Service-model-data joint driving star-ground fusion network resource intelligent collaborative scheduling method and system for new power system

Abstract

The application provides a service-model-data joint driving star-ground fusion network resource intelligent collaborative scheduling method and system for a new power system, and relates to the technical field of power resource scheduling. Considering the communication requirements of different power services in the star-ground fusion network, a dynamic star-ground fusion network and service communication requirement characteristic model is established, and the services are divided into two categories: transmission sensitive and calculation sensitive. In order to reasonably utilize the limited star-ground fusion network resources, a priority processing strategy for service data is formulated, which can allocate and schedule transmission and calculation resources according to the requirements of the services, and unload and process different types of service data in the same period. Under the condition of meeting the communication requirements of different power services, a joint optimization problem considering system delay and energy consumption is proposed to minimize the total cost of the system. The intelligent collaborative scheduling of resources is realized, and the power services are served in real time on demand to meet the requirements of multiple types of power services in the star-ground fusion network.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power resource scheduling, in particular to a business-model-data joint driving star-ground fusion network resource intelligent collaborative scheduling method and system for a new power system. BACKGROUND

[0002] With the development of the new power system, the wide-area distribution of power equipment has brought about a large amount of business data, which has brought great challenges to the ground communication network with limited coverage and transmission capacity. Different power businesses have different communication quality of service requirements. For example, the control type business such as power distribution automation and distributed energy control has a small amount of data and a low requirement on communication bandwidth, but has a high requirement on communication delay and error rate. The information collection type business such as transmission line monitoring and power quality monitoring has a large amount of data and a high requirement on communication bandwidth, but has a low requirement on delay and error rate. However, different types of businesses with different characteristics and requirements often occur at the same time, and the network service capacity is limited within a certain period of time. Therefore, how to model and represent the communication requirements of different types of power businesses and realize clustering and parallel processing is one of the important problems in the current research.

[0003] In view of the problems of insufficient coverage and limited communication capacity of the ground communication network, the star-ground fusion network widens the service range and capacity of the ground communication network by integrating satellite network services, and has become an important construction direction of the next generation of communication networks. Due to the different characteristics of satellite networks and ground networks, the star-ground fusion network has the characteristics of heterogeneity and dynamics, which makes it face problems such as long transmission delay, complex link conditions and dynamic network topology. At the same time, the network node mobility and communication environment complexity in the fusion operation of the two networks bring new problems to task offloading and resource management that cannot be solved by existing methods. Therefore, an unreasonable scheduling strategy will not only cause resource waste, but also increase the system operation cost.

[0004] Therefore, how to realize intelligent collaborative scheduling of resources, real-time service of power businesses on demand, and meet the needs of multiple types of power businesses in the star-ground fusion network has become a technical problem to be solved. SUMMARY

[0005] In order to realize intelligent collaborative scheduling of resources, real-time service of power businesses on demand, and meet the needs of multiple types of power businesses in the star-ground fusion network, the application provides a business-model-data joint driving star-ground fusion network resource intelligent collaborative scheduling method and system for a new power system.

[0006] In the first aspect, the business-model-data joint driving star-ground fusion network resource intelligent collaborative scheduling method for a new power system provided by the application adopts the following technical scheme:

[0007] A service-model-data joint driving star-ground fusion network resource intelligent collaborative scheduling method for a new power system, comprising:

[0008] A star-ground fusion network model is constructed to describe a star-ground fusion network composed of a satellite layer and a ground layer, wherein the satellite layer includes low-orbit satellites, and the ground layer includes a gateway station, a data processing center and a control center, the satellite and the ground node are represented by a bidirectional time-varying graph, and the connectivity, transmission rate, buffer size and CPU cycle frequency of the node and the link dynamically change;

[0009] A channel model in the star-ground fusion network is constructed, an inter-satellite communication model and a star-ground communication model are established, the influence of channel fading, loss and interference on the transmission rate is considered, the inter-satellite communication rate is calculated based on the Shannon formula, and a Rician fading channel model of star-ground communication is established;

[0010] A new power system service model is established, power services are divided into two categories of transmission sensitive and calculation sensitive, the data volume, CPU cycle demand, processing proportion coefficient and QoS demand of each type of service are described, and the services are scheduled according to the service type and demand;

[0011] A service data priority queuing model is constructed, the priority of service data is determined according to the maximum delay tolerance and generation time of the service, service data with low delay tolerance is preferentially processed, and the backlog and processing of service data are managed through an offloading queue and a calculation queue;

[0012] A system cost model is established, the transmission delay, calculation delay, queuing delay and energy consumption cost are comprehensively considered, and the system total cost is defined as the weighted sum of the delay cost and the energy consumption cost;

[0013] A joint optimization problem is established, the system total cost is minimized under the premise of meeting the communication demand of various power services, and the service offloading, service processing, transmission resource allocation and calculation resource allocation decisions are optimized;

[0014] The joint optimization problem is solved to obtain the star-ground fusion network resource intelligent collaborative scheduling result, and efficient processing of power services and optimal allocation of resources are realized.

[0015] Optionally, the step of constructing the star-ground fusion network model comprises:

[0016] A satellite layer is constructed, the satellite layer is composed of low-orbit satellites, and provides wide-area communication coverage, the satellite node is equipped with an edge server to provide calculation services;

[0017] A ground layer is constructed, the ground layer is composed of a gateway station, a data processing center and a control center, and the gateway station is connected with the satellite layer;

[0018] constructing a bidirectional time-varying graph representing dynamic changes of connectivity, transmission rate, buffer size and CPU cycle frequency of nodes and links, the nodes including satellite nodes and ground nodes, the links representing communication connections between the nodes;

[0019] constructing a satellite-ground fusion network model according to the satellite layer, the ground layer and the bidirectional time-varying graph.

