Power business matching method, electronic device and storage medium

Abstract

The present invention provides a power service matching method, electronic device and storage medium, firstly obtaining a target network slice set and a power service set; then determining the scenario requirement information, service decision method and service acquisition frequency of each power service according to the service type of each power service in the power service set; thereby determining the matching degree of each power service with each target network slice in the target network slice set; finally matching each power service with each target network slice in the target network slice set according to the matching degree of each power service with each target network slice. By considering the characteristics of the power service in three aspects, namely, scenario requirement information, service decision method and service acquisition frequency, the matching degree is calculated to evaluate whether the performance required by the power service matches the actual performance of the network slice, so that the power service can be transmitted in the optimal network scenario, effectively improving the transmission effect of the power service.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a power service matching method, electronic equipment and storage medium. Background Art

[0002] 5G technology has promoted the development of power business. There are many types of business in the power system, and different businesses have different requirements for the network. Network slicing, as one of the key technologies of 5G, divides the resources and functions of the actual network to form a virtual network for different application scenarios.

[0003] In the existing technology, services and slices are usually matched in a conventional way. However, services in different fields have different requirements for network bandwidth, latency, connectivity, mobility, security, reliability, etc. Therefore, the accuracy of conventional service matching methods is low, which affects the transmission of power services. Summary of the invention

[0004] In view of this, the present invention provides a power service matching method, an electronic device and a storage medium, aiming to solve the problem that the conventional service matching method in the prior art has low accuracy and affects the transmission of power services.

[0005] A first aspect of an embodiment of the present invention provides a power service matching method, including:

[0006] Obtain the target network slice set and power service set;

[0007] According to the business type of each power business in the power business collection, the scenario demand information, business decision method and business acquisition frequency of each power business are determined;

[0008] Determine the matching degree between each power service and each target network slice in the target network slice set based on the scenario demand information, service decision method and service acquisition frequency of each power service;

[0009] According to the matching degree between each power service and each target network slice, each power service is matched with each target network slice in the target network slice set.

[0010] A second aspect of an embodiment of the present invention provides a power service matching device, including:

[0011] An acquisition module, used to acquire a target network slice set and a power service set;

[0012] A determination module, used to determine the scenario demand information, business decision method and business acquisition frequency of each power business according to the business type of each power business in the power business set;

[0013] A calculation module is used to determine the matching degree of each power service with each target network slice in the target network slice set according to the scenario demand information, service decision method and service acquisition frequency of each power service;

[0014] The matching module is used to match each power service with each target network slice in the target network slice set according to the matching degree between each power service and each target network slice.

[0015] A third aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the power business matching method of the first aspect above are implemented.

[0016] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the power business matching method of the first aspect above are implemented.

[0017] An electric power service matching method, electronic device and storage medium provided by an embodiment of the present invention first obtains a target network slice set and an electric power service set; then, according to the service type of each electric power service in the electric power service set, determines the scenario requirement information, service decision method and service acquisition frequency of each electric power service; then, according to the scenario requirement information, service decision method and service acquisition frequency of each electric power service, determines the matching degree of each electric power service with each target network slice in the target network slice set; finally, according to the matching degree of each electric power service with each target network slice, matches each electric power service with each target network slice in the target network slice set. By considering the characteristics of the electric power service in three aspects, namely, scenario requirement information, service decision method and service acquisition frequency, the matching degree is calculated to evaluate whether the required performance of the electric power service matches the actual performance of the network slice, so that the electric power service can be transmitted in the optimal network scenario, effectively improving the transmission effect of the electric power service. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 This is an application scenario diagram of the power business matching method provided by an embodiment of the present invention;

[0020] Figure 2is a flow chart of an implementation method of a power business matching method provided by an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of the structure of a power service matching device provided by an embodiment of the present invention;

[0022] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0024] Figure 1 : is an application scenario diagram of the power business matching method provided by an embodiment of the present invention. Figure 1 As shown, in some embodiments, the power business matching method provided in the embodiment of the present invention can be applied to, but not limited to, this application scenario. In this embodiment of the invention, the system includes: a collection terminal 11 and an electronic device 12.

[0025] The acquisition terminal 11 may be an inspection robot, a smart meter, a control system, a video surveillance system, etc., which are not limited here. The electronic device 12 may be a terminal or a server, the terminal may be a mobile phone, a base station terminal, etc., which are not limited here, and the server may be a physical server, a cloud server, etc., which are not limited here.

