74 results about "Power matrix" patented technology

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 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 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 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 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.

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 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 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 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.

The present invention relates to a big data power matching method based on wheel side working condition characteristics, comprising the following steps: step one: collecting driving data of market vehicles, including vehicle speed, rotation speed and torque; and pre-processing the driving data to obtain a time-driving data table; step two: extracting wheel side working condition characteristics according to the time-driving data table, including a vehicle speed-wheel side power matrix under acceleration working condition, deceleration working condition and uniform speed working condition; step three: according to the vehicle speed-wheel side power matrix, integrating power configuration parameters, rotation speed theoretical calculation formula, gear matching theoretical method and torque theoretical calculation formula, reversely deriving engine working condition, and obtaining economic parameters and dynamic parameters; step four: normalizing the economic parameters and the dynamic parameters, calculating weights of the economic parameters and the dynamic parameters, calculating comprehensive indicators, ranking the comprehensive indicators, and selecting the optimal power configuration according to the comprehensive indicators.

This invention discloses a dynamic gain-controlled optoelectronic hybrid neural network computing system for low-altitude UAV rubble search and rescue, comprising a UAV camera, a convolutional neural network preprocessing module, a dynamic range adaptive matching module, a matrix calculation mapping control module, a weighted DAC module, an optical matrix calculation chip, a scaling restoration module, and a convolutional neural network post-processing module. The system identifies weak feature regions and performs adaptive gain amplification through the dynamic range adaptive matching module. The matrix calculation mapping control module adapts the optical calculation format, the optical matrix calculation chip performs high-speed, low-power matrix operations, and the scaling restoration module restores the true signal amplitude, ultimately outputting the target recognition result. This invention improves the accuracy of weak vital sign detection, reduces end-to-end latency and power consumption, adapts to UAV SWaP constraints, and is suitable for low-altitude UAV rubble search and rescue scenarios.

The embodiment of the invention discloses an energy storage equipment installation method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the steps of obtaining node topological data, parameter data of each line, a new energy output matrix, an active load matrix and a reactive load matrix; generating an active net injection power matrix and a reactive net injection power matrix; generating voltage deviation average data, an out-of-limit node number set, a node out-of-limit voltage sensitivity matrix and a power sensitivity matrix; generating a time sequence out-of-limit voltage sensitivity matrix; generating a time sequence power sensitivity matrix; determining an energy storage access point data sequence of the energy storage equipment; and controlling the mechanical arm to mount the at least one energy storage device to the corresponding at least one mounting point. According to the embodiment, waste of installation resources during installation of the energy storage equipment can be reduced.

This invention provides a modular, highly maneuverable, lightweight mobile platform with a power matrix structure, comprising at least two hull platforms and flexible connecting devices for 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 based on the required size, shape, and function of the hull platform and assembles them. Then, the flexible connecting devices are used to flexibly connect the hull platforms and install the load. This invention uses 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.

A power distribution method for a redundant electric drive system. The method comprises: on the basis of drive objectives on shafts and states and parameters of batteries and electric motors, determining constraint levels of the batteries, so as to set the maximum power of each battery pack; calculating an optimal power configuration of each battery pack, and on the basis of the optimal power configuration, setting a power matrix, a disturbance term and a target power vector; determining the constraint level of each electric motor, and on the basis of the constraint level, adjusting the power matrix and the disturbance term; on the basis of a full-rank condition for the power matrix and a rationality condition for a power distribution ratio, determining whether a rational power ratio can be obtained; if the conditions are not met, removing constraints layer by layer in ascending order until the conditions are met, otherwise, notifying an upper-layer controller or an upper-layer control module for processing; and on the basis of the obtained power ratio, calculating an output power of each electric motor. The method provides a general method for optimizing the power distribution of a redundant electric drive system under constraint conditions of different levels, thereby simplifying design and development, shortening development cycles and reducing costs; and calculation costs are low, and the method can be implemented in a real-time control system.

The invention discloses a power distribution network rapid automatic reclosing method based on a load balance coefficient, and the method comprises the following steps: S1, calling the power flow data of a line protection device after a fault occurs, and collecting the line power flow data once in real time at time intervals by a collection device, totally d power data; s2, calling the electrical load power of all loads of the line at d moments corresponding to the active power matrix at the line outlet; S3, solving the power generation power of the distributed power supply; s4, solving a weighted load balance coefficient; and S5, selecting different reclosing schemes according to the obtained weighted load balance coefficient. According to the invention, rapid reclosing can be ensured when the proportion of the distributed power supply and the load is in a feasible interval, and splitting reclosing can be ensured to be selected when the power generation power of the distributed power supply is too large, so that safe and reliable operation of the power distribution network is ensured to the greatest extent.

