16 results about "Fuzzy vector" patented technology

The invention provides a cooling and heating equipment energy consumption positioning and fault prediction method under an EMS energy consumption management system, and the method comprises the steps: S1, collecting state original data of target equipment to construct a comprehensive evaluation data set, employing a source data extraction function to enable indexes to be matched to a fuzzy set, forming a fuzzy vector, and carrying out the clustering, sorting and recognition of the important features, thereby achieving the energy consumption positioning and fault prediction of the target equipment; generating device-level time series data; s2, training a health score prediction model and a fault classification model based on the time sequence data vector; and S3, performing health scoring and fault classification prediction on the real-time data by using the training model, and generating early warning information. By defining the fuzzy set and the source data extraction function and extracting key features through clustering analysis, important features are further screened by using the random forest algorithm, so that the accuracy of target equipment performance evaluation is improved, a more accurate basis is provided for subsequent fault diagnosis, and the purposes of simplifying the model, improving the analysis efficiency and reducing the cost are achieved. And remarkable economic benefits are brought to enterprise partners.

The invention discloses a vehicle formation driving control method for preventing eavesdropping attacks, and belongs to the technical field of vehicle networking safety. In a formation vehicle wireless transmission process, a designed fuzzy vector is introduced, state privacy is effectively protected, and a formation control task is completed at the same time. Comprising the following steps: determining a leading vehicle and a following vehicle, and further determining a network topology; the leading vehicle randomly generates a fuzzy vector meeting a condition, and transmits a false state to the following vehicle according to a formation control communication protocol; the following vehicle designs a noise vector, receives a fuzzy vector transmitted by the leading vehicle, and transmits a false state of the following vehicle according to a designed formation control communication protocol when the following vehicle communicates with front and back adjacent vehicles; and constructing a second-order dynamic model of the leading vehicle and the following vehicle, designing control input based on a formation control communication protocol, and completing formation control. The method has the advantages of being easy and convenient to operate, low in calculation complexity and high in real-time performance.

The invention relates to the technical field of vehicle communication, and discloses a vehicle communication control method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting a current speed contradictory index, a current space overlapping degree and a current environment complexity, and carrying out the calculation of the current speed contradictory index, performing fuzzification processing on the current space overlapping degree and the current environment complexity respectively to obtain fuzzy vectors corresponding to the current moments respectively; according to all the fuzzy vectors and a preset emergency degree table, determining trigger intensity and sub-emergency degree values under each emergency condition; determining a current emergency degree value according to the trigger intensity and the sub-emergency degree value under each emergency condition; and determining a current communication strategy according to the current emergency degree value, and controlling vehicle communication according to the current communication strategy. According to the invention, the current communication strategy adapted to control the vehicle communication in the current scene can be determined, the communication redundancy can be avoided while the vehicle communication requirement is met, and the effect of flexibly adjusting the vehicle communication strategy is realized.

The invention relates to the technical field of synthetic aperture radar interferometry, and discloses a clustering multi-baseline phase unwrapping algorithm combined with an iterative local search strategy, which comprises the following steps of: 1, combining interferograms with different baseline lengths in a histogram clustering mode, and separating combinations of different fuzzy vectors; step 2, separating all wave crests on an envelope line obtained after clustering, and obtaining an abscissa corresponding to each type of wave crests as an intercept value of the type; 3, constructing an integer constraint equation of an intercept item; 4, aiming at the integer programming problem, establishing an objective function and a transformation constraint condition; 5, iterating local search to solve a solution set meeting the minimum objective function; and 6, calculating an absolute phase. According to the scheme, when the constructed intercept item constraint equation is searched and solved, the phenomenon of possible local extremum in the discrete optimization process is improved, and the global search capability of the algorithm is enhanced by dynamically adjusting the step length and the solution space range, so that the unwrapping effect is optimized.

The invention provides a steam turbine fault symptom identification method based on fuzzy vector mapping and independent element space reconstruction, relates to the field of steam turbine fault diagnosis, and aims to solve the problems that normal working conditions and fault features are difficult to distinguish and the non-linear and non-stationary signal processing capacity is weak in a traditional vibration analysis method, an oil analysis method and an independent element analysis method. The method comprises the following steps: collecting multivariable sensor data of a steam turbine in a normal state for preprocessing; establishing an initial independent element analysis model; a fuzzy observation vector is constructed by using a Gaussian membership function, and the influence of large-amplitude normal fluctuation is weakened; performing dynamic reconstruction in an independent element space through the hybrid matrix, and calculating a residual vector; and constructing a square prediction error statistic based on the residual vector to carry out fault judgment. According to the method, the problem of order and amplitude uncertainty in independent component analysis is effectively solved, the recognition accuracy of the early weak fault of the steam turbine is remarkably improved, and reliable technical guarantee is provided for safe and stable operation of a power system.

