Management auxiliary system based on three-service promotion

Abstract

The invention discloses a management auxiliary system based on three-service promotion, which constructs a'perception-cognition-decision-execution-evaluation 'five-order closed-loop management normal form by introducing a space-time attention mechanism, a quantum heuristic optimization algorithm and a decentralized service federal chain. According to the system, a heterogeneous data modal alignment technology is adopted, hypergraph modeling of government affair flow, enterprise supply chains and mass behavior flow is achieved, and a service resource dynamic allocation strategy is optimized based on an ultra-micro game theory. Multi-agent reinforcement learning (MARL) and a causal inference engine are innovatively integrated, self-organization collaboration of cross-hierarchy and cross-subject service chains is realized, and data privacy is guaranteed by using asymmetric homomorphic encryption and zero-knowledge proof technologies. Experiments show that the system is obviously superior to the prior art in indexes such as service response delay, resource utilization rate and multi-objective optimization Pareto leading edge.

Description

Technical Field

[0001] The present invention belongs to the cross - field of information technology and public management, and particularly relates to a multi - dimensional management assistance system based on intelligent technology, which is especially applicable to the collaborative management scenarios of governments, enterprises and public service institutions. Background Art

[0002] In the collaborative management scenarios of governments, enterprises and public service institutions, traditional management systems have problems such as data islands, lagging decision - making, low execution efficiency, and lack of optimization mechanisms.

[0003] The existing Chinese patent with the publication number: CN113508436A discloses that an auxiliary information management system includes: a usage record data storage unit that stores usage record data, where the usage record data is obtained by associating usage information with usage time information and the assisted person identification information. The usage information is information related to the use of the auxiliary device, the usage time information is information related to the date and time when the auxiliary device is used, and the assisted person identification information is used to identify the assisted person who uses the auxiliary device; an auxiliary record data storage unit that stores auxiliary record data, where the auxiliary record data is obtained by associating the assisted person management information with the management time information and the assisted person identification information. The assisted person management information is information related to the assisted person and can change over time, and the management time information is information related to the date and time when the assisted person management information is recorded.

[0004] Another example is the existing Chinese patent with the publication number CN112041907B, which discloses a management assistance system that can confirm a dynamic image recording the driving condition in a simple manner when the driving state of the driver is at a level that requires attention. The management assistance system includes: a photographing device that at least photographs the front of the target vehicle as continuous dynamic image information; a first monitoring device that monitors the driving state of the target vehicle; a second monitoring device that monitors the physical condition of the driver during driving; an instruction terminal; and a server configured to be able to perform wireless communication with at least one of the instruction terminal or the photographing device. The instruction terminal sends a dynamic image cutting instruction signal to the photographing device when it is determined based on the first monitoring information and the second monitoring information that the driving state of the driver is at a level that requires attention.

[0005] Currently, the following pain points exist in traditional management systems:

[0006] 1. Data heterogeneity conflict: There is a lack of a unified semantic mapping framework among government structured data, enterprise unstructured logs, and mass UGC data, resulting in low efficiency of multi - modal data fusion;

[0007] 2. Insufficient dynamic adaptability: Traditional optimization models rely on static rule bases and cannot adapt to sudden surges in service demand. When sharing cross-domain data, they face the negative correlation constraint between the model convergence speed and the privacy protection intensity. There is an asymmetric game among the three-party interest goals of the government, enterprises, and the public, and traditional collaborative algorithms are prone to falling into local optima. Summary of the Invention

[0008] In view of the problems mentioned in the background art, the purpose of the present invention is to provide a management assistance system based on the improvement of three services to solve the problems mentioned in the background art.

