Aerospace element knowledge base construction method

Abstract

The invention relates to the crossing field of information processing and aerospace engineering, in particular to a method for constructing an aerospace element knowledge base, which comprises the following steps of: unifying formats and time of design, test, assembly, telemetering and on-orbit image data, eliminating abnormities and complementing the abnormities; physical data is bound into a binary super vector through frequency spectrum coding and semantic text random index, the binary super vector is written into a spiking nerve-photon convolution link, a six-dimensional spatial-temporal index containing a Morton key is generated according to information entropy, and elements, textures, colors and structures are marked; the index outputs a risk index through distributed hash routing, homomorphic memory multiplication and addition and topology coherence; and when the risk exceeds the threshold, solving a secondary unconstrained model through quantum annealing to determine index fission or combination, releasing peripheral gray, generating holographic snapshots, quickly rolling back during abnormality, and adaptively updating an entropy threshold along with global statistics, thereby realizing an encrypted, efficient and self-evolution aerospace element knowledge base.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information processing and aerospace engineering, and in particular to a method for constructing an aerospace element knowledge base. BACKGROUND

[0002] A large number of cultural and functional elements, such as dragon pattern components, honeycomb textures, national color coatings, and folding structures, need to be digested in the design stage of the spacecraft shape, internal installation, and observation load. Constructing an aerospace element knowledge base can unify the coding of concepts such as elements, textures, colors, and structures in the whole life cycle, realize cross-type reuse, rapid iteration, and on-orbit fault diagnosis, and has important significance for shortening the development cycle, protecting the consistency of visual identification, and improving mission reliability. SUMMARY

[0003] In view of the problems existing in the prior art, the present application provides a method for constructing an aerospace element knowledge base. After the multi-source aerospace data is unified on a time axis, a binary hyper vector is generated through spectrum-index binding; a light field entropy is derived through a three-value synapse-photon convolution, and a six-dimensional index fission and fusion is combined according to the entropy; a risk index is output through homomorphic dot product and topological homology, and quantum annealing is adaptively evolved, so as to finally realize an encrypted, efficient, and rollbackable aerospace element knowledge base.

[0004] A method for constructing an aerospace element knowledge base, comprising the steps of:

[0005] Unifying the formats and times of design, test, assembly, telemetry, and on-orbit image data, eliminating abnormalities and completing missing data to obtain multi-modal samples;

[0006] After the physical data of the multi-modal samples is coded by spectrum and the semantic data is indexed by random, it is bound into a binary hyper vector, the pulse nerve synapse weight is set by the binary hyper vector, the light field is formed by sending the light photon interference array through electro-optical modulation, the six-dimensional space-time index containing the Morton key is generated according to the information entropy, and the element, texture, color, and structure labels are recorded;

[0007] The Morton key is hashed and routed in a distributed hash network, the candidate is obtained by homomorphic similarity and in-memory multiplication and addition on the node side, the failure probability is output by photon computing, the abnormality identifier is obtained by combining the topological homology to form a risk index;

[0008] When the risk index meets the error or topological condition, the secondary unconstrained optimization is established according to the local entropy, style consistency, color deviation, and structure topology, the fission or fusion is determined by quantum annealing, the index is updated, the rollback snapshot is generated and released, the abnormality is rolled back, and the threshold value is updated according to the global entropy.

[0009] Preferably, the spectral encoding first performs a discrete Fourier transform on the physical data to obtain the amplitude spectrum, and then maps the amplitude spectrum into a fixed-length codeword according to a preset quantization level, which is used as the physical channel input of the binary supervector.

[0010] Preferably, the random index selects a dimension position by hashing the term and assigns a positive or negative value to form a semantic vector, which is then combined with the physical channel codeword by bitwise XOR to obtain a binary supervector.

[0011] Preferably, the binary supervector is mapped to set the zero value as a static weight, and the positive and negative values ​​are set as synaptic weights that are opposites of each other. The synaptic output is modulated and input into the photon interference array to perform convolution operations to generate a light field.

[0012] Preferably, the information entropy is obtained by dividing the light field energy into four energy level intervals according to a fixed threshold and calculating the probability of each interval, and then obtaining the Shannon entropy. The Shannon entropy is used to determine the six-dimensional spatiotemporal index splitting or merging.

[0013] Preferably, after the Morton key is securely hashed and output, the routing is completed in a distributed hash table. The routing adopts the longest prefix matching strategy and replicates the index record according to the node distance.

[0014] Preferably, the node side performs a binary supervector dot product in the ciphertext domain using a homomorphic encryption scheme and outputs a similarity value through in-memory array multiplication and addition operations. The similarity value is used to select retrieval candidates.

[0015] Preferably, the topological homology is achieved by constructing an Alpha complex and calculating zero-dimensional, one-dimensional, and two-dimensional persistent entries. When the birth-death interval of any entry exceeds a preset threshold, a topological anomaly identifier is generated.

[0016] Preferably, the quadratic unconstrained optimization model linearly combines four indicators: local entropy, style consistency, color deviation, and structural topology. The quantum annealing processor obtains the fission or merging decision by minimizing this combination function and updates the index accordingly.

[0017] Preferably, after generating a rollback snapshot, the new index is released in batches in a gray-scale manner, the prediction error or processing delay is monitored, and if any indicator exceeds the threshold, the most recent snapshot is referenced for rollback, and the entropy threshold is reset based on the global entropy statistics.

[0018] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows:

[0019] By employing spectral encoding, random indexing, and bit binding, a unified binary representation of physical sequences and semantic text is achieved, resolving the multimodal heterogeneity problem. On-chip multiplication and addition are implemented through a spiking neural network-photon convolution link, reducing energy consumption and improving millisecond-level retrieval speed. Adaptive index depth control is achieved through quartic bucket entropy-driven fission and merging, resolving latency fluctuations caused by fixed granularity. Ciphertext domain similarity calculation is implemented through homomorphic cosine and in-memory multiplication and addition, eliminating the risk of data leakage. Geometric distortion detection such as holes and cavities is achieved through topological homology anomaly criteria, improving anomaly capture rate. Quantum annealing secondary optimization enables online index evolution and generates rollback snapshots, ensuring high availability. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0021] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0022] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0023] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0024] like Figure 1 As shown, a method for constructing an aerospace element knowledge base includes the following steps:

[0025] The format and time of design, testing, assembly, telemetry and on-orbit image data are standardized, anomalies are removed and missing data is filled in to obtain multimodal samples;

[0026] Design models, assembly logs, environmental test curves, on-orbit telemetry sequences, and mission camera images generated during spacecraft operation come from different systems, resulting in inconsistent time bases, diverse data formats, and the presence of both defects and noise. Directly inputting these raw data into hypervector encoding would cause vector drift and index fission chaos. This invention first constructs a "multi-source coaxialization" preprocessing link, aiming to output multimodal samples with unified structure, time alignment, and quantifiable quality assessment, laying the foundation for subsequent pulse-photon collaborative computing.

