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

Abstract

The invention discloses an energy-saving evaluation method, system, equipment and program based on multi-parameter coupling. The method comprises the following steps: acquiring parameters; the parameters comprise environment parameters, equipment parameters and operation parameters; calculating the correlation degree among the parameters based on a grey correlation analysis method; establishing a coupling energy consumption model based on the parameters and the correlation degree between the parameters; and performing energy-saving evaluation based on the coupling energy consumption model and the dynamic reference to obtain an energy consumption index. Through the processing scheme disclosed by the invention, the coupling loss can be detected, the evaluation effectiveness is still kept under the unsteady working conditions such as equipment aging and climate abnormality, and the evaluation error is reduced.

Description

Technical Field

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

[0002] Traditional methods only consider the linear change of a single parameter (such as power), ignoring the non-linear coupling effect between parameters, resulting in inaccurate evaluation and easy to cause losses. For example, only adjusting the output power according to the device current, without considering the decrease of heat dissipation efficiency caused by the increase of ambient temperature, leading to equipment overload failure; the adjustment of the blade angle of a wind turbine only depends on the average wind speed, ignoring the cumulative effect of turbulence intensity on the wear rate of the gearbox, resulting in a 20% reduction in the equipment life.

[0003] Moreover, the existing fixed weight allocation method also cannot adapt to dynamic working conditions such as equipment aging and environmental mutations. The energy efficiency benchmark uses static historical data, resulting in the evaluation result deviating from the actual operating state.

[0004] Therefore, it is obvious that the above existing energy conservation evaluation methods still have inconveniences and defects in use and urgently need to be further improved. How to create a new energy conservation evaluation method based on multi-parameter coupling has become an urgent goal to be improved in the current industry. Summary of the Invention

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

[0006] In a first aspect, embodiments of the present disclosure provide an energy conservation evaluation method based on multi-parameter coupling, the method comprising the following steps: Obtain parameters; the parameters include environmental parameters, device parameters and operating parameters; Calculate the correlation degree between the parameters based on the grey relational analysis method; Establish a coupled energy consumption model based on the parameters and the correlation degree between the parameters; Perform energy conservation evaluation based on the coupled energy consumption model and the dynamic benchmark to obtain an energy consumption index.

[0007] According to a specific implementation manner of embodiments of the present disclosure, calculating the correlation degree between the parameters based on the grey relational analysis method includes: Intercept the data sequence of the parameters within the window; Perform a normalization operation on the data sequence; Calculate the correlation degree of the parameters based on the data sequence after the normalization operation; Dynamically update the weight factor, and update the correlation degree based on the updated weight factor.

[0008] According to a specific implementation manner of an embodiment of the present disclosure, calculating the correlation degree of parameters based on the data sequence after standardization includes: Calculating the correlation degree of parameters based on the following formula: ; wherein, is the observed value of the th sequence parameter and the th sequence parameter at time instantaneous correlation coefficient; is weight factor of; is and absolute difference; is and at time absolute difference; is the resolution coefficient; is the current time.

[0009] According to a specific implementation manner of an embodiment of the present disclosure, dynamically updating the weight factor and updating the correlation degree based on the updated weight factor includes: Calculating the correlation degree under multi-time offset based on the following formula: ; wherein, is and distance step correlation coefficient; is the time lag parameter, when is a positive number, it means ahead, when is a negative number, it means lag; is the current time; is the sliding window length; Calculating the maximum correlation direction based on the following formula: ; wherein, is the maximum correlation direction; is the time lag parameter; is the maximum allowable time lag range; is the minimum allowable time lag range; is and distance step correlation coefficient; Updating the weight factor based on the following formula: ; Among them, is the weight factor of the updated ; is the maximum correlation direction; is a constant; Update the correlation degree based on the updated weight factor, which is achieved by the following method: ; Among them, is and at the final grey absolute correlation degree at the moment; is the current moment; is the forgetting factor; is the parameter and at the correlation coefficient at the moment; is the previous moment; is the sliding window length; is the observation value of the th sequence parameter and the observation value of the th sequence parameter at the moment

[0010] According to a specific implementation manner of the embodiment of the present disclosure, the coupling energy consumption model established based on the parameter and the correlation degree between the parameters includes: Establish a coupling energy consumption model based on the following formula: ; Among them, is the energy consumption value predicted by the model; is the basic energy consumption; is the type of parameters affecting the energy consumption; is the observation value of the th sequence parameter is the th sequence parameter at the moment ; is the th sequence parameter at the moment ; is and at the final grey absolute correlation degree at the moment; is the dynamic coupling weight.

