A service platform-based distributed service method and platform

Abstract

The application provides a kind of distributed service method and platform based on service platform, it is related to distributed computing power processing technical field.The method includes collecting the historical computing power period supply data of different distributed servers, the performance analysis for stable available computing power is carried out, and distributed computing power supply data is formed;The total amount of estimated computing power of training object set is obtained, and the computing power supply matching analysis is carried out in combination with distributed computing power supply data, and distributed computing power matching data is formed;The power supply price change data is obtained, and the computing power supplement analysis is carried out in combination with distributed computing power matching data and the historical computing power period supply data of different distributed servers, and distributed computing power supplement data is formed.The method extracts the characteristics of historical computing power supply based on distributed computing power to provide reasonable computing power matching, ensures that the training object can obtain sufficient computing power, and also can realize efficient and reasonable use of total computing power provided by platform.

Description

Technical Field

[0001] The present invention relates to the field of distributed computing power processing technology, and in particular to a distributed service method and platform based on a service platform. Background Art

[0002] In order to cope with the current increasing amount of model training data, a single server can no longer meet such needs. With the development of Internet technology, connecting different servers to form a huge computing power provision system to provide sufficient computing power for model training with huge computing power requirements has become the current popular computing power provision method. Due to the decentralized distribution of servers, this method has also become a distributed computing power provision method.

[0003] Although the distributed computing power provision method solves the huge computing power demand for model training, the computing power that can be provided by different servers varies due to differences in hardware and software performance. At the same time, the time for providing computing power also varies. How to more reasonably allocate the computing power provided by distributed computing power to ensure the computing power demand for model training has become a key issue of concern.

[0004] Therefore, designing a distributed service method and platform based on a service platform, which can reasonably match the distributed computing power based on the extraction of historical computing power supply characteristics, ensure that the training objects obtain sufficient computing power while also achieving efficient and reasonable utilization of the total computing power provided by the platform, is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of the present invention is to provide a distributed service method based on a service platform. By collecting the historical computing power cycle supply data of different distributed servers within the service platform, the characteristics of the stable available computing power supply are extracted, the computing power supply situation of the distributed servers is determined, and the total estimated computing power demand of all training objects to be provided with computing power by the current platform is combined to perform a reasonable computing power matching analysis to ensure that each training object can obtain sufficient and reasonable computing power supply, and arrange the computing power supply as reasonably as possible to avoid wasting the server computing power. At the same time, the periodic volatility of the server computing power supply is fully considered to reserve computing power at low electricity prices to ensure the stability of the computing power supply. In this way, the rationality and efficiency of the computing power utilization of the platform's distributed servers are achieved, and the computing power demand of the training objects can be fully met, making the allocation and use of computing power more efficient and reasonable.

[0006] The purpose of the present invention is also to provide a distributed service platform based on a service platform. The platform obtains the historical computing power cycle supply data of the server through a data acquisition unit, and performs a matching analysis of the computing power supply on a computing power analysis unit, thereby realizing the reasonable allocation and utilization of computing power while also considering computing power storage with low electricity prices, fully realizing the efficiency and rationality of computing power supply, forming an important part of the platform's distributed services, and being an important material basis for realizing the platform's distributed computing power services.

[0007] In the first aspect, the present invention provides a distributed service method based on a service platform, including: collecting historical computing power cycle supply data of different distributed servers, conducting performance analysis on stable available computing power, and forming distributed computing power supply data; obtaining the estimated total power of the training object set, and performing computing power supply matching analysis in combination with the distributed computing power supply data to form distributed computing power matching data; obtaining power grid power supply price change data, and performing computing power supplement analysis in combination with the distributed computing power matching data and the historical computing power cycle supply data of different distributed servers to form distributed computing power supplement data.

[0008] In the present invention, the method collects historical computing power cycle supply data of different distributed servers within the service platform, extracts features of stable available computing power supply, determines the computing power supply situation of the distributed servers, and performs reasonable computing power matching analysis based on the total estimated computing power demand of all training objects to which the current platform is to provide computing power, to ensure that each training object can obtain sufficient and reasonable computing power supply, and to arrange computing power supply as reasonably as possible to avoid wasting server computing power. At the same time, the periodic volatility of server computing power supply is fully considered to reserve computing power at low electricity prices to ensure the stability of computing power supply. In this way, the rationality and efficiency of computing power utilization of the platform's distributed servers are achieved, and the computing power demand of the training objects can be fully met, making the allocation and use of computing power more efficient and reasonable.

[0009] As a possible implementation method, historical computing power cycle supply data of different distributed servers is collected, and performance analysis of stable available computing power is conducted to form distributed computing power supply data, including: extracting the periodic effective computing power supply change information of the distributed servers based on the historical computing power cycle supply data corresponding to the distributed servers; conducting computing power availability analysis based on the periodic effective computing power supply change information corresponding to different distributed servers to form distributed computing power supply data.

[0010] In the present invention, in order to reasonably match the computing power of the distributed server with the training object, it is first necessary to determine the computing power that the distributed server can stably provide. By obtaining the historical computing power cycle supply data of the distributed server, information on the computing power cycle change characteristics is extracted to determine the stable computing power that the distributed server can provide.

[0011] As a possible implementation method, based on the historical computing power cycle supply data corresponding to the distributed server, the periodic effective computing power supply change information of the distributed server is extracted, including: for different distributed servers, extracting the corresponding historical cycle computing power change curve for each cycle , m is the number of different distributed servers, n is the number of different computing power cycles of the distributed server numbered m; compare the computing power change curves of different historical cycles under distributed servers , determine the minimum historical computing power supply at each cycle point, and form the periodic effective computing power supply change information corresponding to the distributed server.

