92 results about "Power matrix" patented technology

The invention discloses an industrial and commercial park electrical load power prediction method, and relates to the technical field of electrical load prediction, and the method comprises the steps: obtaining park electrical equipment power data, and obtaining a standardized time sequence power matrix through normalization preprocessing; performing wavelet denoising and empirical mode decomposition on the power matrix, and separating a power trend component from a fluctuation component; constructing a trend prediction sub-model by using a long-short term memory network, and constructing a fluctuation prediction sub-model by using a seasonal ARIMA model; carrying out weighted combination on the two sub-models through an integrated learning method, and constructing a comprehensive prediction model; and finally, based on the historical prediction error, setting an adaptive threshold value for constraint processing, and outputting a final prediction result. According to the method, power data features are adaptively separated through a multi-level decomposition strategy, a dual-model collaborative prediction mechanism is adopted, environmental factors are introduced to improve prediction precision, a self-adaptive correction mechanism is designed to ensure result reliability, and accuracy and stability of park power load prediction are remarkably improved.

The invention discloses a power distribution network fault section positioning detection method based on mixture of Transform and a particle swarm and a related device. The method comprises the following steps: constructing and initializing an upstream section matrix, a downstream section matrix, an upstream power supply matrix and a downstream power supply matrix at the current moment; reading operation parameters of the regional power distribution network and configuration data of the measurement equipment; carrying out Transform model training by using the operation parameters and the configuration data of the measurement equipment; current fault information is extracted from the feeder terminal unit, and a Transform model is called to complement missing data; and constructing a target function based on the constructed matrix and data obtained after completion of the missing data, and solving the target function to locate the position of the fault section. According to the method, the intelligent algorithm is introduced on the basis of original FTU configuration of the power distribution network, missing data is supplemented, the particle swarm optimization is used for solving, and the calculation precision and robustness of power distribution network fault positioning are improved.

The invention relates to an intelligent available link prediction and optimal selection method for unmanned cluster anti-interference communication. The method comprises the following steps: acquiring a multi-channel interference power matrix; an LSTM-KAN interference prediction model is established; predicting channel interference power of a plurality of time slots in the future by using an LSTM-KAN interference prediction model; channel selection is modeled as a dual-objective optimization model for minimizing channel switching times and minimizing channel average interference power; and solving the dual-objective optimization model based on a classification sorting genetic algorithm, improving a crossover variation strategy and an elitist retention strategy to increase the generation probability of a low-channel switching frequency solution, and retaining the low-channel switching frequency solution as much as possible in an iteration process, and finally generating an optimized channel switching and interference power solution. Compared with the prior art, the method has the advantages of remarkably reducing the channel switching times, improving the throughput and reducing the packet loss rate, and has important application value in resisting unmanned trunking communication in a strong electromagnetic interference environment.

The invention discloses a genetic algorithm-based battery module heat dissipation channel optimization design method. The method comprises the following steps of S1, acquiring operation data of a battery module under a plurality of dynamic load working conditions; s2, constructing an initial heat dissipation channel topological structure model based on a physical packaging structure of the battery module and a flow channel layout constraint; s3, setting a multi-objective optimization objective function set; s4, performing heat-fluid-solid coupling simulation on each heat dissipation channel structure candidate body in the first generation population structure by taking the heat generation power matrix as a heat source boundary condition; s5, performing fitness calculation on the obtained heat-flow performance index set by using a multi-objective genetic algorithm based on Pareto optimal sorting; s6, repeating S4 and S5 until a preset convergence criterion or an upper limit of the number of iterations is met; and S7, carrying out comprehensive performance verification and manufacturing feasibility evaluation on the heat dissipation channel topological structure model. The method has the advantages of being high in global search capability, high in heat source response authenticity and good in structural expression diversity.