[0011] According to a specific implementation manner of the embodiments of the present disclosure, the dynamic coupling weight is calculated based on the following formula: ; wherein, is the dynamic coupling weight; is a constant; is the maximum correlation direction; is and at the final grey absolute correlation degree at the moment.

[0012] According to a specific implementation manner of the embodiments of the present disclosure, the parameter independent influence coefficient is obtained based on the following method : Obtain historical data samples; the historical data samples include parameters and actual energy consumption; Construct a linear regression model based on the historical data samples; Calculate the sum of squared prediction errors based on the linear regression model, and solve for the parameter independent influence coefficient that minimizes the sum of squared prediction errors : ; wherein, is the parameter independent influence coefficient of, ; is the basic energy consumption; is the total number of samples; is the serial number of the samples; is the actual observed value of the th sample; is the predicted value of the th sample; the predicted value of the th sample ; wherein, is the observed value of the th sequence parameter, .

[0013] In a second aspect, the embodiments of the present disclosure provide an energy-saving evaluation system based on multi-parameter coupling, and the system includes: A data acquisition module configured to acquire parameters; the parameters include environmental parameters, device parameters, and operation parameters; A calculation module configured to calculate the correlation degree between the parameters based on the grey correlation analysis method; A coupled energy consumption model construction module, configured to establish a coupled energy consumption model based on the parameters and the correlation degree between the parameters; An evaluation module, configured to perform energy-saving evaluation based on the coupled energy consumption model and a dynamic benchmark to obtain an energy consumption index.

[0014] In a third aspect, an embodiment of the present disclosure further provides an electronic device, which includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor performs the energy-saving evaluation method based on multi-parameter coupling according to any one of the foregoing first aspects or any implementation manner of the first aspect.

[0015] In a fourth aspect, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, and the non-transitory computer-readable storage medium stores computer instructions, and when the computer instructions are executed by at least one processor, the at least one processor performs the energy-saving evaluation method based on multi-parameter coupling according to any one of the foregoing first aspects or any implementation manner of the first aspect.

[0016] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, and the computer program product includes a computing program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, and when the program instructions are executed by a computer, the computer performs the energy-saving evaluation method based on multi-parameter coupling according to any one of the foregoing first aspects or any implementation manner of the first aspect.

[0017] The energy-saving evaluation method based on multi-parameter coupling in the embodiments of the present disclosure breaks through the traditional single-dimensional energy consumption analysis by proposing a three-dimensional coupling model of environmental parameters, equipment state parameters, and operation parameters; adjusts the weight coefficient in real time according to the correlation strength between the parameters to solve the problem of evaluation distortion caused by fixed weights; constructs an adaptive baseline based on historical data and real-time working conditions to improve the accuracy of energy-saving potential evaluation. Description of the Drawings

[0018] Figure 1 It is a schematic flowchart of an energy-saving evaluation method based on multi-parameter coupling provided by an embodiment of the present disclosure; Figure 2 It is a flowchart block diagram of an energy-saving evaluation method based on multi-parameter coupling provided by an embodiment of the present disclosure; Figure 3 It is a schematic structural diagram of an energy-saving evaluation system based on multi-parameter coupling provided by an embodiment of the present disclosure; Figure 4Schematic diagram of the electronic device provided by the embodiments of the present disclosure. Detailed implementation manners

[0019] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0020] The following uses specific specific examples to illustrate the implementation manners of the present disclosure. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific implementation manners, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without making creative efforts belong to the scope of protection of the present disclosure.

[0021] It should be noted that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. In addition, this device and / or method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0022] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0023] The embodiments of the present invention provide an energy-saving evaluation method based on multi-parameter coupling, which establishes an interaction influence model of environmental parameters, equipment parameters, and operation parameters through a three-dimensional coupling parameter system; and introduces an improved grey absolute correlation degree algorithm to capture the causal relationship between parameters in real time, which can detect coupling losses and maintain the evaluation effectiveness under non-steady-state working conditions such as equipment aging and abnormal climate, and reduce evaluation errors.

