Data center computing power network resource scheduling method

Abstract

The invention discloses a data center computing power network resource scheduling method, and belongs to the technical field of cloud data processing, and the method comprises the steps: obtaining the total number of passenger-carrying vehicles in a preset city, carrying out the district division of the preset city, obtaining the number of passenger-carrying vehicles in each divided district in different time periods, and enabling the different time periods to be distinguished with each hour as a time period; a travel information request sent by a client is acquired in a preset city district, the information request comprises a departure place and a destination, network signal quality data of the preset city district is acquired, and network signal quality data of a preset passenger-carrying vehicle is acquired in the preset city district; according to the invention, through a data analysis scheduling technology, network signal quality data changes of a preset passenger-carrying vehicle in a preset city area can be calculated, and network signal quality data changes of a departure place can be collected in the preset city area according to each travel information request to be received by the preset passenger-carrying vehicle; therefore, prediction analysis is provided for the passenger-carrying vehicle, and the operation service efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of cloud data processing technology, and in particular to a method for scheduling computing power network resources in a data center. Background Art

[0002] In recent years, the widespread application of cloud computing in the field of information technology has brought new challenges and opportunities to the scheduling and optimization of data center network resources. The research on the scheduling and optimization methods of data center network resources for cloud computing has become a hot topic in the industry and academia. The essence of this research is the service of computing resources that can be freely scheduled on demand. In the future, corporate customers and individual users will not only need networks and clouds, but also need to flexibly schedule computing tasks to the right place. Among them, the Internet of Vehicles industry has a wide range of use for computing network resource scheduling services, because its intelligent driving technology has high requirements for low latency and computing task scheduling. It is necessary to quickly switch computing nodes with the high-speed movement of vehicles, and according to the specific business applications initiated by different vehicles, and then based on the needs of different businesses, uniformly arrange and schedule applications and network capabilities to each computing node, forming a wide-area computing network service chain. In this computing network service chain, there are many beneficiaries, including the rapidly developing same-city taxi-hailing system, which can analyze user needs and the location of passenger drivers in real time through advanced intelligent algorithms to achieve rapid matching. However, for passenger vehicles that constantly shuttle through the city, their passenger vehicle networks sometimes change due to the location of the vehicles, among which network delay is one of the changes. The reasons for this situation include vehicle computer freezes, insufficient network traffic, vehicle network antenna failure, and surrounding interference signals. These problems will lead to unstable network connections for passenger vehicles, resulting in network delays and / or reduced network quality. If this happens, the passenger end will not be able to receive the taxi information request sent by the client in time, thereby increasing the customer's taxi waiting time and reducing the customer's service experience. The existing computing resource scheduling method applied to the same-city taxi system is difficult to calculate the network signal status of the passenger vehicle in time according to its driving route, resulting in difficulty in scheduling resources for subsequent orders according to the orders being executed by the passenger vehicle. This is not only not conducive to improving the operational efficiency of passenger vehicles for travel services, but also easily leads to an increase in invalid orders, which in turn leads to an increase in the driver's complaint rate, which is not conducive to the service improvement of the same-city taxi operation. Summary of the invention

[0003] In view of the above problems existing in the existing cloud data processing technology field, the present invention is proposed.

[0004] Therefore, one of the objects of the present invention is to provide a data center computing power network resource scheduling method, which calculates the network signal quality data changes of preset passenger vehicles in a preset urban area through data analysis scheduling technology, and then predicts whether the preset passenger vehicles that receive travel information requests can complete the travel order. It can also collect the network signal quality data changes of the departure points in all travel information requests to be received by the preset passenger vehicles in the preset urban area, and calculate the change rules, and then provide predictive analysis for the passenger vehicles, so that they can make order decisions faster and improve operational service efficiency.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] The method for scheduling computing power network resources in a data center includes the following steps:

[0007] Obtain the total number of passenger vehicles in a preset city, divide the preset city into areas, and obtain the number of passenger vehicles in each divided area at different time periods, wherein the different time periods include dividing each hour as a time period;

[0008] Acquire a travel information request sent by a client in the preset urban area, the information request including a departure place and a destination, and acquire network signal quality data of the preset urban area, and also acquire network signal quality data of preset passenger vehicles in the preset urban area;

[0009] Collecting movement data of the preset passenger vehicles in the preset urban area, analyzing the real-time driving routes of the preset passenger vehicles according to the movement data, obtaining changes in network signal quality data based on the analyzed real-time driving routes, and classifying the changes in network signal quality data;

[0010] A safety threshold is preset based on the quality of a network signal capable of receiving a travel information request sent by a client in real time. When the network signal quality of the preset passenger vehicle is lower than the safety threshold, it is determined that the preset passenger vehicle cannot receive the travel information request sent by the client in time, and a low-quality network signal warning is issued. Otherwise, no determination is made;

[0011] When it is determined that the preset passenger vehicle is unable to receive the travel information request issued by the client in a timely manner, other passenger vehicles that are not in the order-accepting state around the preset passenger vehicle are obtained, and the passenger vehicles are distinguished. At the same time, the network signal quality of the passenger vehicles is obtained, and a network signal quality comparison is performed.

