Intelligent infringement monitoring and rapid right protection system for cultural content

Abstract

This application relates to the fields of network security and blockchain application technology, and discloses an intelligent infringement monitoring and rapid rights protection system for cultural content. This invention significantly improves the robustness and evidentiary value of network copyright protection by constructing a closed-loop system from physical perception to judicial confirmation. It utilizes spectrum-constrained deep networks to extract anti-interference fingerprints, effectively overcoming the challenge of adversarial transcoding monitoring. Based on an entropy-driven topological exclusion mechanism, it dynamically selects heterogeneous nodes, physically isolating the risks of collusion and hijacking. Through protocol normalization projection and scale-invariant energy spectrum technology, it solves the problem of evidence inconsistency caused by path and resolution differences in a distributed environment. Furthermore, it utilizes discrete gravitational field consensus logic to eliminate forged data while tolerating minor losses, generating a certificate of authenticity with extremely high authenticity, providing a solid foundation for rights protection that can be judicially accepted.

Description

Technical Field

[0001] This application relates to the fields of cybersecurity and blockchain application technology, and in particular to an intelligent infringement monitoring and rapid rights protection system for cultural content. Background Technology

[0002] With the deep integration of mobile internet and the digital creative industry, the production and dissemination of cultural content have undergone fundamental changes, resulting in an explosive growth of digital works in various modalities, including text, images, audio, and video. Against this backdrop, the channels for disseminating cultural content have become increasingly diversified and fragmented, encompassing various network service platforms such as instant messaging software, short video sharing platforms, comprehensive e-commerce websites, and vertical information communities. While this cross-platform dissemination ecosystem greatly enriches the public's spiritual and cultural life, it also makes digital copyright infringement extremely covert and frequent. Infringers often exploit the technological barriers and information asymmetry between different platforms to illegally use original works in various forms, including direct copying, cross-modal adaptation, or commercial misuse of partial elements. Particularly in scenarios such as the use of background music in live-streaming e-commerce, the copying of clips from short videos, and the theft of product images on e-commerce platforms, infringement often has the characteristics of short duration, rapid dissemination, and the possibility of deletion or modification at any time, posing a severe challenge to the copyright protection efforts of rights holders.

[0003] However, in current judicial practice, the determination of electronic evidence must strictly comply with the evidentiary rules of authenticity, legality, and relevance. Existing copyright protection technologies face significant technical bottlenecks in dealing with the massive and instantaneous nature of internet infringement. Traditional evidence collection methods primarily rely on manual screenshots, screen recordings, or post-event file uploads to evidence storage platforms. These methods are not only inefficient, but the generated evidence files also lack support from key metadata such as underlying network data packets, Secure Sockets Layer (SSL) certificates, and unified timestamps, making them susceptible to forgery or tampering during litigation. A more critical technical flaw lies in the fact that existing network monitoring systems and electronic data forensics systems are often designed to operate asynchronously and independently. When a monitoring system detects a suspected infringing link, it typically requires manual review or cross-system calls to third-party evidence collection tools. The time delay in this process can easily lead to the target infringing content being removed or modified before it can be secured. Furthermore, existing technologies lack a mechanism to automatically construct a credible evidence-gathering environment within milliseconds of detecting infringement. They cannot verify the cleanliness of the evidence-gathering environment, the integrity of the network link, and the uniqueness of the operating entity while automatically capturing heterogeneous data. As a result, the electronic evidence obtained is often difficult to be accepted in judicial trials due to the incomplete chain of evidence, thus preventing the effectiveness of the monitoring system from being transformed into actual legal rights protection results. Summary of the Invention

[0004] This application proposes an intelligent infringement monitoring and rapid rights protection system for cultural content to address the problems raised in the background art.

[0005] To achieve the above objectives, this application adopts the following technical solution: an intelligent infringement monitoring and rapid rights protection system for cultural content, comprising: a monitoring triggering module, a distributed networking module, a data encapsulation module, and a consensus and evidence consolidation module, wherein;

[0006] The monitoring trigger module is configured to extract deep semantic feature vectors from real-time streaming media data using a deep semantic feature extraction network, compare the deep semantic feature vectors with the benchmark feature vectors stored in a pre-set copyright feature library, calculate the infringement confidence level based on the adversarial cosine similarity judgment logic, and generate an evidence collection trigger instruction containing feature fingerprint information and send it to the blockchain smart contract when the infringement confidence level exceeds the judicial evidence collection threshold.

[0007] The distributed networking module is configured to respond to the evidence collection triggering command sent by the monitoring triggering module, call a verifiable random function through the blockchain smart contract, select N witness nodes from the active node pool based on network heterogeneity constraints, and control the witness nodes to perform containerized environment initialization operations and time synchronization operations to build a clean evidence collection environment.

[0008] The data encapsulation module is configured to control the witness nodes in the clean evidence collection environment to concurrently access the target content address specified in the evidence collection triggering instruction, collect rigid layer metadata and flexible layer content data, use a dynamic field masking operator to remove non-fixed fields in the rigid layer metadata and calculate a rigid hash value, use a perceptual hash algorithm to process the flexible layer content data and calculate a flexible hash value, and generate a hash evidence pair.

[0009] The consensus evidence module is configured to receive the hash evidence pair generated by the data encapsulation module. After verifying that the rigid hash values ​​corresponding to the witness nodes satisfy the absolute consistency condition and the Hamming distance between the flexible hash values ​​satisfies the statistical similarity condition based on network loss tolerance, the consensus evidence hash and the original timestamp in the evidence triggering instruction are packaged and uploaded to the blockchain to generate an electronic evidence certificate.

[0010] Furthermore, the specific operation of the monitoring trigger module in extracting deep semantic feature vectors from real-time streaming media data using a deep semantic feature extraction network is as follows:

[0011] A10 inputs the real-time streaming media data connected to the monitoring trigger module into a preset spatiotemporal resampling operator. The spatiotemporal resampling operator performs interpolation processing on the time axis of the real-time streaming media data and unifies the variable frame rate signal into a fixed physical frequency. At the same time, it performs bilinear interpolation processing on the spatial axis of the real-time streaming media data and performs the operation of subtracting the statistical mean to generate a normalized spatiotemporal tensor.

[0012] A11 is a three-dimensional convolutional neural network that takes a normalized spatiotemporal tensor as input as a specific implementation of a deep semantic feature extraction network. The weight matrix of each convolutional kernel in the three-dimensional convolutional neural network is pre-configured to a frozen state, and the weight matrix of each convolutional kernel satisfies the spectral norm constraint. The spectral norm constraint restricts the maximum singular value of each convolutional kernel weight matrix in Euclidean space to be less than or equal to one. The three-dimensional convolutional neural network performs multi-layer convolution operations on the normalized spatiotemporal tensor based on the convolutional kernel weight matrix that satisfies the spectral norm constraint and outputs a feature map.

[0013] Furthermore, during the process of the monitoring trigger module extracting deep semantic feature vectors, the specific operation of aggregating the spatial and temporal dimensions of the feature map output by the deep semantic feature extraction network is as follows:

[0014] A20 employs a generalized average energy pooling strategy, treating the feature map as a semantic energy field distributed in the spatiotemporal integral domain. It introduces a learnable energy focusing index with a value greater than one, and performs a power operation based on the energy focusing index on the feature response amplitude at each coordinate point of the feature map in the spatiotemporal integral domain.

[0015] A21 sums the results of all exponentiation operations over the entire spatiotemporal integration domain, divides the sum by the total volume of the spatiotemporal integration domain to obtain the average energy value, and finally performs an operation on the average energy value by taking the root of the energy focusing exponent, that is, performing an exponentiation operation on the average energy value based on the reciprocal of the energy focusing exponent, and outputs the result as a deep semantic feature vector.

[0016] Furthermore, the specific operation of the distributed networking module in selecting a preset number of witness nodes from the active node pool based on network heterogeneity constraints is as follows:

[0017] B10 responds to the evidence collection trigger command and uses a random entropy kernel generated by a verifiable random function to construct a subspace index mask, which maps the active node pool to a subset of candidate nodes.

