Information processing and promotion system based on big data in combination with RPA

Abstract

The invention discloses an information processing and promotion system based on big data in combination with RPA, and relates to the technical field of data processing, the system comprises a real-time data processing unit, an autonomous decision execution unit and an information promotion unit, the real-time data processing unit adopts a quantum-photon hybrid computing architecture, and the information promotion unit adopts a quantum-photon hybrid computing architecture. The data acquisition unit acquires and preprocesses real-time data streams from various heterogeneous data sources at a high speed, the autonomous decision execution unit adopts a neural symbolic RPA engine to perform intelligent analysis and automatic process scheduling and execution on the preprocessed data, and the information promotion unit automatically generates a promotion scheme according to a received decision result and implements promotion. According to the invention, by designing a quantum-photon hybrid computing architecture, variable combination computing speed optimization and cross-region instantaneous synchronization functions of a super-large-scale data stream are realized, and the problems of high delay, high energy consumption and low computing efficiency when a traditional silicon-based architecture and a GPU cluster process mass data are solved; and the data processing speed and energy efficiency of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and specifically to an information processing and promotion system based on big data in combination with RPA. Background Art

[0002] With the advent of the big data era and the continuous penetration of automated processes into all walks of life, traditional RPA (robotic process automation) and big data systems have exposed problems such as computing bottlenecks, excessive energy consumption, response delays, and insufficient security protection when dealing with massive, multi-source, and real-time data processing. Existing technologies generally rely on silicon-based architectures, GPU clusters, and traditional data storage and promotion models. When dealing with high-dimensional decision-making, cross-regional data synchronization, and complex promotion environments, it is difficult to meet the requirements of speed, accuracy, security, and intelligence.

[0003] In order to break through the limitations of existing technologies, improve data processing efficiency, decision-making intelligence and system security, and meet the needs of future ultra-large-scale data and complex business scenarios, this application proposes an information processing and promotion system based on big data combined with RPA.

[0004] Patent document CN106294515B discloses a promotion information processing method and device. The above patent realizes that in the integrated promotion information, the user promotion information and the selected accompanying promotion information form an accompanying relationship. During the dissemination of the integrated promotion information after release, the user promotion information and the selected accompanying promotion information will be promoted together.

[0005] In summary, the above patent promotes integrated promotion information through free promotion channels based on social networks, which can significantly reduce the number of times ordinary users are disturbed. However, there is still room for optimization in the processing mode of real-time data, which leads to bottleneck problems in data calculation in high-dimensional decision-making. To this end, this application proposes an information processing and promotion system based on big data combined with RPA, which can break through the technical bottlenecks of high latency, high energy consumption and low computing efficiency of traditional silicon-based architecture and GPU clusters when processing massive real-time data. Summary of the invention

[0006] The purpose of the present invention is to provide an information processing and promotion system based on big data combined with RPA to solve the technical problem of bottlenecks in data calculation in high-dimensional decision-making proposed in the above background technology.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: combined with RPA, an information processing and promotion system based on big data includes a real-time data processing unit, an autonomous decision-making execution unit and an information promotion unit. The real-time data processing unit adopts a quantum-photon hybrid computing architecture to collect and preprocess real-time data streams from a variety of heterogeneous data sources at high speed, and transmit the processed data to the autonomous decision-making execution unit. The autonomous decision-making execution unit adopts a neural symbolic RPA engine to perform intelligent analysis, automated process scheduling and execution on the preprocessed data, and generate decision results to transmit to the information promotion unit. The information promotion unit automatically generates a promotion plan based on the received decision results and implements the promotion, and at the same time feeds back the execution status and feedback information as closed-loop data to the real-time data processing unit and the autonomous decision-making execution unit.

[0008] Preferably, the quantum-photonic hybrid computing architecture in the real-time data processing unit uses a photonic chip to process real-time data streams, achieving sub-nanosecond delay, thereby improving data processing speed, and the photonic chip includes an optical sensor and a multi-channel optical fiber interface.

