20results about How to "Improve execution speed" patented technology

The invention relates to a path planning method of a photovoltaic robot, the photovoltaic robot, equipment and a medium, and the method comprises the steps: setting a distance between adjacent paths according to a machine cleaning performance index, generating an internal cleaning path based on a photovoltaic plate map and the distance between the adjacent paths, and discretizing the internal cleaning path into a plurality of path points with expected poses; an acceptance path point is arranged on each sub-cleaning path of the internal cleaning path, and parameters of the acceptance path points comprise an expected cleaning area; executing an internal cleaning path, monitoring a pose error between an actual pose and an expected pose in real time, and determining a cleaning performance error according to an actual cleaning area and an expected cleaning area; when the pose error is greater than the pose tolerance threshold or the cleaning performance error is less than the cleaning tolerance threshold, a target internal cleaning path is generated and executed based on the pose error and / or the cleaning performance error. The execution speed and the cleaning efficiency of the photovoltaic robot on the cleaning path are improved, and meanwhile it is ensured that the cleaning quality reaches the expected standard.

The invention discloses a dynamic module operation management system based on a programming language, and the system comprises a module analyzer which manages the dependency relationship between modules in the system, applies a compiling optimization technology before the modules are loaded, and optimizes the code execution through code expansion and constant folding; operation environment establishment: optimizing execution speed and resource scheduling by integrating a just-in-time compiling JIT technology and an optimization measure during operation; the module warehouse is used for controlling the retrieval and loading process of the module by adopting an optimized organization and index strategy; data structure optimization: using a skip list to adjust data structure design, and controlling data processing efficiency; dynamic optimization: the system dynamically adjusts an optimization strategy according to performance feedback and an operation environment, and controls improvement of self-adaptive performance; garbage collection: dynamically adjusting a garbage collection strategy by adopting a self-adaptive garbage collection algorithm, and reducing the module retrieval time, accelerating the module loading process and improving the data access efficiency.

The invention discloses a tensor program generation method and system based on a large language model, and the method comprises the following steps: (1) generating a random ONNX computational graph through an AI compiler test tool nnsmith; (2) importing an ONNX calculation graph generated by nnsmith into a TVM, and converting the ONNX calculation graph into a RelayIR intermediate representation; (3) based on a model intermediate representation RelayIR in the TVM, adjusting and optimizing the model through an automatic adjusting and optimizing device Ansor in the TVM, generating an optimal tensor program, and outputting a JSON file measured during adjusting and optimizing; (4) continuously carrying out model tuning and tensor program measurement, generating a large number of measurement records during tuning, and forming a tensor program data set which comprises features of a tensor program and a corresponding optimization pass list; and (5) training a large language model according to the generated tensor program data set to realize understanding and generation of the tensor program. Through combination of an automatic tool and a large language model, efficient tensor program generation and optimization can be realized.

The invention provides a synchronous zero voltage modulation system and method of a cascade system, a medium and equipment wherein the synchronous zero voltage modulation system of the cascade system comprises a power circuit module and a modulation system; the power circuit module comprises a front-stage four-switch buck-boost converter and a rear-stage LCC-LCC compensated bidirectional wireless charging system; the modulation system comprises a voltage compensation module, a current compensation module, a current comparison module, a synchronization module and a switch driving signal generation module, the output end of the voltage compensation module is connected with the input end of the synchronization module, and the output end of the current compensation module is connected with the input end of the synchronization module. The output end of the current comparison module is connected with the input end of the synchronization module, and the output end of the synchronization module is connected with the driving signal generation module. According to the invention, a plurality of time scales are adopted, the implementation condition of synchronous zero-voltage switch modulation is accurately calculated, the calculation speed is high, the memory occupation amount is small, and the zero-voltage switch synchronous modulation of the cascade system is realized.

The invention discloses an automatic test case generation and execution method, which comprises the following steps: obtaining input content uploaded by a user, and analyzing the input content uploaded by the user to obtain structured key data; generating a preliminary test case according to the structured key data and a general rule of a knowledge base, optimizing the preliminary test case according to historical data and a code coverage rate, and generating a test case; compiling a test script according to the test case, and executing the test script; analyzing and processing the test execution result, and generating an execution structure and a test case analysis report; and performing optimization processing on the test execution result and the historical data, and dynamically adjusting a test strategy according to an optimization processing result. The invention further discloses a system for realizing the automatic test case generation and execution method. The test efficiency is improved, the test coverage rate is increased, the execution performance is improved, and automatic continuous testing is supported.