[0024] Figure 1 Schematic diagram of the flow of the energy-saving evaluation method based on multi-parameter coupling provided by the embodiments of the present disclosure.

[0025] Figure 2 For Figure 1 The flow block diagram of the energy-saving evaluation method based on multi-parameter coupling corresponding thereto.

[0026] As Figure 1As shown, at step S110, parameters are obtained; the parameters include environmental parameters, device parameters, and operating parameters.

[0027] More specifically, the environmental parameters, device parameters, and operating parameters are obtained through collaborative multi-modal data acquisition.

[0028] The environmental parameters are obtained through an environmental sensor group, including: temperature and humidity, air pressure, light intensity, etc.; The operating parameters are obtained through a device monitoring unit, including: vibration spectrum, current harmonics, etc.; The device parameters are obtained through a control system, including: valve opening, frequency converter frequency, etc.

[0029] Furthermore, the method further includes preprocessing the obtained parameters, including using the SG filtering algorithm to eliminate high-frequency noise and aligning asynchronous data streams based on Dynamic Time Warping (DTW).

[0030] More specifically, next, go to step S120.

[0031] At step S120, the correlation degree between the parameters is calculated based on the grey relational analysis method.

[0032] In the embodiment of the present invention, calculating the correlation degree between the parameters based on the grey relational analysis method includes: intercepting the data sequence of the parameters within the window; performing a normalization operation on the data sequence; calculating the correlation degree of the parameters based on the normalized data sequence; dynamically updating the weight factor, and updating the correlation degree based on the updated weight factor.

[0033] More specifically, the data sequence of each parameter within the window is intercepted in real time : …… Equation 1 where is the observed value of the th sequence parameter at time ; is the current time, and the window range is ; is the sliding window length, indicating the number of local correlation degrees used in each calculation (for example, k = 50 sampling points); Perform Z-score normalization on each parameter within the window to eliminate the dimension difference and highlight the change trend: …… Equation 2 where is the normalized parameter value; , is the parameter within the window The mean and standard deviation.

[0034] In an embodiment of the present invention, calculating the correlation degree of parameters based on the data sequence after standardization operations includes: Calculating the correlation degree of parameters based on the following formula: …… Equation 3 Wherein, is the observed value of the th sequence parameter and the th sequence parameter at time The instantaneous correlation coefficient; is The weight factor; is and The absolute difference; is and at time The absolute difference; is the resolution coefficient ( = 0.5), controlling the sensitivity to differences; is the current time.

[0035] In an embodiment of the present invention, dynamically updating the weight factor and updating the correlation degree based on the updated weight factor includes: Calculating the correlation degree under multi-time offset based on the following formula: …… Equation 4 Wherein, is and The correlation coefficient at a distance of steps; is the time lag parameter for detecting the lead / lag relationship with being positive indicating ahead, and being negative indicating lag; is the current time; is the sliding window length; Calculating the maximum correlation direction based on the following formula: …… Equation 5 Wherein, is the maximum correlation direction; is the time lag parameter; is the maximum allowable time lag range; is the minimum allowable time lag range; For The correlation coefficient with distance for steps; …… Equation 6 Wherein, is the updated weight factor; is the maximum correlation direction; is a constant; that is, the greater the lag, the lower the weight.

[0036] Update the correlation degree based on the updated weight factor, which is achieved by the following method: Use the Exponentially Weighted Moving Average (EWMA) to update the correlation degree to achieve smooth transition and real-time update of the correlation degree: …… Equation 7 Wherein, is the final grey absolute correlation degree between and at time is the current time; is the forgetting factor, (0 < < 1), which controls the attenuation rate of historical data. When the current system changes smoothly, tends to 1 and the weight of historical data is high; when the current system changes rapidly, tends to 0 and the weight of current window data is high; is the parameter the correlation coefficient between and at time is the previous time; is the sliding window length; is the th observed value of the sequence parameter and the th observed value of the sequence parameter at time is the instantaneous correlation coefficient.

