17 results about "Hybrid programming" patented technology

The application relates to the technical field of intelligent production scheduling, and discloses a multi-target intelligent production scheduling method combining AI inventory prediction and hybrid programming. The method first standardizes and integrates multi-source data such as enterprise digital factories and warehouses, and constructs a unified production scheduling database; then a linear programming model is established with the target of maximizing profits, and product production quota optimization is completed in combination with constraints such as production capacity and raw materials; based on a switching cost matrix and production constraints, the lowest cost production sequence is solved through dynamic programming; an AI inventory prediction model is constructed by using a random forest regression, and safe inventory calculation and inventory risk grading early warning are realized; finally, multi-target production scheduling is executed according to the multi-level dynamic adjustment principle, and complete schemes such as hour-level plans, Gantt charts, cost and profit, and inventory curves are output. The application can simultaneously optimize production profit, switching cost, demand satisfaction rate and inventory safety, realize active inventory prediction, effectively reduce production loss caused by material interruption and single insertion, and improve the scientific nature of production scheduling and enterprise operation benefits.

The invention relates to the technical field of radar observation, and discloses a radar observation intelligent collaborative assimilation method, which comprises the steps of constructing a Python-Fortran hybrid programming platform, commanding a weather forecast mode simulation environment, generating multi-scale background field data, and enabling the programming platform and a weather forecast mode to share data mutually. A variational auto-encoder and a decoder are used to realize rapid mutual conversion between a physical space and a low-dimensional hidden space, the encoder realizes data dimension compression through a neural network, and the decoder realizes data physical reconstruction through the neural network; in the hidden space, radar observation is fused into a mode background by using a minimization algorithm to realize multivariable nonlinear assimilation. The physical environment state after assimilation is output to form a continuous time sequence which can be used for three-dimensional environment monitoring service, and an assimilation result at the latest moment is used as an initial state of a weather forecasting mode, so that the weather subjected to numerical simulation naturally expands to the future in a time domain, and forecasting service can be formed. According to the method, the efficiency and precision of three-dimensional environment monitoring and forecasting are improved.

The invention provides a hybrid programming code debugging method, system and device and a medium, and the debugging method comprises the steps: obtaining a code package which comprises a plurality of code files; wherein all code files of the code package are combined into a single execution program in the linking process, and the code package is compiled by adopting at least two programming languages; converting all the code files into intermediate language codes with a unified language format based on a preset code conversion format according to a sequence of executing the program; compiling the intermediate language code into a corresponding target binary code; and running the target binary code, and debugging the code file of the code package based on the running result of the target binary code. The method can support simultaneous mixed compiling and debugging of code files of high-level languages and code files of structured languages, so that the compiling process and the debugging efficiency are improved and optimized.

The invention provides a multi-method dynamic coupling soil and underground water heavy metal adsorption process simulation method, and belongs to the technical field of soil and underground water pollution simulation. Analyzing and screening main control factors by using a random forest and an SHAP value; constructing a plurality of machine learning models to establish a mapping relation between the main control factor and the adsorption coefficient Kd; and the optimal model is embedded into a TOUGHREACT platform, and dynamic prediction and feedback of a Kd value are realized through Fortran and Python hybrid programming. According to the method, the limitation of static setting of traditional model parameters is overcome, the simulation precision of heavy metal migration and transformation under the action of microorganisms is improved, meanwhile, the prediction advantages of mechanical property and machine learning of a physical model are reserved, and the method is suitable for adsorption behavior simulation and risk assessment of various heavy metals such as Cr (VI), Cu and As in a complex environment.

The application discloses a kind of high contrast imaging wave front detection and correction method and system, this method uses FPGA and CPU hybrid programming architecture to realize high-precision high contrast imaging wave front detection and correction, locking is realized to high contrast imaging dark area in closed-loop correction, wherein, high-precision wave front slope calculation is completed in FPGA, sub-aperture image division and wave front recovery calculation are realized in CPU, and voltage control deformable mirror correction wave front aberration is output.The application uses hybrid programming architecture to carry out high contrast imaging wave front detection and correction, realizes high-precision wave front detection and correction, solves the problem of insufficient performance of CPU for aerospace, and then realizes the locking of high contrast imaging dark area of space coronagraph, for direct imaging detection of exoplanet.After wave front detection and correction, the energy stability of high contrast imaging dark area can be guaranteed, and dark area locking is completed.

