92 results about "Power analysis" patented technology

The invention discloses a computing power analysis method used in a cloud computing environment, a matching method and a computer device. The computing power analysis method used in the cloud computing environment comprises the steps that historical task operation data of different types of computing power nodes in a cloud computing platform are acquired, associated computing power portraits corresponding to the computing power nodes are generated through preprocessing, and then a target model is trained in combination with the historical task operation data to obtain a task-computing power mapping model; performing multi-dimensional dynamic and static analysis on a to-be-executed user calculation task to obtain task feature information; and analyzing the task feature information based on the task-computing power mapping model to obtain analysis information of each type of computing power nodes. According to the method, the required computing power resources and the analysis information of each type of computing power nodes can be accurately predicted according to the dynamic and static analysis feature information of the task before the task is executed, so that accurate computing power identification and intelligent matching are realized, the reasonability and efficiency of task allocation are improved, the resource waste rate is effectively reduced, and the adaptation accuracy in a multi-tenant environment is improved.

The invention provides a computing power thermal evaluation method based on multi-dimensional data analysis and a decision making system thereof, and relates to the technical field of computing power analysis, and the method comprises the steps: obtaining an original data set, and carrying out the preprocessing of the original data set to generate a standardized data set; performing vectorization processing and heterogeneous fusion on the standardized data set to generate a unified fusion data set; a hierarchical evaluation system is constructed, correlation analysis is performed based on evaluation index features, and a computing power evaluation feature set is formed; different initial index weights are configured for different types of regions; calculating an initial calculation force thermal value of each region; performing iterative optimization on the initial index weight by using an optimization algorithm; and recalculating the regional computing power thermal value based on the optimized index weight, generating a thermal time-space distribution map, and generating a computing power resource allocation scheme according to the thermal gradient difference. According to the method, the systematicness and objectivity of computing power resource assessment are improved, and a reliable decision basis is provided for regional computing power resource allocation.

The invention provides a chip security key protection method and system capable of resisting side channel attack, and relates to the technical field of chips, which comprises the following steps of: obtaining a key operation request, performing address space mapping based on a dynamic mapping rule to obtain encrypted key data, decrypting the encrypted key data, fragmenting key intermediate data, and allocating an independent processing channel to perform decoupling operation; and injecting a time-varying noise signal to cover the association between the key and the physical characteristics, and finally performing recombination and reverse conversion to generate a final key output. According to the method, side channel attacks such as power consumption analysis and electromagnetic analysis are effectively resisted, and the security of key storage and operation processes is improved.

The invention relates to the field of intelligent monitoring, and discloses an early warning method, which is suitable for a monitoring system, and the monitoring system comprises a sensor, a camera and a computing power analysis module. The method comprises the following steps: determining an initial weight and a position coordinate of each sensor; when the sensor detects an abnormal event, an alarm signal is sent; after receiving the alarm signal, the camera captures an image according to a preset strategy based on an image capture time sequence; inputting the picture group into a computing power analysis module, and selecting an image with the highest confidence coefficient; according to the picture confidence coefficient and the detection rate, whether the event reaches an early warning level is judged, and early warning information is uploaded to a system; and dynamically adjusting the weight of the trigger sensor according to the analysis result of the alarm event. According to the method, the sensor weight and the image capturing strategy are dynamically optimized, double evaluation of the confidence coefficient and the detection rate is combined, accurate judgment and response of the abnormal event are achieved, and the method has the advantages of being efficient, flexible and high in robustness and is suitable for monitoring requirements of various complex scenes.

The invention discloses a big data intelligent analysis method and system based on artificial intelligence, and belongs to the field of electric power analysis. Equipment risk assessment is performed through the acquired environment change data in combination with equipment anomaly analysis, equipment risk influence assessment is performed based on an equipment risk assessment result and an equipment position, and early warning of an equipment risk level is performed based on an equipment risk influence assessment result. According to the method, the operation of the substation equipment is comprehensively evaluated through the operation data and the aging data of the equipment, then the operation stability of the equipment in the future substation is evaluated according to the environment change condition, and appropriate suggestions are made for replacement or maintenance of the equipment under the influence of the environment. And the stability and the safety of the transformer substation under the influence of the environment are further improved.

