Video monitoring data security storage method based on block chain technology

Abstract

The invention discloses a video monitoring data security storage method based on a block chain technology, and relates to the technical field of video monitoring data storage, according to the method, from the elaborate design of a hybrid storage architecture to the application of a storage virtualization technology and then to the selection of a high-reliability storage medium, the integrity of data in a storage link is ensured in an all-around manner, and the storage efficiency is improved. Data incompleteness caused by hardware faults, data loss and the like is effectively prevented, and a solid data basis is provided for security decision. Through reasonable allocation of computing power and optimization of an encryption scheme, it is guaranteed that data can be quickly de-encrypted and efficiently processed when needed, and various query, analysis and decision-making requirements of security and protection services are responded in time. And the high-priority data blocks quickly provide key clues by virtue of preferentially distributed computing power and a simple and efficient encryption and decryption process, so that security personnel can obtain effective information in the first time, emergency disposal actions are powerfully supported, and perfect balance between data availability and security is realized.

Description

Technical Field

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

[0002] In today's era of digitalization sweeping the world, video surveillance systems have become an indispensable key infrastructure in many fields such as maintaining social security, ensuring production operations, and assisting urban management. From the streets and alleys of bustling cities and commercial centers to the parks and factories of various enterprises and institutions, as well as transportation hubs and residential areas, surveillance cameras are like sharp "electronic eyes", capturing massive video data 24 hours a day, providing an intuitive and powerful basis for security decision-making, event tracing, and daily management. Therefore, a video surveillance data security storage method based on blockchain technology has emerged.

[0003] Prior art, such as the invention application patent with announcement number: CN114301904A, discloses a monitoring method, device, monitoring system and readable storage medium for a big data cluster, wherein the method includes: obtaining the operating data of each node in the big data cluster, and analyzing the operating data of each node to determine whether there is an abnormal node in each node; if there is an abnormal node in each node, 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 operating data of each abnormal node. The present invention analyzes the abnormal nodes with potential faults from the operating data of each node, and performs abnormal monitoring on the big data cluster according to its abnormal level and operating data, and performs operation and maintenance processing on the abnormal nodes in a timely manner, which is conducive to the effective operation of the big data cluster.

[0004] In view of the above scheme, the inventor of the present application 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, which does not fully consider the uniqueness of the data involved in different areas and monitoring equipment. In the processing of monitoring data in the bustling commercial areas of the city and the remote suburbs, the same encryption strength and access control mechanism are used equally. The bustling commercial areas are densely populated and commercial activities are frequent, involving a large amount of personal privacy and commercial confidential data, which urgently need high-intensity encryption and fine access control; while the data sensitivity in the remote suburbs is relatively low, and the traditional unified protection not only causes a waste of resources, but also fails to provide sufficient security for highly sensitive areas, which is easy to cause data leakage risks. 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 emergency events or data characteristics. For example, if a large-scale event is held in a certain area, the flow of people will increase sharply in a short period of time and the security level will be increased. The existing security system cannot automatically strengthen the security protection of the monitoring data in the area, such as timely improving the encryption level, increasing the storage backup frequency, etc., resulting in higher risks for data during critical periods.

[0005] 2. Existing technologies tend to evenly distribute storage, computing power, and security protection resources, without making differentiated configurations based on data risk levels. This results in high-value, high-risk data blocks not receiving sufficient resource support at critical moments, and inefficiency in processing complex tasks (such as real-time intelligent analysis and emergency incident backtracking); low-risk data excessively occupies resources, resulting in idle and wasted resources, and the overall security system operating costs are increased but the results are poor. Hidden dangers of resource mismatch: Due to the failure to accurately understand data characteristics, resource mismatch often occurs. For example, valuable high-performance storage media are used to store low-timeliness, low-sensitivity data, while data blocks with extremely high real-time requirements and related to major security incidents use low-speed storage devices, which seriously affects data reading and writing speeds and emergency processing efficiency, and delays the timing of security decision-making. Summary of the invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a video surveillance data secure storage method 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: obtaining regional importance parameters, data sensitivity parameters and data timeliness parameters corresponding to each monitoring device in each area of ​​the target area of ​​the current cycle, and then analyzing to obtain the regional importance assessment value, data sensitivity assessment value and data timeliness assessment value corresponding to each monitoring device in each area.

