288 results about "Decision matrix" patented technology

The invention relates to the technical field of communication, in particular to a dynamic priority dual-mode communication fault-tolerant switching method and equipment, and aims to solve the problems of communication interruption and service quality reduction when a main link is degraded in a complex electromagnetic environment. According to the method, a dynamic priority evaluation model and a dual-mode protocol cooperation mechanism are constructed, throughput, bit error rate, delay jitter, task semantic priority and aging constraint data of a main link and a standby link are collected in real time, a priority weight and a communication mode decision matrix are generated through combined modeling, an atomization switching instruction is executed according to the priority weight and the communication mode decision matrix, and a communication mode is switched. And completing path reconstruction and resource redistribution. The system further introduces link health prediction, a resource reservation pool and an online learning feedback loop, and supports fault pre-judgment, key task preemption and model adaptive optimization. According to the method, the limitation of a static threshold is broken through, the task semantics and channel state joint scheduling is realized, and the communication reliability, the service continuity and the intelligent fault-tolerant capability of a distributed system in a high-risk scene are remarkably improved.

The invention provides a multi-agent cooperative processing system and method for multi-scene legal complaint consultation, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a legal or complaint consultation text inputted by a user, and generating structured consultation request data; automatically generating an evidence obtaining template, and forming a standardized evidence obtaining data packet; constructing a demonstration tree and outputting a multi-scheme decision matrix; performing multi-objective optimization by combining the success rate, the timeliness, the cost and the regional qualification index, and outputting a comprehensive recommendation result; generating a knowledge unit set, and updating a regulation index and agent prompt template library; predicting a re-complaint risk and generating a remedy and optimization suggestion; and outputting review result data, and reversely updating the review result data to the agent configuration strategy in the step of the knowledge construction module. According to the method and the device, the technical targets of multi-agent cooperative processing and dynamic optimization management in the whole legal complaint consultation process can be realized, and the technical effects of improving consultation processing efficiency, enhancing scheme recommendation accuracy, reducing manual participation cost and ensuring data compliance and risk controllability are achieved.

The invention relates to the technical field of industrial platform data analysis, in particular to a renewable resource recovery data management system based on the Internet of Things, which comprises the steps of synchronously acquiring weight, spectrum and microwave characteristic data of a tested resource through a data acquisition module, executing moisture weight decoupling operation by utilizing a resource matching module, and performing data analysis; then, an inventory evolution module tracks performance loss of resources in real time by using an evolution model of an integrated long and short-term memory network, generates a resource attenuation weight, and constructs a two-dimensional decision matrix containing scheduling priority and preprocessing strength grade instructions in combination with real-time inventory saturation; and finally, the clearance scheduling module executes dynamic pruning and bidirectional optimization through a resource scheduling model, accurately allocates the loading share and the access sequence of each node, and generates a dynamic instruction set containing a delivery sequence. According to the method, feeding homogenization is realized through industrial data multi-dimensional collaborative analysis.

The invention discloses an old people safety monitoring method and equipment based on multi-modal sensor fusion, which are applied to the technical field of data processing, and the method comprises the steps: collecting multi-modal monitoring, environment state and equipment operation data, and carrying out synchronous correction, feature extraction and edge end encryption caching to generate a standardized multi-dimensional feature vector; by means of a dynamic weight multi-modal fusion engine, in combination with adaptive weight distribution and scene association analysis, suspected tumble events are recognized, and then the real tumble situation is confirmed through AI agent voice interaction. Based on an emergency linkage decision matrix, a priority notification and a home custom control rule are fused, an emergency linkage scheme is generated, and finally, based on an edge-cloud collaboration architecture and an adaptive adjustment strategy, the security dynamic protection and continuous optimization of the old people are realized, and the home security of the old people is comprehensively guaranteed.