[0020] Optionally, the step of constructing a channel model in the satellite-ground fusion network comprises:

[0021] establishing an inter-satellite communication model, calculating transmission rate between satellite nodes based on Shannon formula, considering free space path loss, additional link loss and signal interference;

[0022] establishing a satellite-ground communication model, calculating transmission rate between satellite and ground nodes based on Rician fading channel model, considering small-scale fading coefficient and rain attenuation caused signal additional loss;

[0023] constructing a channel model in the satellite-ground fusion network according to the inter-satellite communication model and the satellite-ground communication model.

[0024] Optionally, the step of establishing a new power system service model comprises:

[0025] constructing a transmission sensitive service for describing information collection type service;

[0026] constructing a computation sensitive service for describing control type service;

[0027] constructing a service tuple for describing data volume, CPU cycle demand, processing proportion coefficient and QoS demand of each type of service, the QoS demand including target node, maximum delay requirement and minimum bandwidth requirement;

[0028] establishing a new power system service model according to the transmission sensitive service, the computation sensitive service and the service tuple.

[0029] Optionally, the step of constructing a service data priority queuing model comprises:

[0030] constructing a priority function for determining priority of service data according to maximum delay tolerance and generation time of service, and preferentially processing service data with low delay tolerance;

[0031] constructing an offloading queue for storing service data and computation result waiting for offloading, and offloading in priority order;

[0032] constructing a computation queue for storing computation sensitive service data waiting for processing, and processing in priority order;

[0033] According to the priority function, the offloading queue and the computing queue, a service data priority queuing model is constructed.

[0034] Optionally, the step of establishing the system cost model comprises:

[0035] A delay cost is constructed for calculating a sum of a transmission delay, a computing delay and a queuing delay, and the queuing delay is calculated based on Little's law;

[0036] An energy consumption cost is constructed for calculating a sum of a transmission energy consumption, a computing energy consumption and a satellite operation energy consumption, the transmission energy consumption is calculated based on the transmission delay, and the computing energy consumption is calculated based on the computing delay;

[0037] A system total cost is constructed for representing a weighted sum of the delay cost and the energy consumption cost as an optimization objective function;

[0038] According to the delay cost, the energy consumption cost and the system total cost, a system cost model is established.

[0039] Optionally, the step of establishing the joint optimization problem comprises:

[0040] A service offloading decision variable is established for representing whether a service is offloaded from a source node to a target node;

[0041] A service processing decision variable is established for representing a processing proportion of the service on a node;

[0042] A transmission resource allocation decision variable is established for representing a bandwidth allocated to the service by a link;

[0043] A computing resource allocation decision variable is established for representing a CPU cycle frequency allocated to the service by a node;

[0044] A constraint condition is established for ensuring rationality and feasibility of the service offloading, the service processing, the transmission resource allocation and the computing resource allocation, including a bandwidth demand, a computing resource limit and a node storage capability constraint;

[0045] According to the service offloading decision variable, the service processing decision variable, the transmission resource allocation decision variable, the computing resource allocation decision variable and the constraint condition, a joint optimization problem is established.

[0046] In a second aspect, the application provides a service-model-data joint driving star-ground fusion network resource intelligent collaborative scheduling system for a new power system, comprising:

[0047] A fusion network module is configured to construct a star-ground fusion network model, and describe the star-ground fusion network composed of a satellite layer and a ground layer, wherein the satellite layer includes low-orbit satellites, and the ground layer includes a gateway station, a data processing center and a control center, the satellite and the ground node are represented by a bidirectional time-varying graph, and the connectivity, transmission rate, buffer size and CPU cycle frequency of the node and the link dynamically change;

[0048] A channel module is configured to construct a channel model in the star-ground fusion network, establish an inter-satellite communication model and a star-ground communication model, consider the influence of channel fading, loss and interference on the transmission rate, calculate the inter-satellite communication rate based on a Shannon formula, and establish a Rician fading channel model for star-ground communication;

[0049] A service module is configured to establish a new power system service model, divide the power service into two types of transmission-sensitive and computation-sensitive, describe the data volume, CPU cycle demand, processing proportion coefficient and QoS demand of each type of service, and schedule according to the service type and demand;

[0050] A priority module is configured to construct a service data priority queuing model, determine the priority of service data according to the maximum delay tolerance and generation time of the service, preferentially process the service data with low delay tolerance, and manage the backlog and processing of the service data through an offloading queue and a computing queue;

[0051] A cost module is configured to establish a system cost model, comprehensively consider the transmission delay, computation delay, queuing delay and energy consumption cost, and define the total system cost as the weighted sum of the delay cost and the energy consumption cost;

[0052] A joint optimization module is configured to establish a joint optimization problem, minimize the total system cost under the premise of meeting the communication demands of various power services, and optimize the service offloading, service processing, transmission resource allocation and computing resource allocation decisions;

[0053] A solution module is configured to solve the joint optimization problem, obtain intelligent collaborative scheduling results of the star-ground fusion network resources, and realize efficient processing of the power service and optimal allocation of the resources.

[0054] In a third aspect, the present application provides a computer device, which comprises a memory and a processor, wherein the processor executes computer instructions stored in the memory to perform the method described above.

[0055] In a fourth aspect, the present application provides a computer readable storage medium comprising instructions, which, when executed on a computer, cause the computer to perform the method described above.

[0056] In summary, the present application has the following beneficial technical effects:

[0057] The application considers the communication requirements of different power services in the satellite-ground integrated network, establishes a dynamic satellite-ground integrated network and service communication requirement feature model, and divides the services into two categories: transmission sensitive and computation sensitive. In order to reasonably utilize the limited satellite-ground integrated network resources, a service data priority processing strategy is formulated, which can allocate and schedule transmission and computation resources according to the requirements of the services, and unload and process different types of service data in the same period. Under the condition of meeting the communication requirements of different power services, a joint optimization problem considering system delay and energy consumption is proposed to minimize the total system cost. Intelligent collaborative scheduling of resources is realized, and power services are served in real time on demand to meet the requirements of multiple types of power services in the satellite-ground integrated network. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 is a computer device structure schematic diagram of a hardware running environment related to an embodiment scheme of the application.