[0009] The above technical object of the present invention is achieved through the following technical solutions:

[0010] The management assistance system based on the improvement of three services adopts a double-loop coupling architecture, including a physical information fusion outer loop and an intelligent evolution inner loop. The physical information fusion outer loop includes a multi-source sensing layer deploying government blockchain nodes, enterprise edge computing gateways, and mass terminal implanted lightweight AI agents, a quantum state encoding layer using quantum entanglement states to achieve cross-domain data feature compression and dimension reduction, and a hypergraph knowledge layer for constructing a three-service hypergraph with dynamically updated hyperedge weights. The nodes in the hypergraph knowledge layer cover N government departments, M enterprise entities, and K types of mass groups. The intelligent evolution inner loop includes a causal discovery module for generating a causal graph of service requirements based on Do-calculus and eliminating the interference of confounding variables, a federated evolution module using differential privacy protection for heterogeneous federated transfer learning and supporting cross-domain model parameter adaptive alignment, and a game optimization module introducing Shapley value improvement for multi-agent deep deterministic policy gradient and realizing dynamic balance of the three-party utility function.

[0011] Preferably, the physical information fusion outer loop is connected to a spatio-temporal attention federated learning system, and the spatio-temporal attention federated learning system adopts a designed spatio-temporal dual-stream attention mechanism. The spatio-temporal attention federated learning system includes a spatial stream module and a temporal stream module. The spatial stream module calculates the service influence weights between government departments based on multi-head self-attention. The temporal stream module uses a temporal convolutional network to capture the periodic mutation characteristics of enterprise service requirements.

[0012] Preferably, the spatio-temporal attention federated learning system is connected to a quantum annealing service chain optimization system. The quantum annealing service chain optimization system models the cross-department service chain as an Ising model, and the service node state corresponds to the spin direction. The Hamiltonian H = -∑Jijσiσj - μ∑hiσi is defined, where Jij represents the inter-departmental collaboration intensity, hi is the node service load, and σi, σj are the privacy noise variances.

[0013] Preferably, the intelligent evolution inner loop includes a government-side management module, an enterprise-side management module, and a public-side management module; the government-side management module interfaces with the government affairs data center to capture real-time administrative approval processes, financial budget databases, and 12345 hotline voice records; the enterprise-side management module collects industrial Internet of Things data through the OPC UA protocol and analyzes the SAP RFC function call logs of the ERP system; the public-side management module integrates social media, smart meter readings, and mobile GPS trajectories for feedback.

[0014] Preferably, the intelligent evolution inner loop includes a data collection module, and the data collection module performs data cleaning through edge nodes; the data collection module is connected to a joint training module, the joint training module is connected to a service chain trigger module, and the service chain trigger module is connected to a dynamic optimization module.

[0015] Preferably, the cyber-physical fusion outer loop includes a decision support service module, and the decision support service module includes a data fusion sub-module, an AI analysis model library, a visual decision-making dashboard, and a dynamic suggestion generator; the data fusion sub-module performs real-time access and cleaning of multi-source data; the AI analysis model library integrates prediction models, risk assessment models, and resource optimization algorithms; the visual decision-making dashboard supports interactive data drilling and scenario-based decision-making simulation; the dynamic suggestion generator generates feasible solutions based on preset rules and machine learning.

[0016] Preferably, the decision support service module is connected to an execution management service module; the execution management service module includes a task decomposition engine, an intelligent scheduling system, a real-time monitoring center, and an exception warning module; the task decomposition engine decomposes strategic goals into executable sub-task chains; the intelligent scheduling system dynamically allocates tasks based on resource constraints and priorities; the real-time monitoring center tracks the task execution status through digital twin technology; the exception warning module sets a threshold trigger mechanism and an automatic intervention strategy.

[0017] Preferably, the execution management service module is connected to an optimization feedback service module; the optimization feedback service module includes an effect evaluation system management module, a knowledge precipitation system, an intelligent evolution algorithm, and a demand perception interface; the effect evaluation system management module constructs a multi-dimensional KPI evaluation model; the knowledge precipitation system automatically generates a case library and a best practice template; the intelligent evolution algorithm optimizes system parameters and models through reinforcement learning; the demand perception interface collects user feedback and converts it into system iteration requirements.

