Intelligent scheduling and dynamic scheduling optimization method based on Internet of Things

Abstract

The invention discloses an intelligent scheduling and dynamic scheduling optimization method based on the Internet of Things, and belongs to the technical field of production line communication. The method comprises the following steps: acquiring resource utilization rate historical data, network reliability historical data, service quality historical data, resource utilization rate real-time data, network reliability real-time data and service quality real-time data; obtaining a resource utilization rate index, a network reliability index and a service quality index, and obtaining corresponding feedback; integrating the resource utilization rate index, the network reliability index and the service quality index to obtain an intelligent scheduling dynamic scheduling index; and performing intelligent scheduling and dynamic scheduling according to the intelligent scheduling and dynamic scheduling index. According to the invention, intelligent scheduling and dynamic scheduling are carried out through the intelligent scheduling and dynamic scheduling indexes, so that intelligent scheduling and dynamic scheduling are accurately and effectively carried out, and the problem that intelligent scheduling and dynamic scheduling are difficult to accurately carry out in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of production line communication technology, and in particular to an intelligent scheduling dynamic scheduling optimization method based on the Internet of Things. Background Art

[0002] With the development of Internet technology, we have moved from the analog communication era to the mobile communication era, and now to the 5G era. The development of the Internet has brought great convenience to people, facilitated people's lives, and enriched people's lives. With the popularization of the Internet, more and more people are using the Internet. In places where people gather, data traffic will increase significantly, leading to network congestion. The use of intelligent scheduling and dynamic scheduling methods for communication networks is of great significance to improving network quality.

[0003] The existing intelligent scheduling and dynamic scheduling method includes obtaining production line equipment data through a production line equipment data acquisition subunit, storing the production line equipment data through a task scheduling sorting subunit, and sorting the production line equipment data from large to small according to task priority and storing it in a database, and then scheduling and processing high-priority data in turn according to the sorting order of the production line equipment data, thereby realizing intelligent scheduling and dynamic scheduling of communication data.

[0004] For example, the intelligent production line data scheduling communication device disclosed in the invention patent with publication number: CN114386725A includes: production line equipment data acquisition subunit, production line equipment data task scheduling and sorting subunit, address space subscription subunit, data interface management subunit, and task queue buffer subunit. The production line equipment data acquisition subunit can collect control data and sensor data source data of field equipment, and then the production line equipment data task scheduling and sorting subunit performs optimal task list sorting processing, the address space subscription subunit maps the actual field equipment to the address space, and finally puts the data into the response queue cache to schedule the communication data.

[0005] For example, the invention patent with publication number: CN111784189A discloses an intelligent command platform for electric power communication and dispatching, including: the command platform is constructed using a B / S three-tier architecture, and each functional module is divided into a three-tier architecture of a presentation layer, a business logic layer, and a data access layer, and each layer uses an interface to access each other; the database transmits power grid data between the modules of the command platform through the electric power communication network, and the command platform uses the electric power communication network channel to perform network operation management and transaction operation management.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the prior art, since the communication network will only adopt intelligent scheduling and dynamic scheduling to improve the quality of the communication network and ensure people's communication quality when a network failure occurs or a surge in users occurs within a specific period of time, the prior art only unilaterally performs intelligent scheduling and dynamic scheduling based on large data traffic or user congestion, and there is a problem that it is difficult to accurately perform intelligent scheduling and dynamic scheduling. Summary of the invention

[0007] The embodiment of the present application solves the problem that it is difficult to accurately perform intelligent scheduling and dynamic scheduling in the prior art by providing an intelligent scheduling and dynamic scheduling optimization method based on the Internet of Things, and realizes accurate and effective intelligent scheduling and dynamic scheduling.

[0008] The embodiment of the present application provides an intelligent scheduling dynamic scheduling optimization method based on the Internet of Things, comprising the following steps: obtaining resource utilization historical data, network reliability historical data, and service quality historical data from the Internet of Things; obtaining resource utilization real-time data, network reliability real-time data, and service quality real-time data from the Internet of Things; obtaining resource utilization indicators based on resource utilization historical data and resource utilization real-time data and obtaining corresponding feedback; obtaining network reliability indicators based on network reliability historical data and network reliability real-time data and obtaining corresponding feedback; obtaining service quality indicators based on service quality historical data and service quality real-time data and obtaining corresponding feedback; obtaining intelligent scheduling dynamic scheduling indicators by integrating resource utilization indicators, network reliability indicators, and service quality indicators; and performing intelligent scheduling dynamic scheduling according to the intelligent scheduling dynamic scheduling indicators.

[0009] Furthermore, the resource utilization historical data includes CPU utilization historical data, memory utilization historical data, network bandwidth utilization historical data, network interface utilization historical data and storage space utilization historical data; the network reliability historical data includes network delay historical data, network throughput historical data and network packet loss rate historical data; the service quality historical data includes transmission rate historical data, bit error rate historical data, jitter historical data and response time historical data.

[0010] Furthermore, the real-time data on resource utilization includes real-time data on CPU utilization, real-time data on memory utilization, real-time data on network bandwidth utilization, real-time data on network interface utilization, and real-time data on storage space utilization; the real-time data on network reliability includes real-time data on network delay, real-time data on network throughput, and real-time data on network packet loss rate; the real-time data on service quality includes real-time data on transmission rate, real-time data on bit error rate, real-time data on jitter, and real-time data on response time.