[0059] Figure 2 is a flowchart of an embodiment of the service-model-data joint driving satellite-ground integrated network resource intelligent collaborative scheduling method for the new power system of the application.

[0060] Figure 3 is a satellite-ground integrated network power application scenario schematic diagram of the application.

[0061] Figure 4 is a power service data processing flowchart of the application.

[0062] Figure 5 is a structure block diagram of an embodiment of the service-model-data joint driving satellite-ground integrated network resource intelligent collaborative scheduling system for the new power system of the application. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical scheme and advantages of the application clearer, the application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application.

[0064] Referring to Figure 1 , Figure 1 is a computer device structure schematic diagram of a hardware running environment related to an embodiment scheme of the application.

[0065] As Figure 1As shown, the computer device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display and an input unit, such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also be a storage device independent of the processor 1001.

[0066] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0067] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a business-model-data joint-driven satellite-ground fusion network resource intelligent collaborative scheduling program for the new power system.

[0068] exist Figure 1 In the computer device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in this application can be set in the computer device, and the computer device calls the business-model-data joint-driven satellite-ground fusion network resource intelligent collaborative scheduling program for the new power system stored in the memory 1005 through the processor 1001, and executes the business-model-data joint-driven satellite-ground fusion network resource intelligent collaborative scheduling method for the new power system provided in the embodiment of the present application.

[0069] The embodiment of the present application provides a business-model-data joint-driven satellite-ground fusion network resource intelligent collaborative scheduling method for a new power system. Figure 2 , Figure 2The flowchart of an embodiment of the service-model-data joint driving star-ground fusion network resource intelligent collaborative scheduling method for a new power system of the present application.

[0070] In this embodiment, the service-model-data joint driving star-ground fusion network resource intelligent collaborative scheduling method for a new power system includes the following steps:

[0071] Step S10: Construct a star-ground fusion network model to describe the star-ground fusion network composed of a satellite layer and a ground layer, wherein the satellite layer includes low-orbit satellites, and the ground layer includes a gateway station, a data processing center, and a control center. The satellite and ground nodes are represented by a bidirectional time-varying graph. The connectivity, transmission rate, buffer size, and CPU cycle frequency of nodes and links dynamically change.

[0072] It can be understood that in this embodiment, the commonly used technical terms in the art are explained as follows:

[0073] Star-ground fusion network: The star-ground fusion network is a space-based network infrastructure composed of multiple satellites in different orbits, ground gateway stations, and measurement and control stations, and a ground-based network infrastructure composed of ground mobile base stations, WiFi hotspots, and optical fiber networks. Through integrated and fusion design, a multi-dimensional three-dimensional information network is realized, which can provide global ubiquitous communication, navigation, and remote sensing services for users in different application scenarios of space, air, ground, and sea. The star-ground fusion network runs through the ocean, the far border, the space high border, and the network new border. Because of its important position, major space powers in the world have developed development strategies and invested huge amounts of money to layout satellite communication network construction focusing on high-orbit high-throughput satellite communication constellation and low-orbit satellite internet constellation, and seek leading advantages in new technologies, new industries, and space frequency orbital resources. The "full coverage, full time, and full weather" communication services provided by the star-ground fusion network can effectively make up for the systemic short board of the information capability of the new power system "incomplete coverage, uneven performance, insufficient resilience, and not deep integration", and help to realize the "comprehensive observability, accurate measurability, and high controllability" of the new power system, and has a wide application prospect in the field of electric power.

[0074] QoS: Quality of Service

[0075] Shannon's formula

[0076] Boltzmann's constant

[0077] Little's law.

[0078] It should be noted that the steps of constructing a satellite-ground fusion network model include: constructing a satellite layer, which is composed of low-orbit satellites and provides wide-area communication coverage. Satellite nodes are equipped with edge servers to provide computing services; constructing a ground layer, which is composed of a gateway station, a data processing center and a control center, and the gateway station is connected to the satellite layer; constructing a bidirectional time-varying graph, which represents the dynamic changes in the connectivity, transmission rate, buffer size and CPU cycle frequency of nodes and links. Nodes include satellite nodes and ground nodes, and links represent communication connections between nodes; constructing a satellite-ground fusion network model based on the satellite layer, the ground layer and the bidirectional time-varying graph.

[0079] In the specific implementation, a satellite-ground fusion network model is constructed. Figure 3 As shown, this embodiment considers that the satellite-ground fusion network can support distributed energy control, transmission line monitoring and other power services. The satellite-ground fusion network consists of two layers: the satellite layer and the ground layer. In the satellite layer, low-orbit satellites provide wide-area communication coverage; in the ground layer, the gateway station is connected to the data processing center with high-speed data processing capabilities and the control center with communication needs. It is assumed that the satellites are equipped with edge servers that can provide effective computing services. Establish a bidirectional time-varying graph consisting of nodes and links .in, Satellite nodes and ground nodes A collection of nodes, nodes ; is the set of communication links, Represents a slave node To Node communication links; Indicates that The time period of time slots, the time between time slots is ; represents the edge time set, Indicates a communication link connectivity, when When the communication link is connected, On the contrary, nodes are connected when they are within each other's communication range; is the rate time series, Indicates a communication link In the time slot The transmission rate; is a collection of node functions, and Represents nodes The buffer size and CPU cycle frequency. In this bidirectional time-varying graph, and respectively represent the QoS characteristics of links and nodes in terms of communication, computation and storage. Transmission rates of different links are different due to the difference in transmission capability. It is assumed that the network topology and resources remain stable in each time slot, but the topology and resources of different time slots dynamically change.

[0080] Step S20: constructing a channel model in the star-ground fusion network, establishing an inter-satellite communication model and a star-ground communication model, considering the influence of channel fading, loss and interference on transmission rate, calculating the inter-satellite communication rate based on Shannon's formula, and establishing a Rician fading channel model of star-ground communication.

