Wireless network energy consumption evaluation method and device, electronic equipment and storage medium

Abstract

The application discloses a wireless network energy consumption evaluation method and device, electronic equipment and storage medium, belongs to the technical field of wireless communication, and aims to solve the problems of related wireless network energy consumption evaluation technology and poor accuracy of wireless network energy consumption evaluation. The method comprises the following steps: acquiring energy consumption data and a plurality of network data indexes of a wireless base station in a wireless network to be evaluated; determining an association degree coefficient of the energy consumption data and the plurality of network data indexes based on the energy consumption data and the network data indexes; acquiring a network index value parameter, and determining a value weight of the plurality of network data indexes based on the network index value parameter and the network data indexes; and determining an energy consumption evaluation result of the wireless network based on the association degree coefficient and the value weight.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, specifically relating to a wireless network energy consumption assessment method, apparatus, electronic device, and storage medium. Background Technology

[0002] In the field of wireless communication, with the rapid development of 5G network technology, the high energy consumption problem has become increasingly prominent due to the increased bandwidth, number of channels, and more complex signal processing requirements. Current research on wireless network energy consumption mainly focuses on wireless energy-saving technologies, achieving energy savings by reducing hardware power consumption or shutting down network resources. However, there is currently no unified standard in the industry for energy consumption assessment.

[0003] Relevant wireless network energy consumption assessment technologies generally evaluate wireless network energy consumption by measuring the network traffic that can be contributed per unit of network energy consumption. However, assessing wireless network energy consumption solely based on the network traffic contributed per unit of network energy consumption is not accurate enough, leading to unreasonable subsequent wireless energy-saving efforts and wireless network planning and construction.

[0004] In other words, the relevant wireless network energy consumption assessment technologies suffer from poor accuracy in assessing wireless network energy consumption. Summary of the Invention

[0005] This application provides a wireless network energy consumption assessment method, apparatus, electronic device, and storage medium, which can solve the problem of poor accuracy in related wireless network energy consumption assessment technologies.

[0006] In a first aspect, embodiments of this application provide a method for assessing the energy consumption of a wireless network. The method includes: acquiring energy consumption data of a wireless base station in a wireless network to be assessed and multiple network data indicators; determining a correlation coefficient between the energy consumption data and the multiple network data indicators based on the energy consumption data and the network data indicators; acquiring network indicator value parameters and determining value weights of the multiple network data indicators based on the network indicator value parameters and the network data indicators; and determining the energy consumption assessment result of the wireless network based on the correlation coefficient and the value weights.

[0007] Secondly, embodiments of this application provide a wireless network energy consumption assessment device, the device comprising: an acquisition module, configured to acquire energy consumption data of a wireless base station in a wireless network to be assessed and multiple network data indicators; a first determination module, configured to determine a correlation coefficient between the energy consumption data and the multiple network data indicators based on the energy consumption data and the network data indicators; a second determination module, configured to acquire network indicator value parameters and determine value weights of the multiple network data indicators based on the network indicator value parameters and the network data indicators; and a third determination module, configured to determine the energy consumption assessment result of the wireless network based on the correlation coefficient and the value weights.

[0008] Thirdly, embodiments of this application provide an electronic device comprising: a processor; and a memory arranged to store computer-executable instructions configured to be executed by the processor, the executable instructions including instructions for performing the wireless network power consumption assessment method as described in the first aspect.

[0009] Fourthly, embodiments of this application provide a storage medium for storing computer-executable instructions that cause a computer to execute the wireless network power consumption assessment method as described in the first aspect.

[0010] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the wireless network energy consumption assessment method as described in the first aspect.

[0011] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the wireless network power consumption assessment method as described in the first aspect.

