Data computing power recovery and energy consumption control method based on AI and blockchain

Abstract

The present invention relates to the field of artificial intelligence, and discloses a data computing power recovery and energy consumption control method based on AI and blockchain, including a computing power request processing module that generates multiple initial computing power containers according to historical computing power request data and total computing power data, and generates a task list in the initial computing power container; according to the average waiting time of the computing power tasks of the initial computing power container and the average waiting time threshold of the computing power tasks, the required number of computing power containers is obtained, and the required computing power is obtained according to the required number of computing power containers, and the required number of computing power containers is generated according to the required computing power and the idle time sequence of the second initial computing power container, and the tasks are evenly distributed to the generated computing power containers. Through the technical solution provided by the present invention, dynamic allocation of computing resources can be achieved, reducing resource waste and energy consumption.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and specifically to a data computing power recovery and energy consumption control method based on AI and blockchain. Background Art

[0002] With the continuous development of artificial intelligence (AI) and blockchain technology, the demand for data computing has gradually increased, but it has also caused the problem of energy consumption. Large-scale data computing requires a lot of computing power support, and traditional computing methods have the problem of excessive energy consumption to a certain extent. In order to meet this challenge, a data computing power recovery and energy consumption control method is proposed by combining AI and blockchain technology, aiming to improve computing efficiency and reduce energy waste. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a data computing power recovery and energy consumption control method based on AI and blockchain, comprising the following steps:

[0004] Step 1: The computing power request processing module generates multiple initial computing power containers according to the historical computing power request data and the total computing power data, and generates a task list in the initial computing power container;

[0005] Step 2: The task scheduling module obtains the requested computing power according to the computing power request information sent by the device, obtains the computing power difference between the requested computing power and each initial computing power container, obtains the utilization rate of the corresponding initial computing power container, and the initial computing power container corresponding to the maximum utilization rate is the matching initial computing power container. The device computing power request information is sent to the task list of the corresponding initial computing power container, and the task waiting time is collected;

[0006] Step 3: within the set container adjustment period, according to the computing power task list of each initial computing power container, the average waiting time of the computing power tasks of each initial computing power container and the idle time of the initial computing power container are obtained respectively; the computing power request processing module generates the first computing power container sub-table and the second computing power container sub-table respectively according to the average waiting time of the computing power tasks of the initial computing power container and the idle time of the initial computing power container, and obtains the idle time sequence of the second initial computing power container according to the size of the idle time of each initial computing power container in the second computing power container sub-table;

[0007] Step 4: Obtain the usage rate of each initial computing power container in the first computing power container sub-table respectively, and obtain the computing power container first adjustment characteristic value of each initial computing power container respectively according to the usage rate of the initial computing power container and the average waiting time of the computing power task of the corresponding initial computing power container;

[0008] Step 5: According to the set computing power container adjustment threshold, the initial computing power container that is greater than the computing power container adjustment threshold is adjusted, and then the process goes to step 6;

[0009] Step 6: According to the average waiting time of the computing tasks of the initial computing power container and the average waiting time threshold of the computing tasks, the required number of computing power containers is obtained, and the required computing power is obtained according to the required number of computing power containers. According to the required computing power and the idle time sequence of the second initial computing power container, the initial computing power container in the second computing power container subtable is called, and the required number of computing power containers is generated in combination with the initial computing power container, and the tasks are evenly distributed to the generated computing power containers, and then step 7 is entered;

[0010] Step 7: The initial computing power containers that have not been called in the second computing power container sub-table enter sleep mode, completing computing power recovery and energy consumption control.

[0011] Furthermore, the computing power request processing module generates multiple initial computing power containers according to the historical computing power request data and the computing power data, and generates a task list in the initial computing power container, including:

[0012] According to the set computing power level difference, the computing power request frequency of each level of computing power is obtained. According to the computing power request frequency, the initial computing power container and the number of initial computing power containers corresponding to each level of computing power are generated, and a task list is generated in each initial computing power container.