[0009] Preferably, the real-time data processing unit further comprises: Quantum annealing module: The quantum annealing module adopts a solid-state quantum bit structure and is equipped with an annealing control circuit. It uses the quantum annealing algorithm to optimize the multi-objective decision tree to solve extremely high-dimensional problems. A distributed quantum entanglement storage device is configured. The distributed quantum entanglement storage device adopts a storage structure based on a quantum bit chain and is equipped with inter-regional quantum connection nodes to achieve instantaneous synchronization of cross-regional data, while improving computing speed while reducing energy consumption.

[0010] Preferably, the neuro-symbolic RPA engine in the autonomous decision-making execution unit integrates deep reinforcement learning and formal verification to form a dynamic cognitive architecture to achieve semantic-level process understanding, which is based on knowledge graph reasoning and operation.

[0011] Preferably, the neural symbolic RPA engine further comprises: a deep reinforcement learning module, a formal verification module and a dynamic cognitive architecture; The deep reinforcement learning module uses a computing structure based on tensor operations; The formal verification module adopts symbolic logic circuit structure; Dynamic cognitive architecture has: Adaptation in non-deterministic environments, automatically switching decision strategies when the system entropy value is lower than 0.1; Multimodal interface penetration capabilities across AR, VR and brain-computer interfaces; Self-generated digital fingerprint technology to track and record each automated decision chain.

[0012] Preferably, the information promotion unit adopts a generative anti-fragile promotion network, which constructs a dynamic propagation model based on a generative adversarial network, optimizes the promotion strategy through quantum Monte Carlo tree search, and simulates multiple abnormal scenarios in real time to ensure the fidelity of the promoted information in the multi-universe propagation path.

[0013] Preferably, the information promotion unit further includes self-organizing nanoscale propagation nodes, which are based on DNA computing biochip technology and use environmental energy collection functions to achieve group intelligence to construct dynamic propagation topology and ensure the working life of the nodes.

[0014] Preferably, the information processing and promotion system also includes a causal cognition enhancement framework, which realizes real-time analysis of potential causal relationships in the process by establishing a time-varying causal graph model, and is configured with a counterfactual reasoning engine, which generates a dimension of more than 10 4 The potential outcome space of the intervention is constructed using interpretable factor analysis to build an intervention effect propagation model to predict and regulate second-order and higher chain reactions and ensure the safety margin of the strategy.

[0015] Preferably, the information processing and promotion system is further configured with a biomolecular level security protocol, which includes: DNA chain encryption unit, using multi-chain structure and DNA chain encryption algorithm, key space exceeds 10 300 ; The protein folding verification unit is equipped with a molecular conformation matching array. Through the protein folding verification mechanism, dynamic conformation matching authentication is achieved with an error tolerance of less than 10 -9 ; The quantum bio-signature system, including quantum entanglement detection circuit and enzyme catalytic reaction module, realizes the physical non-cloning function through quantum entanglement and enzyme catalytic reaction, thereby ensuring the physical level security of data during transmission and storage.