The invention provides a method, a medium and a system for rapidly judging a damaged or defective lithium battery, and belongs to the technical field of product detection. And multi-dimensional characteristics such as a temperature change matrix, a voltage matrix, a power matrix, a resistance matrix, a cross-correlation index matrix, a harmonic change matrix and a reverse charging and discharging power matrix are constructed, and the battery performance is comprehensively represented. Thermal imaging is adopted to obtain surface temperature distribution, a direct-current internal resistance tester and an alternating-current impedance spectroscopy analyzer are utilized to measure internal resistance, and Fourier transform is carried out to analyze voltage harmonic characteristics. And analyzing the collected multi-dimensional matrix data based on a deep neural network model of a multi-head attention mechanism, and outputting a battery health state score and a damage type. According to the method, electrochemical performance and thermodynamic characteristics of the battery are comprehensively considered, and rapid and accurate evaluation of the lithium battery health state is realized.

The invention relates to a district and county level power industry carbon emission prediction method based on power carbon traceability. The method comprises the following steps: carrying out user side load prediction in different areas and time periods; carrying out distributed power supply output prediction in different regions and different time periods; performing energy storage charging and discharging scheme prediction and source side carbon emission factor prediction; carrying out load flow calculation on each node in different regions and time periods; constructing a power matrix; measuring and calculating a single-time-point carbon emission index; and measuring and calculating the long-time scale carbon emission. According to the method, prediction data of user power utilization, clean energy power generation and energy storage charging and discharging are integrated, the carbon emission measurement and calculation requirements of different time periods and regions can be met, the applicability and flexibility of the model are enhanced, accurate prediction of the carbon emission in the whole power distribution network load storage link can be achieved, and the accuracy of carbon emission measurement and calculation is improved; the method particularly focuses on the influence of policy changes of an electric vehicle, a distributed power supply and the like on the carbon emission, so that the prediction result can reflect actual changes brought by the policy changes.

According to the channel estimation method of the six-dimensional movable antenna base station and the related equipment provided by the embodiment of the invention, the six-dimensional movable antenna base station is provided with at least one antenna surface, the antenna surface comprises at least one six-dimensional antenna, and the method comprises the following steps: acquiring a pilot signal sent by a user terminal, performing statistical channel estimation calculation based on the pilot signal and the intermediate matrix to obtain a channel power matrix and a direction sparsity matrix between the user terminal and the six-dimensional antenna; solving the rate optimization model based on the channel power matrix to obtain a target position rotation parameter, and performing position rotation adjustment on the at least one six-dimensional antenna according to the target position rotation parameter; and instantaneous channel estimation is carried out based on the pilot signal and the direction sparsity matrix to obtain an instantaneous channel estimation result, so that the accuracy of channel estimation between the six-dimensional movable antenna base station and the Internet of Things terminal is improved while the information transmission rate between the six-dimensional movable antenna base station and the user terminal is effectively improved.

The present invention discloses a high-resolution imaging method for a scanning radar under low signal-to-noise ratio conditions, comprising: Step 1: Modeling the echo signal of the scanning radar and initializing the scattering point power matrix; Step 2: Calculating the covariance matrix according to the scattering point power matrix; Step 3: Modeling the radar imaging problem as a least-squares problem by using the covariance matrix and calculating the least-squares solution of the target scattering intensity; Step 4: Calculating the scattering point power matrix according to the least-squares solution of the target scattering intensity; Step 5: Removing the smaller values in the scattering point power matrix to update it; Step 6: Repeating the operations of Step 2 to Step 5 until the maximum number of iterations is reached. The present invention applies the iterative adaptive method based on the least-squares method to scanning radar imaging, and removes the smaller values in the scattering point power matrix during the iteration process, making the target signal more prominent, improving the azimuth resolution under low signal-to-noise ratio conditions, and enhancing the imaging effect.

