calculate the image structure similarity index SSI, the expression is as follows where μ x Represents the pixel mean value of high-resolution image before compression, μ y represents the pixel mean value after high-resolution image compression, σ x Represents the pixel standard deviation of high-resolution image before compression, σ y represents the pixel standard deviation of the high-resolution image after compression, xf represents the covariance of the high-resolution image before and after compression, C1 and C2 are constants used to stabilize the calculation and avoid the denominator being zero;

[0018] Calculate the image compression distortion coefficient Yxys, the expression is as follows Among them YSB j Indicates the image compression ratio of the jth image compression behavior, YFZ j The peak signal-to-noise ratio of the image of the jth image compression behavior, SSI j The image structure similarity index representing the j-th image compression behavior.

[0019] In a preferred embodiment, by acquiring format conversion information of the high-resolution image data access process, analyzing the format conversion situation of the high-resolution image data access process, and obtaining a format conversion abnormality coefficient, the degree of influence of the abnormality of the image format conversion on the high-resolution image data access is measured;

[0020] The logic for obtaining the format conversion anomaly coefficient is as follows:

[0021] Get the storage byte size G1 before high-resolution image format conversion and the storage byte size G2 after format conversion, and calculate the format conversion size difference GS. The expression is as follows Get the start time T1 and end time T2 of the high-resolution image format conversion, and calculate the format conversion time delay GY, which is expressed as follows Where TY represents the expected format conversion time; obtain the pixel value distribution before and after the high-resolution image format conversion, and calculate the entropy value SZ1 before the high-resolution image format conversion. The expression is as follows Among them, P nIt represents the probability of the nth pixel value appearing before the high-resolution image format conversion, which is calculated by dividing the frequency of the pixel value by the total number of pixels, n = {1, 2, ..., N}, where N is a positive integer; the entropy difference SZC of the high-resolution image format conversion is calculated as follows SZC = |SZ1-SZ2|, where SZ2 represents the entropy value after the high-resolution image format conversion; the format conversion anomaly coefficient Gszy is calculated as follows Gszy = e GS+GY+SZC .

[0022] In a preferred embodiment, by obtaining external attack pressure information during the high-resolution image data access process, analyzing the external attack situation during the high-resolution image data access process, and obtaining an external attack pressure coefficient, the degree of external attack pressure during the high-resolution image data access process is measured;

[0023] The logic for obtaining the external attack pressure coefficient is as follows:

[0024] Obtain the network attack traffic WG during the access process of high-resolution image data and calculate the attack intensity GJ. The expression is as follows Where NT represents the normal network traffic level; obtain the number of data access failures SC and the total number of accesses ZF, and calculate the access intensity FW, as shown below: According to the hash algorithm, the data is verified and the amount of tampered data CG is counted, and the data tampering ratio SL is calculated. The expression is as follows Where ZS represents the total data volume; calculate the external attack pressure coefficient Wbgj, the expression is as follows Wbgj=GJ*FW*SL.

[0025] In a preferred embodiment, an image data access hidden danger assessment model is constructed based on the image compression distortion coefficient, format conversion anomaly coefficient, and external attack pressure coefficient to generate an image data access hidden danger assessment index HDI. The model is based on the following formula: Where a1, a2, and a3 represent the preset proportional coefficients of the image compression distortion coefficient, the format conversion abnormality coefficient, and the external attack pressure coefficient, respectively, and a1, a2, and a3 are all greater than 0.

[0026] In a preferred embodiment, the image data access hidden danger assessment index is compared with a preset image data access hidden danger assessment index threshold, and a response is performed to identify potential hidden dangers in the high-resolution image data access process, as follows:

[0027] If the image data access hidden danger assessment index is greater than the image data access hidden danger assessment index threshold, an access hidden danger initial appearance signal is generated;

[0028] If the image data access hidden danger assessment index is less than or equal to the image data access hidden danger assessment index threshold, there is no need to generate an access hidden danger initial occurrence signal.

