Real-time monitoring method and system for energy consumption of industrial equipment based on remote control

Abstract

The present invention provides a remote-based real-time monitoring method and system for energy consumption of industrial equipment. The method includes constructing a data acquisition function and a corresponding energy consumption analysis model based on the operating state parameters and energy consumption characteristics of the industrial equipment; determining an energy consumption calculation function based on the operating state parameters and the data acquisition function; constructing an energy consumption optimization model based on the energy consumption calculation function; and constructing an energy consumption optimization gain coefficient calculation model based on the energy consumption optimization model and the energy consumption analysis model. The present invention can realize real-time monitoring and optimization of energy consumption of industrial equipment, reduce energy consumption costs, and improve energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the field of energy consumption monitoring of industrial equipment, and more specifically, to a remote-based real-time monitoring method and system for energy consumption of industrial equipment. Background Art

[0002] In the industrial production process, the energy consumption management of equipment has always been an important topic. Traditional energy consumption monitoring methods mainly rely on manual records and simple instrument readings. This method is not only inefficient, but also difficult to achieve real-time monitoring and accurate analysis of equipment energy consumption. With the development of industrial automation and informatization, some energy consumption monitoring systems based on sensors and data acquisition technology have gradually emerged. These systems can achieve real-time monitoring of the operating status of equipment, but their energy consumption analysis and optimization functions are still insufficient. For example, existing systems can often only monitor a single energy consumption indicator and lack the ability to comprehensively analyze and optimize the overall energy consumption characteristics of the equipment. In addition, these systems also have certain limitations in data processing and analysis, and it is difficult to adapt to the complex and changeable industrial environment and equipment operation status.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: first, the existing energy consumption monitoring system does not monitor the operating status of the equipment comprehensively, and cannot accurately reflect the energy consumption characteristics of the equipment at different operating stages; second, these systems lack effective models and methods in energy consumption analysis and optimization, and it is difficult to achieve accurate calculation and optimal management of equipment energy consumption; finally, the existing system has problems of low efficiency and insufficient precision in data collection and processing, and cannot meet the real-time and accuracy requirements of industrial equipment energy consumption monitoring. Summary of the invention

[0004] The present invention provides a remote-based real-time monitoring method and system for energy consumption of industrial equipment.

[0005] In a first aspect of the present invention, a remote-based real-time monitoring method for energy consumption of industrial equipment is provided, comprising:

[0006] Based on the operating status parameters and energy consumption characteristics of industrial equipment, construct data acquisition functions and corresponding energy consumption analysis models;

[0007] Determining an energy consumption calculation function based on the operating state parameter and the data acquisition function;

[0008] According to the energy consumption calculation function, an energy consumption optimization model is constructed;

[0009] According to the energy consumption optimization model and the energy consumption analysis model, an energy consumption optimization gain coefficient calculation model is constructed.

[0010] Furthermore, the operating status parameters include: equipment power, equipment operating time, operating status parameters at a first moment and operating status parameters at a second moment;

[0011] The operating state parameter at the first moment includes: one or more of: power when the device is started, voltage when the device is started, current when the device is started, temperature when the device is started, and pressure when the device is started;

[0012] The operating status parameter at the second moment includes: one or more of: power during equipment operation, voltage during equipment operation, current during equipment operation, temperature during equipment operation, and pressure during equipment operation.

[0013] Furthermore, based on the operating state parameters and energy consumption characteristic classification, a data acquisition function and a corresponding energy consumption analysis model are constructed, including:

[0014] According to the operating state parameters, a power model and a temperature model are constructed, wherein the power model and the temperature model correspond to one of the energy consumption feature categories respectively; the power model and the temperature model are both used to characterize the energy consumption components under the corresponding category;

[0015] constructing the data acquisition function according to the operating state parameters, the power model and the temperature model;

[0016] According to the data collection function, construct an energy consumption analysis index function;

[0017] Based on the energy consumption analysis index function, the energy consumption analysis model is constructed.

