68 results about "Electricity" patented technology

A chip package includes a housing, one or more electrical connections coupled to an exterior of the housing, a photonic integrated circuit, and a scanning unit. Both the photonic integrated circuit and the scanning unit are disposed within the housing. The photonic integrated circuit has at least one waveguide designed to guide a beam of light. The scanning unit is designed to laterally scan the beam of light across a focal plane outside of the housing. The scanning unit is aligned with the photonic integrated circuit such that the beam of light is coupled between the photonic integrated circuit and the scanning unit.

The invention provides a test method and device for a motor active short-circuit relay, and a storage medium. The method includes the steps: performing the zero speed detection of a motor after a motor controller completes a low-voltage power-on operation, wherein when the motor controller is powered down, a first relay and a second relay are both switched on; controlling the second relay to be turned on if the motor passes the zero speed detection, and injecting a first excitation signal at the control ends of a first phase winding and a third phase winding through the motor controller; performing injection detection of the first excitation signal to determine whether the first excitation signal is successfully injected or not; controlling the first relay to be turned on if the first excitation signal is successfully injected, and obtaining a voltage across two ends of the first relay; and judging whether the power-on test of the first relay is successful or not according to the voltage across two ends of the first relay. The method can realize the turn-on test of the short-circuit relay, and ensures the reliability of the active short-circuit function of the vehicle motor duringdriving.

The invention discloses a disperse relaxation factor tidal current model based minimum electricity generating cost incremental quantity obtaining method. When a minimum electricity generating cost incremental quantity model only containing one balance node tidal current model is solved, the total demand unit change of a system is fixed on a balance node, and an obtained lambda, namely electricity generating cost minimum incremental quantity cannot reflect actual operation of the system in most cases during total demand unit change of a system. The disperse relaxation factor tidal current model based minimum electricity generating cost incremental quantity obtaining method adopts solves the provided model by means of an original antithesis interior point method, and the lambda of the system is controllable according to selected disperse relaxing factors.

The invention relates to a simple and reliable touch control switch. The simple and reliable touch control switch consists of a 12-V direct-current power supply, a one-way silicon controlled rectifier 1 (SCR1) trigger circuit, a one-way silicon controlled rectifier 2 (SCR2) trigger circuit, a relay control circuit and a load RL circuit. A conventional mechanical self-locking rocker switch or a toggle switch has the defects that actions are mechanical, operating actions are different because of persons, a contact is easy to jitter, a contact is easy to ablate, abrade and the like. The touch control switch is realized by only using a small number of common components and two touch metal pieces, namely an on-load circuit is switched on by touching a touch electrode once, and an off-load circuit is switched off by touching the touch electrode once. The overall touch control switch is made of discrete components, and strong electricity can be effectively isolated from weak electricity through a relay, so that the power supply of a load can use alternating current and also can use direct current. Therefore, the touch control switch has a certain function in the field of household appliances, industrial automation, and optical, mechanical and electrical integration.

The invention provides a multifunctional cycle power generation system and method by a gravity lever. The system comprises a supporting rack and a balance weight arranged on the supporting rack and slidably matched with the supporting rack, wherein engines are arranged on one side or two sides of the balance weight; the input shaft of each engine is connected to a transmission shaft; two or more single-directional clutches are fixedly connected to the middle of the transmission shaft; the locking directions of the single-directional clutches are same, gears are fixed to the peripheries of thesingle-directional clutches, and a rack matched with each gear to drive the transmission shaft to rotate is arranged next to each single-directional clutch; at least one pair of racks is arranged on the upper part and the lower part, separately, the racks and the balance weight which reciprocates are in driving movement and the balance weight is arranged on the supporting rack to swing through a lifting mechanism in a reciprocating manner. As the balance weight which swings in the reciprocating manner pulls the racks to drive the transmission shaft to rotate in the single direction, multiple engines can be driven synchronously to generate electricity synchronously, and the power needed by a supporting rack lifting device is alleviated greatly by means of a swing mode like to a lever, so that the energy sources are saved and the power generation efficiency is improved greatly.

