Video monitoring system and device based on quantum encryption communication

Abstract

The invention discloses a video monitoring system and device based on quantum encryption communication, and relates to the technical field of secure transmission of relay chain video data. The video monitoring system and device based on quantum encryption communication comprise the following modules: a data acquisition module for acquiring transmission data through front-end equipment and performing standardized preprocessing on acquired multi-modal data; the quantum encryption communication module is used for evaluating the security of the video data in a transmission process and dynamically adjusting a transmission strategy based on an evaluation result; the relay chain transmission module is used for analyzing the reliability of a relay chain link and optimizing a link load distribution mode; and the parallel chain data processing and collaborative analysis module comprehensively judges the collaborative response capability of the node in the data transmission process and generates an optimization report based on a judgment result. The problem that in the prior art, when high-intensity interference and data hijacking risks exist, the safety and reliability of a transmission link are difficult to guarantee is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of secure transmission of relay chain video data, and in particular to a video surveillance system and device based on quantum cryptographic communication. Background Art

[0002] With the rapid development of public security, industrial production, and smart city construction, video surveillance systems have become an essential means of sensing and recording critical scene information. However, traditional video surveillance systems generally rely on classical encryption algorithms during data transmission. Their security is vulnerable to the threat posed by the advancement of quantum computing capabilities, posing risks of key cracking and data leakage, making them unable to meet the actual demand for high-level data security.

[0003] While some existing systems have introduced protection measures such as network isolation and data masking, they still lack system-level encryption protection mechanisms from source to endpoint when facing complex attack scenarios such as high-intensity link monitoring, man-in-the-middle attacks, and node tampering. Furthermore, video surveillance data volumes are large and transmission links are complex. Relay scheduling and encryption strength configuration between multiple nodes often rely on fixed policies that are difficult to dynamically adjust based on data risk levels and link status, resulting in insufficient flexibility and intelligence in data security protection.

[0004] In recent years, the development of quantum key distribution technology has provided new ideas for building a highly secure communication system, but its effective integration with actual video surveillance systems still faces many challenges, such as the construction of a collaborative mechanism between quantum keys and traditional encryption parameters, the stability of quantum link relay transmission, and the matching scheduling of video data and encrypted channels. These issues need to be urgently addressed.

[0005] Therefore, in view of the shortcomings of the existing technology, there is an urgent need for a video surveillance system and device based on quantum encryption communication. Summary of the Invention

[0006] Technical problems solved In response to the shortcomings of the existing technology, the present invention provides a video surveillance system and device based on quantum encryption communication, which solves the problem in the existing technology that the security and reliability of the transmission link are difficult to ensure when facing high-intensity interference and data hijacking risks.

[0007] Technical Solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a video surveillance system based on quantum cryptography communication, comprising a data acquisition module, which collects video image data, environmental perception data, link status data, and node interaction data through front-end monitoring equipment, and performs standardized preprocessing on the collected multimodal data to generate a standardized data set; a quantum cryptography communication module, which performs a security metric assessment of video data under a quantum cryptography communication link based on the standardized data set, and dynamically adjusts the encryption parameters and data transmission strategy in the front-end monitoring device based on the security metric assessment results; a relay chain transmission module, which analyzes the load suppression capability and reliability of the relay chain link based on the link bandwidth of each relay chain node, and dynamically reconstructs the relay forwarding path and communication routing strategy of the video data based on the analysis results, thereby optimizing the collaborative scheduling relationship and link load distribution method between nodes; a parallel chain data processing and collaborative analysis module, which comprehensively determines the collaborative response capability of the nodes during the data transmission process based on the security metric assessment results of the video data and the reliability analysis of the relay link, and generates an optimized scheduling model between multiple nodes across the chain based on the node collaborative efficiency assessment results, and further outputs a comprehensive optimization report covering the video data routing path, link resource allocation, and relay load balancing strategy.

[0008] Furthermore, the specific steps of collecting video image data, environmental perception data, link status data and node interaction data through the front-end monitoring equipment are as follows: collecting video image data including high-definition video stream frame rate, encoding format, image noise level, signal strength, facial data and abnormal behavior through the video monitoring module; collecting environmental perception data including gas concentration, temperature, humidity, light intensity and particle concentration through the environmental perception sensor; collecting link status data such as link bandwidth, average transmission delay, packet loss rate, channel interference intensity, interaction frequency and data return success rate through the communication module; collecting node interaction data including the response delay, interaction frequency, block generation interval, task processing time and resource utilization rate of each blockchain node per unit time through the node interface monitoring module, which is used to construct a node collaborative efficiency evaluation model and calculate the actual data processing intensity of the node; generating a high-intensity key stream including quantum key entropy value and deviation detection rate in real time through the quantum key management unit, recording the key generation cycle, key update number and key stream integrity verification result; and recording the collection timestamp, data collection frequency, total number of fields, number of missing fields, data nesting level and field standardization completion ratio of each data source.

[0009] Furthermore, the specific steps of performing standardized preprocessing on the collected multimodal data and generating a standardized data set are as follows: by converting the collected video image data, environmental perception data, link status data and node interaction data into a unified timestamp format, ensuring that all data sources have alignable time identifiers; eliminating null values, error codes or fields that do not conform to the format in the collected data, marking and recording the abnormal data ratio for subsequent anomaly detection and backfilling; performing standardized mapping processing on the data field names from different sources, converting non-uniform field names into predefined standard fields, and recording the field mapping success rate; flattening fields with more than three levels of nested structures, breaking down nested fields into a flat format to improve data parsing efficiency; unifying the physical unit format of each field and retaining the unit conversion accuracy information; counting the number of valid fields, the length of time the field is missing, the standardization completion ratio and the field retention rate in each type of data, and integrating the standardized data into a standardized data set.

[0010] Furthermore, the security metric evaluation of the video data under the quantum encryption communication link is performed based on the standardized data set, and the encryption parameters and data transmission strategy in the front-end monitoring device are dynamically adjusted based on the security metric evaluation result. The specific steps are as follows: extracting the quantum key entropy value, deviation detection rate and channel interference strength from the quantum key management unit, and calculating the quantum key strength value; extracting the interaction frequency of each current link, and calculating the total interaction value; extracting the packet loss rate of the data collected by each front-end device at the current moment and combining it with the total number of front-end devices to obtain the video data security evaluation value; comparing the video data security evaluation value with the security evaluation threshold in real time, the security evaluation threshold includes a first security threshold and a second security threshold: when the video data security evaluation value is greater than the first security threshold, it is determined that the data encryption strength, link interference suppression and transmission integrity of the front-end monitoring device are all at an excellent level, the overall data security is high, and the current encryption parameters and transmission strategy are kept unchanged; when When the video data security assessment value is greater than the second security threshold and less than or equal to the first security threshold, it is judged as medium-risk data, and the security is acceptable but tends to be tense. It is necessary to optimize and adjust the link and data processing strategies. The specific adjustment measures are to dynamically reallocate data processing tasks to reduce the data pressure on high-load relay chain nodes, and at the same time adjust the data return path of the front-end equipment, giving priority to relay nodes with better link quality and higher transmission efficiency for data transfer; when the video data security assessment value is less than or equal to the second security threshold, it is judged as high-risk data, and protection and correction measures must be implemented immediately. The specific method is to suspend the uplink and link return of non-critical data, and give priority to ensuring the transmission security of core monitoring data; enable off-chain buffers to temporarily isolate abnormal data to prevent data leakage and link blockage; perform field standardization and outlier repair on data sources, reduce the data processing priority of affected nodes, and migrate some tasks to redundant nodes.

