80 results about "Chaos theory" patented technology

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.

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 a transformer fault diagnosis method based on chaos theory comparative learning under a strong noise background, and the method comprises the following steps: 1, obtaining a label-free data set and a label data set of vibration signals of a transformer box body, and carrying out the normalization processing of the collected signals; a second step of utilizing a dung beetle optimization algorithm to obtain an optimal parameter for ICEEMDAN decomposition of the vibration signal in each fault state, performing ICEEMDAN decomposition of the vibration signal to obtain a plurality of modal components IMF, calculating a multi-scale fuzzy entropy of each modal component, screening a component with a smaller entropy value and reconstructing the signal, and performing data cleaning and denoising; 3, performing random data enhancement on the de-noised label-free data set, defining positive and negative sample pairs, performing phase-space reconstruction, completing comparative learning of a pre-training model, and migrating trained encoder parameters to a downstream task; 4, performing phase-space reconstruction on the denoised labeled data set, and inputting the denoised labeled data set into a trained encoder after fine tuning; 5, performing preprocessing and phase-space reconstruction on the label-free test data, and inputting the preprocessed label-free test data into the trained encoder; sixthly, the output features are input into a classifier to be classified, and fault diagnosis is completed. According to the method, the problem of low diagnosis precision caused by a strong noise environment and lack of marked data in transformer fault diagnosis and the problems of misjudgment and missed judgment caused by nonlinear characteristics of vibration signals are effectively solved.

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 electronic tracking marks, in particular to a whole-course traceable pig intelligent electronic ear tag and method for livestock breeding. The live pig intelligent electronic ear tag comprises a nonlinear characteristic analysis module, a behavior trend modeling module, a coding strategy adjustment module, a compression efficiency evaluation module, an abnormal mode monitoring module, a resource adjustment strategy module and a behavior early warning feedback module. And the nonlinear characteristic analysis module processes time sequence data by adopting a chaos theory analysis method based on live pig monitoring data. According to the invention, by integrating nonlinear characteristic analysis, behavior trend modeling, coding strategy adjustment, compression efficiency evaluation, abnormal mode monitoring, resource adjustment strategy and behavior early warning feedback modules, the efficiency of livestock breeding management is improved. The application of a chaos theory analysis method and a dynamic modeling technology makes prediction of live pig behaviors and identification of abnormal modes more accurate.

The invention relates to the field of computer science and artificial intelligence, in particular to a high-robustness meta-heuristic optimization method, which comprises the following steps of: firstly, designing a chaos reverse learning strategy and applying the chaos reverse learning strategy to population initialization, and secondly, designing flexible switching of a behavior transition coefficient between a global exploration stage and a local development stage, the method comprises the following steps of: selecting a plurality of stages, and respectively adopting six different search strategies in each stage to find a candidate optimal solution position, and finally, when a preset termination standard is met, outputting an optimal solution found so far. According to the method, the chaos theory and the reverse learning strategy are combined, the defect that individual position distribution is uneven due to the fact that a pseudo-random number is used for initializing a population in a traditional meta-heuristic optimization method is overcome, the obtaining speed of an optimal solution is increased through local mean value development and cooperation, and meanwhile the optimal solution is obtained. And dynamic balance between global search and local development is realized by utilizing the behavior transition coefficient, so that the resource use efficiency and robustness of the algorithm in processing a complex optimization task are remarkably improved.

The invention discloses a postoperative intelligent monitoring system for gastrointestinal tumor patients, particularly relates to the technical field of postoperative monitoring, and is used for solving the problem that a nonlinear mode in data cannot be effectively identified when facing high-dimensional and complex wound image data in the prior art. By deploying an ambient light sensor and an automatic exposure adjustment technology, it is ensured that a wound image can keep high quality under various illumination conditions, rich local and global features are extracted from the wound image, and a detailed data basis is provided for deep learning analysis; the chaos theory analysis module evaluates the complexity of the features by using a chaos theory model, and effectively screens out data which is high in complexity and needs to be deeply analyzed; the convolutional network classification module uses a convolutional neural network to accurately identify and classify different healing stages and infection signs of the wound; and the model calibration feedback module continuously calibrates and optimizes the model through historical healing data, so that the self-updating capability and the long-term operation accuracy of the system are guaranteed.

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 relates to the technical field of PCBA testing, in particular to a PCBA function test optimization method and system based on time sequence characteristic analysis, and the method comprises the steps: obtaining time sequence data in a PCBA function test process; constructing a time sequence feature extraction and topology embedding model based on the time sequence data; according to the time sequence feature extraction and topology embedding model, generating a continuous coherence feature vector; performing group theory transformation and invariant extraction based on the continuous coherence feature vector; constructing a matrix theory spectrum analysis model according to the invariant extraction result; performing number theory and lattice point optimization based on the output of the spectral analysis model; according to the lattice point optimization result, establishing a chaos theory prediction model; based on the chaos theory prediction model, a PCBA function test optimization scheme is output, the method can not only capture complex dynamic characteristics of the PCBA in the time dimension, but also extract more essential and more stable function characteristics through mathematical transformation and optimization.