The invention provides a high-power high-reliability voltage retainer used for an automobile starting and stopping system. The high-power high-reliability voltage retainer is higher in power and reliability and smaller in size and ripple. The high-power high-reliability voltage retainer comprises a through circuit module (1), a booster circuit module (2), a reversed polarity circuit module (3), a CAN transceiver (4), a CPU (5), a DSP (digital signal processor) (6) and a battery. A battery input port and a battery output port are formed in the through circuit module (1), a battery input port and a battery output port are formed in the booster circuit module (2), and the reversed polarity circuit module (3) is electrically connected with the booster circuit module (2); the CAN transceiver (4), the CPU (5) and the DSP (6) are sequentially and electrically connected; the CPU (5) is electrically connected with the through circuit module (1), and the DSP (6) is electrically connected with the through circuit module (1); the DSP (6) is electrically connected with the booster circuit module (2).

The invention belongs to the technical field of geological engineering monitoring and multi-physics field intelligent sensing, and discloses a separation grouting slurry migration monitoring method and system, and the method comprises the steps: constructing an acoustics-optics-electricity-electromagnetism multi-physics field coupling monitoring system; a grouting hole and a monitoring drilling hole array are arranged in a target stratum, a distributed optical fiber sensor, a drilling hole ultrasonic imager, a resistivity measuring device and a transient electromagnetic detection device are installed, and a three-dimensional monitoring network is formed; synchronously acquiring sound field, light field, electric field and magnetic field data; four-field monitoring data and overlying strata multi-parameter observation data are fused, a coal seam mining three-dimensional overlying strata separation model is constructed, and a slurry diffusion three-dimensional visualization model is generated through numerical simulation; and based on the four-field monitoring response difference of the separation space before and after grouting, in combination with a monitoring frequency domain correction model of multiple indexes of grout diffusion, the diffusion range, compactness and filling effect of the grouting grout are evaluated, and abnormal early warning is triggered.

The invention discloses a photovoltaic module early-stage fault early-warning method based on electric-thermal characteristic joint analysis, and belongs to the field of photovoltaic module early-stage fault early-warning, and the method comprises the steps: segmenting an irregular high-temperature or low-temperature region according to a temperature distribution image through employing a data segmentation algorithm, and extracting the boundary contour and area quantification characteristics of the abnormal region; performing joint feature vector construction by using the abnormal region boundary contour and area quantization feature and the current and voltage fluctuation amplitude in the electrical signal time sequence to obtain the comprehensive fault diagnosis vector; according to the preliminary fault classification label, extracting a temperature electrical corresponding association mode of a similar fault case from a historical fault diagnosis database, and obtaining an enhanced fault corresponding relation model; and generating a system risk assessment report by adopting the final fault diagnosis classification result, triggering a corresponding alarm mechanism, and judging the overall risk level of the renewable energy system.

The present invention provides a method, device and storage medium for analyzing electricity theft, and relates to the technical field of safe electricity use. The present invention collects and analyzes historical and current current data, uses a deep neural network to train an electricity theft prediction model, and realizes real-time detection of abnormal power nodes. The first suspicious power node is identified according to the power loss deviation of the abnormal node, and the second suspicious node is screened out by the loss deviation of its adjacent nodes. A normal power value interval is set, and nodes not within this interval are identified as electricity theft risk nodes. Combined with historical electricity use behavior and temperature factors, an electricity calibration coefficient is generated, and the upper and lower thresholds of the power consumption of electricity theft are corrected. By comparing the actual loss deviation with the corrected threshold, the system can accurately determine the type of electricity theft. The present invention realizes accurate detection of electricity theft behavior and efficient management of the power system by constructing a topological network on the electricity consumption side and applying a deep neural network prediction model.