[0011] Furthermore, the specific method for obtaining the resource utilization index is: obtaining historical resource utilization data, numbering the historical resource utilization data; constructing a resource utilization index calculation formula; The specific resource utilization index calculation formula is: ; In the formula, Expressed as a resource utilization indicator, Represents real-time data of CPU utilization. Expressed as CPU utilization historical data, , Represented as the total number of historical data on resource utilization, Represented as real-time data of memory utilization, Expressed as Memory utilization historical data, Represented as real-time data of network bandwidth utilization, Expressed as Historical data of network bandwidth utilization, Represented as real-time data of storage space utilization, Expressed as Storage space utilization historical data, Represented as real-time data of network interface utilization, Expressed as Historical data on network interface utilization.

[0012] Furthermore, the specific analysis process of the resource utilization indicator is: obtaining historical data of the resource utilization indicator, obtaining a first threshold value of the resource utilization indicator and a second threshold value of the resource utilization indicator, comparing the resource utilization indicator with the first threshold value of the resource utilization indicator and the second threshold value of the resource utilization indicator; when the resource utilization indicator is less than the first threshold value of the resource utilization indicator and greater than the second threshold value of the resource utilization indicator, it indicates that the resource utilization indicator is in a normal range, that is, the resource utilization is in a passing range; when in other situations, it indicates that the resource utilization indicator is in an abnormal range, that is, the resource utilization is in a failing range.

[0013] Furthermore, the specific analysis process of the network reliability index is: obtaining historical data of the network reliability index, obtaining a first threshold value of the network reliability index and a second threshold value of the network reliability index, comparing the network reliability index with the first threshold value of the network reliability index and the second threshold value of the network reliability index; when the network reliability index is less than the first threshold value of the network reliability index and greater than the second threshold value of the network reliability index, it indicates that the network reliability index is in a normal range, that is, the network reliability is in a passing range; when it is in other situations, it indicates that the network reliability index is in an abnormal range, that is, the network reliability is in a failing range.

[0014] Furthermore, the specific analysis process of the service quality indicator is: obtaining historical data of the service quality indicator, obtaining a first threshold value of the service quality indicator and a second threshold value of the service quality indicator, comparing the service quality indicator with the first threshold value of the service quality indicator and the second threshold value of the service quality indicator; when the service quality indicator is less than the first threshold value of the service quality indicator and greater than the second threshold value of the service quality indicator, it indicates that the service quality indicator is in a normal range, that is, the network service quality is in a passing range; when in other situations, it indicates that the service quality indicator is in an abnormal range, that is, the network service quality is in a failing range.

[0015] Furthermore, the specific method for obtaining the intelligent scheduling dynamic scheduling index is: obtaining the weight ratio of the preset resource utilization index, network reliability index and service quality index in the intelligent scheduling dynamic scheduling index from the database; constructing an intelligent scheduling dynamic scheduling index calculation formula; the specific intelligent scheduling dynamic scheduling index calculation formula is: ; In the formula, It is represented as the dynamic scheduling index of intelligent scheduling. Expressed as a resource utilization indicator, Expressed as a network reliability index, Expressed as a service quality indicator, It is expressed as the weight ratio of resource utilization index in the dynamic scheduling index of intelligent scheduling. It is expressed as the weight ratio of network reliability index in the dynamic scheduling index of intelligent scheduling. It is expressed as the weight ratio of the service quality index in the intelligent scheduling dynamic scheduling index. is a natural constant.

[0016] Furthermore, the specific analysis process of the intelligent scheduling dynamic scheduling indicator is: obtaining historical data of the intelligent scheduling dynamic scheduling indicator, obtaining the first threshold value of the intelligent scheduling dynamic scheduling indicator and the second threshold value of the intelligent scheduling dynamic scheduling indicator, comparing the intelligent scheduling dynamic scheduling indicator with the first threshold value of the intelligent scheduling dynamic scheduling indicator and the second threshold value of the intelligent scheduling dynamic scheduling indicator, when the intelligent scheduling dynamic scheduling indicator is less than the first threshold value of the intelligent scheduling dynamic scheduling indicator and greater than the second threshold value of the intelligent scheduling dynamic scheduling indicator, it indicates that the intelligent scheduling dynamic scheduling indicator is in a normal range; when in other situations, it indicates that the intelligent scheduling dynamic scheduling indicator is in an abnormal range.

[0017] Furthermore, the specific process of performing intelligent scheduling and dynamic scheduling according to the intelligent scheduling and dynamic scheduling indicators is: when the intelligent scheduling and dynamic scheduling indicators are in the normal range, that is, the quality of the communication network is in a good state, the network does not perform any processing; when the intelligent scheduling and dynamic scheduling indicators are in the abnormal range, that is, there is a problem with the quality of the communication network, the network is intelligently scheduled and dynamically scheduled.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By acquiring historical data of resource utilization, historical data of network reliability and historical data of service quality, real-time data of resource utilization, real-time data of network reliability and real-time data of service quality, resource utilization indicators, network reliability indicators and service quality indicators are obtained. Intelligent scheduling dynamic scheduling indicators are obtained by integrating resource utilization indicators, network reliability indicators and service quality indicators. Intelligent scheduling dynamic scheduling is performed according to the intelligent scheduling dynamic scheduling indicators, thereby realizing accurate and effective intelligent scheduling dynamic scheduling, which effectively solves the problem that it is difficult to accurately perform intelligent scheduling dynamic scheduling in the prior art.