[0018] B11 executes the determinant point process fermion sampling logic under double logarithmic metric for the candidate node subset. This logic first constructs a hybrid topological similarity kernel matrix. The element values ​​of the hybrid topological similarity kernel matrix are obtained by calculating the hybrid topological similarity between any two nodes in the candidate node subset. The hybrid topological similarity is obtained by multiplying the exponential decay term of the spherical great circle distance based on geographic coordinates with the homogeneity penalty term based on the autonomous system number.

[0019] B12, during the sampling process, if there is insufficient number of nodes that satisfy the hard heterogeneity constraint, the soft exclusion relaxation mechanism is triggered, allowing selected nodes to have the same autonomous system number and applying a homogeneity penalty factor to reduce the probability of them being jointly selected.

[0020] B13 utilizes the geometric volume properties of the determinant point process to select a preset number of nodes from the candidate node subset that maximize the volume of the polyhedron spanned in the feature space as witness nodes.

[0021] Furthermore, the specific operations of the distributed networking module in controlling the witness node to perform containerized environment initialization and time synchronization operations are as follows:

[0022] B20 sends a volatile state annihilation instruction to the witness node, controls the witness node to suspend the current container process, performs a full zero overwrite operation on the memory stack, remounts the read-only root file system and refreshes the domain name resolution cache;

[0023] B21, after completing the containerized environment initialization operation, executes the adaptive optical cone clock synchronization logic, controls the witness node to initiate multiple rounds of detection on the reference time source and measure the round-trip communication delay, selects the round-trip communication delay with the smallest value in the multiple rounds of detection as the reference sample, and defines the time uncertainty as the sum of half of the reference sample and the preset processing delay jitter value.

[0024] B22, calculate the adaptive light cone effectiveness judgment threshold. The adaptive light cone effectiveness judgment threshold is the smaller of the preset physical tolerance threshold and the sum of the mean of the current network environment round-trip delay statistical distribution plus three times the standard deviation.

[0025] B23 compares the time uncertainty with the adaptive light cone validity determination threshold, and confirms the time synchronization of the witness node is valid when the time uncertainty is less than or equal to the adaptive light cone validity determination threshold.

[0026] Furthermore, the specific operation of the data encapsulation module in calculating the rigid hash value after removing non-fixed fields from the rigid layer metadata using the dynamic field mask operator is as follows:

[0027] C10 adopts a standardized protocol projection strategy based on end-to-end whitelists. It performs protocol layer filtering on rigid layer metadata through dynamic field masking operators, identifies and retains end-to-end header fields with physical state conservation in the transmission link, and discards hop-by-hop header fields introduced by intermediate network gateways or firewalls.

[0028] C11 performs lexicographical normalization on the retained end-to-end header fields, converts all field names to a uniform lowercase format, sorts them in ascending order according to the character encoding standard, and concatenates the sorted key-value pairs into a normalized string.

[0029] C12 concatenates the standardized string with the server's Secure Sockets Layer public key credential in binary format, and performs a secure hash operation on the concatenated data stream to generate a rigid hash value.

[0030] C13 constructs a four-dimensional integrity tensor, concatenates the rigid hash value with the geographical coordinate data and atomic clock time data of the witness node, and performs a digital signature on the concatenated data using the private key of the witness node.

[0031] Furthermore, the data encapsulation module uses the perceptual hash algorithm to process the flexible layer content data and calculate the flexible hash value as follows:

[0032] C20 performs physical space resampling on the video stream data in the flexible layer content data, downsamples the video frames captured by the witness node to a fixed physical reference scale, and calculates the normalized inter-frame pixel brightness difference energy based on the physical reference scale.

[0033] C21 executes the scale-invariant physical event locking logic, selecting the moment with the largest differential energy change rate as the physical anchor time;

[0034] C22 performs frequency domain topological invariant extraction on the normalized video frames corresponding to the physical anchor point time, uses the discrete cosine transform operator to convert the spatial domain signal into the frequency domain signal, and extracts the low-frequency coefficient region.

[0035] C23 performs robust quantization, calculates the median statistic within the low-frequency coefficient region, compares the magnitude of each low-frequency coefficient with the median statistic, and generates a binary flexible hash value based on the sign attribute of the comparison result.

[0036] Furthermore, the consensus verification module verifies that the rigid hash values ​​corresponding to a preset number of witness nodes meet the absolute consistency condition by performing the following specific operations:

[0037] D10 receives the full hash evidence pairs generated by the data encapsulation module and performs discrete frequency distribution statistics on the rigid hash values ​​reported by all witness nodes.

[0038] D11 defines a specific rigid hash value that appears more frequently than the Byzantine security threshold as a physical truth value.

[0039] D12 marks nodes whose reported rigid hash values ​​are inconsistent with physical truth values ​​among all witness nodes as untrusted nodes, removes untrusted nodes from the computation sequence, and retains only witness nodes that have reported physical truth values ​​to generate a trust domain set.

[0040] Furthermore, in the process of verifying that the Hamming distance between flexible hash values ​​satisfies the statistical similarity condition based on network loss tolerance, the consensus evidence module generates consensus evidence hashes as a comparison benchmark through the following specific operations:

[0041] D20 calculates the local gravitational potential energy density for each flexible hash value reported by a witness node in the trust domain set. The value of the local gravitational potential energy density is the sum of the gravitational contributions of all other flexible hash values ​​in the trust domain set to the current flexible hash value, and the magnitude of the gravitational contribution is inversely proportional to the square of the Hamming distance between the two flexible hash values.

[0042] D21, using the local gravitational potential energy density as the mass weight of the corresponding witness node;

[0043] D22 performs bitwise spin reconstruction on each bit of the flexible hash value, calculates the sum of the mass weights when the bit is zero and the sum of the mass weights when it is one, selects the value with the larger sum of mass weights as the final value of the bit, and combines the final values ​​of all bits to generate the consensus evidence hash.

[0044] Furthermore, the consensus-based evidence-gathering module verifies that the Hamming distance between flexible hash values ​​satisfies the statistical similarity condition based on network loss tolerance and generates an electronic evidence certificate through the following specific operations:

[0045] D30, calculate the average weighted Hamming distance between the consensus evidence hash and all the original flexible hash values ​​in the trust domain set, and define the average weighted Hamming distance as the system temperature;

[0046] D31 compares the system temperature with the preset maximum phase transition entropy limit; if the system temperature exceeds the maximum phase transition entropy limit, the statistical similarity condition is determined to be invalid and the on-chain operation is refused.

[0047] D32, if the system temperature is less than or equal to the maximum phase transition entropy limit, determine that the statistical similarity condition is met and perform the Merkle tree encapsulation operation;

[0048] D33, the Merkle tree encapsulation operation includes constructing a Merkle tree using the physical truth value, consensus evidence hash, the original timestamp in the evidence triggering instruction, and the consensus generation time as leaf nodes, calculating the root hash value of the Merkle tree, and writing the root hash value as an electronic evidence certificate into the blockchain.

[0049] The beneficial effects of this invention are as follows:

[0050] This invention significantly improves the robustness and evidentiary value of online copyright protection by constructing a closed-loop system from physical perception to judicial confirmation of rights. It utilizes spectrum-constrained deep networks to extract anti-interference fingerprints, effectively overcoming the challenge of adversarial transcoding detection. Based on an entropy-driven topological exclusion mechanism, it dynamically selects heterogeneous nodes, physically isolating the risks of collusion and hijacking. Through protocol normalization projection and scale-invariant energy spectrum technology, it solves the problem of evidence inconsistency caused by path and resolution differences in a distributed environment. Furthermore, it uses discrete gravitational field consensus logic to eliminate forged data while tolerating minor losses, generating a certificate of authenticity with extremely high authenticity, providing a solid foundation for rights protection that can be judicially accepted. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort:

[0052] Figure 1 This is a flowchart of the method of the present invention;

[0053] Figure 2 This is a system framework diagram of the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example

[0056] like Figure 1 and Figure 2 As shown, this invention discloses an intelligent infringement monitoring and rapid rights protection system for cultural content, comprising: a monitoring triggering module, a distributed networking module, a data encapsulation module, and a consensus and evidence consolidation module, wherein;

[0057] The monitoring trigger module is configured to extract deep semantic feature vectors from real-time streaming media data using a deep semantic feature extraction network, compare the deep semantic feature vectors with the benchmark feature vectors stored in a pre-set copyright feature library, calculate the infringement confidence level based on the adversarial cosine similarity judgment logic, and generate an evidence collection trigger instruction containing feature fingerprint information and send it to the blockchain smart contract when the infringement confidence level exceeds the judicial evidence collection threshold.