[0013] Furthermore, the above-mentioned respectively obtaining the difference between the requested computing power and the computing power of each initial computing power container to obtain the usage rate of the corresponding initial computing power container includes:

[0014]

[0015] Further, the computing power request processing module generates a first computing power container sub-table and a second computing power container sub-table respectively according to the average waiting time of the computing power tasks of the initial computing power container and the idle time of the initial computing power container, including:

[0016] The initial computing power containers with computing power tasks waiting are listed in the first computing power container sub-table, and the rest are listed in the second computing power container sub-table; the first computing power container sub-table includes the average waiting time of the computing power tasks of each listed initial computing power container and the usage rate of the corresponding initial computing power container; the second computing power container sub-table includes the initial computing power container idle time of each listed initial computing power container.

[0017] Further, the method of respectively obtaining the usage rate of each initial computing power container in the first computing power container sub-table, and respectively obtaining the computing power container first adjustment characteristic value of each initial computing power container according to the usage rate of the initial computing power container and the average waiting time of the computing power task of the corresponding initial computing power container, includes:

[0018] The first adjustment characteristic value = the utilization rate of the initial computing power container × the average waiting time of the computing power task

[0019]

[0020] Furthermore, the required number of computing power containers is obtained according to the average waiting time of the computing power tasks of the initial computing power container and the average waiting time threshold of the computing power tasks, and the required computing power is obtained according to the required number of computing power containers, including:

[0021]

[0022] Required computing power = (computing power of initial computing power container × utilization rate of initial computing power container) required number of computing power containers

[0023] in Indicates rounding up.

[0024] Further, the initial computing power container in the second computing power container subtable is called according to the required computing power and the idle time sequence of the second initial computing power container, and the required number of computing power containers is generated in combination with the initial computing power container, including:

[0025] The required computing power is obtained according to the sum of the computing power of the initial computing power container and the computing power of the initial computing power container in the called second computing power container sub-table, and the required number of computing power containers is generated.

[0026] The beneficial effects of the present invention are as follows: the method combines the advantages of AI and blockchain technology, realizes efficient recovery of data computing power and fine control of energy consumption. By dynamically allocating computing resources, it reduces resource waste and energy consumption. It ensures the transparency and credibility of resource allocation and energy control. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the data computing power recovery and energy consumption control method based on AI and blockchain. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0029] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0030] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention. It should be noted that relational terms such as the terms "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0031] Moreover, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus. In the absence of more restrictions, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0032] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0033] like Figure 1 As shown, the data computing power recovery and energy consumption control method based on AI and blockchain includes the following steps:

[0034] Step 1: The computing power request processing module generates multiple initial computing power containers according to the historical computing power request data and the total computing power data, and generates a task list in the initial computing power container;

[0035] Step 2: The task scheduling module obtains the requested computing power according to the computing power request information sent by the device, obtains the computing power difference between the requested computing power and each initial computing power container, obtains the utilization rate of the corresponding initial computing power container, and the initial computing power container corresponding to the maximum utilization rate is the matching initial computing power container. The device computing power request information is sent to the task list of the corresponding initial computing power container, and the task waiting time is collected;

[0036] Step 3: within the set container adjustment period, according to the computing power task list of each initial computing power container, the average waiting time of the computing power tasks of each initial computing power container and the idle time of the initial computing power container are obtained respectively; the computing power request processing module generates the first computing power container sub-table and the second computing power container sub-table respectively according to the average waiting time of the computing power tasks of the initial computing power container and the idle time of the initial computing power container, and obtains the idle time sequence of the second initial computing power container according to the size of the idle time of each initial computing power container in the second computing power container sub-table;

[0037] Step 4: Obtain the usage rate of each initial computing power container in the first computing power container sub-table respectively, and obtain the computing power container first adjustment characteristic value of each initial computing power container respectively according to the usage rate of the initial computing power container and the average waiting time of the computing power task of the corresponding initial computing power container;