[0029] In a preferred embodiment, when an initial access hazard signal is generated, the output values ​​of the image data access hazard assessment model at different subsequent moments and the data access scale are obtained, and a warning timing prediction model is constructed to generate a warning timing prediction value YJS. The model is based on the following formula: Among them HDI t represents the image data access hidden danger assessment index generated by the image data access hidden danger assessment model at time t, It represents the average value of the image data access risk assessment index. The calculation expression is as follows SJG t represents the data access volume of the system at time t, Represents the average value of data access volume, and the calculation expression is as follows T is a positive integer.

[0030] In a preferred embodiment, the warning timing prediction value is compared with a preset warning timing prediction value threshold to determine the warning timing of high-risk hidden dangers in the high-resolution image data access process, as follows:

[0031] If the warning opportunity prediction value is greater than the warning opportunity prediction value threshold, a warning signal is generated;

[0032] If the warning opportunity prediction value is less than or equal to the warning opportunity prediction value threshold, there is no need to generate a warning signal.

[0033] In a preferred embodiment, the geographic science information intelligent management system based on big data analysis includes an image compression distortion module, an image format conversion module, an external attack pressure module, a comprehensive assessment module, and an early warning module;

[0034] An image compression distortion module is used to obtain image compression distortion information during the high-resolution image data access process, obtain image compression distortion coefficients based on the image compression distortion information, and evaluate the impact of the abnormal degree of image compression distortion on the high-resolution image data access;

[0035] An image format conversion module is used to obtain format conversion information of the high-resolution image data access process, obtain a format conversion anomaly coefficient based on the format conversion information, and evaluate the impact of the anomaly degree of different high-resolution image data format conversions on the high-resolution image data access;

[0036] An external attack pressure module is used to obtain external attack pressure information during the high-resolution image data access process, obtain an external attack pressure coefficient based on the external attack pressure information, and evaluate the impact of the external attack pressure during the data access process on the high-resolution image data access;

[0037] A comprehensive assessment module is used to construct an image data access risk assessment model based on image compression distortion coefficients, format conversion anomaly coefficients, and external attack pressure coefficients, generate an image data access risk assessment index, and respond to and identify potential risks in the high-resolution image data access process;

[0038] The early warning module is used to determine the early warning time of high-risk hidden dangers in the high-resolution image data access process in combination with the current data access scale when there are potential hidden dangers in the high-resolution image data access process.

[0039] The technical effects and advantages of the present invention are as follows:

[0040] 1. The present invention acquires and quantifies image compression distortion information, format conversion information, and external attack pressure information to generate an image compression distortion coefficient, a format conversion anomaly coefficient, and an external attack pressure coefficient, respectively. This accurately assesses the impact of different anomaly factors during high-resolution image data access, comprehensively covering the potential sources of hidden dangers in the access process of high-resolution image data. By integrating the image compression distortion coefficient, format conversion anomaly coefficient, and external attack pressure coefficient, an image data access hidden danger assessment model is constructed to generate an image data access hidden danger assessment index, which quantitatively represents the overall risk level of potential hidden dangers during image data access, enabling real-time assessment of hidden dangers in complex data access environments. After potential hidden dangers are identified, an early warning timing prediction model is constructed based on the real-time data access scale. The predicted warning timing value is calculated based on the dynamic changes in the hidden danger assessment index and the scale of data access. By comparing it with a set threshold, it intelligently determines whether high-risk hidden dangers exist during the high-resolution image data access process. This enables early warning and precise response, avoids fault escalation, significantly improves the real-time performance and responsiveness of the image data management system, effectively reduces resource waste and the risk of decision-making errors caused by access delays or failures, and ensures the stability of the image data management process. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0042] Figure 1 This is a flow chart of the method of Example 1 of the present invention;

[0043] Figure 2 This is a flow chart of the system of Example 2 of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] Example 1: Figure 1 The present invention provides a method for intelligent management of geographic science information based on big data analysis, which includes the following steps:

[0046] Step S1, obtaining image compression distortion information during the high-resolution image data access process, obtaining an image compression distortion coefficient based on the image compression distortion information, and evaluating the impact of the abnormal degree of image compression distortion on the high-resolution image data access;

[0047] Step S2, obtaining format conversion information of the high-resolution image data access process, obtaining a format conversion abnormality coefficient based on the format conversion information, and evaluating the impact of abnormality levels of different high-resolution image data format conversions on the high-resolution image data access;