The present invention discloses a vehicle formation driving control method for defending against eavesdropping attacks, belonging to the field of vehicle network security technology. During the wireless transmission process of the formation vehicles, a designed fuzzy vector is introduced to effectively protect the state privacy and complete the formation control task at the same time. The method comprises the following steps: determining the lead vehicle and the following vehicle, and then determining the network topology; the lead vehicle randomly generates a fuzzy vector that meets the conditions, and transmits the false state to the following vehicle according to the formation control communication protocol; the following vehicle designs a noise vector and receives the fuzzy vector transmitted by the lead vehicle, and when communicating with the front and rear adjacent vehicles, transmits its own false state according to the designed formation control communication protocol; constructs a second-order dynamic model of the lead vehicle and the following vehicle, designs the control input based on the formation control communication protocol, and completes the formation control. The present invention has the advantages of simple operation, low computational complexity, and strong real-time performance.

The present invention discloses a control method and system for a surveillance camera, which relate to the technical field of camera control. By calculating the target defocus rate Tbd within a time window T, the blur state is no longer dependent on instantaneous image judgment, but has statistical trend characteristics, effectively alleviating the false triggering problem caused by occasional factors such as temporary interference and occlusion. The target defocus rate Tbd is compared with the defocus rate judgment threshold Thd to generate a focus control signal Foc, complete automatic focusing on a key target area, avoid the resource waste and focus delay caused by the fixed periodic focusing of the traditional system, re-collect the blur features of the focused image, construct a new fuzzy vector sequence set Ftr4, and compare it with the fuzzy vector sequence set Ftr2 to calculate the focus effect value Qev, realize the closed-loop evaluation and optimization adjustment of the quality of the focusing behavior, and overcome the problem of the lack of a feedback verification mechanism for focus control in the prior art.

The application relates to the field of satellite navigation and timing technology, in particular to a Beidou and INS (inertial navigation system) fused real-time high-precision timing method, which comprises the following steps: generating pseudo-range residuals and code-minus-carrier based on Beidou satellite signals, and generating satellite channel fuzzy vectors by applying fuzzy reasoning; performing spatial clustering based on the multi-dimensional features of satellite signals, and configuring parallel sub-filters for satellite clusters; the sub-filters utilize satellite channel fuzzy vectors and carrier dynamic information to output error correction amounts and uncertainty variances; the satellite observations are corrected by using the error correction amounts, and a composite noise covariance matrix is constructed based on the uncertainty variances; the corrected observations and the composite noise covariance matrix are input into a navigation solver to extract high-precision time information. The application significantly improves the positioning and timing precision of a radio navigation system in a complex dynamic environment by hierarchically and cooperatively estimating and compensating the propagation errors of satellite signals.

The invention discloses a control method and system for a monitoring camera, and relates to the technical field of camera control, and by calculating a target out-of-focus rate Tbd in a time window T, a fuzzy state does not depend on instantaneous image judgment any more, but has a statistical trend characteristic, and the target out-of-focus rate Tbd does not depend on the statistical trend characteristic. The problem of false triggering caused by accidental factors such as temporary interference and shielding is effectively relieved, the target out-of-focus rate Tbd is compared with the out-of-focus rate judgment threshold value Thd, a focusing control signal Foc is generated, automatic focusing of a key target area is completed, resource waste and focusing delay caused by fixed periodic focusing of a traditional system are avoided, and automatic focusing of the key target area is achieved. According to the focusing control method and device, the fuzzy feature of the focused image is recollected, a new fuzzy vector sequence set Ftr4 is constructed, and the fuzzy vector sequence set Ftr4 is compared with the fuzzy vector sequence set Ftr2 to calculate the focusing effect value Qev, so that the quality closed-loop evaluation and optimal adjustment of the focusing behavior are realized, and the problem that the focusing control lacks a feedback verification mechanism in the prior art is solved.

The present invention provides a method for locating and predicting energy consumption of heating and cooling equipment under an EMS energy consumption management system, including S1, collecting the original data of the target equipment status to construct a comprehensive evaluation data set, using the source data extraction function to match the indicators to the fuzzy set to form a fuzzy vector, and generating equipment-level time series data by clustering, sorting and identifying its important features; S2, training a health score prediction model and a fault classification model based on the time series data vector; S3, using the training model to perform health scoring and fault classification prediction on real-time data to generate early warning information. The present invention further screens important features using a random forest algorithm by defining fuzzy sets and source data extraction functions and extracting key features through clustering analysis, which not only improves the accuracy of target equipment performance evaluation, but also provides a more accurate basis for subsequent fault diagnosis, thereby simplifying the model and improving analysis efficiency, and bringing significant economic benefits to corporate partners.

