Video Surveillance Data Secure Storage Method Based on Blockchain Technology

Abstract

The present invention discloses a method for secure storage of video surveillance data based on blockchain technology, which relates to the technical field of video surveillance data storage. From the careful design of the hybrid storage architecture to the application of storage virtualization technology and then to the selection of highly reliable storage media, this application comprehensively ensures the integrity of data in the storage link, effectively preventing data corruption caused by hardware failures, data loss and other problems, and providing a solid data foundation for security decision-making. Through the reasonable allocation of computing power and the optimization of the encryption scheme, it is ensured that the data can be quickly decrypted and efficiently processed when needed, and various queries, analyses and decision-making requirements of security services can be responded to in a timely manner. High-priority data blocks, with the computing power preferentially allocated and a simple and efficient encryption and decryption process, quickly provide key clues to ensure that security personnel can obtain effective information in the first time, strongly supporting emergency response operations and achieving a perfect balance between data availability and security.

Description

Technical Field

[0001] The present invention relates to the technical field of video surveillance data storage, and particularly to a method for securely storing video surveillance data based on blockchain technology. Background Art

[0002] In the current era of the digital wave sweeping across the globe, video surveillance systems have become an indispensable key infrastructure in many fields such as maintaining social security, ensuring production operations, and facilitating urban management. From the streets and commercial centers of bustling cities, to the campuses and factories of various enterprises and institutions, to transportation hubs and residential communities, surveillance cameras, like sensitive "electronic eyes", continuously capture a vast amount of video data 24 hours a day, providing intuitive and powerful evidence for security decision-making, event tracing, and daily management. Therefore, a method for securely storing video surveillance data based on blockchain technology has emerged as the times require.

[0003] The prior art, such as the invention patent application with the publication number: CN114301904A, discloses a monitoring method, device, monitoring system, and readable storage medium for a big data cluster. The method includes: obtaining the operation data of each node in the big data cluster, analyzing the operation data of each node, and determining whether there are abnormal nodes among the nodes; if there are abnormal nodes among the nodes, determining the abnormal level of each abnormal node; and performing abnormal monitoring on the big data cluster according to the abnormal level of each abnormal node and the operation data of each abnormal node. The present invention analyzes potential fault abnormal nodes from the operation data of each node, and performs abnormal monitoring on the big data cluster according to their abnormal levels and operation data, and timely performs operation and maintenance processing on the abnormal nodes, which is beneficial to the effective operation of the big data cluster.

[0004] In view of the above solution, the inventors of the present application have found that the above technology has at least the following technical problems: 1. Most of the existing technologies adopt a unified standard security protection strategy, without fully considering the uniqueness of the data involved in different regions and monitoring devices. For example, in the processing of monitoring data in urban bustling commercial areas and remote suburbs, the same encryption intensity and access control mechanism are applied equally. In bustling commercial areas, where there are dense crowds and frequent commercial activities, a large amount of personal privacy and commercial secret data are involved, and high-strength encryption and fine-grained access control are urgently needed; while in remote suburbs, the data sensitivity is relatively low, and traditional unified protection not only causes resource waste, but also fails to provide sufficient security guarantees for high-sensitivity areas, easily leading to the risk of data leakage. Lack of the ability to respond to real-time dynamic changes. Once the protection parameters are set, it is difficult to flexibly adjust according to sudden emergencies or the evolution of data characteristics. For example, when a large-scale event is held in a certain area, the personnel flow surges and the security level increases in a short period of time. The existing security system cannot automatically strengthen the security protection of the monitoring data in this area, such as timely increasing the encryption level and increasing the storage backup frequency, resulting in higher risks for the data during critical periods.

[0005] 2. Existing technologies tend to evenly allocate storage, computing power, and security protection resources, without differentiating the configuration according to the data risk level. This results in high-value and high-risk data blocks not receiving sufficient resource support at critical moments, and low efficiency in processing complex tasks (such as real-time intelligent analysis and emergency event backtracking); while low-risk data overoccupies resources, causing resource idle waste, and the overall operation cost of the security system increases but the effectiveness is poor. Hidden danger of resource misallocation: Due to the failure to accurately understand the data characteristics, resource misallocation often occurs. For example, valuable high-performance storage media are used to store low-timeliness and low-sensitivity data, while data blocks with extremely high real-time requirements and related to major security events are stored using low-speed storage devices, seriously affecting the data reading and writing speed and emergency handling efficiency, and delaying the security decision-making opportunity. Summary of the Invention

[0006] In view of the above technical deficiencies, the purpose of the present invention is to provide a method for secure storage of video surveillance data based on blockchain technology.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for secure storage of video surveillance data based on blockchain technology, including: Step 1: Analysis of data parameters: Obtain the regional importance parameters, data sensitivity parameters, and data timeliness parameters corresponding to each monitoring device in each region of the target area in the current cycle, and then analyze to obtain the regional importance evaluation values, data sensitivity evaluation values, and data timeliness evaluation values corresponding to each monitoring device in each region.