The invention discloses an intelligent data analysis system and method based on source-network-load-storage integration, and relates to the technical field of electric power analysis, the method comprises the steps: carrying out the statistics of the historical average power consumption of a certain time period, and determining a characteristic time period; a training period is set, the electricity consumption of the load end at the sampling time point is collected, and the electricity consumption fluctuation rate of the load end in the characteristic time period is calculated; drawing an electricity consumption fluctuation graph of the load end in the characteristic time period in the training period, and determining a target load end; generating an energy storage end group, collecting operation parameters of the energy storage end group, calculating an operation score of the energy storage end group, and drawing a feature operation score graph of an analysis period corresponding to the energy storage end group; and acquiring a real-time operation score of the energy storage end group, calculating a real-time analysis period of the energy storage end group, judging an abnormal analysis state of the energy storage end group, and ensuring efficient and stable operation of the source-grid-load-storage integrated power system.

The invention relates to the technical field of elevator operation safety monitoring, and discloses an elevator no-load monitoring method based on AI computing power analysis, RUL prediction and big data optimization, and the method comprises the following steps: S1, a system initialization step; s2, a data acquisition step; s3, a computing power analysis step; s4, a step of AI reasoning and RUL prediction; s5, a monitoring period prediction step; s6, executing a no-load test; s7, a score calculation and optimization step; s8, a data uploading and big data optimization step; and S9, a manual intervention step. According to the elevator no-load monitoring method based on AI computing power analysis, RUL prediction and big data optimization, by arranging a dynamic computing power distribution mechanism and an edge computing RTOS operating system, self-adaptive resource scheduling is achieved, and particularly, according to the idle computing power of edge equipment (if a high-precision AI model is selected when the idle computing power is larger than 50%, otherwise a lightweight model is selected), the optimal scheduling of the edge equipment is achieved. And in combination with task priority scheduling of an RTOS kernel layer, the real-time requirement is ensured.

The invention discloses a distributed storage method and system based on AI computing power analysis. The method comprises the steps that heterogeneous storage resources are constructed in advance, and routing nodes are in communication connection with at least one storage node; obtaining computing power state data of routing nodes and storage nodes in the heterogeneous storage resources according to historical data of the heterogeneous storage resources, and converting computing power state information into computing power feature data of each routing node and each storage node; outputting computing power state data of each routing node and each storage node according to an AI computing power prediction model; and constructing a distributed storage task priority rule, storing the corresponding data packet to the corresponding storage node through the routing node according to the computing power state data of the routing node and the storage node and the distributed storage task priority rule, and updating the storage state data of the storage node and the routing node in real time. The adaptability of the distributed storage method to different storage task rules can be improved, and the stability of distributed storage is greatly improved.

The present invention discloses a data intelligent analysis system and method based on the integration of source, grid, load and storage, which relates to the technical field of power analysis. The present invention counts the historical average power consumption in a certain time period and determines the characteristic time period; sets a training cycle, collects the power consumption of the load end at the sampling time point, and calculates the power consumption fluctuation rate of the load end in the characteristic time period; draws the power consumption fluctuation diagram of the load end in the characteristic time period within the training cycle, and determines the target load end; generates an energy storage end group, collects the operating parameters of the energy storage end group, calculates the operating score of the energy storage end group, and draws the characteristic operating score diagram of the analysis period corresponding to the energy storage end group; obtains the real-time operating score of the energy storage end group, calculates the real-time analysis period of the energy storage end group, and judges the status of the abnormal analysis of the energy storage end group to ensure the efficient and stable operation of the source, grid, load and storage integrated power system.

The invention relates to the technical field of power analysis, and particularly discloses a power distribution network fault judgment method based on voltage amplitude variation, and the method comprises the steps: obtaining network data, historical voltage data and historical fault data of a power distribution network, and determining a plurality of adjustment division regions and adjustment region fault data of each adjustment division region; determining real-time voltage data of the power distribution network; calculating an associated key point set of each key point of each adjustment division region; and constructing a fault prediction model of each adjustment division area, and determining real-time fault data of the power distribution network based on the real-time voltage data and the fault prediction models of all the adjustment division areas. The method can quantify the incidence relation between the key points, achieves the linkage analysis of the fault key points, enables the fault prediction to be upgraded from single-point analysis to regional cooperative judgment, improves the accuracy, real-time performance and intelligent level of the fault diagnosis of the power distribution network, optimizes the operation stability of the power grid, and reduces the fault response time and operation and maintenance cost.