[0018] Furthermore, the power model is generally expressed as:

[0019]

[0020] in, For the device at time The power, is the initial power of the device, is the power change of the equipment, is the amplitude coefficient of power change, is the time attenuation coefficient;

[0021] The general formula of the temperature model is:

[0022]

[0023] in, For the device at time The temperature, is the initial temperature of the device, is the temperature change of the device, is the amplitude coefficient of temperature change, is the angular frequency, is the phase angle.

[0024] Furthermore, constructing a data acquisition function according to the operating state parameter, the power model and the temperature model includes:

[0025] Based on time series analysis, the power model is decomposed into three power components;

[0026] Based on time series analysis, the general temperature model is decomposed into two temperature components;

[0027] Data acquisition functions are constructed respectively according to the three power components and the two temperature components.

[0028] Furthermore, the data acquisition function is specifically:

[0029]

[0030]

[0031]

[0032] in, are the data acquisition gain coefficients respectively.

[0033] Furthermore, constructing an energy consumption analysis index function according to the data acquisition function includes:

[0034] According to the data collection function, construct an energy consumption analysis index function;

[0035] The energy consumption analysis index function is:

[0036]

[0037] The energy consumption analysis model is:

[0038]

[0039] in, For the device at time Energy consumption, For the device at time The total energy consumption in For the The data collection function is in time Energy consumption, is the total number of data collection functions.

[0040] Furthermore, the energy consumption optimization model is constructed based on the operating state parameters, the data acquisition function and the energy consumption calculation function, including:

[0041] According to the data acquisition function, determining a data acquisition gain coefficient for building an energy consumption optimization model;

[0042] Determining an energy consumption calculation function according to the operating state parameters and the data acquisition gain coefficient;

[0043] Determining an energy consumption optimization model according to the energy consumption calculation function;

[0044] The energy consumption calculation function is specifically:

[0045]

[0046] in, For the device at time The optimized energy consumption is obtained by comprehensively calculating the energy consumption caused by power and temperature changes;

[0047] The energy consumption optimization model is:

[0048]

[0049] in, is the total energy consumption after optimization, For the The data collection function is in time Optimized energy consumption.

[0050] Furthermore, the energy consumption optimization gain coefficient calculation model is constructed according to the energy consumption optimization model and the energy consumption analysis model, including:

[0051] Determining that the energy consumption analysis model and the energy consumption optimization model are dual-objective functions;

[0052] Utilizing preset boundary conditions, the dual objective function is optimized to obtain the energy consumption optimization gain coefficient calculation model;

[0053] The dual objective function is:

[0054]

[0055] The preset boundary conditions are:

[0056]

[0057]

[0058] in, is the dual objective function, is the weight coefficient, are the minimum and maximum values ​​of power, are the minimum and maximum values ​​of temperature.

[0059] In a second aspect of the present invention, a remote-based real-time monitoring system for energy consumption of industrial equipment is provided, comprising:

[0060] The data acquisition module is used to construct data acquisition functions and corresponding energy consumption analysis models based on the operating status parameters and energy consumption characteristics of industrial equipment;

[0061] An energy consumption calculation module, used for determining an energy consumption calculation function based on the operating state parameters and the data acquisition function;

[0062] An energy consumption optimization module, used to construct an energy consumption optimization model according to the energy consumption calculation function;

[0063] The energy consumption optimization gain module is used to construct an energy consumption optimization gain coefficient calculation model according to the energy consumption optimization model and the energy consumption analysis model.

[0064] According to the above-mentioned embodiment of the present invention, at least the following beneficial effects are achieved: the method can realize accurate monitoring and analysis of the energy consumption of industrial equipment by constructing a data acquisition function and an energy consumption analysis model based on the equipment operation status parameters and energy consumption characteristic classification. Specifically, the method uses a power model and a temperature model to perform component analysis on the energy consumption of the equipment, and can accurately capture the energy consumption changes of the equipment under different operating conditions, thereby providing reliable data support for energy consumption management and optimization. In addition, the method also improves the efficiency and accuracy of data acquisition through the application of time series analysis and data acquisition gain coefficients, making energy consumption monitoring more real-time and accurate.

