Green computing power network management method, device, equipment, medium and product

Abstract

The invention relates to a green computing power network management method and device, equipment, a medium and a product, and the method comprises the steps: analyzing a service request after the service request for a computing power network is detected, obtaining a service demand, and determining a service demand parameter based on the service demand; determining a candidate computing power node set meeting a preset energy consumption requirement based on the equipment parameters of the equipment in the computing power network; screening target computing power nodes meeting the business demand parameters from the candidate computing power node set; determining a target computing network route distribution path based on the screened target computing power node; wherein the target computing network routing distribution path is used for indicating a routing path between the source node and the target computing power node. According to the invention, the computing power network has diversified green node selection capability.

Description

Technical Field

[0001] The present disclosure relates to the field of transmission and bearer networks, and in particular to a green computing power network management method, device, equipment, medium and product. Background Art

[0002] A computing network is a new type of information infrastructure that allocates and flexibly schedules computing resources, storage resources, and network resources between clouds, networks, and edges based on business needs. When new computing services are added or scheduled, the computing network's management and control system will allocate computing resources and computing network resources based on business needs.

[0003] In the prior art, the allocation of computing nodes in the computing network is achieved by satisfying the computing resources and the key network indicators. However, the allocation indicators selected by this method are relatively simple, which leads to a decrease in the rationality of the allocation of computing resources and computing network resources during the operation and maintenance of existing computing network services. Summary of the invention

[0004] The present disclosure provides a green computing power network management method, device, equipment, medium and product.

[0005] According to a first aspect of the present disclosure, a green computing power network management method is provided, the method comprising:

[0006] After detecting a service request for the computing power network, the service request is parsed to obtain a service requirement, and a service requirement parameter is determined based on the service requirement;

[0007] Determine a set of candidate computing nodes that meet preset energy consumption requirements based on device parameters of each device in the computing network;

[0008] Selecting a target computing power node that meets the business requirement parameters from the candidate computing power node set;

[0009] Determine a target computing network routing allocation path based on the filtered target computing power node; wherein the target computing network routing allocation path is used to indicate a routing path between a source node and the target computing power node.

[0010] Further, determining the business requirement parameters based on the business requirement includes:

[0011] Collecting the operation data of each device in the computing power network and the business data carried;

[0012] Based on the operation data and the business data, determining a routing fusion model of the computing power network; wherein the routing fusion model is used to indicate a routing path between a source node and each computing power device in the computing power network;

[0013] The service requirement is matched with the routing fusion model to obtain the service requirement parameter.

[0014] Furthermore, the determining of a set of candidate computing nodes that meet preset energy consumption requirements based on device parameters of each device in the computing network includes:

[0015] The device parameters of each device in the computing power network are processed through a neural circulatory network to obtain a set of candidate computing power nodes that meet preset energy consumption requirements.

[0016] Furthermore, the step of screening a target computing power node that meets the service requirement parameters from the candidate computing power node set includes:

[0017] Performing minimum granularity data identification processing on the business requirement parameters to obtain minimum requirement parameters that meet the business request;

[0018] Determine a candidate computing power node that meets the minimum requirement parameters in the candidate computing power node set to obtain the target computing power node.

[0019] Further, the determining of the target computing network routing allocation path based on the screened target computing power nodes includes:

[0020] Determine at least one candidate computing network routing allocation path based on the target computing power node and the source node;

[0021] Determine a first optimal computing network routing allocation path in the at least one candidate computing network routing allocation path based on the network performance indicator of each node in each of the candidate computing network routing allocation paths;

[0022] Determine a second optimal computing network routing allocation path in the first optimal computing network routing allocation path based on the network energy consumption index of each node in each of the first optimal computing network routing allocation paths;

[0023] Based on the second optimal computing network routing allocation path, the target computing network routing allocation path is determined.

[0024] Further, the determining of a first optimal computing network routing allocation path in the at least one candidate computing network routing allocation path based on the network performance indicator of each node in each of the candidate computing network routing allocation paths includes:

[0025] Determine a first score of each candidate computing network routing allocation path according to the network performance index of each node in each candidate computing network routing allocation path and the first weight coefficient;

[0026] A first preset number of candidate computing network routing allocation paths with the highest first scores are determined as the first optimal computing network routing allocation path.

[0027] Further, the determining the second optimal computing network routing allocation path in the first optimal computing network routing allocation path based on the network energy consumption index of each node in each of the first optimal computing network routing allocation paths includes:

[0028] Determine a second score of the first optimal computing network routing allocation path according to the network energy consumption index of each node in each of the first optimal computing network routing allocation paths and the second weight coefficient;

[0029] A second preset number of first optimal computing network routing allocation paths with the second highest scores are determined as the second optimal computing network routing allocation paths.

[0030] Further, the determining the target computing network routing allocation path based on the second optimal computing network routing allocation path includes:

[0031] Verify the second optimal computing network routing allocation path based on multiple performance verification indicators to obtain a performance verification result of the second optimal computing network routing allocation path;

[0032] If the performance verification result meets the preset indicator requirements, determine the target computing network routing allocation path based on the second optimal computing network routing allocation path, and run the service request on the target computing network routing allocation path in the computing power network;

[0033] If the verification result does not meet the preset indicator requirement, return to the step of determining a set of candidate computing nodes that meet the preset energy consumption requirement based on the device parameters of each device in the computing power network.

[0034] Furthermore, the method further comprises:

[0035] If the second optimal computing network routing allocation path that meets the preset indicator requirements is not determined for multiple consecutive times, an alarm message is sent to the user that the computing network cannot meet the service request.

[0036] According to a second aspect of the present disclosure, a green computing power network management device is provided, the device comprising:

[0037] A parsing module is used to parse the service request for the computing power network after detecting the service request, obtain the service demand, and determine the service demand parameters based on the service demand;

[0038] A computing module, used to determine a set of candidate computing nodes that meet preset energy consumption requirements based on device parameters of each device in the computing network;

[0039] A screening module, used to screen the target computing power nodes that meet the business requirement parameters from the candidate computing power node set;

[0040] A determination module is used to determine a target computing network routing allocation path based on the screened target computing power node; wherein the target computing network routing allocation path is used to indicate a routing path between a source node and the target computing power node.

