60 results about "Chaos theory" patented technology

The invention provides a multi-sensor electric power intelligent monitoring system, and belongs to the field of electric power monitoring. The system comprises a data processing system, a monitoring center server and a plurality of edge computing nodes deployed on different power devices, and the data processing system comprises a multi-source data acquisition module used for acquiring operation data of the power devices; the fault diagnosis module is used for constructing a distributed knowledge graph according to the basic data and the historical operation data of the power equipment, and storing the constructed knowledge graph in each edge computing node and the monitoring center server in a scattered manner so as to perform preliminary diagnosis on the fault of the power equipment; the early warning module is used for mining a data feature association relationship through a quantum heuristic algorithm and establishing a dynamic early warning mechanism; and the life prediction module is used for predicting the residual life of the power equipment based on the chaos theory and the phase-space reconstruction technology. According to the invention, the problems of low data processing efficiency, inaccurate fault diagnosis, untimely early warning and the like in traditional power monitoring are solved.

The invention discloses a micro-grid load distribution control method, device and equipment based on entropy increase disturbance, and the method comprises the steps: analyzing the operation state of a micro-grid through chaos measurement, and constructing an entropy increase disturbance excitation mechanism to actively excite the vitality of a system; after random disturbance is applied, inherent frequency characteristics of each power supply are extracted, and a frequency synchronization coordination mechanism is established to realize multi-source coordination; adopting counter-example search to actively identify distribution scheme vulnerabilities, and generating an enhanced distribution strategy to improve robustness; evaluating the load bearing capacity of each power supply, constructing a load conduction topology and analyzing pressure distribution; each power supply bearing limit parameter is obtained through boundary active detection, and the equipment potential is fully excavated; and a final control instruction is generated based on a multi-layer time sequence synchronization mechanism, and multi-power-supply coordinated output is realized. A chaos theory is introduced into micro-grid control, local optimum is avoided through active disturbance, a multi-layer coordination mechanism is adopted to adapt to new energy volatility, and a self-adaptive load distribution control scheme is provided for a micro-grid with high-proportion renewable energy access.

The invention discloses an automatic debugging and fault diagnosis system, which relates to the field of automatic equipment maintenance and comprises a data acquisition and preprocessing module, a chaotic feature analysis module, a fault mode identification and prediction module, a debugging and diagnosis execution module and a system management and interaction module. The chaos phenomenon in equipment operation data is deeply analyzed through the chaos feature analysis module, whether the equipment operation state is in a chaos state or not and the chaos degree are accurately judged by using chaos feature parameters such as a Lyapunov index, fractal dimension and correlation dimension, and the fault mode identification and prediction module is combined, so that the fault detection accuracy is improved. The system can recognize a potential intermittent fault mode in advance, predict the fault occurrence time and probability and send out an early warning signal, the fault diagnosis method based on the chaos theory remarkably improves the accuracy and timeliness of fault diagnosis, workers are helped to take preventive maintenance measures in time, the non-planned downtime is shortened, and the fault diagnosis efficiency is improved. The production efficiency is improved.

The invention belongs to the technical field of goods shelf arrangement optimization, and provides a digital twinning-based goods shelf arrangement optimization method, which comprises the following steps of: acquiring storage goods shelf dynamic data in real time and transmitting the storage goods shelf dynamic data to an edge computing node, and performing fusion processing on the storage goods shelf dynamic data by adopting a spatio-temporal data fusion technology, and dynamically refreshing a virtual mirror image of the storage shelf layout through three-dimensional modeling, and constructing a real-time digital twinborn model of the storage shelf. According to the method, the prediction model is constructed by using the Transform time sequence prediction algorithm, whether the orders are in the increasing trend is judged based on the chaos theory, the order change trend can be captured, potential order quantity increase can be perceived in advance, a basis is provided for planning shelf arrangement optimization in advance, unsatisfactory storage coping when the orders are suddenly increased is avoided, the stability of storage operation is guaranteed, and the storage efficiency is improved. And when the order increase trend is predicted, the generated scheme comprehensively considers various factors, so that the goods in-out warehouse time can be shortened, the goods picking path can be optimized, and the overall warehousing operation efficiency can be improved.