[0011] Furthermore, the specific steps for obtaining the video data security assessment value are: calculating the ratio of the quantum key entropy value of the current data to the deviation detection rate, adding one to take the logarithm, and dividing it by the sum of its channel interference intensity plus one to obtain the quantum key strength value of the data; for each data stream, counting its interaction frequency in all nodes, multiplying it by the weight coefficient corresponding to each node, and then weighted summing and dividing it by the maximum value of the interaction frequency of all devices to obtain the total interaction value of the data in the network; then obtaining the packet loss rate of the data at this moment, multiplying it by the quantum key strength value and the total interaction value, summing it and dividing it by the total number of front-end devices to obtain the video data security assessment value at the current time.

[0012] Furthermore, the load suppression capability and reliability of the relay chain link are analyzed based on the link bandwidth of each relay chain node, and the relay forwarding path and communication routing strategy of the video data are dynamically reconstructed based on the analysis results to optimize the collaborative scheduling relationship and link load distribution method between nodes. The specific steps are: extracting the link bandwidth, data flow rate and data acquisition frequency of each current relay link from the standardized data set to obtain the total value of the link reachability; extracting the data nesting level of the current relay link node and the acquisition completeness rate of the key fields at this moment, and comprehensively calculating to obtain the relay link reliability evaluation value; comparing the relay link reliability evaluation value with the link performance threshold in real time, and the link performance threshold includes a first reliability threshold and a second reliability threshold: when the relay link reliability evaluation value is greater than the first reliability threshold, it means that the link transmission capacity is strong and the interference level is low, the link reliability is high, and the current path is maintained. The scheduling strategy and node coordination mode remain unchanged. When the relay link reliability evaluation value is greater than the second reliability threshold and less than or equal to the first reliability threshold, it is determined to be in a marginal link reliability state, with some bandwidth jitter and inter-node interference. Path fine-tuning and traffic optimization are required. Specific measures include dynamically adjusting the relay node forwarding load, replacing some forwarding paths with degraded link quality, and periodically verifying the communication consistency of the primary link to enhance the overall reliability of the relay path. When the relay link reliability evaluation value is less than or equal to the second reliability threshold, it indicates that the link reliability has seriously degraded and there are high-risk transmission hazards. Link reconstruction and node isolation mechanisms need to be immediately enabled, including disabling high-interference links, suspending relay forwarding of secondary data, activating backup low-interference paths for rerouting, and reducing the scheduling frequency of affected nodes, and redistributing link loads and data flows.

[0013] Furthermore, the specific steps of the comprehensive calculation to obtain the relay link reliability evaluation value are: multiplying the link bandwidth and data flow rate of the relay link node at the current moment, and then calculating the ratio by the sum of the data acquisition frequency and the stability factor to obtain the load adjustment gain of the current relay link node; then weighting the sum of the load adjustment gains of all relay links combined with the acquisition completeness rate of the key fields, and multiplying the result by the adjustment factor and subtracting the product of the data nesting level and the penalty factor of the relay link node at the current moment, and finally adding one to the difference and taking the logarithm to obtain the relay link reliability evaluation value.

[0014] Furthermore, the security metric evaluation results based on the video data and the reliability analysis of the relay link are used to comprehensively determine the collaborative response capabilities of the nodes during the data transmission process, and based on the node collaborative efficiency evaluation results, an optimized scheduling model between multiple nodes across the chain is generated, and a comprehensive optimization report covering the video data routing path, link resource allocation and relay load balancing strategy is further output. The specific steps are: obtaining the video data security evaluation value of each data and the relay link reliability evaluation value of each relay link node, and then extracting the current data processing intensity, resource utilization rate and standard deviation of the task processing time of all nodes of each node, and combining the load balancing factor to obtain the current node collaborative efficiency evaluation value; real-time comparison of the node collaborative efficiency evaluation value and the collaborative threshold, the collaborative efficiency threshold includes a first efficiency threshold and a second efficiency threshold: when the node collaborative efficiency evaluation value is greater than the first efficiency threshold, it is determined that the parallel chain node collaborative analysis efficiency is good, and the overall processing capacity, resource utilization and link load are in a high-quality state; at this time, the existing parallel chain task allocation strategy is maintained, the cache tasks of low-priority nodes are streamlined to release resources and maintain high-concurrency data analysis task allocation of high-adaptation nodes; generate a collaborative efficiency maintenance report, including the current node collaborative Efficiency indicators, node load overview and distribution of processing time differences between nodes are provided for administrators' reference; when the node collaborative efficiency evaluation value is greater than the second efficiency threshold and less than or equal to the first efficiency threshold, it is determined that the parallel chain node collaborative processing is in a medium load state, and task and load strategy optimization is required; the node task allocation weight should be adjusted dynamically to reduce the data processing tasks of high-load nodes, and at the same time, the node data stream merging processing mechanism should be enabled to reduce repeated calculations between nodes; the node processing path should be optimized to improve the data return speed and analysis timeliness; a collaborative efficiency optimization and task reallocation recommendation report should be generated, including the node task adjustment method. solutions, load balancing optimization paths and high-time-consuming node processing suggestions; when the node collaborative efficiency evaluation value is less than the second efficiency threshold, it is determined that the parallel chain collaborative processing efficiency is low, the link bottleneck and node performance bottleneck are obvious, and repair and reconstruction measures need to be taken; specifically, reduce the data access frequency of inefficient nodes and enable off-chain auxiliary processing; start node processing capacity self-inspection, identify and isolate high-time-consuming and inefficient nodes; enable inter-chain fault tolerance mechanism and spare node participation in processing to avoid single-point bottlenecks; generate collaborative efficiency fault repair and resource reconstruction report settings, including node isolation measures, inter-chain task migration path and abnormal node performance diagnosis results.

[0015] Furthermore, the specific steps for obtaining the current node collaborative efficiency evaluation value are: extracting the video data security evaluation value of each node in the specified time window, the data processing intensity of the node, and the corresponding relay link reliability evaluation value, multiplying the three and then dividing by the resource utilization rate of the node to obtain the collaborative load score of the node; summing up the collaborative load scores of all nodes in turn to obtain the total collaborative load value at the current moment; extracting the task processing time of each node, calculating its standard deviation, obtaining the degree of task collaborative disturbance, and multiplying it by the load balancing factor to form a task scheduling stability adjustment item; adding one to the adjustment item as the numerator of the node collaborative efficiency evaluation value, and using the total collaborative load value as the denominator to perform a ratio calculation, and finally obtaining the current node collaborative efficiency evaluation value.

[0016] According to a second aspect of the present invention, a video surveillance device based on quantum cryptography communication is provided, comprising: a front-end monitoring device, a quantum cryptography communication device, a relay link processing device, and a collaborative control host; the front-end monitoring device is configured to collect video image data, environmental perception data, link status data, and node interaction data within a monitoring area, and pre-process the multimodal data before uploading; the quantum cryptography communication device is connected to the front-end monitoring device, configured to receive the pre-processed data and perform quantum key encryption, while calculating a security assessment value of the data, and dynamically adjusting the encryption parameters and data transmission strategy of the front-end monitoring device based on the assessment result; the relay link processing device is configured to receive the encrypted data, dynamically select and optimize the forwarding path and communication route between each relay node based on the link assessment condition, and achieve efficient distribution and cache management of data during multi-hop transmission; the collaborative control host is connected to the relay link processing device and the quantum cryptography communication device, and configured to comprehensively analyze the security assessment value and link assessment value of the aforementioned data, calculate the node collaboration efficiency, and generate optimized control instructions accordingly, which are fed back to the front-end monitoring device and the relay link processing device, thereby improving the response efficiency and resource allocation capabilities of the entire system.

[0017] Beneficial effects The present invention has the following beneficial effects: (1) This video surveillance system and device based on quantum cryptographic communication, by building a closed-loop control mechanism that integrates quantum key management and real-time evaluation of video data, realizes adaptive dynamic adjustment of front-end device encryption parameters and link scheduling strategies, effectively solving the problems of static encryption strategies and rigid transmission paths in the existing technology.