[0008] Step 2. Obtaining the data security importance coefficient: Based on the regional importance evaluation value, data sensitivity evaluation value, and data timeliness evaluation value corresponding to each monitoring device in each region, analyze to obtain the data security importance coefficient corresponding to each monitoring device in each region. Furthermore, classify the security levels of each monitoring device in each region, and divide each monitoring device corresponding to the same security level in each region into the same security level data block in the corresponding regional node.

[0009] Step 3. Analyzing the data block resource strategy: Obtain the event correlation rate, data complementarity rate, and data volume size corresponding to each security level data block in each regional node in the current period, analyze to obtain the data risk correlation coefficient corresponding to each security level data block in each regional node in the current period, and further analyze the resource allocation strategy corresponding to each security level data block in each regional node in the current period.

[0010] Step 4. Analyzing the regional node encryption scheme: Obtain the data volume ratio corresponding to each security level data block in each regional node in the current period, and further analyze the encryption scheme corresponding to each regional node in the current period.

[0011] Preferably, the regional importance parameters include the crime rate, population density, and the quantity corresponding to each fixed asset value level; the data sensitivity parameters include the proportion of personally identifiable information, the quantity corresponding to each type of privacy information, the quantity corresponding to each level of military facilities, and the quantity corresponding to each type of government agency; the data timeliness parameters include the required duration of event response, the frequency of business activities, and the required frequency of data update.

[0012] Preferably, the specific analysis process for analyzing the regional importance evaluation value, data sensitivity evaluation value, and data timeliness evaluation value corresponding to each monitoring device in each region is as follows: Q1. Input the crime rate, population density, and the quantity corresponding to each fixed asset value level corresponding to each monitoring device in each region into the plant electro-signal evaluation value analysis model, and output the regional importance evaluation value corresponding to each monitoring device in each region.

[0013] Q2. Input the proportion of personally identifiable information, the quantity corresponding to each type of privacy information, the quantity corresponding to each level of military facilities, and the quantity corresponding to each type of government agency corresponding to each monitoring device in each region into the plant electro-signal evaluation value analysis model, and output the data sensitivity evaluation value corresponding to each monitoring device in each region.

[0014] Q3. Input the required duration of event response, the frequency of business activities, and the required frequency of data update corresponding to each monitoring device in each region into the plant electro-signal evaluation value analysis model, and output the data timeliness evaluation value corresponding to each monitoring device in each region.

[0015] Preferably, the security level classification of each monitoring device in each area is as follows: The data security importance coefficient corresponding to each monitoring device in each area is compared with the data security importance coefficient interval corresponding to each security level in the database. If the data security importance coefficient corresponding to a certain monitoring device in a certain area is within the data security importance coefficient interval corresponding to a certain security level in the database, then the security level in the database is recorded as the security level of the monitoring device in that area. In this way, the security level classification of each monitoring device in each area is carried out, and each monitoring device corresponding to the same security level in each area is divided into the same security level data block in the corresponding area node.

[0016] Preferably, the analysis of the resource allocation strategy corresponding to each security level data block in each area node in the current cycle is as follows: W1. Priority sorting based on coefficients: Sort the data risk correlation coefficients corresponding to each security level data block in each area node in the current cycle from high to low. List the security level data blocks in the top 10% of the data risk correlation coefficients in each area node as high priority, allocate more blockchain storage space for the data blocks with high risk correlation coefficients, and preferentially expand the storage capacity of the node where they are located. For the high-risk data blocks in the top 10%, an additional 50% storage quota is added. At the same time, store the high-risk data blocks on a more reliable storage medium. In the computing power allocation of the blockchain node, tilt towards the high-risk data blocks. For the high-risk data blocks, strengthen the security protection measures, increase the fineness of the firewall rules, deploy more advanced intrusion detection systems and intrusion prevention systems, increase the encryption intensity, adopt an encryption algorithm with a longer key length, and at the same time, increase the complexity of key management, update the key regularly, and reduce the risk of key leakage.

[0017] W2. After the screening of the top 10% data blocks with high priority is completed, the security level data blocks in each area node with the data risk correlation coefficient ranking in the interval of 10%-50% are designated as the medium priority category. In the blockchain management system, add medium priority identifiers to these data blocks, and adopt an elastic storage allocation scheme. On the one hand, based on the monitoring and analysis of the data growth trend, reserve 20%-30% of the expandable storage space in advance. In terms of storage medium selection, build a hybrid storage architecture, mainly using high-performance mechanical hard disks, and matching a certain proportion of solid-state drives as the cache layer. At the same time, optimize the garbage collection mechanism of the storage management system. For the medium priority data blocks, regularly clean the space occupied by invalid data, release the storage resources, improve the storage utilization rate, and ensure that the storage system always maintains a healthy state to adapt to its relatively mild risk situation.