[0012] As a preferred solution of the present invention, other passenger vehicles that are not in the order-receiving state around the preset passenger vehicle are obtained, and the other passenger vehicles include passenger vehicles within a range of 1 to 3 kilometers from the preset passenger vehicle. The passenger vehicles are distinguished in two ways, one of which includes: dividing the passenger vehicles into θ according to the distance from the preset passenger vehicle 1 ,θ 2 , ..., θ n , where n represents the nth passenger vehicle. When it is determined that the preset passenger vehicle cannot receive the travel information request issued by the client in time, the nearest passenger vehicle is recommended to the client who issued the travel information request.

[0013] As a preferred solution of the present invention, another method for distinguishing the passenger vehicles includes sorting the network signal qualities of the passenger vehicles based on their distance from the preset passenger vehicles. When it is determined that the preset passenger vehicle is unable to receive the travel information request issued by the client in time, the passenger vehicle with the highest network signal quality strength is recommended to the client who issued the travel information request.

[0014] As a preferred solution of the present invention, the network signal quality data change is graded, including being graded according to the collected signal strength data, as shown below:

[0015] dBm signal strength value range Corresponding signal quality -40～-60 very good -60～-70 good -70～-80 generally -80～-90 Slightly weak -90～-100 Weaker -100～-110 Difference -Below 110 Very bad

[0016] When the passenger vehicle with the highest network signal quality strength is moving towards the customer, the time required for the passenger vehicle to reach the customer's location is calculated, and the network signal quality of the passenger vehicle is collected every 2 to 4 seconds during the time. When the collected network signal quality is lower than the good level, the passenger vehicle is located and its positioning data is obtained. At the same time, based on the positioning data, other passenger vehicles closest to the passenger vehicle that are not in the order-accepting state are obtained, and the passenger vehicle is given as a substitute passenger vehicle. At the same time, the substitute passenger vehicle is recommended to the customer to complete the travel order.

[0017] As a preferred solution of the present invention, when the network signal quality of the passenger vehicle is collected at a time period of every 2 to 4 seconds, the change pattern of the network signal quality of the passenger vehicle from a very good level to a level below the good level is obtained, the median data of this change process is collected in the change pattern, and a critical threshold is preset based on the median data. When calculating the time required for any passenger vehicle to reach the customer's location in a future time period, if the network signal quality change of the passenger vehicle exceeds the critical threshold, it is determined that the network signal quality of the passenger vehicle will be lower than the good level, otherwise, it is not determined.

[0018] As a preferred solution of the present invention, the travel information requests to be received by the preset passenger vehicles in the preset urban area are counted, and the departure point is obtained in each travel information request, and the network signal quality of the departure point is analyzed at the same time. If the network signal quality of a certain departure point is lower than the good level, it is determined that when the preset passenger vehicle travels to the departure point, its network signal quality will change to a general and / or slightly weaker level, and the corresponding travel information request pushed to the passenger vehicle is canceled; otherwise, no determination is made.

[0019] As a preferred solution of the present invention, when the network signal quality at a certain departure point is lower than the good level, the network signal quality change characteristics of the departure point within 2 to 3 months are analyzed, and a data set is generated, in which the network signal quality change is divided into a test set, a validation set and a training set; at the same time, the network signal quality data closest to the general level in history is collected during the analysis time period, and the tenth network signal quality data closest to the data is collected based on the network signal quality data, and a risk threshold is preset based on the network signal quality data. When the network signal quality data change at the departure point in the future time period exceeds the risk threshold, it is determined that the network signal quality of the departure point will be lower than the good level, and a warning notification is issued to passenger vehicles outside the departure point; otherwise, no determination is made.

[0020] As a preferred solution of the present invention, the tenth network signal quality data collected within the time period is divided into several evaluation indicators, the network signal strength represented by the tenth network signal quality data within the time period is statistically calculated, different network signal strengths are given as different evaluation indicator data, the data is normalized, and the weight of each evaluation indicator is calculated.