[0018] (2) This video surveillance system and device based on quantum encryption communication introduces a video data security assessment method that integrates multiple source indicators, combines data structure complexity, semantic adaptability and time coverage ratio, thereby improving the real-time identification capability of high-risk data sources, and effectively solves the problems of delayed security risk discovery and lack of graded response in existing technologies.

[0019] (3) This video surveillance system and device based on quantum encryption communication, by building a relay link collaborative scheduling model, comprehensively considers quantum key strength, link interference level and node load distribution, and thus realizes intelligent optimization of communication routing paths, effectively solving the problems of uneven link resource allocation and low node communication efficiency in the existing technology.

[0020] (4) This video surveillance system and device based on quantum encryption communication introduces an off-chain buffer and field reconstruction mechanism in the abnormal data processing link, thereby realizing the isolation, repair and retransmission control of abnormal data, effectively solving the problems of weak abnormal data processing capability and poor overall system reliability in the existing technology.

[0021] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of the video surveillance system based on quantum encryption communication of the present invention; Figure 2 This is a structural diagram of the video surveillance system based on quantum encryption communication of the present invention; Figure 3 This is a bar chart showing the distribution of collaborative efficiency evaluation values ​​of each node involved in the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] See also Figure 1-Figure 3An embodiment of the present invention provides a technical solution: a video surveillance system and device based on quantum cryptography communication, comprising a data acquisition module, which collects video image data, environmental perception data, link status data, and node interaction data through a front-end monitoring device, and performs standardized preprocessing on the collected multimodal data to generate a standardized data set; a quantum cryptography communication module, which performs a security metric assessment of the video data under the quantum cryptography communication link based on the standardized data set, and dynamically adjusts the encryption parameters and data transmission strategy in the front-end monitoring device based on the security metric assessment results; a relay chain transmission module, which analyzes the load suppression capability and reliability of the relay chain link based on the link bandwidth of each relay chain node, and dynamically reconstructs the relay forwarding path and communication routing strategy of the video data based on the analysis results, thereby optimizing the collaborative scheduling relationship and link load distribution method between nodes; a parallel chain data processing and collaborative analysis module, which comprehensively determines the collaborative response capability of the nodes during the data transmission process based on the security metric assessment results of the video data and the reliability analysis of the relay link, and generates an optimized scheduling model between multiple nodes across the chain based on the node collaborative efficiency assessment results, and further outputs a comprehensive optimization report covering the video data routing path, link resource allocation, and relay load balancing strategy.

[0025] Specifically, the steps for collecting video image data, environmental perception data, link status data, and node interaction data through front-end monitoring equipment are as follows: First, the target area is captured in real time through the video monitoring module. The acquired video image data includes the frame rate, encoding format, image noise level, signal strength, and other underlying image parameter information of the high-definition video stream. At the same time, key identification content such as facial images, movement trajectories, and behavioral characteristics are extracted. Preliminary label recognition is performed on possible abnormal behaviors such as out-of-bounds movement, abnormal stay, or gathering behavior, providing data support for subsequent security incident analysis. Secondly, environmental perception data is acquired through environmental perception sensors, including temperature and humidity sensors, gas concentration sensors, light detection modules, and particle sensing units. Specifically, this data includes the gas concentration, temperature, humidity, light intensity, and concentration of suspended particles in the monitored area. This data is intended to assist in optimizing video image quality in complex environments and analyzing data interference sources. Subsequently, the communication module monitors the data link from the front-end to the relay node, collecting link status data such as the current link bandwidth, average data transmission delay, packet loss rate, channel interference intensity, interaction frequency between front-end and rear-end nodes, and data return success rate. This data is used to assess link quality, diagnose communication bottlenecks, and dynamically optimize path selection strategies. Next, the node interface monitoring module is used to monitor the performance of each blockchain node during the collaborative processing process. Key performance indicators such as response delay per unit time, frequency of interactions between nodes, block generation interval, task processing time, and local resource utilization are collected. This is used to build a node collaborative efficiency evaluation model and further calculate the actual data processing intensity of each node under different loads, providing a quantitative basis for the parallel chain scheduling strategy. On this basis, a high-intensity key stream is generated in real time through the quantum key management unit, and key security parameters such as quantum key entropy, deviation detection rate, and channel interference response are collected. At the same time, the key generation cycle, key update times, and key stream integrity verification results are recorded to ensure the consistency and tamper resistance of encrypted communication of link data in multi-node collaborative transmission. In addition, the system also synchronously records the collection timestamp, data collection frequency, total number of fields, number of missing fields, nested hierarchical structure and field standardization completion ratio of each data source, which is used to measure the real-time performance, structural complexity and cleaning completeness of each data source, and provide unified standardized input for subsequent data fusion, encryption processing and node collaborative evaluation.

[0026] In this implementation, in the process of collecting video image data, environmental perception data, link status data and node interaction data through front-end monitoring equipment, each collection process works together to achieve comprehensive perception of the monitoring scene and high-security data construction. Specifically, video image data is collected by the video surveillance module, which not only captures high-definition video streams, image clarity, and abnormal behavior, but also records image signal strength and noise levels, providing critical information for subsequent image recognition and encrypted transmission. Environmental perception data is collected through multi-source sensors such as temperature, humidity, light, and gas concentration to characterize the external environmental conditions of the monitored area and assist in evaluating the reliability of the collected images. Link status data collection covers network quality indicators such as transmission bandwidth, latency, packet loss rate, and channel interference intensity to ensure the stability and integrity of data during transmission. Node interaction data is collected through the node interface module to measure the responsiveness, computational load, and task processing latency of each blockchain node, which is used to quantify the collaborative efficiency between nodes and optimize task allocation. The quantum key management unit generates the key stream required for quantum encryption in real time and records its entropy, deviation detection rate, and integrity check results to ensure high-strength security protection during encrypted communication. In addition, the system simultaneously records the acquisition timestamp, frequency, total number of fields, and missing percentage of each data source, assesses its structural complexity and standardization, and ensures that all data is of high quality and in a unified format, providing a solid data foundation for subsequent encrypted transmission, relay optimization, and node collaboration.

[0027] Specifically, the collected multimodal data is pre-processed in a standardized manner to generate a standardized data set. The specific steps are as follows: The pre-processing of the collected multimodal data includes multiple processing flows for data consistency, structural optimization, and format unification. First, the video image data, environmental perception data, link status data, and node interaction data are time-series standardized through unified timestamp format conversion to ensure that all data sources have alignable time identifiers, thereby meeting the requirements for time consistency of subsequent model training and collaborative computing. Subsequently, the system automatically removes null values, abnormal error codes, or fields that do not conform to the format specifications in the data set. At the same time, the number of abnormal data is counted and its distribution location is marked to form an abnormal data ratio indicator to assist in subsequent missing value filling and data quality assessment. In terms of semantic consistency, the naming of multi-source data fields is standardized and mapped, and the different data field names are uniformly converted into system-defined standard field names. The field mapping success rate is calculated to measure the smoothness of multi-source data fusion. For complex data fields, the system flattens those nested more than three levels deep, expanding the nested information into a tiled structure, significantly improving data parsing speed and model readability. Furthermore, the system standardizes the physical unit format for all fields and records the precision parameters for unit conversions to ensure numerical consistency and the accuracy of physical semantics. Finally, the system compiles statistics for each data type, including the number of valid fields, the duration of field loss, the standardization completion rate, and the field retention rate. Based on these statistics, all processed data is consolidated into a standardized dataset with a clear structure and uniform format.