[0018] W3. For each regional node in the range where the data risk correlation coefficient ranking is in the bottom 50%, uniformly classify data blocks of each security level as low priority, adopt a conventional storage configuration, select large-capacity and low-cost mechanical hard drives to build a storage cluster, provide storage space to meet their basic data storage needs, regularly inspect the storage devices, perform routine maintenance operations such as disk health checks and data consistency verification, promptly discover and repair potential storage problems, and at the same time, utilize storage virtualization technology to optimize the utilization rate of storage resources.

[0019] Preferably, the process of obtaining the data volume ratio corresponding to each data block of each security level in each regional node in the current period is as follows: For each regional node, calculate the data volume ratio corresponding to each data block of each security level in the current period respectively. The calculation formula is: The data volume ratio corresponding to each data block of each security level in each regional node in the current period is equal to the total data volume corresponding to each data block of each security level in each regional node in the current period divided by the total data volume corresponding to each regional node in the current period.

[0020] Preferably, the process of analyzing the encryption scheme corresponding to each regional node in the current period is as follows: Compare the data volume ratio corresponding to each data block of each security level in each regional node in the current period with the data volume ratio corresponding to each encryption scheme in the database. If the data volume ratio corresponding to a certain data block of a certain security level in a certain regional node in the current period is the same as the data volume ratio corresponding to a certain encryption scheme in the database, then record the encryption scheme in the database as the encryption scheme corresponding to the regional node in the current period. In this way, analyze the encryption scheme corresponding to each regional node in the current period.

[0021] The beneficial effects of the present invention are as follows: 1. In the embodiments of the present invention, through the comprehensive collection and in-depth analysis of regional importance parameters (such as crime rate, population density, fixed asset value, etc.), data sensitivity parameters (covering personal identity information, privacy information, data related to military facilities and government agencies), and data timeliness parameters (combining event response duration, business activity frequency, and data update frequency), key risk and value information hidden behind a large amount of monitoring data is mined. Such meticulous parameter consideration enables the system to accurately locate the unique status of each monitoring device and data block in the overall security situation. Relying on the accurate parameter evaluation results, the traditional "one-size-fits-all" security model is abandoned, and a dedicated security protection strategy is tailored for each data unit. For monitoring devices in high-crime areas that capture a large amount of sensitive information and have extremely high real-time requirements, allocate top-level security resources to comprehensively improve the protection level from encryption intensity to access control; while for relatively low-risk and low-sensitivity devices, adopt appropriate basic protection measures to avoid resource waste and ensure that the security investment matches the actual needs precisely.

[0022] 2. In the embodiment of the present invention, the adjustment factor in the data security importance coefficient is like a sensitive "risk antenna", which can quickly respond according to the real-time and dynamically changing business scenarios. When an emergency occurs in a certain area, such as the outbreak of a mass security incident, the weight of the data timeliness parameter of the monitoring devices in that area increases instantly. The data security importance coefficient is adjusted in real time through the adjustment factor, enabling the system to quickly focus on the emergency processing and priority protection of the data in that area. The continuous dynamic calculation of the data risk correlation coefficient provides accurate "navigation" for resource allocation. With the continuous generation of monitoring data and the change of the business environment, once the data risk correlation coefficient of a certain data block soars due to frequent association with security incidents, the system immediately activates the resource tilting mechanism, from the emergency expansion of storage resources, the priority guarantee of computing power to the enhanced deployment of security protection resources, to ensure that high-risk data blocks are always under high-intensity protection and seamlessly adapt to the real-time fluctuations of security needs. At the same time, the dynamic adjustment mechanism of the encryption scheme based on the data volume ratio ensures that the encryption strategy always conforms to the change of the actual data distribution. If the data volume ratio of a certain security level data block increases significantly due to business expansion at a certain regional node, the system quickly compares the database and switches to a higher-level encryption scheme that matches it to effectively cope with potential data leakage risks and ensure the timeliness and accuracy of encryption protection.

[0023] 3. In the embodiment of the present invention, the present solution builds a clear "hierarchical structure" for resource allocation according to the high, medium, and low priorities divided by the data risk correlation coefficient. This differential resource allocation mode enables various resources such as storage, computing power, and encryption to accurately flow to the places where they are most needed, avoiding the common resource misallocation and idle waste phenomena in traditional security systems. High-risk data blocks obtain sufficient resource guarantees at critical moments and efficiently complete complex data processing and security protection tasks; low-risk data blocks operate at the lowest cost, achieving the maximization of the overall resource utilization efficiency and enhancing the economy and sustainability of the security system.