[0027] In the format unification stage, 3D design files are converted into triangular meshes and component identifiers in a universally interchangeable format; assembly logs are parsed into hierarchical text; test curves and telemetry sequences are converted into columnar time series tables; and on-orbit images are decoded into floating-point pixel matrices. In the parallel time alignment stage, Coordinated Universal Time (UTC) is used as the reference, mapping the local clocks of various records to a unified second-level raster, and ensuring consistent sampling intervals through linear interpolation or clipping.

[0028] Anomaly removal employs a dual-criteria strategy: firstly, it calculates the quantile range for numerical fields to remove out-of-bounds samples; secondly, it calculates the structural similarity index for image sequences to remove jittery or out-of-focus frames. The missing frame completion stage uses differentiated methods for three types of gaps: designing grid interpolation using shape functions; reconstructing curves and telemetry sequences using splines; and restoring missing frames by rearranging pixels using optical flow from previous and subsequent frames.

[0029] To ensure that the subsequent mutual hash variational entropy coupler considers data reliability when allocating computational resources, this invention calculates a quality score for each sample:

[0030] Q = 1 - Δαt - βr out -γr mis

[0031] Where: Δt represents the difference between the sample recording time and the center of the corresponding second-level raster; r out The percentage of abnormal segments; r mis The reconstructed segment proportions are represented by α, β, and γ, which are weights set by the system. The score Q is used as a weight gating parameter and written to the cache along with the samples. High-scoring samples receive greater gains during the spiking neural weight mapping stage, while the weights of low-scoring samples are suppressed, thereby reducing the interference of noise on the six-dimensional spatiotemporal index entropy distribution.

[0032] This pipeline is implemented using a parallel stream processing framework, capable of processing tens of thousands of samples per second on average, while outputting a "multimodal sample" structure: unique identifier, physical sequence, semantic text, image matrix, unified timestamp, and quality score. This structure is consistent with the names of subsequent steps to avoid semantic ambiguity.

[0033] Example: During attitude maneuvers, a satellite's camera generates continuously jittery images. The system first calculates the structural similarity of this sequence and detects the anomaly percentage r.out The missing frame intervals are reconstructed using optical flow rearrangement of pixels to obtain the missing percentage r. mis The quality assessment formula is used to calculate the score Q. The sample is then bound to the same Morton key with the telemetry sequence within the same second, ensuring precise temporal alignment of the geometric model, process semantics, experimental curves, telemetry sequences, and image textures within the six-dimensional index space. This provides a unified input for subsequent optical field entropy estimation, quantum fission, and self-supervised evolution. Comparative experiments show that this preprocessing link can reduce the void ratio after index fission by approximately 30%, significantly improving subsequent retrieval stability and topology self-healing efficiency.

[0034] The physical data of the multimodal samples are spectrally encoded and the semantic data is randomly indexed and bound into a binary supervector. The weights of the pulsed neural synapses are set using the binary supervector, and the electro-optic modulation is sent into a photonic interference array to form a light field. A six-dimensional spatiotemporal index containing Morton bonds is generated based on information entropy, and element, texture, color, and structure labels are recorded.

[0035] The physical sequence records continuous changes in engine vibration, temperature drift, and cumulative irradiation, while the semantic text contains component specifications, assembly locations, and fault descriptions. These two types of data differ significantly in statistical patterns and syntactic structures; direct concatenation would lead to inconsistencies in the vector space. This invention employs a "spectral encoding-random indexing-bit binding" strategy to unify the expression of different modes. The specific steps are as follows: First, a discrete Fourier transform is performed on the physical sequence, selecting the frequency band with the highest amplitude spectrum energy and quantizing it to obtain a codeword vector P. Then, a sparse vector S is generated from the semantic text using a word hashing method. Using the binding operation of hyperdimensional computation, the corresponding components are XORed bitwise to obtain a binary supervector H. P retains the periodic characteristics of the physical signal, and S maintains the semantic order of the text. After binding, both temporal information and semantic context are preserved.

[0036] Zeros in H are mapped to static weights, positive ones to excitatory synaptic weights, and negative ones to inhibitory synaptic weights, and this mapping is written into a spiking neural chip. Neurons accumulate input using a threshold integral firing model, and the output voltage varies with the sample content. This voltage is electro-optically modulated to drive a photonic interference array. The difference in waveguide lengths within the array determines the convolution kernel weights, and the optical field output exhibits a spatial convolution result. The system uses an optical power detector to divide the optical field energy into four intervals and calculates the Shannon entropy.

[0037]

[0038] Where p i This represents the energy percentage of the i-th energy level interval. A larger entropy value indicates that the information is dispersed, and the index remains coarse-grained; a smaller entropy value indicates that local features are concentrated, which is suitable for splitting into fine-grained fragments.

[0039] To unify global addressing, this invention constructs a six-dimensional coordinate system using spatial three-coordinates, a unified timestamp, the dominant vibration frequency, and cumulative irradiance, generating integer key values ​​according to Morton curve rules. These key values ​​are then combined with entropy values, sample time, and consistency verification values ​​to form a six-dimensional spatiotemporal index. Simultaneously, four types of aerospace element tags are extracted and recorded: element tags correspond to knowledge graph nodes, texture tags are generated from multi-scale texture signatures, color tags use standard color space coordinates, and structural tags employ shape grammar parameters. These tags are appended to the end of the index, allowing for direct style filtering during retrieval and participation in style consistency calculations.

[0040] Through the above-mentioned hyperdimensional bit binding strategy, this invention compresses physical and semantic features with different statistical patterns into a unified binary vector, avoiding the cumbersome process of separate training and alignment; the spiking neural network and photonic convolution link complete multiplication and addition operations at the hardware layer, sinking high-dimensional computation to near physical limits; the entropy-driven fission mechanism realizes adaptive index layering, ensuring that the query latency in hot areas remains low and the storage load is balanced.

[0041] Example: Temperature waveforms and camera log text were acquired during a satellite thermal control experiment. The temperature sequence was analyzed using Discrete Fourier Transform to select the dominant thermal frequency, and the text was hashed to generate a sparse vector. The two were then bound together to form a binary hypervector. This vector was written to a pulse chip to obtain a voltage output, which was then electro-optically modulated and input into a photon interference array to generate a light intensity matrix. The entropy value was calculated to be E = 0.73. The low entropy value triggered fine-grained fission, and the system generated a sub-index along a six-dimensional coordinate system, recording the element label "metal heat sink," the texture label "parallel stripes," the color label "warm color," and the structure label "riveting." When the search criteria included "parallel stripes and warm color," the system located the sample within a single route without additional semantic reasoning, and the retrieval latency remained on the order of milliseconds. This example verifies the effectiveness of the binding strategy for multimodal data fusion and fast retrieval.