The invention relates to the technical field of intelligent monitoring, in particular to an intelligent monitoring system and device for a box transformer substation, and the system comprises a data collection module which collects the initial sensing data in the box transformer substation in real time, and the initial sensing data comprises electrical parameters and environmental parameters; the communication network and data processing module cleans the initial sensing data and transmits the initial sensing data to a data terminal in real time to obtain terminal cleaning data; the performance temperature power analysis module analyzes the insulation performance and the mapping relation between the temperature and the output power to obtain an insulation temperature formula; the control module judges whether the output power exceeds a threshold value or not, if yes, the equivalent resistance of the optimal insulation performance and the optimal temperature is calculated, and the optimal insulation performance and the optimal temperature are calculated according to the equivalent resistance of the optimal insulation performance and the optimal temperature. By analyzing and optimizing the insulation performance and the temperature, unnecessary energy waste is reduced, the energy utilization efficiency is improved, and potential fault hidden dangers are found and processed in time.

The invention relates to the technical field of geophysical exploration and geotechnical engineering monitoring, in particular to a soil seepage channel resistivity or electrical impedance bimodal tomography measuring device. Comprising an excitation signal adjusting module, a data acquisition module, a channel switching module, a sensing electrode module and a computer, the excitation signal adjusting module is composed of a signal generation module and an isolation switch QS. The data acquisition module consists of a power analysis module, a multi-channel synchronous acquisition module, a signal conditioning module and a phase-locked amplification module; the channel switching module consists of a relay switch module A and a relay switch module B; each relay is provided with a plurality of channels, and each channel comprises three general ports, namely a normally open end (NO), a common end (COM) and a normally closed end (NC). The invention provides a resistivity or electrical impedance bimodal tomography measurement device, which is used for realizing rapid high-resolution detection and description of a soil seepage channel with resistivity and electrical impedance combined.

The invention discloses a low-overhead anti-power analysis AES (advanced encryption standard) algorithm mask protection method. According to the method, two shares of a plaintext and a secret key are received in a clock period 0, ten rounds of round functions are executed from the clock period 0 to the clock period 30, each round needs three clock periods, and two shares of a ciphertext are output in a clock period 31; in the implementation of a round function and key expansion, the calculation of an S box (not including input linear mapping) needs three clock periods; in the third clock period after S box calculation is completed, a round of residual operation (wherein the residual operation of a round function comprises a multiplication module of the S box, S box output linear mapping, row shifting, column confusion and round key addition, and the residual operation of key expansion comprises key word XOR generation of a next round of round key) and the next round of S box input linear mapping calculation are completed at the same time. According to the mask, the side channel security capability of first-order power consumption analysis resistance can be provided for the AES algorithm, and the requirements on random numbers and chip area are remarkably reduced.

The invention relates to the technical field of electric meter box fault monitoring, in particular to an electric meter box fault monitoring system based on multi-source data fusion. The system comprises a data acquisition module used for acquiring internal and external temperature data of an electric meter box, total load power of the electric meter box and heat dissipation power of a heat dissipation system of the electric meter box; the temperature analysis module is used for setting a sliding window to synchronously slide on the internal temperature data, the external temperature data, the heat dissipation power and the total load power respectively to obtain a temperature drop coefficient and a heat dissipation effect correction value; the power analysis module is used for calculating a heat dissipation power characteristic value based on the heat dissipation power in the current sliding window, obtaining a working efficiency conversion coefficient of the heat dissipation system, and then obtaining a corrected working efficiency conversion coefficient; the early warning analysis module is used for acquiring an early warning index based on the correction working efficiency conversion coefficient corresponding to each sliding window; and carrying out early warning on the electric meter box fault according to the early warning index. The fault monitoring accuracy of the electric meter box can be improved.