[0016] Preferably, the implementation path of the information processing and promotion system includes: Build a quantum-photon hybrid computing base that breaks through the traditional silicon-based chip architecture; Develop a neural symbolic programming language that combines lambda calculus and tensor flow; Establish a holographic data lake based on metasurface manufacturing technology, achieve 1 EB data storage per cubic centimeter and in-situ photon convolution kernel calculation; Deploy quantum biosafety protocols to achieve room temperature quantum storage; By using PB-level counterfactual data sets to train causal cognitive models, real-time analysis of complex causal relationships can be achieved, achieving an overall order of magnitude improvement in key performance indicators such as energy efficiency, computing speed, and security level.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention realizes the optimization of variable combination calculation speed and cross-regional instant synchronization function for ultra-large-scale data streams by designing a quantum-photon hybrid computing architecture, solving the problems of high latency, high energy consumption and low computing efficiency of traditional silicon-based architecture and GPU clusters when processing massive data, improving the system data processing speed and energy efficiency, and meeting the large-scale implementation processing requirements; 2. The present invention realizes the adaptive adjustment function of data processing in a non-deterministic environment by designing a neural symbolic RPA engine, which makes up for the defects of the traditional RPA system in lacking deep semantic understanding and insufficient flexibility, improves the intelligence level and flexibility of the automation process, and realizes better process optimization and resource scheduling; 3. The present invention realizes high-density data storage and real-time processing functions by designing a holographic data lake, solves the problems of insufficient storage density, real-time performance and security of traditional data storage and query architecture, meets the challenges of ultra-large-scale data processing, and greatly improves data query efficiency and data security; 4. The present invention realizes efficient information distribution function by designing a generative anti-fragile promotion network, solves the problem that traditional information promotion systems cannot dynamically adapt to complex abnormal environments and are prone to information distortion and security risks, effectively improves promotion efficiency and coverage, and reduces risks and interference in the information transmission process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the information processing and promotion process of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1, an embodiment provided by the present invention: an information processing and promotion system based on big data in combination with RPA, including a real-time data processing unit, an autonomous decision-making execution unit and an information promotion unit, wherein the real-time data processing unit adopts a quantum-photon hybrid computing architecture, collects and pre-processes real-time data streams from a variety of heterogeneous data sources at high speed, and transmits the processed data to the autonomous decision-making execution unit, wherein the autonomous decision-making execution unit adopts a neural symbolic RPA engine, performs intelligent analysis, automated process scheduling and execution on the pre-processed data, and generates a decision result and transmits it to the information promotion unit, wherein the information promotion unit automatically generates a promotion plan based on the received decision result and implements the promotion, and at the same time feeds back the execution status and feedback information as closed-loop data to the real-time data processing unit and the autonomous decision-making execution unit; The implementation path of the information processing and promotion system includes: Build a quantum-photon hybrid computing base that breaks through the traditional silicon-based chip architecture; Develop a neural symbolic programming language that combines lambda calculus and tensor flow; Establish a holographic data lake based on metasurface manufacturing technology, achieve 1 EB data storage per cubic centimeter and in-situ photon convolution kernel calculation; Deploy quantum biosafety protocols to achieve room temperature quantum storage; Using PB-level counterfactual data sets to train causal cognitive models, we can achieve real-time analysis of complex causal relationships, and achieve an order of magnitude improvement in key performance indicators such as energy efficiency, computing speed, and security level. Furthermore, the real-time data processing unit based on the quantum-photon computing architecture includes: the photon chip data acquisition module uses ultra-high-speed optical sensors to collect data from heterogeneous data sources such as industrial sensors, network logs, video streams, and voice input in parallel, with a data acquisition frequency of 10GHz; the quantum annealing optimization unit uses quantum annealing algorithms to optimize multivariate data to achieve denoising, data cleaning, and optimal path selection; the distributed quantum entanglement storage device achieves cross-regional data synchronization through the quantum storage network, and the transmission delay is controlled at the nanosecond level; The autonomous decision-making execution unit based on the neural symbolic RPA engine includes: a deep reinforcement learning module for training the automated task decision model and building a decision tree based on historical data; a knowledge graph processing module for building a domain knowledge base, performing semantic analysis on data, and automatically building a logical reasoning network; a digital fingerprint generation module for tracking the execution of automated processes and recording all decision paths; The information promotion unit based on generative antifragile network includes: the adversarial generative network module builds a dynamic propagation model and conducts environmental simulation tests; the quantum Monte Carlo tree search module optimizes the promotion strategy path to ensure the maximum information reach rate; the DNA computing self-organizing propagation node uses DNA storage chips to achieve environmental energy collection and provide long-term autonomous computing capabilities; The real-time data processing unit receives data input from multiple sensors through the photonic chip data acquisition module and converts it into optical computing data format in real time. The quantum annealing optimization unit denoises and formats the collected data, screens redundant data and optimizes the storage structure. The processed data is synchronized to each computing node through the quantum entanglement storage module for use by the autonomous decision-making execution unit; the autonomous decision-making execution unit receives pre-processed data from the real-time data processing unit and uses the knowledge graph processing module to parse the data semantics. The deep reinforcement learning module compares the existing historical data and automatically builds the optimal process path. After generating the decision result, the data fingerprint generation module records the complete execution link and transmits the data to the information promotion unit; the information promotion unit constructs a generative adversarial network based on the decision result provided by the autonomous decision-making execution unit, and tests it by simulating different propagation environments. The quantum Monte Carlo tree search module calculates multiple information propagation paths, screens out the best solution, and delivers information to the target group through DNA computing self-organized propagation nodes, dynamically adjusts the strategy, records the promotion feedback data, and transmits it back to the real-time data processing unit and the autonomous decision-making execution unit through a closed-loop feedback mechanism.