The invention provides a porcelain insulator quality evaluation system based on multi-physics field coupling analysis, and relates to the technical field of rod-column porcelain insulator data processing.The porcelain insulator quality evaluation system adopts an electricity-heat-force multi-physics field joint simulation model to comprehensively reflect the heat accumulation and stress deformation effect caused by electric field concentration; an electric field concentration coefficient, a temperature rise trend coefficient and a stress gradient coefficient at a sharp point can be accurately captured, and degradation path prediction in a real operation environment is formed; according to the technology, the thread coupling abnormal index F alpha i of the ith rod-type column porcelain insulator is constructed, whether the thread structure has the risk of partial discharge, flashover or breakdown or not can be dynamically evaluated, and by introducing equivalent dielectric layer simulation of various environmental media, the simulation of the influence of various pollutants, attachments and climates on electric field concentration in reality is promoted; quantitative evaluation of the corona discharge risk of the sharp point is realized, and the reliability of the structural design of the insulator is promoted and improved.

The invention provides a heat preservation box capable of rapidly replacing a cold source. The heat preservation box comprises a box body and cold storage plates; the box body is provided with a box door, a cold storage area, a control area and a cold source area are arranged in the box body, the cold storage area is separated from the control area and the cold source area through a middle partition board, and the control area and the cold source area are separated through a side partition board; a backflow partition board is arranged in the cold source area, the cold source area is divided into a cold source cavity and a backflow cavity through the backflow partition plate, the cold source cavity communicates with the lower portion of the backflow cavity, a plurality layers of cold sourceframes used for containing the cold storage plates are arranged in the cold source cavity, and the cold source cavity communicates with the cold storage area through an air inlet channel; an exhaustfan is arranged on the air inlet channel, the cold storage area communicates with the backflow cavity through a backflow channel, and an air blower is arranged on the backflow channel; a control module is arranged in the control area, a first temperature sensor is arranged in the cold storage area, the exhaust fan, the air blower, and the first temperature sensor are electrically connected with the control module; and box body, the middle partition plate and the side partition plate are all made of vacuum composite plates. According to the heat preservation box, the replacement efficiency of the cold source is improved.

The invention discloses an abnormal Transform model-based electricity larceny detection method and device and a storage medium, and relates to the field of electric power system electricity larceny detection, and the abnormal Transform model-based electricity larceny detection method comprises the following steps: capturing complex correlation among different time points in sequence data by using a self-attention mechanism of a Transform model; the characteristics of the normal time point and the abnormal time point are effectively distinguished by learning the relevance difference of each time point, the accuracy of electricity stealing user identification is improved, and the misjudgment rate is reduced. The method comprises the following steps: S1, obtaining user multi-dimensional power consumption time series data, preprocessing the data, and constructing a sliding window sample; s2, constructing an electricity larceny detection model based on an Angle-Attention mechanism, wherein the electricity larceny detection model comprises a prior correlation branch and a sequence correlation branch; s3, quantifying the time dependence deviation of the power consumption behavior by taking the relevance difference as an anomaly judgment standard; s4, training the model by using a minimax optimization strategy, and amplifying the relevance difference between normal electricity utilization and electricity stealing electricity utilization; and S5, performing electricity larceny risk prediction and model performance evaluation through the joint score of the relevance difference and the reconstruction error.

The invention belongs to the field of life, and particularly relates to a thermos bottle with visible water storage capacity. The thermos bottle comprises a shell and an inner container arranged inside the shell. A base capable of vertically moving is mounted at the bottom of the shell, an elastic airbag is fixedly connected between the upper end of the base and the lower end of the inner container, a vertical strip opening is hermetically connected onto the side wall of the shell, a transparent tube is fixedly connected into the strip opening, the lower end of the transparent tube is communicated with the elastic airbag, and a piston block is hermetically connected into the transparent tube. The quantity of water in the thermos bottle can be known by observing the position of a piston block, a piezoelectric ceramic is used for power generation when water is injected, an LED (light-emitting diode) lamp in the piston block can emit light, the quantity of the water in the thermos bottlecan be known conveniently by people, and scalding is avoided. In addition, by the aid of an annular air cushion, when the thermos bottle falls, a good buffering function can be achieved, and impact onthe inner container is reduced.