The invention discloses an active power distribution network energy storage optimization configuration method and system based on an improved parrot algorithm, and the method comprises the steps: building a multi-target energy storage optimization configuration model with node voltage deviation, energy storage cost and power distribution network loss as target functions according to a preset constraint condition; a chaos theory is introduced to initialize a parrot algorithm population, in each iteration process, weights of foraging behaviors, staying behaviors, communication behaviors and stranger afraid behaviors in the parrot algorithm are dynamically adjusted according to fitness values of individuals and current iteration times, Gaussian variation or Cauchy variation is selected according to variation probabilities, and a parrot algorithm is obtained. Mutation operation is carried out on the parrot individuals in the iteration process, and finally an improved parrot algorithm is obtained; and solving the multi-target energy storage optimal configuration model according to the improved parrot algorithm, and outputting an energy storage optimal configuration result. Efficient collaborative optimization of multiple objective functions is realized, and more accurate and comprehensive decision support is provided for energy storage optimization configuration of the active power distribution network.

The invention discloses a multi-factor comprehensive monitoring method and system for an ancient tree growth environment, and relates to the technical field of environment monitoring, and the method comprises the following components: S1, a data collection step, S2, a data preprocessing step, S3, a chaos theory model construction step, and S4, a prediction and analysis step. According to the invention, through omnibearing and high-precision data acquisition and preprocessing, prediction and analysis are carried out in combination with a chaos theory model, the accuracy and reliability of ancient tree growth environment monitoring are significantly improved, and the accuracy and reliability of ancient tree growth environment monitoring are improved through real-time monitoring of multi-source information of meteorological data, soil data and pest and disease damage data in the ancient tree growth environment. And the accuracy and availability of the data are ensured through cleaning, denoising and normalization preprocessing operations, so that the data are deeply analyzed by using a chaos theory model, the future change trend of the growth environment of the ancient tree is effectively predicted, and a scientific basis is provided for the protection of the ancient tree.

The invention relates to the technical field of graph neural networks, in particular to an ideological and political education video content compliance examination system based on a dynamic graph neural network. The module comprises three layers of progressive innovation units: a semantic space mapping unit maps text data into a semantic space of a dynamic dimension to form a semantic unit, and can adaptively adjust the dimension according to content complexity; the keyword network construction unit constructs a keyword association network containing a hierarchical subgroup structure based on a group theory principle, so that the network is kept stable and can be flexibly evolved; the network sensitivity analysis unit performs sensitivity analysis on the keyword association network based on the chaos theory and maps the network state to a predefined compliance attractor pattern library, the advanced mathematics theory is applied to the field of ideological and political content auditing, the auditing efficiency and accuracy are remarkably improved, the manual intervention requirement is reduced, and the auditing efficiency is improved. And the adaptability and sustainability of the system are enhanced.

The invention discloses a transformer winding loose fault feature extraction method based on chaos theory and an ephemeral optimization K-means algorithm. The method comprises the following steps: step 1: setting measuring points on a transformer box, obtaining vibration signals of each measuring point, and selecting a measuring point D with the largest vibration amplitude as the optimal measuring point; step 2: calculating the maximum Lyapunov exponent of the vibration signal collected by the measuring point D, and judging its chaotic characteristics; step 3: calculating the correlation dimension and Kolmogorov entropy of the vibration signal with chaotic characteristics as the chaotic characteristics of the transformer winding loose fault; step 4: reconstructing the vibration signal in phase space to obtain a phase space trajectory; step 5: optimizing the initial cluster centers of the K-means algorithm by using the ephemeral optimization algorithm, clustering the phase space trajectory, and finally calculating the sum of the cluster center moments and the radial vector offset of the obtained K cluster centers as the geometric characteristics of the transformer winding loose fault; and step 6: comparing the chaotic characteristics and geometric characteristics of the abnormal state of the transformer winding with reference values ​​to achieve accurate identification.