[0021] See also Figure 1 , an embodiment provided by the present invention: in combination with the RPA information processing and promotion system based on big data, the quantum-photon hybrid computing architecture in the real-time data processing unit uses a photon chip to process the real-time data stream, achieving sub-nanosecond delay, thereby improving the data processing speed, and the photon chip includes an optical sensor and a multi-channel optical fiber interface; The real-time data processing unit further comprises: Quantum annealing module: The quantum annealing module adopts a solid-state quantum bit structure and is equipped with an annealing control circuit. It uses the quantum annealing algorithm to optimize the multi-objective decision tree to solve extremely high-dimensional problems. Configure a distributed quantum entanglement storage device, which adopts a storage structure based on quantum bit chains and is equipped with inter-regional quantum connection nodes to achieve instantaneous synchronization of cross-regional data and improve computing speed while reducing energy consumption; Furthermore, optical sensors are used to collect data streams from multiple heterogeneous data sources such as industrial IoT devices, video surveillance systems, network logs, and voice input. Multi-channel fiber optic interfaces are used to transmit data in parallel, and each fiber optic channel independently processes data from different sources to avoid data conflicts. Photonic computing units are used to perform parallel optical signal processing on raw data to perform signal denoising, pattern recognition, and edge computing. Since photonic computing has sub-nanosecond computing delays, the data processing speed far exceeds the traditional electronic chip architecture. The data processed by photonic computing is converted into a format recognizable by quantum computing and transmitted to the quantum annealing module for optimization. The preprocessed data is subjected to a quantum annealing module, which uses a solid-state quantum bit structure and provides superconductor quantum bits as computing units. Quantum coherent computing is performed between quantum bits through coupling circuits to achieve parallel processing. The quantum annealing module uses a quantum annealing algorithm for multi-objective optimization, including: abnormal data removal: based on the historical data model, abnormal data points are detected, and the optimal data set is selected through quantum computing; multi-objective path decision: for large-scale data sets, the Hamiltonian path calculation is used to select the optimal data transmission path; high-dimensional problem dimensionality reduction: for variables with more than 10 6 Quantum annealing is used to reduce the computational complexity of high-dimensional optimization problems, reducing the computational time from exponential to logarithmic levels. The quantum annealing module outputs the optimized data, which is stored in a distributed quantum entanglement storage device or synchronized to a remote computing node. The processed data is stored in a distributed quantum entanglement storage device, in which the storage medium is based on a quantum bit chain structure and uses topological coding technology to maintain quantum coherence during data storage to avoid data loss. Inter-regional quantum connection nodes use a quantum entanglement mechanism to achieve instantaneous synchronization between different storage nodes. Even if the physical distance exceeds 1,000 kilometers, data transmission can be completed in milliseconds. Compared with traditional distributed storage such as HDFS, this method not only eliminates network latency, but also reduces data transmission energy consumption. The stored data can be called by autonomous decision-making execution units to perform intelligent analysis and automated process scheduling, and the data transmission path is automatically optimized to ensure computing efficiency and storage stability.