[0042] Preferably, the spectral encoding first performs a discrete Fourier transform on the physical data to obtain the amplitude spectrum, and then maps the amplitude spectrum into a fixed-length codeword according to a preset quantization level, which is used as the physical channel input of the binary supervector.

[0043] The spectral coding of the physical channel compresses continuous sequences such as vibration, temperature, or irradiation into codewords of fixed length and bit-level operability, providing a unified input for binary supervectors. First, a discrete Fourier transform is performed on the physical sequence x[n] (sampling index n, sequence length N) to obtain the complex spectrum X[k]. The amplitude spectrum |X[k]| characterizes the energy of each frequency band. The system calculates the energy threshold T. e Select the one that satisfies The frequency set Ω. The amplitude values ​​of the retained frequency band are quantized to level L (L=2) using a linear scale. b (b is consistent with the hardware bit width), the quantization function is:

[0044]

[0045] Where m min With m max These represent the minimum and maximum amplitudes within the set Ω, respectively. The quantized output Q(k) is converted into a binary bit string and then filled to length d using a mapping table. phys The codeword vector P carries both the dominant frequency and amplitude difference, and is of the same length as the semantic channel vector to satisfy subsequent bit binding.

[0046] The Discrete Fourier Transform is performed using the Fast Fourier Unit of the stream processing engine. Amplitude calculation and threshold comparison are performed on the same channel, and a lookup table method is used in the quantization stage to reduce the time consumption of multiplication and division. The output codeword vector directly enters the binder, where it is XORed bit-by-bit with the sparse vector of the semantic channel to generate a binary supervector, which is then written to the synaptic storage matrix.

[0047] By applying spectral coding, this invention can explicitly map the temporal correlation of physical data to the frequency domain, making it easier to distinguish the modes of the same component under different loads; quantization ensures consistent sample bit width, and binding operations can be completed at the logic gate level; energy sorting allows abnormal vibrations to be weighted into the supervector, improving the sensitivity of subsequent entropy estimation.

[0048] Example: The solar array root accelerometer samples at 1250Hz, with an input sequence length of 1024 points. After performing a Fast Fourier Transform, the system selects the frequency band where the energy accumulation reaches 80%, sets the quantization level to 8, and generates a codeword vector of length 10000. Compared with the unencoded scheme, the supervector using spectral coding increases the entropy value of the photon convolution output by approximately 20%, and the average hop count for locating fault modes in low-entropy clusters using quantum fission is reduced by 2, indicating that spectral coding reduces the ambiguity of the six-dimensional index space and improves retrieval accuracy.

[0049] Preferably, the random index selects a dimension position by hashing the term and assigns a positive or negative value to form a semantic vector, which is then combined with the physical channel codeword by bitwise XOR to obtain a binary supervector.

[0050] In multi-source aerospace data, semantic text descriptions cover discrete information such as fault phenomena, assembly steps, material grades, and operational warnings. To share the same bit space with the spectral codewords of the physical channel, this invention employs a hyperdimensional representation scheme based on random indexing. This rapidly maps the text into a sparse vector of the same length as the physical codeword, containing only positive or negative ones. Subsequently, it is combined with the physical codeword through a bitwise XOR operation to obtain a binary hypervector. This hypervector simultaneously encodes the frequency characteristics of the time series and the textual semantics at the bit level, allowing it to enter the pulse-photon computing link without additional alignment.

[0051] The core of random indexing lies in assigning a high-dimensional, nearly uncorrelated sparse vector to each term. Let the global dimension be d and the vocabulary size be V. During system initialization, a fixed and reproducible sparse vector R is generated for each term. j The vector is filled with positive or negative 1 only in s random dimensions, and the remaining dimensions are zero. For the sample text, it consists of the word sequence {w1, w2, ..., w...} m The semantic vector S is obtained by accumulating the corresponding term vectors and performing a bitwise sign function.

[0052]

[0053] Where idx(w) t Return to entry w t Indexes in the vocabulary. The sign function `sgn` maps positive values ​​to positive one, negative values ​​to negative one, and zero to zero. Since each R... j The vectors have an equal number of positive and negative values ​​and are randomly positioned. After accumulation, the probabilities of each dimension are approximately balanced, avoiding vector density. The generated semantic vector is XORed bit-by-bit with the physical codeword P to obtain the binary supervector H:

[0054]

[0055] In the logical implementation, the XOR operation relies on only a single-bit logic gate and can be directly mapped to the write operation of the synaptic storage matrix. If P and S have the same dimension, the output is zero, indicating that the synaptic weight in that dimension remains static; if the signs are opposite, the output is positive or negative, indicating that the synaptic weight will be set to an excitation or inhibition value. Through this bit-by-bit combination, this invention can achieve modal fusion without explicit splicing.

[0056] Semantic random indexing brings three direct benefits. First, when a new term is added, only a sparse vector needs to be generated and written to the dictionary, without affecting the dimension and sparsity of existing vectors, allowing the system to dynamically expand with the load. Second, the supervector obtained after XORing still maintains a binary form, allowing subsequent spiking neural-photon convolution links to process it directly without introducing multiplication. Third, sparsity makes the vector of a single text dispersed in high-dimensional space, which, when superimposed with physical codewords, increases information entropy, enabling the light field to form a more discriminative pattern in energy distribution.

[0057] Example: The assembly log contains the statement "Solar panel installation complete, rubber pad buffer insufficient". During the initialization phase, the system generates sparse vectors for terms such as "solar panel", "panel", "installation", and "buffer". After text parsing, a sequence of terms is obtained, accumulated, and their signs are extracted to obtain a semantic vector. This vector is then XORed bit-by-bit with the physical codewords generated from the synchronously acquired panel assembly acceleration sequence to produce a binary hypervector. After being written to the pulse chip, other samples in the same batch have opposite signs in multiple dimensions due to semantic differences, resulting in a complementary synaptic weight distribution. Electro-optic modulation drives the photonic interference array, making it easier to distinguish between impact-type assembly anomalies and routine assembly actions. Experimental statistics show that when simultaneously searching for the phrase "buffer insufficient" and high-amplitude morphology, the similarity distribution obtained by the XOR-fused hypervector index exhibits a bimodal structure, improving the retrieval accuracy by approximately 10% compared to using only single-channel features. This demonstrates that the random index-bit binding mechanism effectively enhances the discriminative power after multimodal combination.

[0058] Preferably, the binary supervector is mapped to set the zero value as a static weight, and the positive and negative values ​​are set as synaptic weights that are opposites of each other. The synaptic output is modulated and input into the photon interference array to perform convolution operations to generate a light field.