[0024] 4. In the embodiments of the present invention, from the elaborate design of the hybrid storage architecture (such as the combination of high-performance mechanical hard disks and solid-state drive caches for medium-priority data blocks) to the application of storage virtualization technology (optimizing resource utilization for low-priority data blocks through storage virtualization), and then to the selection of highly reliable storage media (solid-state drive storage for high-priority data blocks), it comprehensively ensures the integrity of data during the storage process, effectively preventing data corruption caused by hardware failures, data loss, etc., and providing a solid data foundation for security decision-making. Through the reasonable allocation of computing power and the optimization of encryption schemes, it ensures that data can be quickly decrypted and efficiently processed when needed, and promptly responds to various query, analysis, and decision-making requirements of security services. For example, in the scenario of emergency event backtracking, high-priority data blocks, with the preferentially allocated computing power and simple and efficient encryption and decryption processes, quickly provide key clues, ensuring that security personnel can obtain effective information in the first time, strongly supporting emergency response operations, and achieving a perfect balance between data availability and security. Brief Description of the Drawings

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0026] Figure 1 It is a flowchart of the implementation steps of the method of the present invention. Detailed Embodiments

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0028] The embodiments of the present invention are as Figure 1 shown. A method for secure storage of video surveillance data based on blockchain technology includes:

[0029] Step 1. Analysis of data parameters: Obtain the area importance parameters, data sensitivity parameters, and data timeliness parameters corresponding to each monitoring device in each area of the target area in the current period, and then analyze to obtain the area importance evaluation values, data sensitivity evaluation values, and data timeliness evaluation values corresponding to each monitoring device in each area.

[0030] In a specific embodiment, the regional importance parameters include crime rate, population density and the number corresponding to each fixed asset value level, the data sensitivity parameters include the proportion of identifiable personal identity information, the number corresponding to each category of privacy information, the number corresponding to each level of military facilities and the number corresponding to each category of government agencies, and the data timeliness parameters include the required time for event response, the frequency of business activities and the frequency of data updates.

[0031] It should be noted that a secure and compliant data connection channel is established with the local public security department's crime information database. Through authorized access, the number, type and distribution of criminal cases in each area of the target area in the current cycle are obtained in real time. Using geographic information system (GIS) technology, the crime data is accurately mapped to the corresponding regional grid, and the crime incidence rate per unit area of each area is obtained. This is used as a key indicator to measure the crime rate in the area. In the area covered by each monitoring device, a human target detection algorithm based on deep learning is used to identify and count people in the video in real time. Combined with the shooting angle, coverage and actual geographical area of the monitoring equipment, the average number of people per unit area is obtained as a personnel density indicator. In conjunction with local industrial and commercial, taxation and real estate management departments, the registration information of enterprises, merchants and various fixed assets in each area of the target area is obtained. Fixed assets of different value levels are visually marked on the map through GIS technology, and the quantity distribution of various fixed assets in each area is statistically analyzed to intuitively present the regional fixed asset value structure, providing key economic dimension data support for measuring regional importance, so that security resource allocation can match regional economic value.

[0032] It should also be noted that the natural language processing (NLP) technology combined with image recognition technology is used to conduct in-depth analysis of the images, audio and related metadata in the surveillance video, and the number of items in the video data that can identify personal identity information is counted and divided by the total number of items in the video data to obtain the proportion of identifiable personal identity information. A professional privacy information classification model is built, and privacy information is subdivided into multiple categories such as medical health, financial finance, and home address in accordance with laws, regulations and industry norms. The surveillance data is comprehensively scanned using data mining algorithms to identify and count the frequency and quantity distribution of various types of privacy information. With the help of a technical means combining high-precision satellite remote sensing image data with a geographic information system (GIS), the target area is comprehensively scanned to count the number of military facilities and agencies of different levels involved in each area.

[0033] Once again, it should be noted that according to the functional characteristics and security risk levels of each region in the target area, combined with the local security emergency plan and industry standards, different event response requirement durations are formulated for different regions. Using big data analysis technology, the historical video data collected by each monitoring device is deeply mined to identify the periodic patterns and frequency characteristics of various business activities in different regions. Combining the technical parameters of the monitoring devices (such as camera resolution, frame rate, etc.), the performance indicators of the storage devices (such as storage capacity, read and write speed, etc.) and the security business requirements, the ideal update frequency of the monitoring data in each region is obtained.

[0034] In another specific embodiment, the analysis obtains the regional importance evaluation value, data sensitivity evaluation value and data timeliness evaluation value corresponding to each monitoring device in each region. The specific analysis process is as follows:

[0035] Q1. Input the crime rate, population density and the quantity corresponding to each fixed asset value level corresponding to each monitoring device in each region into the plant electro-signal evaluation value analysis model, and output the regional importance evaluation value corresponding to each monitoring device in each region.

[0036] It should be noted that the analysis process of the regional importance evaluation value corresponding to each monitoring device in each region is as follows: Normalize the crime rate, population density and the quantity corresponding to each fixed asset value level corresponding to each monitoring device in each region, and denote the processed crime rate, population density and the quantity corresponding to each fixed asset value level corresponding to each monitoring device in each region as 、 and , substitute them into the analysis formula , and obtain the plant electro-signal evaluation value corresponding to each monitoring time point in each monitoring region. Among them, v represents the number corresponding to each region, v is a positive integer, x represents the number corresponding to each monitoring device, x is a positive integer, and k represents the number corresponding to each fixed asset value level, k is a positive integer.