[0048] Step S3, obtaining external attack pressure information during the high-resolution image data access process, obtaining an external attack pressure coefficient based on the external attack pressure information, and evaluating the impact of the external attack pressure during the data access process on the high-resolution image data access;

[0049] Step S4: constructing an image data access hidden danger assessment model based on the image compression distortion coefficient, the format conversion anomaly coefficient, and the external attack pressure coefficient, generating an image data access hidden danger assessment index, and responding to and identifying potential hidden dangers in the high-resolution image data access process;

[0050] Step S5, when there are potential hidden dangers in the high-resolution image data access process, determine the early warning time of the high-risk hidden dangers in the high-resolution image data access process in combination with the current data access scale;

[0051] Step S1, obtaining image compression distortion information during the high-resolution image data access process, obtaining an image compression distortion coefficient based on the image compression distortion information, and evaluating the impact of the abnormal degree of image compression distortion on the high-resolution image data access;

[0052] High-resolution image data usually needs to be compressed and aggregated to save storage space. During the access process of high-resolution image data, repeated compression and release of data will inevitably introduce compression distortion and affect the access quality of the image data. The image compression distortion coefficient is used to measure the degree of quality loss caused by data compression during the compression and decompression process of high-resolution image data, as well as the impact of this quality loss on the image data access process. During the data access process, image compression distortion may cause incomplete image information, blurred details or loss of key features, thereby affecting the accuracy and reliability of the accessed data. By calculating the image compression distortion coefficient, problems such as image blurring and detail loss caused by compression during the access process of high-resolution image data can be timely perceived, ensuring the reliability of accessed data. Potential data access problems, such as loss of details due to excessively high compression ratios, can be detected through the image compression distortion coefficient to avoid decision delays due to data defects.

[0053] A larger image compression distortion coefficient indicates that the data quality has been significantly affected due to frequent compression and decompression operations during data access, which may cause key information to be weakened during transmission or storage, thereby increasing data access delays, instability, and potential hidden dangers, affecting the efficiency and accuracy of real-time query and processing, and thus significantly affecting the actual application value of high-resolution image data, such as reducing analysis accuracy, decision reliability, and data availability. On the contrary, it indicates that high-resolution image data has maintained a high quality during the compression and decompression process, key information has not been significantly lost, and the data access process is more stable and efficient, ensuring fast and accurate real-time query and processing. This means that the transmission and storage of image data is more reliable, which can better support the needs of high-resolution image data in various applications, and improve analysis accuracy, decision reliability, and data availability.

[0054] Therefore, by obtaining the image compression distortion information of the high-resolution image data access process, analyzing the image compression distortion of the high-resolution image data access process, and obtaining the image compression distortion coefficient, the influence of the abnormal degree of image compression distortion on the high-resolution image data access is measured;

[0055] The logic for obtaining the image compression distortion coefficient is as follows:

[0056] Get the width, height, and color depth of the high-resolution image before compression, and calculate the size of the high-resolution image before compression. The expression is as follows Where DA1 represents the size of the high-resolution image before compression, Kd represents the width of the high-resolution image, Gd represents the height of the high-resolution image, and Ys represents the color depth of the high-resolution image;

[0057] It should be noted that color depth refers to the number of bits per pixel in a high-resolution image;

[0058] Calculate the image compression ratio YSB, the expression is as follows Where DA2 represents the compressed size of the high-resolution image;

[0059] Calculate the image peak signal-to-noise ratio YFZ, the expression is as follows Where MSE represents the mean square error of the high-resolution image before and after compression, and the calculation expression is as follows Where Yq(x i ) represents the pixel x of the high-resolution image before compression i The value of Yh(x i ) represents the pixel x after high-resolution image compression i , i={1,2,...,I}, I is a positive integer;

[0060] Calculate the image structure similarity index SSI, the expression is as follows where μ x Represents the pixel mean value of high-resolution image before compression, μ y represents the pixel mean value after high-resolution image compression, σ x Represents the pixel standard deviation of high-resolution image before compression, σ y represents the pixel standard deviation of the high-resolution image after compression, xf represents the covariance of the high-resolution image before and after compression, C1 and C2 are constants used to stabilize the calculation and avoid the denominator being zero;

[0061] Calculate the image compression distortion coefficient Yxys, the expression is as follows Among them YSB j Indicates the image compression ratio of the jth image compression behavior, YFZ j The peak signal-to-noise ratio of the image of the jth image compression behavior, SSI j The image structure similarity index representing the j-th image compression behavior;

[0062] It should be noted that before calculating the image compression distortion coefficient, it is necessary to ensure that the image compression ratio, image peak signal-to-noise ratio, and image structure similarity index are all normalized. Commonly used normalization methods include Min-Max normalization and Z-Score normalization.