[0012] In the present invention, for each training project, the distributed computing power server combines its own computing power situation to provide as much computing power as possible. Since the computing power of the distributed server varies in performance, supply time and supply volume, the computing power it provides is in a constantly changing state during the implementation phase of the entire training project. Therefore, it is first necessary to extract the change information of the computing power provided by the distributed server over the entire training cycle of each training project. This is to lay the data foundation for the subsequent determination of the stable computing power provided by the distributed server. Of course, due to the different training projects, the computing power provided by the distributed server each time has differences in size. Therefore, considering the computing power that the distributed server can stably provide, the minimum computing power at each cycle point on all training projects is used as the computing power that can be stably provided to form the periodic effective computing power supply change information of the distributed server. It should be noted that for different training projects, there are certain differences in the training cycle. In order to ensure the comparability and processability of the data, the cycles of different projects can be reasonably reduced to adjust to a unified cycle length. Of course, the computing power supply corresponding to the cycle is also averaged accordingly to achieve this. The computing power change curve can also be scaled according to the scaling ratio of the cycle to form a unified computing power supply curve.

[0013] As a possible implementation method, based on the periodic effective computing power supply change information corresponding to different distributed servers, computing power availability analysis is performed to form distributed computing power supply data, including: for different distributed servers, based on the corresponding periodic effective computing power supply change information, extracting the historical computing power supply provided at all periodic points, and determining the total historical computing power supply for the entire period. ; According to the total historical computing power supply corresponding to the distributed server , determine the corresponding stable available computing power ,in, ; Aggregate the stable available computing power corresponding to different distributed servers , forming distributed computing power supply data.

[0014] In the present application, the periodic effective computing power supply change information obtained has certain fluctuation in the periodic time, and such fluctuation affects the stability of the computing power provided, so for the stable available computing power that the distributed server can provide, the total amount of computing power provided in the whole period is generally taken as the standard, on the one hand, the fluctuation of the computing power supply amount when providing computing power continuously with stable available computing power can be avoided, and a certain computing power surplus for the distributed server to meet its own computing power use demand can be provided.

[0015] As a possible implementation manner, the estimated total amount of computing power of the training object set is obtained, and the computing power supply matching analysis is performed in combination with the distributed computing power supply data to form distributed computing power matching data, including: determining the object computing power estimated total amount corresponding to different training objects according to the estimated total amount of computing power of the training object set , k represents the number of different training objects; determining the minimum splitting unit of the computing power corresponding to the training object according to the data acquisition mode of the different training objects and in combination with the corresponding object computing power estimated total amount ; for different training objects, the corresponding object computing power estimated total amount and the minimum splitting unit of the computing power are combined with the distributed computing power supply data to perform computing power supply matching analysis and form distributed computing power matching data. In the present application, after the stable available computing power data of different distributed servers is extracted, reasonable computing power matching analysis can be performed for the training objects. The provision of computing power is determined by the computing power demand of the training object, so it is necessary to estimate the training required computing power of the training object first. Such estimation includes the estimation of the total amount of computing power of all training objects and the estimation of the computing power of each training object. After all, the estimation of the total amount of computing power can determine how much total amount of computing power needs to be distributed by the distributed server, and the estimation of the computing power of each training object is an important data reference basis for realizing computing power matching. Of course, for different training objects, due to the difference in the type and size of the data to be trained, the minimum demand amount of computing power is also different, so it is necessary to determine the minimum splitting unit of the computing power, which is the evaluation basis for the distributed server to provide the training object with the amount of computing power.

[0016] As a possible implementation manner, for different training objects, the corresponding object computing power estimated total amount and the minimum splitting unit of the computing power are combined with the distributed computing power supply data to perform computing power supply matching analysis and form distributed computing power matching data, including: determining the minimum splitting unit of the computing power corresponding to different training objects according to the corresponding minimum splitting unit of the computing power

[0017] . In the present application, after the stable available computing power data of different distributed servers is extracted, reasonable computing power matching analysis can be performed for the training objects. The provision of computing power is determined by the computing power demand of the training object, so it is necessary to estimate the training required computing power of the training object first. Such estimation includes the estimation of the total amount of computing power of all training objects and the estimation of the computing power of each training object. After all, the estimation of the total amount of computing power can determine how much total amount of computing power needs to be distributed by the distributed server, and the estimation of the computing power of each training object is an important data reference basis for realizing computing power matching. Of course, for different training objects, due to the difference in the type and size of the data to be trained, the minimum demand amount of computing power is also different, so it is necessary to determine the minimum splitting unit of the computing power, which is the evaluation basis for the distributed server to provide the training object with the amount of computing power. ​, sort the matching order of different training objects in descending order to form the object computing power matching order; according to the object computing power matching order, perform distributed server computing power supply matching analysis on different training objects in turn to form distributed computing power matching data.

[0018] In the present invention, the computing power provided by different distributed servers can be matched based on the estimated computing power demand of the training object and the minimum computing power split unit. It can be understood that for the minimum computing power split unit, the smaller the computing power that can be split, the less computing power the distributed server will have left when providing computing power for the training object. The basic remaining amount will not exceed the minimum computing power split unit. When performing computing power matching, computing power matching is performed once in order of size, which can ensure the rational use of the computing power of the distributed servers.