[0012] In this embodiment, energy consumption data of wireless base stations and multiple network data indicators in the wireless network to be evaluated are acquired; based on the energy consumption data and the network data indicators, a correlation coefficient between the energy consumption data and the multiple network data indicators is determined; network indicator value parameters are acquired, and based on the network indicator value parameters and the network data indicators, the value weights of the multiple network data indicators are determined; based on the correlation coefficient and the value weights, the energy consumption evaluation result of the wireless network is determined. Compared with related wireless network energy consumption evaluation technologies, this application conducts a more comprehensive wireless network energy consumption analysis by performing multi-dimensional joint analysis of the energy consumption data of wireless base stations and multiple network data indicators in the wireless network to be evaluated, resulting in a more accurate energy consumption evaluation result for the wireless network; this solves the problem of poor accuracy in wireless network energy consumption evaluation of related technologies. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating a wireless network energy consumption assessment method provided in an embodiment of this application; Figure 2 A flowchart illustrating another wireless network power consumption assessment method provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of a wireless network power consumption assessment device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0016] The wireless network energy consumption assessment method, apparatus, electronic device, and storage medium provided in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0017] Figure 1 This invention illustrates a method for assessing wireless network energy consumption according to an embodiment of the present invention. This method can be executed by an electronic device, which may include a server and / or a terminal device, wherein the terminal device may be, for example, a vehicle-mounted terminal or a mobile phone terminal. The method includes the following steps: S102: Obtain energy consumption data and multiple network data indicators of the wireless base stations in the wireless network to be evaluated.

[0018] In practical applications, energy consumption data of wireless base stations in the wireless network to be evaluated, as well as multiple network data indicators of the wireless base stations, can be obtained. Specifically, the wireless network to be evaluated may include one or more wireless base stations. The following steps S104 to S108 are performed for one or more wireless base stations to determine the energy consumption evaluation results of the wireless network in S108.

[0019] S104: Based on energy consumption data and network data indicators, determine the correlation coefficient between energy consumption data and multiple network data indicators.

[0020] S106: Obtain the network indicator value parameters, and determine the value weights of multiple network data indicators based on the network indicator value parameters and network data indicators.

[0021] S108: Determine the energy consumption assessment results of the wireless network based on the correlation coefficient and value weight.

[0022] The energy consumption assessment result of the wireless network includes an energy consumption value assessment for one or more data acquisition moments of the wireless base station in the wireless network. The larger the energy consumption value assessment value, the greater the energy consumption value of the wireless base station at that data acquisition moment. Optionally, for each data acquisition moment, the energy consumption value assessment value at that moment can be determined by weighted averaging based on multiple correlation coefficients and multiple value weights, or by constructing corresponding matrices and multiplying them separately based on multiple correlation coefficients and multiple value weights to determine the energy consumption value assessment value at that moment; alternatively, the energy consumption value influence factors of multiple network data indicators at the data acquisition moment can be determined based on the correlation coefficients and value weights corresponding to each network data indicator, and the energy consumption value influence factors of multiple network data indicators at the data acquisition moment can be used as the energy consumption value assessment value.

[0023] The wireless network energy consumption assessment method provided in this invention acquires energy consumption data of the wireless base station in the wireless network to be assessed and multiple network data indicators; determines the correlation coefficient between the energy consumption data and the multiple network data indicators based on the energy consumption data and network data indicators; obtains network indicator value parameters, and determines the value weight of the multiple network data indicators based on the network indicator value parameters and network data indicators; and determines the energy consumption assessment result of the wireless network based on the correlation coefficient and value weight. Compared with related wireless network energy consumption assessment technologies, this application conducts a more comprehensive wireless network energy consumption analysis and a more accurate energy consumption assessment result by performing multi-dimensional joint analysis of the energy consumption data of the wireless base station in the wireless network to be assessed and multiple network data indicators; it solves the problem of poor accuracy in assessing wireless network energy consumption in related wireless network energy consumption assessment technologies.

[0024] In one implementation, the aforementioned energy consumption data and network data indicators correspond to multiple data collection moments. Before determining the energy consumption assessment result of the wireless network based on the correlation coefficient and value weight (i.e., S108), step A1 can also be performed to determine the time weights of the multiple data collection moments: Step A1: Obtain the electricity price at multiple data collection times, and determine the time weight of the multiple data collection times based on the electricity price at multiple data collection times.