[0028] This implementation improves data quality and usability through systematic, structured, and standardized preprocessing of multimodal data collected by the front-end, ensuring the accuracy and efficiency of subsequent processes such as quantum encryption, security assessment, link scheduling, and node collaborative analysis. First, by converting to a unified timestamp format, data collected by different devices is comparable and synchronized, avoiding timing misalignments or logical conflicts during data fusion analysis. Second, by eliminating null values, error codes, and non-compliant fields and recording the ratio of abnormal data, this provides a foundation for subsequent data compensation and anomaly detection mechanisms, helping to enhance the robustness and fault tolerance of the system. Furthermore, standardized mapping resolves the issue of field naming differences across various devices, improving the system's compatibility and parsing efficiency for heterogeneous data from multiple sources. Statistics on field mapping success rates can be used to dynamically assess the degree of data interface adaptability. Flattening reduces nesting complexity, making data easier to access and analyze, particularly suitable for rapid response requirements in high-frequency processing scenarios. The unified physical unit format avoids error accumulation during calculations, ensuring the accuracy and consistency of numerical processing. Finally, statistics on indicators such as field validity, missingness, and standardization completion ratio of various types of data will help monitor data quality levels in real time, provide a quantitative basis for data governance, and lay the foundation for generating high-quality, traceable standardized data sets, thereby improving the security processing capabilities and intelligent analysis levels of the entire video surveillance system in a quantum encryption communication environment.

[0029] Specifically, the security metric evaluation of video data in a quantum encryption communication link is performed based on a standardized data set, and the encryption parameters and data transmission strategy in the front-end monitoring device are dynamically adjusted based on the security metric evaluation results. The specific steps are as follows: The core security indicators of the quantum key generated in the current period are extracted from the quantum key management unit, including parameters such as quantum key entropy, deviation detection rate, and channel interference intensity. A comprehensive scoring model is constructed based on these indicators to calculate the quantum key strength value used to measure key stability and anti-interference capabilities. Subsequently, the system extracts the real-time interaction frequency of each communication link, quantifies its activity level and channel utilization, and calculates the total interaction value of the entire link to evaluate the current network's available bandwidth and routing resource distribution. At the same time, the system also extracts the packet loss rate of the data collected by each front-end monitoring device at the current moment, and combines it with the number of all front-end devices in the system to calculate and generate a security assessment value for the video data, which is used to comprehensively reflect the integrity risk and transmission efficiency during the data transmission process.

[0030] During the security assessment phase, the calculated video data security assessment value is compared in real time with the preset security assessment threshold. The threshold is divided into a first security threshold and a second security threshold to support dynamic response mechanisms under different risk levels. The first security threshold is higher than the second security threshold. Specifically, when the video data security assessment value is higher than the first security threshold, it indicates that the current data encryption strength is sufficient, link interference suppression is appropriate, and transmission integrity is good. At this time, the system determines that the overall data security is in an excellent state and can maintain the current encryption parameter settings and data transmission strategy unchanged to ensure stable system operation.

[0031] When the video data security assessment falls between the second and first security thresholds, the system determines this status as medium risk, meaning data security is acceptable but showing a downward trend. To prevent further security deterioration, the system automatically activates an optimization mechanism, dynamically reallocating data processing tasks to alleviate pressure on some heavily loaded relay chain nodes. Simultaneously, based on the latest link status, the system adjusts the data return path of front-end devices in real time, prioritizing relay nodes with excellent link quality, stable signals, and low latency as data forwarding points to reduce potential data loss and transmission risks.

[0032] If the video data security assessment value is lower than or equal to the second security threshold, the system will determine this status as high-risk and must immediately implement mandatory protection and correction strategies. First, non-critical data chain operations and link backhaul tasks will be suspended to prioritize the transmission security and real-time performance of core surveillance video data. Second, an off-chain buffering mechanism will be enabled to temporarily isolate potential abnormal data to prevent it from entering the main chain and causing data pollution or link congestion. Finally, the system will perform field standardization repair and outlier processing on relevant data sources, reduce the task scheduling priority of affected nodes, and migrate some of their computing tasks to redundant nodes with sufficient resources, thereby improving the overall security resilience and responsiveness of the system in extreme situations.

[0033] In this implementation plan, the quantum key entropy value, deviation detection rate and channel interference intensity generated by the quantum key management unit are extracted and quantitatively scored, and combined with the current interaction frequency of each link and the packet loss rate of the front-end device during the acquisition process, a comprehensive security assessment system for video data is constructed; by comparing the assessment value with the set multi-level security threshold in real time, the risk level of the current data transmission is dynamically determined, and adaptive adjustment of encryption parameters, transmission paths and task scheduling strategies is achieved through a hierarchical response mechanism; by setting specific response measures for situations with different risk levels, such as maintaining the current strategy, adjusting the link load or suspending the upload of non-core data, the system's ability to identify, respond to and protect data security situations in complex communication environments is effectively improved, ensuring the encrypted transmission stability of core video data and the overall network collaboration reliability.

[0034] Specifically, the specific steps for obtaining the video data security assessment value are as follows: calculate the ratio of the quantum key entropy value of the current data to the deviation detection rate, add one to take the logarithm, and divide it by the sum of its channel interference intensity plus one to obtain the quantum key strength value of the data; for each data stream, count its interaction frequency in all nodes, multiply it by the weight coefficient corresponding to each node, and then take the weighted sum and divide it by the maximum value of the interaction frequency of all devices to obtain the total interaction value of the data in the network; then obtain the packet loss rate of the data at this moment, multiply it by the quantum key strength value and the total interaction value, sum it and divide it by the total number of front-end devices to obtain the video data security assessment value at the current time.

[0035] The calculation formula for the video data security assessment value is: ; Where N represents the total number of front-end monitoring devices, that is, the number of devices involved in current data collection and transmission; The entropy value of the quantum key generated by the i-th device at the current moment is the entropy measurement value output in real time by the built-in quantum random number generator of the source device, which is used to evaluate the randomness quality of the key; represents the deviation detection value of the i-th device during the quantum key distribution process, which is the link bit error rate data obtained by the self-test module of the source device; represents the channel interference intensity of the i-th device at time t, obtained by the device link status monitoring module; The interaction frequency collected by the i-th device at time t; Indicates the maximum interaction frequency of all devices in the current monitoring scenario; It represents the packet loss rate of the i-th device at time t, which is derived from the comparison results of data packet sending and receiving collected in real time by the device communication module. The weight coefficient for the i-th interaction frequency in the overall security assessment, ranging from 0 to 1, is derived from statistical and structural analysis of the number of interactions per unit time in standardized node interaction data. By calculating each node's average interaction frequency, interaction success rate, and degree of collaborative coupling with other nodes over the monitoring period, the communication activity and criticality of the node in the network structure are assessed. A node that frequently participates in data forwarding, responds promptly, and has a high degree of connectivity in the system is given a higher weight. If a node has fewer interactions or is in a marginal position, the weight coefficient is reduced to balance the influence of different nodes in the overall security assessment model.

[0036] In this implementation plan, by integrating multi-dimensional parameters such as quantum key entropy, deviation detection rate, channel interference intensity and node interaction frequency, the video data security assessment value at the current moment is dynamically calculated. By weighted normalization processing of various indicators, it comprehensively reflects the comprehensive performance of front-end data acquisition integrity, link stability and node collaboration efficiency in the current quantum encryption communication system, and thus provides a basis for subsequent encryption parameter optimization, data scheduling adjustment and transmission path reconstruction, so as to achieve rapid identification and response to high-risk communication status.