[0008] Step 2. Obtaining the data security importance coefficient: According to the regional importance assessment value, data sensitivity assessment value and data timeliness assessment value corresponding to each monitoring device in each area, the data security importance coefficient corresponding to each monitoring device in each area is analyzed, and then the security level of each monitoring device in each area is classified, 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.

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

[0010] 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 scheme corresponding to each regional node in the current period.

[0011] Preferably, 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 required for data updates.

[0012] Preferably, the analysis obtains the regional importance assessment value, data sensitivity assessment value and data timeliness assessment value corresponding to each monitoring device in each area. The specific analysis process is as follows: Q1. The crime rate, population density and the number corresponding to each fixed asset value level corresponding to each monitoring device in each area are input into the plant electrical signal assessment value analysis model, and the regional importance assessment value corresponding to each monitoring device in each area is output.

[0013] Q2. Input the proportion of personally identifiable information corresponding to each monitoring device in each area, the number of each category of privacy information, the number of each level of military facilities, and the number of each category of government agencies into the plant electrical signal evaluation value analysis model, and output the data sensitivity evaluation value corresponding to each monitoring device in each area.

[0014] Q3. Input the event response requirement time, business activity frequency and data update frequency corresponding to each monitoring device in each area into the plant electrical signal evaluation value analysis model, and output the data timeliness evaluation value corresponding to each monitoring device in each area.

[0015] Preferably, the security level classification of each monitoring device in each area is performed, and the specific classification process 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 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 the area. In this way, the security level of each monitoring device in each area is classified, 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.

[0016] Preferably, the resource allocation strategy corresponding to each security level data block in each regional node in the current period is analyzed, and the specific analysis process is as follows: W1. Priority sorting based on coefficients: The data risk correlation coefficients corresponding to each security level data block in each regional node in the current period are sorted from high to low, and the data blocks of each security level in each regional node with the top 10% of the data risk correlation coefficient are listed as high priority, and more blockchain storage space is allocated to the data blocks with high risk correlation coefficients, and the storage capacity of the node where they are located is preferentially expanded. For the top 10% of high-risk data blocks, an additional 50% of the storage quota is added. At the same time, the high-risk data blocks are stored on storage media with higher reliability. In the allocation of computing power of blockchain nodes, the high-risk data blocks are tilted. For high-risk data blocks, security protection measures are strengthened, the sophistication of firewall rules is increased, more advanced intrusion detection systems and intrusion prevention systems are deployed, encryption strength is increased, and encryption algorithms with longer key lengths are adopted. At the same time, the complexity of key management is increased, keys are updated regularly, and the risk of key leakage is reduced.

[0017] W2. After completing the screening of the top 10% of high-priority data blocks, the data blocks of each security level in each regional node with the next data risk correlation coefficient ranking in the range of 10%-50% are classified as medium priority. In the blockchain management system, these data blocks are marked with medium priority and a flexible storage allocation plan is adopted. On the one hand, based on the monitoring and analysis of data growth trends, 20%-30% of expandable storage space is reserved in advance. In terms of storage media selection, a hybrid storage architecture is constructed, with high-performance mechanical hard disks as the main and a certain proportion of solid-state hard disks as the cache layer. At the same time, the garbage collection mechanism of the storage management system is optimized. For medium-priority data blocks, the space occupied by invalid data is regularly cleaned up to release storage resources, improve storage utilization, and ensure that the storage system always remains healthy to adapt to its relatively mild risk status.

[0018] W3. All data blocks of various security levels in the nodes of each region whose data risk correlation coefficient ranks in the bottom 50% are uniformly classified as low priority. Conventional storage configuration is adopted, and large-capacity, low-cost mechanical hard disks are selected to build a storage cluster to provide storage space that meets their basic data storage needs. Storage devices are inspected regularly, and routine maintenance operations such as disk health checks and data consistency checks are performed to promptly discover and repair potential storage problems. At the same time, storage virtualization technology is used to optimize storage resource utilization.