The invention relates to the technical field of intelligent question answering, in particular to an intelligent question answering method and system based on a tax knowledge graph, and the system comprises a knowledge graph construction module, a user question processing module, an answer reasoning generation module and a system iteration module. According to the method, an intention recognition engine comprising a contradictory recognition unit and an anomaly recognition unit is constructed, contradiction detection is realized by utilizing an entity conflict measurement model, a tense interval algebraic contradiction model and the like, anomaly analysis is completed in combination with a chaos theory-Lyapunov index fusion model, and the recognition rate of contradictory problems and anomaly problems is improved; according to the method, a reasoning channel selection mechanism is designed, single-layer retrieval, multi-path reasoning and manual switching are dynamically switched according to contradictory and anomaly analysis results, and a knowledge feedback mechanism of a system iteration module is matched, so that the manual intervention cost of complex question answering is reduced, and the answer accuracy is improved.

The invention discloses a photo-thermal power generation technology-based power and heat combined supply type virtual power plant optimization scheduling method. The method comprises the steps of constructing a multi-energy virtual power plant model comprising wind power generation, photovoltaic power generation, a photo-thermal power plant, an energy storage device and an electric boiler; utilizing Latin hypercube sampling to process wind and light output uncertainty; a double-layer optimization strategy and a hybrid dragonfly differential evolution algorithm (CT-DADE) based on a chaos theory are adopted to carry out optimization scheduling. An upper-layer optimization scheduling model takes wind and light output deviation minimization and economic benefit maximization as targets, and the output deviation is corrected through energy storage and an electric boiler; and the lower-layer optimization scheduling model optimizes the electric heating load curve by combining the time-of-use electricity price and the flexible load regulation characteristics. According to the method, a virtual power plant model including photo-thermal power generation, wind power generation, photovoltaic power generation, energy storage equipment, an electric boiler and a flexible load is constructed, uncertainty disturbance and supply-demand imbalance of new energy consumption are comprehensively considered, and stable supply and economical operation of an electric heating system are achieved.

The invention discloses a water pump pressure pulsation diagnosis method based on variational mode decomposition and chaos theory, which belongs to the technical field of water conservancy equipment pressure pulsation fault diagnosis, and comprises the following steps: setting a pressure pulsation monitoring point, and collecting a pressure signal; decomposing the pressure signal at each monitoring point by adopting variational mode decomposition (VMD); performing spectrum analysis on each modal function of the pressure signal decomposed by the VMD, and extracting spectrum features; frequency components and pressure amplitude are extracted, and internal flow field state information is obtained; in combination with a chaos theory, obtaining different modal attractors and the maximum value lyapunov exponent lymax; and introducing the modal attractors obtained in the VMD and chaos theory analysis and the lymax into a weighted decision model, and obtaining pressure fluctuation information at different monitoring points in combination with influence weights. According to the water pump pressure pulsation diagnosis method based on the variational mode decomposition and chaos theory, the pressure pulsation occurring in the operation of the water pump is monitored, and damage possibly caused by abnormal pressure pulsation is prevented.

The invention discloses a data monitoring method and system for real-time control of a heat exchange unit, and the method comprises the steps: collecting the real-time operation data of the heat exchange unit through a sensor module, carrying out the periodic global optimization of a multivariable heat transfer model of the heat exchange unit through quantum calculation, and forming a global scheduling parameter; an ultra wide band (UWB) sensor is used for collecting micro-vibration data in fluid, and a digital twin model and a chaos theory are combined to carry out short-period dynamic prediction on a heat exchange state; performing abnormal mode recognition on the multi-dimensional sensing data through high-dimensional topological data analysis TDA; and operating parameters of the heat exchange unit are adjusted by integrating calculation results. According to the method, global target parameters are obtained through quantum calculation optimization scheduling, short-period dynamic state prediction is realized by using ultra-wideband sensing and digital twinning combined chaos theory, and abnormal states are analyzed and detected by using high-dimensional topological data, so that final control parameters are generated after data fusion, and the control precision is improved. Precise adjustment and real-time control of key operation parameters of the heat exchange unit are achieved.