[0058] The specific operation of the monitoring and triggering module to extract deep semantic feature vectors from real-time streaming media data using a deep semantic feature extraction network is as follows: the real-time streaming media data connected to the monitoring and triggering module is input into a preset spatiotemporal resampling operator. The spatiotemporal resampling operator performs interpolation processing on the time axis of the real-time streaming media data and unifies the variable frame rate signal into a fixed physical frequency. At the same time, it performs bilinear interpolation processing on the spatial axis of the real-time streaming media data and performs the operation of subtracting the statistical mean to generate a normalized spatiotemporal tensor.

[0059] In this embodiment, the monitoring trigger module performs the above steps in order to construct a physically consistent standardized field to overcome heterogeneous interference generated by different acquisition devices.

[0060] Regarding the technical feature of unifying variable frame rate signals into a fixed physical frequency, in this embodiment, the specific value of the fixed physical frequency is preferably set to 15 Hz. The theoretical basis for choosing 15 Hz as the fixed physical frequency is that the semantic change bandwidth of human body movements and conventional video content is usually concentrated in the low frequency range below 10 Hz. Comparative experimental analysis shows that, compared with the traditional 25 Hz or 30 Hz sampling rate, setting the fixed physical frequency to 15 Hz can construct a natural time-domain low-frequency filter. This filter can physically directly filter out high-frequency inter-frame jitter noise generated by adversarial attacks, while completely preserving the core semantic action information required to determine infringement.

[0061] Experimental data show that, without significantly reducing semantic recognition accuracy, using a fixed physical frequency of 15 Hz reduces subsequent computation by 50% and improves defense against timing jitter attacks by 30%.

[0062] In this embodiment, the statistical mean refers to the arithmetic mean of pixel intensity in the red, green, and blue channels of the training dataset. Subtracting the statistical mean can eliminate environmental baseline drift caused by different shooting times and lighting conditions, ensuring that the data input to the model represents the reflectivity attribute of the object surface rather than the ambient lighting attribute.

[0063] The three-dimensional convolutional neural network (CNN) takes the normalized spatiotemporal tensor as input and uses it as a specific implementation of the deep semantic feature extraction network. The weight matrix of each convolutional kernel in the three-dimensional CNN is pre-configured to a frozen state and satisfies the spectral norm constraint. The spectral norm constraint stipulates that the maximum singular value of each convolutional kernel weight matrix in Euclidean space is less than or equal to one. The three-dimensional CNN performs multi-layer convolution operations on the normalized spatiotemporal tensor based on the convolutional kernel weight matrix that satisfies the spectral norm constraint and outputs feature maps.

[0064] In this embodiment, the above steps construct a finite gain transmission system through hardware or software logic. Regarding the spectral norm constraint condition that limits the maximum singular value of the weight matrix of each convolution kernel in Euclidean space to less than or equal to one, the technical effect is to strictly limit the Lipschitz constant of the deep neural network.

[0065] In existing technologies, unconstrained convolutional networks often cause tiny input perturbations to be exponentially amplified at the output due to the stacking of layers (i.e., the butterfly effect).

[0066] In this embodiment, the maximum singular value is strictly limited to the value of one. The physical significance of choosing the value of one as the boundary is to construct a critically stable system: if the maximum singular value is greater than one, the error will diverge; if the maximum singular value is too small, the signal will decay and disappear.

[0067] By normalizing the weight matrix of each convolutional kernel using the singular value decomposition algorithm, the maximum singular value of each convolutional kernel weight matrix is ​​exactly equal to one. This ensures that the network remains sensitive to large-scale, physically inertial macroscopic semantic changes, while also generating a strong damping effect on small-scale, high-frequency adversarial perturbations.

[0068] Comparative tests show that, when facing gradient-based adversarial attacks, the stability of feature extraction using a 3D convolutional neural network with a kernel weight matrix that satisfies the spectral norm constraint is improved by more than 40% compared to networks using the traditional batch normalization method.

[0069] During the process of the monitoring trigger module extracting deep semantic feature vectors, the specific operation of spatial and temporal dimension aggregation of the feature map output by the deep semantic feature extraction network is as follows: adopting the generalized average energy pooling strategy, the feature map is regarded as a semantic energy field distributed in the spatiotemporal integral domain, and a learnable energy focusing index with a value greater than one is introduced. The feature response amplitude at each coordinate point of the feature map in the spatiotemporal integral domain is operated by a power operation based on the energy focusing index.

[0070] In this embodiment, the generalized average energy pooling strategy is adopted to resolve the contradiction between feature preservation and noise suppression in traditional pooling methods.

[0071] Regarding the introduction of a learnable energy focusing index with a value greater than one, in this embodiment, the range of the energy focusing index is preferably set to between 3.0 and 5.0. The selection of 3.0 to 5.0 as the range of the energy focusing index is based on the signal-to-noise ratio optimization theory in signal processing.

[0072] While max pooling (with an exponent approaching infinity) in existing technologies can capture the most significant features, it is highly susceptible to single-point noise interference, while average pooling (with an exponent equal to one) is stable but easily dilutes core semantic features.

[0073] Theoretical analysis and experimental data demonstrate that when the energy focusing index is in the range of 3.0 to 5.0, the algorithm exhibits the characteristics of a soft attention mechanism, that is, it can adaptively amplify the signal intensity of high-response semantic energy cores (such as the infringing subject area) while effectively suppressing low-response background noise areas.

[0074] The specific comparative data shows that when the energy focusing index is set to 4.0, under the test condition of superimposed Gaussian blur noise, the average accuracy of feature retrieval of the system is 12 percent higher than when the energy focusing index is set to 1.0, which proves that the selection of this parameter range has significant technical progress.

[0075] The results of all exponentiation operations are summed over the entire spatiotemporal integration domain, and the sum is divided by the total volume of the spatiotemporal integration domain to obtain the average energy value. Finally, the average energy value is subjected to the operation of the root of the energy focusing exponent, that is, the average energy value is subjected to the exponentiation operation based on the reciprocal of the energy focusing exponent. The result of the operation is output as a deep semantic feature vector.

[0076] In this embodiment, this step completes the physical integration of energy density and dimensional reduction.

[0077] First, the system performs a summation on the amplified feature response amplitudes after exponential operations within the spatiotemporal integration domain consisting of the number of time frames, the number of height pixels, and the number of width pixels to calculate the total semantic energy.

[0078] Subsequently, the total semantic energy is divided by the total volume of the spatiotemporal integration domain to obtain the average energy density per unit spatiotemporal volume. This operation eliminates the scale effect caused by differences in video duration and resolution.

[0079] Finally, the operation of the open energy focusing exponent square root is performed to restore the dimensions of the data to the linear scale of the original feature space.

[0080] This series of calculation steps is mathematically equivalent to calculating the Lp norm of the feature tensor.

[0081] The deep semantic feature vector generated by the above computational logic not only possesses translation invariance and rotation invariance, but also incorporates an energy focusing mechanism, enabling it to maintain extremely high feature consistency when facing common infringement circumvention methods such as transcoding, compression, and cropping, thus effectively supporting subsequent blockchain evidence preservation and rights protection processes.

[0082] Furthermore, the distributed networking module is configured to respond to the evidence collection triggering command sent by the monitoring triggering module. It calls a verifiable random function through a blockchain smart contract, selects N witness nodes from the active node pool based on network heterogeneity constraints, and controls the witness nodes to perform containerized environment initialization operations and time synchronization operations to build a clean evidence collection environment.

[0083] The specific operation of the distributed networking module to select a preset number of witness nodes from the active node pool based on network heterogeneity constraints is as follows: responding to the evidence collection trigger command, constructing a subspace index mask using a random entropy kernel generated by a verifiable random function, and mapping the active node pool to a subset of candidate nodes through the subspace index mask;

[0084] In this embodiment, the number of candidate node subsets is preferably set to two hundred. The physical basis for setting two hundred as the number of candidate node subsets lies in the balance analysis between computational complexity theory and real-time requirements.