[0037] Specifically, the load suppression capability and reliability of the relay chain links are analyzed based on the link bandwidth of each relay chain node. Based on the analysis results, the relay forwarding path and communication routing strategy for video data are dynamically reconstructed to optimize the collaborative scheduling relationship between nodes and the link load distribution method. The specific steps are as follows: By extracting the key performance indicators of each relay link from a standardized data set, including link bandwidth, data flow rate, and data collection frequency, the overall reachability of the link is first calculated, reflecting the maximum carrying capacity of the link per unit time. Furthermore, the data nesting level of each relay link node and the collection completeness rate of key fields are combined to evaluate the link relay node's processing ability for high-value data, and a comprehensive reliability assessment value for the relay link is obtained. This assessment value is compared in real time with the set link performance threshold. The link performance threshold is divided into a first reliability threshold and a second reliability threshold, with the first reliability threshold being higher than the second reliability threshold. When the relay link reliability assessment value is higher than the first reliability threshold, it indicates that the link has strong data transmission and anti-interference capabilities, and the system can maintain stable operation of the existing path scheduling strategy and node coordination mode. If the relay link reliability assessment value is between the first and second reliability thresholds, the link is in a marginally reliable state, with slight bandwidth fluctuations and communication interference between relay nodes. Lightweight path fine-tuning and data flow optimization measures will be implemented, including redistributing the forwarding load of some relay nodes, replacing some paths with degraded link quality, and introducing a communication consistency periodic verification mechanism to improve the robustness of the relay chain. When the relay link reliability assessment value is lower than or equal to the second reliability threshold, it indicates that the link is in a severely unstable state, with the risk of data transmission interruption or packet loss. Link reconstruction and risk node isolation strategies must be immediately initiated. Specific operations include blocking high-interference links, suspending the relay transmission of non-core data, activating backup low-interference link paths for emergency rerouting, and implementing scheduling frequency reduction measures for high-load or abnormal nodes. Link load and data flow are redistributed to ensure overall system communication stability and critical data accessibility.

[0038] In this implementation, the link bandwidth, data flow rate, data collection frequency, field collection integrity, and data structure complexity of each relay link in the standardized data set are dynamically extracted to comprehensively evaluate the reachability and stability of the current relay link. The relay link reliability evaluation value is then calculated to reflect the data transmission quality and collaborative support capability of the link at a specific moment. By comparing this evaluation value with the preset first and second reliability thresholds in real time, a hierarchical response strategy can be implemented: when the link reliability evaluation value is higher than the first reliability threshold, the system maintains the original relay path and collaborative mode to ensure resource utilization efficiency; when the evaluation value is between the two thresholds, the system determines that the link is in a critical risk state and requires local optimization through traffic fine-tuning, link replacement, and communication consistency verification; and when the evaluation value is lower than the second reliability threshold, the system will promptly execute emergency measures such as link reconstruction, node isolation, and data load redistribution to effectively avoid data transmission interruption or security risks caused by link instability, thereby significantly improving the robustness, reliability, and emergency response capabilities of the overall system.

[0039] Specifically, the steps for comprehensively calculating and obtaining the relay link reliability evaluation value are as follows: The load adjustment gain of the current relay link node is obtained by multiplying the link bandwidth and data flow rate of the relay link node at the current moment and then calculating the ratio by the sum of the data collection frequency and the stability factor. The load adjustment gains of all relay links are then weighted and summed in combination with the collection completeness rate of the key fields. The result is multiplied by the adjustment factor and then subtracted from the product of the data nesting level and the penalty factor of the relay link node at the current moment. Finally, the difference is added by one and the logarithm is taken to obtain the relay link reliability assessment value.

[0040] The specific calculation formula for the relay link reliability evaluation value is: ; Where n represents the collection completeness rate of the key fields of the entity at the current moment; Indicates the link bandwidth of the k-th relay chain node, which is obtained through the real-time link status monitoring of the relay chain node and reflects the node's currently available cache bandwidth resources; represents the data flow rate of the cache area of ​​the k-th relay chain node at time t, which is the real-time data flow of the cache area obtained by the cache monitoring module of the source node; Indicates the data collection frequency of the k-th relay chain node at the current time; Indicates the data nesting level at time t.

[0041] α represents the gain adjustment factor for the relay chain's caching and acceleration capabilities, ranging from 0.1 to 2. This factor is derived from the system's ability to adjust its response to historical data mutations and is used to control the weighting of sudden link performance changes in the overall transmission reliability score. The system's sensitivity to link state changes is determined by retrospectively analyzing the performance gains caused by link state changes within different time windows. A higher value is used when the system needs to amplify the positive impact of link performance changes; a lower value is used to suppress fluctuations and maintain evaluation stability. β represents the performance penalty factor for the main chain, ranging from 0.5 to 5. This factor is derived from the need to negatively control the impact of system load pressure and node anomalies. It is used to set the penalty weight for the number of abnormal nodes. When the system is highly loaded or abnormal nodes frequently occur, affecting data scheduling and transmission stability, β should be increased to increase the negative impact of the number of abnormal nodes on the overall evaluation score. If the system has a robust fault-tolerance mechanism and the impact of abnormal nodes is limited, this factor can be appropriately reduced to prevent excessive penalties that lead to frequent link path adjustments. λ represents the stability factor, with a value range of 1-5. It is derived from link status evaluation and is primarily used to improve the numerical stability of the evaluation function under small data perturbations. By analyzing short-term fluctuations in link transmission parameters such as average rate, jitter frequency, and bandwidth utilization, the model's robustness requirements for small changes are determined. If the link itself is unstable and perturbations are frequent, λ should be appropriately increased to avoid evaluation drift caused by the denominator approaching zero. Conversely, when the link operates smoothly and the parameter distribution is balanced, λ can be reduced to improve model sensitivity.

[0042] In this implementation scheme, by calculating the link stability adjustment value at the current moment, the impact of multiple link state changes on the overall transmission performance and reliability is comprehensively reflected. By introducing a gain adjustment factor, the positive driving effect of link state changes on system performance can be moderately amplified, such as the linkage relationship between the link bandwidth share and capacity changes; by setting the stability factor, the interference on the evaluation results when the link fluctuates violently or the data deviation is large can be reduced, and the numerical stability of the evaluation process can be enhanced; by introducing a penalty factor, the number of abnormal nodes detected in the system at the current moment can be penalized and deducted, reflecting the adverse effects of node failures or abnormal behaviors on link reliability. This formula can quantify the comprehensive change trend of link operation status in a dynamic and complex network environment, and provide a basic basis for intelligent decision-making on link reconstruction, path optimization and node scheduling strategies.

[0043] Specifically, based on the security metric assessment results of video data and the reliability analysis of relay links, the collaborative response capabilities of nodes during data transmission are comprehensively determined. Based on the node collaborative efficiency assessment results, an optimized scheduling model between cross-chain multi-nodes is generated, and a comprehensive optimization report covering video data routing paths, link resource allocation, and relay load balancing strategies is further output. The specific steps are as follows: After obtaining the video data security assessment value and relay link reliability assessment value of each data, the data processing intensity, available computing resource ratio, and memory, bandwidth, and other resource utilization of each node at the current moment are further extracted from the standardized data set. At the same time, the standard deviation of the task processing time of all participating nodes in the system is calculated, and combined with the system's preset load balancing factor, a node collaborative efficiency assessment value is comprehensively generated to quantify the efficiency and load balancing status of the current multi-node collaborative processing tasks. This assessment value is used to measure the stability of the collaborative relationship between nodes, the degree of processing capacity adaptation, and the consistency of data flow, and is an important basis for dynamic optimization scheduling strategies and resource allocation.

[0044] Subsequently, the collaborative efficiency evaluation value is compared with the collaborative efficiency threshold in real time. The collaborative efficiency threshold is divided into two levels: the first efficiency threshold and the second efficiency threshold, so as to finely divide the processing performance under the system state. When the node collaborative efficiency evaluation value is higher than the first efficiency threshold, it means that the nodes in the current system are well coordinated, tasks are properly allocated, resources are used efficiently, link communication delays are low, and the overall operation of the system is at an ideal level. At this time, the existing task allocation strategy and path scheduling mechanism are maintained to avoid unnecessary structural adjustments. At the same time, some low-priority node cache tasks are cleared to release system redundant resources, ensure the continuous high-concurrency operation of high-performance nodes, and automatically generate a "collaborative efficiency maintenance report" to record the current core indicators such as the load level, response capability, and task completion time difference of each node for continuous monitoring and analysis by system administrators.