The invention discloses a security check door security check process optimization scheduling system and method based on big data, and belongs to the technical field of intelligent security check, a whole security check area is divided into a plurality of functional areas and mapped into network nodes according to security check process links and physical space distribution, directed edges among the nodes represent passenger flow paths, and the flow paths of passengers are distributed in the network nodes. A weight matrix of edges is calculated through historical flow data, and a security check area network topology model is constructed; fusing ticket business, historical security check and real-time sensor data, constructing a load evaluation model, and calculating a node load index and an influence conduction coefficient; based on passenger ticket business time and historical behavior records, establishing a time urgency index and risk level model, and forming a two-dimensional decision matrix to dynamically divide priorities; constructing and training a congestion propagation model, predicting a future congestion path in combination with priority distribution, and identifying bottleneck nodes; and according to a prediction result, cooperatively implementing a grooming strategy from four dimensions of personnel, channels, equipment and processes.

The invention discloses a drilling construction intelligent decision support and risk early warning method, device and equipment, and the method comprises the steps: collecting drilling sound wave response data, carrying out harmonic analysis to recognize a reservoir resonance node, and constructing a reservoir energy conduction network; a fluctuation propagation path is tracked based on an energy conduction network, and a construction risk area is identified through stress wave spectrum features; porosity and permeability data are extracted for phase difference analysis, a seepage-construction mismatch factor is generated, and a construction optimization window is actively generated; constructing a geological potential energy gradient field to determine an overflow influence range, and performing tensor coupling on multi-dimensional risk factors to generate a three-dimensional construction decision matrix; an optimal construction scheme is generated through energy minimization and risk-efficiency dynamic balance processing; construction parameter response changes are monitored in real time, abnormal parameter combination symptoms are recognized, a grading risk early warning instruction is generated, and intelligent decision support and risk early warning of drilling construction are achieved.

The invention relates to the technical field of tunnel engineering automation and intelligent control, and discloses a TBM tunneling parameter intelligent optimization decision-making system based on an LSTM network, and the system comprises a data collection and preprocessing module which obtains external data and outputs a tunneling parameter sequence; the probabilistic tunneling trend prediction module is used for outputting a prediction expected value and prediction uncertainty; the prospective geological precursor sensing module is used for matching and identifying known risks and outputting alarm events; and determining an optimal tunneling mode by the dynamic risk avoidance decision matrix. When the prediction uncertainty is too high, activating the prospective template driven by the uncertainty to excavate a new precursor template and update the template library; meanwhile, the decision-efficiency relevance evaluation and strategy self-optimization engine optimizes the decision rule according to the actual tunneling efficiency. According to the method, decision making is carried out through quantitative risk prediction and fusion of multi-source information, and a double learning closed loop of knowledge discovery and strategy optimization is established, so that the reliability, the adaptability and the long-term efficiency of system decision making are remarkably improved.

The invention discloses a multi-modal interaction method and interaction system applied to an intelligent robot. The method comprises the following steps: collecting a scene image and processing the scene image into a three-dimensional point cloud and a two-dimensional texture feature; the Gemini Robotics-ER model is used for extracting features, and the vision-language-action model is used for analyzing a language instruction into a sequence capable of being recognized by a machine; and fusing the features to generate an interactive decision matrix, planning a trajectory, calculating kinetic parameters, driving the robot to execute actions and feeding back in real time. The system comprises a multispectral visual information acquisition and preprocessing unit, a Gemini Robotics-ER model processing unit, a natural language instruction analysis unit, a vision-language-action cooperative processing unit, a trajectory planning and dynamics calculation unit and a motion control and feedback unit, and all the units work cooperatively. According to the method and the system, through multi-modal fusion and closed-loop control, interaction accuracy and real-time performance are improved, and industrial scene requirements are met.