[0019] As a possible implementation method, according to the object computing power matching order, the distributed server computing power supply matching analysis is performed on different training objects in turn to form distributed computing power matching data, including: according to the object computing power matching order, the minimum computing power split unit corresponding to each training object is extracted in turn. and the estimated total computing power of the object , perform the following computing power supply matching analysis: the stable available computing power corresponding to all distributed servers , determine the split unit with the minimum computing power Poor computing power , and according to the computing power difference Sort the distributed servers from small to large to form a distributed server computing power matching order, where: ; Extract in sequence according to the distributed server computing power matching order The number of distributed servers forms the object matching distributed server set corresponding to the training object, and the extracted The number of distributed servers that meet the following requirements: , ,express An integer formed by a further ending method, β represents the minimum allowable computing power difference, and β < 0; excluding the distributed servers that have been matched before, for the remaining distributed servers, continue to extract the minimum computing power split unit corresponding to the training object according to the object computing power matching order and the estimated total computing power of the object , perform computing power matching analysis until the computing power matching analysis of all training objects is completed; determine the object matching distributed server set corresponding to different training objects to form distributed computing power matching data.

[0020] In the present invention, the computing power of distributed servers is matched for different training objects in the order of arrangement of the split units with the smallest computing power. During the matching, the stable available computing power provided by the distributed servers is compared and sorted, and the computing power supply is matched in order from small to large. This ensures that the computing power remaining after the distributed servers supply computing power reaches the minimum, ensuring that the computing power of the distributed servers can be utilized to the greatest extent. The minimum allowable computing power difference is a measure of whether the computing power volatility of the corresponding distributed servers affects the amount of computing power provided after providing the corresponding computing power. Only when there is enough remaining computing power will the volatility of the computing power provided by the server itself not cause an unstable impact on the computing power provided to the training objects. The minimum allowable computing power difference can be set according to actual conditions, or it can be determined based on big data analysis. Of course, if there is still enough computing power remaining for the distributed servers that have already undergone computing power matching, matching can continue to be performed to provide computing power for other training objects.

[0021] As a possible implementation method, the power grid price change data is obtained, and the distributed computing power matching data and the historical computing power cycle supply data of different distributed servers are combined to perform computing power replenishment analysis to form distributed computing power replenishment data, including: according to the distributed computing power matching data, combined with the total historical computing power supply corresponding to different distributed servers Based on the periodic effective computing power supply change information, the following computing power missing amount analysis is performed to form the computing power missing amount analysis result: for each distributed server in the object matching distributed server set corresponding to different training objects, according to the periodic effective computing power supply change information corresponding to the distributed server, it is determined that the historical computing power supply at the periodic point is less than the minimum computing power split unit. The period of insufficient computing power is marked as the period of insufficient computing power; according to the periodic effective computing power supply change information corresponding to the distributed server, the historical computing power supply during the period of insufficient computing power and the minimum split unit of continuous computing power supply during the same period are determined. The difference in computing power of the computing power of the distributed server is used to form a missing computing power; according to the change information of the effective computing power supply of the distributed server, the historical computing power supply in other periods outside the period of insufficient computing power and the minimum split unit of the continuous computing power supply in the same period are determined. The computing power difference between the self-charging computing power and the missing computing power is used to form the self-charging computing power; the following analysis is performed based on the self-charging computing power and the missing computing power: if the self-charging computing power is not less than the missing computing power, the corresponding distributed server is determined as the first-time external computing power server; if the self-charging computing power is less than the missing computing power, the corresponding distributed server is determined as the continuous external computing power server; based on the analysis results of the computing power shortage and combined with the power grid supply price change data, a computing power supplementation analysis is performed to form the computing power supplementation analysis result data.

[0022] In the present invention, although the minimum allowable computing power difference is used to avoid the impact of the volatility of the server computing power supply on the computing power supply when matching the computing power of a single training object, there will still be insufficient computing power in actual situations, especially when the server is providing computing power in the early stage. Before that, a certain amount of computing power is stored, which causes the computing power supply to fluctuate and there is no computing power to supplement when the computing power is insufficient. Therefore, in this case, before providing computing power, it should be considered to store computing power in advance. Since the generation of computing power is closely related to the consumption of electric energy resources, in order to control the cost of computing power production, it is necessary to obtain electricity based on the characteristics of the power supply price changes of the power grid for computing power production. Before production, the actual situation of the computing power of the distributed server should be accurately confirmed. One is that if there is excess computing power in the later computing power fluctuations, it cannot make up for the computing power loss caused by the previous computing power fluctuations. This loss is continuous. The other is that if there is excess computing power in the later computing power fluctuations, it can make up for the computing power loss caused by the previous computing power fluctuations. This loss only occurs in the early stage. When there is excess computing power in the later computing power fluctuations, it can be self-sufficient.

[0023] As a possible implementation method, based on the analysis results of the computing power shortage and combined with the power grid price change data, a computing power replenishment analysis is conducted to form computing power replenishment analysis result data, including: if the distributed server is a server that borrows computing power for the first time, then based on the computing power shortage period of the server that borrows computing power for the first time and the corresponding historical computing power supply in the period, and combined with the power grid price change data, the earliest charging time point for the first borrowed computing power and the charging period during the low electricity price period are determined; if the distributed server is a server that borrows computing power continuously, then: based on the continuous external The computing power server's computing power shortage period and the corresponding historical computing power supply in each period, combined with the power grid price change data, determine the earliest charging time point for the first borrowed computing power and the charging period during the low-price electricity period; the computing power shortage period during which self-charging can be achieved is determined based on the difference between the self-charging computing power and the missing computing power; and for the remaining computing power shortage period that cannot be automatically charged, the earliest charging time point for continuous borrowed computing power and the charging period during the low-price electricity period are determined based on the corresponding historical computing power supply in each period and the power grid price change data.