The invention relates to a big data dynamic matching method based on wheel edge working condition characteristics, and the method comprises the steps: 1, collecting market vehicle driving data, including vehicle speed, rotating speed and torque; preprocessing the driving data to obtain a time-driving data table; 2, according to the time-driving data table, extracting wheel-side working condition characteristics, including vehicle speed-wheel-side power matrixes under an acceleration working condition, a deceleration working condition and a constant-speed working condition; 3, according to the vehicle speed-wheel edge power matrix, integrating power configuration parameters, a rotating speed theoretical calculation formula, a gear matching theoretical method and a torque theoretical calculation formula, reversely obtaining engine working conditions, and obtaining economical efficiency parameters and dynamic performance parameters; and 4, normalizing the economic parameters and the dynamic parameters, calculating weights of the economic parameters and the dynamic parameters, calculating comprehensive indexes, ranking the comprehensive indexes, and selecting optimal dynamic configuration according to the comprehensive indexes.

The invention discloses a fusion and decision-making method based on multi-node spectrum data. The method comprises the following steps: arranging a plurality of nodes in an application area, constructing a receiving signal spectrum sensing model of the nodes, constructing a receiving spectrum matrix, calculating the power of each frequency value in the receiving spectrum matrix, and constructing a receiving power matrix of the nodes by utilizing the compensated power; determining a mean value of each node in a frequency band based on the receiving power matrix of each node, and determining a threshold value of each node by using a variance based on the mean value of each node, thereby setting a weight for the receiving power matrix of each node; setting parameters required by spectrum data fusion of multiple nodes; the multi-node spectrum data is fused, the spectrum data in a certain frequency band and a certain time range is compared with a threshold value, then the idle proportion in each frequency band range is calculated, and the frequency band with the maximum idle proportion serves as the optimal frequency band to be output. The system has the characteristics of high data acquisition efficiency, high reliability and wide application range.

The invention discloses an electric vehicle charging load prediction method in a low data quality scene, and relates to the technical field of electric vehicles, and the method comprises the steps: obtaining a first feature; setting a dynamic space-time weight factor according to the first feature; according to the dynamic space-time weight factors, vacancy values in the space-time associated charging power matrix are complemented, and complete target charging power matrix data are obtained; constructing a three-dimensional input tensor according to the complete target charging power matrix data; inputting the three-dimensional input tensor into a first model to obtain a charging load predicted value; performing trend verification on the charging load prediction value and historical charging load data to obtain a prediction trend; and if the difference value between the prediction trend and the historical trend exceeds a preset threshold value, correcting the charging load prediction value based on the change rate of the historical trend to obtain a target electric vehicle charging load prediction result. According to the method, high-precision load prediction can be realized, and powerful support is provided for power grid demand response.

The invention relates to a method for optimizing energy consumption of a whole vehicle by power distribution of a fuel cell vehicle, which is used for performing power distribution on target demand power Preq output to a fuel cell system and performing whole vehicle energy distribution in cooperation with a power battery, and comprises the following steps: step 1, analyzing an optimal power point PF of the fuel cell system, step 2, performing charge and discharge limit analysis on the power battery, and step 3, analyzing the energy consumption of the whole vehicle. 3, making a gas-electricity power matrix; and 4, carrying out a control strategy of power distribution. On the basis of each power interval of the electric drive demand power Ptmr of the fuel cell and the SOC percentage of the power cell, a gas-electric power matrix is drawn in combination with charging and discharging zoning of the power cell, the gas-electric power matrix is inquired by collecting the current SOC residual electric quantity percentage and the current Ptmr value, Preq is rapidly output and distributed, energy flow distribution of the whole vehicle is achieved, and energy flow distribution of the whole vehicle is achieved. And the power control of the gas-electric vehicle model is realized more efficiently, so that the energy flow management of the whole vehicle is more economical.