[0037] Furthermore, determine the causal relationship based on the final grey absolute correlation degree: If > 0.7 and > 0, it is determined that drives ; If > 0.7 and < 0, it is determined that Drive ; If < 0.3, it is determined that there is no significant causal relationship.

[0038] For example, when the parameter is the fuel flow rate, is the furnace temperature, is the oxygen content in the flue gas, the final grey absolute correlation degree , indicates that the furnace temperature is affected 2 seconds after the fuel flow rate changes; , indicates that the oxygen content in the flue gas decreases 1 second after the furnace temperature rises.

[0039] Next, go to step S130.

[0040] At step S130, a coupled energy consumption model is established based on the parameter and the correlation degree between the parameters.

[0041] Fuse the real-time data of multiple parameters and their absolute correlation degrees ( ), construct an energy consumption prediction model, and quantify the influence of the coupling effect between parameters on the total energy consumption.

[0042] In the embodiment of the present invention, establishing the coupled energy consumption model based on the parameter and the correlation degree between the parameters includes: Establish a coupled energy consumption model based on the following formula: …… Equation 8 Wherein, is the energy consumption value predicted by the model; is the basic energy consumption; is the type of each parameter affecting the energy consumption; is the sequence number of the data sequence; is the observation value of the parameter independent influence coefficient of the th sequence parameter; is the observation value of the th sequence parameter at time ; is the observation value of the th sequence parameter at time ; is the final grey absolute correlation degree between and

[0043] At time …… Equation 9 wherein is the dynamic coupling weight; is a constant; is the maximum correlation direction; is and at the final grey absolute correlation degree at time

[0044] In the embodiment of the present invention, the parameter independent influence coefficient is obtained based on the following method : Obtain historical data samples; the historical data samples include parameters and actual energy consumption; Perform Z-score standardization operation on the historical data samples, and delete the data points exceeding 3 times the standard deviation; Obtain historical data samples; the historical data samples include parameters and actual energy consumption; Construct a linear regression model based on the historical data samples; Calculate the sum of squared prediction errors based on the linear regression model, and solve for the parameter independent influence coefficient that minimizes the sum of squared prediction errors : …… Equation 10 wherein is the parameter independent influence coefficient of ; is the basic energy consumption; is the total number of samples; is the serial number of the sample; is the actual observed value of the th sample; is the predicted value of the th sample; the predicted value of the th sample …… Equation 11 wherein is the observed value of the th sequence parameter, ;

[0045] Next, go to step S140.

[0046] At step S140, perform energy-saving evaluation based on the coupled energy consumption model and the dynamic benchmark to obtain an energy consumption index.

[0047] More specifically, a reference energy consumption that varies with time or operating conditions is established to avoid the errors of traditional fixed references.

[0048] A dynamic reference model is constructed based on the following formula: …… Equation 12 Wherein, is the dynamic reference energy consumption (i.e., the theoretical optimal value); is the basic energy consumption; is the parameter index; represents the th dynamic parameter affecting energy consumption. The dynamic parameters include temperature, load, operating time, etc.; is the theoretical minimum energy consumption coefficient, which can be calibrated through technical manuals or optimal operating conditions; is the time-dependent term; is the current time.

[0049] The reference drift term is calibrated based on a time series model (such as ARIMA) or environmental parameter interpolation method.

[0050] The deviation degree between the actual energy consumption and the dynamic reference is quantified based on the following formula to evaluate the energy-saving potential (energy consumption index): …… Equation 13 Wherein, is the energy consumption index. When the energy consumption index is greater than 100%, it indicates that the energy consumption exceeds the standard. When the energy consumption index is less than 100%, it indicates that it is better than the reference; is the energy consumption value predicted by the model; is the dynamic reference energy consumption; is the noise. When the noise cannot be directly measured, it can be estimated and eliminated through signal processing techniques (such as Kalman filtering, etc.).

[0051] The energy-saving evaluation method based on multi-parameter coupling proposed by the present invention has a significant improvement in evaluation accuracy compared with traditional evaluation methods; the present invention can detect hidden coupling losses that cannot be identified by traditional methods; and the present invention still maintains evaluation effectiveness under non-steady-state conditions such as equipment aging and climate anomalies.