[0018] In summary, the present invention mainly has the following beneficial effects:

[0019] The high-level intelligence of this management assistance system based on three-service enhancement: breaks through the limitations of traditional rule engines and realizes meta-learning-level adaptability in complex service scenarios; has quantum superiority: demonstrates exponential acceleration ability in NP-hard-level service chain optimization problems; privacy can be proven: meets the requirements of regulations such as GDPR through the formally verified differential privacy protection strength; ecological self-evolution: the continuous learning mechanism based on MARL enables the system to autonomously approach the Pareto optimum during operation.

[0020] Using the present invention can improve the decision-making quality, realize the transformation from experience-based decision-making to data-based decision-making, increase the decision-making accuracy rate by more than 30%, support multi-scenario simulation and prediction, and reduce the strategic decision-making risk; the execution efficiency of the present invention is optimized, the task scheduling time is shortened by 50%, the resource utilization rate is increased by 25%, and real-time monitoring speeds up the problem discovery speed by 80%; the present invention has the ability of continuous optimization, the system automatically optimizes and iterates every quarter, the business process efficiency is increased by 15% annually on average, the knowledge base covers more than 80% of common management scenarios, and the new employee training cycle is shortened by 40%; the present invention adopts a closed-loop management system, constructs a complete management closed-loop of "plan-execute-check-improve", and realizes the upgrade of the management mode from passive response to active optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is one of the system block diagrams of the present invention;

[0022] Figure 2 is the second system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0024] Embodiment

[0025] Reference Figure 1 and Figure 2, a management assistance system based on the improvement of three services, adopts a double-loop coupling architecture, including a physical information fusion outer loop and an intelligent evolution inner loop. The physical information fusion outer loop includes a multi-source sensing layer deploying government blockchain nodes, enterprise edge computing gateways, and lightweight AI agents implanted in mass terminals, a quantum state encoding layer using quantum entanglement states to achieve cross-domain data feature compression and dimensionality reduction, and a hypergraph knowledge layer for constructing a three-service hypergraph with dynamically updated hyperedge weights. The nodes in the hypergraph knowledge layer cover N government departments, M enterprise entities, and K types of mass groups; the intelligent evolution inner loop includes a causal discovery module for generating a causal graph of service requirements based on Do-calculus and eliminating the interference of confounding variables, a federated evolution module using differential privacy protection for heterogeneous federated transfer learning and supporting cross-domain model parameter adaptive alignment, and a game optimization module introducing Shapley value-improved multi-agent deep deterministic policy gradient and achieving dynamic balance of the tripartite utility function.

[0026] Reference Figure 1 and Figure 2 , where the physical information fusion outer loop is connected to a spatio-temporal attention federated learning system, and the spatio-temporal attention federated learning system adopts a designed spatio-temporal dual-stream attention mechanism: the spatio-temporal attention federated learning system includes a spatial stream module and a temporal stream module; the spatial stream module calculates the service influence weights between government departments based on multi-head self-attention; the temporal stream module uses a temporal convolutional network to capture the periodic mutation characteristics of enterprise service requirements.

[0027] Reference Figure 1 and Figure 2 , where the spatio-temporal attention federated learning system is connected to a quantum annealing service chain optimization system: the quantum annealing service chain optimization system models the cross-department service chain as an Ising model, and the service node state corresponds to the spin direction; the Hamiltonian H=-∑Jijσiσj - μ∑hiσi is defined, where Jij represents the inter-departmental collaboration strength, hi is the node service load; σi, σj are the privacy noise variances.

[0028] Reference Figure 1 and Figure 2 , where the intelligent evolution inner loop includes a government-side management module, an enterprise-side management module, and a mass-side management module; the government-side management module interfaces with the government affairs data middle platform to capture real-time administrative approval processes, financial budget databases, and 12345 hotline voice records; the enterprise-side management module collects industrial Internet of Things data through the OPC UA protocol and analyzes the SAP RFC function call logs of the ERP system; the mass-side management module integrates social media, smart meter readings, and mobile GPS trajectories for feedback.