The invention discloses a project cost index dynamic analysis and intelligent prediction system and method. The system comprises a multi-dimensional data acquisition module, a data processing module, a dynamic analysis module and an intelligent prediction module. According to the method, the multi-source heterogeneous data is collected, the multi-dimensional feature vectors are extracted through preprocessing, the dynamic analysis model fusing the chaos theory and the game theory is constructed, the reinforcement learning algorithm and the risk adjustment reward function are combined in the extended state space to achieve prediction, and the generalization ability is improved through transfer learning. According to the invention, the problems of insufficient dynamic analysis, low prediction accuracy and weak data processing capability in the prior art are solved, dynamic analysis and accurate prediction can be carried out on the project cost index, comprehensive, timely and accurate decision support is provided for project cost management, and the adaptability of the system to a complex market environment is enhanced.

This invention discloses a phased anomaly detection method for ultra-large-scale industrial software supply chains based on graph neural networks. It divides the software anomaly detection task into two stages. In the rapid contamination stage, a simple model is used to quickly learn global anomaly patterns and filter out potential anomaly nodes and regions. In the precise detection stage, a more complex model is used to detect anomaly nodes. Furthermore, this invention defines the "disorder level" of nodes on the graph based on the Lyapunov coefficients in chaos theory and utilizes the "defect" of rapid oversmoothing in graph neural networks to rapidly propagate the behavioral patterns of anomaly nodes on the graph, simulating the propagation process of the entire chaotic system. In this way, this invention can quickly learn the global node anomaly patterns and quickly and accurately filter out candidate anomaly nodes and regions.

The application discloses a QR code encryption and decryption method and system based on chaos theory and an AES algorithm, relates to the encryption and decryption technical field of QR codes, and comprises an encryption process and a decryption process; the encryption process comprises the following steps: firstly, chaotic sequences generated by improved Logistic mapping and Chebyshev mapping are subjected to XOR to obtain a composite chaotic sequence, the composite chaotic sequence is used as an initial key of AES grouping, and then ciphertext is obtained through AES encryption. Grouping initial keys are dynamically generated, a newly generated key is used for encrypting one group of plaintext data, the keys are completely independent, and therefore, one-time password is realized. Then, the AES algorithm is improved, plaintext information is first encrypted, then secondary encryption is performed on the plaintext information in a Base64 mode, final ciphertext information is subjected to QR code standard coding, and an encrypted QR code is generated. Through the mode of increasing encryption processing, the application effectively reduces information leakage or information tampering problems caused by QR codes.

The invention discloses an automatic monitoring system of a gas turbine generator set, and relates to the technical field of turbine generator set monitoring, the system comprises the following components: a microscopic state monitoring unit, a data collection and transmission unit, a data comprehensive analysis unit, a fault source tracing and data analysis unit and a control and display management unit; according to the invention, by using the knowledge graph integrated by the fault source tracing and data analysis unit, chaos theory analysis and cross-domain knowledge migration functions, the fault source can be quickly and accurately traced, including determination of specific component problems and related pre-operation condition changes or indirect influences of other components, and the fault source can be accurately traced through chaos data analysis. According to the method, potential fault risks which are difficult to perceive by a traditional data analysis method are found, in addition, fault diagnosis and maintenance experience knowledge of other similar complex industrial equipment is migrated to the field of gas turbine generator sets by means of a cross-domain knowledge migration technology, and the content of a knowledge graph is further enriched.

According to the PEM water electrolysis equivalent voltage model and the modeling method, according to electrochemical properties and circuit properties, the total voltage obtained when a PEM electrolytic cell is connected to a power supply is divided into a theoretical voltage threshold value, an ohmic voltage and an electrochemical polarization voltage which can be spontaneously carried out in an electrolytic reaction, and modeling is carried out respectively; constructing an open-circuit reversible voltage during theoretical voltage threshold modeling which can be spontaneously performed based on an electrolytic reaction; when modeling based on ohmic voltage, constructing a proton membrane ohmic resistor based on the proton membrane key parameters; when modeling is carried out based on electrochemical polarization voltage, modeling is carried out based on various key parameters of the proton membrane, a chaos theory algorithm related formula is fused, and the voltage of the PEM electrolyzed water electrolytic tank under the actual working condition is constructed; modeling is carried out based on various key parameters of the proton membrane, the voltage of the PEM electrolyzed water electrolytic cell under the actual working condition is constructed by integrating a chaos theory related algorithm, and the external voltage characteristics of the electrolytic cell under the actual condition can be reflected.