The invention provides a hybrid model fault early warning method and system based on time sequence prediction and fuzzy logic, and the method comprises the steps: collecting operation and maintenance data of equipment in a continuous operation period, constructing an equipment state feature set, carrying out the feature correlation analysis of the equipment state feature set, generating an equipment state potential vector which reflects a dynamic coupling relation between parameters, and carrying out the fault early warning of a hybrid model. Calling a pre-trained fuzzy logic reasoning model to carry out fuzzy rule matching, and generating a fuzzy state set containing a multi-dimensional fuzzy subset; performing space-time correlation processing on the fuzzy state set to generate a multi-dimensional decision cloud picture, and performing spatial reconstruction on the multi-dimensional decision cloud picture through a quantization weight distribution mechanism to obtain an optimized decision matrix; and generating a fault early warning signal containing a fault early warning level and fault position positioning information based on the confidence distribution characteristics corresponding to the fault types. According to the invention, the accuracy and timeliness of fault early warning can be improved, and reliable decision support is provided for equipment operation and maintenance management.

The invention relates to the technical field of vehicle control, and discloses a parking start-stop battery intelligent module management system, which comprises a data acquisition and preprocessing module, a driving intention inference module, a self-adaptive transient power capability evaluation module and a multi-dimensional fusion decision and control module, discrete driving intention states such as congestion crawling and intersection waiting are recognized through a finite state machine; according to the intention state, adaptively selecting an evaluation sub-model from an evaluation sub-model library so as to evaluate the transient power capability of the battery, and outputting a transient power capability score and a corresponding confidence coefficient; and the system fuses the driving intention, the transient power capability score and the confidence coefficient, corrects logic through the preliminary decision matrix and the confidence coefficient, performs rechecking in combination with the whole vehicle level constraint condition, and generates and issues a final start-stop control instruction. According to the method, the problems of single decision and poor situation adaptability in the prior art are solved, the intelligence and reliability of start-stop decision are remarkably improved, and the driving experience is optimized.

The invention relates to the technical field of intelligent driving, and particularly discloses a layered dangerous lane changing intervention method and system based on multi-mode intention recognition, and the method comprises the steps: obtaining a multi-mode intention signal of a driver in real time; judging the current lane changing intention state of the driver based on the multi-mode intention signal; environment information behind the side of the vehicle is detected in real time, and the collision risk level of current lane changing is determined based on the environment information; inputting the lane changing intention state and the collision risk level into a pre-stored decision matrix for matching, and outputting a corresponding hierarchical intervention strategy; the decision matrix comprises intervention strategies corresponding to combinations of different lane changing intention states and different collision risk levels, and early warning and / or vehicle control actions corresponding to the layered intervention strategies are executed. The method solves the core problem that in the prior art, intentional lane changing and unconscious dangerous deviation of a driver cannot be effectively distinguished, and consequently false alarm or untimely intervention is caused.

The invention belongs to the technical field of medicine inventory management, and discloses a Western medicine intelligent inventory optimization and automatic replenishment decision-making method and a Western medicine intelligent inventory optimization and automatic replenishment decision-making system. Comprising the steps of collecting environment data, inventory information and historical inventory consumption data of inventory drugs, and integrating the environment data and the inventory information to obtain a health degree index of each drug; based on the drug health degree index and the consumption prediction result, generating a replenishment decision matrix, and outputting replenishment opportunity, replenishment amount and replenishment priority; in combination with an operation audit result and inventory area division, differential inventory management scheme configuration is realized; and after external information is obtained, a replenishment strategy is dynamically adjusted, and a target inventory optimization decision is formed. According to the method, accurate evaluation of the inventory state and intelligent decision of replenishment response are realized, and the safety, efficiency and refinement level of medicine inventory management are improved.

The embodiment of the invention relates to the technical field of large model data analysis, and discloses a large model evolution multi-agent investment decision matrix system. The investment Transform module is used for acquiring financial data and performing feature extraction and feature fusion on the financial data in combination with a context learning enhancement function and a financial domain knowledge enhancement function; the interpretability analysis module is used for acquiring the feature representation in the investment Transform module and carrying out feature relation mapping and interpretability analysis; the multi-agent decision-making module is used for carrying out collaborative decision-making through multiple agents; the optimization module is used for providing interpretation optimization for the interpretability analysis module and providing strategy optimization for the multi-agent decision-making module; and the knowledge base module is used for providing an investment template for the investment Transformer module, providing causal relationship knowledge for the interpretability analysis module and providing rule constraints for the multi-agent decision-making module. The technical problem of investment decision analysis can be at least solved.