The invention discloses an abnormal power consumption detection method and system based on multi-scale neighborhood density and a storage medium. The method comprises the following steps: S1, preprocessing original data; s2, carrying out adaptive granular ball representation on the original user sample set; s3, calculating a fuzzy relation matrix among the pellets; s4, calculating the fuzzy density of the pellets; s5, calculating a sample neighborhood density based on the Chebyshev boundary; s6, multi-scale neighborhood abnormal factors of all samples are calculated; s7, judging abnormal users in the power consumption data through threshold comparison; according to the method, the limitation of a current density-based anomaly detection method in adaption to the data characteristics of the electric power Internet of Things is solved, and the capability of depicting the multi-scale neighborhood density by the unsupervised learning model is improved.

The invention discloses an electric energy prediction method and system based on a multi-factor correlation coefficient, a storage medium and equipment. The method comprises the following steps: acquiring historical electricity consumption, temperature and date type of a certain region; dividing upper and lower limits of temperature intervals according to temperature characteristics of each season, and calculating a correlation coefficient of historical electricity consumption and temperature of each temperature interval based on a percentile method; taking the historical electricity consumption of the workday as a reference value, performing normalization processing on the historical electricity consumption of each date type based on the reference value, and calculating a correlation coefficient between the historical electricity consumption of each date type and the date type; according to the method, the correlation coefficient of the historical electricity consumption and the temperature and the correlation coefficient of the historical electricity consumption and the date type are used as features and are input into a regional electricity quantity prediction model XG-Boost, the electric energy in a certain time period in the future is predicted, the electric energy prediction precision is effectively improved, and accurate guidance is provided for power supply of a power grid.

The invention relates to a knitting machine and particularly relates to a controller for a yarn changer. The controller comprises a controller skeleton arranged on an outer plate of a yarn changer main body, a transfer bar, a digital circuit and a drive mechanism for driving the transfer bar to carry out linear motion, wherein the transfer bar is connected with the controller skeleton in a sliding manner; the transfer bar drives a yarn clamping and shearing device of the yarn changer to complete yarn selecting and yarn guiding actions while carrying out linear motion; the digital circuit and the drive mechanism are arranged on the controller skeleton separately; and the digital circuit is electrically connected with a drive mechanism. External drive is not needed, and the drive mechanism can be controlled to drive the transfer bar to carry out linear motion by virtue of the digital circuit, so that the transfer bar drives the yarn clamping and shearing device to complete the yarn selecting and yarn guiding actions, and independent control on yarn selecting and yarn guiding of the yarn changer is achieved; and the controller is small in impact vibration, high in applicability, stable in working, simple in structure and beneficial to improvement of the production stability of the knitting machine.

The invention provides a near-electricity risk early warning method and system based on electric field and magnetic field distribution. The method comprises the following steps: acquiring three-dimensional space electromagnetic field data in a target scene every first preset time; preprocessing the three-dimensional space electromagnetic field data, and constructing an electric field distribution model and a magnetic field distribution model according to the preprocessed electric field data and magnetic field data; respectively extracting a field intensity gradient amplitude and an electromagnetic field energy density from the electric field distribution model and the magnetic field distribution model, calculating a risk index of each space point according to the field intensity gradient amplitude and the electromagnetic field energy density, and dividing a risk area according to the risk indexes; and acquiring the current position of the operator, judging whether the current position is in the risk area, and if so, sending out early warning information. According to the method, the near-electricity risk can be comprehensively and accurately evaluated, the accuracy and effectiveness of early warning are improved, and the safety of operators is effectively guaranteed.