The present invention discloses a method, apparatus, device, medium, and product for one-time, one-pad transmission of a hyperchaotic key. The method comprises the following steps: determining a chaotic function based on the chaotic parameters and a large prime number parameter obtained by decrypting a generated symmetric key; generating a key stream based on the generated random number sequence and the large prime number parameter; and transmitting one-time, one-pad data based on the key stream. This application scheme utilizes a symmetric key for preliminary encryption and, in conjunction with chaos theory, generates a one-time, one-pad key stream at each end. The generated key stream is used for one-time, one-pad data transmission, ensuring that the encrypted data can be decrypted while preventing a third party from obtaining the plaintext information. This method avoids security risks arising from key stream sharing and improves the security performance of key transmission.

The invention discloses an amplitude-frequency detection method for a weak current harmonic signal of a cable under a dynamic working condition, and relates to the technical field of cable state monitoring. A signal acquisition step; installing a high-precision current sensor at a key part of the cable, setting a proper sampling frequency according to a cable working frequency range and signal characteristics, and collecting a current signal in the cable in real time; a pretreatment step; collected current signals are preprocessed, a self-adaptive filtering algorithm is adopted to automatically adjust filtering parameters according to signal noise characteristics to remove high-frequency noise and power frequency interference, and noise reduction methods such as wavelet transformation are combined to remove random noise so as to improve the signal-to-noise ratio of the signals. The method has the advantages that the chaos characteristic in the early weak current harmonic signal of the cable is fully utilized, the chaos theory is adopted to analyze and process the signal, and different from a traditional method, weak characteristic information in the signal can be more effectively captured.

The invention relates to the technical field of desertification comprehensive treatment, in particular to a desertification evolution prediction method, system, equipment and medium based on the chaos theory, and the method comprises the following steps: S1, collecting the historical and real-time data of a desertification region, and obtaining the quantification index of the desertification degree and the variation trend; s2, analyzing nonlinear characteristics of desertification monitoring observation data by using a chaos theory, identifying chaos behaviors in a system, and performing phase-space reconstruction on the quantization indexes of the desertification degree and the variation trend obtained in the S1; and S3, establishing a model based on a chaos theory, and simulating a desertification evolution process by taking the desertification monitoring data as an input boundary condition to obtain a prediction result of desertification evolution. According to the method, the chaos theory is combined to establish the regional desertification evolution network model based on the chaos theory, and the established chaos theory model well solves the problem of regional desertification trend prediction by performing phase-space reconstruction on the past regional desertification evolution characteristic indexes.

The invention discloses a three-dimensional point cloud model encryption method based on Logistic mapping, and belongs to the field of information security cryptography. The three-dimensional point cloud data encryption method is constructed based on the chaos theory, and the calculation efficiency is optimized while the encryption complexity and reliability are improved by means of the extreme sensitivity of a chaos system and the mass characteristic of the point cloud data. According to the method, based on preset control parameters and initial values, iterative updating is carried out through an alternating mapping function, then a high-randomness sequence is generated through Logistic mapping, a random unit vector is constructed in combination with a spherical coordinate method, an orthogonal matrix is generated through a vector projection subtraction method, and finally a transformation matrix is dynamically constructed according to the scale of point cloud data to achieve encryption. Experimental results show that the scheme can effectively defend multiple attack modes, has the characteristics of high security and rapid encryption and decryption, and shows excellent protection capability and application value in secure storage and transmission of three-dimensional point cloud data.