[0026] After collecting the power data, the collection terminal 11 reports it to the electronic device 12. The electronic device 12 matches the power service to the corresponding slice network based on the scenario demand information, service decision method and service acquisition frequency, so that the power service can be transmitted quickly in the 5G scenario.

[0027] Figure 2 FIG. 1 is a flow chart of the implementation of the power business matching method provided by an embodiment of the present invention. Figure 2 As shown, in some embodiments, the power business matching method is applied to Figure 1 The electronic device 12 in the method comprises:

[0028] S210, obtain the target network slice set and power service set.

[0029] In the embodiment of the present invention, the three major application scenarios of 5G network include:

[0030] Enhanced Mobile Broadband (eMBB) is used to improve the overall user experience, that is, the network coverage is wider, the speed is faster, and the user capacity is more.

[0031] Massive Machine Type Communication (mMTC) is an application scenario of the Internet of Things. It focuses on the information interaction between people and objects. It mainly relies on the large capacity of 5G to support massive terminal access, thereby realizing smart cities, smart factories, and smart homes.

[0032] Ultra Reliable Low Latency Communication (uRLLC) belongs to the application scenario of the Internet of Things. It focuses on the communication needs between things and mainly relies on the low latency, high reliability and high availability of 5G to realize vehicle networking, autonomous driving, telemedicine, etc.

[0033] In actual applications, 5G networks are further sliced ​​in the above three scenarios to classify and transmit power services in a more refined manner. The basis for slicing includes but is not limited to at least one of the following: communication latency, reliability, broadband performance, accessible terminal level, and service isolation performance.

[0034] The power business includes control business and collection business. The control business includes differential protection, distribution automation, precise load control, etc., and the collection business includes electricity consumption information collection, advanced metering, etc.

[0035] S220, determining scenario demand information, a service decision method, and a service acquisition frequency of each power service according to the service type of each power service in the power service set.

[0036] In the embodiment of the present invention, the scenario requirement information includes but is not limited to at least one of the following: communication delay, reliability requirement, bandwidth requirement, terminal level requirement, service isolation requirement, and service priority. The service decision mode includes local decision, remote decision, and joint decision. The service acquisition frequency is the average acquisition speed of this type of power service within a preset period of time.

[0037] Different power services have different characteristics, and their scenario demand information, service decision methods, and service acquisition frequencies are all different. For example, for the drone inspection service in the power grid, its communication delay is medium, the reliability requirement is high, the bandwidth requirement is high, the terminal level requirement is low, the service isolation requirement is low, and the service priority is medium. The service decision method is joint decision, and the service acquisition frequency is low over a long period of time (every day).

[0038] Among them, the above-mentioned low, medium and high are just simple descriptions by way of example. When performing specific calculations, the above-mentioned items are all accurate values ​​to accurately reflect the actual performance requirements of various power businesses.

[0039] S230, determine the matching degree of each power service with each target network slice in the target network slice set according to the scenario demand information, service decision method and service acquisition frequency of each power service.

[0040] In an embodiment of the present invention, the calculation of the matching degree of the target network slice is mainly used to reflect whether the performance of the slice network meets the actual performance requirements of the power business.

[0041] S240, according to the matching degree between each power service and each target network slice, each power service is matched with each target network slice in the target network slice set.

[0042] In an embodiment of the present invention, the characteristics of the power service are considered in three aspects: scenario demand information, service decision method and service acquisition frequency, so as to calculate the matching degree to evaluate whether the performance required by the power service matches the actual performance of the network slice, so that the power service can be transmitted in the optimal network scenario, effectively improving the transmission effect of the power service.

[0043] In some embodiments, the scenario requirement information includes indicator requirements for multiple scenario indicators; each target network slice is marked with standard values ​​of multiple scenario indicators; accordingly, S230 may include: according to the service decision method and the service acquisition frequency, querying multiple first target network slices corresponding to each power business from a pre-set target network slice adaptation table; for each first target network slice corresponding to the power business, calculating the Euclidean distance between the indicator requirements and the standard values ​​of each scenario indicator of the power business; for each first target network slice corresponding to the power business, multiplying the calculated Euclidean distance by the preset weights of each scenario indicator to obtain the matching degree between the power business and each first target network slice.

