Position allocation method, device, computer equipment and storage medium

Abstract

The present application relates to a location allocation method, apparatus, computer equipment, storage medium and computer program product, and relates to the field of financial technology or other related fields. The method comprises: obtaining location allocation demand data, the location allocation demand data including the resource domain, resource subdomain, identification information and type information of the required target server; searching for the target power consumption data corresponding to the type information of the target server in the correspondence between the pre-configured type and the average power consumption data, and determining the grouping feature information of the target server; based on the grouping feature information and the location allocation optimization function, optimizing the multiple location information contained in the target computer room resource information to obtain the optimized location information of the target server. By adopting the location optimization function in the present disclosure to realize the location allocation of server resources, the automatic allocation of server resource locations can be completed quickly.

Description

Technical Field

[0001] The present application relates to the field of server technology, and in particular to a location allocation method, apparatus, computer equipment, and storage medium. Background Art

[0002] With the development of the information technology industry, cloud computing technology, as a key component of information technology, will be realized through massive servers. Cloud computing data centers are equipped with a large number of servers, each with different functions and application requirements. Therefore, how to allocate the locations of these servers has become a pressing issue.

[0003] In related technologies, server locations are generally allocated manually based on construction schedule requirements and priority levels, while ensuring maximum utilization, using cabinets and computer room resources. However, due to the increase in the number of servers, the efficiency of server location allocation is low. Summary of the Invention

[0004] Based on this, it is necessary to provide a location allocation method, apparatus, computer equipment and storage medium that can improve the efficiency of location allocation in order to address the above technical problems.

[0005] In a first aspect, the present application provides a location allocation method. The method comprises:

[0006] Acquire location allocation requirement data, wherein the location allocation requirement data includes at least a requirement sequence, and the requirement sequence includes a required resource domain, resource subdomain, identification information, and type information of a target server;

[0007] Searching for target power consumption data corresponding to the type information of the target server in a pre-configured correspondence between the type and the average power consumption data;

[0008] Determining group characteristic information of the target server based on the resource domain, resource subdomain, target power consumption data, and identification information of the target server;

[0009] Based on the grouping feature information and the location allocation optimization function, multiple location information included in the target computer room resource information is optimized to obtain the optimized location information of the target server.

[0010] In one embodiment, the optimizing process of the plurality of location information included in the target computer room resource information based on the grouping feature information and the location allocation optimization function to obtain the optimized location information of the target server includes:

[0011] Determining functional characteristics of the target server based on the resource subdomain and identification information included in the grouping characteristic information, and determining target rules corresponding to the functional characteristics;

[0012] Based on the resource domain and resource subdomain included in the grouping feature information, determining an available server location set from the multiple location information included in the target computer room resource information;

[0013] The available server location set is optimized by using a location allocation optimization function, the target rule, and the target power consumption data of the target server to obtain optimized location information of the target server.

[0014] In one embodiment, the optimizing process of the available server location set using the location allocation optimization function, the target rule, and the target power consumption data of the target server to obtain the optimized location information of the target server includes:

[0015] Calculating an initial position corresponding to an initial solution of the position allocation optimization function in the set of available server positions by using the target rule and the target power consumption data of the target server;

[0016] In a case where the initial position meets a preset position output condition, the initial position corresponding to the initial solution is determined as the optimized position information of the target server.

[0017] In one embodiment, the method further comprises:

[0018] Calculating a first optimization function based on available power of the server before allocation and available power of the server after allocation;

[0019] calculating a second optimization function based on the available space data before the server is allocated and the available space data after the server is allocated;

[0020] Calculating a third optimization function based on the available power before allocation, the available power after allocation, the available space data before allocation, the available space data after allocation, the total power data, and the total space data of the server;

[0021] A weighted calculation is performed on the first optimization function, the second optimization function, and the third optimization function to obtain a position allocation optimization function.

[0022] In one embodiment, the target computer room resource information includes identification information of each server, the resource domain to which it belongs, the resource subdomain to which it belongs, rated power data, used power information, total space data, and used space data.

[0023] In one embodiment, the location allocation method further includes:

[0024] Obtain multiple power consumption data of various types of servers within a preset time range;

[0025] Based on the multiple power consumption data corresponding to each type of server, average power consumption data is calculated to obtain a corresponding relationship between the type and the average power consumption data.

[0026] In one embodiment, the location allocation method further includes:

[0027] The correspondence between the type and the average power consumption data is stored in a preset format, where the preset format includes at least one of a dictionary format, a json data format, and a csv format.

[0028] In a second aspect, the present application further provides a position allocation device. The device comprises:

[0029] A first acquisition module is configured to acquire location allocation requirement data, wherein the location allocation requirement data includes at least one requirement sequence, and the requirement sequence includes a required resource domain, resource subdomain, identification information, and type information of a target server;

[0030] A search module, configured to search for target power consumption data corresponding to the type information of the target server in a pre-configured correspondence between types and average power consumption data;

[0031] A first determining module is configured to determine grouping characteristic information of the target server based on the resource domain, resource subdomain, target power consumption data, and identification information of the target server;

[0032] The optimization processing module is used to optimize the multiple location information included in the target computer room resource information based on the grouping feature information and the location allocation optimization function to obtain the optimized location information of the target server.

[0033] In one embodiment, the optimization processing module is specifically configured to:

[0034] Determining functional characteristics of the target server based on the resource subdomain and identification information included in the grouping characteristic information, and determining target rules corresponding to the functional characteristics;

[0035] Based on the resource domain and resource subdomain included in the grouping feature information, determining an available server location set from the multiple location information included in the target computer room resource information;

[0036] The available server location set is optimized by using a location allocation optimization function, the target rule, and the target power consumption data of the target server to obtain optimized location information of the target server.