[0024] In the present invention, based on the determined distributed servers with different types of computing power shortages, reasonable computing power production and storage are performed in combination with low-cost power supply from the power grid to provide sufficient computing power supplement for subsequent computing power supply fluctuations, ensuring that the distributed servers can continuously and effectively provide stable and sufficiently effective computing power.

[0025] In the second aspect, the present invention provides a distributed service platform based on a service platform, including a data acquisition unit for collecting historical computing power cycle supply data of different distributed servers; a computing power analysis unit for performing available computing power performance analysis on the historical computing power cycle supply data obtained by the data acquisition unit to form distributed computing power supply data, performing computing power estimation on the training object set and performing computing power matching analysis in combination with the distributed computing power supply data to form distributed computing power matching data, and performing computing power supplementation analysis based on the power grid power supply price change data to form distributed computing power supplementation data.

[0026] The platform obtains the historical computing power cycle supply data of the server through the data acquisition unit, and performs matching analysis of computing power supply on the computing power analysis unit. While achieving reasonable allocation and utilization of computing power, it also considers computing power storage with low electricity prices, fully realizing the efficiency and rationality of computing power supply, forming an important part of the platform's distributed services, and is an important material basis for realizing the platform's distributed computing power services.

[0027] The distributed service method and platform based on the service platform provided by the present invention have the following beneficial effects:

[0028] This method collects historical computing power cycle supply data of different distributed servers within the service platform, extracts features of stable available computing power supply, determines the computing power supply situation of distributed servers, and conducts reasonable computing power matching analysis based on the total estimated computing power demand of all training objects that the current platform needs to provide computing power. It ensures that each training object can obtain sufficient and reasonable computing power supply, and arranges computing power supply as reasonably as possible to avoid wasting server computing power. At the same time, it fully considers the periodic volatility of server computing power supply and reserves computing power at low electricity prices to ensure the stability of computing power supply. In this way, it not only realizes the rationality and efficiency of computing power utilization of platform distributed servers, but also fully meets the computing power demand of training objects, making the allocation and use of computing power more efficient and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0030] Fig. 1 A step diagram of a distributed service method based on a service platform provided in an embodiment of the present invention;

[0031] Fig. 2A structural schematic diagram of a distributed service platform based on a service platform is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

[0033] In order to cope with the increasing amount of model training data, a single server cannot meet such demand. With the development of Internet technology, different servers are connected to form a huge computing power providing system to provide sufficient computing power for model training with huge computing power demand, which has become a popular computing power providing method. Due to the non-centralized distribution of servers, such method has become a distributed computing power providing method.

[0034] Although the distributed computing power providing method solves the huge computing power demand for model training, the computing power provided by different servers is not the same due to the differences in hardware and software performance, and the time for providing computing power also differs. How to more reasonably allocate the distributed computing power to ensure the computing power demand for model training has become a current focus.

[0035] REFERENCE Figs. 1-2 The embodiment of the present application provides a distributed service method based on a service platform. The training cluster collects historical computing power cycle supply data of different distributed servers within the range of the service platform, extracts the features of stable and available computing power supply, determines the computing power supply of the distributed servers, combines the total amount of estimated computing power demand of all training objects to be provided by the current platform for reasonable computing power matching analysis, ensures that each training object can obtain sufficient and reasonable computing power supply, and reasonably arranges the computing power provision as much as possible to avoid waste of server computing power. At the same time, the periodic fluctuation of server computing power provision is fully considered to reserve computing power at low electricity price to ensure the stability of computing power provision. This not only realizes the rationality and efficiency of computing power utilization of the platform distributed servers, but also fully meets the demand of training objects for computing power, making the allocation and use of computing power more efficient and reasonable.

[0036] The distributed service method based on the service platform specifically includes the following steps:

[0037] S1: Collecting historical computing power cycle supply data of different distributed servers, performing performance analysis for stable and available computing power, and forming distributed computing power supply data.

[0038] Collect historical computing power cycle supply data of different distributed servers, conduct performance analysis on stable and available computing power, and form distributed computing power supply data, including: extracting the periodic effective computing power supply change information of distributed servers based on the historical computing power cycle supply data corresponding to the distributed servers; conduct computing power availability analysis based on the periodic effective computing power supply change information corresponding to different distributed servers to form distributed computing power supply data.

[0039] In order to reasonably match the computing power of the distributed server with the training object, it is first necessary to determine the computing power that the distributed server can stably provide. By obtaining the historical computing power cycle supply data of the distributed server, information on the characteristics of the computing power cycle change can be extracted to determine the stable computing power that the distributed server can provide.

[0040] According to the historical computing power cycle supply data corresponding to the distributed server, the periodic effective computing power supply change information of the distributed server is extracted, including: for different distributed servers, the corresponding historical cycle computing power change curve for each cycle is extracted. , m is the number of different distributed servers, n is the number of different computing power cycles of the distributed server numbered m; compare the computing power change curves of different historical cycles under distributed servers , determine the minimum historical computing power supply at each cycle point, and form the periodic effective computing power supply change information corresponding to the distributed server.