An electronic device is provided for securing and desynchronizing register transfers as a mitigation strategy to side channel attacks that employ power analysis profiling, whereby leakage information produced as a result of register switching at the bit-level, by way of random delay cell insertions, is decorrelated from actual power consumption. The technical effect of the solution constructively produces overlapping of current power profiles / signatures, maximally spanning across bit value profiles of a register, which when analyzed from a Hamming Weight or Distance model introduces a wider time-spread of resulting current power profiles thereby impeding deep learning of the transistor switching / flipping state behavior, and consequently disrupting efficacy of profile / signature matching / mapping of transistor state changes characteristic of a traditional power analysis side channel attack. Other embodiments are disclosed

The invention discloses an electric power market power calculation method based on artificial intelligence, and mainly aims to solve the problems that traditional market power analysis data utilization and interaction are limited, the complex problem processing capability is low, and dynamic supervision requirements are difficult to adapt. The platform is connected with an MCP service and a large language model platform, and comprises the following steps: receiving a query statement, and carrying out keyword matching on the query statement and an RAG knowledge base to obtain a knowledge base retrieval result; an available tool list in the MCP service is obtained, and the available tool list, the query statement and the knowledge base retrieval result are integrated and then sent to the large language model platform; receiving a structured calling instruction returned by the large language model platform, and initiating a tool calling request to the MCP service, so that the MCP service calls and executes calculation operation related to market power analysis; and receiving a calling execution result, integrating the calling execution result with the query statement, and sending the integrated result to the large language model platform, so that the large language model platform generates reply content.

An intelligent task configuration method based on big data SQL code computing power analysis performs computing power evaluation on sql codes to be operated, and the core lies in that a query processing process is decomposed into three key dimensions: an operation type, a data scale and processing complexity. Each dimension is quantized through well-designed parameters, and finally a unified computing power demand calculation formula is integrated. The formula not only considers a single-thread execution scene, but also particularly adds extra overhead brought by parallel processing, so that an evaluation result is closer to an actual operation environment. The SQL code is further stored in a notebook and kept as a file, the hash value of the file is calculated to serve as an identification code, a plan of an sql code running task is configured according to the authority level and the real-time computing power condition of a user, and computing power resource allocation is intelligently optimized.

This invention discloses a security chip based on a configurable lookup table. The security chip includes: at least one configurable logic block, each containing at least one configurable lookup table unit; a dynamic mask generator connected to the configurable lookup table unit, dynamically generating a mask sequence according to the current processing clock cycle; a reconfiguration controller connected to the dynamic mask generator, changing the configuration values ​​of the configurable lookup table units in real time according to a security policy; and a security execution engine, whose input is connected to the output of the configurable lookup table unit, performing secure operations based on the masked lookup results; wherein the configurable lookup table unit presents different truth table mappings for the same logic input in at least two different clock cycles. This invention enables the lookup table unit to present different truth table mappings in different clock cycles, breaking the deterministic correlation between power consumption characteristics and data, and effectively resisting side-channel attacks such as differential power analysis.

The embodiment of the invention provides an SM4 encryption and decryption circuit for realizing side channel protection. The circuit comprises a first round of key expansion module, a first encryption and decryption module, a second round of key expansion module, a second encryption and decryption module, a modular multiplication module, a first summator and a second summator, the circuit carries out masking processing on a secret key input into a first round of secret key expansion module, a secret key input into a second round of secret key expansion module, an adjustment value input into a first encryption and decryption module and to-be-encrypted or to-be-decrypted data input into a first summator through masks, and carries out operation on the to-be-encrypted or to-be-decrypted data after masking processing. And outputting the ciphertext or the plaintext. According to the circuit, a mask is introduced in an encryption / decryption process, and the mask is used for performing mask processing on points which are easily attacked in the encryption / decryption process so as to weaken and even eliminate the correlation between a key and power consumption, so that the encryption / decryption key cannot be obtained through a power consumption analysis attack technology, and the side channel protection capability and the data security are improved.

The invention discloses a computing power analysis method, computing power analysis equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining computing power resource pool information corresponding to a computing power task; based on a first optimization target, analyzing the computing power resource pool information by using a simulated annealing algorithm to obtain a plurality of candidate scheduling schemes; constructing a flow control parameter of a genetic algorithm according to the plurality of candidate scheduling schemes, and analyzing the plurality of candidate scheduling schemes by using the genetic algorithm based on a second optimization target and the flow control parameter to obtain a target scheduling scheme; and executing computing power scheduling according to the target scheduling scheme. The invention aims to improve the accuracy of computing power scheduling so as to accurately meet the computing power demand of a user.