[0037] Q2. Input the proportion of personally identifiable information, the quantity corresponding to each category of privacy information, the quantity corresponding to each level of military facilities and the quantity corresponding to each category of government agencies and institutions corresponding to each monitoring device in each region into the plant electro-signal evaluation value analysis model, and output the data sensitivity evaluation value corresponding to each monitoring device in each region.

[0038] It should be noted that the data sensitivity evaluation value corresponding to each monitoring device in each region is obtained by analyzing according to the above analysis process of the regional importance evaluation value corresponding to each monitoring device in each region.

[0039] Q3. Input the event response requirement duration, business activity frequency, and data update requirement frequency corresponding to each monitoring device in each region into the plant electrical signal evaluation value analysis model, and output the data timeliness evaluation value corresponding to each monitoring device in each region.

[0040] It should be noted that the data timeliness evaluation value corresponding to each monitoring device in each region is obtained through the analysis process of the regional importance evaluation value corresponding to each monitoring device in each region as described above.

[0041] Step 2. Obtaining the data security importance coefficient: According to the regional importance evaluation value, data sensitivity evaluation value, and data timeliness evaluation value corresponding to each monitoring device in each region, analyze to obtain the data security importance coefficient corresponding to each monitoring device in each region. Furthermore, classify the security levels of each monitoring device in each region, and divide the monitoring devices corresponding to the same security level in each region into the same security level data block in the corresponding region node.

[0042] In a specific embodiment, the specific analysis process for analyzing the data security importance coefficient corresponding to each monitoring device in each region is as follows: Denote the regional importance evaluation value, data sensitivity evaluation value, and data timeliness evaluation value corresponding to each monitoring device in each region as 、 and , where v represents the number corresponding to each region, v is a positive integer, and x represents the number corresponding to each monitoring device, x is a positive integer. Substitute into the calculation formula: to obtain the data security importance coefficient corresponding to each monitoring device in each region. Among them, 、 、 are respectively the standard regional importance evaluation value, standard data sensitivity evaluation value, and standard data timeliness evaluation value corresponding to the set monitoring device. 、 、 are respectively the weight factors corresponding to the set monitoring device regional importance evaluation value, data sensitivity evaluation value, and data timeliness evaluation value. 、 、 are respectively the adjustment factors corresponding to the set monitoring device regional importance evaluation value, data sensitivity evaluation value, and data timeliness evaluation value. 、 、 are respectively the permitted monitoring device regional importance evaluation value difference, permitted data sensitivity evaluation value difference, and permitted data timeliness evaluation value difference.

[0043] It should be noted that , , are all greater than 0 and less than 1.

[0044] In a specific embodiment, the security level classification of each monitoring device in each area is as follows: The data security importance coefficient corresponding to each monitoring device in each area is compared with the data security importance coefficient interval corresponding to each security level in the database. If the data security importance coefficient corresponding to a certain monitoring device in a certain area is within the data security importance coefficient interval corresponding to a certain security level in the database, then the security level in the database is recorded as the security level of the monitoring device in that area. In this way, the security level classification of each monitoring device in each area is carried out, and each monitoring device corresponding to the same security level in each area is divided into the same security level data block in the corresponding area node.

[0045] Step 3: Analysis of data block resource strategy: Obtain the event correlation rate, data complementarity rate, and data volume size corresponding to each security level data block in each area node in the current cycle, analyze and obtain the data risk correlation coefficient corresponding to each security level data block in each area node in the current cycle, and then analyze the resource allocation strategy corresponding to each security level data block in each area node in the current cycle.

[0046] It should be noted that, first of all, a security event management platform covering the entire area is built, which can collect and summarize security event log information triggered by various monitoring devices and related security systems (such as intrusion detection systems, access control systems, etc.) in real time. These logs record in detail the time, location, type (such as theft alarm, fire warning, illegal intrusion, etc.) of the event, as well as the key information such as the identification of the monitoring equipment involved. Advanced association analysis algorithms, such as the Apriori algorithm or the improved version of the FP-Growth algorithm, are used to conduct in-depth mining of classified and archived security event logs. Taking a data block of a certain security level in a specific regional node as an example, the algorithm scans the event log to find the combination of events that frequently co-occur within a certain time window (such as the past hour, day, etc., which can be flexibly set according to business needs). The support of these frequent item sets is calculated, that is, the proportion of the combination of events that appear at the same time in the total number of events, which is used as a preliminary indicator to measure the degree of event association. Based on the data complementary relationship sorted out, special data fusion tools and algorithms are developed. Data blocks with complementary potential are paired and fused, and the data complementarity rate is quantified by comparing the effective information gain that can be provided before and after fusion. Specifically, before fusion, the number of key information points that each single data block can independently provide is counted, such as the number of people in a specific area, the characteristics of objects, etc.; after fusion, the additional valid information points that the newly generated comprehensive data can reveal are counted again, such as the interactive relationship between people, the continuity of the movement trajectory of objects, etc. The calculation formula is: data complementarity rate = (number of valid information points after fusion - the sum of valid information points of each data block before fusion) / the sum of valid information points of each data block before fusion, directly using the metadata management function of the blockchain storage system itself, which usually records the detailed storage information of each data block, including the number of bytes of the data block, storage address, creation time and other key metadata. By writing targeted query scripts or calling the API interface provided by the system, the storage capacity of each data block can be accurately retrieved according to the dimensions of regional nodes and security levels.