[0059] After the binary hypervector completes modal binding, it enters the neural-photon coupling computation link. This invention defines a one-to-one mapping rule at the hardware layer: dimensions with a value of 0 in the hypervector do not participate in charge accumulation, and their corresponding synaptic weights are set as static weights; dimensions with a value of +1 correspond to excitatory weights; and dimensions with a value of -1 correspond to inhibitory weights. Let the hypervector be H = [h1, h2, ..., h...]. d The synaptic weight matrix is ​​W = [w ij The mapping formula is:

[0060]

[0061] Here, G represents the synaptic conduction reference value. This mapping eliminates floating-point multiplication, requiring only the detection of a single bit state to complete the weight writing. The spiking neural chip employs a threshold integral-fire model, where each neuron accumulates a weighted input current, and fires a pulse when the membrane potential exceeds the threshold. The pulse width is converted from digital to analog to obtain a voltage value, which directly drives the refractive index change of the electro-optic modulator, realizing the energy transfer from electrons to photons.

[0062] The output of the electro-optic modulator enters a Mach-Zehnder interferometer array via a silicon-based waveguide. The phase difference between the two branches in the array represents the convolution kernel weights. Multiple interferometer elements are connected in series according to a 2D grid, which is equivalent to performing a convolution operation on the input pulse in the optical domain. Since the waveguide length difference is fixed, the convolution kernel weights do not need to be dynamically adjusted; changes in pulse width only affect the intensity of the interference fringes, thus forming a light intensity matrix at the output. An on-chip integrated optical power detector collects the light intensity values, and this matrix is ​​directly used in subsequent information entropy assessment and six-dimensional spatiotemporal index generation.

[0063] The key principle of this scheme lies in using ternary weight mapping to directly inject high-dimensional binary information into the synaptic matrix, reducing the complexity of weighted summation to the physical conduction state; the interference array utilizes optical path difference to complete convolution, overcoming the bandwidth limitation of electronic multiplication and addition. The static weight dimension manifests as a high-resistance branch in the circuit, reducing the influence of uncorrelated features on the pulse background current; the excitation and inhibition dimensions are opposites, causing the convolution kernel to exhibit complementary fringes in the optical domain, enhancing the resolvability of high-frequency details.

[0064] The mapped synaptic array only requires binary storage, significantly reducing chip area and power consumption; the parallel propagation of photonic convolution avoids clock synchronization bottlenecks and can output a complete light field on a sub-microsecond scale; after the light intensity matrix is ​​evaluated by 4-bucket entropy, high-entropy samples tend to maintain coarse-grained indexing, while low-entropy samples trigger fission, forming an adaptive deep and shallow indexing structure.

[0065] Example: A binary supervector with a length of 10000 and a sparsity of approximately 10% (i.e., 1000 non-zero bits) is used. After weight mapping, 1000 synapses in the chip are set to the conducting state, while the remaining dimensions remain high-impedance. The input comes from a pulse stream bound to triaxial acceleration and assembly text. The neuron completes three cycles of integration and firing within 50 microseconds, with the firing pulse width varying between 0.5 and 1.5 volts and the intensity adjusted according to the input features. An electro-optic modulator converts the voltage into a phase difference, and the interference array outputs a 32x32 light intensity matrix. The optical power detector performs energy binning, calculates an entropy value of 0.71, and the system determines that the sample features are concentrated, performing fission at the index layer. Retrieval experiments show that after adopting this weight mapping-photon convolution link, the cosine distance between similar samples in the vector space is reduced by 15 percentage points, the retrieval accuracy is improved by 9 percentage points, and the hardware power consumption is reduced by about 1 / 3 compared to the traditional mixed-signal convolution method.

[0066] Preferably, the information entropy is obtained by dividing the light field energy into four energy level intervals according to a fixed threshold and calculating the probability of each interval, and then obtaining the Shannon entropy. The Shannon entropy is used to determine the six-dimensional spatiotemporal index splitting or merging.

[0067] The intensity matrix output by the photonic interferometer array, after electro-optical conversion, reflects the spatial distribution of the binary hypervector on the convolution kernel in the form of energy. To quantify the dispersion of this distribution, this invention first sets a fixed threshold sequence for the intensity values, dividing the entire light field energy into four non-overlapping intervals: low, second-low, second-high, and high. The thresholds are set equidistantly based on the detector's full-scale range and are not adjusted with samples. The accumulator integrated within the array counts the four intervals respectively, obtaining the cumulative energy values ​​E1, E2, E3, and E4. The total energy is then... Based on this, calculate the energy percentage for each interval:

[0068]

[0069] Where i = 1, 2, 3, 4, the light field information entropy is then calculated according to Shannon's definition:

[0070]

[0071] In the formula, H reflects the uniformity of light energy across the four intervals. When the proportions of the four intervals are close, the entropy value tends to be high, indicating that the light field information is dispersed and the sample features contain multi-frequency components; if the energy is concentrated in a few intervals, the entropy value decreases, indicating that the features are locally focused. The fission or merging of the six-dimensional spatiotemporal index is controlled by the entropy threshold decision module. The system maintains a pair of dynamic thresholds: the upper threshold H... high and lower threshold H low If the sample entropy H is greater than H high This indicates that the current index granularity is insufficient, and the system performs index fission along the six-dimensional coordinates to expand the resolution; if H is less than H0 low If the region is considered too finely granular and merging is more beneficial, the system will trigger a merge and reclaim empty indexes. The threshold is adaptively adjusted based on global entropy statistics to maintain a balance between fission depth and storage load.

[0072] The introduction of the entropy decision mechanism brings two benefits. On the one hand, it automatically adjusts the index granularity based on the complexity of the sample itself, eliminating the need for manual setting of levels. On the other hand, the splitting and merging actions are directly triggered at the light field feature level, avoiding repeated encoding and transfer at the semantic layer.

[0073] Example: After convolution, the energy of a batch of nadir images is mainly distributed in the second-highest and highest regions, accounting for 0.46% and 0.42% respectively, while the lowest and second-lowest regions account for only 0.12%. The entropy value is calculated to be 0.97. The system records the current global H... high 1.10, H low The initial value was 0.85, and 0.97 was found to be between the two, so the existing index granularity was kept unchanged. Subsequently, another batch of images showed that due to cloud occlusion, energy was concentrated in low-energy areas, accounting for 0.78, and the entropy dropped to 0.55, below H. lowThe system performs two-level merging along the six-dimensional coordinate system, reducing invalid index nodes by 35% and decreasing the average image retrieval latency by 14%. This example demonstrates that the four-bucket entropy decision can reflect the light field information structure in real time and guide the adaptive hierarchical adjustment of the index, reducing storage and computational overhead while ensuring retrieval accuracy.

[0074] Preferably, after the Morton key is securely hashed and output, the routing is completed in a distributed hash table. The routing adopts the longest prefix matching strategy and replicates the index record according to the node distance.

[0075] After completing the light field entropy assessment and generating a six-dimensional spatiotemporal index, this invention uses the Morton key within the index as the basis for global positioning. The Morton key is composed of spatial three coordinates, a unified timestamp, the dominant vibration frequency, and cumulative irradiance arranged using an interleaved bit method. While numerically continuous, it is susceptible to load skew due to segment concentration in a distributed environment. To prevent critical coordinate leakage and improve addressing balance, the system first performs a secure hash on the Morton key, using a standard secure hash algorithm to generate a fixed-length hash value. The hashing process is irreversible, ensuring that the original coordinates can only be decoded within nodes with access to the index table.

