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Resource allocation can greatly improve the utilization efficiency of various resources such as energy.

If artificial intelligence can shine in these fields, the future life of human beings will be greatly changed.

The last item, competitive games, is relatively the fairest item, which can objectively reflect the computing power of artificial intelligence.

After confirming these rules, the date of the competition was also set one month later.

"Professor Xu, is one month too short?"

Han Shubin knows that apart from the field of competitive games, we have not yet started researching the other two fields.

On the IBM and Google side, the technology is relatively mature.

"One month is enough, let's set it at this time."

Seeing that Xu You was so confident, Han Shubin didn't ask any more questions.

In fact, even if the time limit is reduced to half a month, Xu You is very confident that he can complete these tasks.

Xu You couldn't wait for this quantum competition.

"Let's start with the weather forecasting program."

Although Xu You has not formally conducted research on the field of artificial intelligence weather forecasting before.

But Xu You is still very familiar with artificial intelligence's method of predicting the weather.

In weather forecasting, artificial intelligence mainly uses methods, including smart grid forecasting and model analysis techniques.

Smart grid forecasting uses big data analysis skills to mine and analyze a large amount of weather data in an all-round way.

Through such a smart grid system, artificial intelligence can accurately predict various weather.

This kind of weather forecasting method is accurate in predicting precipitation, but the disadvantage is that the forecast period is relatively short, and it is impossible to accurately predict the weather in a few days.

Another method of weather forecasting is model analysis technology.

Model analysis technology will also use various big data and artificial intelligence, but the focus is on the need to model a complex meteorological system.

This weather forecast method may not be as accurate as the first method in the short term, but it has a higher forecast accuracy rate in the long run.

The weather within a month can be predicted more accurately.

However, because the amount of calculation is too large, the performance requirements for the computer are very high.

Even many supercomputers are not capable of such a huge amount of calculation.

But these are not a problem for quantum computers with extremely strong calculation speed.

What's more, Xu You also has the skill of brain simulation to help him complete the modeling work.

In just three days, Xu You completed all the programming and modeling work, and taught Suanjing to predict the weather.

"Our weather forecasting system has also learned a number of weather forecasting methods such as smart grid forecasting and model analysis technology. It can accurately predict the weather through its own system scoring mechanism. In the case of sufficient meteorological data , the calculation can predict the weather within 24 hours almost 100%. Even for the weather within a month, the prediction accuracy can be increased to more than 95%. "

Because the weather is affected by too many factors, it is almost impossible to predict the weather in a few days with 100% accuracy.

A butterfly flapping its wings may change the weather of a certain day.

Not to mention, it is artificial rainfall and the like, which artificially changes the weather.

But for the data given by Xu You, a researcher raised his own doubts.

"Professor Xu, I can understand the accuracy of the weather forecast within 24 hours. But...how did you come up with the accuracy of the weather forecast within a month?"

This doubt is very normal, because it has only been three days since Xu You made this weather prediction model.

It is too late to count the accuracy of the model.

"This data is a theoretical value, and we will know the specific accuracy rate later."

As he spoke, Xu You showed on the big screen the weather forecast just made by artificial intelligence.

According to the radar and other data provided by the National Meteorological Observatory, the artificial intelligence has completed the weather forecast for all parts of the world within one month.

However, compared with the weather forecast given by the Meteorological Observatory, the weather forecast calculated by artificial intelligence will have some discrepancies. Even a certain day in a certain place gives a completely different forecast for whether it will be sunny or rainy.

"Professor Xu, if it's just a theoretical value, will this model lack sufficient verification?"

"Observe for half a month first, and if the data does not meet the standard, we will make changes to the model."

In fact, Xu You's confidence is very high. According to the simulation results of Xu You's brain, the accuracy of this model is even higher than the data given by Xu You.

Xu You also understands their skepticism, after all, if the normal procedure is followed, it will definitely need to be verified and modified many times.

"I agree with Professor Xu's statement that the prediction accuracy of the model will be known in a few days." Han Shubin said.

Even Han Shubin couldn't understand how Xu You came up with the theoretical value predicted by the model.

But as long as this achievement comes from Xu You, there is nothing to doubt.

After completing the weather prediction model, Xu You immediately conducted research on the task of resource allocation.

Compared with weather forecasting, resource allocation problems are much less contingency, and the main consideration is the computing power of quantum computers.

For example, in terms of energy deployment, the data provided by the power grid is used to predict the power load, and then provide predictive maintenance measures to provide accurate power supply and demand solutions.

Or in the field of wind power generation, build and train neural network models based on historical power generation data and weather forecast information to optimize wind power generation schemes and improve the efficiency of wind power generation.

Over the past two days, Suanjing AI has learned to solve various resource allocation problems.

Compared with the previous model, the artificial intelligence can improve the efficiency by 20% to 50%, making the allocation of resources more reasonable.

And with the passing of these two days, the accuracy of the weather forecast by artificial intelligence can also be verified.

"Professor Xu, the accuracy rate of our weather forecasts around the world in the past two days has reached 99.9%. Many of the places where the forecasts are inaccurate are also caused by man-made actions such as artificial rainfall, which affects the accuracy of our forecasts." A member of the project team said.

Such an accuracy rate means that only one mistake will be made if artificial intelligence predicts the weather a thousand times.

This is already a very high data for a weather forecast that already has a lot of contingencies.