[0025] Considering the fluctuations in electricity prices during peak and off-peak periods, the data collection times are correlated with electricity prices to determine the time weights of multiple data collection times. Specifically, the time weights of multiple data collection times satisfy the following formula (1): (1) Where t represents the data acquisition time, and T represents the number of data acquisition times; This represents the time weight at the t-th data acquisition time.

[0026] Based on the correlation coefficient and value weight, the energy consumption assessment result of the wireless network is determined (i.e., S108), and the following step A2 can be performed: Step A2: Based on the correlation coefficient, value weight, and time weight, determine the energy consumption assessment results of the wireless network at multiple data collection times.

[0027] For example, the energy consumption value impact factor of the i-th network data indicator at data acquisition time t can be determined based on the correlation coefficient, value weight, and time weight using the following formula (2): (2) Where t represents the data acquisition time; Let be the energy consumption value impact factor of the i-th network data indicator at data acquisition time t. Let be the correlation coefficient of the i-th network data index at data acquisition time t; Let be the value weight of the i-th network data indicator; This represents the time weight at the t-th data acquisition time.

[0028] In this embodiment, by introducing time weighting and taking into account the dynamic changes in electricity prices over time, operators can make more accurate assessments of the energy consumption of wireless networks.

[0029] In one implementation, based on energy consumption data and network data indicators, the correlation coefficient between energy consumption data and multiple network data indicators is determined (i.e., S104), which can be achieved by performing the following step B1: Step B1: Based on energy consumption data and network data indicators, determine the correlation coefficient between energy consumption data and multiple network data indicators through grey relational analysis.

[0030] In this embodiment, grey relational analysis can identify the weak and sparse correlation patterns between energy consumption data and network data indicators, with a small computational load, thus obtaining a more accurate correlation coefficient faster.

[0031] In one implementation, based on network indicator value parameters and network data indicators, the value weights of multiple network data indicators are determined (i.e., step S104), which can be achieved by executing the following step C1: Step C1: Based on the network indicator value parameters and network data indicators, the value weights of multiple network data indicators are determined using the analytic hierarchy process (AHP).

[0032] In this embodiment, the Analytic Hierarchy Process (AHP) is used in conjunction with actual energy consumption, enabling operators to make more accurate assessments of the energy consumption of wireless networks.

[0033] In one implementation, the aforementioned energy consumption data and network data indicators correspond to multiple data collection times. Based on the energy consumption data and network data indicators, the correlation coefficient between the energy consumption data and multiple network data indicators is determined through grey relational analysis (i.e., step B1). Steps b1 to b3 can be executed as follows: Step b1 involves standardizing the energy consumption data and network data indicators to obtain the corresponding standardized energy consumption data and multiple standardized network data indicators.

[0034] In practical applications, energy consumption data can be used as a reference sequence, and network data indicators as a comparison sequence. Considering the different dimensions of the reference sequence and network data indicators, which lead to discrepancies, the energy consumption data and network data indicators are first standardized. Optionally, since the mean method can effectively eliminate the influence of dimensions, making the data comparable, while maintaining the original distribution characteristics of the data well, and having high computational efficiency, the mean method can be used to standardize the energy consumption data and network data indicators. Considering that there may be many outliers in the energy consumption data and network data indicators, when outliers in the energy consumption data and network data indicators are detected to be greater than a preset outlier threshold, the median can be used to replace the mean method to standardize the energy consumption data and network data indicators.

[0035] For example, the energy consumption data of a wireless base station can be ,in, For the first Energy consumption data at each data acquisition moment; the network data indicators of the wireless base station can be n, and the network indicators can be: Taking the standardization of energy consumption data and network data indicators using the mean method as an example, the standardized energy consumption data and n standardized network data indicators are obtained through the following formulas (3) and (4): (3) (4) in, The number of data acquisition moments; For the first Standardized energy consumption data at each data acquisition time, t=1,2,...,T; This indicates that the i-th (or class) is in the i-th position. The standardized network data metrics at each data acquisition time point, i=1,2,...,n. It should be noted that the following... , The meanings of 'i' and 'n' are the same as those mentioned above, and will not be repeated below.