[0041] For each training project, the distributed computing server provides as much computing power as possible based on its own computing power. Since the computing power of the distributed server varies in performance, supply time, and supply quantity, the computing power it provides is in a state of constant change throughout the implementation phase of the training project. Therefore, it is necessary to first extract the computing power change information provided by the distributed server throughout the training cycle of each training project. This lays the data foundation for subsequently determining the stable computing power provided by the distributed server. Of course, due to different training projects, the computing power provided by the distributed server each time varies in size. Therefore, considering the computing power that the distributed server can stably provide, the minimum computing power at each cycle point for all training projects is used as the computing power that can be stably provided, thereby forming the periodic effective computing power supply change information of the distributed server. It should be noted that for different training projects, the training cycle has certain differences. In order to ensure the comparability and processability of the data, the cycles of different projects can be reasonably reduced to adjust to a unified cycle length. Of course, this is achieved by also averaging the computing power supply corresponding to the cycle. The computing power change curve can also be scaled according to the cycle scaling ratio to form a unified computing power supply curve.

[0042] Based on the periodic effective computing power supply change information corresponding to different distributed servers, computing power availability analysis is performed to form distributed computing power supply data, including: for different distributed servers, based on the corresponding periodic effective computing power supply change information, extracting the historical computing power supply provided at all periodic points, and determining the total historical computing power supply for the entire period ; According to the total historical computing power supply corresponding to the distributed server , determine the corresponding stable available computing power ,in, ; Aggregate the stable available computing power corresponding to different distributed servers , forming distributed computing power supply data.

[0043] Considering that the information on changes in the effective computing power supply obtained during the period has certain fluctuations in the periodic time, and this volatility affects the stability of the computing power provided, the total amount of computing power provided during the entire period is generally used as the standard for the stable available computing power that the distributed server can provide. On the one hand, this can avoid fluctuations in the computing power supply when computing power is continuously provided with stable available computing power, and on the other hand, it can provide the distributed server with a certain amount of computing power surplus for its own computing power usage needs.

[0044] S2: Obtain the estimated total computing power of the training object set, and perform computing power supply matching analysis in combination with the distributed computing power supply data to form distributed computing power matching data.

[0045] Obtain the estimated total power of the training object set, and perform power supply matching analysis in combination with the distributed power supply data to form distributed power matching data, including: determining the estimated total object power corresponding to different training objects based on the estimated total power of the training object set , k represents the number of different training objects; according to the data collection mode of different training objects, combined with the corresponding object computing power estimation , determine the minimum computing power split unit corresponding to the training object ; For different training objects, estimate the total amount based on the corresponding object computing power The smallest split unit of computing power , and combined with the distributed computing power supply data, conduct computing power supply matching analysis to form distributed computing power matching data.

[0046] After extracting the stable and available computing power data for different distributed servers, a reasonable computing power matching analysis can be performed for the training object. Considering that the computing power provided is determined by the computing power requirements of the training object, it is first necessary to estimate the computing power required for training the training object. This estimate includes both the total computing power of all training objects and the computing power of each training object. After all, estimating the total computing power can determine how much computing power the distributed servers will need, and estimating the computing power of each training object is an important data reference for achieving computing power matching. Of course, for different training objects, the minimum computing power requirements are different due to the different types and sizes of the training data. Therefore, it is also necessary to determine the minimum computing power split unit. After all, it is the basis for evaluating the computing power provided by the distributed server to the training object.

[0047] For different training objects, estimate the total amount based on the corresponding object computing power The smallest split unit of computing power , and combined with the distributed computing power supply data, perform computing power supply matching analysis to form distributed computing power matching data, including: splitting units based on the minimum computing power corresponding to different training objects , sort the matching order of different training objects in descending order to form the object computing power matching order; according to the object computing power matching order, perform distributed server computing power supply matching analysis on different training objects in turn to form distributed computing power matching data.

[0048] Based on the estimated computing power requirements of the training object and the minimum computing power split unit, the corresponding computing power provided by different distributed servers can be matched. It can be understood that for the minimum computing power split unit, the smaller the computing power that can be split, the less computing power the distributed server will have left when providing computing power for the training object. The basic remaining amount will not exceed the minimum computing power split unit. When performing computing power matching, computing power matching is performed in order of size, which can ensure the rational utilization of the computing power of the distributed servers.

[0049] According to the object computing power matching order, the distributed server computing power supply matching analysis is performed on different training objects in turn to form distributed computing power matching data, including: according to the object computing power matching order, the minimum computing power split unit corresponding to each training object is extracted in turn. and the estimated total computing power of the object , perform the following computing power supply matching analysis: the stable available computing power corresponding to all distributed servers , determine the split unit with the minimum computing power Poor computing power , and according to the computing power difference Sort the distributed servers from small to large to form a distributed server computing power matching order, where: ; Extract in sequence according to the distributed server computing power matching order The number of distributed servers forms the object matching distributed server set corresponding to the training object, and the extracted The number of distributed servers that meet the following requirements: , , express An integer formed by a further ending method, β represents the minimum allowable computing power difference, and β < 0; excluding the distributed servers that have been matched before, for the remaining distributed servers, continue to extract the minimum computing power split unit corresponding to the training object according to the object computing power matching order and the estimated total computing power of the object , perform computing power matching analysis until the computing power matching analysis of all training objects is completed; determine the object matching distributed server set corresponding to different training objects to form distributed computing power matching data.