[0081] It can be understood that the step of constructing a channel model in the star-ground fusion network comprises: establishing an inter-satellite communication model, calculating the transmission rate between satellite nodes based on Shannon's formula, and considering free space path loss, additional link loss and signal interference; establishing a star-ground communication model, calculating the transmission rate between a satellite and a ground node based on a Rician fading channel model, and considering small-scale fading coefficient and signal loss caused by rain attenuation; and constructing a channel model in the star-ground fusion network according to the inter-satellite communication model and the star-ground communication model.

[0082] In a specific implementation, a channel model in the star-ground fusion network is constructed. An inter-satellite communication model and a star-ground communication model are established by considering the influence of channel fading, loss and interference.

[0083] 1) Inter-satellite communication model. According to Shannon's formula, the transmission rate between satellite nodes and in a time slot can be expressed as

[0084]

[0085] In the formula, B represents the bandwidth of the inter-satellite link ; P represents the transmission power of the satellite ; G represents the total signal gain of the transmitting antenna and the receiving antenna; L represents the free space path loss related to signal transmission; L' represents the additional link loss of inter-satellite communication; I represents signal interference; k represents the Boltzmann constant; and T represents the total noise temperature of the system.

[0086] wherein the free space path loss related to signal transmission is expressed as

[0087] ​​​​​​​​

[0088] Where, represents the speed of light; represents the distance between nodes in time slots; represents the center frequency;

[0089] Signal interference Expressed as

[0090]

[0091] Where, Indicates a link The set of interference links; Indicates the link's offload state variable, Indicates that the link actively offloads tasks and can cause interference in the time slot. The opposite is true.

[0092] 2) Satellite-to-ground communication model. The satellite-to-ground communication channel is established as a Rician fading channel. When performing satellite-to-ground communication, the data transmission rate of the link in the time slot is expressed as

[0093]

[0094] Where, represents the small-scale fading coefficient; Represents the additional signal loss caused by rain fade.

[0095] The small-scale fading coefficient is expressed as

[0096]

[0097]

[0098] in, The corresponding Rice fading coefficient; is the influence state coefficient within the line of sight. ; Indicates the random influence within non-line-of-sight; the corresponding interference Expressed as

[0099]

[0100] Step S30: Establish a new power system business model, divide power business into two categories: transmission-sensitive and computation-sensitive, describe the data volume, CPU cycle requirements, processing ratio coefficient and QoS requirements of each type of business, and schedule according to business type and demand.

[0101] In the specific implementation, the steps of establishing a new power system business model include: constructing transmission-sensitive business to describe information collection business; constructing computing-sensitive business to describe control business; constructing business tuples to describe the data volume, CPU cycle requirements, processing ratio coefficient and QoS requirements of each type of business, QoS requirements include target nodes, maximum delay requirements and minimum bandwidth requirements; establishing a new power system business model based on transmission-sensitive business, computing-sensitive business and business tuples.

[0102] It is understandable that a new power system business model is established in the satellite-ground fusion network. In different power scenarios, the source node will generate different types of power services. This embodiment divides the services into transmission-sensitive (information collection) and calculation-sensitive (control) types. Considering different characteristics and requirements Electricity business, establish the electricity business collection as According to the main characteristics of the power business, the business is described as a tuple ,in, Indicates power business The amount of data; Indicates the CPU cycles required to process each bit of data; represents the processing scale factor; Indicates the source node of the business. For computationally sensitive businesses, the amount of data required for computation is For transmission-sensitive services without data processing requirements, the CPU cycles required to process each bit of data , processing scale factor In addition, define the tuple express QoS requirements for type services, including: Indicates the target node of the business; Indicates the maximum delay requirement; represents the minimum bandwidth requirement. Assume that the source node is in time slot Randomly generate a number in the range Various types of business within the time slot Produced Number of business types These services are scheduled based on their needs, scheduling decisions, and allocated resources. During service processing, nodes can offload incoming transmission-sensitive services or computational results, perform local computations, or offload incoming computation-sensitive services. If transmission-sensitive services and computational results reach the destination node within the maximum latency requirement, service processing is complete; otherwise, the processing is terminated and service processing fails.

[0103] Step S40: Construct a service data priority queuing model, determine the priority of service data according to the maximum delay tolerance and generation time of the service, give priority to service data with low delay tolerance, and manage the backlog and processing of service data through unloading queues and computing queues.

[0104] It should be noted that the steps for building a business data priority queuing model include:

[0105] A priority function is constructed to determine the priority of business data based on the maximum delay tolerance and generation time of the business, and business data with low delay tolerance is processed first; an unloading queue is constructed to store business data and calculation results waiting to be unloaded, and unload them in order of priority; a calculation queue is constructed to store calculation-sensitive business data waiting to be processed, and process them in order of priority; a business data priority queuing model is constructed based on the priority function, unloading queue and calculation queue.

[0106] In specific implementation, Figure 4 The power business data processing flow chart shown in the figure establishes a priority queuing model for business data in this embodiment. Given the limited transmission and computing resources of the satellite-ground converged network, some services cannot be processed immediately upon arrival at the node. Therefore, each node is assumed to have two buffer queues: an offload queue and a storage queue to store tasks. The offload queue stores pending offload services and computational results, while the settlement queue stores computationally sensitive tasks awaiting processing. When incoming services exceed the node's storage capacity, the services are discarded.

[0107] First, formulate a business data priority processing strategy. Considering the different time periods of business data generation and the changing latency requirements, prioritize unloading and processing business data with low latency tolerance. The priority function is expressed as the remaining time to complete the business within the maximum tolerable delay. Specifically, the time slot is defined as Middle indivual The priority function for processing business data is as follows:

[0108]

[0109] Where, Representative indivual The time when the business data is generated. Business is a collection Business data generation order, yes The total number of services generated by this type of service in the previous time slot.