The invention discloses a turboshaft engine test bed fuel system digital modeling method and device, and belongs to the technical field of aero-engine fuel systems, and the method comprises the steps: building a test bed fuel system entity digital model according to the composition structure of a test bed fuel system, and carrying out the working mode division; measuring and processing input parameters and output parameters for an entity digital model; establishing a mechanism model based on the entity digital model according to the physical formulas of the input parameters and the output parameters, and optimizing the mechanism model according to the historical collected data of the key characteristic relationship; according to the entity digital model and the optimized mechanism model, C + + and Simulink hybrid programming is adopted to complete simulation model construction of the fuel oil system; measurement data are monitored and processed in real time, a test bed physical mechanism model and the measurement data are fused, the defect of low precision caused by simplified assumption in a traditional mechanism model is overcome, and the modeling and simulation speed is remarkably increased on the premise that the precision is not sacrificed.

The invention discloses a verification method and system for a scientific calculation-oriented hybrid programming model, and relates to the technical field of information, and the method comprises the steps: carrying out the difference analysis of an original programming model and a high-performance programming model; performing initialization of physical parameters and calculation parameters and distributed topology on the original programming model; analyzing the original programming model by adopting a performance analysis tool to obtain a hotspot function; establishing a matching mapping mechanism of the original programming model and the high-performance programming model, and performing hotspot function transplantation; and constructing a verification function, carrying out hybrid compiling with the original programming model, respectively carrying out unit testing and integration testing, and outputting to obtain a verification result. Program vulnerabilities in the transplantation process can be found, and the overload workload of a large amount of output data manual comparison results is avoided. The method supports verification of various hybrid programming models, and can be used as an intermediate process method for conversion of two or more kinds of programming to verify correctness of target codes.

The invention relates to an anti-quantum cryptography hybrid programming language program verification method and an anti-quantum cryptography hybrid programming language program verification system. The method comprises the following steps: inputting a password protocol program to be verified in Rocq based on a hybrid programming language; subprograms in the cryptographic protocol program are modeled in a formalized mode through a module system, and classic program behaviors and quantum program behaviors in the cryptographic protocol program are described based on a unified grammar framework of a hybrid programming language; performing type checking and grammar checking on a program written based on a unified grammar framework and a module system; generating semantics of the program according to grammar for the program passing the check; protocol properties needing to be proved are described in a formalized mode according to grammar; establishing an inference rule based on program rewriting on the basis of grammar; and carrying out interactive certification on the formally described protocol property in the Rocq by using a certification strategy of the Rocq. According to the method, the security of the complex password protocol can be completely and accurately described and automatically verified, and the verification reliability and efficiency are remarkably improved.

The present disclosure provides a hybrid programming method and related products, which can be implemented as a compilation device that can be included in a combined processing device that can also include a general-purpose interconnect interface and other processing devices. The computing processing device interacts with the other processing devices to collectively complete a user-specified computing operation. The combined processing device can also include a storage device connected to the computing processing device and the other processing devices, respectively, for storing data of the computing processing device and the other processing devices.

The application provides a method for realizing deep fusion of artificial intelligence and scientific numerical calculation by Python-Fortran hybrid programming, which comprises four parts of environment setting, recompilation of original Fortran module, Fortran-to-Python and Python-to-Fortran interface, Python master control design and operation, and realizes deep fusion of AI and scientific numerical calculation designed by Python. According to the technical scheme of the application, any existing Fortran scientific numerical calculation module can be directly called by artificial intelligence, any artificial intelligence algorithm obtained by machine learning can be directly called by a scientific calculation module, any scientific calculation module coded in Fortran can be directly called by AI designed in Python without re-coding in Python, and any AI algorithm coded in Python can be directly called by a scientific calculation module without re-coding in Fortran.