[0076] The hash output is written as a key to a distributed hash table. This table uses a multi-level routing tree to map keys to nodes. The routing algorithm uses longest prefix matching: nodes maintain a set of prefix routing table entries, each record pointing to a lower-level node with a longer prefix. When a query key arrives at a node, the node searches its own table for the longest prefix entry that is left-aligned to the key and forwards the query to the node corresponding to that entry. If the current node is already in a leaf node of the routing tree or the prefix is ​​a perfect match, then that node is responsible for storing or returning the query result.

[0077] To improve fault tolerance and near-source read performance, the system also performs index replication based on node distance after routing. The distance metric is the bit difference obtained by XORing the hash values. Let the query key hash value be K, and the target node identifier be N, the distance is defined as:

[0078]

[0079] Among the symbols This represents a bitwise XOR operation. A smaller distance value indicates that the key and node are closer in the hash space. The replication strategy selects several nodes with doubling distances on a logarithmic scale. For example, nodes with distances of D, 2D, 4D, ... from the target node are selected, and one node is chosen at each level as the replica holder. This ensures data redundancy while avoiding local node overload and keeping the average lookup hop count logarithmic.

[0080] Node distance replication also enables load balancing based on geographical location. The routing tree can be hierarchically structured by physical geographic partitioning or communication latency clustering, with nodes having similar prefixes tending to be located in the same region. As the distance between replicas increases, data forms multi-level replicas across regional nodes, enabling both local requests to be fulfilled locally and rapid recovery from remote nodes in case of failure.

[0081] This routing replication mechanism is deeply coupled with subsequent homomorphic retrieval and topology analysis. After parsing the query key, each node directly performs homomorphic cosine similarity and in-memory multiplication and addition locally, without needing to request the original vector from the central node. If a replica node fails, the nearest replica in the upper layer can automatically take over, ensuring that the index availability remains within a high availability range. The replication depth and distance threshold are periodically adaptively adjusted by the scheduler based on node online rate and network latency, maintaining a globally optimal balance between query latency and storage redundancy.

[0082] Example: Assume the six-dimensional spatiotemporal index corresponds to the Morton key value of decimal 13248. The system securely hashes this value to obtain a 256-bit hash value, denoted as key K. The routing tree of the distributed hash table is constructed in four-bit layers. After the root node finds the first four matching entries, it sends the request down. Subsequently, each layer of nodes continues to match longer prefixes, finally locating the storage node N in the fifth hop. The distance D is calculated by XORing K and N. The scheduler selects two replica nodes by doubling the distance expansion, located in the nearby data center and the remote low-Earth orbit satellite link node, respectively, to achieve local fast reading and disaster recovery. Compared with the direct Morton key positioning scheme without hashing, the median hop count of the query for the secure hash plus longest prefix route is reduced by about 20%, the node load variance is reduced by half, and the risk of data leakage is reduced to an irreversible level, verifying the efficiency and security advantages of the routing replication strategy of this invention.

[0083] Morton key hash addressing and routing in a distributed hash network; on the node side, candidates are obtained by homomorphic similarity and in-memory multiplication and addition; photon computation outputs failure probability; and combined with topological homology, anomaly identifiers are obtained to form a risk index.

[0084] The Morton key, after secure hashing, enters the distributed hash network as a global key. After routing, the node containing the key first reads the locally stored binary hypervector and loads the ciphertext form of the query vector. To protect sensitive information from the space mission, the nodes employ a numerical homomorphic encryption scheme to directly calculate the cosine similarity within the ciphertext domain. Let the ciphertext of the query vector be Q, and the ciphertext of the candidate vector be... The node performs homomorphic addition and multiplication operations to obtain the inner product ciphertext. After decrypting the inner product, dividing by the magnitude of each vector yields the similarity score S. j Homomorphic processes prevent plaintext vectors from flowing through the network, thus blocking the risk of side-channel theft at its source.

[0085] The node performs in-memory multiply-add optimization on the top few candidates with the highest similarity. The in-memory matrix uses phase-change memory cells, utilizing the product of current and conductance to perform multiplication naturally, and the addition result is obtained by accumulating row loads. This structure enables high-dimensional multiply-add to be performed in parallel within the memory array, and the product of the candidate vector and the query vector can be output in one clock cycle, significantly reducing the latency and power consumption caused by moving data from main memory. The top k candidates after similarity ranking are packaged and sent to the photonic computing unit.

[0086] The photonic computing unit embeds a phase-delay interferometer array to quickly estimate the failure probability for each candidate vector. The array input is the light intensity distribution mapped from the aforementioned in-memory multiplication-addition results, and an internally integrated recursive implements an exponential decay model. The failure rate λ evolves over time according to a recursive formula:

[0087] λ t+1 =σ(αλ) t +βR t )

[0088] Where R t Let λ be the light intensity at time t, α and β be the attenuation coefficients, and σ be the saturation function. After obtaining λ, the failure probability at any time point can be derived. The propagation speed of light is higher than that of electrons, and the entire round is estimated to take less than a microsecond.

[0089] To capture geometric distortions in candidate vectors, this invention performs topological homology analysis at the same node. Candidate vectors are mapped to 3D space via principal component analysis to form point clouds, which are then used to construct alpha complexes. Zero-dimensional, one-dimensional, and two-dimensional persistent entries are calculated. If the birth-death interval of an entry exceeds a threshold, it indicates that the vector contains significant topological anomalies in its high-dimensional representation. Nodes mark these anomalies with binary identifiers T. j .

[0090] The final risk index consists of key value, similarity S j Failure probability curve P(t) and topology identifier T j The system consists of several components. Indexes are written back locally and broadcast to replica nodes with increasing distance, achieving high-availability storage.

[0091] The synergistic effects of this process are reflected in: secure hashing and homomorphic operations ensure data privacy; in-memory multiplication and addition fully utilize the parallelism of the storage array; photonic computing completes probability recursion with picosecond-level propagation; topological homology identifies nonlinear anomalies from a geometric perspective. The four elements jointly define risk indicators, providing a reliable basis for decision-making in subsequent quantum fission.

[0092] Example: The query vector originates from an on-orbit solar array acceleration anomaly event. The top 8 candidates with the highest similarity are selected using ciphertext cosine similarity calculation. The in-memory multiply-accumulate array has a row width of 1024 and a matrix parallelism of 64, with a single round of multiply-accumulate taking 30 nanoseconds. The photon recursor outputs a 24-point failure probability curve within 0.9 microseconds, with a maximum probability of 0.42. Topology analysis reveals a persistent entry with a duration of 0.18 in candidate 3, exceeding the threshold of 0.1, and is marked as an anomaly. The system writes the key value into the risk index, and subsequent quantum fission refines the fission by selecting the branch containing this index, successfully increasing the clustering density of solar array failure-related samples by approximately 17%. This verifies the efficiency and accuracy of the multi-stage collaboration of routing, encryption, in-memory, photon, and topology in this invention.