The application discloses a steel structure welding quality evaluation system and method based on distributed computing. The system includes a distributed gradient sensing unit, a physical field reconstruction unit, an entropy calculation unit, a chaotic feature extraction unit, a resource scheduling unit, a physical constraint verification unit, a chaotic synchronization analysis unit, a data aggregation processing unit, a quality comprehensive evaluation unit and a knowledge base management unit. By distributed acquisition of multi-physical field signals in the welding process and calculation of spatial gradient, the data transmission amount is significantly reduced; the complete physical field is reconstructed from the gradient information by using the multi-grid method; the potential defect area is identified and the computing resources are dynamically optimized based on information entropy analysis; the nonlinear characteristics of the sound signal are analyzed by using the chaos theory; multi-dimensional cross verification is carried out through physical constraint verification and chaotic synchronization analysis; and the federal learning is used to realize multi-site knowledge sharing. The application realizes real-time, accurate and comprehensive evaluation of the welding quality, and provides reliable technical support for steel structure welding quality control.

The invention relates to the technical field of modern application of traditional Chinese medicine theories, and aims to solve the problem of insufficient quantitative research of the five-element theory of traditional Chinese medicine and the meridian theory under modern scientific contexts. Clinical twelve-meridian bio-electricity signals are collected, the chaos theory is used as the basis, a five-element generation-restriction mechanism is fused, and a five-dimensional nonlinear kinetic equation system is constructed; by establishing an acupuncture point-meridian-five-element two-stage mapping relation, quantitative characterization of five-element characteristic values is realized; parameter identification is completed by adopting a PSO-GA integrated optimization strategy, and chaos characteristics and evolution laws of a five-element system of a human body in healthy, sub-healthy and disease states are represented by calculating a Lyapunov index, calculating fractal dimensions, drawing a chaos bifurcation diagram, a phase diagram and the like. The method can be used for human health status assessment, provides a modeling thought for modernization of traditional Chinese medicine theories, and has clinical application value.

The invention discloses a GIS partial discharge mode recognition method and system based on the chaos theory, and relates to the technical field of GIS partial discharge mode recognition, and the method comprises the steps: collecting a partial discharge signal caused by a typical insulation defect, and forming a PRPD pattern sample; each PRPD pattern sample is preprocessed, and the signal amplitude is normalized; and obtaining chaotic characteristics according to a chaos theory, carrying out classification and identification by using a support vector machine, recording a mode to which partial discharge belongs, and completing identification. According to the method, the recognition accuracy of the insulation defect is remarkably improved, and the robustness and practicability of the system are enhanced.

The invention relates to the field of industrial aluminum alloy, and discloses a method for improving homogeneous cooling uniformity, which comprises the following steps of: determining the water quantity of homogeneous cooling water and the initial opening degree of a water valve of a homogeneous cooling chamber according to the temperature of a bar; chaos state characteristics of homogeneous cooling water are extracted based on the chaos theory, multi-step prediction and risk judgment are carried out, and a neural network model of the homogeneous cooling water temperature and the opening degree adjustment amount of a homogeneous cooling chamber water valve is trained under the constraint of a stable operation interval; the opening degree adjustment amount is output through a neural network model, the water valve opening degree adjustment amount is optimized and decomposed into temperature deviation, energy consumption, chaotic stability and response smoothness target sub-population independent evolution through a co-evolution mechanism, and the global optimal opening degree adjustment amount is obtained through dynamic weighted combination; and based on the global optimal opening degree adjustment amount corresponding to the current homogeneous cooling water temperature and the initial opening degree of the homogeneous cooling chamber water valve, the final opening degree of the homogeneous cooling chamber water valve is determined. And precise, stable and efficient control of homogeneous cooling is realized.

The invention discloses a comprehensive benefit evaluation method and system for a power distribution system, and the method comprises the steps: firstly, collecting the multi-dimensional benefit index data of a power distribution system scheme, and constructing a contribution cloud picture through parameter inversion, so as to describe the fuzziness and uncertainty of the data; secondly, fusing subjective and objective weights by adopting a combined weighting method so as to balance expert experience and data laws; then, performing weighted fusion on the cloud model parameters of the plurality of indexes to quantify the overall benefit performance of the scheme; and finally, in combination with an improved grey clustering analysis method, introducing a Logistic curve to fit a benefit level boundary, in combination with a graph neural network, extracting a complex relation structure between the schemes, fusing a chaos theory to improve a fuzzy boundary identification capability, replacing a traditional whitening function, calculating a membership degree of each scheme to a benefit level, and completing sorting. Compared with a traditional method, the method is better in processing nonlinearity, uncertainty and dynamic information fusion, and the scientificity and decision credibility of comprehensive benefit evaluation of the power distribution system are effectively improved.