The invention belongs to the related technical field of electric power measurement and calculation, and provides a photovoltaic green electricity source tracing method and system based on power distribution and dynamic boundary definition, which introduces enterprise consumption density parameters to judge whether green electricity in a region is completely consumed or not, prevents enterprises in a non-target region from participating in distribution, and cuts off the correlation of the distance to determine the consumption amount from the spatial dimension; green electricity is distributed based on the real-time load proportion of an enterprise, the absorption deviation caused by line impedance difference is thoroughly eliminated, and the equal distribution quantity fairness is achieved when the loads are equal; a generator power matrix, a node flow power matrix and a power flow tracking matrix are constructed according to a power grid real-time power flow calculation result, a core matrix and green power distribution quantity are dynamically updated along with power fluctuation, power change under the influence of distributed photovoltaic illumination / temperature can be matched in real time, and deviation caused by traditional static measurement is avoided.

The application provides a charging pile state prediction method, a computer readable medium and equipment, the method is a health index and double attention optimization GCN charging pile state prediction method, including load data acquisition, dynamic wavelet denoising, health index construction, health state prediction. The principle of the application is: firstly, the charging pile load data is collected and processed, and the server platform data and the voltage, current and power data in the charging station are integrated and processed; secondly, the original data is denoised by using dynamic threshold wavelet transform to obtain a charging pile state data matrix; thirdly, based on the voltage, current and power matrix of the charging pile load data, the health factors of the single state indicators of the charging pile are constructed by using the distance sparsity algorithm, and the health factors are fused into a health index according to the improved objective weighting method; finally, the health state of the charging pile is predicted by using a double attention optimization graph convolutional neural network.

The present invention relates to a method for calculating the SOC of a BMS system based on a metering chip. The metering chip is connected to an MCU and a voltage reference source chip, and the method includes the following steps: the MCU sends a start metering command to the metering chip, and the metering chip calculates the charge and discharge amount containing the voltage coefficient after receiving the start metering command; the MCU reads the charge and discharge amount containing the voltage coefficient calculated by the metering chip according to the acquisition timing defined by the BMS; a single cell power matrix is generated based on the charge and discharge amount containing the voltage coefficient; at the same time, a temperature sampling point distribution matrix is generated; the single cell power matrix and the temperature sampling point distribution matrix are expanded into matrices with the same rows and columns; the two expanded single cell power matrices and temperature sampling point distribution matrices are subjected to Coleman filtering in a one-to-one correspondence to obtain the single cell SOC value. The present invention uses a separate electric energy metering chip to be solely responsible for the integration work, which can greatly save the computing power of the MCU.

The invention provides a power demand prediction method and device based on two-stage optimization, and is suitable for the technical field of power systems, and the method comprises the steps: dividing a to-be-predicted region based on the population density and the area, and obtaining a plurality of regions; acquiring historical power consumption data of each region, determining a first power matrix of each region through a first prediction model, and determining a special region based on all the first power matrixes; determining a second power matrix of the special area based on a second prediction model, and optimizing the corresponding first power matrix based on the second power matrix; and determining a first special value matrix of the special area based on the historical power consumption data of the special area and the plurality of adjacent areas, and optimizing the optimized matrix based on the first special value matrix to obtain a final power matrix. According to the method, the special region can be predicted for multiple times through multiple models, the special region is compared with the adjacent region, the prediction result is optimized twice, and the accuracy of the prediction result of the special region is improved.

This invention provides a modular offshore mobile platform, comprising at least two hull platforms and a flexible connecting device for flexibly connecting adjacent hull platforms. Each hull platform includes at least one modular platform unit, which includes an installation platform and multiple small boats. The installation platform is a truss frame, and the small boats are connected to at least a portion of the frame nodes of the truss frame. This invention selects a certain number of platform units according to the required size, shape, and function of the hull platform, assembles them into a hull platform, and then uses the flexible connecting device to flexibly connect the hull platforms and install the load. This invention employs a grid-distributed array of power sources to form a power matrix chassis, breaking through the limitations of traditional single propulsion modes and achieving high-speed navigation on the water surface. Relying on the independent control of each small boat, it can achieve highly maneuverable maneuvers such as turning in place, sharp-angle turning, and lateral crabbing, greatly enhancing flexibility and sea trial response capabilities.

