A method and device for predicting energy consumption of mobile sites

Abstract

The present invention relates to a method and device for predicting energy consumption of a mobile station. The method comprises: collecting power consumption related data of each power-consuming device of a mobile station; mapping the traffic volume data of a first type of power-consuming device of the mobile station to the real-time power data of the first type of power-consuming device, thereby establishing a traffic volume data-power data mapping table of the first type of power-consuming device; integrating the first type of power-consuming device and the second type of power-consuming device of the mobile station based on the device model, working frequency band, rated power, and common mode, thereby establishing a first type of power-consuming device-second type of power-consuming device mapping table; constructing a power consumption prediction model of the second type of power-consuming device using a multivariate regression algorithm based on the traffic volume data-power data mapping table and the first type of power-consuming device-second type of power-consuming device mapping table; and predicting the power consumption of the second type of power-consuming device based on the power consumption prediction model.

Description

Technical Field

[0001] The present invention relates to data mining in the field of communications, and in particular to a method and device capable of accurately predicting the energy consumption of a mobile station. Background Art

[0002] Since the communication operator transferred the mobile site assets to the tower company, the electricity accounting values ​​of the telecom tower and the tower companies have deviated greatly. There are many reasons for the deviation, which can be roughly divided into 13 types. The main reasons include unreasonable sharing ratio, large deviation in air-conditioning electricity calculation (the main reason is that the two parties have differences in the sharing of air-conditioning electricity, inaccurate air-conditioning resources and difficulty in estimating air-conditioning working hours), and large deviations between the electricity consumption provided by the tower and the actual electricity consumption (the electricity consumption cycle of the meter does not match the actual, electricity theft, etc.). The large-scale deviations in the electricity fee calculation results provided by both parties seriously affect the cost settlement, and have a great impact on the investment, cost planning and real-time business activities of both parties. Summary of the invention

[0003] At present, the following three methods are generally used to deal with the deviation between the calculation results of mobile site electricity consumption and the objective value:

[0004] Manual measurement: Taking the telecommunications equipment in Fujian Province as an example, Fujian Telecom transferred more than 40,000 mobile sites to the Tower Company, involving more than 150,000 network elements. Due to the huge workload and manual measurement errors, only partial sampling can be achieved and the errors are large.

[0005] Comparison with historical data: In order to reduce the deviation of electricity settlement, the comparison of current data with historical data is generally used as the basis for abnormal electricity consumption. In fact, due to expansion, optimization or policies of the competent authorities, the number of mobile sites continues to change over a large area, resulting in objectively large fluctuations in electricity consumption. At the same time, historical data itself may have large deviations, which makes historical data of little reference value.

[0006] Suspension of electricity bill settlement: The electricity bill settlement for the mobile site that was the subject of much dispute between the two parties was suspended, affecting the financial security and normal operations of both companies.

[0007] In summary, although the above three technologies have certain effects in solving the deviation of the electricity bill calculation values ​​of both parties through various different ways, they all have defects and shortcomings to varying degrees.

[0008] Therefore, in view of the various deficiencies in the current methods of operators and tower companies in collecting and calculating the periodic power consumption of mobile sites, an improved method and device for predicting the energy consumption of mobile sites is needed.

[0009] The present invention performs data feature preprocessing on the traffic volume index and power consumption of ZTE manufacturer's equipment, uses machine learning technology to perform regression modeling on the sample energy consumption data index, and combines the timing characteristics of the base station equipment for training and learning, so as to fit and predict the energy consumption index of base station equipment of other manufacturers, and integrate air conditioning, transmission, and other external power-consuming equipment into the total power consumption of the mobile site, and then simultaneously display the periodic power consumption of each mobile site in the existing network. The purpose is to calculate the power consumption of the mobile site in the existing network through a unified model by the system, reduce human measurement errors, and avoid investing huge manpower and material resources in on-site testing and data collection.

[0010] A brief overview of the invention is given below in order to provide a basic understanding of some aspects of the invention. However, it should be understood that this overview is not an exhaustive overview of the invention. It is not intended to identify the key or important parts of the invention, nor is it intended to limit the scope of the invention. Its purpose is merely to give some concepts of the invention in a simplified form as a prelude to a more detailed description that will be given later.