[0019] 2. By acquiring historical data of CPU utilization, historical data of memory utilization, historical data of network bandwidth utilization, historical data of network interface utilization, historical data of storage space utilization, real-time data of CPU utilization, real-time data of memory utilization, real-time data of network bandwidth utilization, real-time data of network interface utilization and real-time data of storage space utilization, resource utilization indicators are obtained. The resource utilization indicators reflect the resource utilization of communication network equipment, and the resource utilization of communication network equipment is evaluated through the resource utilization indicators.

[0020] 3. By acquiring historical data of network delay, historical data of network throughput, historical data of network packet loss rate, real-time data of network delay, real-time data of network throughput and real-time data of network packet loss rate, the network reliability index is obtained. The network reliability index reflects the reliability of the communication network, and the network reliability is evaluated through the network reliability index. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for intelligent scheduling and dynamic scheduling optimization based on the Internet of Things provided in an embodiment of the present application; Figure 2 A curve chart of the intelligent scheduling dynamic scheduling index provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiment of the present application solves the problem that it is difficult to accurately perform intelligent scheduling and dynamic scheduling in the prior art by providing an intelligent scheduling and dynamic scheduling optimization method based on the Internet of Things. By acquiring historical data of resource utilization, historical data of network reliability, and historical data of service quality, real-time data of resource utilization, real-time data of network reliability, and real-time data of service quality, resource utilization indicators, network reliability indicators, and service quality indicators are obtained. The intelligent scheduling and dynamic scheduling indicators are obtained by comprehensively combining the resource utilization indicators, network reliability indicators, and service quality indicators. Intelligent scheduling and dynamic scheduling are performed according to the intelligent scheduling and dynamic scheduling indicators, thereby realizing accurate and effective intelligent scheduling and dynamic scheduling, and effectively solving the problem that it is difficult to accurately perform intelligent scheduling and dynamic scheduling in the prior art.

[0023] The technical solution in the embodiment of the present application is to solve the above-mentioned problem of difficulty in accurately performing intelligent scheduling and dynamic scheduling. The overall idea is as follows: By acquiring historical data of resource utilization, historical data of network reliability and historical data of service quality, real-time data of resource utilization, real-time data of network reliability and real-time data of service quality, we can obtain resource utilization indicators, network reliability indicators and service quality indicators. We can obtain intelligent scheduling dynamic scheduling indicators by integrating resource utilization indicators, network reliability indicators and service quality indicators. We can perform intelligent scheduling dynamic scheduling according to the intelligent scheduling dynamic scheduling indicators.

[0024] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] like Figure 1As shown, it is a flow chart of an intelligent scheduling dynamic scheduling optimization method based on the Internet of Things provided by an embodiment of the present application, and the method includes the following steps: obtaining historical data of resource utilization, historical data of network reliability, and historical data of service quality from the Internet of Things; obtaining real-time data of resource utilization, real-time data of network reliability, and real-time data of service quality from the Internet of Things; obtaining resource utilization indicators and corresponding feedback based on historical data of resource utilization and real-time data of resource utilization; obtaining network reliability indicators and corresponding feedback based on historical data of network reliability and real-time data of network reliability; obtaining service quality indicators and corresponding feedback based on historical data of service quality and real-time data of service quality; obtaining intelligent scheduling dynamic scheduling indicators by integrating resource utilization indicators, network reliability indicators, and service quality indicators; performing intelligent scheduling dynamic scheduling according to the intelligent scheduling dynamic scheduling indicators.

[0026] In this embodiment, under normal circumstances, the resource utilization data, network reliability data, and service quality data of the communication network for 24 hours are taken as the resource utilization historical data, network reliability historical data, and service quality historical data. In this embodiment, the resource utilization index reflects the resource utilization of the network equipment, the network reliability index reflects the reliability of the communication network, and the service quality index reflects the service quality of the communication network. The service quality represents the performance level that can be provided when the communication network transmits data, that is, the degree of realization of the service quality promised by the provider to the user. This embodiment combines the resource utilization index, the network reliability index, and the service quality index to obtain the intelligent scheduling dynamic scheduling index, which can comprehensively evaluate the communication network, so that the communication network can be more timely and accurately intelligently scheduled and dynamically scheduled.

[0027] Furthermore, resource utilization historical data includes CPU utilization historical data, memory utilization historical data, network bandwidth utilization historical data, network interface utilization historical data and storage space utilization historical data; network reliability historical data includes network delay historical data, network throughput historical data and network packet loss rate historical data; service quality historical data includes transmission rate historical data, bit error rate historical data, jitter historical data and response time historical data.

[0028] In this embodiment, resource utilization historical data is obtained through the built-in performance monitoring function of the communication network device. The performance monitoring function can be started on the communication network device, and the interval for collecting data can be set to one hour. The CPU utilization data, memory utilization data, network bandwidth utilization data, and storage space utilization data of the communication network device for 24 hours are the CPU utilization historical data, memory utilization historical data, network bandwidth utilization historical data, network interface utilization historical data, and storage space utilization historical data. The network reliability historical data is obtained through the built-in performance monitoring function of the communication network device. The performance monitoring function can be started on the communication network device, and the interval for collecting data can be set to one hour. The network delay data, network throughput data, and network packet loss rate data of the communication network device for 24 hours are obtained. The network delay historical data, network throughput historical data, and network packet loss rate historical data can be obtained by viewing the historical records of the performance monitoring data through the CLI or Web interface of the device. The service quality historical data can be obtained through a network analyzer.