[0037] In one embodiment, the optimization processing module is further configured to:

[0038] Calculating an initial position corresponding to an initial solution of the position allocation optimization function in the set of available server positions by using the target rule and the target power consumption data of the target server;

[0039] In a case where the initial position meets a preset position output condition, the initial position corresponding to the initial solution is determined as the optimized position information of the target server.

[0040] In one embodiment, the apparatus further comprises:

[0041] A first calculation module, configured to calculate a first optimization function based on the available power of the server before allocation and the available power of the server after allocation;

[0042] a second calculation module, configured to calculate a second optimization function based on the available space data before the server allocation and the available space data after the server allocation;

[0043] a third calculation module, configured to calculate a third optimization function based on the available power before allocation, the available power after allocation, the available space data before allocation, the available space data after allocation, the total power data, and the total space data of the server;

[0044] The fourth calculation module is used to perform weighted calculation on the first optimization function, the second optimization function and the third optimization function to obtain a position allocation optimization function.

[0045] In one embodiment, the position allocation device further includes:

[0046] The second acquisition module is used to obtain a plurality of power consumption data of various types of servers within a preset time range;

[0047] The fifth calculation module is used to calculate average power consumption data based on the multiple power consumption data corresponding to each type of server, and obtain a corresponding relationship between the type and the average power consumption data.

[0048] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are performed:

[0049] Acquire location allocation requirement data, wherein the location allocation requirement data includes at least a requirement sequence, and the requirement sequence includes a required resource domain, resource subdomain, identification information, and type information of a target server;

[0050] Searching for target power consumption data corresponding to the type information of the target server in a pre-configured correspondence between the type and the average power consumption data;

[0051] Determining group characteristic information of the target server based on the resource domain, resource subdomain, target power consumption data, and identification information of the target server;

[0052] Based on the grouping feature information and the location allocation optimization function, multiple location information included in the target computer room resource information is optimized to obtain the optimized location information of the target server.

[0053] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0054] Acquire location allocation requirement data, wherein the location allocation requirement data includes at least a requirement sequence, and the requirement sequence includes a required resource domain, resource subdomain, identification information, and type information of a target server;

[0055] Searching for target power consumption data corresponding to the type information of the target server in a pre-configured correspondence between the type and the average power consumption data;

[0056] Determining group characteristic information of the target server based on the resource domain, resource subdomain, target power consumption data, and identification information of the target server;

[0057] Based on the grouping feature information and the location allocation optimization function, multiple location information included in the target computer room resource information is optimized to obtain the optimized location information of the target server.

[0058] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0059] Acquire location allocation requirement data, wherein the location allocation requirement data includes at least a requirement sequence, and the requirement sequence includes a required resource domain, resource subdomain, identification information, and type information of a target server;

[0060] Searching for target power consumption data corresponding to the type information of the target server in a pre-configured correspondence between the type and the average power consumption data;

[0061] Determining group characteristic information of the target server based on the resource domain, resource subdomain, target power consumption data, and identification information of the target server;

[0062] Based on the grouping feature information and the location allocation optimization function, multiple location information included in the target computer room resource information is optimized to obtain the optimized location information of the target server.

[0063] The above-mentioned location allocation method, device, computer equipment and storage medium obtain location allocation demand data, which includes the resource domain, resource subdomain, available resource domain, identification information and type information of the required target server; in the correspondence between the pre-configured type and the average power consumption data, the target power consumption data corresponding to the type information of the target server is searched; based on the resource domain, resource subdomain, target power consumption data and identification information of the target server, the grouping feature information of the target server is determined; based on the grouping feature information and the location allocation optimization function, the multiple location information contained in the target computer room resource information is optimized to obtain the optimized location information of the target server. By adopting the location optimization function in the present disclosure to realize the location allocation of server resources, the automatic allocation of server resource locations can be completed quickly, while ensuring the improvement of allocation speed, the allocation efficiency of server resource locations is further improved, and the rationality of server resource location allocation can be better maintained, thereby improving the resource utilization of the server as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 1 is a flow chart of a location allocation method according to an embodiment;

[0065] Figure 2 Schematic diagram of a flow chart of the step of calculating optimized location information in one embodiment;

[0066] Figure 3 Schematic diagram of a flow chart of the step of calculating optimized location information in one embodiment;

[0067] Figure 4 A schematic diagram of a flow chart of steps for calculating a position allocation optimization function in one embodiment;

[0068] Figure 5 1. A schematic diagram of a flow chart of steps for calculating type and average power consumption data in one embodiment;

[0069] Figure 6 is a structural block diagram of a position allocation device in one embodiment;

[0070] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0072] The location allocation method provided in the embodiment of the present application can be applied in a cloud environment. The cloud environment may include multiple regions, a single region may include multiple available zones (AZ), and each available zone may contain thousands of physical machines (servers); the cloud resource area may include a network area, a management area, a business area, a public storage area, etc. The management area is divided into a management node and an FCD node, and the management node may contain at least two separate servers. The network area is divided into a network node, a network element block storage, and an FC node. The network node is placed with at least two servers, and the network element block storage and the FC node are placed with at most one server each. The functional type of the server in the business area may include bare metal nodes, etc. Therefore, how to realize the unified scheduling of server resources based on the resource information of multiple servers, the demand information of actual server resources, and the predetermined planning information, and to improve the utilization rate of server resources to a certain extent, is an urgent problem to be solved.

[0073] In one embodiment, Figure 1 As shown, a location allocation method is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understood that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, etc., and the server can be implemented as an independent server or a server cluster consisting of multiple servers. In this embodiment, the location allocation method includes the following steps:

[0074] Step 102: Acquire location allocation demand data.

