Energy-saving evaluation method, system, device and program based on multi-parameter coupling

Abstract

This invention discloses a method, system, device, and program for energy-saving assessment based on multi-parameter coupling. The method comprises: acquiring parameters; the parameters include environmental parameters, equipment parameters, and operating parameters; calculating the correlation between the parameters using a gray correlation analysis method; establishing a coupled energy consumption model based on the parameters and the correlation between the parameters; and performing energy-saving assessment based on the coupled energy consumption model and a dynamic benchmark to obtain an energy consumption index. The disclosed processing scheme can detect coupling loss, maintain assessment validity under non-steady-state conditions such as equipment aging and climate anomalies, and reduce assessment errors.

Description

Technical Field

[0001] The present invention relates to the field of energy-saving technology, and in particular to an energy-saving evaluation method, system, device and program based on multi-parameter coupling. Background Art

[0002] Traditional methods consider only the linear variation of a single parameter (such as power) and ignore the nonlinear coupling effects between parameters, leading to inaccurate assessments and potentially causing losses. For example, adjusting output power based solely on device current fails to account for the decrease in heat dissipation efficiency caused by rising ambient temperature, which can lead to device overload failures. Adjusting wind turbine blade angles relies solely on average wind speed, ignoring the cumulative effect of turbulence intensity on gearbox wear, resulting in a 20% reduction in device lifespan.

[0003] Moreover, the existing fixed weight allocation method is also unable to adapt to dynamic operating conditions such as equipment aging and environmental changes. The energy efficiency benchmark uses static historical data, which causes the evaluation results to deviate from the actual operating status.

[0004] Therefore, the above existing energy-saving evaluation methods still have inconveniences and defects in use and are in urgent need of further improvement. How to create a new multi-parameter coupled energy-saving evaluation method has become an urgent goal for improvement in the current industry. Summary of the Invention

[0005] In view of this, an embodiment of the present disclosure provides an energy-saving evaluation method based on multi-parameter coupling, which at least partially solves the problems existing in the prior art.

[0006] In a first aspect, an embodiment of the present disclosure provides an energy-saving evaluation method based on multi-parameter coupling, the method comprising the following steps:

[0007] Acquiring parameters; the parameters include environmental parameters, equipment parameters and operating parameters;

[0008] Calculate the correlation between the parameters based on the grey correlation analysis method;

[0009] Establishing a coupling energy consumption model based on the parameters and the correlation between the parameters;

[0010] Energy saving evaluation is performed based on the coupled energy consumption model and dynamic benchmark to obtain the energy consumption index.

[0011] According to a specific implementation of the embodiment of the present disclosure, the calculating the correlation between the parameters based on the grey correlation analysis method includes:

[0012] intercepting a data sequence of the parameter within the window;

[0013] performing a standardization operation on the data sequence;

[0014] Calculate the correlation of parameters based on the data sequence after standardization operation;

[0015] The weight factor is dynamically updated, and the relevance is updated based on the updated weight factor.

[0016] According to a specific implementation of the embodiment of the present disclosure, the calculation of the correlation degree of the parameters based on the data sequence after the normalization operation includes:

[0017] The correlation of the parameters is calculated based on the following formula:

[0018] ;

[0019] in, For the Observed values ​​of the sequence parameters With the Observed values ​​of the sequence parameters At the moment The instantaneous correlation coefficient of for The weight factor of for and The absolute difference of for and At the moment The absolute difference of is the resolution coefficient; For the current moment.