[0036] Step b2: Based on the standardized energy consumption data, multiple standardized network data indicators, and preset resolution coefficients, determine the grey relational coefficients of the standardized network data indicators at multiple data acquisition times.

[0037] Specifically, the absolute difference between the standardized energy consumption data and multiple standardized network data indicators at each data acquisition time can be calculated to obtain multiple absolute differences. Among these multiple absolute differences, the maximum and minimum absolute differences can be determined. Then, for each standardized network data indicator, based on the maximum absolute difference, the minimum absolute difference, the absolute difference corresponding to the data acquisition time, and a preset resolution coefficient, the grey relational coefficient for multiple data acquisition times can be determined.

[0038] Following the example above, the absolute difference between the standardized energy consumption data and the i-th standardized network data index at data acquisition time t can be calculated using the following formula (5). : (5) Standardized energy consumption data was obtained and The absolute differences of standardized network data metrics at T data collection times are then used to determine the maximum and minimum absolute differences among these differences. ; Then, for each standardized network data indicator, based on the maximum absolute difference, the minimum absolute difference, the absolute difference corresponding to the data acquisition time, and the preset resolution coefficient, the grey relational coefficients for multiple data acquisition times are determined, as shown in the following formula (6): (6) in, Let be the grey relational coefficient of the i-th standardized network data index at data acquisition time t; The discrimination coefficient has a value range of (0-1), and is generally taken as 0.5. It can be determined according to actual needs, and no specific limitation is made; the grey relational coefficient... The closer it is to 1, the stronger the correlation between the standardized energy consumption data and the standardized network data indicators at the data acquisition time t.

[0039] Step b3: Based on the grey relational coefficient, determine the correlation coefficient between energy consumption data and network data indicators.

[0040] Following the example above, based on the grey relational coefficient, normalization can be performed using the following formula (7) to obtain the correlation coefficient between energy consumption data and network data indicators: (7) in, The correlation coefficient; Same as above.

[0041] In one implementation, based on network indicator value parameters and network data indicators, the value weights of multiple network data indicators are determined using the Analytic Hierarchy Process (AHP) (i.e., step C1). Steps c1 to c2 can be executed as follows: Step c1: Based on the network data indicators and the preset importance value of the i-th network data indicator to the j-th network data indicator under the network indicator value parameter, establish a corresponding judgment matrix for each network indicator value parameter.

[0042] Where i and j are integers greater than zero and less than or equal to the number of network data metrics. There can be multiple network metric value parameters.

[0043] For example, for each network indicator value parameter, the corresponding judgment matrix can be constructed with network data indicators as rows and columns, and the importance value of the i-th network data indicator to the j-th network data indicator under the network indicator value parameter as an element, as shown in the following (8): (8) in, To determine the matrix, Let be the importance value of the i-th network data indicator relative to the j-th network data indicator under the network indicator value parameter; n is the number of network data indicators; and .

[0044] The importance values ​​of the i-th network data indicator to the j-th network data indicator under the network indicator value parameter are shown in Table 1 below: Table 1

[0045] Step c2: For each judgment matrix, determine the first weight of multiple network data indicators under the corresponding network indicator value parameter based on the judgment matrix, and determine the value weight of multiple network data indicators according to the first weight and the second weight of the preset network indicator value parameter.

[0046] Specifically, based on the judgment matrix, the first weight of multiple network data indicators under the corresponding network indicator value parameters can be determined by the sum-product method and the square root method (also known as the geometric mean).

[0047] Furthermore, before determining the value weights of multiple network data indicators based on the first weight and the second weight of the preset network indicator value parameters, the following step c3 can be performed: Step c3 involves performing a consistency check based on the judgment matrix, the number of network data indicators, the preset consistency indicator table, the first weight, and the preset consistency ratio threshold, to obtain the check result; correspondingly, determining the value weights of multiple network data indicators based on the first weight and the second weight of the preset network indicator value parameters can be performed as follows: if the check result indicates that the consistency check has passed, the value weights of multiple network data indicators are determined based on the first weight and the second weight of the preset network indicator value parameters. The preset consistency indicator table can be shown in Table 2 below: Table 2

[0048] Where n is the order of the judgment matrix (i.e., the number of network data indicators).