[0075] The location allocation requirement data includes at least one requirement sequence. The location allocation requirement data may be data carried by the location allocation request, or may be a requirement list. The requirement list may include one or more requirement sequences, each of which includes the required resource domain, resource subdomain, identification information, and type information of the target server.

[0076] In this embodiment, the terminal can obtain a location allocation request and obtain the required location allocation requirement data carried in the location allocation requirement request, and process each of the multiple requirement sequences contained in the location allocation requirement data based on the resource domain, resource subdomain, identification information and type information contained in each requirement sequence. The specific processing process will be described in subsequent embodiments.

[0077] In one embodiment, the requirement sequence may further include configuration information of the required target server, RAC (Real Application Cluster) type information, and required time, etc. The configuration information may be configuration information of the target server, such as capacity configuration information, and the configuration information may be "2*960G+12*3.84T+768G+20,000M". The target server may be the required server resources, and the resource domain of the target server may be, for example, "bd01". The resource subdomain of the target server may be "BMS01" or "BMS02". The identification information may be node name information of the required target server, such as "bare metal". The type information may be model information of the required target server resource, such as "super K620", "2288HV5", or "NF5280M5". The RAC type information may be step 104 indicating the required target server resource. In the pre-configured correspondence between the type and the average power consumption data, the target power consumption data corresponding to the type information of the target server is searched.

[0078] Among them, the type can be the model information of the server, and the average power consumption data can be the average power consumption data after averaging multiple power consumption data of the server of this model in a preset time period. The correspondence between the pre-configured type and the average power consumption data can include the average power consumption data of servers of multiple models.

[0079] In this embodiment, after obtaining the location allocation demand data, the terminal can extract the average power consumption data corresponding to the type information of the target server (i.e., the target server) contained in each demand sequence, from the pre-configured correspondence between the type and the average power consumption data, as the target power consumption data.

[0080] Step 106 : Determine the grouping characteristic information of the target server based on the resource domain, resource subdomain, target power consumption data, and identification information of the target server.

[0081] In this embodiment, the terminal can obtain the required resource domain, resource subdomain and identification information of the target server, and combine the required resource domain, resource subdomain and identification information of the target server to obtain the grouping feature information of the target server.

[0082] In one example, the terminal can determine the required server resources corresponding to each of the multiple requirement sequences contained in the location allocation requirement data, that is, the target server, and extract the resource domain, resource subdomain, identification information and type information of the target server from the multiple data contained in the requirement sequence, and determine the target power consumption data corresponding to the type information; in this way, the terminal can combine the resource domain, resource subdomain, identification information and target power consumption data to obtain the grouping characteristic information of the target server.

[0083] Specifically, the terminal can extract the resource domain (bd01) to which the required server belongs, the resource subdomain (such as BMS01, RAC1) to which the server belongs, and the corresponding available resource domain from the requirements list. The terminal can import the requirements list through the pandas module and convert it into a DataFrame format. In this way, the terminal can obtain the corresponding target power consumption data based on the type information of the target server, and use the resource domain (batch), identification information (node name), resource subdomain (AZ), type information (model), and target power consumption data as the grouping features of the target server.

[0084] Based on this, the terminal can process the grouping feature information corresponding to each demand queue and the quantity required by the target server using a preset summation function to obtain a demand character pair. For example, the data contained in the demand character pair can be "function-environment-power consumption, quantity". In one example, it can be "bare metal RAC1-bd01BMS01-350W, 4", where 350W represents the target power consumption data of the target server; the preset summation function can be DataFrame.groupby.count().

[0085] Step 108 : Based on the grouping feature information and the location allocation optimization function, the multiple location information included in the target computer room resource information is optimized to obtain the optimized location information of the target server.

[0086] The location allocation optimization function may be a preconfigured optimization function obtained based on multiple pre-set evaluation functions, and is used to optimize the location information once or multiple times to obtain an optimal solution, and use the location information corresponding to the optimal solution as the optimized location information of the target server. The target computer room resource information may be a collection of location information of multiple servers.

[0087] In this embodiment, the terminal can optimize the location information of multiple servers contained in the target computer room resource information once or multiple times based on the grouping feature information and the preset location allocation optimization function. When the preset location output conditions are met, the obtained location information is output as the optimized location information, that is, the location of the required target server resource is determined to be the calculated optimized location information.

[0088] In the above-mentioned location allocation method, by obtaining location allocation demand data, the location allocation demand data includes the required resource domain, resource subdomain, identification information and type information of the target server. In the correspondence between the pre-configured type and the average power consumption data, the target power consumption data corresponding to the type information of the target server is searched. Based on the resource domain, resource subdomain, target power consumption data and identification information of the target server, the grouping feature information of the target server is determined. Based on the grouping feature information and the location allocation optimization function, the multiple location information contained in the target computer room resource information is optimized to obtain the optimized location information of the target server. By adopting the location optimization function in the present disclosure to realize the location allocation of server resources, the automatic allocation of server resource locations can be completed quickly, while ensuring the improvement of allocation speed, the allocation efficiency of server resource locations is further improved, and the rationality of server resource location allocation can be better maintained, thereby improving the resource utilization of the server as a whole.

[0089] In one embodiment, Figure 2 As shown, the specific processing process of step 108 "optimizing the multiple location information included in the target computer room resource information based on the grouping feature information and the location allocation optimization function to obtain the optimized location information of the target server" includes:

[0090] Step 202: Based on the resource subdomain and identification information included in the grouping characteristic information, determine the functional characteristics of the target server and determine the target rules corresponding to the functional characteristics.

[0091] The target rule may be a constraint condition (allocation rule) in determining the location information of the target server, a condition on the quantity dimension, or a constraint condition on location allocation, etc.