The invention discloses an electromagnetic heating system, and a method and a device for detecting the thermistor of the electromagnetic heating system. The method comprises the following steps of: recording the temperature value obtained by the thermistor as a first temperature value when the electromagnetic heating system is powered on; recording the temperature value obtained by the thermistoras a second temperature value when the electromagnetic heating system is in a standby state; obtaining the maximum temperature value and the minimum temperature value of the temperature value obtainedby the thermistor in a continuously preset second preset time every other first preset time during the heating process of the electromagnetic heating system; and judging whether the thermistor is ina failure state or not according to the first temperature value and / or the second temperature value, the maximum temperature value, and the minimum temperature value. The method for detecting the thermistor of the electromagnetic heating system judges whether the thermistor fails or not according to the static temperature values at the time of power-on and standby and the dynamic temperature valuein the heating process, so that the accuracy and the reliability of the judgment are greatly improved, and the safety of the system is improved.

The present application belongs to the technical field of electrical equipment state monitoring and fault diagnosis, and particularly relates to an automatic electricity stealing identification method based on data mining technology.The present application constructs an "electric-thermal-acoustic" multi-physical field coupling benchmark physical model through a BP neural network, mines the dynamic correlation law between the current and the temperature, acoustic emission physical quantities through the benchmark physical model, effectively captures the multi-dimensional physical quantity abnormal coupling characteristics caused by electricity stealing behavior, and through the model residual blood sequence output by the benchmark physical model, the deviation degree between the temperature measured value and the acoustic emission measured value of the actual collected power user equipment under the current power utilization state and the predicted value output by the BP neural network can be reflected, so as to strengthen the identification accuracy of electricity stealing behavior.

The present invention relates to the technical field of electric energy collection in distribution networks, and in particular to an electric energy collection method for distribution networks. The method comprises the following steps: collecting electrical data of the distribution network and collecting electromagnetic field data of the distribution network transmission lines and transformers in operation, respectively obtaining transmission electromagnetic data and electromagnetic transformer data; performing three-dimensional surface graph conversion according to the transmission electromagnetic data and performing electromagnetic fluctuation fault identification to obtain electromagnetic fluctuation fault data; performing fluctuation anomaly identification on the electromagnetic fluctuation fault data and performing abnormal voltage fluctuation interval calculation and impact effect analysis and performing performance loss analysis on the distribution network transformer and performing automated electromagnetic abnormality analysis mode design to obtain an automated electromagnetic analysis module; performing electric energy loss compensation design on the automated electromagnetic analysis module to obtain an automated electric energy collection and analysis module. The present invention enables electric energy collection to accurately detect abnormal conditions by improving the electric energy collection technology.

The invention discloses a high-energy-load park electricity consumption prediction method, and the method comprises the steps: obtaining a to-be-predicted time period which comprises a plurality of time points; collecting first data corresponding to the electricity consumption of the high-energy-load park in a historical time sequence, wherein the first data comprises electricity consumption data and a related influence factor sequence; according to the first data and a historical time sequence, performing normalization processing to generate a sample set, and training the sample set to obtain a power consumption prediction model of the high-energy-load park; and inputting the to-be-tested time period into the high-energy-load park electricity consumption prediction model to obtain the high-energy-load park electricity consumption at each time point in the to-be-predicted time period. According to the prediction method provided by the invention, the prediction accuracy is improved, the power utilization safety of the park is ensured, and the situation of power shortage or excess is avoided.

The present invention discloses a multi-factor short-term electricity forecasting method and device. By aligning other factor sequences with historical electricity sequences, effective information in other factors can be fully extracted, thereby improving electricity forecasting accuracy and reducing model training difficulty. At the same time, a variational modal decomposition algorithm is used to decompose the historical electricity sequence, which is more conducive to the model extracting the relationship between modal components and the electricity to be predicted. The factor matrix is ​​obtained by splicing the modal component matrix with other factor matrices, and a mapping relationship between the factor matrix, the modal component matrix and the future electricity sequence is constructed, thereby effectively capturing the correlation between the historical electricity sequence and other factor sequences and the historical electricity modal components, thereby reducing the difficulty of model establishment and improving electricity forecasting accuracy.