[0021] A terminal comprises a processor, an input interface, an output interface and a memory, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 8.

[0022] A computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method according to any one of claims 1 to 8.

[0023] Beneficial effects:

[0024] The present invention calculates the changes in network signal quality data of preset passenger vehicles in a preset urban area through data analysis and scheduling technology, and then predicts whether the preset passenger vehicle that receives the travel information request can complete the travel order, and can analyze the network signal quality strength of other passenger vehicles in its surroundings according to the calculated results of the changes in network signal quality data of the preset passenger vehicle, and then recommend priority passenger vehicles to customers who send travel information requests to complete travel services. On this basis, the changes in network signal quality data of the departure points in all travel information requests to be received by the preset passenger vehicles in the preset urban area can be collected, and the change rules can be calculated, thereby providing predictive analysis for passenger vehicles, enabling them to make order decisions faster and improve operational service efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0026] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0027] Figure 2 It is a schematic diagram of the process structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0029] Since the existing computing resource scheduling method applied to the same-city taxi system is difficult to calculate the network signal status of the passenger vehicle in a timely manner according to the driving route of the passenger vehicle, it is difficult to schedule resources for subsequent orders according to the orders being executed by the passenger vehicle. This is not only not conducive to improving the operational efficiency of passenger vehicles for travel services, but also easily leads to an increase in invalid orders, which in turn leads to an increase in the driver's complaint rate, which is not conducive to the service improvement of the same-city taxi operation.

[0030] Based on this, the present invention proposes a data center computing power network resource scheduling method, which calculates the network signal quality data changes of the preset passenger vehicles in the preset urban area through data analysis scheduling technology, and then predicts whether the preset passenger vehicles that receive the travel information request can complete the travel order. It can also collect the network signal quality data changes of the departure point in the preset urban area according to all travel information requests to be received by the preset passenger vehicles, and calculate the change rules, and then provide predictive analysis for the passenger vehicles, so that they can make order decisions faster and improve operational service efficiency.

[0031] The present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0032] Reference Figure 1 to Figure 2 , is an embodiment of the present invention, which provides a data center computing power network resource scheduling method, comprising the following steps:

[0033] S10: Obtain the total number of passenger vehicles in a preset city, divide the preset city into areas, and obtain the number of passenger vehicles in each divided area at different time periods, where the different time periods are divided into one time period per hour;

[0034] S20: obtaining a travel information request sent by a client in a preset urban area, the information request including a departure place and a destination, and obtaining network signal quality data of the preset urban area, and also obtaining network signal quality data of a preset passenger vehicle in the preset urban area;

[0035] S30: collecting movement data of preset passenger vehicles in a preset urban area, analyzing the real-time driving routes of the preset passenger vehicles according to the movement data of the preset passenger vehicles, obtaining changes in network signal quality data based on the analyzed real-time driving routes, and classifying the changes in network signal quality data;

[0036] Specifically, in this embodiment, the network signal quality data changes are graded, including being graded according to the collected signal strength data, as shown below:

[0037] dBm signal strength value range Corresponding signal quality -40～-60 very good -60～-70 good -70～-80 generally -80～-90 Slightly weak -90～-100 Weaker -100～-110 Difference -Below 110 Very bad

[0038] When the passenger vehicle with the highest network signal quality strength is moving towards the customer, the time required for the passenger vehicle to reach the customer's location is calculated, and the network signal quality of the passenger vehicle is collected every 2 to 4 seconds during the time. When the collected network signal quality is lower than the good level, the passenger vehicle is located and its positioning data is obtained. At the same time, other passenger vehicles that are closest to the passenger vehicle and are not in the order-accepting state are obtained based on the positioning data, and the passenger vehicle is given as a substitute passenger vehicle, and the substitute passenger vehicle is recommended to the customer to complete the travel order;

[0039] On the basis of the above, this embodiment further obtains the change rule of the network signal quality of the passenger vehicle from the very good level to the level below the good level when collecting the network signal quality of the passenger vehicle in a period of 2 to 4 seconds, collects the median data of this change process in the change rule, and presets a critical threshold based on the median data. When calculating the time required for any passenger vehicle to reach the customer's location in the future period, if the change of the network signal quality of the passenger vehicle exceeds the critical threshold, it is determined that the network signal quality of the passenger vehicle will be lower than the good level, otherwise, it is not determined;

[0040] At the same time, this embodiment counts each travel information request to be received by the preset passenger vehicles in the preset urban area, obtains the departure point in each travel information request, and analyzes the network signal quality of the departure point. If the network signal quality of a certain departure point is lower than the good level, it is determined that when the preset passenger vehicle travels to the departure point, its network signal quality will change to the general and / or slightly weak level, and the corresponding travel information request pushed to the passenger vehicle is cancelled; otherwise, it is not determined;