[0019] Preferably, the data volume ratio corresponding to each security level data block in each regional node in the current period is obtained, and the specific acquisition process is as follows: for each regional node, the data volume ratio corresponding to each security level data block in each regional node in the current period is calculated respectively, and the calculation formula is: the data volume ratio corresponding to each security level data block in each regional node in the current period is equal to the total data volume corresponding to each security level data block 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 encryption scheme corresponding to each regional node in the current period is analyzed, and the specific analysis process is as follows: the data volume ratio corresponding to each security level data block in each regional node in the current period is compared with the data volume ratio corresponding to each encryption scheme in the database. If the data volume ratio corresponding to a certain security level data block in a regional node in the current period is the same as the data volume ratio corresponding to a certain encryption scheme in the database, then the encryption scheme in the database is recorded as the encryption scheme corresponding to the regional node in the current period. In this way, the encryption scheme corresponding to each regional node in the current period is analyzed.

[0021] The beneficial effects of the present invention are as follows: 1. 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, military facilities and related data of government agencies), and data timeliness parameters (combined with event response time, business activity frequency and data update frequency), dig out the key risks and value information hidden behind the massive monitoring data. This meticulous parameter consideration enables the system to accurately locate the unique position of each monitoring device and data block in the overall security situation. Relying on accurate parameter evaluation results, abandon the traditional security "one size fits all" model, and tailor a unique security protection strategy for each data unit. For monitoring equipment in high crime rate areas that capture a large amount of sensitive information and have extremely high real-time requirements, top-level security resources are deployed to comprehensively improve the protection level from encryption strength to access control; for relatively low-risk and low-sensitivity equipment, adaptive basic protection measures are adopted to avoid resource waste and ensure that security investment is accurately matched with actual needs.

[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 respond quickly according to the real-time and dynamically changing business scenarios. When an emergency occurs in a certain area, such as a mass security incident, the weight of the data timeliness parameter of the monitoring equipment in the area is instantly increased. The data security importance coefficient is adjusted in real time through the adjustment factor, so that the system can quickly focus on the emergency processing and priority protection of the data in the 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 changes in the business environment, once a data block has a surge in its data risk correlation coefficient due to frequent associated security incidents, the system immediately starts the resource tilt 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 in a high-intensity protection state in all aspects, 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 fits the changes in the actual distribution of data. If the proportion of data volume in a data block of a certain security level increases significantly due to business expansion at a regional node, the system will quickly compare the database and switch to a more advanced encryption solution that matches it, effectively responding to potential data leakage risks and ensuring the timeliness and accuracy of encryption protection.

[0023] 3. In the embodiment of the present invention, this scheme builds a clear "hierarchical architecture" for resource allocation based on the high, medium and low priorities divided by the data risk correlation coefficient. This differentiated resource allocation model allows various types of resources such as storage, computing power, encryption, etc. to flow accurately to where they are most needed, avoiding the common resource mismatch and idle waste 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 maintain operation at the lowest cost, maximizing the overall resource utilization efficiency and improving the economy and sustainability of the security system.

[0024] 4. The embodiments of the present invention, from the careful design of the hybrid storage architecture (such as the combination of high-performance mechanical hard disk + solid-state hard disk cache for medium-priority data blocks) to the application of storage virtualization technology (optimizing resource utilization of low-priority data blocks through storage virtualization), to the selection of high-reliability storage media (solid-state hard disk storage for high-priority data blocks), comprehensively ensure the integrity of data in the storage link, effectively prevent data incompleteness caused by hardware failures, data loss and other problems, and provide a solid data foundation for security decision-making. Through the reasonable allocation of computing power and the optimization of encryption schemes, it is ensured that data can be quickly deencrypted and efficiently processed when needed, and timely respond to various query, analysis and decision-making needs of security services. For example, in the emergency backtracking scenario, high-priority data blocks can quickly provide key clues with the priority allocation of computing power and simple and efficient encryption and decryption processes, ensuring that security personnel can obtain effective information at the first time, effectively supporting emergency response actions, and achieving a perfect balance between data availability and security. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 The present invention is a flowchart of the steps for implementing the method. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0028] Embodiments of the present invention Figure 1 As shown, the video surveillance data security storage method based on blockchain technology includes:

[0029] 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 area of ​​the target area in the current cycle, and then analyze and obtain the regional importance assessment value, data sensitivity assessment value and data timeliness assessment value 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 value structure of regional fixed assets, 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, and a professional privacy information classification model is built. According to laws, regulations and industry norms, privacy information is subdivided into multiple categories such as medical health, financial finance, and home address. 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] It is necessary to explain again that, based on the functional characteristics and security risk levels of each area in the target area, combined with local security emergency plans and industry standards, differentiated event response requirements are formulated for different areas. Big data analysis technology is used to deeply mine the historical video data collected by each monitoring device, identify the periodic laws and frequency characteristics of various business activities in different regions, and combine the technical parameters of the monitoring equipment (such as camera resolution, frame rate, etc.), the performance indicators of the storage device (such as storage capacity, read and write speeds, etc.) and security business needs to derive the ideal update frequency of monitoring data in each area.

[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, and the specific analysis process is as follows:

[0035] Q1. Input the crime rate, population density and number of fixed asset value levels corresponding to each monitoring device in each area into the plant electrical signal evaluation value analysis model, and output the regional importance evaluation value corresponding to each monitoring device in each area.

[0036] It should be noted that the analysis process of the regional importance evaluation value corresponding to each monitoring device in each area is as follows: the crime rate, personnel density and the number of fixed asset value levels corresponding to each monitoring device in each area are normalized, and the crime rate, personnel density and the number of fixed asset value levels corresponding to each monitoring device in each area after processing are recorded as and Substitute into the analysis formula The plant electrical signal evaluation value corresponding to each monitoring area collected at each monitoring time point is obtained, where v represents the number corresponding to each area, 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 corresponding to each monitoring device in each area, the number of each category of privacy information, the number of each level of military facilities, and the number of each category of government agencies into the plant electrical signal evaluation value analysis model, and output the data sensitivity evaluation value corresponding to each monitoring device in each area.

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

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

[0040] It should be noted that the data timeliness evaluation value corresponding to each monitoring device in each area is obtained by analyzing according to the analysis process of the regional importance evaluation value corresponding to each monitoring device in each area mentioned above.

[0041] Step 2. Obtaining the data security importance coefficient: According to the regional importance assessment value, data sensitivity assessment value and data timeliness assessment value corresponding to each monitoring device in each area, the data security importance coefficient corresponding to each monitoring device in each area is analyzed, and then the security level of each monitoring device in each area is classified, 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.

[0042] In a specific embodiment, the analysis obtains the data security importance coefficient corresponding to each monitoring device in each area. The specific analysis process is as follows: the regional importance evaluation value, data sensitivity evaluation value and data timeliness evaluation value corresponding to each monitoring device in each area are respectively recorded as and v represents the number corresponding to each area, v is a positive integer, x represents the number corresponding to each monitoring device, x is a positive integer, substitute into the calculation formula:

[0043]

[0044] Data security importance coefficient corresponding to each monitoring device in each area Among them, Z′, S′, and F′ are the standard regional importance assessment value, standard data sensitivity assessment value, and standard data timeliness assessment value corresponding to the set monitoring equipment, respectively; Ω1, Ω2, and Ω3 are the weight factors corresponding to the set monitoring equipment regional importance assessment value, the weight factors corresponding to the data sensitivity assessment value, and the weight factors corresponding to the data timeliness assessment value, respectively; Ψ1, Ψ2, and Ψ3 are the adjustment factors corresponding to the set monitoring equipment regional importance assessment value, the adjustment factors corresponding to the data sensitivity assessment value, and the adjustment factors corresponding to the data timeliness assessment value, respectively; ΔZ, ΔS, and ΔF are the set permitted monitoring equipment regional importance assessment value differences, permitted data sensitivity assessment value differences, and permitted data timeliness assessment value differences, respectively.

[0045] It should be noted that Ω1, Ω2, and Ω3 are all greater than 0 and less than 1.

[0046] 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 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 the area. In this way, the security level of each monitoring device in each area is classified, 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.