[0011] One aspect of the present invention relates to a method for predicting energy consumption of a mobile station, comprising: collecting power consumption-related data of each power-consuming device of the mobile station; establishing a traffic data-power data mapping table of the first type of power-consuming device by mapping the traffic data of the first type of power-consuming device of the mobile station to the real-time power data of the first type of power-consuming device, wherein the first type of power-consuming device can provide real-time traffic data and real-time power data; integrating the first type of power-consuming device and the second type of power-consuming device of the mobile station based on device model, operating frequency band, rated power, and common mode, thereby establishing a first type of power-consuming device-second type of power-consuming device mapping table, wherein the second type of power-consuming device can provide real-time traffic data but cannot provide real-time power data; based on the traffic data-power data mapping table and the first type of power-consuming device-second type of power-consuming device mapping table, using a multivariate regression algorithm to construct a power consumption prediction model for the second type of power-consuming device; and predicting the power consumption of the second type of power-consuming device based on the power consumption prediction model.

[0012] One aspect of the present invention relates to an apparatus for predicting energy consumption of a mobile site, comprising: a memory; and a processor, the processor being coupled to the memory and configured to execute the above method for predicting energy consumption of a mobile site.

[0013] One aspect of the present invention relates to a computer-readable storage medium having executable instructions stored thereon, and when the executable instructions are executed by a computer, the computer is caused to execute the above-mentioned method for predicting energy consumption of a mobile station.

[0014] The implementation method of the present invention realizes the mapping of equipment models between different manufacturers by classifying nearly 100 types of mobile communication equipment in the existing network according to working frequency and rated power, and using online testing. At the same time, regression modeling is performed on the different models of equipment of ZTE manufacturer through traffic volume and energy consumption, and the mapping relationship between equipment models between manufacturers is used to adapt the electricity cost prediction model of equipment models of other manufacturers to achieve full coverage of electricity cost prediction for all manufacturers. The implementation method of the present invention provides a list of mobile site power-consuming equipment and reference values ​​of power consumption of each equipment for maintenance personnel to verify the accuracy of electricity cost sharing ratio, equipment type and quantity, and at the same time provide a reference basis for finding illegal power theft involving mobile sites.

[0015] Other features and advantages of the present invention will become more apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which constitute a part of the specification, illustrate exemplary embodiments of the present invention and, together with the description, serve to explain the principles of the present invention.

[0017] The present invention may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0018] Figure 1 is a flow chart illustrating a method for predicting energy consumption of a mobile station according to an exemplary embodiment of the present invention;

[0019] Figure 2 is a flow chart showing a method for predicting energy consumption of a mobile station according to another exemplary embodiment of the present invention; and

[0020] Figure 3 is a schematic diagram showing an apparatus for predicting energy consumption of a mobile station according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0021] Hereinafter, preferred exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Note that in this specification and the accompanying drawings, sometimes the same reference numerals are used in common between different drawings to represent the same parts or parts with the same functions, and thus their repeated descriptions are omitted. In this specification, similar reference numerals and letters are used to represent similar items, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0022] For ease of understanding, the positions, sizes, and ranges of various structures shown in the drawings and the like may not represent actual positions, sizes, and ranges, etc. Therefore, the disclosed invention is not limited to the positions, sizes, and ranges disclosed in the drawings and the like.

[0023] Exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these exemplary embodiments do not limit the scope of the present invention.

[0024] The following description of at least one exemplary embodiment is in fact merely illustrative and is in no way intended to limit the present invention and its application or use. That is, the communication devices and communication methods herein are shown in an exemplary manner to illustrate exemplary embodiments of the structures and methods in the present invention. However, those skilled in the art will appreciate that they merely illustrate exemplary ways of the present invention that can be implemented, rather than exhaustive ways. In addition, the drawings need not be drawn to scale, and some features may be enlarged to illustrate details of specific components.

[0025] Technologies, methods, and apparatus known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the authorization specification.

