4results about How to "Realize collaborative scheduling" patented technology

The application relates to the technical field of path planning, in particular to a crowd-sensing cooperative optimization method and system based on evolutionary fusion two-stage mixing. The method comprises the following steps: constructing a multi-agent cooperative optimization model of a heterogeneous space based on a mobile crowd-sensing operation scene; adopting a stage-type evolutionary fusion strategy to deeply fuse MOPSO and NSGA-II, and constructing an EF-DH algorithm; using the EF-DH algorithm to perform unmanned aerial vehicle multi-target path planning based on the constructed multi-agent cooperative optimization model, including first-stage unmanned aerial vehicle cluster path optimization and second-stage ground operation personnel task optimization; and performing air-ground cooperative execution and dynamic re-optimization based on the path planning. The evolutionary fusion two-stage optimization algorithm fusing MOPSO and NSGA-II is constructed, and the global search capability and local convergence performance of the multi-target optimization problem are effectively improved.

This invention discloses an optimized scheduling method for a power system incorporating biomass indirect co-firing units, belonging to the field of power system optimization. The method includes: establishing a power system scheduling model with the objective function of minimizing the sum of power generation cost, coal-fired unit start-up cost, carbon trading cost, and curtailment penalty; and using the following optimization variables: the quality of thermal coal input to the boiler of each coal-fired unit at each time, the quality of biomass input to the pyrolysis furnace of each coal-fired unit at each time, the start-up status of each coal-fired unit, the net carbon dioxide emissions of each coal-fired unit at each time, and the renewable energy curtailment of each renewable energy unit at each time. The optimized scheduling plan is obtained by solving the power system scheduling model under preset constraints. These preset constraints include the operational constraints of the pyrolysis furnace, boiler, and turbine within the coal-fired units. This invention can simultaneously improve the renewable energy absorption capacity and reduce carbon emissions while satisfying the operational constraints of the pyrolysis furnace, boiler, and turbine within the coal-fired units.

The application discloses a cloud-edge-end cooperative industrial Internet of Things dynamic task unloading method and system. The method comprises the following steps: obtaining the running state information of each terminal device in the industrial Internet of Things system, and obtaining the state estimation information of each edge server in the industrial Internet of Things system and the overall load condition of the industrial Internet of Things system from a digital twin layer to obtain the current system state information of the industrial Internet of Things system; obtaining the predicted time sequence feature representation containing the time dependence relationship according to the current system state information and a plurality of historical system state information of the industrial Internet of Things system through each terminal device, then processing the predicted time sequence feature representation according to the trained task unloading strategy generation model issued by the cloud end, generating the task unloading strategy of each terminal device, and executing the task unloaded by the corresponding terminal device through the target edge server, so that efficient, low-delay and stable task unloading is realized.

The application discloses a fire-fighting information transmission method and system based on edge computing, and relates to the technical field of data transmission; each edge node acquires initial fire-fighting data uploaded by a corresponding target area; data cleaning is performed on the initial fire-fighting data to obtain effective fire-fighting data; abnormality detection is performed on the effective fire-fighting data to obtain a detection result of each type of fire-fighting data; target upload data uploaded to the cloud is determined according to the detection result of each type of fire-fighting data; the total amount of data of all target upload data is counted, and the theoretical upload time length required for the edge node to upload the target upload data to the cloud server is calculated according to the current available upload bandwidth of the edge node; if the theoretical upload time length is greater than a preset time threshold, a data offloading request is sent from the edge computing node to a neighboring edge computing node, and fire-fighting information is uploaded. Thus, in the case of high-bandwidth data, the transmission efficiency of fire-fighting information is still guaranteed.