[0092] In this embodiment, the terminal may combine the resource subdomain and identification information included in the target server's grouping feature information to obtain the target server's functional features, and determine the target rule corresponding to the target server's functional features based on a pre-configured correspondence between functional features and rules. The target rule may include one or more allocation rules.

[0093] In one example, after determining the target rule corresponding to the functional characteristics, the terminal further includes: the terminal can determine the allocation rule based on the RAC type information contained in the demand sequence, and use the determined allocation rule and the rule corresponding to the functional characteristics of the target server as the target rule.

[0094] Step 204 : Based on the resource domain and resource subdomain included in the grouping characteristic information, determine the available server location set from the multiple location information included in the target computer room resource information.

[0095] The available server location set may be a location information list containing multiple server locations.

[0096] In this embodiment, the terminal can filter multiple location information included in the target computer room resources based on the resource domain included in the grouping feature information, extract location information whose resource domain information matches the resource domain in the grouping feature information, and determine it as the set of available servers; or the terminal can filter multiple location information included in the target computer room resources, extract location information whose resource subdomain matches the resource subdomain in the grouping feature information, and determine the location information as the set of available servers. In one example, the matching location information can be consistent location information, for example, location information with the same resource domain, or location information with the same resource subdomain.

[0097] In a specific embodiment, the process of the terminal determining the available server set can also be: the terminal can filter the location information with an empty resource domain (which can be recorded as the first location information) from the multiple location information contained in the target computer room resources. In this way, the terminal can filter the location information in the first location information whose resource subdomain is consistent with the resource subdomain in the grouping feature information to form an available server set.

[0098] Optionally, after the terminal filters the first location information and obtains location information whose resource subdomain is consistent with the resource subdomain in the grouping feature information, it can also filter based on the available capacity of each location information, and add the location information whose available capacity meets the preset capacity sufficiency condition to the set of available servers. The preset capacity sufficiency condition may be that the available capacity is greater than or equal to a preset capacity threshold. The preset capacity threshold may be determined based on an actual application scenario, and this disclosure does not limit this.

[0099] In another embodiment, the process of determining the available server set may be: the terminal may first screen the multiple location information contained in the target computer room resources, and extract the location information (which may be recorded as the second location information) whose resource domain information matches the resource domain in the grouping feature information from the multiple location information contained in the second location information. In this way, the terminal may screen the second location information, and extract the location information whose resource subdomain matches the resource subdomain in the grouping feature information from the multiple location information contained in the second location information, and determine the obtained one or more location information as the available server set.

[0100] In this way, the terminal filters the multiple location information contained in the first location information based on the resource subdomain in the group feature information, extracts the location information whose resource subdomain matches the resource subdomain in the group feature information, and uses the obtained one or more location information as the available server set.

[0101] Step 206 : Optimize the available server location set using the location allocation optimization function, target rules, and target power consumption data to obtain optimized location information of the target server.

[0102] In this implementation, the terminal can repeatedly screen the multiple location information contained in the filtered available server set. The specific screening process can be: the terminal can calculate the optimal solution of the location allocation optimization function based on the target rules and target power consumption data, as well as the information of the server corresponding to each location information contained in the available server location set, and use the location information corresponding to the optimal solution as the optimized location information of the target server.

[0103] In this embodiment, the location allocation optimization function is used to optimize the plurality of location information included in the available server set to obtain more suitable location information, thereby improving the efficiency of location allocation.

[0104] In one embodiment, Figure 3 As shown, the specific processing process of step 206 "optimizing the available server location set by using the location allocation optimization function, the target rule, and the target power consumption data of the target server to obtain the optimized location information of the target server" includes:

[0105] Step 302 : Calculate an initial position corresponding to an initial solution of a position allocation optimization function in a set of available server positions using target rules and target power consumption data of the target server.

[0106] In this embodiment, the terminal can calculate the location information that can minimize the function value of the location allocation optimization function based on the power data and space data of the cabinets corresponding to the multiple location information included in the available server set.

[0107] Step 304: When the initial position meets the preset position output condition, the initial position corresponding to the initial solution is determined as the optimized position information of the target server.

[0108] The preset position output condition may be that the number of iterations is greater than or equal to a preset iteration threshold, etc.

[0109] In this embodiment, the terminal can determine whether the obtained initial solution meets the preset location output conditions. If the preset location output conditions are met, the terminal can use the location information corresponding to the initial solution as the optimized location information of the target server. If the initial solution does not meet the preset location output conditions, the terminal can re-execute the steps of the above embodiment until the obtained initial solution meets the preset location output conditions.

[0110] In one example, the process of calculating the optimized location information by the terminal may be: the terminal may calculate the function value of the location allocation optimization function based on the data corresponding to the location information contained in the available server set, while satisfying the target rule, and obtain the arrangement order (which may be recorded as X) of the multiple location information contained in the available server set according to the ascending order of each function value (which may be recorded as z0). The terminal may filter z0 and extract the location information that can maximize the growth of z0, for example, extracting two location information that can maximize the growth of z0 (which may be recorded as R m1 、R m2 ), so that the terminal can R m1 、R m2 The machine scrambles and randomly recombines the data, and the function value under the new combination can be recorded as z1. When z1 meets the preset position output condition, the position information corresponding to z1 can be used as the optimized position information of the target server and output.

[0111] In this embodiment, position information with a high degree of adaptability can be obtained through optimization processing of the position allocation optimization function.

[0112] In one embodiment, Figure 4 As shown, the location allocation method further includes:

[0113] Step 402: Calculate a first optimization function based on the available power before the server is allocated and the available power after the server is allocated.