[0026] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0027] The overall concept of the present invention involves configuring the basic layer, data layer, support layer, application layer, and display layer. The basic layer uses the server and the corresponding cloud computing; the data layer is responsible for basic data configuration, electricity bill data integration, traffic data collection and conversion, and dynamic environment system data processing; the support layer is responsible for electricity fee algorithm models, process services, etc.; the application layer includes electricity fee modules such as mobile site power resource integration, electricity unit price, and shared sharing among different operators, and electricity fee calculation modules for power calculation of various types of equipment; the display layer provides various types of analysis and monitoring results directly applied by users, such as user analysis reports.

[0028] Figure 1 is a flow chart illustrating a method for predicting energy consumption of a mobile station according to an exemplary embodiment of the present invention.

[0029] Specifically, the method 100 for detecting the flow rate of a drain outlet based on image recognition according to an exemplary embodiment of the present invention mainly includes steps S102 to S110.

[0030] In step S102, power consumption data of each power-consuming device of the mobile station is collected.

[0031] For example, the power consumption related data includes the equipment model of each power-consuming device, network management alarm data, traffic volume data, common mode equipment, power data, etc. Specifically, the following information is collected for the main power-consuming device and other power-consuming devices by configuring the resource file: mobile site library information, such as the correspondence between the mobile site and the meter; the model, quantity, static current value, synchronous professional network management, etc. of the main power-consuming device; the model, quantity, static current value and synchronous resource system of other power-consuming devices (such as monitoring equipment, transmission equipment, non-wireless equipment); meter library information, such as meter number, power supply mode, sharing mode, electricity unit price, sharing ratio, etc.; and air conditioner library information, such as type, rated power, sharing ratio, quantity, etc.

[0032] In step S104, a traffic data-power data mapping table of the first type of powered devices is established by mapping the traffic data of the first type of powered devices of the mobile station to the real-time power data of the first type of powered devices, wherein the first type of powered devices can provide real-time traffic data and real-time power data.

[0033] As the first type of power-consuming equipment, for example, various types of ZTE base station equipment are included, which can provide real-time traffic volume data and real-time power data. However, it should be noted that the ZTE base station equipment is only an example of the first type of power-consuming equipment involved in the present invention, and it does not constitute a limitation of the present invention. In other words, any base station equipment that can provide real-time traffic volume data and real-time power data at the same time is included as the first type of power-consuming equipment in the present invention.

[0034] In addition, the first type of electrical equipment may be a 4G base station device or a 5G base station device.

[0035] Specifically, the long-term traffic volume change data of the first type of electrical equipment and the long-term data power data corresponding to the first type of electrical equipment are collected, and the traffic volume and power of the first type of electrical equipment are correlated and fused using big data technology and feature preprocessing is performed to establish a traffic volume data-power data mapping table.

[0036] In addition, the establishment of a traffic volume data-power data mapping table includes configuring the mapping relationship between the traffic volume and the real-time power consumption of various types of equipment under different usage frequencies and different loading powers. It includes the basic power consumption of various types of equipment and the corresponding relationship between traffic volume and real-time power when a device is operating at multiple frequencies.

[0037] At step S106, the first type of power-consuming equipment and the second type of power-consuming equipment of the mobile station are integrated based on the equipment model, operating frequency band, rated power, and common mode, thereby establishing a first type of power-consuming equipment-second type of power-consuming equipment mapping table, wherein the second type of power-consuming equipment can provide real-time traffic data but cannot provide real-time power data.

[0038] For example, as the second type of power-consuming equipment, base station equipment such as Huawei and Nokia can provide real-time traffic data but cannot provide real-time power data. However, it should be noted that Huawei equipment and Nokia base station equipment are only examples of the second type of power-consuming equipment involved in the present invention, and they do not constitute a limitation of the present invention. In other words, any base station equipment that can provide real-time traffic data but cannot provide real-time power data is included as the second type of power-consuming equipment in the present invention.

[0039] In addition, the second type of electrical equipment may be a 4G base station device or a 5G base station device.

[0040] In step S108, based on the traffic data-power data mapping table of the first type of electrical equipment and the first type of electrical equipment-second type of electrical equipment mapping table, a multivariate regression algorithm is used to construct an electricity consumption prediction model for the second type of electrical equipment.