[0029] Furthermore, the real-time data on resource utilization includes real-time data on CPU utilization, real-time data on memory utilization, real-time data on network bandwidth utilization, real-time data on network interface utilization, and real-time data on storage space utilization; the real-time data on network reliability includes real-time data on network delay, real-time data on network throughput, and real-time data on network packet loss rate; the real-time data on service quality includes real-time data on transmission rate, real-time data on bit error rate, real-time data on jitter, and real-time data on response time.

[0030] In this embodiment, the real-time data of resource utilization can be obtained by logging into the Web management interface through the IP address and username and password of the communication network device, and the real-time data of CPU utilization, memory utilization, network bandwidth utilization, network interface utilization and storage space utilization can be obtained in the Web management interface. The real-time data of network delay, network throughput, network packet loss rate, transmission rate, bit error rate, jitter and response time can be obtained through a network analyzer.

[0031] Furthermore, the specific method for obtaining the resource utilization index is: obtaining historical resource utilization data, numbering the historical resource utilization data; constructing a resource utilization index calculation formula; The specific resource utilization index calculation formula is: ; In the formula, Expressed as a resource utilization indicator, Represents real-time data of CPU utilization. Expressed as CPU utilization historical data, , Represented as the total number of historical data on resource utilization, Represented as real-time data of memory utilization, Expressed as Memory utilization historical data, Represented as real-time data of network bandwidth utilization, Expressed as Historical data of network bandwidth utilization, Represented as real-time data of storage space utilization, Expressed as Storage space utilization historical data, Represented as real-time data of network interface utilization, Expressed as Historical data on network interface utilization.

[0032] In this embodiment, It represents the percentage of the total time that the CPU of the communication network device currently executes tasks. Represents the The percentage of the time when the CPU utilization history data of the communication network equipment CPU executes tasks to the total time. Represents the ratio of the amount of memory used by the communication network device to the total amount of memory at the current moment. Represents the The ratio of the amount of memory used by the communication network device to the total amount of memory at the time of each memory utilization historical data. It represents the ratio of the actual bandwidth used in the current network transmission process to the total bandwidth. Represents the The ratio of the actual bandwidth used in the network transmission process to the total bandwidth at the time when the historical data of network bandwidth utilization is located. Represents the ratio of the storage space of the communication network equipment to the total storage space at the current moment. Represents the The ratio of the storage space of the communication network equipment to the total storage space at the time of the storage space utilization history data. It represents the ratio of the bandwidth actually used by the network interface at the current moment to its maximum available bandwidth. Expressed as The ratio of the actual bandwidth used by a network interface to its maximum available bandwidth at the time when the historical data of network interface utilization is collected.

[0033] In this embodiment, the five aspects of CPU utilization real-time data, memory utilization real-time data, network bandwidth utilization real-time data, network interface utilization real-time data and storage space utilization real-time data are integrated, and five factors are taken into consideration, which is conducive to better evaluating the resource utilization of network equipment. When the real-time data of memory utilization is too high, the communication network's data processing capacity will be reduced, thereby causing the real-time data of network bandwidth utilization to decrease. When the real-time data of storage space utilization is too high, the real-time data of CPU utilization, network interface utilization and memory utilization will increase. This embodiment uses logarithmic operations to reduce the impact of outliers, make the obtained data more accurate, and make the resource utilization index more reliable and convincing.

[0034] Furthermore, the specific analysis process of the resource utilization indicator is: obtaining historical data of the resource utilization indicator, obtaining a first threshold value of the resource utilization indicator and a second threshold value of the resource utilization indicator, comparing the resource utilization indicator with the first threshold value of the resource utilization indicator and the second threshold value of the resource utilization indicator; when the resource utilization indicator is less than the first threshold value of the resource utilization indicator and greater than the second threshold value of the resource utilization indicator, it indicates that the resource utilization indicator is in a normal range, that is, the resource utilization is in a passing range; when in other situations, it indicates that the resource utilization indicator is in an abnormal range, that is, the resource utilization is in a failing range.

[0035] In this embodiment, the resource utilization index historical data is obtained from the database, the resource utilization index historical data is averaged, and the average is multiplied by 1.1 to obtain the first threshold value of the resource utilization index. The first threshold value of the resource utilization index is , The value range is , multiply the mean by 0.9 to get the second threshold of resource utilization index. The second threshold of resource utilization index is , The value range is .

[0036] In this embodiment, , , when the resource utilization index is less than the first resource utilization index threshold and greater than the second resource utilization index threshold, that is, , indicating that the resource utilization index is within the normal range, that is, the network resource utilization is within the passing range; when it is in other situations, that is, or When , it means that the resource utilization index is in the abnormal range, that is, the resource utilization is in the unqualified range. When the processing capacity of the feedback communication equipment reaches its upper limit, the network bandwidth should be appropriately increased. When the network resources of the feedback communication equipment are not fully utilized, resulting in resource waste, the network bandwidth is appropriately reduced.

[0037] Furthermore, the specific analysis process of the network reliability index is: obtaining historical data of the network reliability index, obtaining a first threshold value of the network reliability index and a second threshold value of the network reliability index, comparing the network reliability index with the first threshold value of the network reliability index and the second threshold value of the network reliability index; when the network reliability index is less than the first threshold value of the network reliability index and greater than the second threshold value of the network reliability index, it indicates that the network reliability index is in a normal range, that is, the network reliability is in a passing range; when it is in other situations, it indicates that the network reliability index is in an abnormal range, that is, the network reliability is in a failing range.