[0050] Distributed server computing power is matched to different training objects in the order of their smallest computing power splits. During matching, the stable available computing power provided by the distributed servers is compared and ranked, and computing power provision is matched in ascending order. This ensures that the remaining computing power after the distributed servers have provided computing power is minimized, ensuring that the distributed servers' computing power is utilized to the maximum extent possible. The minimum allowable computing power difference measures whether the computing power volatility of the corresponding distributed servers affects the computing power provided after providing the corresponding computing power. Only when there is sufficient remaining computing power can the computing power provided to the training objects be unstable due to the volatility of the server's computing power. The minimum allowable computing power difference can be set based on actual conditions or determined based on big data analysis. Of course, if a distributed server that has already been matched has sufficient computing power remaining, it can continue to be matched to provide computing power for other training objects.

[0051] S3: Obtain the power supply price change data of the power grid, and combine it with the distributed computing power matching data and the historical computing power cycle supply data of different distributed servers to conduct computing power replenishment analysis to form distributed computing power replenishment data.

[0052] Obtain the power supply price change data of the power grid, and combine it with the distributed computing power matching data and the historical computing power cycle supply data of different distributed servers to conduct computing power replenishment analysis to form distributed computing power replenishment data, including: according to the distributed computing power matching data, combined with the total historical computing power supply corresponding to different distributed servers Based on the periodic effective computing power supply change information, the following computing power missing amount analysis is performed to form the computing power missing amount analysis result: for each distributed server in the object matching distributed server set corresponding to different training objects, according to the periodic effective computing power supply change information corresponding to the distributed server, it is determined that the historical computing power supply at the periodic point is less than the minimum computing power split unit. The period of insufficient computing power is marked as the period of insufficient computing power; according to the periodic effective computing power supply change information corresponding to the distributed server, the historical computing power supply during the period of insufficient computing power and the minimum split unit of continuous computing power supply during the same period are determined. The difference in computing power of the computing power of the distributed server is used to form a missing computing power; according to the change information of the effective computing power supply of the distributed server, the historical computing power supply in other periods outside the period of insufficient computing power and the minimum split unit of the continuous computing power supply in the same period are determined. The computing power difference between the self-charging computing power and the missing computing power is used to form the self-charging computing power; the following analysis is performed based on the self-charging computing power and the missing computing power: if the self-charging computing power is not less than the missing computing power, the corresponding distributed server is determined as the first-time external computing power server; if the self-charging computing power is less than the missing computing power, the corresponding distributed server is determined as the continuous external computing power server; based on the analysis results of the computing power shortage and combined with the power grid supply price change data, a computing power supplementation analysis is performed to form the computing power supplementation analysis result data.

[0053] Although the impact of server computing power fluctuations on computing power supply is mitigated by a minimum allowable computing power difference when matching computing power for individual training objects, insufficient computing power may still occur in practice. This is especially true during the initial stages of server computing power provisioning. Previously, a certain amount of computing power was stored, resulting in a lack of computing power to supplement computing power when computing power supply fluctuates. Therefore, in this case, pre-storing computing power should be considered before provisioning computing power. Since computing power generation is closely related to energy consumption, it is necessary to generate computing power based on the fluctuations in grid power prices to control computing power production costs. The actual computing power status of distributed servers should be accurately confirmed before production. One possibility is that even if there is excess computing power in later computing power fluctuations, it will not be able to compensate for the computing power shortage caused by earlier computing power fluctuations, and this shortage will persist. Another possibility is that even if there is excess computing power in later computing power fluctuations, it can compensate for the computing power shortage caused by earlier computing power fluctuations. This shortage will only occur in the early stages, and self-sufficiency will be achieved when excess computing power is generated in later computing power fluctuations.

[0054] Based on the results of the computing power shortage analysis and in combination with the grid power supply price change data, a computing power replenishment analysis is performed to form computing power replenishment analysis result data, including: if the distributed server is a server that is borrowing computing power for the first time, then based on the computing power shortage cycle period of the server that first borrowed computing power and the corresponding historical computing power supply in the period, and in combination with the grid power supply price change data, the earliest charging time point for the first borrowed computing power and the charging time period during the low electricity price period are determined; if the distributed server is a server that continuously borrows computing power, then based on the computing power shortage cycle period of the server that continuously borrowed computing power and the corresponding historical computing power supply in the period, and in combination with the grid power supply price change data, the earliest charging time point for the first borrowed computing power and the charging time period during the low electricity price period are determined; based on the difference between the self-charged computing power and the missing computing power, the computing power shortage cycle period in which self-charging can be achieved is determined, and for the remaining computing power shortage cycle periods that cannot be automatically charged, based on the historical computing power supply corresponding to the period, and in combination with the grid power supply price change data, the earliest charging time point for the continuously borrowed computing power and the charging time period during the low electricity price period are determined.

[0055] Based on the different types of distributed servers that are identified as lacking computing power, reasonable computing power production and storage are carried out in combination with low-cost power supply from the power grid to provide sufficient computing power supplement for subsequent computing power supply fluctuations, ensuring that distributed servers can continue to effectively provide stable and sufficiently effective computing power.

[0056] The present application also provides a distributed service platform based on a service platform, which includes a data collection unit for collecting historical computing power cycle supply data of different distributed servers; a computing power analysis unit for performing available computing power performance analysis on the historical computing power cycle supply data obtained by the data collection unit to form distributed computing power supply data, performing computing power estimation on the training object set and performing computing power matching analysis in combination with the distributed computing power supply data to form distributed computing power matching data, and performing computing power supplementation analysis based on the power grid power supply price change data to form distributed computing power supplementation data.