[0045] If the node collaborative efficiency evaluation value is between the second efficiency threshold and the first efficiency threshold, it means that the system collaborative state is in the critical medium load stage. Individual nodes may face problems such as tight resources, task accumulation, or decreased path efficiency. At this time, the system optimization mechanism needs to be activated. Specifically, it includes dynamically adjusting the task allocation weight of nodes, reducing the task intensity of high-load nodes, introducing data stream merging strategies to reduce the amount of repeated calculations between nodes, and fine-tuning the data processing path based on the on-chain communication efficiency to improve the overall response speed and processing fluency. The system will automatically generate a "Collaborative Efficiency Optimization and Task Reallocation Recommendation Report" and propose node scheduling adjustments, path reconstruction, and task dimensionality reduction suggestions.

[0046] When the node collaborative efficiency evaluation value is lower than the second efficiency threshold, it is determined that the system collaborative ability has seriously declined, the collaborative efficiency between nodes is poor, the task processing differences are large, some links form bottlenecks, and there are hidden dangers in systemic processing. At this time, the fault repair and reconstruction mechanism should be immediately activated, and the access frequency of inefficient node data should be limited first, and secondary tasks should be temporarily undertaken by the off-chain collaborative processing platform; at the same time, the node performance self-test program should be executed to identify nodes with reduced processing capabilities or low communication efficiency, and isolate or downgrade their participation level; enable the inter-chain fault tolerance mechanism and the backup node to take over the data processing tasks, redistribute the on-chain task processing structure, and form a new collaborative model. The system will automatically generate a "Collaborative Efficiency Fault Repair and Resource Reconstruction Report", which covers node isolation strategies, task migration path planning, and abnormal node performance detection results to support administrators in system repairs and structural adjustments.

[0047] In this implementation scheme, this step comprehensively generates a node collaborative efficiency evaluation value by integrating the video data security evaluation value, the relay link reliability evaluation value and the processing status information of each node, which is used to dynamically reflect the stability and efficiency level of multi-node collaborative processing tasks. Among them, extracting the node data processing intensity and resource utilization rate can evaluate the real-time processing capability of each node; statistically analyzing the standard deviation of task processing time can reflect the load balancing status between nodes; and introducing a load balancing factor is used to calibrate the adaptability of collaborative efficiency indicators under different system scales. By comparing this evaluation value with the multi-level collaborative efficiency threshold in real time, it is possible to identify whether the current system collaborative status is at an efficient, medium or abnormal level, thereby guiding the system to take corresponding maintenance, optimization or repair measures to ensure that the distributed system still has stable task scheduling capabilities and efficient resource utilization levels under different loads.

[0048] Specifically, the specific steps for obtaining the node collaborative efficiency evaluation value of the current system are as follows: extract the video data security evaluation value, the data processing intensity of the node, and the corresponding relay link reliability evaluation value of each node in the current system within the specified time window, multiply the three, and then divide by the resource utilization rate of the node to obtain the collaborative load score of the node; sum up the collaborative load scores of all nodes in turn to obtain the total collaborative load value of the system at the current moment; extract the task processing time of each node, calculate its standard deviation, obtain the degree of task collaborative disturbance, and multiply it by the load balancing factor to form a task scheduling stability adjustment item; add one to the adjustment item as the numerator of the node collaborative efficiency evaluation value, and use the total collaborative load value as the denominator to perform a ratio calculation, and finally obtain the node collaborative efficiency evaluation value of the current system.

[0049] The specific calculation formula for the node collaborative efficiency evaluation value is: ; Where M represents the number of nodes currently participating in collaborative analysis, which comes from the real-time online node statistics of the node management system; represents the video data security assessment value at time t under quantum encryption communication; Indicates the actual data processing intensity of the i-th parachain node at time t, which is collected in real time by the node task scheduling system, including the amount of data decrypted, analyzed, and detected by the node processing log statistics; Indicates the reliability evaluation value of the current relay link; Indicates the resource usage of the i-th node, including CPU usage, memory, and I / O rate; ) represents the standard deviation of the processing time of all node tasks at time t, which is calculated by the node task scheduling system based on the processing time statistics of the current batch of tasks. This represents the load balancing factor, ranging from 0.1 to 1. It is derived from an assessment of the dispersion of task response times across task processing nodes in a multi-node distributed system. By calculating the ratio of the standard deviation of task processing times across each node to the average response latency, this factor is used to measure the balance of task distribution in the system. When processing times fluctuate significantly between nodes and there is significant task concentration, the value is adjusted upward to increase the penalty for load balancing in the collaborative efficiency assessment. When task distribution is relatively even and response times are concentrated, the factor is adjusted downward to mitigate its impact on the overall assessment.

[0050] In this embodiment, the data processing intensity of node 1 is set to 0.68, the resource utilization rate is 0.72, the task processing time is 12.5, the corresponding video data security evaluation value is 0.85, and the node collaborative efficiency evaluation value is 0.88; the data processing intensity of node 2 is set to 0.55, the resource utilization rate is 0.66, the task processing time is 14.2, the corresponding video data security evaluation value is 0.76, and the node collaborative efficiency evaluation value is 0.82; the data processing intensity of node 3 is set to 0.62, the resource utilization rate is 0.78, the task processing time is 15.6, the corresponding video data security evaluation value is 0.79, and the node collaborative efficiency evaluation value is 0.85; the data processing intensity of node 4 is set to 0.49, the resource utilization rate is 0.63, the task processing time is 17.1, the corresponding video data security evaluation value is 0.73, and the node collaborative efficiency evaluation value is 0.81.

[0051] Table SEQ Table\* ARABIC 1 node collaborative efficiency evaluation data table node Data processing intensity Resource utilization Task processing time Node collaboration efficiency evaluation value Node 1 0.85 1.2 12.5 0.441 Node 2 0.76 0.9 14.2 0.526 Node 3 0.68 1.0 15.6 0.424 Node 4 0.92 1.5 17.1 0.382 like Figure 3 As shown in Table 1 and Figure 3As can be seen, node 2 has the highest node collaborative efficiency evaluation value, indicating moderate data processing intensity, low resource utilization, and reasonable task processing time. This reflects the node's good stability and high operational efficiency in the current link. Therefore, it can be prioritized as a high-reliability link node to improve overall link stability and load balancing. Node 4 has the lowest node collaborative efficiency evaluation value. Despite its high data processing intensity, its resource utilization is high and its task processing time is long, resulting in a low overall reliability score. This node will automatically be prioritized for scheduling and will be included in the list of key targets for link load adjustment and performance optimization. The distribution bar chart of each node's collaborative efficiency evaluation value reflects the stability and reliability distribution of each node in the current link system. The higher the evaluation value, the better the node's link carrying capacity and operational efficiency, and the more likely it is to be prioritized for link task distribution and load balancing.