The invention discloses a traffic signal control method based on an edge roadside. The method comprises the following steps: acquiring the traffic flow of each intersection at the current moment; calculating the proportion of the traffic flow of each intersection to the total flow of the intersection to obtain first data; processing the first data through Logistic mapping, and predicting to obtain second data; calculating based on the second data to obtain a Jacobian matrix, and obtaining a Lyapunov index according to the Jacobian matrix; if the Lyapunov index is a positive number, the noise suppression intensity is adjusted based on the Lyapunov index, and a noise suppression factor is obtained; processing the first data based on the noise suppression factor to obtain denoised first data; the denoised first data is processed based on the chaos theory, the signal period and the green light time are dynamically adjusted, and then the pollutant emission amount is calculated; and adjusting the signal period and the green light time again based on the target pollutant discharge amount. According to the invention, the road passing efficiency and the vehicle passing rate are improved, and the parking time and the pollutant discharge amount are reduced.

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.

The invention relates to the technical field of image encryption and decryption, and discloses an image encryption and decryption method and system based on a waterfowl-plant hyperchaotic system, and the method comprises the steps: obtaining an original color image, and carrying out the preprocessing of the original color image, and obtaining a to-be-encrypted color image; the method comprises the following steps: constructing a waterfowl-plant hyper-chaotic system based on a relationship between consumers and resources of two habitats in ecology to generate a pseudo-random sequence; scrambling the color image to be encrypted by using the pseudo-random sequence to generate an intermediate encrypted image; according to an operation rule of a finite field GF (28), performing diffusion operation on the intermediate encrypted image to generate a final encrypted image; according to an inverse operation rule of a finite field GF (28), performing inverse diffusion operation on the final encrypted image to generate an intermediate decrypted image; and performing inverse scrambling operation on the intermediate decrypted image to generate a final decrypted image corresponding to the color image to be encrypted. According to the method, the chaos theory and finite field operation are combined, and the security and complexity of image encryption and decryption are 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 application provides a communication performance test method and system for the transmission path on the network side of a base station. Among them, the target monitoring node is determined from multiple monitoring nodes deployed on the transmission path on the network side of the base station to be tested, and the transmission delay data, packet loss rate data and bandwidth utilization data of the test data stream on the specific section of the transmission path are monitored in real time; the periodic and non-periodic changes of the transmission delay are detected to obtain analysis results; the impact of the packet loss rate data on the transmission performance is evaluated to obtain evaluation results; the change trend of the bandwidth utilization data is predicted to obtain prediction results; the three results are combined to calculate the communication performance index of the specific section; according to the comparison result of whether the communication performance of the specific section meets the predetermined standard, a communication performance test report is generated. The present application comprehensively evaluates the communication performance of the transmission path on the network side of the base station by integrating chaos theory, fuzzy logic system and deep learning algorithm, thereby improving the efficiency of communication performance testing.

The invention discloses a DICOM (Digital Imaging and Communications in Medicine) medical image data encryption and desensitization method based on a chaos theory. The method comprises the following steps: (1) a system reads medical image data in a DICOM format; (2) extracting an encryption item and a desensitization item: extracting an item needing to be encrypted (namely encryption item data) and an item needing to be desensitized (namely desensitization item data) from the DICOM data; (3) judging whether the data is a desensitization item or an encryption item: judging the extracted data to determine whether the data is the desensitization item or the encryption item; (4) desensitization item data processing: if the data are desensitization item data, setting the data to be null; (5) encryption item data processing: if the data are encryption item data, forming an encryption item P, and entering an encryption process; and (6) decrypting the data of the encryption item P. According to the method, the sensitive information in the medical image data can be desensitized, meanwhile, the non-sensitive information is encrypted to ensure the privacy and security of the data, and the method has good practical application value.

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 discloses a method and a system for generating a high-security key based on hardware features. The method comprises the steps of extracting terminal hardware features, analyzing feature levels and performing feature fusion. And reinforcing the fused features by using a noise processing algorithm based on a chaos theory. And generating a high-security key based on the enhanced fusion feature. According to the method and the system for generating the high-security key based on the hardware features, the hardware features are effectively utilized, and high-quality basic data are provided for subsequent key generation. The potential of hardware features is fully mined, and the randomness and stability of feature data are improved. And deep optimization and randomness enhancement of feature data are realized for feature enhancement, and the security of the key is effectively improved. Finally, the pseudo-random number sequence is converted into a high-security key through a secure hash algorithm, and it is ensured that the key is difficult to crack by an attacker. The generation of a high-quality key is realized, and the security of data transmission and storage is ensured.