[0110] Since the service data will not be processed once the remaining time exceeds the maximum delay tolerance, the priority function Based on the service data priority handling policy, the service data in the offloading queue and the computing queue are arranged in order of priority. In the priority queue, the smaller the function value is, the higher the priority is, and the data will be offloaded or processed earlier.

[0111] Secondly, the queuing queue analysis is carried out. The service data backlog of the offloading queue and the computing queue at the node is respectively represented by Considering the differences in service characteristics and requirements, the queuing queue is analyzed according to the service type. Based on this, the service backlog of the offloading queue and the computing queue at the node is respectively defined as and The service backlog of the offloading queue and the computing queue at the node is respectively represented as:

[0112] - +

[0113] - +

[0114] In the formula, Q and Q respectively represent the amount of service data of the type that has been offloaded and processed at the time slot t; and Q respectively represent the amount of new offloading and computing service data that arrives at the node at the time slot t. In summary, and Q

[0115] can be respectively represented as:

[0116]

[0117]

[0118] Define as the offloading decision variable of the power service. When the service of the type is offloaded from the node to the node , , otherwise, the opposite. In order to effectively utilize the limited network transmission resources, different types of services are offloaded in parallel by allocating bandwidth to the services that need to be offloaded. Define as the bandwidth allocated to the service when offloading the service of the type through the link . The offloaded ​​​The number of the type 1 service is calculated as follows:

[0119]

[0120] wherein, in the time slot with the bandwidth of the data transmission rate. Further, the number of the type 1 service offloaded to other nodes is calculated as follows: The total number of the type 1 service is calculated as follows:

[0121]

[0122] Meanwhile, to make full use of the computing resources, the computing resources of the node are allocated to each type of the computing sensitive service for parallel processing of the service. The definition in the time slot The CPU cycle frequency allocated to the type 1 service of the node is calculated as follows: The processing amount of the type 1 service is calculated as follows:

[0123] After that, the generated computing result is distributed to the computing queue, waiting to be offloaded to the target node.

[0124] Among the newly arrived services of each node, part of the computing sensitive service waits for processing in the computing queue, and the rest of the services are scheduled to the offloading queue. The definition

[0125] is a service processing decision variable, which determines the processing proportion of the newly arrived type 1 service of the node Based on this, the number of the type 1 service scheduled to the computing queue is calculated as follows:

[0126] For the transmission sensitive service and the computing result, . The number of the type 1 service scheduled to the offloading queue of the node is calculated as follows:

[0127] Considering the number of offloaded tasks, the number of generated tasks and the computing result, the total number of the type 1 service arriving at the node

[0128] is calculated as follows:

[0129]

[0130]

[0131] ​​​​​​Through the above analysis, all the queue information affected by the scheduling decision can be obtained, and the offloading decision variable of the link is derived based on this

[0132]

[0133] In the formula, represents that the service can be offloaded only when the allocated bandwidth is greater than the minimum bandwidth required by the service, that is, when , .

[0134] Step S50: Establish a system cost model, comprehensively consider the transmission delay, calculation delay, queuing delay and energy consumption cost, and define the system total cost as the weighted sum of the delay cost and the energy consumption cost.

[0135] It should be noted that the step of establishing the system cost model includes: constructing a delay cost for calculating the sum of transmission delay, calculation delay and queuing delay, and calculating the queuing delay based on Little's law; constructing an energy consumption cost for calculating the sum of transmission energy consumption, calculation energy consumption and satellite operation energy consumption, the transmission energy consumption is calculated based on the transmission delay, and the calculation energy consumption is calculated based on the calculation delay; constructing a system total cost for representing the weighted sum of the delay cost and the energy consumption cost as an optimization objective function; establishing a system cost model according to the delay cost, the energy consumption cost and the system total cost.

[0136] In specific implementation, the system cost model. Comprehensive consideration of data transmission, data calculation and service queuing delay and other factors in the process of resource scheduling of satellite-ground integrated network, analysis of energy consumption generated by transmission, calculation and system operation, definition of system cost including delay cost and energy consumption cost, as follows:

[0137]

[0138] In the formula, and represent the delay cost and the energy consumption cost respectively.

[0139] 1) Delay cost: considering the case of parallel offloading, the maximum time spent by the link offloading type service in time slot is represented as:

[0140]

[0141] Similarly, considering the calculation delay of parallel processing of calculation sensitive type service, it is represented as:

[0142]

[0143] Based on Little's law, the queuing delay of node at time slot is:

[0144]

[0145] In summary, the total queuing delay of node at time slot is:

[0146]

[0147] 2) Energy cost: Based on link , the maximum time spent by type traffic to be offloaded at time slot , the corresponding transmission energy consumption of link at time slot is:

[0148]

[0149] Similarly, based on the computation delay , the computation energy consumption is:

[0150]

[0151] where is the computation energy consumption parameter.

[0152] In addition, the running energy consumption of satellite node is considered. Let the running power of node at time slot be , then the satellite running energy consumption is:

[0153]

[0154] In summary, the total system energy consumption at time slot is:

[0155] .

[0156] Step S60: Establish a joint optimization problem to minimize the total system cost under the premise of meeting the communication requirements of various power traffic, and optimize the traffic offloading, traffic processing, transmission resource allocation and computation resource allocation decisions.

[0157] In a specific implementation, the step of establishing the joint optimization problem includes: establishing a service offloading decision variable set, representing whether a service is offloaded from a source node to a target node; establishing a service processing decision variable set, representing a processing proportion of a service on a node; establishing a transmission resource allocation decision variable set, representing a bandwidth allocated to a service by a link; establishing a computing resource allocation decision variable set, representing a CPU cycle frequency allocated to a service by a node; establishing a constraint condition, ensuring rationality and feasibility of the service offloading, the service processing, the transmission resource allocation and the computing resource allocation, including bandwidth demand, computing resource limitation and node storage capability constraint; and establishing the joint optimization problem according to the service offloading decision variable set, the service processing decision variable set, the transmission resource allocation decision variable set, the computing resource allocation decision variable set and the constraint condition.

