Abstract: Provided is a cloud-edge collaboration forecasting system and method for aluminum oxide production indexes. The forecasting system performs forecasting algorithm selection, parameter configuration and model training on indexes and variables of the aluminum oxide production process at a cloud model training server, performs evaluation and parameter correction on the trained model to obtain an optimal training model, and pre-processes the data in the aluminum oxide production process at an aluminum oxide production index forecasting computer at an edge end. The trained model parameters are imported from the cloud, and further the trained forecasting model is used for forecasting aluminum oxide production indexes for different production processes.

Abstract: Provided is an aluminum oxide production operation optimization system and method based on a cloud-edge collaboration, which relates to the technical field of an aluminum oxide production operation optimization. According to the system and method, firstly the whole-flow data in the aluminum oxide production process is acquired, the data is pre-processed, then the pre-processed data is transmitted to a local collaboration production operation optimization unit, the local collaboration production operation optimization unit firstly judges working conditions for the current aluminum oxide production process, an optimization strategy needing to be operated at present is automatically switched according to the working condition, and the local operation optimization strategy obtains the actual setting value of the aluminum oxide production operation indexes.

Abstract: The invention provides a decision-making method of comprehensive alumina production indexes based on a multi-scale deep convolutional network. The method mainly consists of several sub-models: a multi-scale deep splicing convolutional neural network prediction sub-model reflecting the influence of bottom-layer production process indexes on the comprehensive alumina production indexes, a full connecting neural network prediction sub-model reflecting the influence of upper-layer dispatching indexes on the comprehensive alumina production indexes, a full connecting neural network prediction sub-model reflecting the influence of the comprehensive alumina production indexes at a past time on current comprehensive alumina production indexes, and a multi-scale information neural network integrated model for collaborative optimization of sub-model parameters.

Abstract: A multi-scale data acquiring and processing device and method for an aluminum oxide production process. The device includes a production index and variable configuring module, a data acquiring module, a data storing module, a main control module, a display module, a data processing module and a data transmitting module. The main control module is used for emitting a command, acquiring production indexes and variables generated in the aluminum oxide production process by different process control devices, and is used for performing unified processing, storage and display on the data, and further the data is transmitted through a transmitting module to systems or devices using the data.

Abstract: Provided is an aluminum oxide production operation optimization system and method based on a cloud-edge collaboration, which relates to the technical field of an aluminum oxide production operation optimization. According to the system and method, firstly the whole-flow data in the aluminum oxide production process is acquired, the data is pre-processed, then the pre-processed data is transmitted to a local collaboration production operation optimization unit, the local collaboration production operation optimization unit firstly judges working conditions for the current aluminum oxide production process, an optimization strategy needing to be operated at present is automatically switched according to the working condition, and the local operation optimization strategy obtains the actual setting value of the aluminum oxide production operation indexes.

Abstract: Provided is a cloud-edge collaboration forecasting system and method for aluminum oxide production indexes. The forecasting system performs forecasting algorithm selection, parameter configuration and model training on indexes and variables of the aluminum oxide production process at a cloud model training server, performs evaluation and parameter correction on the trained model to obtain an optimal training model, and pre-processes the data in the aluminum oxide production process at an aluminum oxide production index forecasting computer at an edge end. The trained model parameters are imported from the cloud, and further the trained forecasting model is used for forecasting aluminum oxide production indexes for different production processes.

Abstract: The invention provides a visualized time sequence pattern matching method based on Hough transformation, and relates to the technical field of data visualization analysis. The method comprises the steps of: firstly, judging whether historical data to be matched is one-dimensional time sequence data or multi-dimensional time sequence data, and if the historical data to be matched is the multi-dimensional time sequence data, performing normalization processing; performing time sequence selection: selecting a time sequence to be matched from the historical data in a time window pattern, and eliminating the selected time sequence from the historical data; converting a time sequence image in original coordinates to Hough space through the Hough transformation, and judging the similarity matching situation of the time sequence through a voting mechanism; and finally, screening the finally-matched results according to the voting results.

Abstract: The invention provides a decision-making method of comprehensive alumina production indexes based on a multi-scale deep convolutional network. The method mainly consists of several sub-models: a multi-scale deep splicing convolutional neural network prediction sub-model reflecting the influence of bottom-layer production process indexes on the comprehensive alumina production indexes, a full connecting neural network prediction sub-model reflecting the influence of upper-layer dispatching indexes on the comprehensive alumina production indexes, a full connecting neural network prediction sub-model reflecting the influence of the comprehensive alumina production indexes at a past time on current comprehensive alumina production indexes, and a multi-scale information neural network integrated model for collaborative optimization of sub-model parameters.