The invention relates to a method for predicting the corrosion rate of a natural gas pipeline, in particular to the field of natural gas pipeline corrosion prediction.The high-precision dynamic early warning is achieved through the four-step cooperative technology, firstly, visible light and radar data are fused to generate a three-dimensional corrosion base film fully covering the pipeline, and blind areas caused by the complex environment are effectively eliminated; secondly, speculating corrosion probability distribution of a shielding area based on a physical constraint generative adversarial network, and solving a hidden area prediction problem; then, in combination with the stress field and historical data, deducing a space-time diffusion path of corrosion through a graph neural network, and outputting a future corrosion thermodynamic diagram; and finally, dynamically adjusting model parameters according to actually measured data to form a corrosion risk decision matrix of closed-loop optimization, thereby realizing the transformation of pipeline corrosion from passive protection to intelligent prevention in a breakthrough manner.

The invention discloses a pipe network leakage point evaluation method and system, and the method comprises the steps: constructing an evaluation decision matrix, carrying out the normalization processing of the evaluation decision matrix, and obtaining the initial weight of each evaluation index; calculating a dynamic adjustment factor based on real-time data and historical data of the pipe network, updating the weight of the evaluation index, performing sensitivity verification, and applying a weight value passing the sensitivity verification as a final weight of the evaluation index; performing weighted normalization processing on the final weight pair by using the initial weight to obtain an index value of the evaluation index; an ideal solution of the pipe network leakage point is constructed based on the index value, the distance between the pipe network leakage point and the ideal solution is calculated, and the relative closeness of the pipe network leakage point is obtained according to the calculation result; and obtaining the relative closeness of each pipe network leakage point, and outputting a priority ranking result based on the relative closeness. According to the method, the weight of the evaluation index can be adaptively adjusted according to the real-time working condition, and the accuracy of judging the priority of the leakage point of the pipe network is improved.

The invention relates to the technical field of network data security, in particular to a power fgOTN power low-delay secure transmission method. Obtaining a service bearing network topology and link transmission parameters, and constructing a network connection matrix; simulating a flow redistribution process after a fault of a single node, and calculating a topology betweenness centrality index of the substation node; the method comprises the following steps: analyzing payload contents of power business data frames at a fgOTN mapping preceding stage, extracting continuous monitoring features and discrete control features, and calculating a real-time business criticality index of each data frame in combination with a frequency deviation and a voltage fluctuation state of a current network; establishing a two-dimensional decision matrix, and generating a defense strategy according to mapping positions of the topological betweenness centrality index and the criticality index value in the matrix; processing the service data according to the generated defense strategy, and mapping the service data to a fine granularity time slot of the fgOTN; and embedding a security state signaling in the frame overhead, and sending the security state signaling to opposite-end equipment through an optical transmission unit.

The invention relates to a single-machine obstacle avoidance method and system based on a millimeter wave radar and a camera, and the method comprises the steps: transmitting a frequency modulation continuous wave signal through the millimeter wave radar, collecting and preprocessing environment original data, and outputting a target list; based on the target list, when the target meets a preset signal-to-noise ratio condition and the speed exceeds a preset speed threshold value, triggering a camera to collect an image, performing real-time classification on the image through a lightweight convolutional neural network model, and outputting a classification result and a bounding box; performing space-time alignment on a target list and a classification result, dynamically allocating weights of radar data and visual data by adopting a confidence weighted fusion algorithm, associating the same target through distance calculation, and outputting a fused target attribute; based on the fused target attributes, constructing a three-dimensional decision matrix, and generating an obstacle avoidance strategy according to a preset priority rule; and converting the obstacle avoidance strategy into a bus control instruction, and controlling the vehicle to execute a corresponding alarm or brake response action.