[0041] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0042] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above method of the present disclosure is implemented.

[0043] According to a fifth aspect of the present disclosure, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the method of the present disclosure is implemented.

[0044] A green computing power network management method, apparatus, device, medium and product provided by the embodiments of the present disclosure, in the embodiments of the present application, first, after detecting a business request for the computing power network, the business request is parsed to obtain the business demand, and the business demand parameters are determined based on the business demand; then, a set of candidate computing power nodes that meet the preset energy consumption requirements is determined based on the device parameters of each device in the computing power network; then, a target computing power node that meets the business demand parameters is screened from the candidate computing power node set; finally, a target computing network routing allocation path is determined based on the screened target computing power nodes; wherein the target computing network routing allocation path is used to indicate the routing path between the source node and the target computing power node.

[0045] From the above description, it can be seen that the technical solution disclosed in the present invention determines a set of candidate computing power nodes that meet the preset energy consumption requirements through the device parameters of each device in the computing power network, and then screens the target computing power nodes that meet the business demand parameters from the candidate computing power node set. The target computing power nodes screened in this way can meet the business demand parameters while also meeting the preset energy consumption requirements, so that the computing power network has a diversified green node selection capability, thereby being able to screen out more reasonable computing power nodes; at the same time, the technical solution disclosed in the present invention can determine the target computing network routing allocation path based on the screened target computing power nodes. When the business request runs on the target computing network routing allocation path, it can not only ensure the network performance of the business request, but also reduce the energy consumption of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0047] Figure 1 A flowchart of a green computing power network management method provided for an exemplary embodiment of the present disclosure;

[0048] Figure 2 A green computing power network management logic diagram provided for an exemplary embodiment of the present disclosure;

[0049] Figure 3 A schematic diagram of a routing fusion model provided for an exemplary embodiment of the present disclosure;

[0050] Figure 4 A flowchart of a green computing power network management method provided for another exemplary embodiment of the present disclosure;

[0051] Figure 5 A flowchart of a green computing power network management method provided for another exemplary embodiment of the present disclosure;

[0052] Figure 6 A flowchart of a green computing power network management method provided for another exemplary embodiment of the present disclosure;

[0053] Figure 7 A green computing power network management logic diagram provided for another exemplary embodiment of the present disclosure;

[0054] Figure 8 A flowchart of a green computing power network management method provided for another exemplary embodiment of the present disclosure;

[0055] Fig. 9 A schematic block diagram of functional modules of a green computing power network management device provided by an exemplary embodiment of the present disclosure;

[0056] Fig.10 A structural block diagram of an electronic device provided for an exemplary embodiment of the present disclosure;

[0057] Fig.11 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure;

[0058] Fig.12 A structural block diagram of a computer program product provided for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0059] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0060] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0061] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0062] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0063] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0064] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0065] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0066] As an optional but non-limiting implementation, in response to receiving an active request from the user, the method of sending a prompt message to the user may be, for example, a pop-up window, in which the prompt message may be presented in text. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0067] In one embodiment, Figure 1 As shown, a green computing power network management method is provided, comprising the following steps:

[0068] Step 101, after detecting a service request for the computing power network, parse the service request to obtain the service demand, and determine the service demand parameters based on the service demand.

[0069] Here, the execution subject may be a computing power network management terminal, which may parse the service request after detecting the service request for the computing power network, obtain the service demand, and determine the service demand parameters based on the service demand. The service demand may include adding new computing power services and computing power service scheduling.

[0070] In a possible embodiment, Figure 2 As shown, Figure 2 The green computing network management logic diagram is shown as an example. After detecting a service request for the computing network, the computing network management terminal first analyzes the service request and determines the service demand, where the service demand includes adding new computing services and computing service scheduling. Then, the computing network management terminal determines the service demand parameters based on the service demand.

[0071] In a possible embodiment, determining a service requirement parameter based on the service requirement includes the following steps:

[0072] Collect the operation data of each device in the computing network and the business data it carries;

[0073] Determine the routing fusion model of the computing network based on operation data and business data;

[0074] Match the business requirements with the routing fusion model to obtain business requirement parameters.

[0075] In the disclosed embodiment, after the computing power network management terminal parses the service request to obtain the service demand, first, it collects the operation data of each device in the computing power network and the business data carried. The equipment in the computing power network may include network equipment, computing power equipment, refrigeration equipment and dynamic environment system. Among them, the network equipment is a router that carries and forwards user requests. It should be noted that the router includes "access, aggregation, core" layer routers; the computing power equipment is a computing power server that provides computing and training for user needs in data centers, cloud resource pools and other areas; the refrigeration equipment is a device that performs hot and cold exchange or dissipates heat generated in the computer room and rack; the power environment equipment is mainly a device that monitors and counts the power consumption and temperature of the computer room, rack, equipment, etc. in real time. The computing power network management terminal collects the operation data of network equipment, computing power equipment, refrigeration equipment, and power environment equipment and the business data carried by them in the form of direct protocol acquisition and interface synchronization. Among them, the collected data specifically includes equipment resource data, business information, computing power data, end-to-end path routing data, cooling parameters, power consumption data, ambient temperature data, transmission bandwidth, performance data, etc.

[0076] Then, the computing network management terminal cleans and analyzes the collected operation data and business data to determine the routing fusion model of the computing network, where the routing fusion model is used to indicate the routing path between the source node and each computing device in the computing network. Figure 3 As shown, Figure 3 A schematic diagram of a routing fusion model is shown exemplarily, in which the routing path between the source node and the computing power device in the computing power network is: source node - device 1 - device 2 - device 3 - device N - computing power device, where the source node is a network device, and devices 1-N are the next-hop routes of the network device, i.e., intermediate nodes, and the computing power device is the destination node. In addition, the routing fusion model includes information fields such as the province, city, region, computer room / base station, rack, network / computing power device name, device IP, device type, device / application port, IP prefix, power consumption, occupied bandwidth / computing power, local temperature, and network performance (bandwidth, latency, packet loss) of each device in the computing power network.