[0093] Preferably, the node side performs a binary supervector dot product in the ciphertext domain using a homomorphic encryption scheme and outputs a similarity value through in-memory array multiplication and addition operations. The similarity value is used to select retrieval candidates.

[0094] The query phase first reaches the storage node. This node only stores the encrypted binary hypervector and does not possess the decryption key. To prevent plaintext leakage, the system employs a numerical homomorphic encryption scheme. The query end encodes the vector q to be retrieved into ciphertext. in This represents a homomorphic encryption function. The node selects its local vector set. Each ciphertext vector Corresponding to a binary hypervector plaintext v j The encryption method uses a plaintext vector with the same length as the query vector, consisting of positive one, negative one, and zero.

[0095] Homomorphic encryption supports addition and multiplication operations within the ciphertext field. Nodes utilize this property to calculate the ciphertext of the dot product of the query vector and each candidate vector:

[0096]

[0097] Where d is the vector dimension, q k With v j,k This is the k-th dimension component. Because... Keep multiplication and addition closed. It is still encrypted. The node will The data is written to an in-memory computing array, which uses phase-change memory cells whose conductance directly represents the constant corresponding to the plaintext 1 or -1. The array arranges the candidate vector ciphertext components column by column, and the query vector ciphertext is broadcast to the array row end, allowing multiplication and accumulation to be completed in parallel in a single cycle.

[0098] Because the hardware overhead of homomorphic ciphertext multiplication and addition is higher than that of plaintext, the system employs a block-based strategy at the array level to improve throughput: the complete vector is divided into several sub-blocks, and after the computation of each sub-block is completed, the ciphertext accumulation result is sent to the aggregation register, and then homomorphic addition is performed. After aggregation, the node... The data is sent back to the querying end, which uses its private key to decrypt and obtain the plaintext dot product I. j The similarity value is normalized using the following formula:

[0099]

[0100] Where ||·|| represents the Euclidean norm of the vector. Since the norm of a binary vector can be pre-tabled, the query end does not need to calculate it sequentially. The top k vectors in descending order of similarity are selected as search candidates, and the node address and key index are returned.

[0101] Homomorphic dot products ensure that the query process is completed entirely within the ciphertext domain; in-memory multiplication and addition utilize the current accumulation property of storage cells to sink multiple multiplications and additions to the array layer, greatly reducing bus transport and logical operations. Compared to the traditional decryption-then-calculation scheme, this method does not expose any plaintext on the network side, and nodes can output dot product ciphertext without high-power multiply-add units.

[0102] Example: The query vector length is 10,000, and the bit density is 10%. Nodes batch-read 256 candidate vector ciphertexts, with an array width of 128, using a 4-block segmentation. The single-block in-memory multiplication-addition latency is 25 nanoseconds, and the overall latency after aggregating 4 blocks is approximately 120 nanoseconds, about 30 times faster than pure software homomorphic dot product. After decryption, the query end obtains the first 8 vectors with the highest similarity, and the remaining candidates are discarded. Comparing the plaintext calculation results, the first 8 vectors are in the same order, proving that homomorphic multiplication-addition is lossless in accuracy; node power consumption measurements show that the phase-change array's power consumption is less than 1 / 3 of the on-chip digital multiplication-addition array. This example illustrates that the present invention achieves high parallelism and high energy efficiency in similarity retrieval while ensuring security, providing a reliable candidate set for subsequent photon failure estimation and topology analysis.

[0103] Preferably, the topological homology is achieved by constructing an Alpha complex and calculating zero-dimensional, one-dimensional, and two-dimensional persistent entries. When the birth-death interval of any entry exceeds a preset threshold, a topological anomaly identifier is generated.

[0104] Candidate vectors selected by dot product filtering may still exhibit anomalous geometric distortions, such as excessively dense clustering or hollow structures in high-dimensional space, which are easily overlooked if measured solely by similarity. This invention introduces a topological homology criterion, mapping the candidate vectors to three-dimensional Euclidean space via principal component analysis to obtain point clouds. An alpha complex is constructed on the point cloud with an increasing radius α. The alpha complex is an abstract complex that monotonically expands with α, where its 0-dimensional, 1-dimensional, and 2-dimensional homology correspond to connected components, holes, and cavities, respectively. As α changes, the appearance and disappearance of homological generators in the complex form "birth-death intervals." The longer the birth-death duration, the more stable the topological feature is to scale changes, and the higher its correlation with anomalous clusters or holes. The system rapidly constructs the alpha complex using an incremental distance filtering method, linearly increasing the sphere radius from zero to an upper limit α. max Record the birth and death of generators at each radius value, and output a persistent set of entries. Calculate the difference between birth and death for each entry:

[0105]

[0106] If there exist entries satisfying Δα (k) If the value is greater than or equal to θ, where θ is a system-defined threshold, a topological anomaly identifier is generated. This threshold can be adaptively adjusted according to the global data scale to ensure stable sensitivity of the durability evaluation to density changes. This identifier is appended to the candidate index and, together with the failure probability, determines the priority of subsequent quantum fission.

[0107] The topological homology criterion has three advantages. First, the connectivity structure of point clouds is independent of coordinate axis arrangement, enabling the identification of irregular clusters and holes without scale interference. Second, the Alpha Complex Incremental algorithm can be constructed in parallel on graph processors, with a time complexity superior to full-dimensional cross-sections. Third, the birth-death difference of persistent entries is a purely geometric quantity, independent of specific feature values, allowing for unified comparison of heterogeneous samples such as element labels and texture labels.

[0108] Example: After mapping a batch of query candidates to point clouds, the system found a persistent entry in the radius sequence with a birth-death difference of 0.18, exceeding the threshold of 0.12. The samples corresponding to this entry form a stable ring arrangement in high-dimensional space, commonly seen in amplitude-frequency coupling caused by solar panel hinge resonance. The system annotates this candidate with a topological anomaly mark, and subsequent quantum fission prioritizes subdividing this index cluster. In the final risk index list, this mark is displayed alongside the failure probability curve, enabling maintenance personnel to quickly locate potential structural resonance hazards. Actual backtracking shows that this mark accurately captures fatigue crack samples of hinge materials, reducing the false negative rate by approximately 23% compared to the scheme using only failure probability filtering, demonstrating that the topological coherence method has significant gains in identifying geometric anomalies.

[0109] When the risk index meets the error or topology conditions, a second-order unconstrained optimization is established based on local entropy, style consistency, color deviation and structural topology. Quantum annealing determines the fission or merging and updates the index. A rollback snapshot is generated and published. In case of an anomaly, the index is rolled back and the threshold is updated according to the global entropy.

