8results about How to "Effective data support" patented technology

The invention relates to the technical field of intelligent early warning, in particular to a corn disease monitoring and early warning system based on agricultural Internet of Things, which comprises a data fusion calibration module, a multi-dimensional feature extraction module, a self-adaptive fusion reference construction module, a feature evaluation and fusion module, a classification and recognition module and an early warning decision generation module, carrying out fusion calibration on the visible light image data and the corresponding environment data to obtain standard monitoring data; performing multi-dimensional feature extraction on the standard monitoring data to obtain image feature information and environment feature information; constructing a multi-source feature adaptive fusion reference; evaluating the disease influence degree of the image feature information and the environment feature information to obtain a comprehensive feature index; identifying a disease type and a disease grade as a disease identification result of the corn; according to a disease early warning mapping relation in a disease early warning rule base and a continuous monitoring period condition required for triggering early warning, obtaining early warning information; according to the invention, the efficiency of corn disease monitoring and early warning can be improved.

The application discloses an operating mechanism data acquisition system and a driving loop module and method thereof, and the module comprises a high-voltage direct-current power supply HM, a high-voltage direct-current power supply KM, a power adapter DP, an embedded control module QK, a control switching plug HT, a supply and discharge circuit, a closing and opening knob KK and a rotary switch ZH2; the high-voltage direct-current power supply HM provides an energy storage power supply and a closing power supply for a measured operating mechanism, the high-voltage direct-current power supply KM provides an opening power supply for the measured operating mechanism, the power adapter DP provides a working power supply for the embedded control module QK, and the required operating power supply voltage for testing can be met; the embedded control module QK can realize program control of driving, the rotary switch ZH2 can realize mode conversion between manual control and program control of driving, and the closing and opening knob KK can realize manual control of driving, so that the problem that traditional operating mechanism research experiments cannot simultaneously integrate automatic control of multiple characteristic parameters is solved.

The present application provides a kind of sedimentation tank sludge quantity prediction system and method, it is related to sedimentation tank sludge control technical field, comprising: for each sedimentation period of sedimentation tank, at least one group of historical water inlet parameters and corresponding historical sludge-water interface height in non-sludge discharge period are obtained respectively, and total actual sludge production after sludge discharge period ends and corresponding total sludge-water interface height;According to each historical sludge-water interface height, total actual sludge production and total sludge-water interface height, the real-time actual sludge production corresponding to historical water inlet parameters is obtained by processing;Real-time water inlet parameters are obtained as input, and the sludge quantity prediction model corresponding to the real-time actual sludge production as output is trained;A group of real-time water inlet parameters of sedimentation tank in non-sludge discharge period is obtained, and real-time water inlet parameters are input into sludge quantity prediction model, to obtain the sludge quantity prediction result of sedimentation tank.The beneficial effect is that the real-time sludge production of sedimentation tank can be effectively predicted;Self-adaptive training and correction of sludge quantity prediction model can be realized.

The utility model discloses a fault bearing test platform, comprising a signal acquisition device which comprises a bottom plate, and a force application driver, a bearing installation seat and a rotation driver which are successively arranged on the bottom plate, the bearing installation seat comprises an outer ring seat and an inner ring seat which are coaxially sleeved from outside to inside, and a fault bearing is installed between the outer ring seat and the inner ring seat; the outer ring seat is rotatably arranged, the driving end of the rotary driver is connected with the outer ring seat and is used for driving the outer ring seat to rotate, the driving end of the force application driver is coaxially connected with the inner ring seat and is used for applying an axial load to a fault bearing, and a sensor is arranged in the inner ring seat. The fault bearing test platform can simulate the real use condition of a fault bearing, can provide effective data support for a bearing diagnosis algorithm, and also can verify the reliability of a sensor and a cable thereof under the real use condition.

This invention discloses a method, apparatus, electronic device, and readable storage medium for NCFI carbon emission responsibility allocation that considers fairness. The allocation apparatus includes a power flow distribution acquisition module, an original allocation result acquisition module, an allocation fairness interval acquisition module, an original satisfaction acquisition module, and a satisfaction optimization and adjustment module. The satisfaction optimization and adjustment module includes an original satisfaction calculation module, a first satisfaction optimization and adjustment module, and a second satisfaction optimization and adjustment module. This invention, on the one hand, can better link the historical emission responsibility of load nodes, individual development opportunities, and node carbon emission responsibility, considering fairness without changing the total responsibility allocation, thus avoiding the phenomenon of unfair allocation schemes; on the other hand, it reduces the responsibility allocation of most load nodes based on reasonable and feasible allocation results, providing effective data support for the formulation of corporate carbon reduction strategies, and is of great significance to coordinated economic development.

This invention discloses a method and system for detecting the cleanliness of photovoltaic panels, relating to the field of photovoltaic panel detection technology. The method includes first collecting benchmark data of a standard clean photovoltaic panel, dividing the effective light-receiving surface of the panel under test into several independent detection zones, calculating the cleanliness of each zone, extracting the spatial distribution characteristics of cleanliness, calculating the uniformity of light absorption in combination with local light absorption intensity, and obtaining an initial judgment result of overall cleanliness through correlation determination; then, based on the cleanliness threshold of each zone and the natural contamination pattern of the array, selecting contamination characteristic areas and clean benchmark areas to obtain on-site verification data, calibrating the initial judgment result, and obtaining the final detection result. The system includes modules corresponding to the steps of the method. This method can provide specific information on the contamination distribution in each area for the clean operation and maintenance of photovoltaic panels, improve the pertinence and actual implementation effect of photovoltaic panel clean operation and maintenance work, and provide effective data support for the daily operation and maintenance of photovoltaic power plants.

The invention discloses a handheld image point cloud and laser point cloud fusion detection method for a bridge prefabricated part. The method comprises the following steps: synchronously obtaining a laser point cloud and a sequence image of the prefabricated part; reconstructing the sequence image to obtain an image point cloud; performing normalization by taking the laser point cloud as a reference to obtain the laser point cloud and the image point cloud with consistent physical scales; initial attitude alignment of the two sets of point clouds is completed, rigid fine registration is carried out, the laser point clouds are corrected, and fused point clouds are obtained; generating a standard virtual point cloud; performing semantic segmentation on the fused point cloud, and outputting a point cloud set of each sub-component; extracting line-plane features of the key points; calculating the key geometric dimension of the prefabricated part, comparing the key geometric dimension with the standard virtual point cloud, and judging whether the dimension deviation meets a preset deviation or not; and evaluating the flatness quality of the surface of the component, and generating a three-dimensional full-field deviation chromatogram which covers the whole surface of the component. According to the invention, rapid, comprehensive and high-precision detection of the geometric dimension and the appearance flatness of the prefabricated part is realized.

This application provides a method, apparatus, electronic device, and storage medium for testing the capacity of a storage battery. The method includes: receiving discharge start information and periodically recording the discharge load current and individual battery cell voltages; the discharge start information is the start of battery discharge when the power supply system fails; calculating the discharged capacity based on the discharge load current and individual battery cell voltages, and plotting a time-capacity discharge curve and a voltage-capacity discharge curve; comparing the time-capacity discharge curve and the voltage-capacity discharge curve with a standard discharge curve to evaluate the remaining battery capacity; and generating first capacity evaluation information based on the remaining battery capacity, the first capacity information including the battery's remaining runtime. This method reduces the workload of the capacity testing process and can also provide timely and effective data support for emergency response and dispatch.