[0077] Afterwards, if Figure 2 As shown, the computing power network management terminal matches the business demand with the routing fusion model, and decomposes the business demand to obtain the business demand parameters of the business demand under the routing fusion model, wherein the business demand parameters include: user information parameters, computing power demand parameters, network demand parameters, energy consumption threshold parameters, temperature range parameters, and computing power type parameters.

[0078] Specifically, for user information parameters, the computing power network management terminal determines the user-side access device based on the user information in the service requirements, thereby determining the operator edge-side router device in the routing fusion model, and determining the source node in the routing fusion model. For computing power demand parameters, the computing power network management terminal decomposes the computing power quantity, specifications, usage time period and other data required to support the service operation in the service requirements. For network demand parameters, the computing power network management terminal decomposes the data supporting the stable operation of network-side services in the routing fusion model, such as bandwidth, latency, packet loss, number of items, overhead, utilization, etc. For energy consumption threshold parameters, the computing power network management terminal defines the upper limit of the energy consumption requirements of node devices such as computing power servers and routers. For temperature range parameters, the computing power network management terminal defines the local temperature range of the computer room for node devices such as computing power servers and routers. For computing power type parameters, the computing power network management terminal identifies the type of computing power required for the service, including computing, storage, training, and multi-dimensional requirements such as sensitive / non-sensitive, large / medium / small scale, and fixed / flexible computing rules.

[0079] Step 102: Determine a set of candidate computing nodes that meet preset energy consumption requirements based on the device parameters of each device in the computing network.

[0080] Here, after determining the business demand parameters based on the business demand, the computing network management terminal can determine the candidate computing node set that meets the preset energy consumption requirements based on the equipment parameters of each device in the computing network. Among them, the equipment parameters of each device in the computing network include: configuration, performance, operation indicators, power consumption, computing resource capacity and other parameters related to the computing server, climate, local rack temperature, room cooling capacity, disposable power capacity, green electricity coverage scale (the proportion of green energy use, such as photovoltaic power generation, hydropower generation, wind power generation, etc.), computing business type, etc.

[0081] In a possible embodiment, determining a set of candidate computing nodes that meet preset energy consumption requirements based on device parameters of each device in the computing network includes the following steps:

[0082] The device parameters of each device in the computing power network are processed through a neural recurrent network to obtain a set of candidate computing power nodes that meet the preset energy consumption requirements.

[0083] Specifically, after determining the business demand parameters based on the business demand, the computing power network management terminal processes the device parameters of each device in the computing power network through the neural loop network to obtain a set of candidate computing power nodes that meet the preset energy consumption requirements.

[0084] It should be noted that the neural circulation network needs to select equipment parameters of at least 6 months as training data, and use recent weekly granularity data as target verification data for model verification, so as to ensure the robustness and accuracy of the model. When the verification result meets the target verification data, the neural circulation network can be released and used; when the verification result does not meet the target verification data, adjust the neural circulation network parameters to continue training. When the verification result does not meet the target verification data for more than the preset number of times, the neural circulation network verification result abnormal alarm is reported, and the neural circulation network iteration is stopped and rolled back to the neural circulation network released in the previous version.

[0085] Here, considering the frequency of network cutover of important nodes in the computing network, such as data centers, aggregation rooms, and core buildings, the training time of the neural circulatory network can be flexibly set. For example, the neural circulatory network can be trained with incremental data at 3 to 4 a.m. every day, while performing real-time iterations.

[0086] Step 103: Filter the target computing nodes that meet the business requirement parameters from the candidate computing nodes set.

[0087] After the computing power network management terminal determines a set of candidate computing power nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network, it can filter the target computing power nodes that meet the business demand parameters from the candidate computing power node set.

[0088] In a possible embodiment, selecting a target computing node that meets the service requirement parameters from a set of candidate computing nodes includes the following steps:

[0089] Perform minimum granularity data identification processing on business requirement parameters to obtain the minimum requirement parameters that meet the business request;

[0090] Determine the candidate computing power node that meets the minimum required parameters in the candidate computing power node set and obtain the target computing power node.

[0091] Specifically, Figure 2 As shown, after the computing power network management terminal determines the set of candidate computing power nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network, the computing power network management terminal first performs minimum granularity data identification processing on the business demand parameters to obtain the minimum demand parameters that meet the business request.

[0092] Exemplarily, when the network requirement parameters in the service requirement parameters are processed with minimum granularity data identification, the minimum bandwidth that meets the user's service activation requirements can be obtained; then, the computing power network management terminal determines the candidate computing power node that meets the minimum requirement parameters in the candidate computing power node set to obtain the target computing power node.

[0093] Step 104: determine the target computing network routing allocation path based on the screened target computing power nodes.

[0094] Here, if Figure 2 As shown, after the computing power network management terminal selects the target computing power node that meets the business demand parameters from the candidate computing power node set, it can determine the target computing network routing allocation path based on the selected target computing power node. Among them, the target computing network routing allocation path is used to indicate the routing path between the source node and the target computing power node.

[0095] In a possible embodiment, determining a target computing network routing allocation path based on the screened target computing power nodes includes the following steps:

[0096] Determine at least one candidate computing network routing allocation path based on the target computing power node and the source node;

[0097] Determine a first optimal computing network routing assignment path in at least one candidate computing network routing assignment path based on a network performance indicator of each node in each candidate computing network routing assignment path;

[0098] Determine a second optimal computing network routing allocation path in the first optimal computing network routing allocation path based on the network energy consumption index of each node in each first optimal computing network routing allocation path;

[0099] Based on the second optimal computing network routing allocation path, a target computing network routing allocation path is determined.

[0100] Specifically, after the computing power network management terminal selects the target computing power node that meets the business demand parameters from the candidate computing power node set, first, the computing power network management terminal determines at least one candidate computing network routing allocation path based on the target computing power node and the source node, wherein the source node is the core device of the upstream operator that the user accesses the computing power network terminal, that is, the router device, and the target computing power node is the computing power device. Exemplarily, the computing power network management terminal can determine candidate computing network routing allocation path A, candidate computing network routing allocation path B, and candidate computing network routing allocation path C based on the target computing power node and the source node.