[0038] In this embodiment, the specific method for obtaining the network reliability index is: obtaining network reliability historical data, numbering the network reliability historical data; constructing a network reliability index calculation formula; the specific network reliability index calculation formula is: ; In the formula, Expressed as a network reliability index, Represented as network delayed real-time data, Expressed as Historical data of network delays, , Represented as the total number of historical network reliability data obtained, Represented as real-time data of network throughput, Expressed as Historical data of network throughput, Represented as real-time data of network packet loss rate, Expressed as Historical data of network packet loss rate, is a natural constant.

[0039] In this embodiment, Represents the time required for the communication network data packet to be sent from the source address to the destination address at the current moment. Represents the The network delay historical data is the time required for the communication network data packet to be sent from the source address to the destination address at the moment. Represents the amount of data successfully transmitted per unit time in the current communication network. Represents the The amount of data successfully transmitted per unit time in the communication network at the time when the network throughput history data is located. It represents the ratio of the data packets lost during the data transmission of the communication network at the current moment to the total data packets sent. Represents the The network packet loss rate historical data is the ratio of the data packets lost during the communication network data transmission process to the total data packets sent at the moment.

[0040] In this embodiment, the real-time data of network delay, network throughput and network packet loss rate all reflect the network reliability during data transmission. When the real-time data of network delay increases, the real-time data of network throughput will decrease and the network packet loss rate will increase. This embodiment combines the three factors of real-time data of network delay, real-time data of network throughput and real-time data of network packet loss rate, and can more accurately evaluate the network reliability. In this embodiment, exponential operation is used to make the change of network reliability index more obvious, which is conducive to obtaining more accurate network reliability index and making the network reliability index more explanatory.

[0041] In this embodiment, the historical data of the network reliability index is obtained from the database, the historical data of the network reliability index is averaged, and the average is multiplied by 1.1 to obtain the first threshold value of the network reliability index. The first threshold value of the network reliability index is , The value range is , multiply the mean by 0.9 to get the second threshold of network reliability index. The second threshold of network reliability index is , The value range is .

[0042] In this embodiment, , , when the network reliability index is less than the first threshold of the network reliability index and greater than the second threshold of the network reliability index, that is, , indicating that the network reliability index is within the normal range, that is, the network reliability is within the passing range; when it is in other situations, that is, or When , it means that the network reliability index is in the abnormal range, that is, the network reliability is in the unqualified range. When the reliability of the feedback communication network is higher than the required network reliability, it may lead to low cost-effectiveness. Re-evaluate the network services and optimize the network architecture. When the network reliability is too low, the network may be faulty. Perform network repair and improve network configuration.

[0043] Furthermore, the specific analysis process of the service quality indicator is: obtaining historical data of the service quality indicator, obtaining a first threshold value of the service quality indicator and a second threshold value of the service quality indicator, comparing the service quality indicator with the first threshold value of the service quality indicator and the second threshold value of the service quality indicator; when the service quality indicator is less than the first threshold value of the service quality indicator and greater than the second threshold value of the service quality indicator, it indicates that the service quality indicator is in a normal range, that is, the network service quality is in a passing range; when in other situations, it indicates that the service quality indicator is in an abnormal range, that is, the network service quality is in a failing range.

[0044] In this embodiment, the specific method for obtaining the service quality indicator is: obtaining the service quality historical data, numbering the service quality historical data; constructing a service quality indicator calculation formula; the specific service quality indicator calculation formula is: ; In the formula, Expressed as a service quality indicator, Expressed as transfer rate real-time data, Expressed as Transmission rate historical data, , Represents the total number of historical service quality data obtained, Expressed as real-time data of bit error rate, Expressed as Bit error rate historical data, Expressed as jitter real-time data, Expressed as Jitter history data, Represented as response time real-time data, Expressed as Response time history data.

[0045] In this embodiment, It represents the amount of data transmitted through the network per unit time at the current moment. Represents the The amount of data transmitted through the communication network per unit time at the moment when the transmission rate historical data is located. It represents the ratio of error bits to total transmission bits in the communication network during data transmission at the current moment. Represents the The ratio of error bits to total transmission bits in the communication network during data transmission at the time when the network delay history data is located, Represents the fluctuation of the time required for a data packet in the communication network to travel from one point to another at the current moment. Represents the The fluctuation of the time required for a data packet to travel from one point to another in the communication network at the time when the bit error rate historical data is located. Represents the time from sending a request to receiving a response in the communication network at the current moment. Represents the The historical data of response time is the time from sending a request to receiving a response in the communication network.

[0046] In this embodiment, four factors are taken into consideration, namely, transmission rate real-time data, bit error rate real-time data, jitter real-time data and response time real-time data. The transmission rate real-time data, bit error rate real-time data, jitter real-time data and response time real-time data all reflect the service quality of the communication network. An increase in the transmission rate real-time data will lead to an increase in the bit error rate real-time data and jitter real-time data, thereby leading to an increase in the response time real-time data. This embodiment combines the four factors together to more accurately evaluate the reliability of the communication network. This embodiment uses square root processing to make the service quality indicators more reliable and accurate.

[0047] In this embodiment, the historical data of the service quality index are obtained from the database, the historical data of the service quality index are averaged, and the average is multiplied by 1.1 to obtain the first threshold value of the service quality index. The first threshold value of the service quality index is , The value range is , multiply the mean by 0.9 to get the second threshold of the service quality indicator. The second threshold of the service quality indicator is , The value range is .