[0052] A second aspect of the present invention provides a video surveillance device based on quantum cryptography communication, comprising a front-end monitoring device, a quantum cryptography communication device, a relay link processing device, and a collaborative control host. The front-end monitoring device is deployed at a key location within the monitoring area and is responsible for real-time sensing and collection of multi-source information in the environment, including video image data, environmental perception data, link status data, and interaction data between nodes. The device integrates a video acquisition module, a sensor interface, and a communication module. It not only captures high-definition video streams and facial and behavioral information, but also simultaneously senses environmental parameters such as gas concentration, temperature, humidity, and light intensity. It also monitors network status such as link bandwidth, packet loss rate, and interaction frequency, and collects interaction information such as node response delay and block generation time difference. After completing preprocessing operations such as structural cleaning and field standardization of the raw data, the processed multimodal data is uploaded to the quantum cryptography communication device via a high-speed channel. The quantum cryptography communication device, serving as the core node for secure front-end data processing, establishes a secure connection with multiple front-end monitoring devices, receives preprocessed data, and performs quantum key encryption operations. The device generates a high-strength key stream in real time, assesses quantum entropy and channel deviation, and adjusts encryption strength accordingly. The system also calculates a security assessment of the current system data by comprehensively analyzing metrics such as the packet loss rate, link interference level, and key consistency of uploaded data. Based on these assessment results, the device automatically adjusts the encryption parameters and data upload cadence of the front-end equipment to ensure controlled and secure information transmission in complex environments. The relay link processing device receives encrypted data from the quantum cryptography communication device and dynamically schedules and optimizes relay paths and forwarding strategies based on the real-time link status. The device's embedded link assessment module assesses the availability and stability of each relay node based on multiple metrics, including current link bandwidth, transmission rate, node responsiveness, and path interference intensity. It then selects the optimal path for efficient data distribution and cache management. It also features abnormal link isolation and backup link activation, ensuring continuity and reliability during multi-hop transmission. The collaborative control host, serving as the system's intelligent scheduling and control hub, connects the relay link processing device and the quantum cryptography communication device and is responsible for global data scheduling and policy coordination. The host receives security and link reliability assessments in real time and calculates the system's node collaboration efficiency by combining node data processing intensity, resource utilization, and the standard deviation of task processing time. When it is found that the collaborative efficiency has decreased or the load of some nodes is abnormal, the host will promptly generate optimization control instructions, including task redistribution suggestions, path reconstruction strategies and node resource release mechanisms, and feed back the instructions to the front-end monitoring equipment and relay link processing devices, thereby achieving dynamic optimization of the overall system resources and continuous improvement of response efficiency.

[0053] In this implementation, a complete closed loop from data acquisition, encryption protection, link transmission, to node coordination is constructed through front-end monitoring equipment, quantum cryptography communication devices, relay link processing devices, and a collaborative control host. The system first uses the front-end monitoring equipment to collect video images, environmental parameters, link status, and node interaction data within the monitoring area, and performs preliminary standardization and structured processing locally. Subsequently, the quantum cryptography communication device receives the pre-processed data, performs encryption operations using quantum keys, and simultaneously calculates the data's security assessment value. Based on the security status, it dynamically adjusts the encryption strength and transmission strategy of the front-end equipment to ensure data anti-tampering and anti-eavesdropping capabilities during upload. The relay link processing device evaluates the current link quality based on indicators such as link bandwidth, rate, and interference. Based on this, it dynamically optimizes the data forwarding path and load distribution strategy between relay nodes, achieving efficient data distribution and cache management in multi-hop links. Finally, the collaborative control host comprehensively analyzes the aforementioned security assessment values ​​and link reliability indicators, calculates the collaborative efficiency of each node in the system, and generates optimized control instructions to guide the scheduling strategy adjustments of the front-end equipment and relay links, thereby improving overall processing capacity, resource utilization efficiency, and system response speed. Through the linkage between modules, the system realizes efficient, secure and stable processing of monitoring data in complex environments, and is suitable for intelligent monitoring and edge computing deployment needs in multiple scenarios.

[0054] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0055] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A video surveillance system based on quantum encryption communication, characterized in that: include: The data acquisition module collects video image data, environmental perception data, link status data, and node interaction data through front-end monitoring equipment, and performs standardized preprocessing on the collected multimodal data to generate a standardized data set; The quantum encryption communication module performs security measurement evaluation of video data under the quantum encryption communication link based on a standardized data set, and dynamically adjusts the encryption parameters and data transmission strategy in the front-end monitoring device based on the security measurement evaluation results; The relay chain transmission module analyzes the load suppression capability and reliability of the relay chain based on the link bandwidth of each relay chain node. Based on the analysis results, it dynamically reconstructs the relay forwarding path and communication routing strategy of the video data, and optimizes the collaborative scheduling relationship between nodes and the link load distribution method. The parallel chain data processing and collaborative analysis module comprehensively determines the collaborative response capabilities of nodes during data transmission based on the security measurement evaluation results of video data and the reliability analysis of relay links. Based on the node collaborative efficiency evaluation results, it generates an optimized scheduling model between multiple nodes across the chain, and further outputs a comprehensive optimization report covering video data routing paths, link resource allocation and relay load balancing strategies.

2. A video surveillance system based on quantum encryption communication according to claim 1, characterized in that: The specific steps of collecting video image data, environmental perception data, link status data and node interaction data through the front-end monitoring equipment are as follows: The video surveillance module collects video image data including HD video stream frame rate, encoding format, image noise level, signal strength, facial data, and abnormal behavior; Collect environmental perception data including gas concentration, temperature, humidity, light intensity and particle concentration through environmental perception sensors; The communication module collects link status data such as link bandwidth, average transmission delay, packet loss rate, channel interference intensity, interaction frequency, and data return success rate; The node interface monitoring module collects node interaction data including the response delay, interaction frequency, block generation interval, task processing time, and resource utilization rate of each blockchain node per unit time, which is used to build a node collaboration efficiency evaluation model and calculate the actual data processing intensity of the node; Generate high-strength key streams in real time through the quantum key management unit, including quantum key entropy and deviation detection rate, and record key generation cycles, key update times, and key stream integrity verification results; At the same time, the collection timestamp, data collection frequency, total number of fields, number of missing fields, data nesting level and field standardization completion ratio of each data source were recorded.

3. The video surveillance system based on quantum encryption communication according to claim 1, characterized in that: The specific steps of performing standardization preprocessing on the collected multimodal data to generate a standardized data set are as follows: By converting the collected video image data, environmental perception data, link status data, and node interaction data into a unified timestamp format, we ensure that all data sources have aligned timestamps. Eliminate empty values, error codes, or fields that do not conform to the format in the collected data, mark and record abnormal data ratios for subsequent anomaly detection and backfilling; Standardize and map data field names from different sources, convert non-uniform field names into predefined standard fields, and record the field mapping success rate; Fields with more than three nested levels are flattened, breaking nested fields into a flat format to improve data parsing efficiency; the physical unit format of each field is unified and unit conversion accuracy information is retained; The number of valid fields, the length of time the fields are missing, the standardization completion rate, and the field retention rate in each type of data are counted, and the standardized data are integrated into a standardized data set.

4. The video surveillance system based on quantum encryption communication according to claim 1, characterized in that: The specific steps of performing security metric assessment on video data under a quantum encryption communication link based on a standardized data set and dynamically adjusting encryption parameters and data transmission strategies in a front-end monitoring device based on the security metric assessment results are as follows: Extract the quantum key entropy, deviation detection rate, and channel interference strength from the quantum key management unit to calculate the quantum key strength value; extract the interaction frequency of each current link and calculate the total interaction value; Extract the packet loss rate of the data collected by each front-end device at the current moment and combine it with the total number of front-end devices to obtain the video data security assessment value; Compare the video data security assessment value with the security assessment threshold in real time, where the security assessment threshold includes a first security threshold and a second security threshold: When the video data security assessment value is greater than the first security threshold, it is determined that the data encryption strength, link interference suppression, and transmission integrity of the front-end monitoring device are all at an excellent level, and the overall data security is high, and the current encryption parameters and transmission strategy remain unchanged; When the video data security assessment value is greater than the second security threshold and less than or equal to the first security threshold, it is judged as medium-risk data. The security is acceptable but tends to be tense. It is necessary to optimize and adjust the link and data processing strategies. The specific adjustment measures are to dynamically reallocate data processing tasks to reduce the data pressure of high-load relay chain nodes. At the same time, adjust the data return path of the front-end device and give priority to relay nodes with better link quality and higher transmission efficiency for data transfer. When the video data security assessment value is less than or equal to the second security threshold, it is determined to be high-risk data and protective and corrective measures must be implemented immediately. The specific method is to suspend the uplink and link return of non-critical data, giving priority to ensuring the transmission security of core monitoring data; activate the off-chain buffer to temporarily isolate abnormal data to prevent data leakage and link blockage; Standardize fields and repair outliers for data sources, lower the data processing priority of affected nodes, and migrate some tasks to redundant nodes.