[0041] For example, constructing a power consumption prediction model for the second type of power consumer includes establishing a traffic volume-power conversion relationship between the first type of power consumer and the second type of power consumer based on the equipment model, operating frequency band, rated power, and common mode through a multivariate regression algorithm, and then predicting the power consumption of the second type of power consumer based on the traffic volume data of the second type of power consumer.

[0042] In step S110, the power consumption of the second type of electrical equipment is predicted based on the power consumption prediction model.

[0043] In addition, although not shown, the electricity charges of all first-type and second-type electric users may be predicted through electricity-electricity charge conversion based on the acquired electricity consumption of the first-type electric users and the predicted electricity consumption of the second-type electric users.

[0044] Figure 2 is a flow chart illustrating a method for predicting energy consumption of a mobile station according to another exemplary embodiment of the present invention.

[0045] and Figure 1 Compared to the method 100 shown, Figure 2 The method 200 shown includes the processing shown in steps S102-S110, and further includes the processing shown in steps S202-S210.

[0046] it goes without saying, Figure 1 The steps of the method 100 shown are also applicable to Figure 2 Therefore, the detailed description of the method 200 will be omitted here. That is, only the prediction of the third type of electric devices of the mobile station is described.

[0047] In step S202, a standard model correspondence table of electricity consumption of the third type of electricity consuming equipment is established by performing a large number of field tests on the third type of electricity consuming equipment of the mobile station and collecting typical electricity consumption values ​​as standard values, so as to calculate the electricity consumption of the third type of electricity consuming equipment, wherein the third type of electricity consuming equipment cannot provide real-time traffic data and real-time power data.

[0048] As the typical value of power consumption, for example, the average power consumption of the third type of electrical equipment collected by a large number of field tests can be selected. It should be noted that the average is only an example, and the typical value of power consumption is not limited to the average, but can also be the median, etc.

[0049] The third type of electrical equipment includes, for example, 3G equipment, repeater, transmission equipment, air conditioning equipment, etc. Similarly, 3G equipment, repeater, transmission equipment, air conditioning equipment are merely examples of the third type of electrical equipment involved in the present invention, and do not constitute a limitation to the present invention. In other words, any base station equipment that cannot provide real-time traffic volume data and real-time power data is included as the third type of electrical equipment in the present invention.

[0050] In addition, when calculating the power consumption of the air-conditioning equipment of the mobile station, the working hours of the air-conditioning equipment are estimated by collecting the temperature change curves of each region throughout the year provided by the meteorological station and setting the cooling start-up temperature of the air-conditioning equipment; and the estimated working hours of the air-conditioning equipment are combined with the rated power of the air-conditioning equipment and the actual power of the air-conditioning equipment in different types of computer rooms to estimate the power consumption of the air-conditioning equipment.

[0051] Specifically, the air conditioning types, ventilation system efficiency ratios, and real-time power of normal use of all mobile sites in the existing network are collected and sorted out in combination with the types of computer rooms and the configuration of air conditioners in integrated cabinets, and then their real-time working power is determined according to different temperatures. In addition, the temperature change trends in various regions of the province are collected, and a corresponding table of air conditioning power consumption is prepared for different types of air conditioning equipment in mobile sites of various types of computer rooms. When calculating the power consumption of air conditioning equipment in mobile sites, the corresponding table can be called to estimate the power consumption of different types of computer rooms in different seasons.

[0052] In step S204, different conversion losses are applied to the first type of electrical equipment, the second type of electrical equipment and the third type of electrical equipment in the mobile site according to the city power and the actual power consumption type, thereby obtaining the conversion estimated power consumption of each type of electrical equipment.

[0053] Different types of electrical equipment are equipped with power conversion modules, but due to differences in design and process among manufacturers, the power conversion coefficients are different. Therefore, the actual conversion coefficients of various types of electrical equipment are used as calculation factors to adjust the conversion estimated power consumption to accurately calculate the actual power consumption. In addition, the mains electricity is what we call industrial frequency alternating current (AC), which is characterized by the three commonly used quantities of alternating current: voltage, current, and frequency. Table 1 shows the types of mobile site electrical equipment, corresponding power consumption parameters, and power consumption conversion values ​​by way of example.