[0038] Step 5: According to the set computing power container adjustment threshold, the initial computing power container that is greater than the computing power container adjustment threshold is adjusted, and then the process goes to step 6;

[0039] Step 6: According to the average waiting time of the computing tasks of the initial computing power container and the average waiting time threshold of the computing tasks, the required number of computing power containers is obtained, and the required computing power is obtained according to the required number of computing power containers. According to the required computing power and the idle time sequence of the second initial computing power container, the initial computing power container in the second computing power container subtable is called, and the required number of computing power containers is generated in combination with the initial computing power container, and the tasks are evenly distributed to the generated computing power containers, and then step 7 is entered;

[0040] Step 7: The initial computing power containers that have not been called in the second computing power container sub-table enter sleep mode, completing computing power recovery and energy consumption control.

[0041] The computing power request processing module generates multiple initial computing power containers according to the historical computing power request data and computing power data, and generates a task list in the initial computing power container, including:

[0042] According to the set computing power level difference, the computing power request frequency of each level of computing power is obtained. According to the computing power request frequency, the initial computing power container and the number of initial computing power containers corresponding to each level of computing power are generated, and a task list is generated in each initial computing power container.

[0043] The above-mentioned respectively obtaining the difference between the requested computing power and the computing power of each initial computing power container, and obtaining the usage rate of the corresponding initial computing power container, includes:

[0044]

[0045] The computing power request processing module generates a first computing power container sub-table and a second computing power container sub-table according to the average waiting time of the computing power tasks of the initial computing power container and the idle time of the initial computing power container, respectively, including:

[0046] The initial computing power containers with computing power tasks waiting are listed in the first computing power container sub-table, and the rest are listed in the second computing power container sub-table; the first computing power container sub-table includes the average waiting time of the computing power tasks of each listed initial computing power container and the usage rate of the corresponding initial computing power container; the second computing power container sub-table includes the initial computing power container idle time of each listed initial computing power container.

[0047] The method of respectively obtaining the usage rate of each initial computing power container in the first computing power container sub-table, and respectively obtaining the computing power container first adjustment characteristic value of each initial computing power container according to the usage rate of the initial computing power container and the average waiting time of the computing power task of the corresponding initial computing power container, includes:

[0048] The first adjustment characteristic value = the utilization rate of the initial computing power container × the average waiting time of the computing power task

[0049]

[0050] The method of obtaining the required number of computing power containers according to the average waiting time of the computing power tasks of the initial computing power container and the average waiting time threshold of the computing power tasks, and obtaining the required computing power according to the required number of computing power containers, includes:

[0051]

[0052] Required computing power = (computing power of initial computing power container × utilization rate of initial computing power container) required number of computing power containers

[0053] in Indicates rounding up.

[0054] The method of calling the initial computing power container in the second computing power container subtable according to the required computing power and the idle time sequence of the second initial computing power container, and generating the required number of computing power containers in combination with the initial computing power container, includes:

[0055] The required computing power is obtained according to the sum of the computing power of the initial computing power container and the computing power of the initial computing power container in the called second computing power container sub-table, and the required number of computing power containers is generated.