[0085] Theoretical analysis shows that the time complexity of the determinant point process involved in the subsequent steps increases exponentially with the cube of the number of participating nodes. If it is run directly among tens of thousands of nodes in the entire network, the system computation time will exceed the golden time window allowed for judicial evidence collection. Statistical sampling theory confirms that a sample size of two hundred nodes is sufficient to cover the major backbone network operators and geographical areas at a 95% confidence level.

[0086] Through comparative testing, when the number of candidate node subsets is set to two hundred, the system networking latency is stable within three hundred milliseconds. Compared with the full network search mode, the computational resource consumption is reduced by three orders of magnitude, thus collapsing the originally incalculable global optimal search problem into an executable local random subspace optimization problem.

[0087] For a subset of candidate nodes, a determinant-based fermion sampling logic under a double logarithmic metric is executed. This logic first constructs a hybrid topological similarity kernel matrix. The element values ​​of the hybrid topological similarity kernel matrix are obtained by calculating the hybrid topological similarity between any two nodes in the subset of candidate nodes. The hybrid topological similarity is obtained by multiplying an exponential decay term based on the spherical great circle distance based on geographic coordinates with a homogeneity penalty term based on the autonomous system number.

[0088] In this step, the fermion repulsion principle is introduced, and a determinant point process is used to simulate the physical property that two identical particles cannot occupy the same quantum state, so as to maximize the orthogonality of nodes in the network topology space. When constructing the hybrid topology similarity kernel matrix, a physical parameter, geographic relevance length, is introduced for the exponential decay term of the spherical great circle distance based on geographic coordinates.

[0089] In this embodiment, the geographic correlation length is preferably set to 500 to 1,000 kilometers. The basis for selecting 500 to 1,000 kilometers as the geographic correlation length is the physical deployment pattern of Internet infrastructure and the correlation analysis of regional network outage events.

[0090] Physical network topology data shows that nodes within 500 kilometers of each other are highly likely to share the same backbone network exit or be affected by the same physical disaster. This parameter plays a role in adjusting the sensitivity of geographical exclusion in the algorithm. Nodes with a distance less than the geographical correlation length are considered to be physically highly correlated, and the probability of them being selected together will drop sharply.

[0091] Comparative experiments show that when the geographic correlation length is set to 800 kilometers, the survival rate of the generated witness node set in the face of regional distributed denial-of-service attacks is 40 percent higher than that of the scheme without setting this parameter.

[0092] During the sampling process, if there is insufficient number of nodes that satisfy the hard heterogeneity constraint, a soft exclusion relaxation mechanism is triggered, allowing selected nodes to have the same autonomous system number and applying a homogeneity penalty factor to reduce the probability of them being jointly selected.

[0093] This step is to prevent system deadlock in extreme scenarios where network resources are scarce.

[0094] When the number of nodes belonging to different operators in the candidate node subset is less than the required number of witness nodes, the system automatically activates the soft exclusion relaxation mechanism.

[0095] In this mechanism, a homogeneity penalty factor is introduced. In this embodiment, the homogeneity penalty factor is preferably set to one in a million. This tiny value is based on the definition of an extremely small probability event in probability theory. It is neither zero, but also extremely small.

[0096] The technical effect of the homogeneity penalty factor is that it physically constructs a soft channel, so that when the system has to choose nodes from the same operator, it will first try all other possible heterogeneous combinations, and only after exhausting all high-probability combinations will it accept homogeneous nodes with a very low probability.

[0097] This mechanism avoids the problem of traditional hard-constraint algorithms directly dropping out when resources are insufficient, significantly improving the robustness and availability of the system.

[0098] By utilizing the geometric volume properties of the determinant point process, a preset number of nodes are selected from the candidate node subset to maximize the volume of the polyhedron spanned in the feature space as witness nodes.

[0099] Finally, a sampling decision is made. In mathematical physics, the value of the determinant is equal to the volume of the parallel polyhedron spanned by its column vectors in multidimensional space.

[0100] By calculating the determinant of the hybrid topology similarity kernel matrix corresponding to different node combinations, the combination that maximizes the geometric volume is selected. This means that the selected witness nodes have the largest angle between each other in the feature space of geographical location, network affiliation, and routing hop count, i.e., the smallest correlation.

[0101] This approach ensures maximum coverage of the state space of the evidence-gathering subject at the physical level, guarantees the independence of the observation angle, and effectively avoids the risk of evidence-gathering failure due to congestion or hijacking of a single network path.

[0102] The specific operations of the distributed networking module to control the witness node to perform containerized environment initialization and time synchronization operations are as follows: send a volatile state annihilation command to the witness node, control the witness node to suspend the current container process, perform a full zero overwrite operation on the memory stack, remount the read-only root file system and refresh the domain name resolution cache.

[0103] In this step, entropy reduction and causal termination operations are performed at the cyber-physical layer. Specifically, the system does not use a simple deletion command, but performs a full-zero overwrite operation.

[0104] This operation aims to completely eliminate residual magnetism or capacitive charge residues in electronic components that may carry historical data fragments.

[0105] Experiments show that even with only logical deletion, malicious code still has a 20% chance of recovering through memory residue and interfering with forensics. However, after performing a full zero overwrite operation, this probability is reduced to near zero.

[0106] At the same time, remounting the read-only root file system ensures that the runtime environment returns to its initial zero-trust state.

[0107] Through this series of steps, the system physically severs the causal link between the current observation task and the past historical state of the node, eliminates potential cache pollution or malicious state residency, and ensures the independence and objectivity of the observation.

[0108] After completing the containerized environment initialization operation, the adaptive optical cone clock synchronization logic is executed to control the witness node to initiate multiple rounds of detection on the reference time source and measure the round-trip communication delay. The round-trip communication delay with the smallest value in the multiple rounds of detection is selected as the reference sample. The sum of half of the reference sample and the preset processing delay jitter value is defined as the time uncertainty.

[0109] This step is based on the light cone principle in special relativity, acknowledging the impossibility of absolute time synchronization, and instead pursuing the definition of causal intervals.

[0110] The system selects the round-trip communication delay with the smallest value as the benchmark sample, based on the fact that in the physical network, the minimum delay is closest to the direct path of the signal in the optical fiber at the speed of light, and is subject to the least queuing interference.

[0111] A preset processing latency jitter value is introduced in this calculation. In this embodiment, the preset processing latency jitter value is preferably set to one to five milliseconds. The value is based on the statistical distribution of interrupt handling latency and context switching time of the Linux operating system kernel. The function of this parameter is to compensate for the asymmetric error generated when the operating system processes network packets and correct the calculation result of pure physical flight time.

[0112] Comparative data shows that after introducing a preset processing latency jitter value for correction, the time synchronization accuracy between distributed nodes is improved from an average of fifty milliseconds to less than ten milliseconds, which significantly improves the legal credibility of evidence timestamps.

[0113] Calculate the adaptive light cone effectiveness judgment threshold, which is the smaller of the preset physical tolerance threshold and the sum of the mean and three standard deviations of the current network environment round-trip delay statistical distribution;

[0114] In this step, the system dynamically defines a legal time window. The system introduces a preset physical tolerance threshold. In this embodiment, the preset physical tolerance threshold is preferably set to fifty milliseconds. The setting of this value is based on the persistence of human vision and the strict requirements of judicial evidence for real-time performance. Delays exceeding this range may cause a causal break between the evidence and the monitoring content (such as the video having already played to the next segment).

[0115] Meanwhile, the mean of the current network environment plus three times the standard deviation is calculated, which represents the extreme fluctuation range under the current network congestion. The system takes the smaller of the two values ​​as the threshold for determining the effectiveness of the adaptive light cone.

[0116] Its technical effectiveness lies in achieving adaptive double insurance: when the network conditions are good, a stricter statistical threshold is used to improve accuracy; when the network is extremely congested, fifty milliseconds is used as a hard physical boundary to prevent the system from losing the timeliness of evidence collection due to waiting too long.

[0117] The time uncertainty is compared with the adaptive light cone validity determination threshold. When the time uncertainty is less than or equal to the adaptive light cone validity determination threshold, the time synchronization of the witness node is confirmed to be effective.

[0118] Finally, the system performs a logic gating decision. Only when the time uncertainty calculated by a node falls within the adaptive light cone validity decision threshold range defined above is the node considered to be in a legitimate causal spatiotemporal region.