[0057] The platform obtains the historical computing power cycle supply data of the server through the data acquisition unit, and performs matching analysis of computing power supply on the computing power analysis unit. While achieving reasonable allocation and utilization of computing power, it also considers computing power storage with low electricity prices, fully realizing the efficiency and rationality of computing power supply, forming an important part of the platform's distributed services, and is an important material basis for realizing the platform's distributed computing power services.

[0058] In summary, the distributed service method and platform based on the service platform provided by the embodiments of the present invention have the following beneficial effects:

[0059] This method collects historical computing power cycle supply data from different distributed servers within the service platform, extracts features of stable available computing power supply, determines the computing power supply status of distributed servers, and performs reasonable computing power matching analysis based on the estimated total computing power demand of all training objects currently being provided by the platform. This ensures that each training object can obtain sufficient and reasonable computing power supply, and arranges computing power supply as reasonably as possible to avoid wasting server computing power. At the same time, it fully considers the cyclical volatility of server computing power supply and reserves computing power during low electricity prices to ensure the stability of computing power supply. This method not only achieves the rational and efficient utilization of computing power of the platform's distributed servers, but also fully meets the computing power needs of training objects, making the allocation and use of computing power more efficient and reasonable. It is an important component of the platform's distributed services and a key material foundation for realizing the platform's distributed computing power services.

[0060] In the embodiment of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association relationship between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (such as specified in the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0061] In addition, the specific indication method can also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can be referred to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, there may be a situation where the indication methods for different information are different. In the specific implementation process, the required indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0062] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiments of this application. The sending period and / or sending time of these sub-information can be predefined, for example, predefined according to a protocol, or can be configured by the transmitting device by sending configuration information to the receiving device.

[0063] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present application do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, and the embodiments of the present application do not limit this.

[0064] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems. The embodiments of the present application do not make specific limitations on this.

[0065] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will perform corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to perform judgment actions when implemented, nor does it mean that there are other limitations.

[0066] In the description of the embodiments of the present application, unless otherwise specified, " / " represents that the objects before and after the " / " are in an "or" relationship, for example, A / B can represent A or B; "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. "At least one of the following" or the like means any combination of the items, including any combination of single item or multiple items. For example, at least one of a, b or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, and c can be single or multiple. In addition, in order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, "first", "second", and the like are used to distinguish the same items or similar items with basically the same function and role. Those skilled in the art can understand that "first", "second", and the like do not limit the quantity and execution order, and "first", "second", and the like do not necessarily mean different. At the same time, in the embodiments of the present application, "exemplary" or "for example" means to serve as an example, illustration or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, "exemplary" or "for example" is used to present the relevant concept in a specific manner, for understanding.

[0067] It should be understood that the processor in the embodiments of the present application can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0068] It should also be understood that the memory in the embodiments of the present application can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. Among them, the nonvolatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, and not limitation, many forms of random access memory (RAM) can be used, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0069] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0070] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0071] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0072] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0073] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0074] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0075] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0076] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0077] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0078] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0079] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A distributed service method based on a service platform, characterized in that: Configured to: Collect historical computing power cycle supply data from different distributed servers, conduct performance analysis on stable available computing power, and generate distributed computing power supply data; Obtaining the estimated total computing power of the training object set, and performing computing power supply matching analysis in combination with the distributed computing power supply data to form distributed computing power matching data; Obtaining grid power supply price change data, and combining the distributed computing power matching data and the historical computing power cycle supply data of different distributed servers to perform computing power replenishment analysis to form distributed computing power replenishment data; Among them, according to the distributed computing power matching data, combined with the total historical computing power supply corresponding to different distributed servers Based on the periodic effective computing power supply change information, the following computing power shortage analysis is performed to form the computing power shortage analysis results: For each of the distributed servers in the object matching distributed server set corresponding to different training objects, according to the periodic effective computing power supply change information corresponding to the distributed server, it is determined that the historical computing power supply at the periodic point is less than the minimum computing power split unit q k The period of time is marked as the period of insufficient computing power; According to the periodic effective computing power supply change information corresponding to the distributed server, determine the historical computing power supply during the computing power shortage period and the minimum split unit q of the computing power that is continuously supplied during the same period. k The computing power of the two parties is poor, resulting in a lack of computing power; According to the periodic effective computing power supply change information corresponding to the distributed server, the historical computing power supply in other periods outside the period of insufficient computing power and the minimum split unit q of the computing power continuously supplied in the same period are determined. k The computing power of the two is poor, forming a self-charging computing power; The following analysis is performed based on the self-charging computing power and the missing computing power: If the self-charged computing power is not less than the missing computing power, the corresponding distributed server is determined as a server for first-time borrowing computing power; If the self-charged computing power is less than the missing computing power, the corresponding distributed server is determined as a server for continuous borrowing computing power; Based on the computing power shortage analysis results and in combination with the power grid price change data, a computing power replenishment analysis is performed to generate computing power replenishment analysis result data.

2. The distributed service method based on the service platform according to claim 1, characterized in that: The historical computing power cycle supply data of different distributed servers is collected, and performance analysis of stable available computing power is performed to form distributed computing power supply data, including: Extracting periodic effective computing power supply change information of the distributed server according to the historical computing power cycle supply data corresponding to the distributed server; Based on the periodic effective computing power supply change information corresponding to different distributed servers, computing power availability analysis is performed to form the distributed computing power supply data.