[0056] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A data computing power recovery and energy consumption control method based on AI and blockchain, characterized in that: The steps include: Step 1: The computing power request processing module generates multiple initial computing power containers according to the historical computing power request data and the total computing power data, and generates a task list in the initial computing power container; Step 2: The task scheduling module obtains the requested computing power according to the computing power request information sent by the device, obtains the computing power difference between the requested computing power and each initial computing power container, obtains the utilization rate of the corresponding initial computing power container, and the initial computing power container corresponding to the maximum utilization rate is the matching initial computing power container. The device computing power request information is sent to the task list of the corresponding initial computing power container, and the task waiting time is collected; Step 3: within the set container adjustment period, according to the computing power task list of each initial computing power container, the average waiting time of the computing power tasks of each initial computing power container and the idle time of the initial computing power container are obtained respectively; the computing power request processing module generates the first computing power container sub-table and the second computing power container sub-table respectively according to the average waiting time of the computing power tasks of the initial computing power container and the idle time of the initial computing power container, and obtains the idle time sequence of the second initial computing power container according to the size of the idle time of each initial computing power container in the second computing power container sub-table; Step 4: Obtain the usage rate of each initial computing power container in the first computing power container sub-table respectively, and obtain the computing power container first adjustment characteristic value of each initial computing power container respectively according to the usage rate of the initial computing power container and the average waiting time of the computing power task of the corresponding initial computing power container; Step 5: According to the set computing power container adjustment threshold, the initial computing power container that is greater than the computing power container adjustment threshold is adjusted, and then the process goes to step 6; Step 6: According to the average waiting time of the computing tasks of the initial computing power container and the average waiting time threshold of the computing tasks, the required number of computing power containers is obtained, and the required computing power is obtained according to the required number of computing power containers. According to the required computing power and the idle time sequence of the second initial computing power container, the initial computing power container in the second computing power container subtable is called, and the required number of computing power containers is generated in combination with the initial computing power container, and the tasks are evenly distributed to the generated computing power containers, and then step 7 is entered; Step 7: The initial computing power containers that have not been called in the second computing power container sub-table enter sleep mode, completing computing power recovery and energy consumption control.

2. The data computing power recovery and energy consumption control method based on AI and blockchain according to claim 1 is characterized in that: The computing power request processing module generates multiple initial computing power containers according to the historical computing power request data and computing power data, and generates a task list in the initial computing power container, including: According to the set computing power level difference, the computing power request frequency of each level of computing power is obtained. According to the computing power request frequency, the initial computing power container and the number of initial computing power containers corresponding to each level of computing power are generated, and a task list is generated in each initial computing power container.

3. The data computing power recovery and energy consumption control method based on AI and blockchain according to claim 1 is characterized in that: The above-mentioned respectively obtaining the difference between the requested computing power and the computing power of each initial computing power container, and obtaining the usage rate of the corresponding initial computing power container, includes: 。 4. The data computing power recovery and energy consumption control method based on AI and blockchain according to claim 1 is characterized in that: The computing power request processing module generates a first computing power container sub-table and a second computing power container sub-table according to the average waiting time of the computing power tasks of the initial computing power container and the idle time of the initial computing power container, respectively, including: The initial computing power containers with computing power tasks waiting are listed in the first computing power container sub-table, and the rest are listed in the second computing power container sub-table; the first computing power container sub-table includes the average waiting time of the computing power tasks of each listed initial computing power container and the usage rate of the corresponding initial computing power container; the second computing power container sub-table includes the initial computing power container idle time of each listed initial computing power container.

5. The data computing power recovery and energy consumption control method based on AI and blockchain according to claim 1 is characterized in that: The method of respectively obtaining the usage rate of each initial computing power container in the first computing power container sub-table, and respectively obtaining the computing power container first adjustment characteristic value of each initial computing power container according to the usage rate of the initial computing power container and the average waiting time of the computing power task of the corresponding initial computing power container, includes: 。 6. The data computing power recovery and energy consumption control method based on AI and blockchain according to claim 1 is characterized in that: The method of obtaining the required number of computing power containers according to the average waiting time of the computing power tasks of the initial computing power container and the average waiting time threshold of the computing power tasks, and obtaining the required computing power according to the required number of computing power containers, includes: in Indicates rounding up.

7. The data computing power recovery and energy consumption control method based on AI and blockchain according to claim 1 is characterized in that: The method of calling the initial computing power container in the second computing power container subtable according to the required computing power and the idle time sequence of the second initial computing power container, and generating the required number of computing power containers in combination with the initial computing power container, includes: The required computing power is obtained according to the sum of the computing power of the initial computing power container and the computing power of the initial computing power container in the called second computing power container sub-table, and the required number of computing power containers is generated.

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