[0049] Following the example above, taking the determination of the first weight using the square root method, with the network indicator value parameters being revenue and cost, and step c3 as an example, the process of determining the value weights of multiple network data indicators is shown in steps 1 to 6 below: Step 1: Calculate the geometric mean of each row of the judgment matrix, that is, the geometric mean of n network data indicators under the value parameter of the network indicator, as shown in the following formula (9): (9) Step 2: Normalize the geometric mean to obtain the first weight, as shown in the following formula (10): (10) Thus, we can obtain the first weight of each network data indicator under the network indicator value parameter, that is, the first weight of the i-th network data indicator under the revenue. and the first weight of the i-th network data metric under cost. .

[0050] Step 3: Multiply the judgment matrix by the first weight to obtain the vector AW = (AW)1, (AW)2, ..., (AW) n Based on the number of network data indicators n, the vector, and the first weight, the largest eigenvalue of the judgment matrix is ​​determined by the following formula (11). : (11) Step 4: Based on the preset consistency index table and the number of network data indicators (that is, the order of the judgment matrix and the largest eigenvalue of the judgment matrix), determine the consistency ratio using the following formulas (12) to (13). : (12) (13) in, The average random consistency index is obtained by querying a pre-defined consistency index table based on the number of network data indicators; C I This serves as a consistency indicator.

[0051] Step 5: Based on the preset consistency ratio threshold and the consistency ratio, perform a consistency check to obtain the check result. The preset consistency ratio threshold can be set according to actual needs, such as 0.1, and there are no specific restrictions. Specifically, a check result indicating that the consistency check has passed can be obtained when the consistency ratio is less than or equal to the preset consistency ratio threshold; a check result indicating that the consistency check has failed can be obtained when the consistency ratio is greater than the preset consistency ratio threshold.

[0052] Step 6: If the consistency verification result passes, then proceed according to the first weight. and The second weight of the preset network indicator value parameter and The value weight of the i-th network data indicator is determined by the following formula (14), and the value weight is normalized to obtain the network indicator value weight between 0 and 1. As shown in the following formula (15): (14) (15) In this embodiment, by transforming experts’ experience and subjective judgments (such as their views on the importance of different network data indicators to the value parameters of network indicators) into calculable numerical weights, qualitative analysis can also be quantified.

[0053] In one implementation, the aforementioned network data metrics include, but are not limited to, traffic, network utilization, and the number of connected users; the network metric value parameters include, but are not limited to, one or more of revenue and cost.

[0054] Figure 2 This is a flowchart illustrating another wireless network power consumption assessment method provided in an embodiment of this application. Figure 2 As shown, the method includes: Step 202: Obtain the energy consumption data and multiple network data indicators of the wireless base station in the wireless network to be evaluated.

[0055] Among them, energy consumption data and network data indicators correspond to multiple data collection times; multiple network data indicators include traffic, network utilization, and the number of connected users.

[0056] Step 204: Obtain the electricity price at multiple data collection times, and determine the time weight of the multiple data collection times based on the electricity price at multiple data collection times.

[0057] Step 206: Based on energy consumption data and network data indicators, determine the correlation coefficient between energy consumption data and multiple network data indicators through grey relational analysis.

[0058] Step 208: Obtain the network indicator value parameters, and based on the network indicator value parameters and network data indicators, determine the value weights of multiple network data indicators through the analytic hierarchy process.

[0059] Among them, network indicator value parameters include one or more of the benefits and costs.

[0060] Step 210: Based on the correlation coefficient, value weight, and time weight, determine the energy consumption assessment results of the wireless network at multiple data acquisition times.

[0061] The specific processes of steps 202 to 210 have been described in detail in the above embodiments and will not be repeated here.