[0048] In this embodiment, ,Pick , when the service quality indicator is less than the first threshold of the service quality indicator and greater than the second threshold of the service quality indicator, that is, , indicating that the service quality index is within the normal range, that is, the network service quality is within the passing range; when it is in other situations, that is, or When , it means that the service quality index is in an abnormal range, that is, the network service quality is in a substandard range. When the network resources are not fully utilized, feedback is given and business needs are reassessed to improve cost-effectiveness. When the network is congested, the service quality is reduced, and the network architecture is optimized and the bandwidth is appropriately increased.

[0049] Furthermore, the specific method for obtaining the dynamic scheduling index of intelligent scheduling is: obtaining the weight ratio of the preset resource utilization index, network reliability index and service quality index in the dynamic scheduling index of intelligent scheduling from the database; constructing the calculation formula of the dynamic scheduling index of intelligent scheduling; the specific calculation formula of the dynamic scheduling index of intelligent scheduling is: ; In the formula, It is represented as the dynamic scheduling index of intelligent scheduling. Expressed as a resource utilization indicator, Expressed as a network reliability index, Expressed as a service quality indicator, It is expressed as the weight ratio of resource utilization index in the dynamic scheduling index of intelligent scheduling. It is expressed as the weight ratio of network reliability index in the dynamic scheduling index of intelligent scheduling. It is expressed as the weight ratio of the service quality index in the intelligent scheduling dynamic scheduling index. is a natural constant.

[0050] In this embodiment, is the weight ratio corresponding to the resource utilization index, which represents the numerical value of the influence of the resource utilization index on the comprehensive value. When used, the weight ratio corresponding to the resource utilization index can be directly obtained from the preset database. The corresponding relationship can be a preset mapping relationship. For example, the resource utilization index and the weight corresponding to the resource utilization index in the preset database form a mapping set. After the real-time resource utilization index is input into the mapping set, the corresponding weight will be obtained. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0,1]. is the weight ratio corresponding to the network reliability index, which indicates the numerical value of the influence degree of the network reliability index on the comprehensive value. When used, the weight ratio corresponding to the network reliability index can be directly obtained from the preset database. The corresponding relationship can be a preset mapping relationship. For example, the network reliability index and the weight corresponding to the network reliability index in the preset database form a mapping set. After the real-time network reliability index is input into the mapping set, the corresponding weight will be obtained. The mapping relationship can be a one-to-one correspondence, a one-to-many relationship, or a many-to-one relationship. In this example, its value range is [0,1]. It is the weight ratio corresponding to the service quality indicator, which represents the numerical value of the influence degree of the service quality indicator on the comprehensive value. When used, the weight ratio corresponding to the service quality indicator can be directly obtained from the preset database. The corresponding relationship can be a preset mapping relationship. For example, the service quality indicator and the weight corresponding to the service quality indicator in the preset database form a mapping set. After the real-time service quality indicator is input into the mapping set, the corresponding weight will be obtained. The mapping relationship can be a one-to-one correspondence, a one-to-many relationship, or a many-to-one relationship. In this example, its value range is [0,1].

[0051] In this embodiment, the intelligent scheduling dynamic scheduling index is the basis for whether to perform intelligent scheduling dynamic scheduling, and also reflects the quality of the communication network. This embodiment comprehensively evaluates the quality of the communication network by comprehensively considering resource utilization index, network reliability index and service quality index. Resource utilization index, network reliability index and service quality index are the basis for whether the communication network performs intelligent scheduling dynamic scheduling. This embodiment integrates three factors to make the basis for intelligent scheduling dynamic scheduling more comprehensive, which is conducive to timely and accurate intelligent scheduling dynamic scheduling. This embodiment uses logarithmic operations to smooth the influence of abnormal values, so that the obtained intelligent scheduling dynamic scheduling index is more accurate and effective.

[0052] like Figure 2 As shown, it is a curve chart of the intelligent scheduling dynamic scheduling index provided by the embodiment of the present application, with the resource utilization index as the independent variable and the intelligent scheduling dynamic scheduling index as the dependent variable. Through the increase and decrease analysis of curves 1, 2 and 3, it can be seen that when the resource utilization index is larger, the intelligent scheduling dynamic scheduling index is larger; when the resource utilization index is smaller, the intelligent scheduling dynamic scheduling index is smaller. The statistical table of the intelligent scheduling dynamic scheduling index related data is shown in Table 1: Table 1 Statistics of relevant data of intelligent scheduling dynamic scheduling indicators ; It can be seen from Table 1 that for curve 1, curve 2 and curve 3, when the resource utilization index of curve 1, curve 2 and curve 3 is 1.00, when the network reliability index of curve 1, curve 2 and curve 3 is 0.50, 1.00 and 1.50 respectively, and other parameters are consistent, the intelligent scheduling dynamic scheduling index of curve 1, curve 2 and curve 3 is 2.22, 2.26 and 2.30 respectively. It can be seen that when the resource utilization index is consistent with other parameters, when the network reliability index is larger, the intelligent scheduling dynamic scheduling index is larger.