[0101] Then, the computing power network management terminal determines the first optimal computing network routing allocation path in at least one candidate computing network routing allocation path based on the network performance indicators of each node in each candidate computing network routing allocation path, wherein the network performance indicators of each node include node delay, node bandwidth, node utilization, node packet loss rate, node hop count, node overhead, whether the node is a must-pass node, etc.

[0102] Continuing with the above example, the computing power network management terminal determines candidate computing network routing allocation path A, candidate computing network routing allocation path B and candidate computing network routing allocation path C based on the target computing power node and the source node, wherein candidate computing network routing allocation path A includes node 1, node 2, and node 3, candidate computing network routing allocation path B includes node 4, node 5, and node 6, and candidate computing network routing allocation path C includes node 7, node 8, and node 9. The computing power network management terminal determines the scores of candidate computing network routing allocation paths A, B, and C respectively according to the network performance indicators of each node in the candidate computing network routing allocation paths A, B, and C, and determines the two candidate computing network routing allocation paths with the highest scores as the first optimal computing network routing allocation paths.

[0103] Afterwards, the computing power network management terminal determines the second optimal computing network routing allocation path in the first optimal computing network routing allocation path based on the network energy consumption indicators of each node in each first optimal computing network routing allocation path, wherein the network energy consumption indicators of each node include node energy consumption, node local temperature, node area cooling capacity, node power distribution capacity, etc.

[0104] Continuing with the above example, the computing power network management terminal determines the first optimal computing network routing allocation path A and the first optimal computing network routing allocation path B. The computing power network management terminal determines the scores of the first optimal computing network routing allocation paths A and B respectively according to the network energy consumption indicators of each node in the first optimal computing network routing allocation paths A and B, and determines the first optimal computing network routing allocation path with the highest score as the second optimal computing network routing allocation path; finally, the computing power network management terminal verifies the second optimal computing network routing allocation path based on multiple performance verification indicators, obtains the performance verification results of the second optimal computing network routing allocation path, and determines the target computing network routing allocation path based on whether the verification results meet the preset energy consumption requirements.

[0105] From the above description, it can be seen that the technical solution disclosed in the present invention determines a set of candidate computing power nodes that meet the preset energy consumption requirements through the device parameters of each device in the computing power network, and then screens the target computing power nodes that meet the business demand parameters from the candidate computing power node set. The target computing power nodes screened in this way can meet the business demand parameters while also meeting the preset energy consumption requirements, so that the computing power network has a diversified green node selection capability, thereby being able to screen out more reasonable computing power nodes; at the same time, the technical solution disclosed in the present invention can determine the target computing network routing allocation path based on the screened target computing power nodes. When the business request runs on the target computing network routing allocation path, it can not only ensure the network performance of the business request, but also reduce the energy consumption of the network.

[0106] In one embodiment, Figure 4As shown, in step 104, based on the network performance indicators of each node in each candidate computing network routing allocation path, a first optimal computing network routing allocation path is determined in at least one candidate computing network routing allocation path, and the following steps are also included:

[0107] Step 401: Determine a first score of each candidate computing network routing allocation path according to the network performance index of each node in the candidate computing network routing allocation path and a first weight coefficient.

[0108] Here, after the computing power network management terminal determines at least one candidate computing network routing allocation path based on the target computing power node and the source node, it can determine the first score of the candidate computing network routing allocation path according to the network performance indicators and the first weight coefficient of each node in each candidate computing network routing allocation path.

[0109] In a possible embodiment, after the computing power network management terminal determines at least one candidate computing network routing allocation path, it determines the first score of the candidate computing network routing allocation path according to the network performance index and the first weight coefficient of each node in each candidate computing network routing allocation path. Exemplarily, the candidate computing network routing allocation paths determined by the computing power network management terminal are candidate computing network routing allocation paths A, B, and C, wherein the first weight coefficient λ of candidate computing network routing allocation path A is 0.6, and candidate computing network routing allocation path A includes nodes 1, 2, and 3, and the network performance index scores of these three nodes are 30, 40, and 55, respectively. Then, the first score of candidate computing network routing allocation path A is (30+40+55)*0.6=75, and the first scores of candidate computing network routing allocation paths B and C can be obtained by the same method, which will not be repeated here. It should be noted that the first weight coefficient λ reflects the weight of the network performance index in the candidate computing network routing allocation path, which can be flexibly set according to demand and is not limited here.

[0110] Step 402: determine a first preset number of candidate computing network routing assignment paths with the highest first scores as the first optimal computing network routing assignment path.

[0111] Here, after the computing power network management terminal determines the first score of the candidate computing network routing allocation path, it can determine a first preset number of candidate computing network routing allocation paths with the highest first scores as the first optimal computing network routing allocation path.

[0112] In a possible embodiment, after the computing power network management terminal determines the first score of the candidate computing network routing allocation path, the first preset number of candidate computing network routing allocation paths with the highest first score are determined as the first optimal computing network routing allocation path. Exemplarily, the computing power network management terminal determines that the first scores of the candidate computing network routing allocation paths A, B, and C are 75, 90, and 80, respectively, where the first preset number is 2, and the two candidate computing network routing allocation paths B and C with the highest first score are determined as the first optimal computing network routing allocation path. It should be noted that the first preset number can be flexibly set according to demand and is not limited here.

[0113] In this embodiment, first, the computing power network management terminal determines the first score of each candidate computing network routing allocation path based on the network performance indicators and the first weight coefficient of each node in each candidate computing network routing allocation path; then, a first preset number of candidate computing network routing allocation paths with the highest first scores are determined as the first optimal computing network routing allocation path.

[0114] From the above description, it can be seen that the computing power network management terminal determines the first score of each candidate computing network routing allocation path based on the network performance index and the first weight coefficient, and determines the first preset number of candidate computing network routing allocation paths with the highest first score as the first optimal computing network routing allocation path. The determined first optimal computing network routing allocation path achieves the optimal choice in terms of network performance, thereby improving the reliability of the computing power network management method.

[0115] In one embodiment, Figure 5 As shown, in step 104, based on the network energy consumption index of each node in each first optimal computing network routing allocation path, a second optimal computing network routing allocation path is determined in the first optimal computing network routing allocation path, and the following steps are also included:

[0116] Step 501, determining a second score of the first optimal computing network routing allocation path according to the network energy consumption index of each node in each first optimal computing network routing allocation path and the second weight coefficient.