[0022] See also Figure 1 , an embodiment provided by the present invention: combined with the RPA information processing and promotion system based on big data, the neural symbolic RPA engine in the autonomous decision-making execution unit integrates deep reinforcement learning and formal verification to form a dynamic cognitive architecture to achieve semantic-level process understanding, and the semantic-level process understanding is based on knowledge graph reasoning and operation; The neural symbolic RPA engine further includes: a deep reinforcement learning module, a formal verification module and a dynamic cognitive architecture; The deep reinforcement learning module uses a computing structure based on tensor operations; The formal verification module adopts symbolic logic circuit structure; Dynamic cognitive architecture has: Adaptation in non-deterministic environments, automatically switching decision strategies when the system entropy value is lower than 0.1; Multimodal interface penetration capabilities across AR, VR and brain-computer interfaces; Self-generated digital fingerprint technology to track and record each automated decision chain; Furthermore, the pre-processed data, including text, voice, video, IoT data, etc., is obtained from the real-time data processing unit. The computing architecture based on tensor operations is used to convert the data into semantic vectors, mapped to the knowledge graph, and the semantic association of the data is extracted through the graph convolutional neural network GCN. The formal verification module is combined to perform logical consistency checks to ensure that the process understanding complies with business rules, generate executable automated task sequences, and predict the optimal execution path based on historical data; The policy gradient algorithm DQN is used to train RPA robots to improve the adaptability of automated tasks. The self-supervised learning method is used to enable the RPA engine to learn the optimal execution strategy from unlabeled data. In a non-deterministic environment, the system monitors the entropy value, that is, the information uncertainty, in real time. When the entropy value is lower than 0.1, the system automatically switches to the optimal strategy to ensure the stability and robustness of decision-making. The symbolic logic circuit structure is used to convert the task process into a verifiable logical expression, and the symbolic constraint check is performed on the decision tree to ensure that there are no conflicting paths. The SAT solver is used to analyze whether there are loopholes in the decision logic. If errors are found, the system triggers an automatic repair mechanism to generate an optimized process model. The multimodal interface penetration of the dynamic cognitive architecture includes: cross-platform interaction: applicable to AR, VR and brain-computer interface, through voice recognition, gesture tracking, brain wave signal decoding, multimodal input is realized, and a unified interaction protocol is adopted to enable RPA to adapt to different hardware environments; interface penetration: through OCR and UI element detection, undisclosed API interfaces are automatically identified, and reverse engineering analysis is used to extract interface interaction logic to realize automated operation; Each time RPA performs a task, the system automatically generates a unique data fingerprint to record the data, reasoning path, and execution results involved in the task. The Merkle tree structure is used to store the decision history to ensure that the data cannot be tampered with. When an abnormal decision occurs, the system can trace the entire decision chain, locate the problem node, and perform self-correction. Combined with blockchain storage, the integrity and completeness of all decision chain data can be ensured.