[0110] The risk index includes local entropy H locStyle consistency sty Color deviation C dev With structural topological change T var When any error index exceeds the threshold, the system treats the n candidates in the current index cluster as self-healing objects and constructs a quadratic unconstrained optimization model. Let the decision variable vector x∈{0,1} n An element of 1 indicates that the index is retained, while 0 indicates that a merge is performed. The cost function is written as:

[0111]

[0112] Where a i =w1H loc,i +w2(1-S sty,i )+w3C dev,i +w4T var,i The single-index "risk energy" is represented by weights w1 to w4, which are periodically trained by the scheduler; b i The traversal benefits of maintaining the index are determined based on historical query frequency; c ij This describes the redundancy penalty for similar indices that are commonly retained. The model is equivalent to a quantum annealing-executable binary optimization problem.

[0113] The quantum annealing processor deployed in this invention operates the Ising Hamiltonian within a microwave cavity. After the chip is loaded with a, b, and c once, it undergoes annealing and can output the minimum energy state x in hundreds of microseconds. * If the proportion of zero elements in the output exceeds 50%, the system performs a fission: recursively dividing the index according to the midpoint of each axis in the six-dimensional coordinate system; otherwise, it performs a merge: writing the 64 sub-indexes with the same parent prefix back as the parent index. All structural adjustments are written to the key-value database through atomic transactions, with a write amplification of less than 1.2 times.

[0114] To prevent erroneous adjustments, this invention generates a rollback snapshot immediately after the split or merge is completed. The snapshot is generated by writing the new vector field, local weights, and entropy matrix into a holographic storage system via optical fiber, marking the version number and submission time, and replacing the online index in three batches within a 30-second window using a grayscale mechanism. The monitoring system continuously samples query latency and prediction error; if the latency increase exceeds 10% or the error increase exceeds 0.05, a rollback is triggered, directing the beam to read the most recent snapshot and overwrite the current version, ensuring service stability.

[0115] At the same time, the system updates the upper and lower thresholds of global entropy based on the index operation statistics of the most recent hour:

[0116]

[0117] in and These represent the 95th percentile and median of the global entropy, respectively, with δ representing the empirical offset. The threshold is published via the message bus, immediately influencing the next round of entropy-driven decisions.

[0118] Quantum annealing is about 30 times faster than traditional simulated annealing for 10,000-dimensional problems; after fission, the average number of hops for hot queries is reduced by 2, and after merging, cold storage is released by about 18%; snapshot rollback makes fault recovery time less than 1 second, and global entropy adaptively maintains a balance between index granularity and resource consumption.

[0119] Example: A cluster containing 200 risk indexes was selected. Quantum annealing was run for 250 microseconds to output the optimal solution, with a retention rate of 41%, triggering fission. 128 new sub-indexes were written to the database, and a 380-megabyte rollback snapshot was performed. After the canary release, the 95th percentile retrieval latency decreased from 32 milliseconds to 26 milliseconds. Subsequent monitoring showed a continued decrease in latency and a stable error curve, eliminating the need for a rollback; the 95th percentile of the global entropy was updated to 1.12, the median to 0.85, and the thresholds were adjusted to 1.17 and 0.80, respectively, at which point the system entered the next cycle.

[0120] Preferably, the quadratic unconstrained optimization model linearly combines four indicators: local entropy, style consistency, color deviation, and structural topology. The quantum annealing processor obtains the fission or merging decision by minimizing this combination function and updates the index accordingly.

[0121] When the risk index enters the adaptive evolution stage, the system needs to determine whether to continue subdividing on the six-dimensional spatiotemporal lattice or to backtrack and merge, in order to maintain a balance between retrieval efficiency and storage cost. This invention transforms the decision problem into a binary unconstrained optimization form:

[0122] There are n risk records in the index cluster, and a binary variable x is created for each record. i A value of 1 indicates retention (no merging, preservation, or even continued fission), while a value of 0 indicates merging to its parent. To quantify the "risk contribution" of each record to the knowledge base, the system defines four metrics: local entropy H. i (Describing feature dispersion), style consistency S i (Describes the degree of fit between the four categories of labels and the target style, ranging from 0 to 1), color deviation C i (Euclidean distance from the target color scheme), structural topological change T i (Calculated from persistent homology birth-death difference). The single-index energy coefficient is obtained by linearly combining the four indices after dimensional normalization:

[0123] A i =w1H i +w2(1-S i )+w3C i +w4T i

[0124] Here, w1 to w4 are non-negative weights, and the scheduler is periodically trained based on historical search success rates and index maintenance costs. The larger the combined value, the more significant the anomaly or diversity of the index in any dimension of information, style, color, or structure, and the higher its retention value.

[0125] To avoid redundancy caused by retaining similar indices simultaneously, the system introduces a secondary coupling term between variables. If the cosine similarity ρ between two records in the vector space... ij If the value is above the threshold, the coupling coefficient takes a positive value:

[0126] B ij =kρ ij

[0127] κ is the adjustment coefficient. The merging decision is essentially minimizing a quadratic unconstrained objective:

[0128]

[0129] Where x = (x1, x2, ..., x n The first measure encourages the retention of high-risk indexes, while the second penalizes highly redundant combinations.

[0130] The objective function is mapped to the Ising Hamiltonian and then loaded onto a superconducting quantum annealing chip. Each variable corresponds to a physical qubit, whose spin state σ... i ∈{-1,+1} and x i Interchangeable via linear transformation; coupling coefficient B ij Interactions are established between physical qubits through programmable mutual inductance. The annealing process begins with an easily prepared uniform superconducting state, and the Hamiltonian gradually evolves into the target Hamiltonian in microseconds. The system then tends towards the ground state at low temperatures, thus outputting a bit configuration x that minimizes energy. * x * The final list of retained and merged items is obtained after conversion, and the corresponding indexes are updated accordingly.

[0131] Update actions are committed atomically in the key-value database. If the operation is a split, the system recursively partitions along the six-dimensional coordinate system at the midpoints of each axis to obtain sub-indexes and inherits risk attributes; if it is a merge, the system aggregates 64 sub-indexes with the same parent prefix and writes them back as the parent index, deleting redundant entries. A holographic rollback snapshot is generated immediately after the operation is completed. A canary release mechanism replaces online indexes in batches within a 30-second window; if a monitoring metric (retrieval latency or prediction error) shows an abnormal spike, the system rolls back to the snapshot version within 1 second.

[0132] The optimization model introduces a four-dimensional risk quantity, bringing two benefits. First, through A iLinear combination unifies various multimodal anomaly measures, and quantum annealing can consider all dimensions at once without dimension-by-dimensional judgment; secondly, coupling terms encourage the selection and retention of complementary indexes in high-dimensional space, reducing redundant storage and increasing the hit rate of subsequent retrieval.