[0117] Here, the computing power network management terminal determines the first optimal computing network routing allocation path in at least one candidate computing network routing allocation path based on the network performance indicators of each node in each candidate computing network routing allocation path. It can then determine the second score of the first optimal computing network routing allocation path based on the network energy consumption indicators and the second weight coefficient of each node in each first optimal computing network routing allocation path.

[0118] In a possible embodiment, after the computing power network management terminal determines the first optimal computing network routing allocation path, it determines the second score of the first optimal computing network routing allocation path according to the network energy consumption index and the second weight coefficient of each node in each first optimal computing network routing allocation path.

[0119] Exemplarily, the first optimal computing network routing allocation path determined by the computing power network management terminal is the first optimal computing network routing allocation path A and B, wherein the second weight coefficient β of the first optimal computing network routing allocation path A is 0.5, and the first optimal computing network routing allocation path A includes node 1, node 2, and node 3. The network energy consumption index scores of these three nodes are 60, 70, and 70, respectively. Then, the second score of the first optimal computing network routing allocation path A is (60+70+70)*0.5=100. The second score of the first optimal computing network routing allocation path B can be obtained by the same method, which will not be repeated here.

[0120] Step 502: Determine a second preset number of first optimal computing network routing allocation paths with the second highest scores as second optimal computing network routing allocation paths.

[0121] Here, after determining the second score of the first optimal computing network routing assignment path, the computing power network management terminal may determine a second preset number of first optimal computing network routing assignment paths with the highest second scores as the second optimal computing network routing assignment path.

[0122] In a possible embodiment, after determining the second score of the first optimal computing network routing allocation path, the computing power network management terminal determines a second preset number of first optimal computing network routing allocation paths with the highest second scores as the second optimal computing network routing allocation path.

[0123] Exemplarily, the computing power network management terminal determines that the second scores of the first optimal computing network routing allocation paths A and B are 100 and 90 respectively, and the second preset number is 1, then the first optimal computing network routing allocation path A with the highest second score is determined as the second optimal computing network routing allocation path. It should be noted that the second preset number can be flexibly set according to demand and is not limited here.

[0124] In this embodiment, first, the computing power network management terminal determines the second score of the first optimal computing network routing allocation path based on the network energy consumption index of each node in each first optimal computing network routing allocation path and the second weight coefficient; then, the computing power network management terminal determines the second preset number of first optimal computing network routing allocation paths with the highest second scores as the second optimal computing network routing allocation path.

[0125] From the above description, it can be seen that the computing power network management terminal determines the second score of the first optimal computing network routing allocation path according to the network energy consumption index and the second weight coefficient, and determines the second preset number of first optimal computing network routing allocation paths with the highest second scores as the second optimal computing network routing allocation path. The determined second optimal computing network routing allocation path achieves the optimal choice in terms of network performance, and also achieves the optimal choice in terms of network energy consumption. The two optimal calculations further improve the reliability of the computing power network management method.

[0126] In one embodiment, Figure 6 As shown, in step 104, based on the second optimal computing network routing allocation path, the target computing network routing allocation path is determined, and the following steps are also included:

[0127] Step 601, verify the second optimal computing network routing allocation path based on multiple performance verification indicators to obtain the performance verification result of the second optimal computing network routing allocation path.

[0128] Here, after the computing power network management terminal determines the second preset number of first optimal computing network routing allocation paths with the second highest scores as the second optimal computing network routing allocation path, it can verify the second optimal computing network routing allocation path from multiple performance verification indicators to obtain the performance verification result of the second optimal computing network routing allocation path.

[0129] In a possible embodiment, Figure 7 As shown, Figure 7 Another green computing power network management logic diagram is shown as an example. After the computing power network management terminal determines the second optimal computing network routing allocation path, it can call the digital twin computing model and input the second optimal computing network routing allocation path into the digital twin pre-verification model. The second optimal computing network routing allocation path is verified through the digital twin pre-verification technology to obtain the performance verification result of the second optimal computing network routing allocation path, where the performance verification indicators include computing power performance verification, business performance verification, and green energy saving verification.

[0130] Specifically, first, the digital twin computing model needs to be input before being called: the computing power configuration requirements and business scale parameters of the business traffic size, the occupancy data of the computing power equipment and routing equipment, the network side indicator thresholds, such as latency, bandwidth, packet loss, utilization, overhead, etc., and the green energy-saving indicator thresholds, such as energy consumption, local temperature, etc. are input into the digital twin computing model; then, the digital twin computing model provides network simulation computing capabilities based on the input data, and verifies the second optimal computing network routing allocation path. The verification specifically includes computing power performance verification, business performance verification, and green energy-saving verification. Among them, the computing power performance verification is mainly to judge the connection of the computing power network business routing. The main purpose is to determine whether the computing network service routing meets the basic capability requirements of data request and forwarding. The main purpose of service performance verification is to determine whether the service performance indicators are met, that is, to determine whether the network performance meets the user's service needs. The main purpose of green energy-saving verification is to determine whether the performance of the computing server during operation meets the service needs, whether the energy consumption and temperature data of the new service do not exceed the threshold, whether the energy consumption and temperature data after service scheduling do not exceed the data before scheduling, and whether the service in the original path after scheduling is not affected. Finally, the computing network management terminal obtains the computing performance verification results, service performance verification results, and green energy-saving verification results of the second optimal computing network routing allocation path.

[0131] Step 602: When the performance verification result meets the preset indicator requirements, determine the target computing network routing allocation path based on the second optimal computing network routing allocation path, and run the service request on the target computing network routing allocation path in the computing power network.

[0132] Here, after obtaining the performance verification results of the second optimal computing network routing allocation path, the computing power network management terminal can determine the target computing network routing allocation path based on the second optimal computing network routing allocation path if the performance verification results meet the preset indicator requirements, and run the service request on the target computing network routing allocation path in the computing power network.

[0133] In a possible embodiment, Figure 7 As shown, after obtaining the performance verification result of the second optimal computing network routing allocation path, the computing power network management terminal judges the performance verification result. When the performance verification result meets the preset indicator requirements, the target computing network routing allocation path is determined based on the second optimal computing network routing allocation path.