[0063] Step S2, obtaining format conversion information of the high-resolution image data access process, obtaining a format conversion abnormality coefficient based on the format conversion information, and evaluating the impact of abnormality levels of different high-resolution image data format conversions on the high-resolution image data access;

[0064] The format conversion anomaly coefficient is a key indicator used to measure the degree of anomalies caused by format conversion during the data access process of high-resolution imagery, as well as its impact on image access efficiency and data integrity. By calculating the format conversion anomaly coefficient, the degree of anomalies in the format conversion process of high-resolution imagery data can be accurately assessed, and the impact of data size changes, increased latency, and image quality degradation caused by the conversion can be quantified, providing support for identifying performance bottlenecks. At the same time, the format conversion anomaly coefficient comprehensively reflects the all-round impact of conversion on storage efficiency, access speed, and image quality, helping to optimize the storage and access strategies of high-resolution imagery data, improve the stability, accuracy, and practical application value of data processing, and thus ensure efficient access and reliable use of image data in multiple scenarios.

[0065] A larger format conversion anomaly coefficient may reduce access efficiency, weaken data stability, and damage image quality. Specifically, abnormal format conversion may increase processing time, reduce data access speed, affect real-time requirements, and cause data loss or damage, making it difficult to ensure data consistency and integrity during the access process. It may also cause image quality degradation, such as reduced resolution or loss of details, further affecting the accurate interpretation and subsequent analysis of the image, and ultimately weakening the practical application value of high-resolution image data. Conversely, a smaller format conversion anomaly coefficient indicates that the format conversion process is more efficient and stable, data access speed is faster, consistency and integrity are guaranteed, image quality can be better preserved, ensuring accurate interpretation and analysis of image data, thereby enhancing the practical application value of high-resolution image data.

[0066] Therefore, by obtaining the format conversion information of the high-resolution image data access process, analyzing the format conversion situation of the high-resolution image data access process, and obtaining the format conversion abnormality coefficient, the impact of the abnormality of image format conversion on the high-resolution image data access is measured;

[0067] The logic for obtaining the format conversion anomaly coefficient is as follows:

[0068] Get the storage byte size G1 before high-resolution image format conversion and the storage byte size G2 after format conversion, and calculate the format conversion size difference GS. The expression is as follows Get the start time T1 and end time T2 of the high-resolution image format conversion, and calculate the format conversion time delay GY, which is expressed as follows Where TY represents the expected format conversion time; obtain the pixel value distribution before and after the high-resolution image format conversion, and calculate the entropy value SZ1 before the high-resolution image format conversion. The expression is as follows Among them, P nIt represents the probability of the nth pixel value appearing before the high-resolution image format conversion, which is calculated by dividing the frequency of the pixel value by the total number of pixels, n = {1, 2, ..., N}, where N is a positive integer; the entropy difference SZC of the high-resolution image format conversion is calculated as follows SZC = |SZ1-SZ2|, where SZ2 represents the entropy value after the high-resolution image format conversion; the format conversion anomaly coefficient Gszy is calculated as follows Gszy = e GS+GY+SZC ;

[0069] It should be noted that before calculating the format conversion anomaly coefficient, it is necessary to ensure that the format conversion size difference, format conversion time delay, and entropy value difference of high-resolution image format conversion are all normalized;

[0070] Step S3, obtaining external attack pressure information during the high-resolution image data access process, obtaining an external attack pressure coefficient based on the external attack pressure information, and evaluating the impact of the external attack pressure during the data access process on the high-resolution image data access;