The embodiment of the invention discloses a safety production socialization service quality evaluation method and device, and relates to the field of safety production management. The method comprises the following steps: constructing a hierarchical structure index system model; constructing a judgment matrix corresponding to each hierarchy, and determining the weight of each index relative to the index of the previous hierarchy according to the proportion of the feature elements of the feature vectors corresponding to the judgment matrixes; constructing an evaluation set, calculating a fuzzy vector of a last-stage index, and generating a fuzzy evaluation matrix; performing fuzzy synthesis on the fuzzy evaluation matrix and the weight of the last-stage index, and performing fuzzy synthesis upwards step by step until a fuzzy vector of the target layer is obtained; and searching the maximum membership in the fuzzy vector of the target layer, and generating a quality evaluation result according to the comment corresponding to the maximum membership. According to the embodiment of the invention, the service quality of the service institution can be evaluated more comprehensively and accurately, the deviation between the evaluation result and the actual situation is smaller, and a purchaser can select a proper service institution more easily.

The invention provides a method for processing an error virtual loop after verification of a secondary virtual loop of an intelligent substation, and belongs to the technical field of intelligent substation model verification. Comprising the following steps: acquiring all possible error reasons of the secondary virtual loop; acquiring knowledge of the secondary loop which is called and still not matched correctly; defining a fuzzy matrix between the secondary loop knowledge and the error reason; according to the fuzzy vector of the to-be-detected error virtual loop, utilizing the fuzzy matrix to obtain a fuzzy vector of an error reason; and diagnosing the error cause of the to-be-detected error virtual loop according to the sorting result of the error cause fuzzy vectors. According to the method, the error result of the virtual loop verification is analyzed and processed, the error reason of the wrong virtual loop can be positioned, a user is helped to quickly position the problem, and the integrity of the virtual loop verification is improved.

The invention discloses an electric vehicle battery system high-voltage accessory fault diagnosis device and method. A fault diagnosis system and a high-voltage load are connected in parallel to a high-voltage bus; the fault diagnosis system comprises a signal generation module and a data acquisition module, and a response current signal is obtained through the data acquisition module; decomposing the response current signal into a plurality of time sequence prototype features, calculating the prototype matching degree of each prototype feature, combining the prototype matching degree into a matching degree vector as a fault symptom fuzzy vector, obtaining a fuzzy diagnosis matrix, and further establishing a relationship between the fault symptom fuzzy vector and a fault mode fuzzy vector; and selecting the mode corresponding to the value with the maximum membership degree from the fault mode fuzzy vector to obtain the high-voltage accessory fault of the battery system of the electric vehicle. According to the invention, the fault diagnosis of the high-voltage accessory key part of the electric vehicle battery system is realized, the safety and reliability of the electric vehicle battery system can be improved, and the safety of the vehicle and the vehicle-mounted personnel is guaranteed.

The invention relates to the technical field of dam safety, in particular to a dam site area seismic intensity calculation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining seismic intensity data; presetting a membership function of each evaluation factor in the seismic intensity data to an evaluation target, and constructing a fuzzy relation matrix; based on the fuzzy relation matrix, obtaining an evaluation factor fuzzy vector; performing fuzzy discrimination based on the evaluation factor fuzzy vector to obtain a seismic intensity fuzzy reasoning relation; and performing multi-level evaluation on the fuzzy reasoning relationship to obtain the final seismic intensity. Based on the relationship between seismic oscillation parameters such as seismic oscillation frequency spectrum, amplitude and duration and seismic intensity, the high concrete dam intensity is quickly calculated, a fuzzy method is adopted to obtain the relationship between the intensity and each related physical quantity, judgment results of different factors are integrated to obtain a seismic intensity value, and the seismic intensity is calculated. Complete analysis of the relationship between the seismic oscillation parameter and the seismic intensity is realized, and the seismic intensity encountered by the dam is accurately reflected.

The invention discloses a multi-baseline INSAR phase unwrapping method, system and device and a medium, and the method comprises the steps: constructing a to-be-clustered data set: obtaining the intercept information corresponding to each pixel according to the interferograms corresponding to different vertical baselines, then taking the position information of each pixel as the multi-dimensional clustering feature of the pixel, and obtaining the to-be-clustered data set; taking the pixel as a to-be-clustered target to obtain a to-be-clustered data set; wavelet clustering processing: performing clustering processing on the data set to be clustered based on wavelet clustering; clustering result correction: aiming at pixels in each noise cluster, correcting cluster labels of the pixels; phase unwrapping is carried out cluster by cluster: on the basis of the corrected cluster distribution, a fuzzy vector of each cluster is calculated by using a closed solution formula or a sparse-TSPA method, and the absolute phase of each pixel in the corresponding cluster is calculated on the basis of the fuzzy vector of each cluster. When the method is used for processing a large-size interference pattern, the method has more advantages in the aspects of efficiency, precision and base line ratio adaptability compared with an existing multi-base line phase unwrapping method.