[0134] Exemplarily, the computing power network management terminal determines the second optimal computing network routing allocation paths A and B, and the verification result of the second optimal computing network routing allocation path A obtained from the digital twin pre-verification model is: the computing power performance verification result passed, the business performance verification result passed, and the green energy saving verification result failed. The verification result of the second optimal computing network routing allocation path B obtained from the digital twin pre-verification model is: the computing power performance verification result passed, the business performance verification result passed, and the green energy saving verification result passed. The preset indicator requirements are that the computing power performance verification result, the business performance verification result, and the green energy saving verification result all pass. Then, the second optimal computing network routing allocation path A does not meet the preset indicator requirements, and the second optimal computing network routing allocation path B meets the preset indicator requirements. The computing power network management terminal determines the second optimal computing network routing allocation path B as the target computing network routing allocation path, and runs the business request on the target computing network routing allocation path in the computing power network.

[0135] In an optional embodiment, during the operation of the service request on the target computing network routing allocation path, the computing power network management terminal performs overall monitoring of the computing power network, that is, detects new service requests for the computing power network. Specifically, the overall monitoring of the computing power network by the computing power network management terminal includes the operation monitoring of the underlying infrastructure and equipment, the operation quality monitoring of the service request on the target computing network routing allocation path, and the monitoring of the change operation requirements of the computing power network. The monitoring indicators include network operation performance indicators, power consumption, energy saving indicators, and change requirements. When the nodes on the target computing network routing allocation path have poor network, energy consumption, temperature, etc., the computing power network management terminal receives the corresponding alarm and switches all service requests of the corresponding nodes to the backup node or path to ensure the stable operation of the service request; when it is detected that there is a new service in the computing power network, the target computing network routing allocation path is calculated. The specific steps of calculating the target computing network routing allocation path can be found in the above-mentioned embodiment applied to the computing power network management terminal, which will not be repeated here.

[0136] Step 603, when the verification result does not meet the preset indicator requirements, return to the step of determining a set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing network.

[0137] Here, after obtaining the performance verification result of the second optimal computing network routing allocation path, if the verification result does not meet the preset indicator requirements, the computing power network management terminal can return to the step of determining a set of candidate computing power nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network.

[0138] In a possible embodiment, Figure 7As shown, after the computing power network management terminal obtains the performance verification result of the second optimal computing network routing allocation path, if the verification result does not meet the preset indicator requirements, the second optimal computing network routing allocation path is marked as a blacklist, and step 102 is re-executed, that is, the set of candidate computing power nodes that meet the preset energy consumption requirements is re-determined based on the device parameters of each device in the computing power network. After the candidate computing power node set is determined, first, the second optimal computing network routing allocation path is re-determined, and the re-determined second optimal computing network routing allocation path is input into the digital twin computing model, and then the performance verification result of the second optimal computing network routing allocation path is obtained. Finally, the target computing network routing allocation path is determined according to the performance verification result. It should be noted that the specific steps for determining the target computing network routing allocation path according to the performance verification results can be found in the embodiments in steps 602 and 603, which will not be repeated here.

[0139] In this embodiment, first, the computing power network management terminal verifies the second optimal computing network routing allocation path from multiple performance verification indicators to obtain the performance verification result of the second optimal computing network routing allocation path; then, when the performance verification result meets the preset indicator requirements, the computing power network management terminal determines the target computing network routing allocation path based on the second optimal computing network routing allocation path, and runs the business request on the target computing network routing allocation path in the computing power network; when the verification result does not meet the preset indicator requirements, it returns to execute the step of determining a set of candidate computing power nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network.

[0140] From the above description, it can be seen that the computing power network management terminal obtains the performance verification result by verifying the second optimal computing network routing allocation path, and the target computing network routing allocation path determined according to the performance verification result meets the preset index requirements, further improving the reliability of the computing power network management method. And in the case that the performance verification result fails to be verified, the computing power network management terminal will recalculate the candidate computing power node set until the target computing network routing allocation path that meets the preset index requirements is determined according to the candidate computing power node set, thereby improving the flexibility of the computing power network management method.

[0141] In one embodiment, Figure 8 As shown, a green computing power network management method is also provided, comprising the following steps:

[0142] Step 801, when the second optimal computing network routing allocation path that meets the preset indicator requirements is not determined for multiple consecutive times, an alarm message is sent to the user that the computing network cannot meet the service request.

[0143] In a possible embodiment, after the computing power network management terminal returns to execute the step of determining a set of candidate computing power nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network, if the second optimal computing network routing allocation path that meets the preset indicator requirements is not determined for multiple consecutive times, the computing power network management terminal sends an alarm message to the user that the computing power network cannot meet the service request.

[0144] Exemplarily, a parameter T is set, and T is used to indicate the number of times that the computing power network management terminal fails to determine the second optimal computing network routing allocation path that meets the preset index requirements. When T>3, the computing power network management terminal sends a warning message to the user that the computing power network cannot meet the service request. It should be noted that the value of parameter T can be set according to actual conditions and is not limited here.

[0145] In this embodiment, when the computing power network management terminal fails to determine the second optimal computing network routing allocation path that meets the preset indicator requirements for multiple consecutive times, it sends an alarm message to the user that the computing power network cannot meet the service request. While ensuring the rigor of the computing power network management method, it notifies the user in an interactive manner that the current computing power network cannot meet the service request, thereby improving the flexibility of the computing power network management method.

[0146] From the above description, it can be seen that the technical solution disclosed in the present invention upgrades the traditional computing power network to a "green" computing power network through a new computing power network management method, so that the existing computing power resources and computing network resources have green and energy-saving attributes; the technical solution disclosed in the present invention can realize the automatic allocation of target computing power nodes, thereby determining the target computing network routing allocation path, and in the process of determining the target computing network routing allocation path, there are two calculations to allocate definable weight coefficients, which ensures that the determined target computing network routing allocation path can achieve the optimal path effect; the technical solution disclosed in the present invention defines a new green computing power network service end-to-end routing fusion model, and at the same time defines all information fields in the model, which can be directly applied to the underlying infrastructure in the computing power network; the technical solution disclosed in the present invention can realize the daily operation monitoring of the computing power network. When a new business is added, changed, or a quality alarm occurs, the target computing network routing allocation path can be automatically determined, and verification is performed based on the digital twin pre-verification technology, and the computing power network data is driven to continue monitoring after being updated, thereby ensuring the reliability of the business request operation.