3. The distributed service method based on the service platform according to claim 2, characterized in that: The extracting, based on the historical computing power cycle supply data corresponding to the distributed server, the periodic effective computing power supply change information of the distributed server includes: For different distributed servers, extract the historical cycle computing power change curve of each corresponding cycle m is the number of different distributed servers, and n is the number of different computing cycles of the distributed server numbered m; Compare the different historical period computing power change curves under the distributed server The minimum historical computing power supply at each periodic point is determined to form the periodic effective computing power supply change information corresponding to the distributed server.

4. The distributed service method based on the service platform according to claim 3, characterized in that: The performing of computing power availability analysis based on the periodic effective computing power supply change information corresponding to different distributed servers to form the distributed computing power supply data includes: For different distributed servers, according to the corresponding periodic effective computing power supply change information, extract the historical computing power supply provided at all periodic points, and determine the total historical computing power supply for the entire period According to the total historical computing power supply corresponding to the distributed server Determine the corresponding stable available computing power q m ,in, Aggregate the stable available computing power q corresponding to different distributed servers m , forming the distributed computing power supply data.

5. The distributed service method based on the service platform according to claim 4, characterized in that: The obtaining of the estimated total computing power of the training object set and performing computing power supply matching analysis in combination with the distributed computing power supply data to form distributed computing power matching data includes: According to the estimated total computing power of the training object set, the estimated total computing power Q corresponding to different training objects is determined. k , k represents the number of different training objects; According to the data collection mode of different training objects, combined with the corresponding object computing power estimation total Q k , determine the minimum computing power split unit q corresponding to the training object k ; For different training objects, the total amount Q is estimated based on the corresponding object computing power k and the minimum split unit q of the computing power k , and combined with the distributed computing power supply data, perform computing power supply matching analysis to form the distributed computing power matching data.

6. The distributed service method based on the service platform according to claim 5, characterized in that: For different training objects, the total amount Q is estimated based on the corresponding object computing power. k and the minimum split unit q of the computing power k , and combined with the distributed computing power supply data, perform computing power supply matching analysis to form the distributed computing power matching data, including: The minimum split unit q corresponding to the computing power of different training objects k , sorting the matching order of different training objects in descending order to form an object computing power matching order; According to the object computing power matching order, the computing power supply matching analysis of the distributed server is performed on different training objects in turn to form the distributed computing power matching data.

7. The distributed service method based on the service platform according to claim 6, characterized in that: The step of performing the distributed server's computing power supply matching analysis on different training objects in sequence according to the object computing power matching order to form the distributed computing power matching data includes: According to the object computing power matching order, the minimum computing power split unit q corresponding to each training object is extracted in turn. k and the estimated total computing power of the object Q k , perform the following hashrate supply matching analysis: The stable available computing power q corresponding to all distributed servers m , determine the split unit q with the minimum computing power k The computing power difference C m , and according to the computing power difference C m The distributed servers are sorted in ascending order to form a distributed server computing power matching order, where C m =q m -q k ; According to the distributed server computing power matching order, A is extracted in sequence. k The number of distributed servers forms the object matching distributed server set corresponding to the training object, and the extracted A k The number of distributed servers that meet the following requirements: m ≥β, express An integer formed by rounding up, β represents the minimum allowed computing power difference, and β<0; Excluding the distributed servers that have been matched before, for the remaining distributed servers, continue to extract the minimum computing power split unit q corresponding to the training object according to the object computing power matching order k and the estimated total computing power of the object Q k , perform computing power matching analysis until the computing power matching analysis of all training objects is completed; Determine the object matching distributed server sets corresponding to different training objects to form the distributed computing power matching data.

8. The distributed service method based on the service platform according to claim 7, characterized in that: The computing power replenishment analysis is performed based on the computing power shortage analysis results and combined with the power grid price change data to form computing power replenishment analysis result data, including: If the distributed server is the server that is lending computing power for the first time, then according to the computing power shortage period of the server that is lending computing power for the first time and the historical computing power supply corresponding to the period, and in combination with the power supply price change data of the power grid, determine the earliest charging time point for the first lending computing power and the charging period during the low electricity price period; If the distributed server is the continuously borrowed computing power server, then: Based on the period of insufficient computing power of the server continuously lending computing power and the corresponding historical computing power supply during the period, and in combination with the power supply price change data of the power grid, determine the earliest charging time point for the first lending computing power and the charging period during the period of low electricity price; The period of insufficient computing power during which self-charging can be achieved is determined based on the difference between the self-charging computing power and the missing computing power. For the remaining period of insufficient computing power during which self-charging cannot be achieved, the earliest charging time point for continuously borrowing computing power and the charging period during the period of low electricity prices are determined based on the historical computing power supply corresponding to the period and the power grid price change data.

9. A distributed service platform based on a service platform, characterized in that: The distributed service method based on the service platform according to any one of claims 1 to 8 comprises: Data collection unit, used to collect historical computing cycle supply data of different distributed servers; The computing power analysis unit is used to perform available computing power performance analysis on the historical computing power cycle supply data obtained by the data acquisition unit to form distributed computing power supply data, estimate the computing power of the training object set and perform computing power matching analysis in combination with the distributed computing power supply data to form distributed computing power matching data, and perform computing power supplementation analysis based on the power grid power supply price change data to form distributed computing power supplementation data.

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