[0047] Step 3: Analysis of data block resource strategy: Obtain the event correlation rate, data complementarity rate and data volume corresponding to each security level data block in each regional node in the current period, analyze and obtain the data risk correlation coefficient corresponding to each security level data block in each regional node in the current period, and then analyze the resource allocation strategy corresponding to each security level data block in each regional node in the current period.

[0048] It should be noted that, first of all, a security event management platform covering the entire region is constructed, 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, a special data fusion tool and algorithm 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.

[0049] 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:

[0050]

[0051] The data risk correlation coefficient corresponding to each security level data block in each regional node in the current period is obtained

[0052] Among them, E′, G′, and H′ are respectively the standard event association rate, standard data complementarity rate, and standard data size corresponding to the set data blocks of each security level, Ξ1, Ξ2, and Ξ3 are respectively the weight factors corresponding to the event association rate, data complementarity rate, and data size of the set data blocks of each security level, and π is a natural constant.

[0053] It should be noted that Ξ1, Ξ2, and Ξ3 are all greater than 0 and less than 1.

[0054] In another specific embodiment, the resource allocation strategy corresponding to each security level data block in each regional node in the current period is analyzed, and the specific analysis process is as follows: W1. Priority sorting based on coefficients: The data risk correlation coefficients corresponding to each security level data block in each regional node in the current period are sorted from high to low, and the data risk correlation coefficients of each security level data block in each regional node ranked in the top 10% are listed as high priority, and more blockchain storage space is allocated to the data blocks with high risk correlation coefficients, and the storage capacity of the node where they are located is preferentially expanded. For the top 10% of high-risk data blocks, an additional 50% storage quota is added. At the same time, the high-risk data blocks are stored on storage media with higher reliability. In the allocation of computing power of blockchain nodes, the high-risk data blocks are tilted. For high-risk data blocks, security protection measures are strengthened, the sophistication of firewall rules is increased, more advanced intrusion detection systems and intrusion prevention systems are deployed, encryption strength is increased, and encryption algorithms with longer key lengths are adopted. At the same time, the complexity of key management is increased, keys are updated regularly, and the risk of key leakage is reduced.

[0055] W2. After completing the screening of the top 10% of high-priority data blocks, the data blocks of each security level in each regional node with the next data risk correlation coefficient ranking in the range of 10%-50% are classified as medium priority. In the blockchain management system, these data blocks are marked with medium priority and a flexible storage allocation plan is adopted. On the one hand, based on the monitoring and analysis of data growth trends, 20%-30% of expandable storage space is reserved in advance. In terms of storage media selection, a hybrid storage architecture is constructed, with high-performance mechanical hard disks as the main and a certain proportion of solid-state hard disks as the cache layer. At the same time, the garbage collection mechanism of the storage management system is optimized. For medium-priority data blocks, the space occupied by invalid data is regularly cleaned up to release storage resources, improve storage utilization, and ensure that the storage system always remains healthy to adapt to its relatively mild risk status.

[0056] W3. All data blocks of various security levels in the nodes of each region whose data risk correlation coefficient ranks in the bottom 50% are uniformly classified as low priority. Conventional storage configuration is adopted, and large-capacity, low-cost mechanical hard disks are selected to build a storage cluster to provide storage space that meets their basic data storage needs. Storage devices are inspected regularly, and routine maintenance operations such as disk health checks and data consistency checks are performed to promptly discover and repair potential storage problems. At the same time, storage virtualization technology is used to optimize storage resource utilization.

[0057] 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 scheme corresponding to each regional node in the current period.

[0058] In a specific embodiment, the data volume ratio corresponding to each security level data block in each regional node in the current period is obtained, and the specific acquisition process is as follows: for each regional node, the data volume ratio corresponding to each security level data block in each regional node in the current period is calculated respectively, and the calculation formula is: the data volume ratio corresponding to each security level data block in each regional node in the current period is equal to the total data volume corresponding to each security level data block in each regional node in the current period divided by the total data volume corresponding to each regional node in the current period.