[0147] In the case of dividing each functional module according to each function, an embodiment of the present disclosure provides a green computing power network management device, which can be a server or a chip applied to a server. Fig. 9 This is a schematic block diagram of the functional modules of a green computing power network management device provided by an exemplary embodiment of the present disclosure. Fig. 9 As shown, the green computing power network management device includes:

[0148] The parsing module 901 is used to parse the service request for the computing power network after detecting the service request, obtain the service demand, and determine the service demand parameters based on the service demand;

[0149] A computing module 902 is used to determine a set of candidate computing nodes that meet preset energy consumption requirements based on device parameters of each device in the computing network;

[0150] A screening module 903 is used to screen a target computing power node that meets the business requirement parameters from the candidate computing power node set;

[0151] The determination module 904 is used to determine the target computing network routing allocation path based on the screened target computing power node; wherein the target computing network routing allocation path is used to indicate the routing path between the source node and the target computing power node.

[0152] In one embodiment, the parsing module 901 includes:

[0153] A collection unit, used to collect the operation data of each device in the computing power network and the business data carried;

[0154] A first determining unit is used to determine a routing fusion model of the computing power network based on the operation data and the service data; wherein the routing fusion model is used to indicate a routing path between a source node and each computing power device in the computing power network;

[0155] The second determining unit is used to match the service demand with the routing fusion model to obtain the service demand parameter.

[0156] In one embodiment, the operation module 902 includes:

[0157] The third determination unit is used to process the device parameters of each device in the computing power network through a neural circulatory network to obtain a set of candidate computing power nodes that meet preset energy consumption requirements.

[0158] In one embodiment, the screening module 903 includes:

[0159] A minimum granularity data identification unit, used to perform minimum granularity data identification processing on the service requirement parameters to obtain the minimum requirement parameters that meet the service request;

[0160] The fourth determination unit is used to determine the candidate computing power node that meets the minimum requirement parameters in the candidate computing power node set to obtain the target computing power node.

[0161] In one embodiment, the determination module 904 includes:

[0162] A fifth determining unit, configured to determine at least one candidate computing network routing allocation path based on the target computing power node and the source node;

[0163] A sixth determining unit, configured to determine a first optimal computing network routing allocation path in the at least one candidate computing network routing allocation path based on a network performance indicator of each node in each of the candidate computing network routing allocation paths;

[0164] A seventh determining unit, configured to determine a second optimal computing network routing allocation path in the first optimal computing network routing allocation path based on the network energy consumption index of each node in each of the first optimal computing network routing allocation paths;

[0165] An eighth determining unit is used to determine the target computing network routing allocation path based on the second optimal computing network routing allocation path.

[0166] In one embodiment, the determination module 904 includes:

[0167] A first scoring unit, configured to determine a first score of each candidate computing network routing allocation path according to a network performance indicator and a first weight coefficient of each node in each candidate computing network routing allocation path;

[0168] The first optimal computing network routing assignment path determination unit is used to determine a first preset number of candidate computing network routing assignment paths with the highest first scores as the first optimal computing network routing assignment path.

[0169] In one embodiment, the determination module 904 includes:

[0170] A second scoring unit, configured to determine a second score of the first optimal computing network routing allocation path according to a network energy consumption index of each node in each of the first optimal computing network routing allocation paths and a second weight coefficient;

[0171] The second optimal computing network routing assignment path determination unit is used to determine a second preset number of first optimal computing network routing assignment paths with the second highest scores as the second optimal computing network routing assignment path.

[0172] In one embodiment, the determination module 904 includes:

[0173] A verification unit, configured to verify the second optimal network routing allocation path based on a plurality of performance verification indicators, and obtain a performance verification result of the second optimal network routing allocation path;

[0174] An operation unit, configured to determine the target computing network routing allocation path based on the second optimal computing network routing allocation path when the performance verification result meets the preset indicator requirements, and to run the service request on the target computing network routing allocation path in the computing power network;

[0175] The return unit is used to return to the step of determining a set of candidate computing nodes that meet the preset energy consumption requirements based on the device parameters of each device in the computing power network when the verification result does not meet the preset indicator requirements.

[0176] In one embodiment, the apparatus further comprises:

[0177] The alarm unit is used to send an alarm message to the user that the computing network cannot meet the service request when the second optimal computing network routing allocation path that meets the preset indicator requirements has not been determined for multiple consecutive times.

[0178] An embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the above method disclosed in the embodiment of the present disclosure.

[0179] Fig.10 The structure diagram of an electronic device provided by an exemplary embodiment of the present disclosure is shown in FIG. Fig.10 As shown, the electronic device 1000 includes at least one processor 1001 and a memory 1002 coupled to the processor 1001 , and the processor 1001 can execute corresponding steps in the above method disclosed in the embodiment of the present disclosure.

[0180] The processor 1001 may also be referred to as a central processing unit (CPU), which may be an integrated circuit chip having signal processing capabilities. Each step in the method disclosed in the embodiment of the present disclosure may be completed by an integrated logic circuit of hardware in the processor 1001 or by instructions in the form of software. The processor 1001 may be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in the memory 1002, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, or other mature storage media in the art. The processor 1001 reads the information in the memory 1002 and completes the steps of the method in combination with its hardware.

[0181] In addition, when various operations / processes according to the present disclosure are implemented by software and / or firmware, they can be transmitted from a storage medium or a network to a computer system having a dedicated hardware structure, such as Fig.11 The computer system 1100 shown is installed with the programs constituting the software. When the various programs are installed, the computer system can perform various functions, including the functions described above. Fig.11 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure.