5. The video surveillance system based on quantum encryption communication according to claim 4, characterized in that: The specific steps of obtaining the video data security assessment value are as follows: Calculate the ratio of the quantum key entropy value of the current data to the deviation detection rate, add one to the logarithm, and divide it by the sum of the channel interference strength plus one to obtain the quantum key strength value of the data; For each data stream, count its interaction frequency across all nodes, multiply it by the weight coefficient corresponding to each node, then take the weighted sum and divide it by the maximum interaction frequency of all devices to obtain the total interaction value of the data in the network. Then obtain the packet loss rate of the data at this moment, multiply it by the quantum key strength value and the total interaction value, sum it up and divide it by the total number of front-end devices to obtain the video data security assessment value at the current time.

6. The video surveillance system based on quantum encryption communication according to claim 1, characterized in that: The specific steps of analyzing the load suppression capability and reliability of the relay chain link based on the link bandwidth of each relay chain node, dynamically reconstructing the relay forwarding path and communication routing strategy of the video data based on the analysis results, and optimizing the collaborative scheduling relationship between nodes and the link load distribution method are as follows: The link bandwidth, data flow rate, and data collection frequency of each relay link are extracted from the standardized data set to obtain the total link reachability value. The data nesting level of the current relay link node and the collection completeness rate of the key fields at this moment are extracted and comprehensively calculated to obtain the relay link reliability assessment value. Compare the relay link reliability evaluation value with the link performance threshold in real time. The link performance threshold includes the first reliability threshold and the second reliability threshold: When the relay link reliability evaluation value is greater than the first reliability threshold, it indicates that the link transmission capability is strong and the interference level is low, and the link reliability is high. The current path scheduling strategy and node coordination mode are maintained unchanged. When the relay link reliability evaluation value is greater than the second reliability threshold and less than or equal to the first reliability threshold, the link reliability is determined to be marginal, with some bandwidth jitter and inter-node interference. Path fine-tuning and traffic optimization are required. Specific measures include dynamically adjusting the relay node forwarding load, replacing some forwarding paths with degraded link quality, and periodically verifying the communication consistency of the primary link to enhance the overall reliability of the relay path. When the relay link reliability assessment value is less than or equal to the second reliability threshold, it indicates that the link reliability has seriously declined and there is a high-risk transmission risk. The link reconstruction and node isolation mechanism must be immediately enabled, including disabling high-interference links, suspending the relay forwarding of secondary data, activating backup low-interference paths for rerouting, reducing the scheduling frequency of affected nodes, and redistributing link loads and data flows.

7. A video surveillance system based on quantum cryptography communication according to claim 6, characterized in that: The specific steps of comprehensively calculating and obtaining the relay link reliability evaluation value are as follows: The load adjustment gain of the current relay link node is obtained by multiplying the link bandwidth and data flow rate of the relay link node at the current moment and then calculating the ratio by the sum of the data collection frequency and the stability factor. The load adjustment gains of all relay links are then weighted and summed in combination with the collection completeness rate of the key fields. The result is multiplied by the adjustment factor and then subtracted from the product of the data nesting level and the penalty factor of the relay link node at the current moment. Finally, the difference is added by one and the logarithm is taken to obtain the relay link reliability assessment value.

8. The video surveillance system based on quantum encryption communication according to claim 1, characterized in that: The security metric evaluation results based on video data and the reliability analysis of relay links are used to comprehensively determine the collaborative response capabilities of nodes during data transmission. Based on the node collaborative efficiency evaluation results, an optimized scheduling model between cross-chain multiple nodes is generated, and a comprehensive optimization report covering video data routing paths, link resource allocation, and relay load balancing strategies is further output. The specific steps are as follows: Obtain the video data security assessment value of each data and the relay link reliability assessment value of each relay link node, then extract the current data processing intensity, resource utilization rate and standard deviation of the task processing time of all nodes of each node, and combine the load balancing factor to obtain the current node collaborative efficiency assessment value; Compare the node collaboration efficiency evaluation value with the collaboration threshold in real time. The collaboration efficiency threshold includes a first efficiency threshold and a second efficiency threshold: When the node collaborative efficiency evaluation value is greater than the first efficiency threshold, it is determined that the parallel chain node collaborative analysis efficiency is good, and the overall processing capacity, resource utilization and link load are in a high-quality state; at this time, the existing parallel chain task allocation strategy is maintained, the cache tasks of low-priority nodes are streamlined to release resources and maintain the high-concurrency data analysis task allocation of high-adaptability nodes; a collaborative efficiency maintenance report is generated, including the current node collaborative efficiency index, node load overview and processing time difference distribution between nodes for administrator reference; When the node collaborative efficiency evaluation value is greater than the second efficiency threshold and less than or equal to the first efficiency threshold, it is determined that the parallel chain node collaborative processing is in a medium load state, and task and load strategy optimization is required; the node task allocation weight should be dynamically adjusted to reduce the data processing tasks of high-load nodes, and at the same time, the node data stream merging processing mechanism should be enabled to reduce repeated calculations between nodes; Optimize node processing paths to improve data return speed and analysis timeliness; Generate collaborative efficiency optimization and task reallocation recommendation reports, including node task adjustment plans, load balancing optimization paths, and high-time-consuming node processing recommendations; When the node collaborative efficiency evaluation value is less than the second efficiency threshold, it is determined that the parallel chain collaborative processing efficiency is low, the link bottleneck and node performance bottleneck are obvious, and repair and reconstruction measures need to be taken; specifically, the data access frequency of inefficient nodes is reduced and off-chain auxiliary processing is enabled; Start node processing capacity self-check to identify and isolate time-consuming and inefficient nodes; enable inter-chain fault tolerance mechanism and spare nodes to participate in processing to avoid single point bottlenecks; generate collaborative efficiency fault repair and resource reconstruction report settings, including node isolation measures, inter-chain task migration path and abnormal node performance diagnosis results.

9. The video surveillance system based on quantum encryption communication according to claim 8, characterized in that: The specific steps of obtaining the current node collaboration efficiency evaluation value are as follows: Extract the video data security assessment value, data processing intensity, and corresponding relay link reliability assessment value of each node within the specified time window, multiply the three, and then divide by the resource utilization rate of the node to obtain the collaborative load score of the node; Sum up the collaborative load scores of all nodes in turn to get the total collaborative load value at the current moment; Extract the task processing time of each node, calculate its standard deviation, obtain the task coordination disturbance degree, and multiply it by the load balancing factor to form the task scheduling stability adjustment item; The adjustment item is added by one and used as the numerator of the node collaborative efficiency evaluation value. The total collaborative load value is used as the denominator to perform a ratio calculation to finally obtain the current node collaborative efficiency evaluation value.

10. A video surveillance device based on quantum encryption communication, characterized in that: include: Front-end monitoring equipment, quantum encryption communication device, relay link processing device and collaborative control host; The front-end monitoring device is used to collect video image data, environmental perception data, link status data and node interaction data in the monitoring area, and upload the multimodal data after pre-processing; The quantum encryption communication device is connected to the front-end monitoring device, and is used to receive the pre-processed data and perform quantum key encryption, while calculating the security evaluation value of the data, and dynamically adjusting the encryption parameters and data transmission strategy of the front-end monitoring device according to the evaluation result; The relay link processing device is used to receive encrypted data and dynamically select and optimize the forwarding path and communication route between each relay node based on the link evaluation conditions to achieve efficient distribution and cache management of data during multi-hop transmission; The collaborative control host is connected to the relay link processing device and the quantum encryption communication device, and is used to comprehensively analyze the security assessment value and link assessment value of the aforementioned data, calculate the node collaboration efficiency, and generate optimized control instructions based on this, and feed them back to the front-end monitoring equipment and the relay link processing device, thereby improving the response efficiency and resource allocation capabilities of the entire system.

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