The invention discloses an interface testing method and device based on a cloud platform and a nonvolatile storage medium. The method comprises the steps of obtaining target interface test case data used for target interface test from interface test equipment of a cloud platform; sending a first request message to a server based on a target interface corresponding to the target interface test case data, the first request message being used for indicating an operation type executed by the server; receiving a first response message corresponding to the first request message, and extracting verification information used for verifying an interface test result in the first response message; and verifying the verification information, and determining that the interface corresponding to the interface test case data passes the test under the condition that the verification is passed. The technical problems that a traditional interface testing method generally depends on manual operation, and is low in testing efficiency, difficult to use and unstable are solved.

The invention relates to a quick expansion method based on run length coding, which comprises the following steps of: S1, taking a binary image as an input region and carrying out run length coding to obtain a run length; inputting expansion parameter height and width; s2, judging whether the height and width of the expansion parameters are less than or equal to 0; s3, judging whether the input area is rectangular or not; s4, calculating the number of possible maximum distribution runs, wherein the maximum run number is obtained by multiplying the run number of the input area by the input expansion height; s5, calculating the number of times required to be iterated; s6, executing expansion in the horizontal direction; s7, expansion in the vertical direction is executed; and S8, merging all the moved run length codes. According to the method, the run length coding technology is introduced, the algorithm execution process and the data representation mode are optimized, the defects and problems existing in the prior art are solved from the aspects of storage, calculation, internal storage, real-time performance and the like, the efficiency and performance of binary image expansion operation are improved, and important technical progress is brought to the field of image processing.

The invention relates to the technical field of RISC-V instruction set processors, in particular to an RISC-V-based AI processor design method for small convolution kernel acceleration, which comprises the following steps: determining the acceleration demand of a 3 * 3 convolution kernel in a convolutional neural network (CNN), selecting an open architecture RV32I of RISC-V as a basis, defining a core architecture of an AI processor, comprising a calculation unit, a register file and a control unit, the processing of the small convolution kernel is optimized; designing and realizing a vector arithmetic logic unit (VALU) and a convolution operation unit (CUU); the VALU has parallel computing capability and can execute addition, subtraction and multiplication vector operations to support instruction set extension; a convolution arithmetic unit CUU processes 3 * 3 convolution operations, including dedicated hardware logic to perform element-by-element multiplication and accumulation operations, thereby accelerating the convolution calculation process.

The present subject matter discloses a system and a method for integration application environment migration utilizing hub and spoke architecture. In one implementation, the method for application environment migration comprising assessing at least one source application code of corresponding integration application environment by a processor (122) of an integration application server. The processor forecasts an assessment statistic (302) that provides at least one functional readiness (304) and a timeline (306) to complete the migration of the each source application code. The processor (122) further scans the each source application code of corresponding source application for generating the integration applications compatible with a target application environment and creates a relationship map to match components of the integrating applications with components of the target application environment. The processor (122) generates a re-factored code for the each source application code by breaking the each source application code into macro-services (426a, ..., 426n) and repackaging the macro-services (426a, ..., 426n) in accordance with the target application code. Thereby, updating components of the integration application environment as per the forecasted assessment statistic (302) and the re-factored code and thus migrating the integration application environment to the target application environment while re-platforming the updated components and the re-factored code of the integration application environment to the target application environment.

The invention relates to the technical field of computer software demand analysis and automation, and discloses an automatic writing method and system oriented to demand analysis, and the method comprises the steps: constructing a domain knowledge graph containing a component mutual exclusion and dependency relationship, and deducing and generating a component compatibility matrix; converting the unstructured demand text into structured features by using a natural language processing algorithm; generating a virtual configuration list in combination with the atlas, and performing conflict detection based on a compatibility matrix and a supervision protocol; when violation is detected, the topological connectivity is quantified by calculating the sum of in-degree and out-degree of the component in the map, and the component is retrieved and replaced according to the topological connectivity for automatic correction; monitoring a change event, calculating an influence domain by using a weight conduction algorithm, and triggering local re-verification; and finally, generating a descriptive text, and executing consistency check by reversely extracting numerical values. According to the method, the configuration verification efficiency and the correction stability are improved through matrix operation and topology analysis, and the accuracy of the demand analysis document is ensured.