The invention provides a product full life cycle management method based on digital twinning, a medium and a product full life cycle management system based on digital twinning, and belongs to the technical field of digital twinning. Based on a super-sparse pre-training model of a liquid neural network architecture, in combination with spectral clustering and an attention mechanism, precise prediction of product performance degradation is realized, and intelligent maintenance suggestion generation and dynamic optimization of operation parameters are realized through a Hilbert matrix state evaluation and decision adjustment mechanism. The balance between calculation efficiency and prediction precision is realized by using a multi-precision simulation analysis and incremental updating technology, a backtracking decision matrix is established to support the optimal decision of product decommissioning and recovery, and the technical problem that the real-time perception and dynamic prediction optimization management of the full-life-cycle multi-dimensional state of the product cannot be realized in the prior art is solved.

The invention relates to a secure transmission method and system for encrypted network data. According to the method, a resource state vector is generated by collecting resource state data of Internet of Things equipment, and an environmental risk level is evaluated by using a pre-training decision model in combination with attacked information; performing content feature scanning on the to-be-transmitted data, and performing data sensitivity grading based on key field identification; querying an encryption decision matrix according to the resource state vector, the environmental risk level and the data sensitivity level, and determining a target encryption scheme; a private key and a public key are dynamically generated at an equipment end, a shared key seed is calculated after the public key is exchanged with a communication opposite end, and a target encryption scheme and the shared key seed are used for encrypting data to generate a transmission frame; periodically optimizing the encryption decision matrix through a reinforcement learning algorithm based on encryption time consumption, resource consumption and transmission success rate indexes; the encryption strength is adaptively adjusted, resource utilization is optimized while the security is ensured, and the overall efficiency of the system is improved through continuous feedback optimization.

The invention relates to the technical field of smart home control, and particularly discloses a smart home central control system and method based on multi-mode perception, and the system comprises the steps: synchronously collecting a voice audio signal, a gesture image signal, an infrared thermal imaging signal and a millimeter wave radar signal through a plurality of groups of sensors; performing blind source separation processing on the voice and gesture signals, extracting a voice command component and a gesture action component which are independent in statistics, and performing space-time alignment and Kalman filtering fusion on the infrared and radar signals to generate a dynamic environment sensing graph; voice intention features, gesture track features and environment anomaly features are extracted through Mel frequency cepstrum coefficient analysis, skeleton key point tracking and multi-level convolution processing; constructing a three-dimensional decision matrix based on the features, performing weighted evaluation through a fuzzy logic rule base to generate a control instruction priority sequence, and dynamically adjusting an equipment operation mode according to the priority; according to the invention, the problems of control conflict and response delay caused by multi-mode signal coupling are solved.

The invention belongs to the technical field of satellite communication, and discloses a satellite communication beam switching method. The positions of a satellite and an aircraft are determined under a geocentric inertial coordinate system, an elevation angle is calculated, and visible wave beams are screened to form a neighbor cell list. And screening out a candidate beam list from the neighbor cell list based on the received power threshold. And if the list is not empty, constructing a decision matrix containing the receiving power, the signal-to-noise ratio, the number of available channels and the satellite residual service time for each candidate beam. And objectively calculating the weight of each performance index based on a matrix by using a CRITIC method. And by using the weight, a TOPSIS model based on Tanimoto similarity optimization is adopted to calculate the relative close degree of each candidate wave beam. And finally, selecting the candidate beam with the maximum relative closeness as the optimal beam to trigger switching. According to the method, through the objective weight and the optimization model, unnecessary frequent switching is reduced, and the communication stability and the system resource utilization rate are improved.

The invention relates to the technical field of computer simulation, and provides an ecological restoration measure carbon sink effect quantitative simulation prediction method and system based on digital modeling. The method comprises the following steps: collecting ecological element information of a target drainage basin, extracting a multi-dimensional spatial distribution feature set, and establishing an ecological environment response simulation model under engineering disturbance in combination with digital baseline data; constructing a carbon sink effect quantitative model based on construction operation situation information, and embedding the carbon sink effect quantitative model into associated nodes of the ecological environment response simulation model to realize coupling; the dynamic influence of engineering construction operation on the drainage basin carbon sink capacity is simulated based on the coupling model, the multi-target optimization decision matrix is generated, the pre-optimization decision suggestion is output, collaborative simulation of ecological environment response and carbon sink effect quantification is achieved, and the accuracy of hydropower engineering carbon sink influence prospective evaluation and the scientificity of decision suggestion are improved.