[0047] In a specific embodiment, the analysis obtains the data risk correlation coefficient corresponding to each security level data block in each regional node in the current period. The specific analysis process is as follows: the event correlation rate, data complementarity rate and data volume corresponding to each security level data block in each regional node are respectively recorded as , and , g represents the number corresponding to each regional node, g is a positive integer, h represents the number corresponding to each security level data block, h is a positive integer, substitute into the calculation formula: The data risk correlation coefficient corresponding to each security level data block in each regional node in the current period is obtained ,in, , , The standard event correlation rate, standard data complementarity rate, and standard data volume size corresponding to the data blocks of each set security level, , , The weight factors corresponding to the event correlation rate of the data blocks of each set security level, the weight factors corresponding to the data complementarity rate, and the weight factors corresponding to the data volume size, is the natural constant.

[0048] It should be noted that, , , are all greater than 0 and less than 1.

[0049] In another specific embodiment, the analysis of the resource allocation strategy corresponding to the data blocks of each security level in each regional node of the current cycle is as follows: W1. Priority sorting based on coefficients: Sort the data risk correlation coefficients corresponding to the data blocks of each security level in each regional node of the current cycle from high to low. List the data blocks of each security level in the regional nodes with the top 10% of the data risk correlation coefficients as high priority. Allocate more blockchain storage space for the data blocks with high risk correlation coefficients, and preferentially expand the storage capacity of the nodes where they are located. For the high-risk data blocks ranked in the top 10%, an additional 50% storage quota is added. At the same time, store the high-risk data blocks on a more reliable storage medium. In the computing power allocation of the blockchain nodes, tilt towards the high-risk data blocks. For high-risk data blocks, strengthen security protection measures, increase the fineness of firewall rules, deploy more advanced intrusion detection systems and intrusion prevention systems, increase the encryption intensity, use encryption algorithms with longer key lengths, and at the same time, increase the complexity of key management and update keys regularly to reduce the risk of key leakage.

[0050] W2. After screening the top 10% of the high-priority data blocks, classify the data blocks of each security level in each regional node with the next data risk correlation coefficients ranked in the range of 10%-50% as the medium-priority category. Add medium-priority identifiers to these data blocks in the blockchain management system. Adopt an elastic storage allocation scheme. On the one hand, based on the monitoring and analysis of the data growth trend, reserve 20%-30% of the expandable storage space in advance. In terms of storage medium selection, build a hybrid storage architecture, mainly using high-performance mechanical hard disks, and matching a certain proportion of solid-state drives as the cache layer. At the same time, optimize the garbage collection mechanism of the storage management system. For medium-priority data blocks, regularly clean up the space occupied by invalid data, release storage resources, improve storage utilization, and ensure that the storage system always maintains a healthy state to adapt to its relatively mild risk situation.

[0051] W3. For each regional node in the range where the data risk correlation coefficient ranking is in the lower 50%, uniformly classify data blocks of each security level as low priority, adopt a conventional storage configuration, select large-capacity and low-cost mechanical hard disks to build a storage cluster, provide storage space to meet its basic data storage needs, regularly inspect the storage devices, perform routine maintenance operations such as disk health checks and data consistency verification, promptly discover and repair potential storage problems. At the same time, utilize storage virtualization technology to optimize the utilization rate of storage resources.

[0052] IV. Analysis of the regional node encryption scheme: Obtain the data volume ratios corresponding to data blocks of each security level in each regional node in the current period, and then analyze the encryption scheme corresponding to each regional node in the current period.

[0053] In a specific embodiment, the process of obtaining the data volume ratios corresponding to data blocks of each security level in each regional node in the current period is as follows: For each regional node, calculate the data volume ratios corresponding to data blocks of each security level in the current period respectively. The calculation formula is: The data volume ratio corresponding to data blocks of each security level in each regional node in the current period is equal to the total data volume corresponding to data blocks of each security level in each regional node in the current period divided by the total data volume corresponding to each regional node in the current period.

[0054] In another specific embodiment, the process of analyzing the encryption scheme corresponding to each regional node in the current period is as follows: Compare the data volume ratios corresponding to data blocks of each security level in each regional node in the current period with the data volume ratios corresponding to each encryption scheme in the database. If the data volume ratio corresponding to a certain security level data block in a certain regional node in the current period is the same as the data volume ratio corresponding to a certain encryption scheme in the database, then record the encryption scheme in the database as the encryption scheme corresponding to the regional node in the current period. In this way, analyze the encryption scheme corresponding to each regional node in the current period.