The invention discloses a power domain interference pattern estimation method in a cellular network multi-cell scene, and the method comprises the steps: constructing a transmission power matrix based on the condition of power allocated to a plurality of resource blocks by each base station in a preset time period; based on the maximum energy efficiency and the minimum condition number of the sending power matrix, creating an objective function, and constructing an optimization model by taking the allocation condition of the resource blocks, the sending power range and the full rank of the sending power matrix as constraint conditions of the objective function; decomposing the optimization model to obtain a first sub-optimization model and a second sub-optimization model, solving the first sub-optimization model, and solving the second sub-optimization model based on the solution of the first sub-optimization model to obtain an optimal sending power matrix; based on the optimal sending power matrix and the receiving power, the equivalent channel vector of the user is calculated, the original time domain / frequency domain is expanded to the power domain, and the problems that a traditional method is large in measurement overhead and high in time synchronization precision requirement are fundamentally solved.

The present invention provides a method, medium and system for quickly determining damaged or defective lithium batteries, belonging to the technical field of product detection. By collecting data such as temperature, voltage, current, etc. of the battery under different charge and discharge states, the present invention constructs multi-dimensional features such as a temperature change matrix, a voltage matrix, a power matrix, a resistance matrix, a cross-correlation index matrix, a harmonic change matrix and a reverse charge and discharge power matrix to comprehensively characterize the battery performance. Thermal imaging is used to obtain the surface temperature distribution, and a DC internal resistance tester and an AC impedance spectroscopy analyzer are used to measure the internal resistance, and Fourier transform is performed to analyze the voltage harmonic characteristics. Based on a deep neural network model with a multi-head attention mechanism, the collected multi-dimensional matrix data is analyzed to output the battery health state score and the damage type. This method comprehensively considers the electrochemical performance and thermodynamic characteristics of the battery, and realizes the rapid and accurate evaluation of the health state of lithium batteries.

The invention relates to a method for comparing and selecting typical wiring modes of a district and county-level power grid under a carbon reduction demand. The method comprises the following steps: carrying out graphic modeling on typical wiring modes of multiple voltage levels of the district and county-level power grid; carrying out typical wiring mode load flow calculation; constructing a power matrix; constructing a carbon emission intensity matrix; calculating a carbon emission index of each typical wiring mode; and comparing and selecting typical wiring modes of the district and county-level power grid. According to the method, carbon reduction requirements are taken as a target, carbon emission calculation is carried out on multiple typical wiring modes by building a graphic model, setting boundary conditions and utilizing an electrical carbon conduction mechanism, carbon emission conditions of different wiring modes are accurately quantified, and low-carbon emission sequencing of different wiring modes is provided for district and county-level power grids through transverse comparison; according to the method, the problem that the carbon reduction target is difficult to quantify in the power grid planning is solved, the carbon emission intensity of the district and county level power grid is reduced, and a reproducible and generalizable technical path is provided for the construction of a novel power system.

The application discloses a power control method and device based on multi-source cooperation and a reconfigurable power matrix. The method comprises the following steps: in response to a charging power scheduling instruction, current power supply parameter sets corresponding to multi-source power supply modules are acquired; at least three-phase alternating current power grids, photovoltaic modules and energy storage modules are included in the current power supply parameter sets; according to a preset power supply mode decision strategy and the current power supply parameter sets, current on-off states of switch groups in the multi-source power supply modules and current charging power distribution strategies in the multi-source power supply modules are determined; and the current charging power distribution strategies are executed, so that the power control module charges the to-be-charged equipment connected to the charging terminal group. According to the conditions corresponding to the working parameters of the three-phase alternating current power grids, the photovoltaic modules and the energy storage modules in the multi-source power supply modules of the charging station, the power supply mode and the charging power distribution mode in the charging station can be flexibly adjusted.