[0114] The available power before the server is allocated can be the available power data before the server corresponding to the location information included in the available server set (which can be recorded as P i1 ), the first optimization function may be an optimization function for performing an optimization process of the power optimization type.

[0115] In this embodiment, the terminal may calculate the first optimization function based on the available power before the server allocation and the available power after the server allocation. Specifically, the calculation process may be that the terminal may calculate the difference between the available power before the server allocation and the available power after the server allocation, and multiply the difference by the available power after the server allocation to obtain the first optimization function. For example, the function value corresponding to the first optimization function (which may be denoted as f1) may be calculated using the following formula:

[0116] f1=∑ i (P i1 -P i2 ) i2 .

[0117] Specifically, the first optimization function is to calculate the difference in electricity (power) before and after allocation to the server.

[0118] Step 404 : Calculate a second optimization function based on the available space data before the server allocation and the available space data after the server allocation.

[0119] The second optimization function may be a space optimization type function.

[0120] In this embodiment, the terminal may calculate a second optimization function based on the available space before the server allocation and the available space after the server allocation. Specifically, the terminal may calculate the difference between the available space before the server allocation and the available space after the server allocation, and multiply the difference by the available space after the server allocation to obtain the second optimization function. For example, the function value corresponding to the second optimization function (which may be denoted as f2) may be calculated using the following formula:

[0121]

[0122] Specifically, the second optimization function is to calculate the difference between the spatial data of the server before and after the server is allocated.

[0123] Step 406 : Calculate a third optimization function based on the available power before allocation, the available power after allocation, the available space data before allocation, the available space data after allocation, the total power data, and the total space data of the server.

[0124] The third optimization function may be a load balancing function, or an optimization function for balancing the loads of the servers.

[0125] In this embodiment, the terminal may calculate a function value corresponding to the third optimization function based on the server's available power before allocation, available power after allocation, available space data before allocation, available space data after allocation, total power data, and total space data. The terminal may calculate the division of the server's available power before allocation and the server's available space data before allocation, and similarly calculate the division of the available power after allocation and the available space data after allocation, as well as the division of the server's total power data and the total space data, and perform calculations based on the above-calculated divisions to obtain the function value of the third optimization function.

[0126] For example, the function value corresponding to the third optimization function (which can be recorded as f3) can be calculated by the following formula:

[0127]

[0128] Step 408 : Perform weighted calculation on the first optimization function, the second optimization function, and the third optimization function to obtain a position allocation optimization function.

[0129] In this embodiment, the terminal pole can determine the weight of the first optimization function, the weight of the second optimization function, and the weight of the third optimization function based on the constraints of the weights of each optimization function, and perform weighted calculation based on the weights of each optimization function to obtain the position allocation optimization function.

[0130] In this embodiment, the position allocation optimization function obtained by combining multiple types of optimization functions can consider the position allocation from an overall perspective to obtain the optimal allocation effect.

[0131] In one embodiment, the target computer room resource information includes identification information of each server, the resource domain to which it belongs, the resource subdomain to which it belongs, rated power data, used power information, total space data, and used space data.

[0132] In this embodiment, the identification information of the server can be the server numbering information, for example, the cabinet numbering information, 1F-01-02; the resource domain to which the server belongs can be the identification information of the resource domain to which the server belongs, for example, bd01; the rated power data can be the power threshold of the server, for example, 2200W; the used power information can be the power data that has been used by the server, for example, the server can be a power value of 1200W that has been used, the total space data can be the total number of spaces included, for example, 42, and the used space data can be the number of spaces that have been used in the server, for example, 8.

[0133]

[0134] In this embodiment, the comprehensiveness of the obtained optimized location information can be improved through the target computer room resource information.

[0135] In one embodiment, Figure 5 As shown, the location allocation method further includes:

[0136] Step 502: Acquire a plurality of power consumption data of various types of servers within a preset time range.

[0137] The preset time range may be one week or 24 hours, and may be determined based on actual application scenarios, which is not limited in this disclosure.

[0138] In this embodiment, for each of the multiple types of servers included in the target computer room resource information, the terminal can obtain multiple power consumption data of servers of this type within a preset time range. For example, it can obtain power consumption data of a target number of servers of this type within a preset time range.

[0139] Step 504 : Calculate average power consumption data based on the multiple power consumption data corresponding to each type of server, and obtain a corresponding relationship between the type and the average power consumption data.

[0140] In this embodiment, the terminal can calculate the average power consumption data of the target number of servers of the same type within a preset time range, and use the average value to determine the average power consumption data of the servers of the same type. In this way, the terminal can obtain a correspondence between the type and the average power consumption data based on the server types and the average power consumption data corresponding to each type of server, and store the correspondence between the type and the average power consumption data in a preset storage space.

[0141] In this embodiment, the speed of calculating the optimized location information can be increased by obtaining the average power consumption data of each type of server.

[0142] In one embodiment, the correspondence between the type and the average power consumption data is stored in a preset format, and the preset format includes at least one of a dictionary format, a Json (Javascript Object Notation) data format, and a CSV format.

[0143] In this embodiment, the terminal can store the correspondence between the type and the average power consumption data in a preset storage space in a preset format, for example, it can be stored in a dictionary format in the preset storage space, or the data can be stored in json format or csv format.

[0144] Specifically, the terminal can collect the power consumption data of the server within a preset time based on the out-of-band management API (Application Program Interface) configured by the server, collect three servers of each type and configuration, calculate the average power consumption, and use this data as the server's power consumption data. The various types of data are summarized to create a server power consumption data table, and the data is stored in dictionary form, json data format or csv file.

[0145] For example, the power consumption data of the server can be: {Super K620: {2*960G+12*3.84T+768G+20,000 Gigabit: 50W}}, where the server type can be "Super K620: {2*960G+12*3.84T+768G+20,000 Gigabit", and 50W is the calculated power consumption data (average power consumption data) of this server type.