[0071] During the access process of high-resolution image data, external attack pressure may affect the access speed, integrity and security of the data. The external attack pressure coefficient is used to measure the severity of external attacks during the data access process of high-resolution image data, as well as the degree of impact on the access of high-resolution image data, taking into account various attack methods (such as denial of service attacks, malware infection, data theft, tampering, etc.); by calculating the external attack pressure coefficient, the impact of external attacks on the access process of high-resolution image data can be comprehensively evaluated, especially in terms of the speed, stability, security and integrity of data access. The external attack pressure coefficient can quantify the intensity of external attacks, the impact on data availability and integrity, and the potential threat to security, thereby providing a strong basis for data protection and emergency response;

[0072] Specifically, when the external attack pressure coefficient is large, it indicates that there is strong attack pressure, which may lead to various problems in the data access process, including reduced access speed, data loss, tampering or leakage risks. At this time, the availability and integrity of the data will be threatened, and it may even cause the system to be unable to provide efficient and stable services, affecting the practical application of high-resolution imagery. In addition, the severity of the external attack may also undermine the security of the data, leading to the leakage of sensitive information or malicious tampering, which will cause long-term damage to the credibility and reliability of the entire image data, reducing the practical application value of the data;

[0073] Conversely, a smaller external attack pressure coefficient indicates lower attack pressure and less impact on data access, ensuring fast and stable access to high-resolution image data, and effectively protecting the security and integrity of the data. Therefore, by calculating the external attack pressure coefficient, we can not only identify potential attack risks, but also take appropriate protective measures when attack pressure is high, such as optimizing the data transmission process, strengthening security protection, and improving the system's fault tolerance, thereby enhancing the security and reliability of high-resolution image data and ensuring its normal use in complex environments.

[0074] Therefore, by obtaining the external attack pressure information of the high-resolution image data access process, the external attack situation in the high-resolution image data access process is analyzed, and the external attack pressure coefficient is obtained to measure the external attack pressure degree in the high-resolution image data access process;

[0075] The logic for obtaining the external attack pressure coefficient is as follows:

[0076] Obtain the network attack traffic WG during the access process of high-resolution image data and calculate the attack intensity GJ. The expression is as follows Where NT represents the normal network traffic level; obtain the number of data access failures SC and the total number of accesses ZF, and calculate the access intensity FW, as shown below: According to the hash algorithm, the data is verified and the amount of tampered data CG is counted, and the data tampering ratio SL is calculated. The expression is as follows Where ZS represents the total data volume; calculate the external attack pressure coefficient Wbgj, the expression is as follows Wbgj=GJ*FW*SL;

[0077] Step S4: constructing an image data access hidden danger assessment model based on the image compression distortion coefficient, the format conversion anomaly coefficient, and the external attack pressure coefficient, generating an image data access hidden danger assessment index, and responding to and identifying potential hidden dangers in the high-resolution image data access process;

[0078] An image data access hazard assessment model is constructed based on the image compression distortion coefficient, format conversion anomaly coefficient, and external attack pressure coefficient to generate the image data access hazard assessment index HDI. The model is based on the following formula: Where a1, a2, and a3 represent the preset proportional coefficients of the image compression distortion coefficient, format conversion abnormality coefficient, and external attack pressure coefficient, respectively, and a1, a2, and a3 are all greater than 0;

[0079] It should be noted that before building the image data access risk assessment model, it is necessary to ensure that the image compression distortion coefficient, format conversion anomaly coefficient, and external attack pressure coefficient are all normalized; a1, a2, and a3 are set according to actual conditions. For example, the expert empowerment method can be adopted, that is, experts in related fields are invited to determine the preset proportional coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0080] From the above calculation expression, it can be seen that the larger the image compression distortion coefficient, the larger the format conversion anomaly coefficient, and the larger the external attack pressure coefficient, the larger the image data access hidden danger assessment index, indicating that the abnormal degree of image compression distortion, the abnormal degree of conversion of different high-resolution image data formats, and the external attack pressure of the data access process have a greater impact on high-resolution image data access. Conversely, the smaller the image compression distortion coefficient, the smaller the format conversion anomaly coefficient, and the smaller the external attack pressure coefficient, the smaller the image data access hidden danger assessment index, indicating that the abnormal degree of image compression distortion, the abnormal degree of conversion of different high-resolution image data formats, and the external attack pressure of the data access process have a lesser impact on high-resolution image data access.