[0020] According to a specific implementation of the embodiment of the present disclosure, dynamically updating the weight factor and updating the association degree based on the updated weight factor includes:

[0021] The correlation degree under multiple time offsets is calculated based on the following formula:

[0022] ;

[0023] in, for and distance Correlation coefficient of steps; is the time lag parameter, when When it is a positive number, it means ahead. When it is a negative number, it indicates lag; For the current moment; is the sliding window length;

[0024] The maximum correlation direction is calculated based on the following formula:

[0025] ;

[0026] in, is the maximum correlation direction; is the time lag parameter; is the maximum allowed time lag range; is the minimum allowed time lag range; for and distance Correlation coefficient of steps;

[0027] Update the weight factor based on the following formula:

[0028] ;

[0029] in, For the updated The weight factor of is the maximum correlation direction; is a constant;

[0030] Update the relevance based on the updated weight factor, which is achieved by:

[0031] ;

[0032] in, for and exist The final gray absolute correlation of the moment; For the current moment; For the forgetting factor; For parameters and exist Correlation coefficient of the moment; For the previous moment; is the sliding window length; For the Observed values ​​of the sequence parameters With the Observed values ​​of the sequence parameters At the moment The instantaneous correlation coefficient.

[0033] According to a specific implementation of the embodiment of the present disclosure, establishing a coupling energy consumption model based on the parameters and the correlation between the parameters includes:

[0034] The coupling energy consumption model is established based on the following formula:

[0035] ;

[0036] in, The energy consumption value predicted by the model; is the basic energy consumption; Types of parameters that affect energy consumption; is the ordinal number of the data sequence; For the Observed values ​​of the sequence parameters The parameter independent influence coefficient of For the Sequence parameters at time Observed values ​​of For the Sequence parameters at time Observed values ​​of for and exist The final gray absolute correlation of the moment; is the dynamic coupling weight.

[0037] According to a specific implementation of the embodiment of the present disclosure, the dynamic coupling weight is calculated based on the following formula:

[0038] ;

[0039] in, is the dynamic coupling weight; is a constant; is the maximum correlation direction; for and exist The final gray absolute correlation of the moment.

[0040] According to a specific implementation of the embodiment of the present disclosure, the parameter independent influence coefficient is obtained based on the following method: :

[0041] Obtaining historical data samples; the historical data samples include parameters and actual energy consumption;

[0042] Constructing a linear regression model based on the historical data sample;

[0043] Based on the linear regression model, the minimum sum of squares of the prediction error is calculated, and the independent influence coefficient of the parameters that minimizes the sum of squares of the prediction error is solved. :

[0044] ;

[0045] in, for The parameter-independent influence coefficient of ; is the basic energy consumption; is the total number of samples; is the serial number of the sample; For the The actual observed value of the sample; For the The predicted value of samples;

[0046] No. The predicted value of samples Calculated by the following formula:

[0047] ;

[0048] in, For the The observed values ​​of the sequence parameters, .

[0049] In a second aspect, an embodiment of the present disclosure provides an energy-saving evaluation system based on multi-parameter coupling, the system comprising:

[0050] A data acquisition module is configured to acquire parameters; the parameters include environmental parameters, equipment parameters and operating parameters;

[0051] A calculation module is configured to calculate the correlation between the parameters based on a grey correlation analysis method;

[0052] a coupling energy consumption model building module, configured to establish a coupling energy consumption model based on the parameters and the correlation between the parameters;

[0053] The evaluation module is configured to perform energy-saving evaluation based on the coupled energy consumption model and the dynamic benchmark to obtain an energy consumption index.

[0054] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0055] at least one processor; and,

[0056] a memory communicatively connected to the at least one processor; wherein,

[0057] The memory stores instructions that can be executed by the at least one processor. When the instructions are executed by the at least one processor, the at least one processor implements the energy-saving evaluation method based on multi-parameter coupling as described in any one of the aforementioned first aspect or any one of the implementations of the first aspect.

[0058] In a fourth aspect, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, which stores computer instructions, and when the computer instructions are executed by at least one processor, the at least one processor executes the energy-saving assessment method based on multi-parameter coupling in the aforementioned first aspect or any implementation of the first aspect.

[0059] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the energy-saving evaluation method based on multi-parameter coupling in the aforementioned first aspect or any implementation of the first aspect.