[0059] In another specific embodiment, the encryption scheme corresponding to each regional node in the current period is analyzed, and the specific analysis process is as follows: the data volume ratio corresponding to each security level data block in each regional node in the current period is compared with the data volume ratio corresponding to each encryption scheme in the database. If the data volume ratio corresponding to a certain security level data block in a regional node in the current period is the same as the data volume ratio corresponding to a certain encryption scheme in the database, then the encryption scheme in the database is recorded as the encryption scheme corresponding to the regional node in the current period. In this way, the encryption scheme corresponding to each regional node in the current period is analyzed.

[0060] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A video surveillance data security storage method based on blockchain technology, characterized in that: include: 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 area of ​​the target area in the current cycle, and then analyze and obtain the regional importance assessment value, data sensitivity assessment value and data timeliness assessment value corresponding to each monitoring device in each area; Step 2: Obtaining the data security importance coefficient: According to the regional importance assessment value, data sensitivity assessment value and data timeliness assessment value corresponding to each monitoring device in each area, the data security importance coefficient corresponding to each monitoring device in each area is analyzed and obtained, and then the security level of each monitoring device in each area is classified, 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 regional node; Step 3: Analysis of data block resource strategy: Obtain the event correlation rate, data complementarity rate and data volume corresponding to each security level data block in each regional node in the current cycle, analyze and obtain the data risk correlation coefficient corresponding to each security level data block in each regional node in the current cycle, and then analyze the resource allocation strategy corresponding to each security level data block in each regional node in the current cycle; 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 scheme corresponding to each regional node in the current period.

2. The video surveillance data security storage method based on blockchain technology as claimed in claim 1 is characterized in that: 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 personally identifiable 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; the data timeliness parameters include the required time for event response, the frequency of business activities and the frequency of data updates.

3. The video surveillance data security storage method based on blockchain technology as claimed in claim 2 is characterized in that: The analysis obtains the regional importance assessment value, data sensitivity assessment value and data timeliness assessment value corresponding to each monitoring device in each area. The specific analysis process is as follows: Q1. Input the crime rate, population density and number of fixed assets value levels corresponding to each monitoring device in each area into the plant electrical signal evaluation value analysis model, and output the regional importance evaluation value corresponding to each monitoring device in each area; Q2. Input the proportion of personally identifiable information corresponding to each monitoring device in each area, the number of each category of privacy information, the number of each level of military facilities, and the number of each category of government agencies into the plant electrical signal evaluation value analysis model, and output the data sensitivity evaluation value corresponding to each monitoring device in each area; Q3. Input the event response requirement time, business activity frequency and data update frequency corresponding to each monitoring device in each area into the plant electrical signal evaluation value analysis model, and output the data timeliness evaluation value corresponding to each monitoring device in each area.

4. The video surveillance data security storage method based on blockchain technology as claimed in claim 3 is characterized in that: The analysis obtains the data security importance coefficient corresponding to each monitoring device in each area. The specific analysis process is as follows: The regional importance assessment value, data sensitivity assessment value and data timeliness assessment value corresponding to each monitoring device in each area are respectively recorded as and v represents the number corresponding to each area, v is a positive integer, x represents the number corresponding to each monitoring device, x is a positive integer, substitute into the calculation formula: The data security importance coefficient corresponding to each monitoring device in each area is obtained Among them, Z′, S′, and F′ are the standard regional importance assessment value, standard data sensitivity assessment value, and standard data timeliness assessment value corresponding to the set monitoring equipment, respectively; Ω1, Ω2, and Ω3 are the weight factors corresponding to the set monitoring equipment regional importance assessment value, the weight factors corresponding to the data sensitivity assessment value, and the weight factors corresponding to the data timeliness assessment value, respectively; Ψ1, Ψ2, and Ψ3 are the adjustment factors corresponding to the set monitoring equipment regional importance assessment value, the adjustment factors corresponding to the data sensitivity assessment value, and the adjustment factors corresponding to the data timeliness assessment value, respectively; ΔZ, ΔS, and ΔF are the set permitted monitoring equipment regional importance assessment value differences, permitted data sensitivity assessment value differences, and permitted data timeliness assessment value differences, respectively.