The present disclosure provides a data processing method and device, electronic equipment and computer readable storage medium, the method comprising: obtaining configuration information to be processed, wherein the configuration information is information for indicating target data to be deleted; generating a target deletion execution instruction according to the configuration information; sending the target deletion execution instruction to a database storing the target data based on a preset data operation interface, the database being configured to precompile the target deletion execution instruction, wherein the preset data operation interface supports placeholders and precompilation of the target deletion execution instruction; and deleting the target data in the database according to the target deletion execution instruction. According to the embodiments of the present disclosure, the target data in the database can be conveniently and efficiently deleted.

This invention discloses a method and system for processing personnel information for business administration based on big data, relating to the field of personnel information data processing technology. The method includes the following steps: acquiring personnel information; preprocessing the personnel information to obtain preprocessed text information; processing the preprocessed text information through an embedding layer based on a CNN-BERT model to obtain a word vector matrix; processing the word vector matrix sequentially through a CNN convolutional layer, pooling layer, and fully connected layer to obtain structured data; using an improved ant colony optimization algorithm to perform path optimization for multi-condition combined queries on the structured data to obtain the globally optimal retrieval path; retrieving personnel information that satisfies the multi-condition combined query from the structured data based on the globally optimal retrieval path; and adaptively updating the global pheromone of the improved ant colony optimization algorithm after each round of multi-ant colony collaborative search to accurately locate the target personnel information.

The present application discloses a fault diagnosis method based on random walk and machine learning. First, the vibration signal data of the moving parts are collected and preprocessed. For the preprocessed vibration signal, the fault feature extraction algorithm is used to extract the feature parameters, and the obtained feature parameters are subjected to principal component analysis PCA. Then, a random walk optimization algorithm is constructed, and the random walk optimization algorithm is used to optimize the hyperparameters of the machine learning model. Then, the sample data after PCA dimension reduction is divided into a training set and a test set, and the training set is put into the optimized machine learning model for training. The test set is used for fault prediction to obtain the prediction accuracy under different features. Finally, a comparative analysis is performed to select the feature with the highest accuracy as the best feature. This method solves the problem that the traditional algorithm takes too long to find the optimal solution and cannot find the global optimal solution. While ensuring the prediction performance of the model, the prediction accuracy of the model is significantly improved, thereby improving the efficiency of fault diagnosis.

The invention discloses a parameter-containing quantum circuit training method and a related device. The method comprises the following steps: constructing a parameter-containing quantum circuit; obtaining a training set, splitting data of the training set into a plurality of sub-data sets, and distributing the plurality of sub-data sets to at least two real quantum computers; data values of the sub-data sets are coded to the first part of quantum logic gates to serve as fixed parameters; initializing trainable parameters of the second part of quantum logic gates, wherein loss operation is randomly performed on at least one second part of quantum logic gate; running the parameter-containing quantum circuit to obtain an output quantum state; and judging whether the output quantum state meets a preset requirement, if not, updating the trainable parameters to meet the preset requirement, and obtaining a trained parameter-containing quantum circuit. Compared with the prior art, the method has the advantages that the execution burden of a single quantum computer is relieved, the execution speed of the parameter-containing quantum circuit is increased, and meanwhile, the overfitting phenomenon occurring in the training process is prevented.

The invention relates to a software development application data processing method based on cloud computing, which belongs to the technical field of software development and comprises the following steps: S1, data receiving and preprocessing: receiving data from different sources based on application data information generated by a software development platform; the integrity and accuracy of data receiving are ensured, and errors in the data are identified and corrected through a check and error processing mechanism; and cleaning the received data, and removing invalid, repeated or abnormal data. According to the software development application data processing method based on cloud computing, by adopting an advanced encryption technology, multi-factor authentication and a strict data access control strategy, the security and privacy protection level of data can be remarkably improved; data leakage and unauthorized access can be effectively prevented, and the integrity and confidentiality of the data in the transmission and storage process can be ensured.