[0044] In the embodiment of the present invention, scenario demand information, service decision method and service acquisition frequency will affect the target network slice matched by the power service, but the content of the service decision method and service acquisition frequency is relatively simple, so they are first screened to select the first target network slice that is relatively more matched with the power service, and then further screened.

[0045] Among them, the target network slice adaptation table can be obtained in the grid access test before the 5G network is put into use in the grid. The scenario indicators depend on the division basis of the target network slice. If the target network slice is divided according to communication delay and reliability, the scenario indicators also set the communication delay indicator and reliability indicator accordingly. The preset weights of each scenario indicator can be determined by expert scoring, or an initial value can be designed first. During the transmission of power services, the difference between the actual transmission rate and the expected transmission rate is minimized, and it is optimized by the particle swarm optimization algorithm, which is not limited here.

[0046] In some embodiments, S230 may include: inputting scenario demand information, service decision method and service acquisition frequency into a pre-established neural network to obtain the matching degree of each power service with each target network slice.

[0047] In an embodiment of the present invention, the neural network may be a BP neural network or a convolutional neural network. When training the neural network, 20 data access processes of power services may be selected in each power scenario, and the scenario demand information, service decision method, and service acquisition frequency of the data when accessing the 5G network are recorded. After the access is completed, the scenario indicators such as the communication delay and reliability of the data are recorded. The neural network is trained with the scenario demand information, service decision method, and service acquisition frequency as input and the scenario indicators as output.

[0048] In some embodiments, S240 may include: matching each power service with each target network slice in the target network slice set according to the matching degree of each power service with each target network slice and the matching game algorithm.

[0049] In an embodiment of the present invention, the matching game algorithm can be specifically a matching game bilateral selection algorithm, in which the electric power service selects the target network slice with the highest matching degree. The target network slice is sorted according to the matching degree with each electric power service based on the selection of the electric power service, and the matching request of the electric power service with the highest matching degree among the selectable electric power services is accepted. Then the matched electric power services and network slices are removed (the z network slice is removed only when the matching electric power services reach the maximum number), and the remaining electric power services re-select the remaining target network slices according to the matching degree. At the same time, the remaining target network slices are still sorted according to the matching degree with each electric power service to select the electric power service with the highest matching degree, and then the remaining electric power services are rejected. The above process is executed repeatedly until all electric power services and target network slices are matched, and the algorithm ends.

[0050] In some embodiments, S240 may include: determining the second target network slice and the third target network slice for each power service based on the matching degree of each power service with each target network slice and the matching game algorithm; wherein the second target network slice and the third target network slice are the two target network slices with the highest matching degree with the power service in the target network slice set; and matching the power service with the second target network slice and the third target network slice.

[0051] In the embodiment of the present invention, due to the particularity of some power services, it is required to be processed on two different slice networks. For example, some power services need to be processed in a target network slice in the eMBB scenario and a target network slice in the URLLC scenario at the same time. Therefore, the two target network slices with the highest matching degree can be selected to transmit the power services.

[0052] In some embodiments, after matching the power service with the second target network slice and the third target network slice, the method also includes: calculating multiple collection intervals for each data in the power service; calculating the collection stability parameter of each data according to the multiple collection intervals; dividing the power service into first category data and second category data according to the collection stability parameters of each data; transmitting the first category data on the second target network slice, and transmitting the second category data on the third target network slice; wherein the collection stability parameter of the first category data is greater than the collection stability parameter of the second category data.

[0053] In the embodiment of the present invention, for data with uniform collection intervals, the calculated collection stability parameter is small, indicating that the power data is stably and continuously transmitted, so it is placed on the third target network slice with a relatively poor matching degree. For data with uneven collection intervals, the calculated collection stability parameter is large, indicating that the power data is temporary data with strong time-varying characteristics, so it is placed on the second target network slice with a relatively high matching degree to quickly transmit and process it.

[0054] In some embodiments, the acquisition stability parameter of each data item is calculated according to a plurality of acquisition intervals, including:

[0055]

[0056] Among them, x j is the acquisition stability parameter of the jth data, t i is the i-th acquisition interval, σ j (t i ) is the sampling interval variance of the jth data, is the mean of the variance of the collection intervals of each data item.

[0057] In the embodiment of the present invention, the acquisition stability parameter is used to reflect the stability of the power service during the data transmission process.

[0058] In some embodiments, after matching each power service with each target network slice in the target network slice set, the method also includes: allocating computing resources to each target network slice according to a dynamic resource optimization allocation algorithm.