[0081] The image data access hidden danger assessment index is compared with the preset image data access hidden danger assessment index threshold to respond and identify potential hidden dangers in the high-resolution image data access process, as follows:

[0082] If the image data access hidden danger assessment index is greater than the image data access hidden danger assessment index threshold, it indicates that the potential hidden danger situation in the high-resolution image data access process is more serious, and an access hidden danger initial appearance signal is generated;

[0083] If the image data access hidden danger assessment index is less than or equal to the image data access hidden danger assessment index threshold, it indicates that there are no potential hidden dangers in the high-resolution image data access process, and there is no need to generate an access hidden danger initial appearance signal;

[0084] Step S5, when there are potential hidden dangers in the high-resolution image data access process, determine the early warning time of the high-risk hidden dangers in the high-resolution image data access process in combination with the current data access scale;

[0085] When the initial access hazard signal is generated, the output values ​​of the image data access hazard assessment model at different subsequent times and the data access scale are obtained, and an early warning timing prediction model is constructed to generate the early warning timing prediction value YJS. The model is based on the following formula: Among them HDI t represents the image data access hidden danger assessment index generated by the image data access hidden danger assessment model at time t, It represents the average value of the image data access risk assessment index. The calculation expression is as follows SJG t represents the data access volume of the system at time t, Represents the average value of data access volume, and the calculation expression is as follows T is a positive integer;

[0086] The warning timing prediction value is compared with the preset warning timing prediction value threshold to determine the warning timing of high-risk hidden dangers in the high-resolution image data access process, as follows:

[0087] If the warning opportunity prediction value is greater than the warning opportunity prediction value threshold, it indicates that there is a high-risk hidden danger in the high-resolution image data access process, and a warning signal is generated;

[0088] If the warning opportunity prediction value is less than or equal to the warning opportunity prediction value threshold, it indicates that the hidden dangers in the high-resolution image data access process and the current access scale are still within the controllable range, and there is no need to generate a warning signal;

[0089] The present invention acquires and quantifies image compression distortion information, format conversion information, and external attack pressure information to generate image compression distortion coefficients, format conversion anomaly coefficients, and external attack pressure coefficients, respectively. This accurately assesses the impact of different anomaly factors during high-resolution image data access, comprehensively covering the potential sources of hidden dangers in the access process of high-resolution image data. By integrating the image compression distortion coefficients, format conversion anomaly coefficients, and external attack pressure coefficients, an image data access hidden danger assessment model is constructed to generate an image data access hidden danger assessment index, which quantitatively represents the overall risk level of potential hidden dangers during image data access, enabling real-time assessment of hidden dangers in complex data access environments. After potential hidden dangers are identified, a warning timing prediction model is constructed based on the real-time data access scale. The predicted warning timing value is calculated based on the dynamic changes in the hidden danger assessment index and the scale of data access. By comparing it with a set threshold, it intelligently determines whether high-risk hidden dangers exist during the high-resolution image data access process. This enables early warning and precise response, avoids fault escalation, significantly improves the real-time performance and responsiveness of the image data management system, effectively reduces resource waste and the risk of decision-making errors caused by access delays or failures, and ensures the stability of the image data management process.

[0090] Example 2: This example is an introduction to the geographical science information intelligent management system based on big data analysis. Figure 2 As shown, it includes an image compression distortion module, an image format conversion module, an external attack pressure module, a comprehensive evaluation module, and an early warning module;

[0091] An image compression distortion module is used to obtain image compression distortion information during the high-resolution image data access process, obtain image compression distortion coefficients based on the image compression distortion information, and evaluate the impact of the abnormal degree of image compression distortion on the high-resolution image data access;

[0092] An image format conversion module is used to obtain format conversion information of the high-resolution image data access process, obtain a format conversion anomaly coefficient based on the format conversion information, and evaluate the impact of the anomaly degree of different high-resolution image data format conversions on the high-resolution image data access;

[0093] An external attack pressure module is used to obtain external attack pressure information during the high-resolution image data access process, obtain an external attack pressure coefficient based on the external attack pressure information, and evaluate the impact of the external attack pressure during the data access process on the high-resolution image data access;

[0094] A comprehensive assessment module is used to construct an image data access risk assessment model based on image compression distortion coefficients, format conversion anomaly coefficients, and external attack pressure coefficients, generate an image data access risk assessment index, and respond to and identify potential risks in the high-resolution image data access process;

[0095] The early warning module is used to determine the early warning time of high-risk hidden dangers in the high-resolution image data access process in combination with the current data access scale when there are potential hidden dangers in the high-resolution image data access process.