[0182] Computer system 1100 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0183] like Fig.11As shown, the computer system 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1102 or a computer program loaded from a storage unit 1108 to a random access memory (RAM) 1103. In the RAM 1103, various programs and data required for the operation of the computer system 1100 can also be stored. The computing unit 1101, the ROM 1102, and the RAM 1103 are connected to each other via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0184] A plurality of components in the computer system 1100 are connected to the I / O interface 1105, including: an input unit 1106, an output unit 1107, a storage unit 1108, and a communication unit 1109. The input unit 1106 may be any type of device capable of inputting information to the computer system 1100, and the input unit 1106 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 1107 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1108 may include, but is not limited to, a disk, an optical disk. The communication unit 1109 allows the computer system 1100 to exchange information / data with other devices over a network such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0185] The computing unit 1101 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1101 performs the various methods and processes described above. For example, in some embodiments, the above methods disclosed in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1108. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1000 via the ROM 1102 and / or the communication unit 1109. In some embodiments, the computing unit 1101 may be configured to perform the above methods disclosed in the embodiments of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0186] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above method disclosed in the embodiment of the present disclosure.

[0187] The computer-readable storage medium in the disclosed embodiments may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specifically, the computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0188] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0189] Fig.12 A computer program product is provided for an exemplary embodiment of the present disclosure. The computer program product 1200 includes a computer program 1201. When the computer program 1201 is executed by a processor, the method disclosed in the embodiment of the present disclosure is implemented.

[0190] In embodiments of the present disclosure, computer program codes for performing the operations of the present disclosure may be written in one or more programming languages ​​or combinations thereof, including but not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0191] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0192] The modules, components or units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the names of the modules, components or units do not, in some cases, limit the modules, components or units themselves.

[0193] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0194] The above descriptions are only some embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, a technical solution formed by replacing the above features with the technical features with similar functions disclosed in the present disclosure (but not limited to).

[0195] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A green computing power network management method, characterized in that: include: After detecting a service request for the computing power network, the service request is parsed to obtain a service requirement, and a service requirement parameter is determined based on the service requirement; Determine a set of candidate computing nodes that meet preset energy consumption requirements based on device parameters of each device in the computing network; Selecting a target computing power node that meets the business requirement parameters from the candidate computing power node set; Determine a target computing network routing allocation path based on the filtered target computing power node; wherein the target computing network routing allocation path is used to indicate a routing path between a source node and the target computing power node.

2. The method according to claim 1, characterized in that: The determining of the business requirement parameters based on the business requirement includes: Collecting the operation data of each device in the computing power network and the business data carried; Based on the operation data and the business data, determining a routing fusion model of the computing power network; wherein the routing fusion model is used to indicate a routing path between a source node and each computing power device in the computing power network; The service requirement is matched with the routing fusion model to obtain the service requirement parameter.

3. The method according to claim 1, characterized in that The determining of a set of candidate computing nodes that meet preset energy consumption requirements based on device parameters of each device in the computing network includes: The device parameters of each device in the computing power network are processed through a neural circulatory network to obtain a set of candidate computing power nodes that meet preset energy consumption requirements.

4. The method according to claim 1, characterized in that: The step of selecting a target computing power node that meets the service requirement parameters from the candidate computing power node set includes: Performing minimum granularity data identification processing on the business requirement parameters to obtain minimum requirement parameters that meet the business request; Determine a candidate computing power node that meets the minimum requirement parameters in the candidate computing power node set to obtain the target computing power node.

5. The method according to claim 1, characterized in that The determining of the target computing network routing allocation path based on the screened target computing power nodes includes: Determine at least one candidate computing network routing allocation path based on the target computing power node and the source node; Determine a first optimal computing network routing allocation path in the at least one candidate computing network routing allocation path based on the network performance indicator of each node in each of the candidate computing network routing allocation paths; Determine a second optimal computing network routing allocation path in the first optimal computing network routing allocation path based on the network energy consumption index of each node in each of the first optimal computing network routing allocation paths; Based on the second optimal computing network routing allocation path, the target computing network routing allocation path is determined.

6. The method according to claim 5, characterized in that The determining of a first optimal computing network routing allocation path in at least one candidate computing network routing allocation path based on the network performance indicator of each node in each candidate computing network routing allocation path includes: Determine a first score of each candidate computing network routing allocation path according to the network performance index of each node in each candidate computing network routing allocation path and the first weight coefficient; A first preset number of candidate computing network routing allocation paths with the highest first scores are determined as the first optimal computing network routing allocation path.

7. The method according to claim 6, characterized in that The determining the second optimal computing network routing allocation path in the first optimal computing network routing allocation path based on the network energy consumption index of each node in each of the first optimal computing network routing allocation paths includes: Determine a second score of the first optimal computing network routing allocation path according to the network energy consumption index of each node in each of the first optimal computing network routing allocation paths and the second weight coefficient; A second preset number of first optimal computing network routing allocation paths with the second highest scores are determined as the second optimal computing network routing allocation paths.

8. The method according to claim 5, characterized in that The determining the target computing network routing allocation path based on the second optimal computing network routing allocation path includes: Verify the second optimal computing network routing allocation path based on multiple performance verification indicators to obtain a performance verification result of the second optimal computing network routing allocation path; If the performance verification result meets the preset indicator requirements, determine the target computing network routing allocation path based on the second optimal computing network routing allocation path, and run the service request on the target computing network routing allocation path in the computing power network; If the verification result does not meet the preset indicator requirement, return to the step of determining a set of candidate computing nodes that meet the preset energy consumption requirement based on the device parameters of each device in the computing power network.

9. The method according to claim 8, characterized in that The method further comprises: If the second optimal computing network routing allocation path that meets the preset indicator requirements is not determined for multiple consecutive times, an alarm message is sent to the user that the computing network cannot meet the service request.

10. A green computing power network management device, characterized in that: include: A parsing module is used to parse the service request for the computing power network after detecting the service request, obtain the service demand, and determine the service demand parameters based on the service demand; A computing module, used to determine a set of candidate computing nodes that meet preset energy consumption requirements based on device parameters of each device in the computing network; A screening module, used to screen the target computing power nodes that meet the business requirement parameters from the candidate computing power node set; A determination module is used to determine a target computing network routing allocation path based on the screened target computing power node; wherein the target computing network routing allocation path is used to indicate a routing path between a source node and the target computing power node.

11. An electronic device, characterized in that: include: at least one processor; a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method as claimed in any one of claims 1 to 9.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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