Abstract: The invention provides a visualized time sequence pattern matching method based on Hough transformation, and relates to the technical field of data visualization analysis. The method comprises the steps of: firstly, judging whether historical data to be matched is one-dimensional time sequence data or multi-dimensional time sequence data, and if the historical data to be matched is the multi-dimensional time sequence data, performing normalization processing; performing time sequence selection: selecting a time sequence to be matched from the historical data in a time window pattern, and eliminating the selected time sequence from the historical data; converting a time sequence image in original coordinates to Hough space through the Hough transformation, and judging the similarity matching situation of the time sequence through a voting mechanism; and finally, screening the finally-matched results according to the voting results.

Abstract: A multi-scale data acquiring and processing device and method for an aluminum oxide production process. The device includes a production index and variable configuring module, a data acquiring module, a data storing module, a main control module, a display module, a data processing module and a data transmitting module. The main control module is used for emitting a command, acquiring production indexes and variables generated in the aluminum oxide production process by different process control devices, and is used for performing unified processing, storage and display on the data, and further the data is transmitted through a transmitting module to systems or devices using the data.

Abstract: Provided is an optimized decision-making system for multiple ore dressing production indexes based on a cloud server and mobile terminals, including mobile intelligent terminals, a cloud server, a mobile industrial private cloud server, a collecting computer and process controllers PLC or DCS. The mobile industrial private cloud server calculates out multiple decision-making result solution sets; the intelligent mobile terminals determine the final decision-making results; the mobile industrial private cloud server calculates out process control set values; the mobile intelligent terminals determine the final process control set values; and the process controllers PLC or DCS control equipment on a production line for production according to the final process control set values. The present invention further provides an optimized decision-making method for multiple ore dressing production indexes adopting the optimized decision-making system.

Abstract: Provided is an optimized decision-making system for multiple ore dressing production indexes based on a cloud server and mobile terminals, including mobile intelligent terminals, a cloud server, a mobile industrial private cloud server, a collecting computer and process controllers PLC or DCS. The mobile industrial private cloud server calculates out multiple decision-making result solution sets; the intelligent mobile terminals determine the final decision-making results; the mobile industrial private cloud server calculates out process control set values; the mobile intelligent terminals determine the final process control set values; and the process controllers PLC or DCS control equipment on a production line for production according to the final process control set values. The present invention further provides an optimized decision-making method for multiple ore dressing production indexes adopting the optimized decision-making system.

Abstract: A construction process for the composite pile foundation which consists of the expanded belled base and the pile shaft, the construction process comprising the steps of: a) Aligning the pile tip at the pile location and place the casing at the top of the pile tip; b) Lifting the heavy hammer to enter the casing, repeatedly lift the heavy hammer to reach the given height and back press the casing, let it fall freely to ram the pile tip and the soil until a hole forms at the pile location; c) Lifting the heavy hammer higher than the feed inlet of the casing and fill solid material from the inlet into the casing; d) Ramming the above-mentioned solid material and repeat the filling and ramming operations; e) Controlling the penetration of the last three blows; f) Filling graded aggregate or stiff consistency concrete and repeatedly ram it to form the expanded belled base; g) pull out the casing; h) Aligning the pile shaft at the pile hole and repeatedly back press and blow slightly with the heavy hammer to drive

Abstract: A construction process for the composite pile foundation which consists of the expanded belled base and the pile shaft, the construction process comprising the steps of: a) Aligning the pile tip at the pile location and place the casing at the top of the pile tip; b) Lifting the heavy hammer to enter the casing, repeatedly lift the heavy hammer to reach the given height and back press the casing, let it fall freely to ram the pile tip and the soil until a hole forms at the pile location; c) Lifting the heavy hammer higher than the feed inlet of the casing and fill solid material from the inlet into the casing; d) Ramming the above-mentioned solid material and repeat the filling and ramming operations; e) Controlling the penetration of the last three blows; f) Filling graded aggregate or stiff consistency concrete and repeatedly ram it to form the expanded belled base; g) pull out the casing; h) Aligning the pile shaft at the pile hole and repeatedly back press and blow slightly with the heavy hammer to drive