In the existing French Named Entity Recognition (NER) research, the machine learning models mostly use the character morphological features of words, and the multilingual generic named entity models use the semantic features represented by word embedding, both without taking into account the semantic, character morphological and grammatical features comprehensively. Aiming at this shortcoming, a deep neural network based model CGC-fr was designed to recognize French named entity. Firstly, word embedding, character embedding and grammar feature vector were extracted from the text. Then, character feature was extracted from the character embedding sequence of words by using Convolution Neural Network (CNN). Finally, Bi-directional Gated Recurrent Unit Network (BiGRU) and Conditional Random Field (CRF) were used to label named entities in French text according to word embedding, character feature and grammar feature vector. In the experiments, F1 value of CGC-fr model can reach 82.16% in the test set, which is 5.67 percentage points, 1.79 percentage points and 1.06 percentage points higher than that of NERC-fr, LSTM(Long Short-Term Memory network)-CRF and Char attention models respectively. The experimental results show that CGC-fr model with three features is more advantageous than the others.

For the traditional 3D robot scanners, the measuring precision is dependent on the positioning precision of the robot, and it is difficult to achieve high measuring precision. A photogrammetric method was proposed to track and position the 3D scanning probe accurately. First, a probe tracking system consisting of multiple industrial cameras was set up, and coded markers were pasted on the probe. Then, the camera was calibrated with high precision and interior and exterior parameters of the camera were obtained. Second, all cameras were synchronized, the markers in the image were matched according to the coding principle, and the projection matrix was obtained. Finally, the 3D coordinates of the markers in space were computed to track and position the probe. The experimental results show that the mean error of the marker position is 0.293 mm, the average angle error is 0.136°, and the accuracy of the algorithm is within reasonable range. The photogrammetric method can improve the positioning precision of the probe, so as to achieve high precision measurement.

Aiming at efficient data acquisition, real-time precise positioning and attitude measurement problems of geostress low-frequency electromagnetic monitoring, real-time data acquisition system was designed and implemented in combination with positioning and attitude measurement module. The hardware system took ARM microprocessor (S3C6410) as control core based on embedded Linux. The hardware and software design architecture were introduced in detail. In addition, the algorithm of positioning and attitude measurement characteristics data extraction was proposed. Monitoring terminal of data acquisition and processing was designed using Qt/Embedded GUI programming technique based on LCD (Liquid Crystal Display) and achieved human-computer interaction. Meanwhile, the required data could be real-time stored to SD card. The results of system debugging and actual field experiments indicate that the system can complete the positioning and attitude data acquisition and processing, effectively solve the problem of real-time positioning for in-situ monitoring. It also can realize geostress low-frequency electromagnetic monitoring with high-speed, real-time and high reliability.

Concerning the perspective of supply chain integration, a blood supply model was developed, which aimed to minimize the blood acquisition risk, system operation cost, the punishment for both excessive and insufficient acquisition by the multi-objective programming method. Taking into account the feature that the amount of expired blood is proportional to time, as well as the cost for expired blood processing, a regional supply and demand equilibrium model characterized by stochastic demand of the four types of blood was built. The model was proved to be convex, and the variational inequality of the blood supply and demand network equilibrium was derived. By modified quasi-Newton method, the solutions of the blood supply chain supply and demand equilibrium under stochastic demand condition were obtained. Finally, a case study in Chengdu verified the model's applicability.