[0062] This embodiment acquires energy consumption data and multiple network data indicators of the wireless base station in the wireless network to be evaluated; based on the energy consumption data and network data indicators, it determines the correlation coefficient between the energy consumption data and the multiple network data indicators; it acquires the value parameters of the network indicators, and based on the value parameters of the network indicators and the network data indicators, it determines the value weights of the multiple network data indicators; based on the correlation coefficients and value weights, it determines the energy consumption assessment result of the wireless network. Compared with related wireless network energy consumption assessment technologies, this application conducts a more comprehensive wireless network energy consumption analysis and a more accurate energy consumption assessment result by performing multi-dimensional joint analysis of the energy consumption data of the wireless base station in the wireless network to be evaluated and multiple network data indicators; it solves the problem of poor accuracy in assessing wireless network energy consumption in related wireless network energy consumption assessment technologies.

[0063] Corresponding to the wireless network power consumption assessment method provided in the above embodiments, based on the same technical concept, the present invention also provides a wireless network power consumption assessment device. Figure 3 This is a schematic diagram of a wireless network power consumption assessment device according to an embodiment of the present invention. The wireless network power consumption assessment device is used to perform... Figures 1 to 2 The described wireless network power consumption assessment method, such as Figure 3 As shown, the wireless network energy consumption assessment device includes: an acquisition module 310, a first determination module 320, a second determination module 330, and a third determination module 340.

[0064] The acquisition module 310 is used to acquire energy consumption data and multiple network data indicators of the wireless base station in the wireless network to be evaluated. The first determining module 320 is used to determine the correlation coefficient between energy consumption data and multiple network data indicators based on energy consumption data and network data indicators. The second determining module 330 is used to obtain network indicator value parameters and determine the value weights of multiple network data indicators based on the network indicator value parameters and network data indicators. The third determining module 340 is used to determine the energy consumption assessment results of the wireless network based on the correlation coefficient and value weight.

[0065] In one implementation, the aforementioned energy consumption data and network data indicators correspond to multiple data acquisition times; the wireless network energy consumption assessment device further includes a fourth determining module; the fourth determining module is used for: Obtain the electricity price at multiple data collection times, and determine the time weight of multiple data collection times based on the electricity price at multiple data collection times; The third determining module is specifically used for: Based on correlation coefficient, value weight, and time weight, the energy consumption assessment results of the wireless network at multiple data collection times are determined.

[0066] In one implementation, the first determining module 320 includes: The first determining unit is used to determine the correlation coefficient between energy consumption data and multiple network data indicators through grey relational analysis, based on energy consumption data and network data indicators.

[0067] In one implementation, the second determining module 330 is specifically used for: The second determining unit is used to determine the value weights of multiple network data indicators based on network indicator value parameters and network data indicators, using the analytic hierarchy process (AHP).

[0068] In one implementation, the aforementioned energy consumption data and network data indicators correspond to multiple data acquisition times; the first determining unit is specifically used for: The energy consumption data and network data indicators are standardized to obtain the corresponding standardized energy consumption data and multiple standardized network data indicators. Based on standardized energy consumption data, multiple standardized network data indicators, and preset resolution coefficients, the grey relational coefficients of the standardized network data indicators at multiple data acquisition times are determined. Based on the grey relational coefficient, the correlation coefficient between energy consumption data and multiple network data indicators is determined.

[0069] In one implementation, the second determining unit is specifically used for: Based on the network data indicators and the preset importance value of the i-th network data indicator to the j-th network data indicator under the network indicator value parameter, a corresponding judgment matrix is ​​established for each network indicator value parameter; where i and j are integers that are greater than zero and less than or equal to the number of network data indicators. For each judgment matrix, based on the judgment matrix, determine the first weight of multiple network data indicators under the corresponding network indicator value parameter, and determine the value weight of multiple network data indicators according to the first weight and the second weight of the preset network indicator value parameter.

[0070] In one implementation, the aforementioned network data metrics include multiples of traffic, network utilization, and number of connected users; the value parameters of the aforementioned network metrics include one or more of revenue and cost.