[0060] The energy-saving assessment method based on multi-parameter coupling in the embodiment of the present disclosure breaks through the traditional single-dimensional energy consumption analysis by proposing a three-dimensional coupling model of environmental parameters, equipment status parameters, and operating parameters; adjusts the weight coefficient in real time according to the correlation strength between parameters to solve the problem of assessment distortion caused by fixed weights; and constructs an adaptive baseline based on historical data and real-time operating conditions to improve the accuracy of energy-saving potential assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic flow chart of an energy-saving evaluation method based on multi-parameter coupling provided in an embodiment of the present disclosure;

[0062] Figure 2 A flowchart of an energy-saving evaluation method based on multi-parameter coupling provided in an embodiment of the present disclosure;

[0063] Figure 3 A schematic diagram of the structure of an energy-saving evaluation system based on multi-parameter coupling provided in an embodiment of the present disclosure;

[0064] Figure 4 A schematic diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0065] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0066] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0067] It should be noted that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or functionality described herein is illustrative only. Based on this disclosure, those skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. In addition, other structures and / or functionality other than one or more of the aspects described herein may be used to implement this apparatus and / or practice this method.

[0068] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0069] An embodiment of the present invention provides an energy-saving evaluation method based on multi-parameter coupling, which establishes an interactive influence model of environmental parameters, equipment parameters, and operating parameters through a three-dimensional coupling parameter system; and introduces an improved grey absolute correlation algorithm to capture the causal relationship between parameters in real time, which can detect coupling loss and maintain the effectiveness of the evaluation under non-steady-state conditions such as equipment aging and climate anomalies, thereby reducing evaluation errors.

[0070] Figure 1 A schematic diagram of the process of the energy-saving evaluation method based on multi-parameter coupling provided in an embodiment of the present disclosure.

[0071] Figure 2 For Figure 1 Corresponding flow chart of the energy-saving assessment method based on multi-parameter coupling.

[0072] like Figure 1 As shown, in step S110, parameters are acquired; the parameters include environmental parameters, equipment parameters and operating parameters.

[0073] More specifically, environmental parameters, equipment parameters, and operating parameters are acquired through collaborative multimodal data collection.

[0074] Obtain environmental parameters through the environmental sensor group, including temperature, humidity, air pressure, light intensity, etc.;

[0075] Obtain operating parameters through the equipment monitoring unit, including vibration spectrum, current harmonics, etc.;

[0076] Obtain equipment parameters through the control system, including valve opening, inverter frequency, etc.

[0077] Furthermore, the method further includes preprocessing the acquired parameters, including eliminating high-frequency noise using an SG filtering algorithm, and aligning asynchronous data streams based on dynamic time warping (DTW).

[0078] More specifically, the process proceeds to step S120.

[0079] In step S120 , the correlation between the parameters is calculated based on the grey correlation analysis method.

[0080] In an embodiment of the present invention, the calculation of the correlation between the parameters based on the grey correlation analysis method includes: intercepting the data sequence of the parameters in the window; performing a standardization operation on the data sequence; calculating the correlation of the parameters based on the data sequence after the standardization operation; dynamically updating the weight factor, and updating the correlation based on the updated weight factor.

[0081] More specifically, the data sequence of each parameter in the window is intercepted in real time :

[0082] ...Formula 1

[0083] in, For the Sequence parameters at time Observed values ​​of is the current moment, and the window range is ; is the sliding window length, which indicates the number of local correlations used in each calculation (e.g., k = 50 sampling points);

[0084] Perform Z-score normalization on each parameter within the window to eliminate dimensional differences and highlight change trends:

[0085] ...Formula 2

[0086] in, is the standardized parameter value; , Parameters within the window The mean and standard deviation of .

[0087] In the embodiment of the present invention, the step of calculating the correlation of parameters based on the data sequence after the normalization operation includes:

[0088] The correlation of the parameters is calculated based on the following formula:

[0089] ...Formula 3

[0090] in, For the Observed values ​​of the sequence parameters With the Observed values ​​of the sequence parameters At the moment The instantaneous correlation coefficient of for The weight factor of for and The absolute difference of for and At the moment The absolute difference of is the resolution coefficient ( = 0.5), controlling for sensitivity to differences; For the current moment.