[0096] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0097] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0098] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0099] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the system and method described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways.

[0101] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An intelligent management method for geographic science information based on big data analysis, characterized by: The steps include: Step S1, obtaining image compression distortion information during the high-resolution image data access process, obtaining an image compression distortion coefficient based on the image compression distortion information, and evaluating the impact of the abnormal degree of image compression distortion on the high-resolution image data access; Step S2, obtaining format conversion information of the high-resolution image data access process, obtaining a format conversion abnormality coefficient based on the format conversion information, and evaluating the impact of abnormality levels of different high-resolution image data format conversions on the high-resolution image data access; Step S3, obtaining external attack pressure information during the high-resolution image data access process, obtaining an external attack pressure coefficient based on the external attack pressure information, and evaluating the impact of the external attack pressure during the data access process on the high-resolution image data access; Step S4: constructing an image data access hidden danger assessment model based on the image compression distortion coefficient, the format conversion anomaly coefficient, and the external attack pressure coefficient, generating an image data access hidden danger assessment index, and responding to and identifying potential hidden dangers in the high-resolution image data access process; Step S5, when there are potential hidden dangers in the high-resolution image data access process, determine the early warning time of the high-risk hidden dangers in the high-resolution image data access process in combination with the current data access scale; By acquiring image compression distortion information during the access process of high-resolution image data, analyzing the image compression distortion during the access process of high-resolution image data, and obtaining the image compression distortion coefficient, the influence of the abnormal degree of image compression distortion on the access of high-resolution image data is measured; The logic for obtaining the image compression distortion coefficient is as follows: Get the width, height, and color depth of the high-resolution image before compression, and calculate the size of the high-resolution image before compression. The expression is as follows ,in Indicates the size of high-resolution image before compression. Indicates the width of the high-resolution image, Indicates the height of the high-resolution image, Indicates the color depth of high-resolution images; Calculate image compression ratio , the expression is as follows ,in Indicates the compressed size of high-resolution images; Calculate image peak signal-to-noise ratio , the expression is as follows ,in It represents the mean square error of high-resolution image before and after compression. The calculation expression is as follows ,in Represents the pixels of high-resolution image before compression The value of Represents the pixels after high-resolution image compression The value of , is a positive integer; Calculate image structure similarity index , the expression is as follows ,in represents the average pixel value of the high-resolution image before compression, represents the average pixel value after high-resolution image compression, Represents the pixel standard deviation of high-resolution image before compression, Represents the pixel standard deviation of high-resolution image compression, represents the covariance of high-resolution images before and after compression, 、 is a constant used to stabilize calculations and avoid the denominator being zero; Calculate image compression distortion coefficient , the expression is as follows ,in represents the image compression ratio of the jth image compression behavior, represents the peak signal-to-noise ratio of the image at the jth image compression behavior, The image structure similarity index representing the j-th image compression behavior; By obtaining external attack pressure information during the high-resolution image data access process, analyzing the external attack situation during the high-resolution image data access process, and obtaining the external attack pressure coefficient, the degree of external attack pressure during the high-resolution image data access process is measured; The logic for obtaining the external attack pressure coefficient is as follows: Obtaining network attack traffic during access to high-resolution image data , calculate attack strength , the expression is as follows ,in Indicates normal network traffic level; number of failed attempts to obtain data access and total visits , calculate the access intensity , the expression is as follows ; Verify the data according to the hash algorithm to count the amount of tampered data , calculate the data tampering ratio , the expression is as follows ,in Indicates the total amount of data; calculates the external attack pressure coefficient , the expression is as follows ; An image data access hazard assessment model is constructed based on the image compression distortion coefficient, format conversion anomaly coefficient, and external attack pressure coefficient, and an image data access hazard assessment index is generated. The model is based on the following formula , where Respectively represent the preset proportional coefficients of image compression distortion coefficient, format conversion abnormality coefficient, and external attack pressure coefficient, and Both are greater than 0.