[0054] Table 1: Types of mobile site electrical equipment, corresponding power consumption parameters and power consumption conversion values

[0055] Mobile site electrical equipment Power consumption parameters Power consumption conversion value L Network BBU Quiescent current I I×53.5V / 0.85 / 1000×24 C network base station without RFU Quiescent current I I×53.5V / 0.85 / 1000×24 C network base station has RFU Current value I = 4A + number of RFUs × 3 I×53.5V / 0.85 / 1000×24 C Network RRU Quiescent current I I×53.5V / 0.85 / 1000×24 L Network ZTE RRU Performance collection RRU power consumption W W×1.4 / 1000 L network Huawei RRU Performance collection base station power consumption W W×1.4 / 1000×RRU ratio L Network Nokia RRU Performance measurement RRU power consumption W W×model coefficient×1.1765 / 1000 Non-wireless devices Quiescent current I I×53.5V / 0.85 / 1000×24

[0056] In step S206, the converted estimated power consumption of the first type of power-consuming equipment, the second type of power-consuming equipment and the third type of power-consuming equipment are summed to obtain the total estimated power consumption of the mobile site, and the total estimated power consumption is displayed to the maintenance personnel.

[0057] In step S208, the total estimated power consumption is compared with the actual total power consumption.

[0058] At step S210, when the difference value is greater than or equal to a certain threshold, the process returns to step S102, the acquired power consumption related data is corrected, and the multivariate regression algorithm is optimized at step S108. For example, the threshold can be set in advance.

[0059] Figure 3 is a schematic diagram showing an apparatus for predicting energy consumption of a mobile station according to an exemplary embodiment of the present invention.

[0060] In addition, the apparatus 300 for predicting mobile station energy consumption according to an embodiment of the present invention may include a processor 301 , a memory 302 , a display 303 , a communication unit 304 , an external interface (I / F) 305 , and a bus 306 .

[0061] The processor 301 may be, for example, a central processing unit (CPU) or a digital signal processor (DSP), and controls functions of the device 300 .

[0062] The memory 302 includes a random access memory (RAM) and a read only memory (ROM), and stores data and a program executed by the processor 301. The memory 302 is used as a work area for the processor 301. When a program such as an application is executed, various data required for the execution is loaded into the memory 302.

[0063] Furthermore, instead of or in addition to the ROM, a HDD (Hard Disk Drive), a flash memory, or another nonvolatile memory such as a solid-state memory may be provided. Then, in these storage devices, the above-mentioned various data or programs may be stored.

[0064] The display 303 is a display device using liquid crystal, electroluminescence (EL), etc. The display 303 is used to display data processed by the processor 301 and data loaded from the memory 302, etc.

[0065] The communication unit 304 is used to connect the device according to the embodiment of the present invention to each mobile station. For example, as the communication unit 304, a wireless LAN module such as WIFI or a short-range wireless module such as Bluetooth (registered trademark) is provided.

[0066] The external interface (I / F) 305 is an interface for connecting to an external device based on standards such as USB and HDMI (registered trademark) (High Definition Multimedia Interface). In addition, an interface for connecting to various memory cards such as a memory stick may be configured.

[0067] The bus 306 connects the processor 301, the memory 302, the display 303, the communication unit 304, and the external interface (I / F) 305 to each other. The bus 306 may include two or more buses each having a different speed such as a high-speed bus and a low-speed bus.

[0068] The information processing performed by the device according to the embodiment of the present invention having the above-mentioned hardware configuration is realized by the cooperation of the software stored in the ROM, etc. and the hardware resources of the device. For example, the drain outlet flow detection method according to the present technology is realized by the processor (CPU) loading the program (application) according to the present technology stored in the ROM, etc. into the RAM to execute it. In order to realize these blocks, dedicated hardware can be used appropriately.

[0069] A program such as an application is installed in the device according to the embodiment of the present invention from, for example, a global network. Alternatively, the program may be installed in the device according to the embodiment of the present invention via a recording medium.