The application provides a JPEG color image encryption method based on chaos theory and belongs to the technical field of image encryption. The method performs block scrambling in the (1) DCT transformation stage, only uses the image block as a unit to perform scrambling, and does not affect the compression performance; (2) the DC coefficient is subjected to XOR operation with a pseudo-random key sequence, which can effectively protect the value of the DC coefficient, improve the encryption security, and the operation is simple, which can reduce the encryption time; (3) the non-zero AC coefficient is subjected to sign transformation, the distribution relationship of the zero value AC coefficient and the non-zero value AC coefficient is not changed, the influence on the entropy encoding is not caused, and the compression performance is not lost; (4) the RSV pair recombination is proposed in the entropy encoding stage, on the one hand, the adjacent pixel correlation is effectively reduced, and then the encryption security is improved, on the other hand, compared with the existing scheme, no excessive data is added, and the influence on the compression performance is effectively reduced.

The present invention provides a JPEG reversible data hiding method based on chaos theory and histogram shifting. First, a pseudo-random sequence is generated based on a JPEG image using a chaotic system. Then, the JPEG image is entropy decoded to obtain quantized discrete cosine transform (DCT) coefficients, resulting in a DCT coefficient block. All DCT coefficient blocks are scrambled using a pseudo-random sequence x for data embedding, thereby enhancing the concealment and security of data embedding. The present invention does not modify the zero AC coefficients in the quantization block, but only expands the AC coefficients with an absolute value of 1 to carry secret information. Furthermore, a new coefficient block selection strategy is proposed, in which, during the information embedding process, the embedding coefficient block is selected based on the ratio R of the number of AC coefficients with a value of 1 to the number of other non-zero AC coefficients. This method effectively preserves the visual quality and storage size of the image, and has a high embedding capacity, which depends solely on the number of AC coefficients with an absolute value of 1 in the DCT coefficient block.

The invention provides a mountain area continuous downhill tunnel road section accident early warning method and system, and relates to the technical field of traffic control, and the method comprises the steps: obtaining the digital road section information of a mountain area continuous downhill tunnel road section; performing non-uniform segmentation on the digital road section information by combining cosine similarity and an exponential weighted Euclidean distance to obtain a plurality of sub-road sections; acquiring historical traffic accident data; establishing a random forest model in combination with a chaos theory and a particle swarm optimization algorithm; screening dangerous sub-road sections by using the model; judging whether the dangerous sub-road sections comprise a target traffic accident road section or not, if so, outputting the dangerous sub-road sections, otherwise, increasing the number of the preset sub-road sections by taking the first coverage rate and the second coverage rate of the traffic accident road section as guidance, and returning to re-segmentation; and setting an early warning identifier at each dangerous sub-road section so as to carry out safety early warning and safety guidance on the vehicle entering each dangerous sub-road section. Excessive reminding is avoided while the dangerous road section reminding coverage rate is ensured, and the early warning effect is improved.

The application discloses a kind of based on improved ascidian algorithm's workflow optimization scheduling method.This method considers the time delay and reliability problem existing in workflow scheduling, utilizes reliability model to define the scheduling failure factor of workflow, and the weighted sum of time delay and failure factor is used as fitness function to evaluate workflow scheduling scheme, and the ascidian algorithm is improved, in population initialization, the standard deviation of population is used in the optimal population selected in multiple populations generated.In the updating of individual in population, chaos theory and updating algorithm are combined, the different method of using chaos mapping to replace pseudo-random sequence, and the leader and follower are updated using chaos mechanism, the global search speed in potential search area is improved, the speed of searching the optimal workflow scheduling scheme is improved, the edge server time delay is minimized, and the task scheduling reliability is improved, and finally the optimal workflow scheduling scheme is obtained.