The invention discloses a method for positioning the occurrence position of a cable external damage event, and belongs to the technical field of electric power safety monitoring. According to the method, the positions and the number of the intelligent ground nails are set, the reference ground nails are set, the intelligent ground nails synchronously collect vibration data of a cable external damage event, an area where the external damage event possibly occurs near the cable is gridded, distance vectors between each grid node and all the intelligent ground nails and difference vectors of distances between the grid nodes and the reference ground nails are calculated respectively, and the vibration data of the cable external damage event are acquired. Calculating an auto-covariance matrix of the external damage event vibration data, calculating a phase delay focusing direction vector of each grid node by using the distance difference vector, and calculating an external damage event vibration power matrix corresponding to each grid node by using the auto-covariance matrix of the external damage event vibration data and the phase delay focusing direction vector of each grid node; the grid node where the maximum vibration power is located in the external damage event vibration power matrix is the occurrence position of the cable external damage event, and the defect that an existing monitoring device cannot achieve external damage positioning is overcome.

The present invention relates to the field of automatic control technology, and further to an intelligent control method and system for a variable frequency centrifugal unit. The method comprises: step 1: establishing a state space model of the variable frequency centrifugal unit based on the dynamic characteristics of the variable frequency centrifugal unit; step 2: designing an adaptive fuzzy neural network controller based on the state space model, and the adaptive fuzzy neural network controller outputs an adaptive power matrix; step 3: estimating load changes through a load torque observer, calculating a load compensation control input matrix, and ensuring the stable operation of the variable frequency centrifugal unit; step 4: calculating a robust power matrix through a robust H∞ controller based on the state space model; and calculating the final control input power matrix of the variable frequency centrifugal unit. The present invention effectively improves the control accuracy, dynamic response capability, nonlinear adaptability and robustness of the variable frequency centrifugal unit under complex working conditions.

The invention discloses a rural micro-grid maximum access capacity optimization method fusing demand response, and belongs to the technical field of micro-grids, and the method comprises the following steps: obtaining a typical load normalized power curve, demand response unit price and a power distribution network flow topological graph of a rural micro-grid; determining active power and reactive power of each node load, extracting standard power flow calculation nodes by combining the power flow topological graph of the power distribution network, establishing an active power balance equation and a reactive power balance equation of the power distribution network, and determining system constraint conditions of the power distribution network; and obtaining a node new energy injection power matrix, taking a system constraint condition of the power distribution network as a boundary, taking maximization of the new energy capacity of each node of the power distribution network as a target, and carrying out iterative updating on the new energy capacity of each node by adopting a particle swarm optimization algorithm to obtain the maximum new energy access capacity of each node of the rural microgrid. According to the invention, the problem of insufficient maximum access capacity under the condition that high-proportion new energy accesses the rural microgrid can be solved.

The present application discloses a method for estimating a power domain interference graph in a multi-cell scenario of a cellular network. The method includes: constructing a transmission power matrix based on the power allocation of each base station to multiple resource blocks within a preset time period; creating an objective function based on maximizing the energy efficiency and minimizing the condition number of the transmission power matrix, and constructing an optimization model with the allocation of resource blocks, the transmission power range, and the full rank of the transmission power matrix as the constraint conditions of the objective function; decomposing the optimization model to obtain a first sub-optimization model and a second sub-optimization model, solving the first sub-optimization model, and based on the solution of the first sub-optimization model, solving the second sub-optimization model to obtain an optimal transmission power matrix; calculating the equivalent channel vector of the user based on the optimal transmission power matrix and the received power, expanding the original time domain / frequency domain to the power domain, and fundamentally solving the problems of large measurement overhead and high time synchronization accuracy requirements of traditional methods.

The invention provides a voltage fitting method without an electrical model. The method comprises the steps that an active power matrix and a reactive power matrix of a low-voltage power distribution network are acquired, and an input data set is constructed; inputting the input data set into a width learning model to obtain an output voltage of the low-voltage power distribution network and taking the output voltage as an output data set of the width learning model; acquiring an original input data set of the low-voltage power distribution network, performing dynamic principal component analysis on the original input data set to obtain a dynamic latent variable data set, and inputting the dynamic latent variable data set instead of the original input data set into the width learning model for training; extracting a dynamic feature weight in the dynamic latent variable data set, and replacing a random weight of the width learning model with the dynamic feature weight to obtain a width learning voltage fitting model; and performing parameter optimization according to a preset particle swarm algorithm to obtain an optimal parameter of the voltage fitting model. According to the technical scheme of the embodiment, the precision of voltage fitting in the low-voltage power distribution network can be remarkably improved.