[0070] Notice, Figure 3 The device 300 shown is also suitable for performing the above-mentioned reference Figure 1 and Figure 2 However, in order to avoid unnecessary ambiguity of the present invention, a detailed description thereof is omitted here.

[0071] Based on the above reference Figure 1 , Figure 2 and Figure 3The method or device for predicting the energy consumption of a mobile site according to an exemplary embodiment of the present invention can be clearly understood by those skilled in the art that the present invention systematically extracts the power consumption of the equipment corresponding to the real-time parameters of the existing network, establishes a mapping relationship between traffic data and power consumption, and then obtains the power consumption through the mapping relationship between traffic volume and power consumption, thereby avoiding errors in manual judgment of the rationality of power consumption and improving the uniformity and accuracy of power consumption calculation.

[0072] It should be known to those skilled in the art that the present disclosure is implemented as a method, system, device, or computer-readable medium (e.g., non-transient storage medium) as a computer program product. Therefore, the present disclosure can be implemented in various forms, such as a complete hardware embodiment, a complete software embodiment (including firmware, resident software, microprogram code, etc.), or can also be implemented as a software and hardware implementation, which will be referred to as a "circuit", "module" or "system" below. In addition, the present disclosure can also be implemented as a computer program product in any tangible media form, which has a computer-usable program code stored thereon.

[0073] The relevant description of the present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, systems, devices and computer program products according to the specific embodiments of the present disclosure. It can be understood that each block in each flowchart and / or block diagram, and any combination of blocks in the flowchart and / or block diagram, can be implemented using computer program instructions. These computer program instructions can be executed by a machine composed of a processor of a general-purpose computer or a special computer or other programmable data processing device, and the instructions are processed by a computer or other programmable data processing device to implement the functions or operations described in the flowchart and / or block diagram.

[0074] The accompanying drawings show the flowcharts and block diagrams of the architecture, functions and operations that can be implemented according to the methods, systems, devices and computer program products of various embodiments of the present disclosure. Therefore, each block in the flowchart or block diagram can represent a module, segment, or part of the program code, which includes one or more executable instructions to implement the specified logical function. It should also be noted that in some other embodiments, the functions described in the blocks may not be performed in the order shown in the figure. For example, two blocks connected to each other in the figure can actually be executed simultaneously, or in some cases according to the functions involved, they can also be executed in the opposite order of the icons. It should also be noted that each block of the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a system based on special hardware, or by a combination of special hardware and computer instructions to perform specific functions or operations.

[0075] As used herein, the word "exemplary" means "serving as an example, instance or illustration" rather than as a "model" to be exactly copied. Any implementation described as an example herein is not necessarily to be construed as preferred or advantageous over other implementations. Moreover, the present invention is not limited by any expressed or implied theory given in the above technical field, background technology, summary of the invention or detailed description.

[0076] As used herein, the word "substantially" is meant to include any minor variations due to design or manufacturing imperfections, device or component tolerances, environmental effects, and / or other factors. The word "substantially" also allows for deviations from a perfect or ideal situation due to parasitic effects, noise, and other practical considerations that may be present in actual implementations.

[0077] In addition, the terms "first", "second" and the like may also be used herein for reference purposes only and are not intended to be limiting. For example, the terms "first", "second" and other such numerical terms referring to structures or elements do not imply a sequence or order unless the context clearly indicates otherwise.

[0078] It should also be understood that when the term “include / comprises” is used in this document, it indicates the presence of the specified features, integers, steps, operations, units and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, units and / or components and / or their combinations.

[0079] In the present invention, the term "provide" is used in a broad sense to cover all ways of obtaining an object, and thus "providing an object" includes but is not limited to "purchasing", "preparing / manufacturing", "arranging / setting up", "installing / assembling", and / or "ordering" an object, etc.

[0080] Those skilled in the art will appreciate that the boundaries between the above operations are merely illustrative. Multiple operations may be combined into a single operation, a single operation may be distributed in additional operations, and operations may be performed at least partially overlapping in time. Moreover, alternative exemplary embodiments may include multiple instances of a particular operation, and the order of operations may be changed in various other exemplary embodiments. However, other modifications, variations, and replacements are equally possible. Therefore, this specification and the accompanying drawings should be considered illustrative, not restrictive.