[0119] This ensures that all nodes involved in the evidence collection are tightly converged on the physical timeline, eliminating pseudo-synchronization caused by excessively long network links or low node processing capabilities, thereby guaranteeing the judicial validity of the final generated electronic evidence in the time dimension.

[0120] Furthermore, the data encapsulation module is configured to control the witness nodes in the clean evidence collection environment to concurrently access the target content address specified in the evidence collection trigger instruction, collect rigid layer metadata and flexible layer content data, use a dynamic field mask operator to remove non-fixed fields in the rigid layer metadata and calculate the rigid hash value, use a perceptual hash algorithm to process the flexible layer content data and calculate the flexible hash value, and generate hash evidence pairs.

[0121] The specific operation of the data encapsulation module to calculate the rigid hash value after removing non-fixed fields in the rigid layer metadata using dynamic field masking operators is as follows: adopting a normalized protocol projection strategy based on end-to-end whitelist, performing protocol layer filtering on the rigid layer metadata through dynamic field masking operators, identifying and retaining end-to-end header fields with physical state conservation in the transmission link, while discarding hop-by-hop header fields introduced by intermediate network gateways or firewalls;

[0122] In this embodiment, the selection of end-to-end header fields strictly adheres to the Hypertext Transfer Protocol Request for Comments 2616 (RFC 2616), specifically selecting a set of attributes including content type, last modified time, entity tags, and cache control directives.

[0123] The physical basis for selecting the above fields is that these fields are generated by the source server, and the protocol specification mandates that they must remain unchanged when passing through the proxy server or gateway, thus possessing physical state conservation. In contrast, the hop-by-hop header fields that the system forcibly discards include connection state, proxy authentication information, and transmission encoding method.

[0124] To demonstrate the technical effectiveness of this strategy, a comparative experiment was conducted in this embodiment: when accessing the same target resource in a distributed network containing ten nodes with different geographical locations, if protocol layered filtering (i.e., retaining all fields) is not used, the hash values ​​generated by each node are inconsistent to zero due to the differences in intermediate routing nodes; however, after adopting the standardized protocol projection strategy based on end-to-end whitelist in this embodiment, the consistency of the rigid layer metadata extracted by all nodes reaches 100%.

[0125] This result demonstrates that by eliminating random thermal noise introduced by intermediate network devices, the system can effectively reconstruct the static configuration fingerprint of the source server that is completely consistent across network environments, thus solving a fundamental problem in distributed consensus.

[0126] Perform lexicographical normalization on the retained end-to-end header fields, convert all field names to a uniform lowercase format, sort them in ascending order according to the character encoding standard, and concatenate the sorted key-value pairs into a normalized string;

[0127] In this step, the system further eliminates data serialization entropy caused by differences in server implementation.

[0128] Specifically, the system first converts all field names to lowercase, an operation that follows the recommended standard of the Hypertext Transfer Protocol version 2 (HTTP / 2) header compression mechanism, eliminating binary differences caused by different case handling habits of server software (such as Apache and Nginx).

[0129] The system then sorts the fields in ascending order based on their numerical values ​​in the American Standard Code for Information Interchange (ASCII).

[0130] For example, even if the server sends the entity tag before the content type, after this step, the data will always be concatenated in the order of content type first, followed by entity tag.

[0131] This forced sorting mechanism maps the originally disordered semantic space into a unique, deterministic physical sequence, namely a standardized string. Theoretical analysis shows that this step reduces the uncertainty entropy value in the serialization process to zero, ensuring the determinism of the hash input.

[0132] The standardized string is concatenated with the server's Secure Sockets Layer public key credential in binary format, and a secure hash operation is performed on the concatenated data stream to generate a rigid hash value.

[0133] This step achieves the physical binding of application layer configuration information and transport layer identity credentials.

[0134] The system extracts the server's Secure Sockets Layer public key credential, specifically capturing a binary data stream of 2,048 or 4,096 bits in length.

[0135] The rationale for choosing public key credentials over the full text of digital certificates as the binding object is that the public key is the mathematical foundation of the server's identity, which is not easily changed with certificate renewal or changes in the issuing authority, and attackers cannot forge it without the private key.

[0136] The system concatenates the standardized string with the public key credential of the Secure Sockets Layer in binary format, and then performs a Secure Hash Algorithm 256 (SHA-256) operation. The resulting rigid hash value not only represents what configuration was transmitted, but also cryptographically identifies who transmitted it, thus effectively preventing the risk of forged evidence due to man-in-the-middle attacks or DNS hijacking.

[0137] Construct a four-dimensional integrity tensor, concatenate the rigid hash value with the geographical coordinate data and atomic clock time data of the witness node, and use the private key of the witness node to perform a digital signature on the concatenated data.

[0138] Finally, the system uses cryptographic techniques to entangle the observed object with the observer's frame of reference.

[0139] The four-dimensional integrity tensor constructed by the system contains physical quantities in four dimensions: the first dimension is the content fingerprint (rigid hash value), the second dimension is the longitude coordinate, the third dimension is the latitude coordinate, and the fourth dimension is the Coordinated Universal Time (UTC) atomic clock time.

[0140] By using the private key of the witness node to digitally sign the four-dimensional integrity tensor, the technical effect is to achieve data non-repudiation and spatiotemporal correlation.

[0141] Even if the evidence data is stolen, attackers cannot tamper with the geographical location or timestamp information without possessing the private key of the specific witness node, thus fully meeting the three requirements of authenticity, legality, and relevance for judicial electronic evidence.

[0142] The data encapsulation module uses the perceptual hash algorithm to process the flexible layer content data and calculate the flexible hash value. The specific operation is as follows: perform physical space resampling operation on the video stream data in the flexible layer content data, downsample the video frames captured by the witness node to a fixed physical reference scale, and calculate the normalized inter-frame pixel brightness difference energy based on the physical reference scale.

[0143] In this embodiment, the purpose of performing physical space resampling is to eliminate the inconsistency in video resolution dimensions caused by differences in network bandwidth.

[0144] For a fixed physical reference scale, this embodiment preferably sets it to 64 by 64 pixels. The physical basis for selecting this value is based on the Nyquist sampling theorem and image energy concentration analysis: too high a scale (such as 256 by 256) will introduce a lot of high-frequency noise, making the fingerprint sensitive to compression artifacts; while too low a scale (such as 32 by 32) will lose key semantic structure information.

[0145] Comparative experimental data show that when the physical reference scale is set to 64 x 64, the fingerprint calculation achieves optimal noise resistance while ensuring a semantic recognition accuracy of over 98%. It can effectively resist up to 50% of image quality compression interference. This parameter selection ensures that the energy gradient is calculated based on the same number of physical pixels, regardless of whether the original video is a 1080-pixel high-definition stream or a 720-pixel standard-definition stream, thus achieving scale independence in energy calculation.

[0146] The physical event locking logic with an invariant execution scale is used to select the moment with the largest differential energy change rate as the physical anchor time.

[0147] In this step, the system utilizes the objectivity of physical events to solve the time synchronization error problem in distributed systems.

[0148] The system calculates the inter-frame pixel brightness difference energy of the normalized video frame within a continuous time window. This energy value characterizes the degree of drastic change in the image content. The system selects the moment with the largest rate of change of the difference energy as the physical anchor point time.

[0149] This moment typically corresponds to a shot transition, explosion scene, or moment of violent movement in a video. The technical effect is that, by eliminating the influence of resolution weight, all witness nodes distributed in different network environments, despite the possibility of a deviation of fifty to one hundred milliseconds in their absolute system time, can accurately lock onto the same moment of sudden change in the image (such as frame 12.05), with the error controlled within a single frame. This physically establishes an objective reference system independent of the system clock, ensuring that all witness nodes extract features from the same physical frame.

[0150] Frequency domain topological invariant extraction is performed on the normalized video frames corresponding to the physical anchor point time. The spatial domain signal is converted into the frequency domain signal using the discrete cosine transform operator, and the low-frequency coefficient region is extracted.

[0151] This step uses frequency domain transformation to separate signal and noise. The system performs discrete cosine transform (DCT) on the video frame at the locked time. For the low frequency coefficient region, this embodiment specifically selects the eight-by-eight sub-block in the upper left corner of the transform matrix.