[0059] In the embodiment of the present invention, the dynamic resource optimization allocation algorithm can keep the network resource utilization rate within a reasonable range while allocating more bandwidth to the slice. Compared with the traditional resource optimization allocation algorithm, the dynamic resource optimization allocation algorithm introduces the idea of ​​admission control, so that the controller adjusts the resources only when the network is running in the Congested and Wasted states, thereby improving the efficiency of network resource scheduling. In the selection of admission control strategy, the use of artificial swarm intelligence algorithm and the setting of protection domain parameters can enable the slice to strictly control the network state within the Feasible range.

[0060] The beneficial effects of the present invention are specifically as follows:

[0061] By considering the characteristics of the power service in three aspects: scenario demand information, service decision method and service acquisition frequency, the matching degree is calculated to evaluate whether the performance required by the power service matches the actual performance of the network slice, so that the power service can be transmitted in the optimal network scenario, effectively improving the transmission effect of the power service.

[0062] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0063] Figure 3 Schematic diagram of the structure of the power business matching device provided by the embodiment of the present invention. Figure 3 As shown, in some embodiments, the power business matching device 3 includes:

[0064] The acquisition module 310 is used to obtain the target network slice set and power service set.

[0065] The determination module 320 is used to determine the scenario requirement information, service decision method and service acquisition frequency of each power service according to the service type of each power service in the power service set.

[0066] The calculation module 330 is used to determine the matching degree of each power service with each target network slice in the target network slice set according to the scenario demand information, service decision method and service acquisition frequency of each power service.

[0067] The matching module 340 is used to match each power service with each target network slice in the target network slice set according to the matching degree between each power service and each target network slice.

[0068] Optionally, the scenario requirement information includes indicator requirements for multiple scenario indicators; each target network slice is marked with standard values ​​of multiple scenario indicators; accordingly, the calculation module 330 is specifically used to query multiple first target network slices corresponding to each power business from a preset target network slice adaptation table according to the business decision method and the business acquisition frequency; for each first target network slice corresponding to the power business, the Euclidean distance between the indicator requirements and the standard values ​​of each scenario indicator of the power business is calculated; for each first target network slice corresponding to the power business, the calculated Euclidean distance is multiplied by the preset weights of each scenario indicator to obtain the matching degree between the power business and each first target network slice.

[0069] Optionally, the calculation module 330 is specifically used to input scenario demand information, service decision method and service acquisition frequency into a pre-established neural network to obtain the matching degree of each power service with each target network slice.

[0070] Optionally, the matching module 340 is specifically used to match each power service with each target network slice in the target network slice set according to the matching degree of each power service with each target network slice and the matching game algorithm.

[0071] Optionally, the matching module 340 is specifically used to determine the second target network slice and the third target network slice of each power service according to the matching degree of each power service with each target network slice and the matching game algorithm; wherein the second target network slice and the third target network slice are the two target network slices with the highest matching degree with the power service in the target network slice set; the power service is matched with the second target network slice and the third target network slice; wherein the collection stability parameter of the first category of data is greater than the collection stability parameter of the second category of data.

[0072] Optionally, the power service matching device also includes: a service division module, used to calculate multiple collection intervals for each data in the power service; calculate the collection stability parameters of each data according to the multiple collection intervals; divide the power service into first category data and second category data according to the collection stability parameters of each data; transmit the first category data on the second target network slice, and transmit the second category data on the third target network slice.

[0073] Optional, service division module, used to:

[0074]

[0075] Among them, x j is the acquisition stability parameter of the jth data, t i is the i-th acquisition interval, σ j (t i ) is the sampling interval variance of the jth data, is the mean of the variance of the collection intervals of each data item.

[0076] Optionally, the power business matching device also includes: a resource allocation module, used to allocate computing resources to each target network slice according to a dynamic resource optimization allocation algorithm.

[0077] The power service matching device provided in this embodiment can be used to execute the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be repeated here.

[0078] Figure 4 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an electronic device 4 provided in one embodiment of the present invention includes: a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. When the processor 40 executes the computer program 42, the steps in the above-mentioned various power business matching method embodiments are implemented, such as Figure 2 Alternatively, when the processor 40 executes the computer program 42, the functions of each module / unit in the above-mentioned system embodiments are implemented, for example Figure 3 The functions of modules 310 to 340 are shown.