[0081] In addition, the embodiments of the present invention may also include the following examples:

[0082] (1) A method for predicting energy consumption of a mobile station, characterized in that the method comprises:

[0083] Collecting power consumption data of each power-consuming device of the mobile station;

[0084] By mapping the traffic volume data of the first type of electric device of the mobile station to the real-time power data of the first type of electric device, a traffic volume data-power data mapping table of the first type of electric device is established, wherein the first type of electric device can provide real-time traffic volume data and real-time power data;

[0085] Integrate the first type of electric device and the second type of electric device of the mobile station based on device model, operating frequency band, rated power, and common mode, thereby establishing a first type of electric device-second type of electric device mapping table, wherein the second type of electric device can provide real-time traffic data but cannot provide real-time power data;

[0086] Based on the traffic data-power data mapping table and the first type of electrical equipment-second type of electrical equipment mapping table, constructing a power consumption prediction model for the second type of electrical equipment using a multivariate regression algorithm; and

[0087] The power consumption of the second type of electrical equipment is predicted based on the power consumption prediction model.

[0088] (2) The method according to (1), characterized in that it also includes

[0089] By conducting a large number of field tests on the third type of power-consuming equipment of the mobile station and collecting typical power consumption values ​​as standard values, a corresponding table of standard power consumption models of the third type of power-consuming equipment is established to calculate the power consumption of the third type of power-consuming equipment, wherein the third type of power-consuming equipment cannot provide real-time traffic data and real-time power data.

[0090] (3) The method according to (2), characterized in that:

[0091] The third type of electrical equipment includes 3G equipment, repeaters, transmission equipment, air conditioning equipment, etc.

[0092] (4) The method according to (3), characterized in that the method further comprises:

[0093] When the third type of electrical equipment is an air conditioning device,

[0094] By collecting the temperature change curves of various regions throughout the year provided by the meteorological station and setting the cooling start temperature of the air-conditioning equipment, the working hours of the air-conditioning equipment can be estimated; and

[0095] The power consumption of the air-conditioning equipment is estimated by combining the estimated working hours of the air-conditioning equipment with the rated power of the air-conditioning equipment and the actual power of the air-conditioning equipment in different types of computer rooms.

[0096] (5) The method according to (2), characterized in that it also includes

[0097] Different conversion losses are taken for the first type of electrical equipment, the second type of electrical equipment and the third type of electrical equipment in the mobile site according to the city power and the actual power consumption type, thereby obtaining the conversion estimated power consumption of each type of electrical equipment.

[0098] (6) The method according to (5), characterized in that

[0099] The actual conversion coefficient of each type of electrical equipment is used as a calculation factor to adjust the converted estimated power consumption.

[0100] (7) The method according to (5), characterized in that

[0101] The converted estimated power consumption of the first type of power-consuming equipment, the second type of power-consuming equipment and the third type of power-consuming equipment are summed to obtain the total estimated power consumption of the mobile site, and the total estimated power consumption is displayed to the maintenance personnel.

[0102] (8) The method according to (7), characterized in that it also includes

[0103] comparing the total estimated electricity usage with the actual total electricity usage, and

[0104] When the difference value is greater than or equal to a certain threshold, the collected power consumption related data is corrected and the multivariate regression algorithm is optimized.

[0105] (9) The method according to (1), characterized in that

[0106] The power consumption related data includes the equipment model of each power-consuming equipment, network management alarm data, traffic volume data, common mode equipment, power data, etc.

[0107] (10) The method according to (1), characterized in that

[0108] The establishing of the traffic volume data-power data mapping table includes using big data technology to correlate and fuse the traffic volume and power of the first electrical device and perform feature preprocessing.

[0109] (11) A device for predicting energy consumption of a mobile station, comprising:

[0110] Memory; and

[0111] A processor is coupled to the memory and is configured to execute the method according to any one of (1) to (10).

[0112] (12) A computer-readable storage medium having executable instructions stored thereon, which, when executed by a computer, causes the computer to perform the method according to any one of (1) to (10).