The embodiment of the invention provides a resource allocation method, a model training method, system and device, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring historical flow data, a request priority analysis result for a user request, and a computing power analysis result for each computing power partition; and performing data analysis on the historical flow data, the request priority analysis result and the computing power analysis result through a resource allocation model, and outputting a resource allocation scheme for the user request. According to the embodiment of the invention, the advantages of the historical flow data, the request priority analysis result and the computing power analysis result can be integrated, and the resource allocation scheme is comprehensively generated by combining the features of multiple dimensions, so that the output resource allocation scheme can be globally optimized, the computing power requirement of an actual task is met, refined load balancing is realized, and the resource allocation efficiency is improved. The real-time performance and reliability of processing large-scale data are improved, resource idleness is reduced, and the resource utilization rate is increased.

The application provides a line loss analysis method and system based on carbon flow tracking, and belongs to the technical field of power data anomaly analysis. CE = CE in ‑ CE o , CE The total loss carbon amount of the node i ; CE in The total input carbon amount of the node i ; CE o The total output carbon amount of the node i ; an XGBoost model for carbon amount anomaly screening is constructed based on a gradient decision tree XGBoost, and a target function of the model; each node in a network topology is analyzed based on the XGBoost model, and a carbon amount anomaly node is predicted based on the power and carbon emission density of each node; each user corresponding to the carbon amount anomaly node is subjected to power analysis, the power analysis includes high-voltage user abnormal power analysis and low-voltage user abnormal power analysis, and the type of abnormal situation existing in the user is obtained.

The invention relates to a multi-party security sharing method and device under power data flow, computer equipment, a computer readable storage medium and a computer program product, and is applied to a first participant, and the method comprises the steps: constructing the calculation logic of power analysis functions, needing to participate in calculation, of the first participant and a second participant into a confusion circuit, issuing a confusion table corresponding to the confusion circuit and the first replacement tag to the second participant; each of the first participant and the second participant holds a part of secret components of the first participant and the second participant; wherein the second participant is used for performing decryption based on the confusion table according to the second replacement tag and the first replacement tag to obtain a decryption result replacement tag; and replacing the mapping relation of the label in the confusion table according to the received decryption result, determining the decryption result, and taking the decryption result as the power analysis result of the first participant and the second participant. By adopting the method, the security of the electric power analysis data of the participant can be improved.

The application provides a chip security key protection method and system against side channel attacks, relates to the technical field of chips, and comprises the following steps: obtaining a key operation request, performing address space mapping based on a dynamic mapping rule to obtain encrypted key data and decryption, fragmenting key intermediate data and allocating independent processing channels for decoupling operation, injecting a time-varying noise signal to mask the association between the key and physical characteristics, and finally recombining and reversely converting to generate a final key output. The application effectively resists side channel attacks such as power analysis and electromagnetic analysis, and improves the security of the key storage and operation process.

The invention relates to a stator core fault standard local excitation temperature rise measurement method and system, computer equipment, a storage medium and a computer program product. The method is applied to a temperature rise measurement system, and comprises the following steps: after a standard local excitation frequency is applied to a fault tooth part, calculating first flux density power according to the reading of a first power analyzer and the reading of a second power analyzer; calculating standard flux density power based on the first flux density power, the standard local excitation frequency and the target local excitation frequency; after the target local excitation frequency is applied to the fault tooth part, second flux density power is calculated according to the reading of the first power analyzer and the reading of the second power analyzer; and when the second flux density power is equal to the standard flux density power, carrying out temperature rise measurement for preset time to obtain the standard local excitation temperature rise of the fault tooth part. By adopting the method, the standard local excitation temperature rise of the stator core fault can be measured under the condition that the standard excitation frequency is changed.