[0091] In an embodiment of the present invention, dynamically updating the weight factor and updating the association degree based on the updated weight factor includes:

[0092] The correlation degree under multiple time offsets is calculated based on the following formula:

[0093] ...Formula 4

[0094] in, for and distance Correlation coefficient of steps; is the time lag parameter used to detect right The leading / lagging relationship is When it is a positive number, it means ahead. When it is a negative number, it indicates lag; For the current moment; is the sliding window length;

[0095] The maximum correlation direction is calculated based on the following formula:

[0096] ...Formula 5

[0097] in, is the maximum correlation direction; is the time lag parameter; is the maximum allowed time lag range; is the minimum allowed time lag range; for and distance Correlation coefficient of steps;

[0098] Update the weight factor based on the following formula:

[0099] ...Formula 6

[0100] in, For the updated The weight factor of is the maximum correlation direction; is a constant; that is, the larger the lag, the lower the weight.

[0101] Update the relevance based on the updated weight factor, which is achieved by:

[0102] Exponentially Weighted Moving Average (EWMA) is used to update the correlation, achieving smooth transition and real-time update of the correlation:

[0103] ...Formula 7

[0104] in, for and exist The final gray absolute correlation of the moment; For the current moment; is the forgetting factor, (0< <1), controls the decay rate of historical data. When the current system changes steadily, Approaching 1, historical data has a high weight; when the current system changes rapidly, Approaching 0, the current window data weight is high; For parameters and exist Correlation coefficient of the moment; For the previous moment; is the sliding window length; For the Observed values ​​of the sequence parameters With the Observed values ​​of the sequence parameters At the moment The instantaneous correlation coefficient.

[0105] Furthermore, the causal relationship is determined based on the final grey absolute correlation degree:

[0106] like >0.7 and >0, judged as drive ;

[0107] like >0.7 and <0, judged as drive ;

[0108] like <0.3, it was judged that there was no significant causal relationship.

[0109] For example, when the parameter is the fuel flow rate, is the furnace temperature, When the oxygen content of flue gas is , Indicates that the change in fuel flow affects the furnace temperature after 2 seconds; , It means that the oxygen content in the flue gas decreases after the furnace temperature rises for 1 second.

[0110] Next, go to step S130.

[0111] In step S130 , a coupling energy consumption model is established based on the parameters and the correlation between the parameters.

[0112] The real-time data of multiple parameters and their absolute correlation ( ) are integrated to build an energy consumption prediction model and quantify the impact of the coupling between parameters on the total energy consumption.

[0113] In an embodiment of the present invention, establishing a coupling energy consumption model based on the parameters and the correlation between the parameters includes:

[0114] The coupling energy consumption model is established based on the following formula:

[0115] ...Formula 8

[0116] in, The energy consumption value predicted by the model; is the basic energy consumption; Types of parameters that affect energy consumption; is the ordinal number of the data sequence; For the Observed values ​​of the sequence parameters The parameter independent influence coefficient of For the Sequence parameters at time Observed values ​​of For the Sequence parameters at time Observed values ​​of for and exist The final gray absolute correlation of the moment; is the dynamic coupling weight.

[0117] In an embodiment of the present invention, the dynamic coupling weight is calculated based on the following formula:

[0118] ...Formula 9

[0119] in, is the dynamic coupling weight; is a constant; is the maximum correlation direction; for and exist The final gray absolute correlation of the moment.