The invention relates to the field of voice questions and answers, and particularly discloses a large model customer service system based on multi-modal double-layer shunt reasoning, which introduces semantic complexity evaluation on a text input by a user, and forms a three-dimensional decision matrix in combination with confidence of front-end voice recognition. By dynamically weighting the intention confidence, the semantic complexity score and the speech recognition confidence, the system can accurately recognize queries which seem to be simple and actually complex. In this way, the traditional confidence trap is solved, even if a complex query generates virtually high intention confidence due to the fact that the complex query contains keywords, the high complexity score of the complex query can correct the shunting decision, and the query is guided to a heavy weight model capable of conducting deep semantic understanding. Finally, it is ensured that all kinds of user requests can be properly processed by the system level, and the accuracy and the problem solving rate of a customer service system are remarkably improved.

The invention belongs to the technical field of battery health degree evaluation, and particularly relates to a battery health degree evaluation method and system, electronic equipment and a storage medium. Based on the original sample data of the battery health degree evaluation indexes, establishing a standardized decision matrix to eliminate the dimensional difference between the indexes; constructing a correlation matrix according to the matrix, and evaluating index correlation; extracting common factors of a correlation matrix, calculating a core quantitative index, determining a main factor by taking standard reaching of an accumulated variance contribution rate as a standard, realizing data dimension reduction and retaining original index core information; and calculating a main factor score in combination with the original data and the rotated factor matrix, performing weighting according to a variance contribution rate, and performing weighted summation to obtain a health degree comprehensive score. And the health difference of the batteries can be judged in an auxiliary manner through clustering analysis, and data support is provided for reasonable configuration and fine management of the batteries. According to the method, dimension reduction weighting is carried out on various related indexes through a factor analysis method, a result is objectively deduced based on data features, subjective influences are reduced, and evaluation scientificity and reliability are improved.

The invention discloses a hydrogen-electricity hybrid energy management method for a low-altitude operation composite wing unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle energy management, and the method comprises the steps: S1, constructing an energy management system which comprises a working condition recognition module, an energy state monitoring module, a decision module and an execution module; s2, real-time operation parameters of the composite-wing unmanned aerial vehicle are collected through a working condition recognition module, and the current operation working condition of the composite-wing unmanned aerial vehicle is judged; s3, collecting real-time energy parameters of the hydrogen fuel cell and the power cell through an energy state monitoring module; s4, the decision module outputs a target energy supply strategy based on the current operation condition and the real-time energy parameters; and S5, the execution module controls the energy supply states of the hydrogen fuel cell and the power cell according to the target energy supply strategy. The three typical working conditions of the low-altitude operation composite wing unmanned aerial vehicle are accurately judged through the working condition recognition module, a decision matrix is constructed in combination with multi-dimensional energy parameters, dynamic matching of energy supply strategies is achieved, and the working condition adaptability is enhanced.

The embodiment of the invention provides a power distribution network load storage edge smoothing control method and related equipment, and belongs to the technical field of power dispatching. According to the method, in an edge calculation unit, source network load storage data and environment data in a power distribution network are collected, reduction of power grid fluctuation is taken as an optimization target, and network load storage state optimization is carried out based on the collected data to obtain a target adjustment strategy; and then, according to the target adjustment strategy, through the dynamic collaborative decision matrix, an adjustment instruction for distributed power supply output, flexible load and energy storage charging and discharging is generated, rapid collaborative adjustment of various devices of the power distribution network is realized, the fluctuation ratio of the power grid is reduced, and the new energy consumption capability and the system operation stability are improved. In addition, according to the power distribution demand scene, each demand dimension in the action decision target is subjected to weight distribution, a dynamic collaborative decision matrix is formed, the action decision instruction obtained based on the dynamic collaborative decision matrix meets the power distribution demand scene, and the system adjustment adaptability in different power distribution demand scenes is improved.