The invention discloses a power load characteristic multi-scale analysis method based on multi-source data and dynamic clustering, and relates to the technical field of power analysis, and the method comprises the following steps: fusing power, production, policy, environment and other multi-source heterogeneous data, and carrying out space-time alignment; cleaning the load data and identifying a characteristic time period influenced by the specific factor according to the auxiliary information; decomposing the load curve and recombining the load curve into components with definite physical significance according to auxiliary data such as a production time sequence; performing context-fused dynamic clustering and mode correlation analysis on each load component according to the time scale; and extracting refined characteristics and adjustable potential indexes, and generating a visual resource map capable of supporting scheduling. According to the method, by introducing a polymorphic load component separation and multi-scale collaborative clustering mechanism, cause decoupling and refined mode recognition of complex industrial loads can be realized, and the problems that in an existing method, mode confusion is likely to occur, and load characteristics under different driving mechanisms are difficult to distinguish are solved.

The present invention relates to a resource allocation method and system based on a virtual power plant, belonging to the field of power technology. The method includes: respectively obtaining the power basic data corresponding to different historical time periods in the area covered by the virtual power plant; analyzing the power basic data to obtain the power analysis data corresponding to the power basic data in different historical time periods; constructing an initial power prediction model according to the power analysis data; obtaining the fluctuation coefficient corresponding to the power prediction model to correct the initial power prediction model and obtain a target power prediction model; performing supply-demand balance prediction based on the target power prediction model and the power data corresponding to the current time period, and allocating power resources according to the prediction result. The present invention can further effectively schedule and allocate power resources.

The present invention provides a fire detection method and system based on power data analysis. After obtaining a set of power equipment to be analyzed and a historical abnormal monitoring data set of each power equipment to be analyzed in the set of power equipment to be analyzed, one or more abnormal operation events are determined in the historical abnormal monitoring data set. According to the historical abnormal monitoring data set, the power equipment to be analyzed and the abnormal operation events are used as graph vertices to generate a power analysis graph. The power analysis graph is mined for characterization information to obtain a power equipment characterization vector of the power equipment to be analyzed. Based on the power equipment characterization vector, the power equipment to be analyzed is grouped to obtain one or more pending power equipment groups, and a target power equipment group is determined in the pending power equipment groups. The present application can improve the accuracy of the determined power equipment groups, help focus on monitoring the equipment in the group during fire monitoring, and make the fire monitoring more targeted.

The invention provides a power anti-violation recognition computing power dynamic allocation method and system based on concurrent perception, and relates to the technical field of computing power allocation, and the method comprises the steps: carrying out the interaction of concurrent perception task information, carrying out the execution parameter, processing amount and computing power analysis of the perception task information, and obtaining the perception task computing power description information; obtaining the concurrent processing amount of the system, and carrying out multi-dimensional analysis on the concurrent processing amount of the system; according to the concurrency constraint parameters, computing power surface layer magnitude distribution is carried out on the sensing task computing power description information; and carrying out concurrent processing optimization based on a surface level matching relationship, obtaining a final computing power distribution strategy by taking maximization of a perception task completion effect and minimization of a concurrent processing amount as targets, and carrying out computing power configuration of power violation detection. According to the method and the device, the technical problem of unreasonable power anti-violation recognition calculation power dynamic distribution in the prior art can be solved, and the technical effect of improving the power anti-violation recognition calculation power dynamic distribution reasonability is achieved.

The utility model relates to the technical field of instrument analysis, and discloses a multifunctional wavelet transform electric power analysis all-in-one machine which comprises a tool box, the outer side of the tool box is fixedly connected with a connecting block, the interior of the connecting block is rotatably connected with a rotating plate, the interior of the rotating plate is fixedly connected with a second spring, and the second spring is fixedly connected with a second motor. A cover is rotatably connected to the outer side of the tool box, a second sealing gasket is fixedly connected to the inner side of the cover, a first sealing gasket is fixedly connected to the top of the tool box, a limiting assembly used for limiting a wire is fixedly connected to the interior of the cover, and the limiting assembly comprises two fixing blocks. The inner sides of the two fixing blocks are fixedly connected to the interior of the cover. According to the utility model, dust is prevented from entering, so that the service life of detection equipment can be prolonged, equipment faults and maintenance cost caused by dust are reduced, electronic elements on the detection board are short-circuited or poor in contact due to dust, and the risk can be effectively reduced by dustproof measures.