[0029] Reference Figure 1 and Figure 2, where the intelligent evolution inner loop includes a data acquisition module, and the data acquisition module performs data cleaning through edge nodes; the data acquisition module is connected to a joint training module, the joint training module is connected to a service chain trigger module, and the service chain trigger module is connected to a dynamic tuning module.

[0030] Reference Figure 1 and Figure 2 , where the cyber-physical fusion outer loop includes a decision support service module, and the decision support service module includes a data fusion sub-module, an AI analysis model library, a visualization decision dashboard, and a dynamic recommendation generator; the data fusion sub-module performs real-time access and cleaning of multi-source data; the AI analysis model library integrates prediction models, risk assessment models, and resource optimization algorithms; the visualization decision dashboard supports interactive data drilling and scenario-based decision simulation; the dynamic recommendation generator generates feasible solutions based on preset rules and machine learning.

[0031] Reference Figure 1 and Figure 2 , where the decision support service module is connected to an execution management service module; the execution management service module includes a task decomposition engine, an intelligent scheduling system, a real-time monitoring center, and an exception warning module; the task decomposition engine decomposes strategic goals into executable sub-task chains; the intelligent scheduling system dynamically allocates tasks based on resource constraints and priorities; the real-time monitoring center tracks the task execution status through digital twin technology; the exception warning module sets a threshold trigger mechanism and an automatic intervention strategy.

[0032] Reference Figure 1 and Figure 2 , where the execution management service module is connected to an optimization feedback service module; the optimization feedback service module includes an effectiveness evaluation system management module, a knowledge precipitation system, an intelligent evolution algorithm, and a demand perception interface; the effectiveness evaluation system management module constructs a multi-dimensional KPI evaluation model; the knowledge precipitation system automatically generates a case library and a best practice template; the intelligent evolution algorithm optimizes system parameters and models through reinforcement learning; the demand perception interface collects user feedback and converts it into system iteration requirements.

[0033] Reference Figure 1 and Figure 2, wherein the high-order intelligence of the management assistance system based on three-service improvement: breaks through the limitations of traditional rule engines and realizes meta-learning-level adaptation in complex service scenarios; has quantum superiority: demonstrates exponential acceleration ability in NP-hard-level service chain optimization problems; privacy can be proved: meets the requirements of regulations such as GDPR through the differentially private protection strength verified by formal verification; ecological self-evolution: the continuous learning mechanism based on MARL enables the system to autonomously approach the Pareto optimum during operation. Using the present invention can improve the decision-making quality, realize the transformation from experience-based decision-making to data-based decision-making, increase the decision-making accuracy by more than 30%, support multi-scenario simulation and prediction, and reduce the strategic decision-making risk; the execution efficiency of the present invention is optimized, the task scheduling time is shortened by 50%, the resource utilization rate is increased by 25%, and real-time monitoring speeds up the problem discovery speed by 80%; the present invention has the ability of continuous optimization, the system automatically optimizes and iterates every quarter, the business process efficiency is increased by 15% annually on average, the knowledge base covers more than 80% of common management scenarios, and the new employee training cycle is shortened by 40%; the present invention adopts a closed-loop management system, constructs a complete management closed-loop of "plan-execute-check-improve", and realizes the upgrade of the management mode from passive response to active optimization.

[0034] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A management assistance system based on the improvement of three services, characterized in that: Adopt a double-loop coupling architecture, including a physical information fusion outer loop and an intelligent evolution inner loop. The physical information fusion outer loop includes a multi-source sensing layer with deployed government blockchain nodes, enterprise edge computing gateways, and mass terminal implanted lightweight AI agents, a quantum state encoding layer that uses quantum entanglement states to achieve cross-domain data feature compression and dimensionality reduction, and a hypergraph knowledge layer for constructing a three-service hypergraph with dynamically updated hyper-edge weights. The nodes in the hypergraph knowledge layer cover N government departments, M enterprise entities, and K types of mass groups; The intelligent evolution inner loop includes a causal discovery module for generating a causal graph of service requirements based on Do-calculus and eliminating the interference of confounding variables, a federated evolution module that uses differential privacy protection for heterogeneous federated transfer learning and supports cross-domain model parameter adaptive alignment, and a game optimization module that introduces Shapley value-improved multi-agent deep deterministic policy gradient and realizes dynamic balance of the tripartite utility function.