[0120] In an embodiment of the present invention, the parameter independent influence coefficient is obtained based on the following method: :

[0121] Obtaining historical data samples; the historical data samples include parameters and actual energy consumption;

[0122] Perform Z-score standardization on historical data samples and delete data points that exceed 3 times the standard deviation;

[0123] Obtaining historical data samples; the historical data samples include parameters and actual energy consumption;

[0124] Constructing a linear regression model based on the historical data sample;

[0125] Based on the linear regression model, the minimum sum of squares of the prediction error is calculated, and the independent influence coefficient of the parameters that minimizes the sum of squares of the prediction error is solved. :

[0126] ...Formula 10

[0127] in, for The parameter-independent influence coefficient of ; is the basic energy consumption; is the total number of samples; is the serial number of the sample; For the The actual observed value of the sample; For the The predicted value of samples;

[0128] No. The predicted value of samples Calculated by the following formula:

[0129] ...Formula 11

[0130] in, For the The observed values ​​of the sequence parameters, .

[0131] Next, go to step S140.

[0132] In step S140 , energy-saving evaluation is performed based on the coupled energy consumption model and the dynamic benchmark to obtain an energy consumption index.

[0133] More specifically, a benchmark energy consumption that changes over time or operating conditions is established to avoid the errors of traditional fixed benchmarks.

[0134] A dynamic benchmark model is constructed based on the following formula:

[0135] ...Formula 12

[0136] in, is the dynamic benchmark energy consumption (i.e., the theoretical optimal value); is the basic energy consumption; is the parameter index; Indicates the Dynamic parameters that affect energy consumption include temperature, load, operating time, etc. The theoretical minimum energy consumption coefficient can be calibrated through technical manuals or optimal operating conditions; is a time dependency; For the current moment.

[0137] The benchmark drift term is calibrated based on a time series model (such as ARIMA) or an environmental parameter interpolation method.

[0138] The energy saving potential (energy consumption index) is evaluated by quantifying the deviation of actual energy consumption from the dynamic benchmark based on the following formula:

[0139] ...Formula 13

[0140] in, Energy consumption index: when the energy consumption index is greater than 100%, it means that the energy consumption exceeds the standard; when the energy consumption index is less than 100%, it means that it is better than the benchmark; The energy consumption value predicted by the model; is the dynamic baseline energy consumption; For noise, when noise When it cannot be measured directly, it can be estimated and eliminated through signal processing technology (such as Kalman filtering).

[0141] The energy-saving evaluation method based on multi-parameter coupling proposed in the present invention has significantly improved evaluation accuracy compared with traditional evaluation methods; the present invention can detect implicit coupling losses that traditional methods cannot identify; and the present invention still maintains evaluation effectiveness under non-steady-state conditions such as equipment aging and climate anomalies.

[0142] Figure 3 The energy-saving evaluation system 300 based on multi-parameter coupling provided by the present invention is shown, including a data acquisition module 310 , a calculation module 320 , a coupling energy consumption model construction module 330 and an evaluation module 340 .

[0143] The data acquisition module 310 is used to acquire parameters; the parameters include environmental parameters, equipment parameters and operating parameters;

[0144] The calculation module 320 is used to calculate the correlation between the parameters based on the grey correlation analysis method;

[0145] The coupling energy consumption model building module 330 is used to build a coupling energy consumption model based on the parameters and the correlation between the parameters;

[0146] The evaluation module 340 is used to perform energy-saving evaluation based on the coupled energy consumption model and the dynamic benchmark to obtain an energy consumption index.

[0147] See also Figure 4 The present disclosure further provides an electronic device 40, which includes:

[0148] at least one processor; and,

[0149] a memory communicatively connected to the at least one processor; wherein,

[0150] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to execute the energy-saving evaluation method based on multi-parameter coupling in the aforementioned method embodiment.

[0151] An embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, which stores computer instructions for causing the computer to execute the energy-saving assessment method based on multi-parameter coupling in the aforementioned method embodiment.

[0152] An embodiment of the present disclosure also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the energy-saving evaluation method based on multi-parameter coupling in the aforementioned method embodiment.