[0052] Figure 3 Fig. shows an energy-saving evaluation system 300 based on multi-parameter coupling provided by the present invention, including a data acquisition module 310, a calculation module 320, a coupling energy consumption model construction module 330, and an evaluation module 340.

[0053] The data acquisition module 310 is used to acquire parameters; the parameters include environmental parameters, equipment parameters, and operating parameters; The calculation module 320 is used to calculate the correlation degree between the parameters based on the grey relational analysis method; The coupled energy consumption model construction module 330 is used to establish a coupled energy consumption model based on the parameters and the correlation degree between the parameters; 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.

[0054] See Figure 4 , embodiments of the present disclosure further provide an electronic device 40, which includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the energy-saving evaluation method based on multi-parameter coupling in the foregoing method embodiments.

[0055] Embodiments of the present disclosure further provide a non-transitory computer-readable storage medium, which stores computer instructions for causing the computer to execute the energy-saving evaluation method based on multi-parameter coupling in the foregoing method embodiments.

[0056] Embodiments of the present disclosure further provide a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer is caused to execute the energy-saving evaluation method based on multi-parameter coupling in the foregoing method embodiments.

[0057] Next, refer to Figure 4 , which shows a schematic structural diagram of an electronic device 40 suitable for implementing embodiments of the present disclosure. The electronic device in embodiments of the present disclosure may include, but is 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 (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is only an example and should not impose any limitations on the functions and usage scopes of embodiments of the present disclosure.

[0058] As Figure 4As shown, the electronic device 40 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 401, which may perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage device 408 into the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 40 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0059] Generally, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a 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 wiredly to exchange data. Although the figure shows an electronic device 40 having various devices, it should be understood that it is not required to implement or include all the shown devices. More or fewer devices may be implemented or included alternatively.

[0060] Specifically, according to an embodiment of the present disclosure, the process described above with reference to the flowchart may 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 contains program codes for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from the network through 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 functions defined in the method of the embodiment of the present disclosure are executed.

[0061] It should be noted that the above-mentioned computer-readable medium in the present disclosure can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable signal medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0062] The above-mentioned computer-readable medium can be included in the above-mentioned electronic device; or it can exist separately without being assembled into the electronic device.

[0063] The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device is caused to: obtain at least two Internet protocol addresses; send a node evaluation request including the at least two Internet protocol addresses to a node evaluation device, where the node evaluation device selects an Internet protocol address from the at least two Internet protocol addresses and returns it; receive the Internet protocol address returned by the node evaluation device; where the obtained Internet protocol addresses indicate edge nodes in a content distribution network.

[0064] Alternatively, the computer-readable medium carries one or more programs that, when executed by the electronic device, cause 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; return the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content delivery network.

[0065] Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages or combinations thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of 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).

[0066] The flowcharts and block diagrams in the figures illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the figures. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0067] The units described in the embodiments of the present disclosure may be implemented in software or in hardware. Wherein, the name of the unit does not constitute a limitation to the unit itself in some cases. For example, the first acquisition unit may also be described as "the unit for acquiring at least two Internet Protocol addresses".

[0068] It should be understood that the various parts of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

[0069] As described above, it is only the specific implementation manner of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present disclosure should be covered within the protection scope of the present disclosure.

Claims

1. An energy-saving evaluation method based on multi-parameter coupling, characterized in that The method includes the following steps: Obtain parameters; the parameters include environmental parameters, device parameters, and operating parameters; Calculate the correlation degree between the parameters based on the grey relational analysis method; Establish a coupled energy consumption model based on the parameters and the correlation degree between the parameters; Conduct an energy-saving evaluation based on the coupled energy consumption model and a dynamic benchmark to obtain an energy consumption index; wherein, the dynamic benchmark is a benchmark energy consumption that varies with time or operating conditions; The calculation of the correlation degree between the parameters based on the grey relational analysis method includes: Intercept the data sequence of the parameters within a window; Perform a normalization operation on the data sequence; Calculate the correlation degree of the parameters based on the data sequence after the normalization operation; Dynamically update the weight factor and update the correlation degree based on the updated weight factor; The establishment of the coupled energy consumption model based on the parameters and the correlation degree between the parameters includes: Establish a coupled energy consumption model based on the following formula: ； Among them, is the energy consumption value predicted by the model; is the basic energy consumption; is the type of parameters affecting energy consumption; is the sequence number of the data sequence; is the observed value of the parameter independent influence coefficient of the th sequence parameter; is the observed value of the th sequence parameter at time is the th sequence parameter; observed value of the is and at the final grey absolute correlation degree at time; is the dynamic coupling weight.