[0113] Although some specific exemplary embodiments of the present invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are intended to be illustrative only and not to limit the scope of the present invention. The various exemplary embodiments disclosed herein may be combined in any manner without departing from the spirit and scope of the present invention. It will also be appreciated by those skilled in the art that various modifications may be made to the exemplary embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A method for predicting energy consumption of a mobile station, characterized in that: The method comprises: Collecting power consumption data of each power-consuming device of the mobile station, wherein the power consumption data includes device model, traffic volume data, and power data; By mapping the traffic volume data of the first type of electric device of the mobile station to the real-time power data of the first type of electric device, a traffic volume data-power data mapping table of the first type of electric device is established, wherein the first type of electric device is a device capable of providing real-time traffic volume data and real-time power data; Integrate the first type of electric device and the second type of electric device of the mobile station based on device model, operating frequency band, rated power, and common mode, thereby establishing a first type of electric device-second type of electric device mapping table, wherein the second type of electric device is a device that can provide real-time traffic data but cannot provide real-time power data; Based on the traffic volume data-power data mapping table and the first type of electrical equipment-second type of electrical equipment mapping table, a power consumption prediction model for the second type of electrical equipment is constructed using a multiple regression algorithm, including establishing a traffic volume data-power data conversion relationship between the first type of electrical equipment and the second type of electrical equipment based on equipment model, operating frequency band, rated power, and common mode through a multiple regression algorithm; and The power consumption of the second type of power consumption equipment is predicted based on the power consumption prediction model and the traffic data of the second type of power consumption equipment.

2. The method according to claim 1, characterized in that Also includes By conducting a large number of field tests on the third type of power-consuming equipment of the mobile station and collecting typical power consumption values ​​as standard values, a corresponding table of standard power consumption models of the third type of power-consuming equipment is established to calculate the power consumption of the third type of power-consuming equipment, wherein the third type of power-consuming equipment is a device that cannot provide real-time traffic data and real-time power data.

3. The method according to claim 2, characterized in that The third type of electrical equipment includes 3G equipment, repeaters, transmission equipment, and air conditioning equipment.

4. The method according to claim 3, characterized in that The method further comprises When the third type of electrical equipment is an air conditioning device, By collecting the temperature change curves of various regions throughout the year provided by the meteorological station and setting the cooling start temperature of the air-conditioning equipment, the working hours of the air-conditioning equipment can be estimated; and The power consumption of the air-conditioning equipment is estimated by combining the estimated working hours of the air-conditioning equipment with the rated power of the air-conditioning equipment and the actual power of the air-conditioning equipment in different types of computer rooms.

5. The method according to claim 2, characterized in that: Also includes Different conversion losses are taken for the first type of electrical equipment, the second type of electrical equipment and the third type of electrical equipment in the mobile site according to the city power and the actual power consumption type, thereby obtaining the conversion estimated power consumption of each type of electrical equipment.

6. The method according to claim 5, characterized in that The actual conversion coefficient of each type of electrical equipment is used as a calculation factor to adjust the converted estimated power consumption.

7. The method according to claim 5, characterized in that The converted estimated power consumption of the first type of power-consuming equipment, the second type of power-consuming equipment and the third type of power-consuming equipment are summed to obtain the total estimated power consumption of the mobile site, and the total estimated power consumption is displayed to the maintenance personnel.

8. The method according to claim 7, characterized in that Also includes comparing the total estimated electricity usage with the actual total electricity usage, and When the difference value is greater than or equal to a certain threshold, the collected power consumption related data is corrected and the multivariate regression algorithm is optimized.

9. The method according to claim 1, characterized in that: The power consumption related data includes the equipment model of each power-consuming equipment, network management alarm data, traffic volume data, common mode equipment, and power data.

10. The method according to claim 1, characterized in that The establishing of the traffic volume data-power data mapping table includes using big data technology to associate the traffic volume and power of the first type of electrical equipment and perform feature preprocessing.

11. A device for predicting energy consumption of a mobile station, comprising: Memory; as well as A processor coupled to the memory and configured to perform the method according to any one of claims 1-10.

12. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a computer, causes the computer to perform the method according to any one of claims 1 to 10.

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