[0158] In a specific implementation, while meeting various new power system service communication demands under the star-ground fusion network, the system cost is minimized, so the objective function is defined as The joint optimization problem is established by considering meeting the service offloading, the service processing, the transmission resource allocation and the computing resource allocation demand. The service offloading variable set is defined as , the service processing decision variable set is defined as , the link bandwidth resource allocation decision variable set is defined as , and the node computing resource allocation decision variable set is defined as .

[0159] In summary, the joint optimization problem is established as follows:

[0160] (28)

[0161]

[0162] (29)

[0163] (30)

[0164] (31)

[0165] (32)

[0166] (33)

[0167] (34)

[0168] (35)

[0169] (36)

[0170] (37)

[0171] Among them, formula (29) represents the binary service offloading decision variable; formula (30) represents the service processing decision continuous variable; formula (31) represents the service only when the link Only when connected can the slave node Offloading to Node ; Formula (32) indicates that the service offloading decision is affected by the bandwidth demand. The allocated bandwidth should be greater than the minimum bandwidth demand of the service, and bandwidth is not allocated to services that do not need to be offloaded; Formula (33) represents the continuous variable for allocating computing resources; Formula (34) indicates that the bandwidth allocated to the link cannot be greater than the total bandwidth of each link; Formula (35) indicates that the CPU cycle frequency allocated to the node is limited by the available computing resources of the node; Formula (36) indicates that the number of services in the node queue cannot exceed the storage capacity of the node; Formula (37) indicates that the priority function cannot be negative, that is, when the delay exceeds the maximum delay tolerance of the unprocessed service, the service will not be processed.

[0172] Step S70: Solve the joint optimization problem to obtain the result of intelligent collaborative scheduling of satellite-ground integrated network resources, so as to achieve efficient processing of power services and optimal allocation of resources.

[0173] This example establishes an intelligent collaborative scheduling strategy for satellite-ground converged network resources that considers the communication needs of diverse new power system services. This strategy allocates and schedules network resources based on the needs of different services, offloading and processing tasks according to priority. With the goal of minimizing total system cost, a joint optimization problem for communication latency and system energy consumption is proposed.

[0174] The method of this embodiment characterizes multiple service characteristics and communication service quality, and classifies service requirements accordingly. This approach can meet the communication requirements of various new power system services in a satellite-ground converged network while minimizing system costs, ensuring the communication service quality required by new power system services, and improving system efficiency.

[0175] This embodiment considers the communication requirements of different power services in a satellite-ground fusion network, establishes a dynamic satellite-ground fusion network and service communication requirement characteristic model, and categorizes services into two main categories: transmission-sensitive and computation-sensitive. To rationally utilize limited satellite-ground fusion network resources, a priority processing strategy for service data is developed that can allocate and schedule transmission and computation resources according to service requirements, and unload and process different types of service data in the same time period. While meeting the communication requirements of different power services, a joint optimization problem that considers system latency and energy consumption is proposed to minimize the total system cost. Intelligent collaborative resource scheduling is achieved, and on-demand real-time services for power services are provided to meet the needs of multiple types of power services in a satellite-ground fusion network.

[0176] Further, the embodiment of the present application also proposes a computer readable storage medium, and the storage medium stores a program for the service-model-data joint driving intelligent collaborative scheduling of star-ground fusion network resources for a new power system. The program for the service-model-data joint driving intelligent collaborative scheduling of star-ground fusion network resources for a new power system is executed by a processor to realize the steps of the method for the service-model-data joint driving intelligent collaborative scheduling of star-ground fusion network resources for a new power system.

[0177] Referring to Figure 5 , Figure 5 FIG. 1 is a structural block diagram of an embodiment of the service-model-data joint driving intelligent collaborative scheduling system of star-ground fusion network resources for a new power system according to the present application.

[0178] As shown in Figure 5 , the service-model-data joint driving intelligent collaborative scheduling system of star-ground fusion network resources for a new power system according to the embodiment of the present application comprises:

[0179] The fusion network module 10 is configured to construct a star-ground fusion network model, and describe the star-ground fusion network composed of a satellite layer and a ground layer. The satellite layer comprises low-orbit satellites, and the ground layer comprises a gateway station, a data processing center and a control center. The satellite and the ground node are represented by a bidirectional time-varying graph. The connectivity, transmission rate, buffer size and CPU cycle frequency of the node and the link dynamically change.

[0180] The channel module 20 is configured to construct a channel model in the star-ground fusion network, establish an inter-satellite communication model and a star-ground communication model, consider the influence of channel fading, loss and interference on the transmission rate, calculate the inter-satellite communication rate based on the Shannon formula, and establish a Rician fading channel model for star-ground communication.

[0181] The service module 30 is configured to establish a new power system service model, divide the power service into two types of transmission sensitive and calculation sensitive, describe the data volume, CPU cycle demand, processing proportion coefficient and QoS demand of each type of service, and schedule according to the service type and demand.

[0182] The priority module 40 is configured to construct a service data priority queuing model, determine the priority of service data according to the maximum delay tolerance and generation time of the service, preferentially process the service data with low delay tolerance, and manage the backlog and processing of service data through the unloading queue and the calculation queue.

[0183] The cost module 50 is configured to establish a system cost model, comprehensively consider the transmission delay, calculation delay, queuing delay and energy consumption cost, and define the total cost of the system as the weighted sum of the delay cost and the energy consumption cost.

[0184] The joint optimization module 60 is configured to establish a joint optimization problem, minimize the total cost of the system, and optimize the service offloading, service processing, transmission resource allocation, and computing resource allocation decisions under the premise of meeting various power service communication requirements.

[0185] The solving module 70 is configured to solve the joint optimization problem, obtain the intelligent collaborative scheduling result of the integrated satellite-terrestrial network resources, and realize efficient processing of the power service and optimal allocation of the resources.

[0186] It should be understood that the above is only illustrative, and does not limit the technical solutions of the present application. In specific applications, those skilled in the art can set up according to the needs, and the present application does not limit this.