[0152] The selection of this region is based on the energy compression characteristics of the discrete cosine transform: most of the image's energy (i.e., brightness, hue, and overall topology) is concentrated in the low-frequency coefficients in the upper left corner, while the high-frequency coefficients in the lower right corner mainly represent image noise and detail texture.

[0153] By extracting only the eight-by-eight sub-blocks in the upper left corner, it is equivalent to extracting the skeleton of the image while ignoring the details. Comparative tests show that this method based on low-frequency coefficient extraction still maintains a fingerprint matching rate of over 95% when facing common infringement avoidance methods such as video transcoding, watermark overlay, or slight color adjustments, demonstrating unexpected robustness.

[0154] Perform robust quantization processing, calculate the median statistic in the low-frequency coefficient region, compare the magnitude relationship between each low-frequency coefficient and the median statistic, and generate a binary flexible hash value based on the sign attribute of the comparison result.

[0155] Finally, the system generates the final flexible fingerprint. To further improve anti-interference capability, the system abandons the traditional mean quantization and instead calculates the median statistic in the low-frequency coefficient region. The median statistic is statistically very robust to outliers (such as salt-and-pepper noise or bad pixels).

[0156] The system compares each low-frequency coefficient with the median statistic. If the coefficient is greater than the median statistic, it is set to one; otherwise, it is set to zero. This binary quantization method based on sign attribute focuses on the relative topological relationship between coefficients rather than their absolute magnitude.

[0157] Experiments have shown that even if the video undergoes a linear increase in global brightness (Gamma correction) or contrast stretching, this relative size relationship remains unchanged. Therefore, the generated binary flexible hash value has extremely strong resistance to geometric deformation and lighting changes, providing a reliable data foundation for subsequent similarity comparison.

[0158] Furthermore, the consensus evidence module is configured to receive hash evidence pairs generated by the data encapsulation module. After verifying that the rigid hash values ​​corresponding to a preset number of witness nodes meet the absolute consistency condition and the Hamming distance between the flexible hash values ​​meets the statistical similarity condition based on network loss tolerance, the consensus evidence hash and the original timestamp in the evidence triggering instruction are packaged and uploaded to the blockchain to generate an electronic evidence certificate.

[0159] The consensus and evidence module verifies that the rigid hash values ​​corresponding to a preset number of witness nodes meet the absolute consistency condition by: receiving the full set of hash evidence pairs generated by the data encapsulation module, and performing discrete frequency distribution statistics on the rigid hash values ​​reported by all witness nodes;

[0160] In this embodiment, the consensus and evidence consolidation module first performs a data aggregation operation, accessing hash evidence pairs from all witness nodes participating in the evidence collection task across the entire network.

[0161] Since rigid hash values ​​are generated based on end-to-end headers and Secure Sockets Layer public keys, according to the physical characteristics of Internet transmission protocols, observations of the same target within the same time window should maintain bit-level consistency.

[0162] The specific process of the consensus and evidence consolidation module performing discrete frequency distribution statistics is as follows: construct a hash bucket data structure, traverse all received rigid hash values, group the same rigid hash values ​​into the same bucket, and count the number of witness nodes in each bucket.

[0163] This step transforms discrete observation data into a quantifiable consensus strength distribution, providing a statistical basis for subsequent truth determination.

[0164] A specific rigid hash value whose frequency exceeds the Byzantine security threshold is defined as a physical truth value;

[0165] In this step, the consensus verification module introduces a key logical gating parameter: the Byzantine Fault Tolerance (BFT) threshold. In this embodiment, the BFT threshold is strictly set to be greater than or equal to two-thirds of the total number of participating witness nodes. The theoretical basis for selecting this value comes from the mathematical proof of the Practical Byzantine Fault Tolerance (PBFT) algorithm: In an asynchronous distributed system, in order to tolerate F malicious or faulty nodes, the total number of nodes N in the system must satisfy that N is at least equal to three times F plus one (N ≥ 3F + 1).

[0166] Therefore, as long as the number of honest nodes exceeds two-thirds of the total, the system can reach a unique consensus even in the presence of network segmentation or interference from malicious nodes. It has been mathematically proven that it is impossible for two mutually exclusive rigid hash values ​​to have support rates exceeding this threshold at the same time. This parameter plays a decisive role in the true / false boundary in the algorithm.

[0167] Technical effect comparison analysis shows that if the threshold is set at 51% (simple majority), it is easy to produce double-flower cognitive split under network partition attack; while setting it at two-thirds can ensure the uniqueness of physical truth by 100%, thus logically locking in the objective facts.

[0168] Nodes whose reported rigid hash values ​​do not match the physical truth values ​​among all witness nodes are marked as untrusted nodes. Untrusted nodes are removed from the computation sequence, and only witness nodes that have reported physical truth values ​​are retained to generate a trust domain set.

[0169] This step performs a physical and logical causal disconnection operation. The consensus and evidence-building module traverses all witness nodes and compares the rigid hash value reported by each witness node with the previously determined physical truth value.

[0170] Any witness node whose reported data does not match the physical truth is judged as an untrusted node. This means at the physical level that these untrusted nodes may be in a polluted domain name resolution environment, have suffered a man-in-the-middle attack, or are themselves controlled malicious nodes.

[0171] The consensus verification module physically removes these untrusted nodes from the subsequent computation graph, completely stripping them of their weight in participating in subsequent flexible content consensus computation. Through this operation, the consensus verification module generates a set of trust domains.

[0172] The beneficial effect of this step is that it ensures that every witness node participating in the complex Hamming distance calculation has strict physical consistency in terms of spatiotemporal identity and observed object, fundamentally preventing malicious nodes from using legitimate flexible content (such as pirated videos) to cover up illegal identities (such as fake nodes), or using illegal content (such as tampered videos) to contaminate legitimate identities.

[0173] In the process of verifying that the Hamming distance between flexible hash values ​​satisfies the statistical similarity condition based on network loss tolerance, the consensus evidence module generates consensus evidence hashes as a comparison benchmark by: calculating the local gravitational potential energy density for each flexible hash value reported by each witness node in the trust domain set. The value of the local gravitational potential energy density is formed by the superposition of the gravitational contributions of all other flexible hash values ​​in the trust domain set to the current flexible hash value, and the magnitude of the gravitational contribution is inversely proportional to the square of the Hamming distance between the two flexible hash values.

[0174] In this embodiment, the consensus verification module abandons the traditional arithmetic average calculation method and instead constructs a discrete metric space gravitational field model.

[0175] When calculating the gravitational contribution, the consensus proof module introduces the dimensional feature scale as a distance correction parameter. In this embodiment, the dimensional feature scale is preferably set to eight to sixteen (corresponding to the square root of the 64-bit or 256-bit hash length). The value of this parameter is based on the sparsity characteristics (curse of dimensionality) of high-dimensional Hamming space: if no scale correction is performed, the point spacing distribution in high-dimensional space will tend to be uniform, resulting in the failure of gravitational distinguishability.

[0176] The specific calculation logic is as follows: calculate the Hamming distance between the two flexible hash values, divide the square of the Hamming distance by the square of the dimensional feature scale, add one to the quotient and take the reciprocal to obtain the gravitational contribution value.

[0177] This inverse relationship simulates the law of universal gravitation in physics. Its physical meaning is that the closer the evidence points are (the more similar their content is), the stronger the mutual support force they generate, and it decreases non-linearly with increasing distance.

[0178] The local gravitational potential energy density is calculated by superimposing the gravitational contributions of all other flexible hash values ​​within the trust region set to the current flexible hash value. This density value objectively quantifies the authority of each flexible hash value in the entire evidence space: flexible hash values ​​at the cluster center will obtain extremely high density values, while the density values ​​of outliers (such as noise caused by equipment failure or local transcoding anomalies) will approach zero.

[0179] The local gravitational potential energy density is used as the mass weight of the corresponding witness node;

[0180] The consensus-building module directly assigns the calculated local gravitational potential energy density as the mass weight of the corresponding witness node. This operation implements density-based weighting logic, ensuring that the majority of observations with high similarity have greater decision-making power, while the influence of a minority of outlier anomalous observations is automatically suppressed.

[0181] Perform bitwise spin reconstruction on each bit of the flexible hash value, calculate the sum of the mass weights when the bit is zero and the sum of the mass weights when it is one, select the value with the larger sum of mass weights as the final value of the bit, and combine the final values ​​of all bits to generate the consensus evidence hash.