[0023] See also Figure 1, an embodiment provided by the present invention: in combination with the RPA information processing and promotion system based on big data, the information promotion unit adopts a generative anti-fragile promotion network, the generative anti-fragile promotion network builds a dynamic propagation model based on the adversarial generative network, optimizes the promotion strategy through quantum Monte Carlo tree search, and simulates multiple abnormal scenarios in real time to ensure the fidelity of the promotion information in the multi-universe propagation path; The information promotion unit further includes a self-organizing nanoscale propagation node, which is based on the biochip technology of DNA computing and utilizes the environmental energy collection function to realize group intelligence to construct a dynamic propagation topology and ensure the working life of the node; Furthermore, the dynamic propagation model of the generative anti-fragile promotion network: the real-time data processing unit provides market data, user behavior data, social network data, etc., uses natural language processing NLP and graph neural network GNN for data cleaning and semantic modeling, and identifies efficient promotion patterns through self-supervised learning; adversarial generative network modeling: generator G: generates efficient propagation paths and simulates multiple propagation environments; discriminator D: scores the promotion results to distinguish high-quality from low-quality propagation paths; iteratively optimizes the propagation strategy through adversarial training to make the promotion effect more anti-fragile; Quantum Monte Carlo tree search optimization promotion strategy: Different promotion strategies are modeled as decision trees, each node represents a different propagation mode, and the information gain of each strategy is calculated to form a multi-path propagation network. In the ultra-large-scale combinatorial search space, the quantum superposition state is used to search for the optimal path, and the quantum annealing algorithm is combined for local optimization to ensure that the convergence speed of the optimal strategy is increased by 10 3 times, calculate the reward function through real-time feedback data, prune inefficient strategies, and adopt entropy monitoring mechanism to ensure the diversity of promotion strategies and prevent the single propagation mode; Multi-universe propagation path simulation: Causal graph modeling is used to simulate the propagation path of promotion information in different social networks, search engines, and IoT environments. The optimal propagation trajectory is predicted using quantum path integral calculations. 10 6 In order to deal with various promotion anomalies, such as social media blocking and ad blocking, distributed reinforcement learning is used to enable the system to automatically adapt to different environments to avoid promotion failures. Information entropy calculation is used to ensure that information is not tampered with or lost during the dissemination process. Data watermarks and hash signatures are combined to achieve full traceability and verification of information. The working process of the self-organizing nanoscale propagation node is as follows: the DNA computing biochip uses DNA analysis and calculation for data storage and logical operations. The environmental energy collection unit is based on photovoltaic nano-batteries, which absorb environmental light energy and thermal energy for power supply, and uses swarm intelligence algorithms to build dynamic propagation topologies. Each nano-node has autonomous decision-making capabilities, can analyze the optimal propagation path in low-power mode, and uses a distributed hash table structure for dynamic networking to achieve rapid information transmission between nodes. It collects light energy, thermal energy, vibration energy, etc. through environmental energy for power supply, achieves maintenance-free operation, and uses a biological nano-scale protective layer to ensure stable operation in extreme environments.

[0024] See also Figure 1 , an embodiment provided by the present invention: in combination with an information processing and promotion system based on big data of RPA, the information processing and promotion system also includes a causal cognition enhancement framework, which implements real-time analysis of potential causal relationships in the process by establishing a time-varying causal graph model, and configures a counterfactual reasoning engine, which generates a dimension of more than 10 4 The potential outcome space of the intervention is analyzed, and an intervention effect propagation model is constructed using interpretable factor analysis to predict and regulate second-order and higher chain reactions and ensure the safety margin of the strategy. The information processing and promotion system is further configured with a biomolecular level security protocol, which includes: DNA chain encryption unit, using multi-chain structure and DNA chain encryption algorithm, key space exceeds 10 300 ; The protein folding verification unit is equipped with a molecular conformation matching array. Through the protein folding verification mechanism, dynamic conformation matching authentication is achieved with an error tolerance of less than 10 -9 ; The quantum bio-signature system includes a quantum entanglement detection circuit and an enzyme catalytic reaction module, which realizes the physical non-cloning function through quantum entanglement and enzyme catalytic reaction, thereby ensuring the physical level security of data during transmission and storage; Furthermore, the system collects event data, state data and log data from various data sources, and constructs an initial causal graph based on timestamp information. Each node in the graph represents a specific event or state, and the edge represents the potential causal relationship. Using time series data, the correlation coefficients between nodes are stored in matrix form, and the sliding window algorithm is used to periodically update the causal graph. During the update process, the causal edges are corrected by weight adjustment to form a causal graph model that reflects the latest time-varying information. Based on the updated time-varying causal graph model, disturbances are applied to selected key nodes, and high-dimensional matrix operation units are called to generate counterfactual simulation data. Parallel computing is used to generate a potential result space with a dimension of more than 10 4, each dimension corresponds to a different value of a single variable in the causal diagram, and the generated high-dimensional result space is stored in the form of a tensor and passed to the interpretable factor analysis module to perform interpretable factor decomposition on the potential result space. The algorithm based on factor load is used to separate the factors of each dimension, and the propagation relationship between the factors is recorded in the form of a matrix. The intervention effect propagation model is constructed based on the decomposition results to form a causal propagation matrix that records the second-order and higher chain relationships. The model is output in a predefined data format and stored in the system's internal database for subsequent calls; After the input data is binary encoded, the DNA chain encryption algorithm is called to map the binary code to a multi-chain DNA sequence. The multi-chain structure is used to perform independent encryption processing on each chain. The data of each chain is cross-validated to form a chain encryption sequence. The key generation module calculates the key space to ensure that the key value exceeds 10 300 The mapping rules and multi-chain cross-structure in the encryption process are implemented by the fixed algorithm circuit, and the result is output in the form of a digital sequence; the above DNA encryption output data is converted into the corresponding protein conformation through the preset protein folding simulation algorithm, and the molecular conformation matching array is configured to collect the simulated protein structure in real time. The molecular conformation is compared with the pre-stored standard template one by one through the high-speed sensor array to verify the built-in dynamic matching algorithm of the circuit to ensure that the matching error is controlled within 10 -19 Within, the matching result is output and recorded as a data signal; Before data transmission, quantum entangled pairs are generated in the quantum entanglement detection circuit and the entangled state parameters are recorded. The detection circuit is implemented using solid-state quantum bits. At the same time, the enzyme catalytic reaction module is started on the biochip, and the specific enzyme catalyzes the generation of biomarkers through preset reaction conditions. This process is collected synchronously with the quantum entanglement detection data. The quantum biosignature data is jointly output by the detection circuit and the biochip acquisition module, and transmitted to the security verification module through the digital interface to form a non-replicable biosignature sequence.