[0187] The embodiment considers the communication requirements of different power services in the integrated satellite-terrestrial network, establishes a dynamic integrated satellite-terrestrial network and a communication requirement feature model of the service, and classifies the service into two categories of transmission-sensitive and computing-sensitive. In order to reasonably utilize the limited integrated satellite-terrestrial network resources, a priority processing strategy for service data is formulated, which can allocate and schedule transmission and computing resources according to the requirements of the service, and offload and process different types of service data in the same period. Under the premise of meeting the communication requirements of different power services, a joint optimization problem considering the system delay and energy consumption is proposed to minimize the total cost of the system. Intelligent collaborative scheduling of resources is realized, and power services are served in real time on demand to meet the requirements of multiple types of power services in the integrated satellite-terrestrial network.

[0188] It should be noted that the above-described workflow is only illustrative and does not limit the scope of protection of the present application. In actual applications, those skilled in the art can select part or all of them to achieve the purpose of the embodiment, and this place does not limit it.

[0189] In addition, technical details not described in detail in the embodiment can be referred to the method for intelligent collaborative scheduling of service-model-data joint driven integrated satellite-terrestrial network resources for new power systems provided by any embodiment of the present application, which will not be described here.

[0190] In addition, it should be noted that in this paper, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or system. Without more limitations, the element defined by the statement "includes a" does not exclude the presence of another identical element in the process, method, article or system including the element.

[0191] The above application embodiment serial numbers are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0192] Through the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and the necessary general hardware platform, of course, it can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk), and includes a plurality of instructions for causing an end device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present application.

[0193] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A business-model-data-jointly driven satellite-ground fusion network resource intelligent collaborative scheduling method for new power systems, characterized by: include: A satellite-ground fusion network model is constructed to describe the satellite-ground fusion network consisting of a satellite layer and a ground layer. The satellite layer includes low-orbit satellites, and the ground layer includes gateways, data processing centers, and control centers. Satellite and ground nodes are represented by a bidirectional time-varying graph, and the connectivity, transmission rate, buffer size, and CPU cycle frequency of nodes and links change dynamically. Construct a channel model for satellite-ground fusion networks, establish inter-satellite communication models and satellite-ground communication models, consider the impact of channel fading, loss, and interference on transmission rates, calculate the inter-satellite communication rate based on the Shannon formula, and establish a Ricean fading channel model for satellite-ground communication; Establish a new power system service model, classifying power services into transmission-sensitive and computation-sensitive categories. Describe the data volume, CPU cycle requirements, processing scaling factor, and QoS requirements for each type of service, and schedule them based on service type and requirements. The steps of establishing a new power system service model include: constructing transmission-sensitive services to describe information collection services; constructing computing-sensitive services to describe control services; constructing service tuples to describe the data volume, CPU cycle requirements, processing ratio coefficients, and QoS requirements of each type of service, where the QoS requirements include target nodes, maximum latency requirements, and minimum bandwidth requirements; and establishing a new power system service model based on the transmission-sensitive services, computing-sensitive services, and service tuples. Establishing a power business collection , according to the main characteristics of the power business, the business is described as a tuple ,in, Indicates power business The amount of data; Indicates the CPU cycles required to process each bit of data; represents the processing scale factor; Represents the source node of the business. For computationally sensitive businesses, the amount of data required for computation is For transmission-sensitive services without data processing requirements, the CPU cycles required to process each bit of data , processing scale factor ; Define tuple express QoS requirements for type services, including: Indicates the target node of the business; Indicates the maximum delay requirement; represents the minimum bandwidth requirement, assuming that the source node is in time slot Randomly generate a number in the range Various types of business within the time slot Produced Number of business types express; Build a service data priority queuing model to prioritize service data based on its maximum latency tolerance and generation time, prioritize service data with low latency tolerance, and manage service data backlogs and processing through offload queues and compute queues. The steps of managing the backlog and processing of business data through the offload queue and the computing queue include: Define time slots Middle indivual The priority function for processing type business data is: Where, Representative indivual Generation time of the business data; against Business is a collection Business data generation order, yes The total number of services generated by this type of service in the previous time slot; The priority function is expressed as ,Based on the business data priority processing strategy, the business data in the offloading queue and computing queue are arranged in order of priority; use and Indicates that the unloading and computing queues are on the node The amount of business data backlog at and They are Type of business at the node The number of business backlogs at , expressed as: - + - + Where, and Represents the time slot Uninstalled and processed Type of business data volume; and Represents the time slot Arrival Node New offload and computing business data volume; and They can be expressed as: definition is the unloading decision variable of the power business, when Business slave node Offloading to Node hour, , otherwise the opposite; definition To pass the link uninstall The bandwidth allocated to the service for the offloaded The calculation method for the number of business types is as follows: Where, Indicates time slot Bandwidth The data transmission rate from the node Unload to other nodes The total number of type business is expressed as: definition For the time slot node Assigned to The CPU cycle frequency of the business type, so The processing volume of type business is expressed as: Distribute the generated calculation results to the calculation queue, waiting to be unloaded to the target node; Among the newly arrived services at each node, some computationally intensive services are placed in the computation queue for processing, while the remaining services are dispatched to the offload queue. definition For business processing decision variables, decide at node Newly arrived Based on the processing ratio of the business type, the The number of business types is calculated as: For transmission-sensitive services and calculation results, , calculation scheduling to nodes Unload queue The number of business types is: Arrival Node of The total number of type business is expressed as: link The uninstall decision variables are: Where, Indicates that the service can be offloaded only when the allocated bandwidth is greater than the minimum bandwidth required by the service. , ; Establish a system cost model that comprehensively considers transmission delay, computing delay, queuing delay, and energy cost, and defines the total system cost as the weighted sum of delay cost and energy cost; The step of establishing a system cost model includes: constructing a delay cost for calculating the sum of transmission delay, computing delay and queuing delay, and calculating the queuing delay based on Little's law; constructing an energy consumption cost for calculating the sum of transmission energy consumption, computing energy consumption and satellite operation energy consumption, wherein the transmission energy consumption is calculated based on the transmission delay, and the computing energy consumption is calculated based on the computing delay; constructing a total system cost for representing the weighted sum of the delay cost and the energy consumption cost as the optimization objective function; and establishing a system cost model based on the delay cost, the energy consumption cost and the total system cost; Establish a joint optimization problem to minimize the total system cost while meeting the communication needs of various power services, and optimize the decisions on service offloading, service processing, transmission resource allocation, and computing resource allocation; Solve the joint optimization problem and obtain the results of intelligent collaborative scheduling of satellite-ground integrated network resources, realizing efficient processing of power business and optimal allocation of resources.