5. The video surveillance data security storage method based on blockchain technology as claimed in claim 4 is characterized in that: 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 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 the area. In this way, the security level of each monitoring device in each area is classified, 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 video surveillance data security storage method based on blockchain technology as claimed in claim 5 is characterized in that: 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 association rate, data complementation rate and data size 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 Among them, E′, G′, and H′ are respectively the standard event association rate, standard data complementarity rate, and standard data size corresponding to the set data blocks of each security level, Ξ1, Ξ2, and Ξ3 are respectively the weight factors corresponding to the event association rate, data complementarity rate, and data size of the set data blocks of each security level, and π is a natural constant.

7. The video surveillance data security storage method based on blockchain technology as claimed in claim 6 is characterized in that: The resource allocation strategy corresponding to each security level data block in each regional node in the current cycle is analyzed, and the specific analysis process is as follows: W1. Priority sorting based on coefficients: The data risk correlation coefficients corresponding to the data blocks of each security level in the nodes of each region in the current period are sorted from high to low, and the data blocks of each security level in the nodes of each region with the top 10% of the data risk correlation coefficients are listed as high priority. More blockchain storage space is allocated to the data blocks with high risk correlation coefficients, and the storage capacity of the nodes where they are located is expanded first. For the top 10% of high-risk data blocks, an additional 50% storage quota is added. At the same time, high-risk data blocks are stored on storage media with higher reliability. In the allocation of computing power of blockchain nodes, high-risk data blocks are tilted. For high-risk data blocks, security protection measures are strengthened, the sophistication of firewall rules is increased, more advanced intrusion detection systems and intrusion prevention systems are deployed, encryption strength is increased, and encryption algorithms with longer key lengths are adopted. At the same time, the complexity of key management is increased, keys are updated regularly, and the risk of key leakage is reduced; W2. After completing the screening of the top 10% of high-priority data blocks, the data blocks of each security level in each regional node whose data risk correlation coefficient is ranked in the range of 10%-50% are classified as medium priority. In the blockchain management system, medium priority labels are added to these data blocks, and a flexible storage allocation plan is adopted. On the one hand, based on the monitoring and analysis of data growth trends, 20%-30% of expandable storage space is reserved in advance. In terms of storage media selection, a hybrid storage architecture is constructed, with high-performance mechanical hard drives as the main component and a certain proportion of solid-state drives as the cache layer. At the same time, the garbage collection mechanism of the storage management system is optimized. For medium-priority data blocks, the space occupied by invalid data is regularly cleaned up to release storage resources, improve storage utilization, and ensure that the storage system always remains healthy to adapt to its relatively mild risk situation; W3. All data blocks of various security levels in the nodes of each region whose data risk correlation coefficient ranks in the bottom 50% are uniformly classified as low priority. Conventional storage configuration is adopted, and large-capacity, low-cost mechanical hard disks are selected to build a storage cluster to provide storage space that meets their basic data storage needs. Storage devices are inspected regularly, and routine maintenance operations such as disk health checks and data consistency checks are performed to promptly discover and repair potential storage problems. At the same time, storage virtualization technology is used to optimize storage resource utilization.

8. The video surveillance data security storage method based on blockchain technology as claimed in claim 7 is characterized in that: The specific acquisition process of obtaining the data volume ratio corresponding to each security level data block in each regional node in the current cycle is as follows: For each regional node, the data volume ratio corresponding to each security level data block in each regional node in the current period is calculated respectively. The calculation formula is: the data volume ratio corresponding to each security level data block in each regional node in the current period is equal to the total data volume corresponding to each security level data block in each regional node in the current period divided by the total data volume corresponding to each regional node in the current period.

9. The video surveillance data secure storage method based on blockchain technology as claimed in claim 8, characterized in that: The encryption scheme corresponding to each regional node in the current period is analyzed, and the specific analysis process is as follows: The data volume ratio corresponding to each security level data block in each regional node in the current period is compared with the data volume ratio corresponding to each encryption scheme in the database. If the data volume ratio corresponding to a certain security level data block in a regional node in the current period is the same as the data volume ratio corresponding to a certain encryption scheme in the database, then the encryption scheme in the database is recorded as the encryption scheme corresponding to the regional node in the current period. In this way, the encryption schemes corresponding to each regional node in the current period are analyzed.

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