[0146] In this embodiment, the correspondence between the server type and the average power consumption data calculated by collecting power consumption data within a preset time range can improve the accuracy of the power consumption data, and the convenience of data storage can be guaranteed by storing the multiple types of power consumption data in multiple formats.

[0147] The following describes in detail the specific implementation process of the location allocation method in the above embodiment with reference to a specific embodiment:

[0148] In the related art, the location allocation rule for scattered servers is that if there is available power in the cabinet (the cabinet has electricity), it can be put on the shelf. In other words, the location allocation rule is only a restriction on the power consumption data, that is, the power consumption data of the server. In addition, when the number of servers increases, the location allocation rule can also be a restriction on the number of network switch ports. With the widespread use of cloud computing, the cloud environment architecture divides server nodes according to the function of each node. With the emergence of different functional nodes, the services carried by each functional node in the cloud computing scenario will also be different, that is, each type of functional node is configured with a different disaster recovery mode.

[0149] In this way, since nodes of different functional types are configured with different disaster recovery modes, even in the same cloud environment, there may be differences in the demand for cloud resources. There may be situations where capacity reduction (providing machine resources) and capacity expansion (occupying machine resources) occur at the same time. The demand for server resources may also be provided in batches. Demand allocation is becoming increasingly complex, and the efficiency and accuracy of manual allocation will be greatly limited. Therefore, the present disclosure provides a location allocation method that can realize automated location allocation and ensure the adaptability of location allocation. In this application, there are multiple parameters, at least as follows: P i Indicates the rated power of cabinet i, U irepresents the total number of u in cabinet i, P i,1 Indicates the current available power of the cabinet, P i,2 Indicates the remaining power after cabinet allocation, C i Indicates the resource domain to which the cabinet (server) belongs. i Indicates the resource subdomain to which the cabinet (server) belongs, Z i Indicates the available resource domain of the cabinet (server). Cabinet refers to the server.

[0150] S1, obtain existing computer room resource information (which can be denoted as E) through the API: The terminal can obtain the power consumption data of each type of server within a preset time range through the out-of-band management API interface provided by the server. For example, the power consumption data of a target number of servers within a preset time range can be collected. The preset time range can be one week, and the target number can be three. In this way, for each type of server, the terminal can perform average processing based on the collected power consumption data and use the calculated average as the average power consumption data of that type. Based on the average power consumption data of each type, the terminal can combine and obtain the power consumption data table corresponding to each type of server, and can also store the power consumption data table in dictionary form, json data format, or csv file.

[0151] S2, obtaining a list of requirements. In one embodiment, the location allocation requirement data including multiple requirement sequences may be as shown in Table 1 below:

[0152] Table 1

[0153]

[0154] As shown in Table 2, for each demand sequence, the terminal can extract the resource domain, resource subdomain and corresponding available resource domain to which the required server resources belong. The terminal can import a demand list containing multiple demand sequences through the pandas module and convert the demand list into a DataFrame type. Based on this, the terminal can obtain the power consumption data corresponding to the target server from the power consumption data table based on the type information and configuration information of the target server, and obtain the grouping features based on the batch, node name, AZ, model and power consumption data of the target server. The terminal uses the DataFrame.groupby.count() function to perform summation to obtain a demand character pair, which can be "function-environment-power consumption, quantity", for example: bare metal RAC1-bd01BMS01-350W, 4.

[0155] S3, the terminal may traverse each character pair in the required character pairs, and for each character pair, the terminal may determine a corresponding target rule, ie, a position allocation rule, based on the functional characteristics of the target server corresponding to the character pair.

[0156] Specifically, different target server identification information and RAC type information may correspond to different location allocation rules. Multiple location batching rules may be as follows:

[0157] Rule 1: Machines of different RAC types cannot be in the same cabinet: Due to multi-active requirements, machines have RAC types, with RAC1 and RAC2 acting as both active and standby. If one machine becomes unavailable due to a cabinet power outage, the other machine must take over. Considering the extreme case where both RAC1 and RAC2 become unavailable simultaneously, a standby machine is required to take over the service. Therefore, the standby machine should also be treated as a RAC type. In other words, target servers corresponding to different RAC types are assigned to different cabinets to ensure active and standby availability of the servers.

[0158] Rule 2: The same group of storage nodes must be divided into four cabinets. A three-replica architecture is used for storage nodes. For disaster recovery at the cabinet level, a group of storage nodes must be divided into four cabinets. Due to storage node architecture requirements, capacity expansion is generally done in multiples of four, so four nodes can be grouped together.

[0159] Rule 3: The number of network function nodes in a single cabinet must be less than two. These network function nodes include bare metal gateways, network service nodes, and network management nodes. A gateway manages seven bare metal servers. To account for extreme power outages in a single cabinet, a cabinet can accommodate no more than two gateway servers.

[0160] Rule 4: Bare metal nodes must be placed in the same cabinet as the gateway. This can reduce cabling costs and improve troubleshooting efficiency.

[0161] In summary, the terminal can determine the target rule corresponding to the character pair based on the functional features in the character pair, and filter the available cabinet subset from the target computer room resource data according to the environmental keywords, that is, obtain the available server set.

[0162] For example, when the character pair is "bare metal RAC1-bd01BMS01-350W,4", the terminal can filter the resource domain and resource subdomain corresponding to the target server after the target computer room resource data, and find a cabinet list whose resource domain is bd01 or is empty and whose resource subdomain is bms01 and has sufficient capacity as the available server set.

[0163] S4, calculating the optimized position information according to the allocation rule and the optimization function.