2. The business-model-data jointly driven satellite-ground fusion network resource intelligent collaborative scheduling method for new power systems according to claim 1 is characterized in that: The steps of constructing the satellite-ground fusion network model include: Building a satellite layer, which consists of low-orbit satellites to provide wide-area communication coverage, and satellite nodes equipped with edge servers to provide computing services; Constructing a ground layer, which consists of a gateway station, a data processing center, and a control center, and the gateway station is connected to the satellite layer; Constructing a bidirectional time-varying graph, wherein the bidirectional time-varying graph represents the dynamic changes of connectivity, transmission rate, buffer size, and CPU cycle frequency of nodes and links, wherein the nodes include satellite nodes and ground nodes, and the links represent the communication connections between the nodes; A satellite-ground fusion network model is constructed according to the satellite layer, the ground layer and the bidirectional time-varying graph.

3. The business-model-data jointly driven satellite-ground fusion network resource intelligent collaborative scheduling method for new power systems according to claim 1 is characterized in that: The step of constructing a channel model in a satellite-ground fusion network includes: Establish an intersatellite communication model and calculate the transmission rate between satellite nodes based on the Shannon formula, taking into account free space path loss, additional link loss and signal interference; A satellite-to-ground communication model was established to calculate the transmission rate between the satellite and the ground node based on the Rice fading channel model, taking into account the additional signal loss caused by small-scale fading coefficients and rain attenuation. A channel model in a satellite-ground fusion network is constructed according to the inter-satellite communication model and the satellite-ground communication model.

4. The business-model-data jointly driven satellite-ground fusion network resource intelligent collaborative scheduling method for new power systems according to claim 1 is characterized in that: The step of constructing a service data priority queuing model includes: Construct a priority function to determine the priority of service data based on the service's maximum delay tolerance and generation time, giving priority to service data with low delay tolerance; Build an offload queue to store business data and calculation results waiting to be offloaded, and offload them in order of priority; Build a computing queue to store computing-sensitive business data waiting to be processed and process them in order of priority; A service data priority queuing model is constructed based on the priority function, the offloading queue, and the computing queue.

5. The business-model-data jointly driven satellite-ground fusion network resource intelligent collaborative scheduling method for new power systems according to claim 1 is characterized in that: The step of establishing the joint optimization problem includes: Establish a service offloading decision variable to indicate whether the service is offloaded from the source node to the target node; Establish business processing decision variables to represent the processing ratio of business on the node; Establish a transmission resource allocation decision variable to represent the bandwidth allocated to the service; Establish a computing resource allocation decision variable to represent the CPU cycle frequency allocated by the node to the business; Establish constraints to ensure the rationality and feasibility of service offloading, service processing, transmission resource allocation, and computing resource allocation, including bandwidth requirements, computing resource limitations, and node storage capacity constraints; A joint optimization problem is established according to the service offloading decision variables, service processing decision variables, transmission resource allocation decision variables, computing resource allocation decision variables and constraint conditions.

6. A business-model-data-jointly driven satellite-ground integrated network resource intelligent collaborative dispatching system for new power systems, characterized by: Executing the method according to claim 1, the business-model-data jointly driven satellite-ground fusion network resource intelligent collaborative dispatching system for the new power system includes: The fusion network module is used to build a satellite-ground fusion network model. It describes the satellite-ground fusion network consisting of a satellite layer and a ground layer. The satellite layer includes low-orbit satellites, and the ground layer includes gateways, data processing centers, and control centers. Satellite and ground nodes are represented by a bidirectional time-varying graph. The connectivity, transmission rate, buffer size, and CPU cycle frequency of nodes and links change dynamically. The channel module is used to build the channel model in the satellite-ground fusion network, establish the inter-satellite communication model and the satellite-ground communication model, consider the impact of channel fading, loss and interference on the transmission rate, calculate the inter-satellite communication rate based on the Shannon formula, and establish the Rice fading channel model for satellite-ground communication; The business module is used to establish a new power system business model, classifying power services into two categories: transmission-sensitive and computation-sensitive. It describes the data volume, CPU cycle requirements, processing ratio coefficient, and QoS requirements of each type of service, and schedules services based on service type and requirements. The priority module is used to build a priority queuing model for service data. It determines the priority of service data based on the service's maximum latency tolerance and generation time, prioritizes service data with low latency tolerance, and manages the backlog and processing of service data through offload queues and computing queues. The cost module is used to establish a system cost model, comprehensively considering transmission delay, computing delay, queuing delay and energy consumption cost, and defining the total system cost as the weighted sum of delay cost and energy consumption cost; Joint optimization module, used to establish joint optimization problems, minimize total system cost, and optimize business offloading, business processing, transmission resource allocation, and computing resource allocation decisions while meeting the communication needs of various power business operations; The solution module is used to solve the joint optimization problem and obtain the results of intelligent coordinated scheduling of satellite-ground integrated network resources, thereby achieving efficient processing of power services and optimal allocation of resources.

7. A computer device, characterized in that: The device comprises: a memory and a processor, wherein the processor executes the method according to any one of claims 1 to 5 when running computer instructions stored in the memory.

8. A computer-readable storage medium, characterized in that The method comprises instructions which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 5.

Images