33results about How to "Save manpower, material resources and time" patented technology

The invention discloses an automatic silicon crystal plate counting and integer packaging device. The automatic silicon crystal plate counting integer packaging device comprises a first conveyor, a first photoelectric induction switch, a second conveyor, suction cup moving devices, a second photoelectric induction switch, a distribution box, an infrared induction counter, a first lifting machine,a dragging plate moving device, a weight inductor, a packaging conveyor, a second lifting machine and triangular iron blocks. The first conveyor and the second conveyor are vertically connected, the right side of the first conveyor is connected with the first lifting machine, the suction cup moving devices are arranged on the two sides of the first conveyor, the suction cup moving devices stretchacross the second conveyor, the dragging plate moving device is arranged on the first lifting machine, the right side of the first lifting machine is connected with the packaging conveyor, the tail end of the packaging conveyor is connected with the second lifting machine, a material plate is arranged on the packaging conveyor, and the front end and the rear end of the material plate are fixedly provided with the triangular iron blocks. According to the device, cost is saved, and use is convenient.

The invention provides a method for recognizing urban road driving direction on the basis of floating car GPS information. GPS positioning point data of a floating car in a preset period of time is obtained, wherein the GPS positioning point data includes positioning point position information and the positioning point driving direction; the GPS positioning point data of the floating car is matched with a road section; according to the positioning point position information, position information of a first break point and a second break point adjacent to a positioning point starts to be found from the starting point of the road section; the included angle between a vector composed of the first break point and the second break point and the positioning point driving direction is calculated, and the road section driving direction is judged according to the included angle; if the included angle is larger than 90 degrees, the road section has the directivity of reverse driving, and otherwise, the road section has the directivity of forward driving. The driving direction of the floating car is calculated according to single GPS positioning point data and the break points of the road section, the driving direction of the road section is further judged, and calculation is quick and convenient.

The invention belongs to the technical field of pile foundation engineering field tests, and relates to a method for monitoring the soil pressure and displacement of a miniature steel pipe pile body. The hole is drilled at a predetermined position outside the pile body and the thickness of welding is slightly lower than that of the XY-TY02A resistance miniature soil pressure gauge. The metal protective shell of the sensor is attached to the pile body, and the XY‑TY02A resistive miniature earth pressure gauge is pasted on the pile body. The transmission line of the XY‑TY02A resistive miniature earth pressure meter is led out from the steel pipe pile through the sensor installation hole to communicate with the data acquisition system, and is connected to the micro The steel pipe pile preset position is welded with reflective sheet base and pasted with reflective sheet to realize displacement monitoring. The device adopted is simple in structure, simple and convenient in operation, reliable and stable, high in measurement accuracy and survival rate, low in cost, saves manpower, material resources and time, and can monitor in real time The change of earth pressure of the micro-steel pipe pile support of rock-soil foundation pit provides experimental data for on-site monitoring of the micro-steel pipe pile support theory of rock-soil foundation and provides reference value for engineering practice.

The invention discloses a method and system for monitoring and analyzing the movement trajectory of personnel. MAC acquisition equipment is installed in the monitoring space, WIFI signals are transmitted to the outside, and signals sent by terminal equipment are automatically collected; the movement trajectory analysis platform is initialized; the MAC acquisition equipment real-time The scanned MAC is sent to the motion trajectory analysis platform; after the motion trajectory analysis platform receives the collected MAC, it intelligently merges and stores the data. If the signal of the smart device corresponding to a certain MAC cannot be received within the specified time, then It is considered that the detection object has left the area; the running track of the MAC that needs to be tracked is displayed; if the alarm area of ​​the MAC has been set, the judgment is made and the alarm information is pushed in real time. The present invention does not need manual visual inspection or manual detection. Tracking can be collected by using the existing network system, without additional wiring construction, and can automatically analyze and display the real-time or historical movement trajectory of personnel.

The invention discloses an automatic model evaluation method and device based on a batch normalization layer. The method comprises the following steps: after first training is completed, extracting batch normalization layer parameters of a fourth module of a model as a measurement reference; training the model for the second time on a label-free test data set needing to be evaluated, wherein in the second training process, parameters of a non-batch normalization layer do not need to be updated, gradient back propagation is not needed, and only the two parameters of the mean value and the variance of the batch normalization layer need to be updated; after the second training is completed, extracting batch normalization layer parameters of the fourth module of the model after the second training; respectively carrying out Gaussian normalization on the reference parameters obtained by the first training and the parameters extracted after the second training on the new test set, then calculating the Euclidean distance between the two groups of parameters, and using the distance to evaluate the precision of the label-free test set on the original model.