[0164] Specifically, the terminal can consider specific needs based on actual conditions. For example, is it electricity or space that limits the installed capacity of a computer room? What factor causes the installed capacity of the computer room to fail to reach the theoretical upper limit? Is it due to excessive scattered electricity or excessive scattered space? Therefore, three evaluation functions are proposed:

[0165] The terminal can calculate the first optimization function (first evaluation function) by the following formula:

[0166] f1=∑ i (P i1 -P i2 ) i2 ,

[0167] Among them, the first optimization function considers the difference in power before and after allocation. The optimal solution is that when one allocation is made, the power of this cabinet is not used or the power of this cabinet is fully used.

[0168] The terminal can calculate the second optimization function (second evaluation function) by the following formula:

[0169]

[0170] The second optimization function considers the difference between the space before and after allocation. The optimal situation is that during one allocation, the cabinet is not used or the space of the cabinet is fully used.

[0171] The terminal can calculate the third optimization function (third evaluation function) by the following formula:

[0172]

[0173] The third optimization function considers the difference between the load of the cabinet when in use and the average load of the cabinet before and after allocation. It can consider the environment globally, making the load of each cabinet relatively even, thereby reducing cooling-related costs.

[0174] In one example, the terminal may weight the first optimization function, the second optimization function, and the third optimization function using a target weight function to obtain a position allocation optimization function. For example, the target weight function may be as follows:

[0175]

[0176] Among them, w i is the weight corresponding to the i-th optimization function, w1 is the weight corresponding to the first optimization function, w2 is the weight corresponding to the second optimization function, and w3 is the weight corresponding to the third optimization function.

[0177] For example, the objective weight function can be as follows:

[0178]

[0179] or,

[0180]

[0181] Among them, P i1 : The available power (available power) of the i-th cabinet before allocation;

[0182] P i2 : The available power after allocation to the i-th cabinet;

[0183] P i : Total power of the i-th cabinet;

[0184] U i1 : Available space after allocation of the i-th cabinet;

[0185] U i2 : Available space after allocation of the i-th cabinet;

[0186] U i : The total space of the i-th cabinet.

[0187] Step 4.1: Based on the multiple location information contained in the available server set and the target rules corresponding to each node, we can try to place the nodes into the cabinet one by one in a way that minimizes the growth of z, and obtain a set of initial solutions, denoted as X, whose optimization function value is z0.

[0188] Step 4.2: Select two cabinets from z0 that maximize the growth of z0, denoted as R m1 、R m2 .

[0189] Step 4.3, R m1 、R m2 The machine scrambles and randomly reorganizes the data, and the result of the new combination is recorded as z1.

[0190] Step 4.4, if z1 < z0, then re-execute Step 4.2, otherwise, re-execute Step 4.3 until the preset position output condition is met. The preset position output condition can be executed 5 times under the same optimization value, then execute Step 4.5.

[0191] Step 4.5, output the location information allocation result (which can be recorded as res). The location information allocation result can be the node information stored in each cabinet, such as {1F-01-02: [sn1, sn2]}, that is, the optimized location information corresponding to demand sequence 1 is 1F-01-02, and the optimized location information corresponding to demand sequence 2 is 1F-01-02.

[0192] S5, traversing the location information allocation results to obtain optimized location information corresponding to the multiple demand sequences included in the location allocation demand data, and storing the cabinet information in the blank location information column in the demand table.

[0193] In one embodiment, the present disclosure provides a location allocation system that automatically acquires computer room resource information, automatically classifies servers by brand, configuration, and function based on installation requirements, calculates power consumption, and ultimately implements location allocation, thereby obtaining optimized location information corresponding to each demand sequence. The location allocation system primarily includes the following modules:

[0194] The computer room equipment resource information acquisition module is used to interact with various API interfaces and obtain the environmental data required by the system from the interfaces.

[0195] A demand conversion module is constructed to execute S2 and S3 in the above embodiment and transfer the processed data to the device allocation module.

[0196] The equipment allocation module is used to execute S4 and S5 in the above embodiment, realize automatic allocation of equipment, and write the result into the demand sheet.

[0197] The allocation result export module is used to execute S1, and is used to provide the import function of demand orders, the query function of historical scenarios, and the export function of various file formats of the results.

[0198] Optionally, cloud environment disaster recovery rules may include: 1. Storage nodes: a three-replica architecture, requiring the master node and replica to belong to different cabinets; 2. Management nodes and network nodes: no more than two per cabinet; 3. Different AZ machines should belong to different cabinets, that is, only machines in the same resource domain and the same AZ should be placed in the same cabinet; 4. Bare metal gateways: no more than two per cabinet.

[0199] The present disclosure provides a method for resource optimization based on cloud rule machine location allocation, which can quickly complete the automatic allocation of large-scale resource shelf locations, improve rapid operation and maintenance efficiency and capacity expansion efficiency, and generally improve cabinet resource utilization, realize the mining of computer room resources, save computer room resources, and improve utilization.

[0200] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0201] Based on the same inventive concept, embodiments of the present application also provide a position allocation device for implementing the aforementioned position allocation method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more embodiments of the position allocation device provided below can be found in the above-described limitations on the position allocation method and will not be further elaborated here.

[0202] In one embodiment, Figure 6 As shown, a position allocation device 600 is provided, comprising:

[0203] A first acquisition module 602 is configured to acquire location allocation requirement data, the location allocation requirement data including at least one requirement sequence, the requirement sequence including the required resource domain, resource subdomain, identification information, and type information of the target server;

[0204] A search module 604 is configured to search for target power consumption data corresponding to the type information of the target server in a pre-configured correspondence between the type and the average power consumption data;

[0205] A first determining module 606 is configured to determine grouping characteristic information of the target server based on the resource domain, resource subdomain, target power consumption data, and identification information of the target server;

[0206] The optimization processing module 608 is used to optimize the multiple location information included in the target computer room resource information based on the grouping feature information and the location allocation optimization function to obtain the optimized location information of the target server.