[0025] Working principle: The real-time data processing unit uses the quantum-photon hybrid computing architecture to collect and pre-process data from multiple heterogeneous data sources at high speed. The autonomous decision-making execution unit uses the neural symbolic RPA engine to realize intelligent data analysis and process scheduling. The information promotion unit automatically generates and implements promotion strategies based on the generative anti-fragile promotion network. A closed-loop data flow is formed between each module. Real-time data is transmitted to the autonomous decision-making execution unit after high-precision collection and quantum optimization. The decision results are jointly generated by deep reinforcement learning and formal verification. The information promotion unit then constructs a dynamic propagation model and optimizes the promotion path in turn, and at the same time feeds back the execution status, forming an adaptive iterative update mechanism. In addition, the system also integrates a causal cognitive enhancement framework and biomolecular-level security protocols, and uses time-varying causal graph models and counterfactual reasoning to perform real-time analysis of potential causal relationships in the process. It also uses DNA chain encryption, protein folding verification, and quantum biosignatures to ensure the physical level security of data transmission and storage, and achieve intelligent and secure management of the entire process from data collection to decision execution and promotion and implementation.

[0026] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. Combined with RPA, the information processing and promotion system based on big data includes a real-time data processing unit, an autonomous decision-making execution unit and an information promotion unit, which is characterized by: The real-time data processing unit adopts a quantum-photon hybrid computing architecture to collect and pre-process real-time data streams from a variety of heterogeneous data sources at high speed, and transmits the processed data to the autonomous decision-making execution unit. The autonomous decision-making execution unit adopts a neural symbolic RPA engine to perform intelligent analysis, automated process scheduling and execution on the pre-processed data, and generate decision results for transmission to the information promotion unit. The information promotion unit automatically generates a promotion plan based on the received decision results and implements the promotion, and at the same time feeds back the execution status and feedback information as closed-loop data to the real-time data processing unit and the autonomous decision-making execution unit.

2. The information processing and promotion system based on big data combined with RPA according to claim 1 is characterized in that: The quantum-photon hybrid computing architecture in the real-time data processing unit uses a photonic chip to process real-time data streams, achieving sub-nanosecond delay, thereby improving data processing speed. The photonic chip includes an optical sensor and a multi-channel optical fiber interface.