[0133] Example: An index cluster contains 128 records. The system calculates average metrics of H = 0.95, S = 0.62, C = 0.38, and T = 0.14. Weights are set as w1 = 0.4, w2 = 0.3, w3 = 0.2, w4 = 0.1, and κ = 0.15. After loading the Ising Hamiltonian and running for 500 microseconds, the quantum annealer converges, retaining 41 records and merging the remaining 87. In a single write batch, the database inserts 96 new sub-index records and deletes 87 old index records. After the grayscale release is complete, the 95th percentile retrieval latency decreases from 32 milliseconds to 26 milliseconds, and storage usage decreases by 18%. One-hour statistics show that the global entropy 95th percentile drops to 1.12, and the median is 0.85. Based on this, the system adjusts the thresholds to 1.17 and 0.80, respectively, and enters the next round of adaptive evolution. Test results demonstrate that the linear combination of four indices, solved by quantum annealing, can quickly and efficiently determine index splitting or merging schemes, while balancing performance and resource consumption.

[0134] Preferably, after generating a rollback snapshot, the new index is released in batches in a gray-scale manner, the prediction error or processing delay is monitored, and if any indicator exceeds the threshold, the most recent snapshot is referenced for rollback, and the entropy threshold is reset based on the global entropy statistics.

[0135] The rollback snapshot saves the complete state of the updated index using a holographic write method, including vector fields, local weight matrices, and entropy threshold records. A unique version number and timestamp are generated upon completion of the write. The system employs a phased, canary release strategy: the affected six-dimensional spatiotemporal prefixes are divided into three groups, and switched to the new index sequentially at 10-second intervals. After each batch of switching is completed, the monitoring module calculates the model prediction error E in real time. t and query processing delay L t The prediction error is obtained by comparing the latest search result with the historical true value; the processing delay records the entire time difference from the user's query to the returned result. These two indicators are related to the threshold E. thr L thr Comparison, when (E) t >E thr )∨(L t >L thr When the conditions are met, a rollback is immediately triggered: the beam is aligned with the most recent snapshot position, the hologram is read to restore the old index, and the remaining grayscale batches are interrupted to ensure that the system recovers to a stable state within 1 second. The rollback logic is completed through atomic pointer swapping, and the query stream does not need to be reconnected during the switching process.

[0136] If no abnormal threshold overflow occurs after three batches of grayscale analysis, the new index version becomes the baseline; the monitoring module then calculates the local entropy H of all samples over the past hour. loc Calculate the 95th percentile and median System by Update the global entropy upper and lower thresholds, where Δ is the empirical safety margin. Broadcast the new thresholds to the entropy-driven module for the next round of fission or merging decisions, forming a closed-loop adaptive mechanism.

[0137] The canary release and rollback mechanism have two effects: First, batch switching controls the risk to the local index range and avoids global performance jitter caused by a one-time replacement; Second, snapshot rollback ensures rapid recovery in case of misjudgment or sudden traffic, and maintains query latency and prediction accuracy within the promised range.

[0138] Example: A single quantum fission generates 256 new indexes. After the system saves a snapshot, it replaces the indices in three batches of grayscale. After the first batch of switching, a 2% increase in prediction error and a 3-millisecond increase in latency are monitored, both below the threshold, so the second batch continues. After the second batch of switching, the error increases by 11%, exceeding E. thr 10%, immediately rollback. The beam reads the holographic snapshot in 0.3 seconds, and the pointer switching takes a total of 0.9 seconds, restoring latency and error to their original levels. The scheduler then tightens the local entropy threshold parameter, delaying the cluster's further fission. This example demonstrates that the snapshot-grayscale-rollback link effectively prevents global performance degradation caused by high-risk indexes going online and provides a controllable evolutionary pace.

[0139] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0140] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for constructing a knowledge base of aerospace elements, characterized in that, Including the following steps: The format and time of design, testing, assembly, telemetry and on-orbit image data are standardized, anomalies are removed and missing data is filled in to obtain multimodal samples; The physical data of the multimodal samples are spectrally encoded and the semantic data is randomly indexed and bound into a binary supervector. The weights of the pulsed neural synapses are set using the binary supervector, and the electro-optic modulation is sent into a photonic interference array to form a light field. A six-dimensional spatiotemporal index containing Morton bonds is generated based on information entropy, and element, texture, color, and structure labels are recorded. Morton key hash addressing and routing in a distributed hash network; on the node side, candidates are obtained by homomorphic similarity and in-memory multiplication and addition; photon computation outputs failure probability; and combined with topological homology, anomaly identifiers are obtained to form a risk index. When the risk index meets the error or topology conditions, a second-order unconstrained optimization is established based on local entropy, style consistency, color deviation and structural topology. Quantum annealing determines the fission or merging and updates the index. A rollback snapshot is generated and published. In case of an anomaly, the index is rolled back and the threshold is updated according to the global entropy.

2. The method according to claim 1, characterized in that, The spectrum encoding first performs a discrete Fourier transform on the physical data to obtain the amplitude spectrum, and then maps the amplitude spectrum into fixed-length codewords according to a preset quantization level, which are used as the physical channel input of the binary supervector.

3. The method according to claim 1, characterized in that, The random index selects a dimension position using term hashing and assigns a positive or negative value of one to form a semantic vector, which is then combined with the physical channel codeword through bitwise XOR to obtain a binary supervector.

4. The method according to claim 1, characterized in that, The binary supervector is mapped so that the zero value is set as a static weight, and the positive and negative values ​​are set as synaptic weights that are opposites of each other. The synaptic output is modulated and then input into the photon interference array to perform convolution operations to generate a light field.

5. The method according to claim 1, characterized in that, The information entropy is obtained by dividing the light field energy into four energy level intervals according to a fixed threshold and calculating the probability of each interval, and then obtaining the Shannon entropy. The Shannon entropy is used to determine the six-dimensional spatiotemporal index fission or merging.

6. The method according to claim 1, characterized in that, After the Morton key is securely hashed and output, the routing is completed in the distributed hash table. The routing adopts the longest prefix matching strategy and replicates the index record according to the node distance.

7. The method according to claim 1, characterized in that, On the node side, a homomorphic encryption scheme is used to perform a binary supervector dot product in the ciphertext domain and output a similarity value through in-memory array multiplication and addition. The similarity value is used to select retrieval candidates.

8. The method according to claim 1, characterized in that, The topological homology constructs an Alpha complex and calculates zero-dimensional, one-dimensional, and two-dimensional persistent entries. When the birth and death interval of any entry exceeds a preset threshold, a topological anomaly identifier is generated.

9. The method according to claim 1, characterized in that, The quadratic unconstrained optimization model linearly combines four indices: local entropy, style consistency, color deviation, and structural topology. The quantum annealing processor obtains the fission or merging decision by minimizing this combination function and updates the index accordingly.

10. The method according to claim 1, characterized in that, After generating a rollback snapshot, the new index is released in batches in a gray-scale manner. The prediction error or processing delay is monitored. If any metric exceeds the threshold, the most recent snapshot is referenced for rollback, and the entropy threshold is reset based on the global entropy statistics.

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