2. The energy-saving evaluation method based on multi-parameter coupling according to claim 1, wherein The calculation of the correlation degree of the parameters based on the data sequence after the normalization operation includes: Calculate the correlation degree of the parameters based on the following formula: ； Among them, is the observed value of the th sequence parameter and the observed value of the th sequence parameter at time ; the instantaneous correlation coefficient is the weight factor of ; is the absolute difference between and ; is the absolute difference between and at time ; is the discrimination coefficient; is the current time.

3. The energy-saving evaluation method based on multi-parameter coupling according to claim 1, characterized in that The dynamic update of the weight factor and the update of the correlation degree based on the updated weight factor include: Calculate the correlation degree under multiple time offsets based on the following formula: ； Among them, is the correlation coefficient with distance steps; is the time lag parameter. When is positive, it indicates lead. When is negative, it indicates lag; is the current moment; is the sliding window length; Calculate the maximum correlation direction based on the following formula: ； Among them, is the maximum correlation direction; is the time lag parameter; is the maximum allowable time lag range; is the minimum allowable time lag range; is and distance step correlation coefficient; Update the weight factor based on the following formula: ； Among them, is the weight factor after update; ; is the maximum correlation direction; is a constant; The update of the correlation degree based on the updated weight factor is achieved through the following method: ； Among them, is the final gray absolute correlation degree at the moment; is the current moment; is the forgetting factor; is the parameter between and the correlation coefficient at the moment; is the previous moment; is the sliding window length; is the observed value of the th sequence parameter and the observed value of the th sequence parameter at the moment of the instantaneous correlation coefficient.

4. The energy-saving evaluation method based on multi-parameter coupling according to claim 1, characterized in that Calculate the dynamic coupling weight based on the following formula: ； Among them, is the dynamic coupling weight; is a constant; is the maximum correlation direction; is with at the final grey absolute correlation degree at the moment.

5. The energy-saving evaluation method based on multi-parameter coupling according to claim 1, characterized in that Obtaining parameter-independent influence coefficients based on the following method :[[]]END]] Obtain historical data samples; the historical data samples include parameters and actual energy consumption; Construct a linear regression model based on the historical data samples; Calculate the sum of squared prediction errors based on the linear regression model and solve for the parameter independent influence coefficients that minimize the sum of squared prediction errors : ; Among them, is the parameter independent influence coefficient, ; is the basic energy consumption; is the total number of samples; is the serial number of the samples; is the th actual observed value of the sample; is the th predicted value of the sample; The predicted value of the first sample is calculated by the following formula: ； Among them, is the observed value of the th sequence parameter, .

6. An energy-saving evaluation system based on multi-parameter coupling, characterized in that, The system includes: A data acquisition module configured to obtain parameters; the parameters include environmental parameters, device parameters, and operating parameters; A calculation module configured to calculate the correlation degree between the parameters based on the grey relational analysis method; the calculation of the correlation degree between the parameters based on the grey relational analysis method includes: intercepting the data sequence of the parameters within a window; performing a normalization operation on the data sequence; calculating the correlation degree of the parameters based on the data sequence after the normalization operation; dynamically updating the weight factor and updating the correlation degree based on the updated weight factor; the establishment of the coupled energy consumption model based on the parameters and the correlation degree between the parameters includes: establishing a coupled energy consumption model based on the following formula: ； 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; A coupled energy consumption model construction module configured to establish a coupled energy consumption model based on the parameters and the correlation degree between the parameters; An evaluation module configured to conduct an energy-saving evaluation based on the coupled energy consumption model and a dynamic benchmark to obtain an energy consumption index; wherein, the dynamic benchmark is a benchmark energy consumption that varies with time or operating 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 executes the energy-saving evaluation method based on multi-parameter coupling as described in any one of claims 1 to 5.

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

Images