The invention discloses a circuit reclosing scheme selection method suitable for a small hydropower station. The circuit reclosing scheme selection method comprises the following steps: S1, after a fault occurs, performing circuit reclosing; calling a plurality of groups of active power data of all hydroelectric generating sets and active power data of all loads of the line from the sampling moment closest to the fault moment, and respectively forming a hydroelectric generating set power matrix and a power matrix of all loads of the line; s2, predicting the power at the fault moment, wherein the step a comprises the following sub-steps: a, selecting a set frequency function and fitting parameters to fit a power curve; b, calculating a power value at a fault moment; s3, selecting a reclosing scheme, wherein the reclosing scheme comprises the following sub-steps: c, calculating a power-to-use ratio; and d, selecting a reclosing scheme according to different power-to-use ratios. According to the method, different reclosing strategies are selected by considering the ratio relation between the hydroelectric generating set and the load, and synchronous grid-connected reclosing and small water electrolysis reclosing in the absence of line voltage judgment are realized by optimizing the reclosing action time.

1. Name of the product in this design: Modular Energy Storage Cabinet (Power Matrix). 2. Purpose of this design: for the storage and release of electrical energy. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

Hierarchical Coordination Prediction Method for Photovoltaic Power under Probabilistic Framework, which solves the problem of how to generate a high-quality and aggregated consistent photovoltaic power prediction distribution in the multi-level structure of the power system, belongs to the field of photovoltaic power generation technology. The present invention includes: S1. Generating a basic photovoltaic prediction power matrix χ for each level of the power system and including a prediction range h; S2. According to χ, calculating the basic photovoltaic prediction power error for one step forward and forming a basic photovoltaic prediction power error matrix, and using the block bootstrap method to draw J error block matrices ε<supgt;(j)< / supgt; from the basic photovoltaic prediction power error matrix with replacement, and superimposing them into χ to obtain a matrix χ<supgt;(j)< / supgt>; S3. Extracting the h-th column vector in each matrix χ<supgt;(j)< / supgt>, and arranging all the extracted column vectors into H new basic photovoltaic prediction power matrices of m×J order, and obtaining the harmonic photovoltaic prediction power sequences for each level of the power system according to the new basic photovoltaic prediction power matrices

The application belongs to the technical field of target detection, and specifically discloses a two-dimensional adaptive constant false alarm rate detection method of a grid complementary reference sliding window, which comprises the following steps: constructing a grid complementary reference sliding window under a detection matrix and distributing reference units; initializing key parameters of the detection matrix, and converting an original image signal matrix into a signal power matrix; judging and assigning the coordinate features of the reference units based on a window type topological structure, and calculating the mean value after traversal to extract background clutter; calculating a second order statistic VI and a mean ratio MR, and calculating a detection threshold by means of three threshold factor calculation formulas and in combination with a judgment rule to select a constant false alarm rate algorithm. The two-dimensional adaptive constant false alarm rate detection method of the grid complementary reference sliding window has the advantages that the detection accuracy is improved by 49.4% under a multi-target and clutter interference environment, the threshold stability and boundary processing capacity are better under different backgrounds, the false alarm and missed alarm rates are effectively reduced, and the target detection performance is improved.

The present invention discloses a method for maximizing the safe computing rate in a wireless power supply edge computing network, wherein the wireless power supply edge computing network includes a base station, a wireless device and an eavesdropper, and the base station transmits wireless radio frequency energy to each wireless device to charge the wireless device, and the base station transmits artificial noise to interfere with the eavesdropper's eavesdropping on each wireless device. The method for maximizing the safe computing rate in the wireless power supply edge computing network first constructs an objective function for maximizing the safe computing rate of the first channel gain, the second channel gain, the third channel gain, the preset unloading decision, the first unloading time ratio, the time ratio of the base station charging the wireless device and the power matrix of the base station transmitting artificial noise, and solves the unloading decision, the first unloading time ratio and the time ratio of the base station charging the wireless device in the objective function, and calculates the objective function to obtain the maximized safe computing rate.