The application relates to the technical field of modulation identification, in particular to a self-adaptive integrated feature selection mixed signal modulation identification method, which comprises the following steps: dividing a received signal into a plurality of small signals by using a sliding window; determining the position where the mixed signal occurs and the position where the mixed signal ends based on the power matrix of each small signal, and extracting the mixed signal; performing feature extraction on the mixed signal, and constructing a feature set of the mixed signal; selecting each feature in the feature set by using five feature selection methods, namely, a minimum redundancy maximum correlation criterion selection method, a multi-cluster feature selection method, a correlation feature selection method, a recursive feature elimination selection method and a random forest feature importance selection method, and preliminarily screening five stable feature subsets; performing self-adaptive optimization on the five stable feature subsets by using an HGSO algorithm to obtain an optimal feature subset; and performing modulation identification based on the optimal feature subset to obtain a modulation identification result. The method effectively improves the efficiency and accuracy of mixed signal modulation identification.

The present invention relates to the technical field of electric power systems, and in particular to an energy system transformation scheduling optimization method and system considering energy quality, which calculates all node sets, a set of equipment types containing energy quality information, and a set of all energy forms containing energy quality information of each node; calculates a system connection matrix and a power correction matrix to establish a collaborative optimization model with the purpose of optimizing equipment capacity and equipment power; solves the collaborative optimization model using an adaptive solution algorithm to obtain an optimal equipment capacity matrix and an optimal equipment power matrix; determines a transformation optimization result and a scheduling optimization result based on the optimal equipment capacity matrix and the optimal equipment power matrix, and considers the energy quality in the energy system and analyzes all energy system structures.

The present invention discloses a calculation method for the acoustic characteristics of a periodic ribbed bridge deck based on advanced statistical energy analysis, comprising the following steps: S1. Divide the periodic ribbed bridge deck into subsystems to obtain a number of subsystems; S2. Calculate the average transmission coefficient and the average internal loss coefficient of the periodic ribbed bridge deck according to the subsystems; S3. Calculate the free power matrix and the fixed power matrix in the advanced statistical analysis power flow equation according to the average transmission coefficient and the average internal loss coefficient of the periodic ribbed bridge deck; S4. Substitute the free power matrix and the fixed power matrix into the power flow equation to obtain the vibration energy vectors of each subsystem; S5. Calculate the sound radiation efficiency of each subsystem based on the sound radiation principle; S6. Calculate the acoustic characteristics of the periodic ribbed bridge deck according to the sound radiation efficiency and the vibration energy vectors of each subsystem. The present invention improves the accuracy of calculating various acoustic characteristics of the periodic ribbed bridge deck, and provides technical support for the design of the periodic ribbed bridge deck and the research on its acoustic characteristics.

The invention relates to the technical field of new energy vehicles, and provides a power supply system and a power supply method of a vehicle-mounted power supply. The power supply system of the vehicle-mounted power supply comprises a battery pack; a single monitoring module; the MOS driving and holding module comprises a D trigger and is used for generating a driving signal according to a line or logic relation between the control signal and the holding signal; the power MOS matrix comprises a first power MOS matrix and a second power MOS matrix, the on-off of the first power MOS matrix is controlled by a first driving signal, and the on-off of the second power MOS matrix is controlled by a second driving signal; and the controller is used for acquiring the output voltage of the battery pack, the monitoring signal output by the single monitoring module, the driving signal output by the MOS driving and maintaining module and the branch current of the parallel branch where the power MOS matrix is located so as to judge whether the first battery pack and the battery pack break down or not, and generating a fault signal when a fault occurs.