[0041] It should be emphasized in this embodiment that when the network signal quality at a certain departure location is lower than the good level, the network signal quality change characteristics of the departure location within 2 to 3 months are analyzed, and a data set is generated, in which the test set, verification set and training set of the network signal quality change are divided; at the same time, the network signal quality data closest to the general level in history is collected during the analysis period, and the tenth network signal quality data closest to the data is collected based on the network signal quality data, and a risk threshold is preset based on the network signal quality data. When the network signal quality data change at the departure location in the future time period exceeds the risk threshold, it is determined that the network signal quality of the departure location will be lower than the good level, and a warning notification is issued to the passenger vehicles outside the departure location; otherwise, no determination is made;

[0042] Further, the tenth network signal quality data collected within the time period is divided into several evaluation indicators, the network signal strength represented by the tenth network signal quality data within the time period is counted, different network signal strengths are given as different evaluation indicator data, the data is normalized, and the weight of each evaluation indicator is calculated;

[0043] S40: Presetting a safety threshold based on the quality of a network signal capable of receiving a travel information request sent by a client in real time. When the quality of a network signal of a preset passenger vehicle is lower than the safety threshold, it is determined that the preset passenger vehicle cannot receive the travel information request sent by the client in time, and a low-quality network signal warning is issued. Otherwise, no determination is made.

[0044] S50: when it is determined that the preset passenger vehicle cannot receive the travel information request sent by the client in time, other passenger vehicles that are not in the order receiving state around the preset passenger vehicle are obtained, and the passenger vehicles are distinguished, and the network signal quality of the passenger vehicles is obtained, and the network signal quality is compared;

[0045] It should be noted that in this embodiment, other passenger vehicles include passenger vehicles within a range of 1 to 3 kilometers from the preset passenger vehicle, and there are two ways to distinguish passenger vehicles. One way includes: dividing passenger vehicles into θ according to the distance from the preset passenger vehicle. 1 ,θ 2 , ..., θ n , where n represents the nth passenger vehicle. When it is determined that the preset passenger vehicle cannot receive the travel information request sent by the client in time, the nearest passenger vehicle is recommended to the client who sent the travel information request in priority;

[0046] On the basis of the above, this embodiment provides another way to distinguish between passenger vehicles, including sorting the network signal quality of passenger vehicles based on their distance from preset passenger vehicles. When it is determined that the preset passenger vehicle cannot receive the travel information request issued by the client in time, the passenger vehicle with the highest network signal quality strength is recommended to the customer who issued the travel information request.

[0047] A terminal comprises a processor, an input interface, an output interface and a memory, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute a method as claimed in any one of claims 1 to 8.

[0048] A computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method as claimed in any one of claims 1 to 8.

[0049] To summarize, the present invention calculates the changes in network signal quality data of preset passenger vehicles in a preset urban area through data analysis and scheduling technology, and then predicts whether the preset passenger vehicle that receives the travel information request can complete the travel order, and can analyze the network signal quality strength of other passenger vehicles around it according to the calculated results of the changes in network signal quality data of the preset passenger vehicle, and then recommend priority passenger vehicles to customers who issue travel information requests to complete travel services. On this basis, the changes in network signal quality data of the departure points in all travel information requests to be received by the preset passenger vehicles in the preset urban area can be collected, and the change rules can be calculated, thereby providing predictive analysis for passenger vehicles, enabling them to make order decisions faster and improve operational service efficiency.

[0050] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A data center computing power network resource scheduling method, characterized in that: The following steps are involved: Obtain the total number of passenger vehicles in a preset city, divide the preset city into areas, and obtain the number of passenger vehicles in each divided area at different time periods, wherein the different time periods include dividing each hour as a time period; Acquire a travel information request sent by a client in the preset urban area, the information request including a departure place and a destination, and acquire network signal quality data of the preset urban area, and also acquire network signal quality data of preset passenger vehicles in the preset urban area; Collecting movement data of the preset passenger vehicles in the preset urban area, analyzing the real-time driving routes of the preset passenger vehicles according to the movement data, obtaining changes in network signal quality data based on the analyzed real-time driving routes, and classifying the changes in network signal quality data; A safety threshold is preset based on the quality of a network signal capable of receiving a travel information request sent by a client in real time. When the network signal quality of the preset passenger vehicle is lower than the safety threshold, it is determined that the preset passenger vehicle cannot receive the travel information request sent by the client in time, and a low-quality network signal warning is issued. Otherwise, no determination is made; When it is determined that the preset passenger vehicle is unable to receive the travel information request issued by the client in a timely manner, other passenger vehicles that are not in the order-accepting state around the preset passenger vehicle are obtained, and the passenger vehicles are distinguished. At the same time, the network signal quality of the passenger vehicles is obtained, and a network signal quality comparison is performed.