[0079] Exemplarily, the computer program 42 may be divided into one or more modules / units, one or more modules / units are stored in the memory 41, and are executed by the processor 40 to implement the present invention. One or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 42 in the electronic device 4.

[0080] The electronic device 4 may be a terminal or a server. The terminal may be a mobile phone, MCU, ECU, industrial computer, etc., which are not limited here. The server may be a physical server, a cloud server, etc., which are not limited here. The electronic device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0081] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0082] The memory 41 may be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 41 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 is used to store computer programs and other programs and data required by the electronic device. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0083] An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned power business matching method embodiment are implemented.

[0084] The computer readable storage medium stores a computer program 42, which includes program instructions. When the program instructions are executed by the processor 40, all or part of the processes in the above-mentioned embodiment method are implemented. The computer program 42 can also be used to instruct related hardware to complete. The computer program 42 can be stored in a computer readable storage medium. When the computer program 42 is executed by the processor 40, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program 42 includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0085] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0086] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0087] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0088] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0090] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0093] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0094] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A power business matching method, characterized in that: include: Obtain the target network slice set and power service set; Determine, according to the service type of each power service in the power service set, the scenario demand information, service decision method and service acquisition frequency of each power service; Determine the matching degree between each power service and each target network slice in the target network slice set based on the scenario demand information, service decision method and service acquisition frequency of each power service; According to the matching degree between each power service and each target network slice, each power service is matched with each target network slice in the target network slice set; The scenario requirement information includes the indicator requirements of multiple scenario indicators; each target network slice is marked with the standard values ​​of multiple scenario indicators; According to the scenario demand information, service decision method and service acquisition frequency of each power service, the matching degree between each power service and each target network slice is determined, including: According to the service decision mode and the service acquisition frequency, query multiple first target network slices corresponding to each power service from a preset target network slice adaptation table; For each first target network slice corresponding to the electric power service, calculating the Euclidean distance between the indicator requirements of each scenario indicator of the electric power service and the standard value; For each first target network slice corresponding to the power service, the calculated Euclidean distance is multiplied by the preset weights of each scenario indicator to obtain the matching degree between the power service and each first target network slice.

2. The power business matching method according to claim 1, characterized in that: According to the scenario demand information, service decision method and service acquisition frequency of each power service, the matching degree between each power service and each target network slice is determined, including: The scenario demand information, service decision method and service acquisition frequency are input into the pre-established neural network to obtain the matching degree between each power service and each target network slice.

3. The power business matching method according to claim 1, characterized in that: According to the matching degree between each power service and each target network slice, each power service is matched with each target network slice in the target network slice set, including: According to the matching degree of each power service with each target network slice and the matching game algorithm, each power service is matched with each target network slice in the target network slice set.

4. The power business matching method according to claim 1, characterized in that: According to the matching degree between each power service and each target network slice, each power service is matched with each target network slice in the target network slice set, including: According to the matching degree of each power service with each target network slice and the matching game algorithm, determine the second target network slice and the third target network slice of each power service; wherein the second target network slice and the third target network slice are the two target network slices with the highest matching degree with the power service in the target network slice set; Match the power service with the second target network slice and the third target network slice.

5. The power business matching method according to claim 4, characterized in that: After matching the power service with the second target network slice and the third target network slice, the method further includes: Calculating multiple collection intervals for each data item in the power business; Calculating a collection stability parameter of each item of data according to the multiple collection intervals; According to the collection stability parameters of each data, the power business is divided into the first category of data and the second category of data; The first category data is transmitted on the second target network slice, and the second category data is transmitted on the third target network slice; wherein the collection stability parameter of the first category data is greater than the collection stability parameter of the second category data.

6. The power business matching method according to claim 5, characterized in that: The step of calculating the acquisition stability parameter of each item of data according to the plurality of acquisition intervals comprises: Among them, x j is the acquisition stability parameter of the jth data, t i is the i-th acquisition interval, σ j (t i ) is the sampling interval variance of the jth data, is the mean of the variance of the collection intervals of each data item.

7. The power business matching method according to any one of claims 1 to 6, characterized in that: After matching each power service with each target network slice in the target network slice set, the method further includes: According to the dynamic resource optimization allocation algorithm, the computing resources of each target network slice are allocated.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the power business matching method as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the power business matching method as described in any one of claims 1 to 7 are implemented.

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