The present invention relates to the technical field of electricity consumption data analysis, and specifically discloses a method for judging abnormal electricity consumption in a building based on an intelligent power distribution system, the method comprising: S1: real-time monitoring of environmental information, electricity consumption information and information of electrical equipment in the building; S2: calculating the overall electricity consumption risk value of the building according to the collected information; S3: judging whether the overall electricity consumption risk value of the building exceeds a preset overall electricity consumption risk value; by respectively calculating the electricity consumption risk value of each electrical equipment and calculating the overall electricity consumption risk value of the building, not only can the abnormality monitoring of electricity consumption be performed within a smaller range, thereby improving the sensitivity of monitoring and thus improving the accuracy of the judgment result, but also by respectively calculating the electricity consumption risk value of each electrical equipment, if an abnormal electricity consumption occurs, the source of the abnormal electricity consumption can be checked, thereby improving the inspection and maintenance efficiency of maintenance personnel.

The present invention discloses a method and system for detecting electricity theft, the method comprising: obtaining electricity consumption data of a user to be detected on the same day; wherein the electricity consumption data comprises: an electricity consumption time series, a first electricity consumption statistical feature and an electricity parameter time series; calculating the similarity between the electricity consumption data of the user to be detected and the reference electricity consumption data of the electricity consumption mode category to which it belongs; when the similarity exceeds a preset range, determining that the user to be detected is an abnormal electricity user; based on the electricity parameter time series of the abnormal electricity user, obtaining characteristic data and operation data of the electrical equipment; inputting the characteristic data into an equipment identification model to obtain the type of the electrical equipment; and determining whether the abnormal electricity user has electricity theft behavior by comparing the operation data with the reference operation data of the type of the electrical equipment. The present invention can effectively improve the accuracy of detecting the user's electricity theft behavior by making multi-level judgments on the user's electricity consumption behavior through multi-dimensional electricity consumption data.

The invention relates to the technical field of electric power measurement, in particular to a method and a device for identifying the electric quantity of an electric energy substitution project, and the method comprises the steps: firstly obtaining a first electricity consumption data queue of the substitution project and a first factor influencing the electricity consumption of the substitution project; then selecting a dimension first conversion matrix according to the first factor, and performing dimension reduction and reproduction on the first electricity consumption data queue through the first dimension conversion matrix to obtain a second electricity consumption data queue; calculating the deviation between the first power consumption data queue and the second power consumption data queue, and standardizing the obtained deviation to obtain a first deviation queue; and finally, inputting the first deviation queue into a deviation analysis model, and determining the credibility of the first electricity consumption data queue according to the output of the deviation analysis model. The deviation analysis model is simple, so that resources consumed in the construction process and the application process are few, and the method is suitable for popularization.

The invention discloses an electricity consumption prediction method capable of actively correcting influence factors and a related device, and belongs to the technical field of electricity consumption prediction. The method comprises the following steps: analyzing power consumption influence factors of a target area to obtain a plurality of main influence factors; correcting the data of the plurality of main influence factors, and inputting the data into a pre-screened annual electricity consumption prediction model to obtain an annual electricity consumption prediction result of the target area; and obtaining historical monthly electricity consumption data, performing time sequence preprocessing on the historical monthly electricity consumption data, and inputting the historical monthly electricity consumption data into a pre-screened monthly electricity consumption prediction model to obtain a monthly electricity consumption prediction result of the target area. According to the method, main influence factors of the electricity consumption of the target area are deeply analyzed, and the data of the factors are accurately corrected, so that the prediction accuracy of the annual electricity consumption can be remarkably improved; the historical monthly electricity consumption data are preprocessed through the time sequence, and the prediction accuracy of the monthly electricity consumption can be greatly improved.