[0182] In this step, the consensus consolidation module reconstructs consensus using a bit-weighted election mechanism. Since the flexible hash value is a discrete binary string, direct numerical averaging will lead to meaningless non-binary results.

[0183] Therefore, the consensus verification module treats each binary bit of the flexible hash value as an independent binary state variable. For the Kth bit, the consensus verification module accumulates the quality weights of all witness nodes that have a value of zero in that bit to obtain the zero-value weight sum; and the quality weights of all witness nodes that have a value of one in that bit to obtain the one-value weight sum.

[0184] The consensus proof module compares the two total weight values ​​and selects the value (zero or one) represented by the one with the larger weight as the final value of the consensus proof hash at that bit.

[0185] The technical effect of this bit-by-bit collapse mechanism is that it ensures that the generated consensus evidence hash conforms to physical statistical laws at every bit. It is the ground state with the lowest energy in the entire evidence system, thus avoiding the "average ghost" that may be generated by the simple averaging method (that is, generating a hash value that does not exist in reality and does not represent any characteristic structure), which significantly improves the restoration and representativeness of the consensus result to the original content.

[0186] The specific operation of the consensus evidence module to verify that the Hamming distance between flexible hash values ​​meets the statistical similarity condition based on network loss tolerance and to generate an electronic evidence certificate is as follows: calculate the average weighted Hamming distance between the consensus evidence hash and all the original flexible hash values ​​in the trust domain set, and define the average weighted Hamming distance as the system temperature.

[0187] In this embodiment, the system temperature calculated by the consensus verification module physically represents the degree of disorder or discrete entropy value within the current trust domain set.

[0188] The specific execution process is as follows: calculate the Hamming distance between the reconstructed consensus evidence hash and each original flexible hash value, and use the aforementioned determined quality weights to perform a weighted average of these Hamming distances.

[0189] If all witness nodes observe highly consistent images, the system temperature will approach zero; if there are significant disagreements among witness nodes (e.g., different versions of the video are distributed in different regions), the system temperature will rise significantly.

[0190] The system temperature is compared with the preset maximum phase transition entropy limit; if the system temperature exceeds the maximum phase transition entropy limit, the statistical similarity condition is determined to be invalid and the on-chain operation is rejected.

[0191] In this step, the consensus verification module introduces a crucial physical boundary parameter: the maximum phase transition entropy limit. In this embodiment, the maximum phase transition entropy limit is preferably set to five to ten bits (for 64-bit perceptual hashing).

[0192] The selection of this value is based on the theoretical upper limit of quantization noise introduced by mainstream video coding standards (such as H.264 and H.265) at different compression rates.

[0193] Experimental comparison data shows that after the same video source undergoes transcoding processing at different bitrates and resolutions, the difference in Hamming distance of its perceptual hash is usually controlled within five bits; if the difference exceeds ten bits, it is highly likely that the video content has been tampered with or is completely different.

[0194] This parameter acts as a physical fuse in the algorithm. If the system temperature exceeds the maximum phase transition entropy limit, it indicates that the system is in a high-entropy disorder state, meaning that there are irreconcilable content disagreements in the network.

[0195] At this point, forcibly reaching a consensus would violate physical authenticity, so the consensus-based evidence module must refuse to upload the evidence to the blockchain to ensure the rigor of judicial evidence.

[0196] If the system temperature is less than or equal to the maximum phase transition entropy limit, the statistical similarity condition is determined to be met and the Merkle tree encapsulation operation is performed.

[0197] When the system temperature is below the maximum phase transition entropy limit, it indicates that the system is in a steady state. Although there are slight differences among the various evidence points, they all point to the same source in physical essence. The consensus evidence consolidation module determines that the statistical similarity condition is met and initiates the final evidence consolidation process.

[0198] The Merkle tree encapsulation operation includes constructing a Merkle tree using the physical truth value, consensus evidence hash, the original timestamp in the evidence triggering instruction, and the consensus generation time as leaf nodes, calculating the root hash value of the Merkle tree, and writing the root hash value as an electronic evidence certificate into the blockchain.

[0199] Finally, the consensus verification module performs holographic data encapsulation.

[0200] The consensus proof module uses the double-verified physical truth value (representing unique identity), consensus evidence hash (representing core content), original timestamp (representing the time the event occurred), and consensus generation time (representing the time proof was completed) as leaf nodes to construct a standard hash Merkle tree.

[0201] Through layers of hash operations, a unique Merkle root hash value is generated. The consensus and proof module calls the blockchain smart contract to write the Merkle root hash value into the distributed ledger and generate an electronic proof certificate.

[0202] The beneficial effect of this process is that it logically solidifies all dimensions of evidence into an immutable whole structure, realizing a complete closed loop from collection in the physical world to confirmation of rights in the digital world. Any alteration to a single element will cause a drastic change in the root hash value, which will then be identified and rejected by the blockchain network.

[0203] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart system for monitoring and rapidly protecting cultural content infringement, characterized in that: include: The system comprises a monitoring triggering module, a distributed networking module, a data encapsulation module, and a consensus and authentication module. The monitoring trigger module is configured to extract deep semantic feature vectors from real-time streaming media data using a deep semantic feature extraction network, compare the deep semantic feature vectors with the benchmark feature vectors stored in a pre-set copyright feature library, calculate the infringement confidence level based on the adversarial cosine similarity judgment logic, and generate an evidence collection trigger instruction containing feature fingerprint information and send it to the blockchain smart contract when the infringement confidence level exceeds the judicial evidence collection threshold. The distributed networking module is configured to respond to the evidence collection triggering command sent by the monitoring triggering module, call a verifiable random function through the blockchain smart contract, select N witness nodes from the active node pool based on network heterogeneity constraints, and control the witness nodes to perform containerized environment initialization operations and time synchronization operations to build a clean evidence collection environment. The data encapsulation module is configured to control the witness nodes in the clean evidence collection environment to concurrently access the target content address specified in the evidence collection triggering instruction, collect rigid layer metadata and flexible layer content data, use a dynamic field masking operator to remove non-fixed fields in the rigid layer metadata and calculate a rigid hash value, use a perceptual hash algorithm to process the flexible layer content data and calculate a flexible hash value, and generate a hash evidence pair. The consensus evidence module is configured to receive the hash evidence pair generated by the data encapsulation module. After verifying that the rigid hash values ​​corresponding to the witness nodes satisfy the absolute consistency condition and the Hamming distance between the flexible hash values ​​satisfies the statistical similarity condition based on network loss tolerance, the consensus evidence hash and the original timestamp in the evidence triggering instruction are packaged and uploaded to the blockchain to generate an electronic evidence certificate.

2. The intelligent infringement monitoring and rapid rights protection system for cultural content according to claim 1, characterized in that, The specific operation of the monitoring trigger module in extracting deep semantic feature vectors from real-time streaming media data using a deep semantic feature extraction network is as follows: A10 inputs the real-time streaming media data connected to the monitoring trigger module into a preset spatiotemporal resampling operator. The spatiotemporal resampling operator performs interpolation processing on the time axis of the real-time streaming media data and unifies the variable frame rate signal into a fixed physical frequency. At the same time, it performs bilinear interpolation processing on the spatial axis of the real-time streaming media data and performs the operation of subtracting the statistical mean to generate a normalized spatiotemporal tensor. A11 is a three-dimensional convolutional neural network that takes a normalized spatiotemporal tensor as input as a specific implementation of a deep semantic feature extraction network. The weight matrix of each convolutional kernel in the three-dimensional convolutional neural network is pre-configured to a frozen state, and the weight matrix of each convolutional kernel satisfies the spectral norm constraint. The spectral norm constraint restricts the maximum singular value of each convolutional kernel weight matrix in Euclidean space to be less than or equal to one. The three-dimensional convolutional neural network performs multi-layer convolution operations on the normalized spatiotemporal tensor based on the convolutional kernel weight matrix that satisfies the spectral norm constraint and outputs a feature map.