[0153] Reference below Figure 4 , which shows a schematic structural diagram of an electronic device 40 suitable for implementing an embodiment of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0154] like Figure 4 As shown, electronic device 40 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 402 or programs loaded from a storage device 408 into a random access memory (RAM) 403. RAM 403 also stores various programs and data required for the operation of electronic device 40. Processing device 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0155] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 408 including, for example, a magnetic tape, hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 40 to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows the electronic device 40 with various devices, it should be understood that not all of the devices shown are required to be implemented or present. More or fewer devices may alternatively be implemented or present.

[0156] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0157] It should be noted that the computer-readable medium described above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0158] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0159] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including the at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0160] Alternatively, the computer-readable medium carries one or more programs, which, when executed by the electronic device, causes the electronic device to: receive a node evaluation request including at least two Internet Protocol addresses; select an Internet Protocol address from the at least two Internet Protocol addresses; and return the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0161] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0163] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses."

[0164] It should be understood that various parts of the present disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0165] The above description is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with this technical field within the technical scope disclosed in this disclosure should be covered by the protection scope of the present disclosure.

Claims

1. An energy-saving evaluation method based on multi-parameter coupling, characterized in that: The method comprises the following steps: Acquire parameters; the parameters include environmental parameters, equipment parameters, and operating parameters; acquire environmental parameters, equipment parameters, and operating parameters through multimodal data collaborative acquisition; acquire environmental parameters through an environmental sensor group, including temperature, humidity, air pressure, and light intensity; acquire operating parameters through an equipment monitoring unit, including vibration spectrum and current harmonics; acquire equipment parameters through a control system, including valve opening and inverter frequency; Calculate the correlation between the parameters based on the grey correlation analysis method; Establishing a coupling energy consumption model based on the parameters and the correlation between the parameters; An energy consumption index is obtained by performing an energy saving assessment based on a coupled energy consumption model and a dynamic benchmark, wherein the dynamic benchmark is a benchmark energy consumption that changes over time or operating conditions; The calculation of the correlation between the parameters based on the grey correlation analysis method includes: intercepting a data sequence of the parameter within the window; performing a standardization operation on the data sequence; Calculate the correlation of parameters based on the data sequence after standardization operation; Dynamically update the weight factor, and update the correlation degree based on the updated weight factor to obtain the final grey absolute correlation degree; The establishing of the coupling energy consumption model based on the parameters and the correlation between the parameters includes: The coupling energy consumption model is established based on the following formula: in, is the energy consumption value predicted by the model; β0 is the basic energy consumption; n is the type of parameters affecting energy consumption; i is the order number of the data sequence; β i is the observed value X of the i-th sequence parameter i The parameter independent influence coefficient of X i (t) is the observed value of the i-th sequence parameter at time t; X j (t) is the observed value of the jth sequence parameter at time t; Γ ij (t) is X i With X j The final grey absolute correlation degree at time t; w ij (t) is the dynamic coupling weight.

2. The energy-saving evaluation method based on multi-parameter coupling according to claim 1 is characterized in that: The calculation of the correlation degree of the parameters based on the data sequence after the normalization operation includes: The correlation of the parameters is calculated based on the following formula: Among them, γ ij (t) is the observed value X of the i-th sequence parameter i The observed value X of the jth sequence parameter j The instantaneous correlation coefficient at time t; ω j For X j The weight factor of ij For X i With X j The absolute difference of ij (t) is X i With X j The absolute difference at time t; ρ is the resolution coefficient; t is the current time.

3. The energy-saving evaluation method based on multi-parameter coupling according to claim 1 is characterized in that: The dynamically updating the weight factor and updating the association degree based on the updated weight factor includes: The correlation degree under multiple time offsets is calculated based on the following formula: in, For X j With X i The correlation coefficient of the distance τ steps; τ is the time lag parameter, when τ is a positive number, it means leading, when τ is a negative number, it means lagging; t is the current time; k is the sliding window length; The maximum correlation direction is calculated based on the following formula: Among them, τ opt is the maximum correlation direction; τ is the time lag parameter; T is the maximum allowed time lag range; -T is the minimum allowed time lag range; For X j With X i Correlation coefficient of distance τ steps; Update the weight factor based on the following formula: Among them, w j ′ is the updated X j The weight factor of opt is the maximum correlation direction; e is a constant; Update the relevance based on the updated weight factor, which is achieved by: Among them, Γ ij (t) is X i With X j The final grey absolute correlation degree at time t; t is the current moment; α is the forgetting factor; γ ij (t-1) is the parameter X i With X j The correlation coefficient at time t-1; t-1 is the previous moment; k is the sliding window length; γ ij (t) is the observed value X of the i-th sequence parameter i The observed value X of the jth sequence parameter j The instantaneous correlation coefficient at time t.