[0071] This embodiment acquires energy consumption data and multiple network data indicators of the wireless base station in the wireless network to be evaluated; based on the energy consumption data and network data indicators, it determines the correlation coefficient between the energy consumption data and the multiple network data indicators; it acquires the value parameters of the network indicators, and based on the value parameters of the network indicators and the network data indicators, it determines the value weights of the multiple network data indicators; based on the correlation coefficients and value weights, it determines the energy consumption assessment result of the wireless network. Compared with related wireless network energy consumption assessment technologies, this application conducts a more comprehensive wireless network energy consumption analysis and a more accurate energy consumption assessment result by performing multi-dimensional joint analysis of the energy consumption data of the wireless base station in the wireless network to be evaluated and multiple network data indicators; it solves the problem of poor accuracy in assessing wireless network energy consumption in related wireless network energy consumption assessment technologies.

[0072] Those skilled in the art will understand that the above-described wireless network power consumption assessment device can be used to implement the wireless network power consumption assessment method described above. The detailed description therein should be similar to the method section described above. To avoid repetition, it will not be repeated here.

[0073] Based on the same technical concept, embodiments of this application also provide an electronic device for performing the above-described wireless network power consumption assessment method. Figure 4 This is a schematic diagram of the structure of an electronic device to implement various embodiments of this application. The electronic device can vary significantly due to differences in configuration or performance, and may include a processor 410, a communications interface 420, a memory 430, and a communication bus 440. The processor 410, communications interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call a computer program stored in the memory 430 and executable on the processor 410 to perform the following steps: Acquire energy consumption data and multiple network data metrics of the wireless base stations in the wireless network to be evaluated; Based on energy consumption data and network data indicators, determine the correlation coefficient between energy consumption data and multiple network data indicators; Obtain network indicator value parameters, and determine the value weights of multiple network data indicators based on network indicator value parameters and network data indicators; The energy consumption assessment results of the wireless network are determined based on the correlation coefficient and value weight.

[0074] This embodiment acquires energy consumption data and multiple network data indicators of the wireless base station in the wireless network to be evaluated; based on the energy consumption data and network data indicators, it determines the correlation coefficient between the energy consumption data and the multiple network data indicators; it acquires the value parameters of the network indicators, and based on the value parameters of the network indicators and the network data indicators, it determines the value weights of the multiple network data indicators; based on the correlation coefficients and value weights, it determines the energy consumption assessment result of the wireless network. Compared with related wireless network energy consumption assessment technologies, this application conducts a more comprehensive wireless network energy consumption analysis and a more accurate energy consumption assessment result by performing multi-dimensional joint analysis of the energy consumption data of the wireless base station in the wireless network to be evaluated and multiple network data indicators; it solves the problem of poor accuracy in assessing wireless network energy consumption in related wireless network energy consumption assessment technologies.

[0075] The specific implementation steps can be found in the various steps of the above-described wireless network energy consumption assessment method embodiment, and can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0076] It should be noted that the electronic devices in the embodiments of this application include: servers, terminals, or other devices besides terminals.

[0077] The above electronic device structure does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or arrange them differently. For example, an input unit may include a Graphics Processing Unit (GPU) and a microphone, and a display unit may use a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar display panels. User input units include at least one of a touch panel and other input devices. A touch panel is also called a touchscreen. Other input devices may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be elaborated further here.

[0078] Memory can be used to store software programs and various data. Memory can primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area can store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, memory can include volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0079] The processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor, wherein the application processor mainly handles operations related to the operating system, user interface, and applications, while the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor.

[0080] This application also provides a storage medium storing computer-executable instructions. When these computer-executable instructions are executed by a processor, they implement the various processes of the above-described wireless network energy consumption assessment method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0081] The processor is the processor in the electronic device described in the above embodiments. The storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0082] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described wireless network energy consumption assessment method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0083] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0084] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the various processes of the above-described wireless network energy consumption assessment method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0085] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include multitasking and parallel processing according to the functions involved, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of this application.