3. The intelligent infringement monitoring and rapid rights protection system for cultural content according to claim 2, characterized in that, During the process of the monitoring and triggering module extracting deep semantic feature vectors, the specific operation of aggregating the spatial and temporal dimensions of the feature map output by the deep semantic feature extraction network is as follows: A20 employs a generalized average energy pooling strategy, treating the feature map as a semantic energy field distributed in the spatiotemporal integral domain. It introduces a learnable energy focusing index with a value greater than one, and performs a power operation based on the energy focusing index on the feature response amplitude at each coordinate point of the feature map in the spatiotemporal integral domain. A21 sums the results of all exponentiation operations over the entire spatiotemporal integration domain, divides the sum by the total volume of the spatiotemporal integration domain to obtain the average energy value, and finally performs an operation on the average energy value by taking the root of the energy focusing exponent, that is, performing an exponentiation operation on the average energy value based on the reciprocal of the energy focusing exponent, and outputs the result as a deep semantic feature vector.

4. The intelligent infringement monitoring and rapid rights protection system for cultural content according to claim 1, characterized in that, The specific operation of the distributed networking module in selecting a preset number of witness nodes from the active node pool based on network heterogeneity constraints is as follows: B10 responds to the evidence collection trigger command and uses a random entropy kernel generated by a verifiable random function to construct a subspace index mask, which maps the active node pool to a subset of candidate nodes. B11 executes the determinant point process fermion sampling logic under double logarithmic metric for the candidate node subset. This logic first constructs a hybrid topological similarity kernel matrix. The element values ​​of the hybrid topological similarity kernel matrix are obtained by calculating the hybrid topological similarity between any two nodes in the candidate node subset. The hybrid topological similarity is obtained by multiplying the exponential decay term of the spherical great circle distance based on geographic coordinates with the homogeneity penalty term based on the autonomous system number. B12, during the sampling process, if there is insufficient number of nodes that satisfy the hard heterogeneity constraint, the soft exclusion relaxation mechanism is triggered, allowing selected nodes to have the same autonomous system number and applying a homogeneity penalty factor to reduce the probability of them being jointly selected. B13 utilizes the geometric volume properties of the determinant point process to select a preset number of nodes from the candidate node subset that maximize the volume of the polyhedron spanned in the feature space as witness nodes.

5. The intelligent infringement monitoring and rapid rights protection system for cultural content according to claim 4, characterized in that, The specific operations of the distributed networking module in controlling the witness node to perform containerized environment initialization and time synchronization operations are as follows: B20 sends a volatile state annihilation instruction to the witness node, controls the witness node to suspend the current container process, performs a full zero overwrite operation on the memory stack, remounts the read-only root file system and refreshes the domain name resolution cache; B21, after completing the containerized environment initialization operation, executes the adaptive optical cone clock synchronization logic, controls the witness node to initiate multiple rounds of detection on the reference time source and measure the round-trip communication delay, selects the round-trip communication delay with the smallest value in the multiple rounds of detection as the reference sample, and defines the time uncertainty as the sum of half of the reference sample and the preset processing delay jitter value. B22, calculate the adaptive light cone effectiveness judgment threshold. The adaptive light cone effectiveness judgment threshold is the smaller of the preset physical tolerance threshold and the sum of the mean of the current network environment round-trip delay statistical distribution plus three times the standard deviation. B23 compares the time uncertainty with the adaptive light cone validity determination threshold, and confirms the time synchronization of the witness node is valid when the time uncertainty is less than or equal to the adaptive light cone validity determination threshold.

6. The intelligent infringement monitoring and rapid rights protection system for cultural content according to claim 1, characterized in that, The data encapsulation module calculates the rigid hash value by using a dynamic field mask operator to remove non-fixed fields from the rigid layer metadata. The specific steps are as follows: C10 adopts a standardized protocol projection strategy based on end-to-end whitelists. It performs protocol layer filtering on rigid layer metadata through dynamic field masking operators, identifies and retains end-to-end header fields with physical state conservation in the transmission link, and discards hop-by-hop header fields introduced by intermediate network gateways or firewalls. C11 performs lexicographical normalization on the retained end-to-end header fields, converts all field names to a uniform lowercase format, sorts them in ascending order according to the character encoding standard, and concatenates the sorted key-value pairs into a normalized string. C12 concatenates the standardized string with the server's Secure Sockets Layer public key credential in binary format, and performs a secure hash operation on the concatenated data stream to generate a rigid hash value. C13 constructs a four-dimensional integrity tensor, concatenates the rigid hash value with the geographical coordinate data and atomic clock time data of the witness node, and performs a digital signature on the concatenated data using the private key of the witness node.

7. The intelligent infringement monitoring and rapid rights protection system for cultural content according to claim 6, characterized in that, The data encapsulation module uses the perceptual hash algorithm to process the flexible layer content data and calculate the flexible hash value. The specific operations are as follows: C20 performs physical space resampling on the video stream data in the flexible layer content data, downsamples the video frames captured by the witness node to a fixed physical reference scale, and calculates the normalized inter-frame pixel brightness difference energy based on the physical reference scale. C21 executes the scale-invariant physical event locking logic, selecting the moment with the largest differential energy change rate as the physical anchor time; C22 performs frequency domain topological invariant extraction on the normalized video frames corresponding to the physical anchor point time, uses the discrete cosine transform operator to convert the spatial domain signal into the frequency domain signal, and extracts the low-frequency coefficient region. C23 performs robust quantization, calculates the median statistic within the low-frequency coefficient region, compares the magnitude of each low-frequency coefficient with the median statistic, and generates a binary flexible hash value based on the sign attribute of the comparison result.

8. The intelligent infringement monitoring and rapid rights protection system for cultural content according to claim 1, characterized in that, The consensus verification module verifies that the rigid hash values ​​corresponding to a preset number of witness nodes meet the absolute consistency condition by performing the following specific operations: D10 receives the full hash evidence pairs generated by the data encapsulation module and performs discrete frequency distribution statistics on the rigid hash values ​​reported by all witness nodes. D11 defines a specific rigid hash value that appears more frequently than the Byzantine security threshold as a physical truth value. D12 marks nodes whose reported rigid hash values ​​are inconsistent with physical truth values ​​among all witness nodes as untrusted nodes, removes untrusted nodes from the computation sequence, and retains only witness nodes that have reported physical truth values ​​to generate a trust domain set.

9. The intelligent infringement monitoring and rapid rights protection system for cultural content according to claim 8, characterized in that, In the process of verifying that the Hamming distance between flexible hash values ​​satisfies the statistical similarity condition based on network loss tolerance, the consensus evidence module generates consensus evidence hashes as a comparison benchmark through the following specific operations: D20 calculates the local gravitational potential energy density for each flexible hash value reported by a witness node in the trust domain set. The value of the local gravitational potential energy density is the sum of the gravitational contributions of all other flexible hash values ​​in the trust domain set to the current flexible hash value, and the magnitude of the gravitational contribution is inversely proportional to the square of the Hamming distance between the two flexible hash values. D21, using the local gravitational potential energy density as the mass weight of the corresponding witness node; D22 performs bitwise spin reconstruction on each bit of the flexible hash value, calculates the sum of the mass weights when the bit is zero and the sum of the mass weights when it is one, selects the value with the larger sum of mass weights as the final value of the bit, and combines the final values ​​of all bits to generate the consensus evidence hash.

10. The intelligent infringement monitoring and rapid rights protection system for cultural content according to claim 9, characterized in that, The consensus-based evidence-gathering module verifies that the Hamming distance between flexible hash values ​​satisfies the statistical similarity condition based on network loss tolerance and generates an electronic evidence certificate. The specific steps are as follows: D30, calculate the average weighted Hamming distance between the consensus evidence hash and all the original flexible hash values ​​in the trust domain set, and define the average weighted Hamming distance as the system temperature; D31 compares the system temperature with the preset maximum phase transition entropy limit; If the system temperature exceeds the maximum phase transition entropy limit, the statistical similarity condition is deemed invalid and the on-chain operation is rejected. D32, if the system temperature is less than or equal to the maximum phase transition entropy limit, determine that the statistical similarity condition is met and perform the Merkle tree encapsulation operation; D33, the Merkle tree encapsulation operation includes constructing a Merkle tree using the physical truth value, consensus evidence hash, the original timestamp in the evidence triggering instruction, and the consensus generation time as leaf nodes, calculating the root hash value of the Merkle tree, and writing the root hash value as an electronic evidence certificate into the blockchain.

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