4. The energy-saving evaluation method based on multi-parameter coupling according to claim 1 is characterized in that: The dynamic coupling weight is calculated based on the following formula: Among them, w ij (t) is the dynamic coupling weight; e is a constant; τ opt is the maximum correlation direction; Γ ij (t) is X i With X j The final grey absolute correlation degree at time t.

5. The energy-saving evaluation method based on multi-parameter coupling according to claim 1 is characterized in that: The parameter independent influence coefficient β is obtained based on the following method i : Obtaining historical data samples; the historical data samples include parameters and actual energy consumption; Constructing a linear regression model based on the historical data sample; Based on the linear regression model, the minimum sum of squares of the prediction error is calculated, and the independent influence coefficient β of the parameter that minimizes the sum of squares of the prediction error is solved. i : Among them, β i For X i The independent influence coefficient of the parameters, i = 1, 2, ..., m; β0 is the basic energy consumption; N is the total number of samples; r is the serial number of the sample; E r is the actual observation value of the rth sample; is the predicted value of the rth sample; The predicted value of the rth sample Calculated by the following formula: Among them, X i is the observed value of the i-th sequence parameter, i = 1, 2, ..., m.

6. An energy-saving evaluation system based on multi-parameter coupling, characterized in that: The system comprises: A data acquisition module is configured to acquire parameters; the parameters include environmental parameters, equipment parameters, and operating parameters; the environmental parameters, equipment parameters, and operating parameters are acquired through multimodal data collaborative acquisition; environmental parameters are acquired through an environmental sensor group, including temperature, humidity, air pressure, and light intensity; operating parameters are acquired through an equipment monitoring unit, including vibration spectrum and current harmonics; and equipment parameters are acquired through a control system, including valve opening and inverter frequency; A calculation module is configured to calculate the correlation between the parameters based on a grey correlation analysis method; the calculation of the correlation between the parameters based on the grey correlation analysis method includes: intercepting a data sequence of the parameters within a window; performing a standardization operation on the data sequence; calculating the correlation between the parameters based on the data sequence after the standardization operation; dynamically updating a weight factor, and updating the correlation based on the updated weight factor to obtain a final grey absolute correlation; the establishment of a coupling energy consumption model based on the parameters and the correlation between the parameters includes: establishing the coupling energy consumption model based on the following formula: in, is the energy consumption value predicted by the model; β0 is the basic energy consumption; n is the type of parameters affecting energy consumption; i is the order number of the data sequence; β i is the observed value X of the i-th sequence parameter i The parameter independent influence coefficient of X i (t) is the observed value of the i-th sequence parameter at time t; X j (t) is the observed value of the jth sequence parameter at time t; Γ ij (t) is X i With X j The final grey absolute correlation degree at time t; w ij (t) is the dynamic coupling weight; a coupling energy consumption model building module, configured to establish a coupling energy consumption model based on the parameters and the correlation between the parameters; The evaluation module is configured to perform energy-saving evaluation based on a coupled energy consumption model and a dynamic benchmark to obtain an energy consumption index; wherein the dynamic benchmark is a benchmark energy consumption that changes with time or working conditions.

7. An electronic device, characterized in that: The electronic device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is caused to execute the energy-saving assessment method based on multi-parameter coupling according to any one of claims 1 to 5.

8. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the energy-saving assessment method based on multi-parameter coupling according to any one of claims 1 to 5.

Images