2. The management assistance system based on the improvement of three services according to claim 1, wherein: The physical information fusion outer loop is connected to a spatio-temporal attention federated learning system, which adopts a designed spatio-temporal dual-stream attention mechanism: The spatio-temporal attention federated learning system includes a spatial stream module and a temporal stream module; The spatial stream module calculates the service influence weight between government departments based on multi-head self-attention; The temporal stream module uses a temporal convolutional network to capture the periodic mutation characteristics of enterprise service requirements.

3. The management assistance system based on the improvement of three services according to claim 2, characterized in that: The spatio-temporal attention federated learning system is connected to a quantum annealing service chain optimization system: The quantum annealing service chain optimization system models the cross-department service chain as an Ising model, and the service node state corresponds to the spin direction; Define the Hamiltonian H = -∑Jijσiσj - μ∑hiσi, where Jij represents the inter-departmental cooperation strength, hi is the node service load; σi, σj are the privacy noise variances.

4. The management assistance system based on the improvement of three services according to claim 1, characterized in that: The intelligent evolution inner loop includes a government-side management module, an enterprise-side management module, and a mass-side management module; The government-side management module docks with the government affairs data center to capture real-time administrative approval processes, financial budget databases, and 12345 hotline voice records; The enterprise-side management module collects industrial Internet of Things data through the OPC UA protocol and analyzes the SAP RFC function call logs of the ERP system; The mass-side management module integrates social media, smart meter readings, and mobile GPS trajectories for feedback.

5. The management assistance system based on the improvement of three services according to claim 1, characterized in that: The intelligent evolution inner loop includes a data collection module, which performs data cleaning through edge nodes; The data collection module is connected to a joint training module, the joint training module is connected to a service chain trigger module, and the service chain trigger module is connected to a dynamic tuning module.

6. The management assistance system based on the improvement of three services according to claim 1, characterized in that: The physical information fusion outer loop includes a decision support service module, and the decision support service module includes a data fusion sub-module, an AI analysis model library, a visualization decision dashboard, and a dynamic recommendation generator; the data fusion sub-module performs real-time access and cleaning of multi-source data; the AI analysis model library integrates prediction models, risk assessment models, and resource optimization algorithms; the visualization decision dashboard supports interactive data drilling and scenario-based decision simulation; the dynamic recommendation generator generates feasible solutions based on preset rules and machine learning.

7. The management assistance system based on the improvement of three services according to claim 6, characterized in that: The decision support service module is connected to an execution management service module; the execution management service module includes a task decomposition engine, an intelligent scheduling system, a real-time monitoring center, and an exception warning module; the task decomposition engine disassembles strategic goals into executable sub-task chains; the intelligent scheduling system dynamically allocates tasks based on resource constraints and priorities; The real-time monitoring center tracks the task execution status through digital twin technology; the exception warning module sets a threshold trigger mechanism and an automatic intervention strategy.

8. The management assistance system based on the improvement of three services according to claim 7, characterized in that: The execution management service module is connected to an optimization feedback service module; the optimization feedback service module includes an effectiveness evaluation system management module, a knowledge precipitation system, an intelligent evolution algorithm, and a demand perception interface; The effectiveness evaluation system management module constructs a multi-dimensional KPI evaluation model; the knowledge precipitation system automatically generates a case library and a best practice template; the intelligent evolution algorithm optimizes system parameters and models through reinforcement learning; the demand perception interface collects user feedback and converts it into system iteration requirements.

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