[0087] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for evaluating the energy consumption of a wireless network, characterized in that, The method includes: Acquire energy consumption data and multiple network data metrics of the wireless base stations in the wireless network to be evaluated; Based on the energy consumption data and the network data indicators, determine the correlation coefficient between the energy consumption data and multiple network data indicators; Obtain network indicator value parameters, and determine the value weights of multiple network data indicators based on the network indicator value parameters and the network data indicators; Based on the correlation coefficient and the value weight, the energy consumption assessment result of the wireless network is determined.

2. The method according to claim 1, characterized in that, The energy consumption data and the network data indicators correspond to multiple data collection times; before determining the energy consumption assessment result of the wireless network based on the correlation coefficient and the value weight, the method further includes: Obtain the electricity price at multiple data collection times, and determine the time weight of the multiple data collection times based on the electricity price at the multiple data collection times; The determination of the energy consumption assessment result of the wireless network based on the correlation coefficient and the value weight includes: Based on the correlation coefficient, the value weight, and the time weight, the energy consumption assessment results of the wireless network at multiple data collection times are determined.

3. The method according to claim 1, characterized in that, The step of determining the correlation coefficient between the energy consumption data and multiple network data indicators based on the energy consumption data and the network data indicators includes: Based on the energy consumption data and the network data indicators, the correlation coefficients between the energy consumption data and multiple network data indicators are determined through grey relational analysis.

4. The method according to claim 1, characterized in that, The step of determining the value weights of multiple network data indicators based on the network indicator value parameters and the network data indicators includes: Based on the network indicator value parameters and the network data indicators, the value weights of multiple network data indicators are determined using the analytic hierarchy process (AHP).

5. The method according to claim 3, characterized in that, The energy consumption data and the network data indicators correspond to multiple data collection times; the step of determining the correlation coefficient between the energy consumption data and the multiple network data indicators based on the energy consumption data and the network data indicators through grey relational analysis includes: The energy consumption data and the network data indicators are standardized to obtain the corresponding standardized energy consumption data and multiple standardized network data indicators. Based on the standardized energy consumption data, multiple standardized network data indicators, and a preset resolution coefficient, the grey relational coefficients of the standardized network data indicators at multiple data acquisition times are determined. Based on the grey relational coefficient, the correlation coefficient between the energy consumption data and multiple network data indicators is determined.

6. The method according to claim 4, characterized in that, The step of determining the value weights of multiple network data indicators based on the network indicator value parameters and the network data indicators using the analytic hierarchy process includes: Based on the network data indicators and the preset importance value of the i-th network data indicator to the j-th network data indicator under the network indicator value parameter, a corresponding judgment matrix is ​​established for each network indicator value parameter; where i and j are integers greater than zero and less than or equal to the number of network data indicators. For each judgment matrix, based on the judgment matrix, a first weight of multiple network data indicators under the corresponding network indicator value parameter is determined, and the value weight of multiple network data indicators is determined according to the first weight and the preset second weight of the network indicator value parameter.

7. The method according to claim 1, characterized in that, The network data metrics include multiple of traffic, network utilization, and number of connected users; the network metric value parameters include one or more of revenue and cost.

8. A wireless network energy consumption assessment device, characterized in that, The device includes: The acquisition module is used to acquire energy consumption data and multiple network data indicators of the wireless base stations in the wireless network to be evaluated. The first determining module is used to determine the correlation coefficient between the energy consumption data and multiple network data indicators based on the energy consumption data and the network data indicators; The second determining module is used to obtain network indicator value parameters and determine the value weights of multiple network data indicators based on the network indicator value parameters and the network data indicators. The third determining module is used to determine the energy consumption assessment result of the wireless network based on the correlation coefficient and the value weight.

9. An electronic device, characterized in that, include: processor; as well as A memory configured to store computer-executable instructions configured to be executed by the processor, the executable instructions including instructions for performing a wireless network power consumption assessment method as described in any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium is used to store computer-executable instructions that cause a computer to perform the wireless network power consumption assessment method as described in any one of claims 1-7.

11. A computer program product, characterized in that, The system includes a computer program that, when executed by a processor, implements the wireless network power consumption assessment method as described in any one of claims 1-7.

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