The invention relates to the technical field of the intelligent power grid industry, in particular to a power distribution and utilization ring network fault intelligent troubleshooting method and system. According to the method, a looped network situation primitive is generated through multi-source heterogeneous data fusion. The primitives are input into synchronous compression transformation to extract transient fault initial features, and the transient fault initial features are mapped into fault propagation path features through a graph convolutional network considering topological structure constraints; then, inputting the features into a space-time correlation prediction model, integrating defect type judgment and trend pre-judgment features, and generating a fault diagnosis evidence set; and starting an investigation mechanism based on the evidence set to determine an interval to be investigated, and performing closed-loop verification by constructing a fusion judgment matrix based on multi-modal physical sensing information to realize physical space locking of a fault defect point. According to the method, deep learning diagnosis and multi-mode positioning are integrated, and the detection precision and the positioning efficiency of looped network defects and the identification capability of transient hidden faults are effectively improved.

The invention discloses a load response capability evaluation method and system, and the method comprises the following steps: 1, obtaining multi-dimensional power consumer information data, carrying out the statistics and summarization of the obtained multi-dimensional power consumer information data, and constructing a power consumer response potential evaluation index system; according to the method, multi-dimensional power consumer information is acquired, an evaluation index system is constructed, data are converted into fuzzy rough numbers, then a decision matrix is established, an entropy weight method and a step-by-step weighting evaluation analysis ratio method are fused to calculate a comprehensive weight, a weighting matrix is obtained through normalization, and then a comprehensive evaluation value is calculated through a fuzzy rough multi-attribute boundary approximation region comparison method. Screening users with high response potential; according to the matching system, through cooperation of seven modules, collection frequency self-adaption and power grid dispatching two-way communication are achieved, the problems that in the prior art, user screening is not accurate, evaluation is not comprehensive, and weight subjectivity is high are solved, and evaluation accuracy and reliability are improved.

The invention provides a goaf tunnel surrounding rock stability progressive evaluation method and system, and relates to the technical field of geotechnical engineering. The method comprises the following steps: constructing a virtual training sample set by utilizing an optimization process fused with active learning based on goaf and tunnel engineering key parameters and a Knoth time function; establishing a two-stage prediction architecture comprising a rapid screening model and an accurate rechecking model; carrying out risk primary judgment and shunting on the target section by utilizing a rapid screening model; for a section needing to be rechecked, outputting a surrounding rock safety coefficient and a probability prediction value of convergence displacement by using an accurate rechecking model in combination with Bayesian inference; outputting a differentiated support scheme in combination with the multi-level decision matrix; and executing parameter reverse reconstruction and model increment evolution based on the form deviation index of the field actual measurement data and the prediction data. The problem that the stability of the surrounding rock is difficult to accurately quantify under the small sample condition is solved, and self-adaptive evolution of an evaluation system is realized.

The invention discloses an electric heating system control method based on source load fluctuation response, which belongs to the technical field of electric heat storage system control, and comprises the following steps: an edge gateway collects and uploads various equipment data; the data center carries out cleaning, alignment and storage; constructing a source and load prediction model based on the cleaning data; the optimization module formulates a control strategy to generate a decision matrix; finally, a control instruction is generated and issued to the equipment, dynamic regulation and control are completed, dynamic cooperative control over all the control equipment in the electric heating system is achieved, the peak clipping and valley filling capacity and the energy utilization efficiency of the whole system are improved, high-precision trend prediction is achieved, the control target is optimized in real time according to the source load prediction result, and the control efficiency is improved. A coordination control instruction of multiple control devices is formulated, various control device protocols are adapted through a uniformly packaged control instruction format, accurate issuing and state feedback of the instruction are realized, and the robustness of the system is improved.