2. The data center computing power network resource scheduling method according to claim 1, characterized in that: Obtain other passenger vehicles that are not in the order-receiving state around the preset passenger vehicle, wherein the other passenger vehicles include passenger vehicles within a range of 1 to 3 kilometers from the preset passenger vehicle, and distinguish the passenger vehicles in two ways, one of which includes: dividing the passenger vehicles into θ1, θ2, ..., θ n , where n represents the nth passenger vehicle. When it is determined that the preset passenger vehicle cannot receive the travel information request issued by the client in time, the nearest passenger vehicle is recommended to the client who issued the travel information request.

3. The data center computing power network resource scheduling method according to claim 2, characterized in that: Another way to distinguish the passenger vehicles includes sorting the network signal qualities of the passenger vehicles based on their distance from the preset passenger vehicles. When it is determined that the preset passenger vehicle is unable to receive the travel information request issued by the client in a timely manner, the passenger vehicle with the highest network signal quality strength is recommended to the client who issued the travel information request.

4. The data center computing power network resource scheduling method according to claim 3, characterized in that: The network signal quality data changes are graded, including according to the collected signal strength data, as shown below: When the passenger vehicle with the highest network signal quality strength is moving towards the customer, the time required for the passenger vehicle to reach the customer's location is calculated, and the network signal quality of the passenger vehicle is collected every 2 to 4 seconds during the time. When the collected network signal quality is lower than the good level, the passenger vehicle is located and its positioning data is obtained. At the same time, based on the positioning data, other passenger vehicles closest to the passenger vehicle that are not in the order-accepting state are obtained, and the passenger vehicle is given as a substitute passenger vehicle. At the same time, the substitute passenger vehicle is recommended to the customer to complete the travel order.

5. The data center computing power network resource scheduling method according to claim 4, characterized in that: When the network signal quality of the passenger vehicle is collected at a time period of every 2 to 4 seconds, the change pattern of the network signal quality of the passenger vehicle from a very good level to a level below the good level is obtained, the median data of this change process is collected in the change pattern, and a critical threshold is preset based on the median data. When calculating the time required for any passenger vehicle to reach the customer's location in a future time period, if the network signal quality change of the passenger vehicle exceeds the critical threshold, it is determined that the network signal quality of the passenger vehicle is lower than the good level; otherwise, it is not determined.

6. The data center computing power network resource scheduling method according to claim 5, characterized in that: In the preset urban area, each travel information request to be received by the preset passenger vehicles is counted, and the departure point is obtained in each travel information request. At the same time, the network signal quality of the departure point is analyzed. If the network signal quality of a certain departure point is lower than the good level, it is determined that when the preset passenger vehicle travels to the departure point, its network signal quality will change to a general and / or slightly weaker level, and the corresponding travel information request pushed to the passenger vehicle is canceled; otherwise, no determination is made.

7. The data center computing power network resource scheduling method according to claim 6, characterized in that: When the network signal quality at a certain departure point is lower than the good level, the network signal quality change characteristics of the departure point within 2 to 3 months are analyzed, and a data set is generated, in which the network signal quality change is divided into a test set, a validation set and a training set; at the same time, the network signal quality data that is closest to the general level in history is collected during the analyzed time period, and the tenth network signal quality data closest to the data is collected based on the network signal quality data, and a risk threshold is preset based on the network signal quality data. When the network signal quality data change at the departure point in the future time period exceeds the risk threshold, it is determined that the network signal quality of the departure point will be lower than the good level, and a warning notification is issued to passenger vehicles outside the departure point; otherwise, no determination is made.

8. The data center computing power network resource scheduling method according to claim 7, characterized in that: The tenth network signal quality data collected within the time period is divided into several evaluation indicators, the network signal strength represented by the tenth network signal quality data within the time period is statistically calculated, different network signal strengths are given as different evaluation indicator data, the data is normalized, and the weight of each evaluation indicator is calculated.

9. The data center computing power network resource scheduling method according to claim 1, characterized in that: A terminal comprises a processor, an input interface, an output interface and a memory, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 8.

10. The data center computing power network resource scheduling method according to claim 1, characterized in that: A computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method according to any one of claims 1 to 8.

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