[0053] Furthermore, the specific analysis process of the intelligent scheduling dynamic scheduling indicator is: obtaining the historical data of the intelligent scheduling dynamic scheduling indicator, obtaining the first threshold of the intelligent scheduling dynamic scheduling indicator and the second threshold of the intelligent scheduling dynamic scheduling indicator, comparing the intelligent scheduling dynamic scheduling indicator with the first threshold of the intelligent scheduling dynamic scheduling indicator and the second threshold of the intelligent scheduling dynamic scheduling indicator, when the intelligent scheduling dynamic scheduling indicator is less than the first threshold of the intelligent scheduling dynamic scheduling indicator and greater than the second threshold of the intelligent scheduling dynamic scheduling indicator, it indicates that the intelligent scheduling dynamic scheduling indicator is in a normal range; when in other situations, it indicates that the intelligent scheduling dynamic scheduling indicator is in an abnormal range.

[0054] In this embodiment, the historical data of the intelligent scheduling dynamic scheduling index is obtained from the database, the historical data of the intelligent scheduling dynamic scheduling index is averaged, and the average is multiplied by 1.1 to obtain the first threshold value of the intelligent scheduling dynamic scheduling index. The first threshold value of the intelligent scheduling dynamic scheduling index is , The value range is , multiply the mean by 0.9 to get the second threshold of the dynamic scheduling index of intelligent scheduling. The second threshold of the dynamic scheduling index of intelligent scheduling is , The value range is .

[0055] In this embodiment, ,Pick When the intelligent scheduling dynamic scheduling index is less than the first threshold of the intelligent scheduling dynamic scheduling index and greater than the second threshold of the intelligent scheduling dynamic scheduling index, that is, , indicating that the dynamic scheduling index of intelligent scheduling is in the normal range. When it is in other situations, that is, or , it indicates that the dynamic scheduling index of intelligent scheduling is in an abnormal range.

[0056] Furthermore, the specific process of performing intelligent scheduling and dynamic scheduling according to the intelligent scheduling and dynamic scheduling indicators is: when the intelligent scheduling and dynamic scheduling indicators are in the normal range, that is, the quality of the communication network is in a good state, the network does not perform any processing; when the intelligent scheduling and dynamic scheduling indicators are in the abnormal range, that is, there is a problem with the quality of the communication network, the network is intelligently scheduled and dynamically scheduled.

[0057] In this embodiment, when the intelligent scheduling dynamic scheduling index is in the normal range, it indicates that the quality of the communication network is good and no processing is required on the communication network. When the intelligent scheduling dynamic scheduling index is in the abnormal range, it indicates that there is a problem with the communication quality and the communication network needs to be intelligently scheduled and dynamically scheduled to obtain better communication quality and ensure people's communication status.

[0058] In this embodiment, intelligent dynamic scheduling can specifically be: either starting or adjusting a load balancer to distribute traffic to multiple servers or network paths; or starting a content distribution network service to distribute user requested content to multiple geographic locations to reduce requests to a single server; or immediately implementing access control list rules to limit the use of non-critical traffic and the bandwidth of non-critical services; or guiding users to reduce unnecessary service requests or avoid using service requests during peak hours through push notifications; or when implementing emergency measures, adding new servers, configuring the new servers, and making network configuration changes to increase network bandwidth.

[0059] In summary, the embodiments of the present application obtain resource utilization historical data, network reliability historical data and service quality historical data, resource utilization real-time data, network reliability real-time data and service quality real-time data, thereby obtaining resource utilization indicators, network reliability indicators and service quality indicators, and obtain intelligent scheduling dynamic scheduling indicators by integrating resource utilization indicators, network reliability indicators and service quality indicators, and then perform intelligent scheduling and dynamic scheduling according to the intelligent scheduling and dynamic scheduling indicators, thereby realizing accurate and effective intelligent scheduling and dynamic scheduling, and effectively solving the problem that it is difficult to accurately perform intelligent scheduling and dynamic scheduling in the prior art.

[0060] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0064] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0065] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A dynamic scheduling optimization method for intelligent scheduling based on the Internet of Things, characterized in that: The following steps are involved: Obtain historical data on resource utilization, network reliability, and service quality from the Internet of Things; Obtain real-time data on resource utilization, network reliability, and service quality from the Internet of Things; Obtain resource utilization indicators and corresponding feedback based on historical resource utilization data and real-time resource utilization data; Obtain network reliability indicators and corresponding feedback based on historical network reliability data and real-time network reliability data; Obtain service quality indicators based on historical service quality data and real-time service quality data and obtain corresponding feedback; Comprehensive resource utilization index, network reliability index and service quality index to obtain intelligent scheduling dynamic scheduling index; Intelligent scheduling and dynamic scheduling are carried out according to the intelligent scheduling and dynamic scheduling indicators.

2. The method for optimizing intelligent scheduling and dynamic scheduling based on the Internet of Things as claimed in claim 1, characterized in that: The resource utilization historical data includes CPU utilization historical data, memory utilization historical data, network bandwidth utilization historical data, network interface utilization historical data and storage space utilization historical data; The network reliability historical data includes network delay historical data, network throughput historical data and network packet loss rate historical data; The service quality historical data includes transmission rate historical data, bit error rate historical data, jitter historical data and response time historical data.

3. The method for optimizing intelligent scheduling and dynamic scheduling based on the Internet of Things as claimed in claim 1, characterized in that: The resource utilization real-time data includes CPU utilization real-time data, memory utilization real-time data, network bandwidth utilization real-time data, network interface utilization real-time data and storage space utilization real-time data; The network reliability real-time data includes network delay real-time data, network throughput real-time data and network packet loss rate real-time data; The service quality real-time data includes transmission rate real-time data, bit error rate real-time data, jitter real-time data and response time real-time data.