3. The information processing and promotion system based on big data combined with RPA according to claim 1 is characterized in that: The real-time data processing unit further comprises: Quantum annealing module: The quantum annealing module adopts a solid-state quantum bit structure and is equipped with an annealing control circuit. It uses the quantum annealing algorithm to optimize the multi-objective decision tree to solve extremely high-dimensional problems. A distributed quantum entanglement storage device is configured. The distributed quantum entanglement storage device adopts a storage structure based on a quantum bit chain and is equipped with inter-regional quantum connection nodes to achieve instantaneous synchronization of cross-regional data, while improving computing speed while reducing energy consumption.

4. The information processing and promotion system based on big data combined with RPA according to claim 1 is characterized in that: The neuro-symbolic RPA engine in the autonomous decision-making execution unit integrates deep reinforcement learning and formal verification to form a dynamic cognitive architecture to achieve semantic-level process understanding, which is based on knowledge graph reasoning and operation.

5. The information processing and promotion system based on big data combined with RPA according to claim 1 is characterized in that: The neural symbolic RPA engine further includes: a deep reinforcement learning module, a formal verification module and a dynamic cognitive architecture; The deep reinforcement learning module uses a computing structure based on tensor operations; The formal verification module adopts symbolic logic circuit structure; Dynamic cognitive architecture has: Adaptation in non-deterministic environments, automatically switching decision strategies when the system entropy value is lower than 0.1; Multimodal interface penetration capabilities across AR, VR and brain-computer interfaces; Self-generated digital fingerprint technology to track and record each automated decision chain.

6. The information processing and promotion system based on big data combined with RPA according to claim 1 is characterized by: The information promotion unit adopts a generative anti-fragile promotion network, which builds a dynamic propagation model based on a generative adversarial network, optimizes the promotion strategy through quantum Monte Carlo tree search, and simulates multiple abnormal scenarios in real time to ensure the fidelity of the promoted information in the multi-universe propagation path.

7. The information processing and promotion system based on big data combined with RPA according to claim 1 is characterized in that: The information promotion unit further includes self-organizing nanoscale propagation nodes, which are based on DNA computing biochip technology and use environmental energy collection functions to achieve group intelligence to construct dynamic propagation topology and ensure the working life of the nodes.

8. The information processing and promotion system based on big data combined with RPA according to claim 1 is characterized in that: The information processing and promotion system also includes a causal cognition enhancement framework, which realizes real-time analysis of potential causal relationships in the process by establishing a time-varying causal graph model, and is configured with a counterfactual reasoning engine, which generates more than 10 dimensions of counterfactual reasoning engine. 4 The potential outcome space of the intervention is constructed using interpretable factor analysis to build an intervention effect propagation model to predict and regulate second-order and higher chain reactions and ensure the safety margin of the strategy.

9. The information processing and promotion system based on big data combined with RPA according to claim 1 is characterized in that: The information processing and promotion system is further configured with a biomolecular level security protocol, which includes: DNA chain encryption unit, using multi-chain structure and DNA chain encryption algorithm, key space exceeds 10 300 ; The protein folding verification unit is equipped with a molecular conformation matching array. Through the protein folding verification mechanism, dynamic conformation matching authentication is achieved with an error tolerance of less than 10 -9 ; The quantum bio-signature system, including quantum entanglement detection circuit and enzyme catalytic reaction module, realizes the physical non-cloning function through quantum entanglement and enzyme catalytic reaction, thereby ensuring the physical level security of data during transmission and storage.

10. The information processing and promotion system based on big data combined with RPA according to claim 1 is characterized by: The implementation path of the information processing and promotion system includes: Build a quantum-photon hybrid computing base that breaks through the traditional silicon-based chip architecture; Develop a neural symbolic programming language that combines lambda calculus and tensor flow; Establish a holographic data lake based on metasurface manufacturing technology, achieve 1 EB data storage per cubic centimeter and in-situ photon convolution kernel calculation; Deploy quantum biosafety protocols to achieve room temperature quantum storage; By using PB-level counterfactual data sets to train causal cognitive models, real-time analysis of complex causal relationships can be achieved, achieving an order of magnitude improvement in key performance indicators such as energy efficiency, computing speed, and safety level.

Images