The invention provides a container sinking method and device based on a DPU, a storage medium and a program product, and relates to the technical field of computers.The method comprises the steps that under the condition that a first containerization platform receives a container operation request, calling information is generated based on target container information carried in the container operation request, and a first container network interface plug-in is called; under the condition that the calling information carries a preset sinking identifier, generating a hypertext transfer protocol request based on the target container information through the first container network interface plug-in, and sending the hypertext transfer protocol request to a second container module; receiving the hypertext transfer protocol request through a second container network interface plug-in in a second container module, and extracting target container information; executing an operation corresponding to the container operation request based on the target container information through the second containerization platform; the problem that the execution efficiency and the execution speed of a service application are low due to the fact that all containers run on a CPU of a server node can be solved.

The application discloses a kind of multi-task constraint end-to-end noisy speech emotion recognition methods based on teacher-student model, comprising: propose an end-to-end multi-task constraint network based on teacher-student model, draw lessons from the idea of knowledge distillation, after the multi-task training of teacher model is completed, the mature teacher model that has been trained is extracted to pure, significant emotional features, to guide the training process of student model;A multi-level enhancement loss mechanism is introduced, so that the student model can learn the key emotional information extracted from the clean speech by the teacher model in the middle layer;The idea of speech enhancement auxiliary task is adopted, and the speech enhancement auxiliary task is combined with multi-level enhancement loss;During the test phase, only the core process specific to speech emotion recognition classification in the student model needs to be focused on, effectively improving the execution rate of the test.The application realizes the speech emotion recognition task in noisy environment, improves the accuracy of emotion classification, and is suitable for intelligent customer service and mental health monitoring.

The invention belongs to the field of fault diagnosis, and particularly relates to a steam turbine fault diagnosis method based on multi-channel time-frequency spectrum cloud model fusion, and the method comprises the steps: carrying out the time-frequency transformation through multi-channel sensor data, and obtaining a plurality of time-frequency spectrums; then, dividing the cloud model into a plurality of sub-regions along the frequency direction, overlapping the sub-regions, and calculating feature parameters of the cloud model of each sub-region, namely a mean value, entropy and hyper-entropy, so as to form three matrixes; three convolutional neural networks are established respectively, the matrixes serve as input of the convolutional neural networks respectively, decision layer weighted fusion is carried out on output, a known state data set is used for training a model, and the output after model fusion is the fault type of the steam turbine; three feature matrixes are calculated according to the above steps for the to-be-tested monitoring data, and the three feature matrixes are input into the trained neural network model to diagnose the fault type of the steam turbine. The fault diagnosis precision can be effectively improved, early warning is provided, and safe and stable operation of the steam turbine is ensured.

The invention provides an identification method and system for a jacking mode of ship structural parts, and the method comprises the steps: dividing the ship structural parts into plate parts and profile parts through a modeling mode, judging whether the plate parts belong to batten parts or not, and carrying out the gantry jacking of battens if the plate parts belong to the batten parts; and if not, carrying out numerical control nesting on the plate. Only the section shape of the section part needs to be observed, and if the section shape is non-rectangular, section nesting is carried out; and if the section shape is rectangular, further judging whether the profile part belongs to a batten part or not so as to judge the nesting mode. The identification method can quickly identify the jacking mode of the ship structural part, and is simple and convenient to operate and wide in practicability; and parallel operation can be realized, so that the execution speed and efficiency are improved.

The invention relates to a data processing method and device, electronic equipment and a storage medium. The data processing method provided by the invention comprises the following steps: in response to a starting event for a target application instance, creating a target thread for running the target application instance based on a single-thread application framework; wherein the target application instance is an instance for opening a target application program to run; and in the process of running the target thread, running the target application instance based on the resource data of the target application instance bound in the target thread. According to the data processing method provided by the invention, the target application instance is operated based on the resource data of the target application instance bound in the target thread, so that the situation that the resource management identifier needs to be frequently transmitted when the resource data of the target application instance is used in the target thread is reduced; and the difficulty of managing the application instance resources through the resource management identifier is also reduced.