2. The method for intelligent management of geographic science information based on big data analysis according to claim 1, characterized in that: By acquiring the format conversion information of the high-resolution image data access process, analyzing the format conversion situation of the high-resolution image data access process, and obtaining the format conversion abnormality coefficient, the impact of the abnormality of image format conversion on the high-resolution image data access is measured; The logic for obtaining the format conversion anomaly coefficient is as follows: Get the storage byte size of the high-resolution image before format conversion and storage byte size after format conversion , calculate the format conversion size difference , the expression is as follows ; Get the start time of high-resolution image format conversion and end time , calculate the format conversion time delay , expressed as follows ,in Indicates the expected format conversion time; obtains the pixel value distribution before and after high-resolution image format conversion, and calculates the entropy value before high-resolution image format conversion , the expression is as follows ,in It represents the probability of the nth pixel value appearing before the high-resolution image format conversion, which is calculated by dividing the frequency of the pixel value by the total number of pixels. , Is a positive integer; calculate the entropy difference of high-resolution image format conversion , the expression is as follows ,in Indicates the entropy value after high-resolution image format conversion; Calculate the format conversion anomaly coefficient , the expression is as follows .

3. The method for intelligent management of geographic science information based on big data analysis according to claim 1, characterized in that: The image data access hidden danger assessment index is compared with the preset image data access hidden danger assessment index threshold to respond and identify potential hidden dangers in the high-resolution image data access process, as follows: If the image data access hidden danger assessment index is greater than the image data access hidden danger assessment index threshold, an access hidden danger initial appearance signal is generated; If the image data access hidden danger assessment index is less than or equal to the image data access hidden danger assessment index threshold, there is no need to generate an access hidden danger initial occurrence signal.

4. The method for intelligent management of geographic science information based on big data analysis according to claim 3 is characterized by: When the initial access hazard signal is generated, the output values ​​of the image data access hazard assessment model at different subsequent times and the data access scale are obtained, and the warning timing prediction model is constructed to generate the warning timing prediction value. The model is based on the following formula ,in represents the image data access hidden danger assessment index generated by the image data access hidden danger assessment model at time t, It represents the average value of the image data access risk assessment index. The calculation expression is as follows , represents the data access volume of the system at time t, Represents the average value of data access volume, and the calculation expression is as follows , , Is a positive integer.

5. The method for intelligent management of geographic science information based on big data analysis according to claim 4 is characterized in that: The warning timing prediction value is compared with the preset warning timing prediction value threshold to determine the warning timing of high-risk hidden dangers in the high-resolution image data access process, as follows: If the warning opportunity prediction value is greater than the warning opportunity prediction value threshold, a warning signal is generated; If the warning opportunity prediction value is less than or equal to the warning opportunity prediction value threshold, there is no need to generate a warning signal.

6. A geographic science information intelligent management system based on big data analysis, for implementing the geographic science information intelligent management method based on big data analysis as described in any one of claims 1 to 5, characterized in that: It includes image compression distortion module, image format conversion module, external attack pressure module, comprehensive assessment module, and early warning module; An image compression distortion module is used to obtain image compression distortion information during the high-resolution image data access process, obtain image compression distortion coefficients based on the image compression distortion information, and evaluate the impact of the abnormal degree of image compression distortion on the high-resolution image data access; An image format conversion module is used to obtain format conversion information of the high-resolution image data access process, obtain a format conversion anomaly coefficient based on the format conversion information, and evaluate the impact of the anomaly degree of different high-resolution image data format conversions on the high-resolution image data access; An external attack pressure module is used to obtain external attack pressure information during the high-resolution image data access process, obtain an external attack pressure coefficient based on the external attack pressure information, and evaluate the impact of the external attack pressure during the data access process on the high-resolution image data access; A comprehensive assessment module is used to construct an image data access risk assessment model based on image compression distortion coefficients, format conversion anomaly coefficients, and external attack pressure coefficients, generate an image data access risk assessment index, and respond to and identify potential risks in the high-resolution image data access process; The early warning module is used to determine the early warning time of high-risk hidden dangers in the high-resolution image data access process in combination with the current data access scale when there are potential hidden dangers in the high-resolution image data access process.

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