The present application belongs to the technical field of microgrid stability analysis, and particularly relates to a stability analysis and early warning method for a building hydrogen-electricity coupling direct current microgrid, which comprises the following steps: obtaining electromagnetic transient state variables of an electrical subsystem in the direct current microgrid and electrochemical reaction state variables of a hydrogen energy subsystem, and constructing a unified feature matrix of time scale dynamic characteristics; extracting stability features, projecting the stability features to a low-dimensional space through nonlinear mapping, forming a representation fingerprint, and calculating an instantaneous stability margin under a current state; performing time sequence deduction, calculating a stability evolution trajectory within a future time window, and generating a predicted offset vector of an offset direction and amplitude; when the predicted offset vector exceeds the instantaneous stability margin, taking minimization of the predicted offset vector as an optimization objective, and generating a collaborative control strategy for collaborative regulation of a hydrogen energy subsystem operating point and an electrical subsystem operating point. The present application can realize perception and early warning of instability risks, and guarantee stable and reliable operation of the microgrid.

The invention provides an electric quantity theft detection method and system based on an ultrasonic vibration spectrum, and belongs to the technical field of electric quantity detection, and the method comprises the steps: constructing a correlation model, analyzing a target region feature and a user portrait, and presetting a feature threshold value of an electricity theft type and an abnormal triggering condition; capturing an ultrasonic signal, calling electricity load of an electricity meter and running state data of equipment, and carrying out time calibration and data fusion; converting the ultrasonic signal into a frequency spectrum feature vector, and screening out double abnormal targets with an ultrasonic feature anomaly and a behavior track anomaly; signal enhanced acquisition, power utilization data time sequence reconstruction and equipment state remote diagnosis are started for the double abnormal targets, and the electricity stealing behavior is confirmed; and outputting a targeted treatment instruction, and updating the feature threshold value and the abnormal triggering condition. According to the electric quantity theft detection method based on the ultrasonic vibration spectrum provided by the invention, the accuracy, real-time performance and high efficiency of electric quantity theft detection are realized through an active identification mode of accurate screening, multi-dimensional verification, targeted treatment and dynamic optimization.

The present application relates to the field of power system safety, and particularly relates to a high-voltage sensitive user electricity hidden danger identification method, system, device and medium, the method comprises obtaining a plurality of disaster-causing factors based on the electricity data of high-voltage sensitive users, and constructing a multi-dimensional factor vector with the plurality of disaster-causing factors as elements; obtaining a plurality of electricity hidden danger types based on historical electricity hidden danger and fault data, and constructing a hidden danger risk vector with the plurality of electricity hidden danger types as elements; using a multi-dimensional mapping function to establish a hidden danger risk mapping model of the multi-dimensional factor vector and the hidden danger risk vector, to obtain the risk values of various hidden dangers under the influence of the multi-dimensional factor vector; weighting the risk values of various hidden dangers according to the hidden danger severity weight to obtain a comprehensive risk index; using the comprehensive risk index to identify and warn the electricity hidden danger of high-voltage sensitive users, which significantly improves the scientificity and accuracy of identification.

The present invention relates to a method and medium for predicting electric quantity based on component decomposition and correction, and relates to the field of electric quantity prediction. The method comprises: constructing a daily electric quantity data decomposition model; correcting the daily average temperature of historical days according to an average temperature correction model; obtaining a trend electric quantity prediction value and a period electric quantity prediction value through a trend electric quantity model and a period electric quantity model; obtaining the meteorological electric quantity of historical days through a meteorological electric quantity model, performing segmented fitting on the meteorological electric quantity of historical days, and obtaining a meteorological electric quantity curve; correcting the average temperature of a day to be predicted according to an average temperature correction model; predicting the meteorological electric quantity according to the daily average temperature to be predicted and the meteorological electric quantity curve; and obtaining an electric quantity prediction value according to the trend electric quantity, the period electric quantity and the meteorological electric quantity. The present invention fully considers the characteristics of electric quantity per day, the limit temperature, the maximum cumulative days and the cumulative effect coefficient and other factors of electric quantity per day through the electric quantity per day decomposition model and the temperature correction model, and improves the accuracy of electric quantity prediction.