The invention provides an SOME / IP protocol test method and system of a distributed architecture, a medium and equipment, based on a test script, a test control instruction is generated, and the test script comprises a Python test script; converting the test control instruction into a test structure body representing SOME / IP protocol stack interaction; sending the test structure body to a tested target machine; receiving test feedback information from the tested target machine; acquiring a global interpreter lock, and pushing test feedback information to a test script layer by adopting a callback function to determine a test result; by introducing an endpoint module of a client / server, the limitation of a physical space is broken, a test architecture is more flexible, the probability of occurrence of common deadlock and crash problems in hybrid programming is reduced through management of a global interpreter lock, and meanwhile, by means of conversion of a test control instruction, the reliability of hybrid programming is improved. Complex memory management and pointer operation of a bottom layer are shielded, and only business logic of a Python layer needs to be concerned in a test, so that the test development efficiency is improved.

The invention discloses a task scheduling method of a quantum classical hybrid computing system and a related device. The task scheduling method comprises the steps of obtaining a first task request submitted by a user; the first task request is an application program with task coding characteristics based on quantum computing hybrid programming, and a quantum part and a classical part of the same application program establish a first linkage relationship based on the task coding characteristics; determining a first scheduling sequence of the first task based on computing resources required by the first task, computing resources of the current quantum classical hybrid computing system and a first self-defined scheduling strategy; respectively configuring the quantum part and the classic part of the first task to a quantum task queue and a classic task queue according to the first scheduling sequence; scheduling a quantum task queue and a classical task queue based on the quantum classical hybrid computing system and the first linkage relationship; by introducing a linkage mechanism between a quantum part and a classic part, the overall calculation efficiency of a quantum and classic hybrid application calculation task is improved.

Various example embodiments for supporting deterministic communications in communication networks may be based on use of a hybrid Programming Protocol-Independent Packet Processor (P4) deterministic switch. The hybrid P4 deterministic switch may include an electronic communication element configured to compensate for the jitter of incoming traffic to match a desired level of jitter for end-to-end deterministic communication of the incoming traffic and an optical communication element configured to support optical propagation of deterministic traffic received from the electronic communication element. The electronic communication element may include electronic packet queues, programmable electronic schedulers serving the electronic packet queues, and a P4 control element, where the electronic packet queues, the programmable electronic schedulers, and the P4 control element cooperate to support end-to-end deterministic communication of traffic based on control plane programming of the programmable electronic schedulers to serve the electronic packet queues in a manner providing end-to-end deterministic communication of the traffic.

The invention provides an intelligent air-sea nonlinear strong coupling assimilation method based on Python-Fortran hybrid programming, and the method comprises the steps: constructing a Python-Fortran hybrid programming platform Hf2pMDA-CM2CDA, achieving the bidirectional calling and efficient integration of Python and Fortran codes through an f2py protocol, and achieving the nonlinear strong coupling data assimilation SCDA in a hidden space through combining with a variational auto-encoder VAE. The method comprises the specific steps that a hybrid computing environment is set up, a CM-CDA Fortran module is packaged into a Python callable module, a Python master control program PMC is designed, dimension reduction and reconstruction from a physical space to a hidden space are conducted through VAE, observation items are introduced into the hidden space, minimization optimization is conducted through a three-dimensional variation method, and finally cross-ring-layer collaborative assimilation of atmosphere and ocean observation data is achieved. The non-linear strong coupling assimilation between the ocean atmosphere can be quickly and efficiently realized, and the three-dimensional monitoring and forecasting capability of extreme weather (such as typhoon) and climate (such as ocean heat wave, atmosphere MJO, ENSO and the like) phenomena related to the air-sea interaction can be continuously improved. And the method has a wide application prospect and a very high practical value.

This application discloses a hybrid programming method, apparatus, device, storage medium, and program product, relating to the field of embedded system programming technology. The hybrid programming method includes: acquiring a trigger signal and parsing the trigger signal according to a preset priority rule to obtain an initial startup intent; performing a self-check on the chip's system status using an intelligent diagnostic engine to obtain a system health report; programming the target startup mode of the chip based on the system health report and the initial startup intent; and programming the bootloader corresponding to the target startup mode. By parsing the trigger signal using a preset priority rule, an initial startup intent is determined from multiple possible startup sources. This allows startup to no longer rely on a single hardware pin and can respond to signals from multiple sources such as remote commands and timed events. Simultaneously, the priority rule avoids multi-source conflicts, ensuring that the system executes the most reasonable startup intent according to its importance, thus improving the flexibility and controllability of startup.