4. The method for optimizing intelligent scheduling and dynamic scheduling based on the Internet of Things as claimed in claim 1, characterized in that: The specific method for obtaining the resource utilization index is: Obtain historical resource utilization data and number the historical resource utilization data; Construct resource utilization index calculation formula; The specific resource utilization index calculation formula is: ； In the formula, Expressed as a resource utilization indicator, Represents real-time data of CPU utilization. Expressed as CPU utilization historical data, , Represented as the total number of historical data on resource utilization, Represented as real-time data of memory utilization, Expressed as Memory utilization historical data, Represented as real-time data of network bandwidth utilization, Expressed as Historical data of network bandwidth utilization, Represented as real-time data of storage space utilization, Expressed as Storage space utilization historical data, Represented as real-time data of network interface utilization, Expressed as Historical data on network interface utilization.

5. The method for optimizing intelligent scheduling dynamic scheduling based on the Internet of Things as claimed in claim 4, characterized in that: The specific analysis process of the resource utilization index is as follows: Obtain historical data of resource utilization indicators, obtain a first threshold value of resource utilization indicators and a second threshold value of resource utilization indicators, compare the resource utilization indicator with the first threshold value of resource utilization indicators and the second threshold value of resource utilization indicators, and when the resource utilization indicator is less than the first threshold value of resource utilization indicators and greater than the second threshold value of resource utilization indicators, it indicates that the resource utilization indicator is within a normal range, that is, the resource utilization is within a passing range; When it is in other situations, it means that the resource utilization indicator is in an abnormal range, that is, the resource utilization is in a failing range.

6. The method for optimizing intelligent scheduling dynamic scheduling based on the Internet of Things as claimed in claim 1, characterized in that: The specific analysis process of the network reliability index is as follows: Acquire historical data of network reliability indicators, acquire a first threshold value of the network reliability indicator and a second threshold value of the network reliability indicator, compare the network reliability indicator with the first threshold value of the network reliability indicator and the second threshold value of the network reliability indicator, and when the network reliability indicator is less than the first threshold value of the network reliability indicator and greater than the second threshold value of the network reliability indicator, it indicates that the network reliability indicator is within a normal range, that is, the network reliability is within a passing range; When it is in other situations, it means that the network reliability index is in an abnormal range, that is, the network reliability is in a failing range.

7. The method for optimizing intelligent scheduling and dynamic scheduling based on the Internet of Things as claimed in claim 1, characterized in that: The specific analysis process of the service quality indicator is as follows: Acquire historical data of the service quality indicator, acquire a first threshold value of the service quality indicator and a second threshold value of the service quality indicator, compare the service quality indicator with the first threshold value of the service quality indicator and the second threshold value of the service quality indicator, and when the service quality indicator is less than the first threshold value of the service quality indicator and greater than the second threshold value of the service quality indicator, it indicates that the service quality indicator is within a normal range, that is, the network service quality is within a passing range; When it is in other situations, it means that the service quality indicator is in an abnormal range, that is, the network service quality is in a failing range.

8. The method for optimizing intelligent scheduling and dynamic scheduling based on the Internet of Things as claimed in claim 1, characterized in that: The specific method for obtaining the intelligent scheduling dynamic scheduling index is: Obtaining from the database the weight ratios of preset resource utilization indexes, network reliability indexes and service quality indexes in the intelligent scheduling dynamic scheduling indexes; Construct a calculation formula for dynamic scheduling indicators for intelligent scheduling; The specific calculation formula of intelligent scheduling dynamic scheduling index is: ； In the formula, It is represented as the dynamic scheduling index of intelligent scheduling. Expressed as a resource utilization indicator, Expressed as a network reliability index, Expressed as a service quality indicator, It is expressed as the weight ratio of resource utilization index in the dynamic scheduling index of intelligent scheduling. It is expressed as the weight ratio of network reliability index in the dynamic scheduling index of intelligent scheduling. It is expressed as the weight ratio of the service quality index in the intelligent scheduling dynamic scheduling index. is a natural constant.

9. The method for optimizing intelligent scheduling dynamic scheduling based on the Internet of Things as claimed in claim 8, characterized in that: The specific analysis process of the intelligent scheduling dynamic scheduling index is as follows: Acquire historical data of the intelligent scheduling dynamic scheduling indicator, acquire a first threshold value of the intelligent scheduling dynamic scheduling indicator and a second threshold value of the intelligent scheduling dynamic scheduling indicator, compare the intelligent scheduling dynamic scheduling indicator with the first threshold value of the intelligent scheduling dynamic scheduling indicator and the second threshold value of the intelligent scheduling dynamic scheduling indicator, and when the intelligent scheduling dynamic scheduling indicator is less than the first threshold value of the intelligent scheduling dynamic scheduling indicator and greater than the second threshold value of the intelligent scheduling dynamic scheduling indicator, it indicates that the intelligent scheduling dynamic scheduling indicator is within a normal range; In other cases, it means that the dynamic scheduling index of intelligent scheduling is in an abnormal range.

10. The method for optimizing intelligent scheduling dynamic scheduling based on the Internet of Things as claimed in claim 1, characterized in that: The specific process of performing intelligent scheduling and dynamic scheduling according to the intelligent scheduling and dynamic scheduling indicators is as follows: When the dynamic scheduling index of intelligent scheduling is within the normal range, that is, the communication network quality is in good condition, the network does not perform any processing; When the intelligent scheduling dynamic scheduling index is in an abnormal range, that is, there is a problem with the communication network quality, the network is intelligently scheduled dynamically.

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