[0055] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the concept of the invention or exceed the scope defined by this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for secure storage of video surveillance data based on blockchain technology, characterized in that, Including: Step 1, analysis of data parameters: Obtain the regional importance parameters, data sensitivity parameters, and data timeliness parameters corresponding to each monitoring device in each region of the target area in the current period, and then analyze to obtain the regional importance evaluation values, data sensitivity evaluation values, and data timeliness evaluation values corresponding to each monitoring device in each region; Step 2, obtaining the data security importance coefficient: According to the regional importance evaluation values, data sensitivity evaluation values, and data timeliness evaluation values corresponding to each monitoring device in each region, analyze to obtain the data security importance coefficients corresponding to each monitoring device in each region, and then classify the security levels of each monitoring device in each region, and divide the monitoring devices corresponding to the same security level in each region into the same security level data blocks in the corresponding regional nodes; Step 3, analysis of data block resource strategies: Obtain the event correlation rate, data complementarity rate, and data volume size corresponding to each security level data block in each regional node in the current period, analyze to obtain the data risk correlation coefficients corresponding to each security level data block in each regional node in the current period, and then analyze the resource allocation strategies corresponding to each security level data block in each regional node in the current period; The process of analyzing to obtain the data risk correlation coefficients corresponding to each security level data block in each regional node in the current period is as follows: Denote the event correlation rate, data complementarity rate, and data volume size corresponding to each data block of each security level in each regional node as , and respectively. Let g represent the number corresponding to each regional node, where g is a positive integer, and h represent the number corresponding to each data block of each security level, where h is a positive integer. Substitute them into the calculation formula: to obtain the data risk correlation coefficient corresponding to each data block of each security level in each regional node in the current period. Among them, , , are the standard event correlation rate, standard data complementarity rate, and standard data volume size corresponding to each data block of each security level set respectively, , , are the weight factors corresponding to the event correlation rate, data complementarity rate, and data volume size of each data block of each security level set respectively, is the natural constant; Pair and fuse the data blocks with complementary potential, and quantify the data complementarity rate by comparing the effective information gain provided before and after fusion; Data complementarity rate = (number of effective information points after fusion - sum of effective information points of each data block before fusion) / sum of effective information points of each data block before fusion; Step 4, analysis of regional node encryption schemes: Obtain the data volume ratio corresponding to each security level data block in each regional node in the current period, and then analyze the encryption schemes corresponding to each regional node in the current period.

2. The method for securely storing video surveillance data based on blockchain technology according to claim 1, wherein The regional importance parameters include the crime rate, population density, and the quantity corresponding to each fixed asset value level. The data sensitivity parameters include the proportion of personally identifiable information, the quantity corresponding to various types of privacy information, the quantity corresponding to military facilities at each level, and the quantity corresponding to various types of government agencies. The data timeliness parameters include the required duration of event response, the frequency of business activities, and the frequency of data update requirements.

3. The method for secure storage of video surveillance data based on blockchain technology according to claim 2, wherein, The process of analyzing to obtain the regional importance evaluation values, data sensitivity evaluation values, and data timeliness evaluation values corresponding to each monitoring device in each region is as follows: Q1. Input the crime rate, population density, and the quantity corresponding to each fixed asset value level corresponding to each monitoring device in each region into the plant electrical signal evaluation value analysis model, and output the regional importance evaluation values corresponding to each monitoring device in each region; The analysis process of the regional importance evaluation value corresponding to each monitoring device in each region is as follows: normalize the crime rate, population density, and the quantity corresponding to each fixed asset value level corresponding to each monitoring device in each region, and record the processed crime rate, population density, and the quantity corresponding to each fixed asset value level corresponding to each monitoring device in each region as , and , substitute them into the analysis formula , and obtain the plant electrical signal evaluation value corresponding to each monitoring time point and each monitoring region. Among them, v represents the number corresponding to each region, v is a positive integer, x represents the number corresponding to each monitoring device, x is a positive integer, and k represents the number corresponding to each fixed asset value level, k is a positive integer; Q2. Input the proportion of personally identifiable information, the quantity corresponding to various types of privacy information, the quantity corresponding to military facilities at each level, and the quantity corresponding to various types of government agencies corresponding to each monitoring device in each region into the plant electrical signal evaluation value analysis model, and output the data sensitivity evaluation values corresponding to each monitoring device in each region; Q3. Input the event response requirement duration, business activity frequency, and data update requirement frequency corresponding to each monitoring device in each area into the plant electro-signal evaluation value analysis model, and output the data timeliness evaluation value corresponding to each monitoring device in each area.