[0207] In one embodiment, the optimization processing module is specifically configured to:

[0208] Determining functional characteristics of the target server based on the resource subdomain and identification information included in the grouping characteristic information, and determining target rules corresponding to the functional characteristics;

[0209] Based on the resource domain and resource subdomain included in the grouping feature information, determining an available server location set from the multiple location information included in the target computer room resource information;

[0210] The available server location set is optimized by using a location allocation optimization function, the target rule, and the target power consumption data of the target server to obtain optimized location information of the target server.

[0211] In one embodiment, the optimization processing module is further configured to:

[0212] Calculating an initial position corresponding to an initial solution of the position allocation optimization function in the set of available server positions by using the target rule and the target power consumption data of the target server;

[0213] In a case where the initial position meets a preset position output condition, the initial position corresponding to the initial solution is determined as the optimized position information of the target server.

[0214] In one embodiment, the apparatus further comprises:

[0215] A first calculation module, configured to calculate a first optimization function based on the available power of the server before allocation and the available power of the server after allocation;

[0216] a second calculation module, configured to calculate a second optimization function based on the available space data before the server allocation and the available space data after the server allocation;

[0217] a third calculation module, configured to calculate a third optimization function based on the available power before allocation, the available power after allocation, the available space data before allocation, the available space data after allocation, the total power data, and the total space data of the server;

[0218] The fourth calculation module is used to perform weighted calculation on the first optimization function, the second optimization function and the third optimization function to obtain a position allocation optimization function.

[0219] In one embodiment, the position allocation device further includes:

[0220] The second acquisition module is used to obtain a plurality of power consumption data of various types of servers within a preset time range;

[0221] The fifth calculation module is used to calculate average power consumption data based on the multiple power consumption data corresponding to each type of server, and obtain a corresponding relationship between the type and the average power consumption data.

[0222] Each module in the above-mentioned position allocation device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0223] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data related to server resources. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a location allocation method.

[0224] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0225] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0226] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0227] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0228] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0229] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0230] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0231] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A location allocation method, characterized in that: The method comprises: Acquire location allocation requirement data, wherein the location allocation requirement data includes at least a requirement sequence, and the requirement sequence includes a required resource domain, resource subdomain, identification information, and type information of a target server; Searching for target power consumption data corresponding to the type information of the target server in a pre-configured correspondence between the type and the average power consumption data; Determining group characteristic information of the target server based on the resource domain, resource subdomain, target power consumption data, and identification information of the target server; Based on the grouping feature information and the location allocation optimization function, multiple location information included in the target computer room resource information is optimized to obtain the optimized location information of the target server.

2. The method according to claim 1, characterized in that The optimizing process of the plurality of location information included in the target computer room resource information based on the grouping feature information and the location allocation optimization function to obtain the optimized location information of the target server includes: Determining functional characteristics of the target server based on the resource subdomain and identification information included in the grouping characteristic information, and determining target rules corresponding to the functional characteristics; Based on the resource domain and resource subdomain included in the grouping feature information, determining an available server location set from the multiple location information included in the target computer room resource information; The available server location set is optimized by using a location allocation optimization function, the target rule, and the target power consumption data to obtain optimized location information of the target server.

3. The method according to claim 2, characterized in that The optimizing process of the available server location set by using the location allocation optimization function, the target rule, and the target power consumption data of the target server to obtain the optimized location information of the target server includes: Calculating an initial position corresponding to an initial solution of the position allocation optimization function in the set of available server positions by using the target rule and the target power consumption data of the target server; In a case where the initial position meets a preset position output condition, the initial position corresponding to the initial solution is determined as the optimized position information of the target server.

4. The method according to claim 3, characterized in that The method further comprises: Calculating a first optimization function based on available power of the server before allocation and available power of the server after allocation; calculating a second optimization function based on the available space data before the server is allocated and the available space data after the server is allocated; Calculating a third optimization function based on the available power before allocation, the available power after allocation, the available space data before allocation, the available space data after allocation, the total power data, and the total space data of the server; A weighted calculation is performed on the first optimization function, the second optimization function, and the third optimization function to obtain a position allocation optimization function.

5. The method according to claim 1 or 2, characterized in that The target computer room resource information includes identification information of each server, the resource domain to which it belongs, the resource subdomain to which it belongs, rated power data, used power information, total space data, and used space data.

6. The method according to any one of claims 1 to 4, characterized in that Also includes: Obtain multiple power consumption data of various types of servers within a preset time range; Based on the multiple power consumption data corresponding to each type of server, average power consumption data is calculated to obtain a corresponding relationship between the type and the average power consumption data.

7. The method according to any one of claims 1 to 4, characterized in that Also includes: The correspondence between the type and the average power consumption data is stored in a preset format, where the preset format includes at least one of a dictionary format and a json data format.

8. A position allocation device, characterized in that: The device comprises: A first acquisition module is configured to acquire location allocation requirement data, wherein the location allocation requirement data includes at least one requirement sequence, and the requirement sequence includes a required resource domain, resource subdomain, identification information, and type information of a target server; A search module, configured to search for target power consumption data corresponding to the type information of the target server in a pre-configured correspondence between types and average power consumption data; A first determining module is configured to determine grouping characteristic information of the target server based on the resource domain, resource subdomain, target power consumption data, and identification information of the target server; The optimization processing module is used to optimize the multiple location information included in the target computer room resource information based on the grouping feature information and the location allocation optimization function to obtain the optimized location information of the target server.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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