De Blau got Guardiola’s support in his recovery, but the Manchester City manager hinted that if he didn’t play his best, there would be no guarantee that he would get the starting position. De Braune recently missed two games due to injury, and resumed starting in last week’s game. Guardiola called his performance in the first half "passive", but after the intermission, the Belgian’s performance improved and scored a wonderful goal.

However, in the game against Newcastle last week, De Braune was somewhat out of step, which prompted the manager to tell him to "get back to basics" in this week’s training. With Manchester City’s away game against Crystal Palace on Saturday, Guardiola supported him: recovering from a slight decline.

Guardiola explained: De Braune is ready to play, just like other Manchester City players. On the first day of our arrival, everyone stayed late for training. At the same time, Gua Shuai also said: He reserved the possibility of rotating the lineup at Selhurst Park. I know him well and know how important he is. At the same time, there are ups and downs, just like Phil Forden and other players, they compete with other players. At a certain stage of the season, it is normal that one player may be better than another player and the other player may be worse. I hope the team can win this special game. Football is always so unpredictable. In these seven years, De Braune has always been himself. When he missed the game, he was either rested, tired or injured.

Guardiola said that the next week will be "decisive" for Manchester City, which will be their last Premier League game before the international match day, followed by the second leg of the Champions League 16 against Leipzig and the FA Cup quarter-final against Burnley. After announcing that De Braune was ready for these games, Guardiola insisted: The only focus of Manchester City is to go to London to challenge Crystal Palace.

"I will never give priority to the Champions League. We are fighting for the Premier League. The most important thing is the league. We have had a long week, so we need to have a good rest, rest for two days and try to play well. This has nothing to do with Leipzig. The strength of Crystal Palace deserves attention. They have many ace players in the frontcourt, and their games are unbelievable. We have to pay attention to a lot. The attack of Crystal Palace is incredible. They scored two goals through positioning. We must be careful. The players know this! "

Guardiola also talked about how Manchester City adapted to Harland’s tactics: "Find this balance in the right way, because when Harland starts running with strength, he tends to give him the ball because he is going to score!"! But there were several accidents in the game against Newcastle. I won’t rely on Harland alone. Our tactics are very rich! "

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With the continuous development of artificial intelligence technology, the upsurge of AI big model is on the rise. Large model refers to a deep learning model with billions or tens of billions of parameters, which can be used in various fields such as natural language processing, image processing and speech recognition. The rise of the AI ? ? big model will have a far-reaching impact on developers and enterprises. Below we will analyze it from the following aspects:

Building a digital China is an important engine to promote Chinese modernization in the digital age and a powerful support to build a new competitive advantage of the country. The development of artificial intelligence can provide strong support for improving the construction of digital China. A number of members from Chinese People’s Political Consultative Conference put forward suggestions to incorporate digital citizens into the top-level design system of digital China, accelerate the efficient circulation and use of data elements, improve the standard system of artificial intelligence, and accelerate the construction of a pioneer city of artificial intelligence. While our state-owned enterprises and institutions actively promote informatization and digitalization, they also promote the training of relevant talents.

With the acceleration of urbanization and the popularity of automobiles, traffic congestion has become a problem in many cities. Because of the difference of traffic signal control mechanism, navigation software sometimes leads drivers to crowded sections, which slows down the travel speed and affects the road traffic flow. In recent years, more and more navigation software began to try to predict the change of traffic lights through intelligent technology, so that drivers can travel more efficiently. So, how does the navigation know that the red light is about to turn green? This involves black technology such as machine learning and Internet of Things technology, which will be introduced in detail below.

First, the traffic light control mechanism

Before introducing the change mechanism of navigation forecasting traffic lights, let’s first understand the principle of traffic light control. Signal light control mainly adopts timing control and induction control.

Timing control: the signal light changes according to the preset time, for example, the parking red light is on for 3 minutes and the green light is on for 1 minute. This method is mainly suitable for areas with small human flow, such as night or remote areas.

Induction control: the signal lamp responds to the traffic flow on the road through the induction equipment, and dynamically adjusts the time of light color change. For example, when it is detected that the traffic volume increases and there is accumulation at the same time, the signal lamp will adjust the color change time according to the situation to give priority to ensuring the smooth flow of traffic.

Second, how does navigation predict the change of signal lights?

Based on the different control mechanisms of traffic lights, navigation software can use different technologies to predict the changes of traffic lights.

1. Timing prediction

For the signal lamp with timing control, navigation can calculate the period and time of signal lamp change by recording historical data, and compare it with the current time, thus predicting the time of signal lamp change. This method is simple and reliable, and is suitable for areas with small human flow. However, because this method can’t consider external factors such as traffic flow changes on roads, the accuracy is low for urban areas with traffic congestion.

2. Sensor prediction

For traffic lights controlled by inductive devices, navigation software can obtain real-time traffic flow data through the Internet of Things technology, so as to predict the changes of traffic lights. This method needs to cooperate with the sensor equipment on the road, and establish a data model by collecting data such as traffic flow and speed, so as to accurately predict the changing time of traffic lights. For example, Google tried this method in its own map, and established a global traffic information database by collecting users’ location data and traffic condition data, thus providing real-time congestion information on a global scale.

3. Artificial intelligence prediction

In addition to sensor prediction, machine learning can also be applied to signal light change prediction. Navigation software can build a model to predict the change of traffic lights by collecting users’ position, speed and various data used for road features, such as historical traffic volume, rush hour travel time, weather, etc., and then using machine learning algorithms to analyze these data. Using artificial intelligence technology to predict traffic lights can predict the changing time of traffic lights more accurately, and at the same time, take road traffic conditions into consideration to enhance the stability and accuracy of prediction.

III. Summary of the article

With the continuous development of Internet of Things technology and artificial intelligence, the ability of navigation software to predict the change of traffic lights will become stronger and stronger. To sum up, navigation can predict the change of traffic lights by recording historical data, sensor prediction and artificial intelligence prediction. However, at present, the accuracy of navigation software to predict the change of traffic lights still needs to be improved, and more accurate data collection and more efficient algorithms are needed to strengthen the prediction model. The technology of predicting the change of traffic lights can better realize the intelligence of urban traffic and provide drivers with a more convenient and efficient travel experience.