4. The method for secure storage of video surveillance data based on blockchain technology according to claim 3, characterized in that, The data security importance coefficient corresponding to each monitoring device in each area is obtained through the following specific analysis process: Denote the area importance evaluation value, data sensitivity evaluation value, and data timeliness evaluation value corresponding to each monitoring device in each area as , and respectively. Let v represent the number corresponding to each area, where v is a positive integer, and let x represent the number corresponding to each monitoring device, where x is a positive integer. Substitute them into the calculation formula: to obtain the data security importance coefficient corresponding to each monitoring device in each area. Among them, , , are the set standard area importance evaluation value, standard data sensitivity evaluation value, and standard data timeliness evaluation value corresponding to the monitoring device respectively. , , are the weight factors corresponding to the set area importance evaluation value, data sensitivity evaluation value, and data timeliness evaluation value of the monitoring device respectively. , , are the adjustment factors corresponding to the set area importance evaluation value, data sensitivity evaluation value, and data timeliness evaluation value of the monitoring device respectively. , , are the set permitted differences in area importance evaluation value, data sensitivity evaluation value, and data timeliness evaluation value of the monitoring device respectively.

5. The method for secure storage of video surveillance data based on blockchain technology according to claim 4, characterized in that, The security level classification of each monitoring device in each area is carried out through the following specific classification process: Compare the data security importance coefficient corresponding to each monitoring device in each area with the data security importance coefficient interval corresponding to each security level in the database. If the data security importance coefficient corresponding to a certain monitoring device in a certain area is within the data security importance coefficient interval corresponding to a certain security level in the database, then record the security level in the database as the security level of the monitoring device in this area. In this way, the security level classification of each monitoring device in each area is carried out, and the monitoring devices corresponding to the same security level in each area are divided into the same security level data block in the corresponding area node.

6. The method for securely storing video surveillance data based on blockchain technology according to claim 1, wherein, The analysis of the resource allocation strategy corresponding to each security level data block in each area node in the current cycle is carried out through the following specific analysis process: W1. Priority sorting based on coefficients: Sort the data risk correlation coefficients corresponding to each security level data block in each area node in the current cycle from high to low. List the security level data blocks with the top 10% data risk correlation coefficients in each area node as high priority. Allocate more blockchain storage space for the data blocks with high risk correlation coefficients, and give priority to expanding the storage capacity of the node where they are located. For the top 10% high-risk data blocks, an additional 50% storage quota is added. At the same time, store the high-risk data blocks on a more reliable storage medium. In the computing power allocation of the blockchain node, tilt towards the high-risk data blocks. For high-risk data blocks, strengthen security protection measures, increase the fineness of firewall rules, deploy more advanced intrusion detection systems and intrusion prevention systems, increase the encryption intensity, use encryption algorithms with longer key lengths, and at the same time, increase the complexity of key management, update keys regularly, and reduce the risk of key leakage; W2. After completing the screening of the top 10% high-priority data blocks, classify the security level data blocks with the next data risk correlation coefficients ranked in the 10%-50% interval in each area node as the medium-priority category. In the blockchain management system, add medium-priority identifiers to these data blocks. Adopt an elastic storage allocation scheme. On the one hand, based on the monitoring and analysis of the data growth trend, reserve 20%-30% of the expandable storage space in advance. In terms of storage medium selection, build a hybrid storage architecture, mainly using high-performance mechanical hard disks, and matching a certain proportion of solid-state drives as the cache layer. At the same time, optimize the garbage collection mechanism of the storage management system. For medium-priority data blocks, regularly clean up the space occupied by invalid data, release storage resources, improve storage utilization, and ensure that the storage system always maintains a healthy state to adapt to its relatively mild risk situation; W3. Uniformly classify the data blocks of each security level in the regional nodes within the range of the last 50% in the ranking of data risk correlation coefficients as low priority. Adopt a conventional storage configuration, select large-capacity and low-cost mechanical hard disks to build a storage cluster, provide a storage space that meets its basic data storage requirements, regularly inspect the storage devices, perform routine maintenance operations such as disk health checks and data consistency verification, promptly discover and repair potential storage problems, and at the same time, utilize storage virtualization technology to optimize the utilization rate of storage resources.

7. The method for secure storage of video surveillance data based on blockchain technology according to claim 6, characterized in that, The specific process of obtaining the data volume ratios of the data blocks of each security level in each regional node in the current period is as follows: For each regional node, calculate the data volume ratios of the data blocks of each security level in each regional node in the current period respectively. The calculation formula is: the data volume ratio of the data blocks of each security level in each regional node in the current period is equal to the total data volume corresponding to the data blocks of each security level in each regional node in the current period divided by the total data volume corresponding to each regional node in the current period.

8. The method for secure storage of video surveillance data based on blockchain technology according to claim 7, characterized in that, The specific analysis process of analyzing the encryption schemes corresponding to each regional node in the current period is as follows: Compare the data volume ratios of the data blocks of each security level in each regional node in the current period with the data volume ratios of each encryption scheme in the database. If the data volume ratio of the data blocks of a certain security level in a certain regional node in the current period is the same as the data volume ratio of a certain encryption scheme in the database, then record the encryption scheme in the database as the encryption scheme corresponding to the regional node in the current period. In this way, analyze the encryption schemes corresponding to each regional node in the current period.

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