[0065] Furthermore, this method can effectively optimize the energy consumption of the equipment by constructing an energy consumption optimization model and an energy consumption optimization gain coefficient calculation model. Through the optimization of the dual objective function and the application of preset boundary conditions, this method can achieve the minimum control of energy consumption while ensuring the normal operation of the equipment. This not only helps to reduce the energy consumption cost of the enterprise, but also improves the energy utilization efficiency, providing strong support for the sustainable development of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0067] Figure 1 A flowchart of a remote industrial equipment energy consumption real-time monitoring method provided by an embodiment of the present invention;

[0068] Figure 2 A schematic diagram of the structure of a remote industrial equipment energy consumption real-time monitoring system provided by an embodiment of the present invention;

[0069] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0071] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0072] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0073] Reference below Figure 1 , Figure 1 The flowchart of the method for real-time monitoring of energy consumption of industrial equipment based on remote control is provided in one embodiment of the present invention. Figure 1 As shown, a remote-based real-time monitoring method 100 for industrial equipment energy consumption includes:

[0074] Step 101, constructing a data acquisition function and a corresponding energy consumption analysis model based on the operating status parameters and energy consumption characteristics of the industrial equipment;

[0075] Step 102, determining an energy consumption calculation function based on the operating state parameter and the data acquisition function;

[0076] Step 103, constructing an energy consumption optimization model according to the energy consumption calculation function;

[0077] Step 104: construct an energy consumption optimization gain coefficient calculation model based on the energy consumption optimization model and the energy consumption analysis model.

[0078] It should be noted that the core of this method is to achieve real-time monitoring and optimization of industrial equipment energy consumption by constructing data acquisition functions and energy consumption analysis models. The data acquisition function refers to a function designed to collect equipment operation data based on the equipment's operating status parameters and energy consumption characteristics. The energy consumption analysis model is a mathematical model used to analyze and calculate equipment energy consumption. It can accurately analyze and predict the equipment's energy consumption based on the collected data.

[0079] Specifically, the operating status parameters of the equipment include equipment power, equipment operating time, etc. These parameters can be collected in real time through sensors and other equipment. Energy consumption characteristics are classified according to the energy consumption characteristics of the equipment under different operating conditions, such as the energy consumption characteristics when the equipment is started and the energy consumption characteristics during equipment operation.

[0080] More specifically, the power model and temperature model are constructed based on the operating status parameters of the device to characterize the power and temperature changes of the device at different time points. For example, the power model can be expressed as a function in which the power of the device at a certain time point is equal to the initial power plus the power change multiplied by the time attenuation coefficient and other factors.

[0081] Preferably, the data collection function can be adjusted according to the specific operation conditions and energy consumption characteristics of the device. For example, if the energy consumption of the device changes greatly during startup, the data collection frequency and accuracy during the startup phase can be increased to more accurately capture the energy consumption changes of the device.

[0082] Furthermore, the energy consumption analysis model can also be optimized according to actual application requirements, for example, by introducing more energy consumption influencing factors such as ambient temperature, equipment load, etc., to improve the accuracy and applicability of the model.

[0083] In some embodiments, the operating status parameters include: device power, device operating time, operating status parameters at a first moment, and operating status parameters at a second moment;

[0084] The operating state parameter at the first moment includes: one or more of: power when the device is started, voltage when the device is started, current when the device is started, temperature when the device is started, and pressure when the device is started;

[0085] The operating status parameter at the second moment includes: one or more of: power during equipment operation, voltage during equipment operation, current during equipment operation, temperature during equipment operation, and pressure during equipment operation.

[0086] It should be noted that the operating status parameters mentioned in this method refer to various measurable physical quantities and status indicators of the equipment during operation. These parameters are crucial for building energy consumption analysis models because they can reflect the energy consumption characteristics of the equipment at different operating stages. Equipment power refers to the amount of electrical energy consumed by the equipment at a certain moment, and equipment operating time refers to the duration from the start to the stop of the equipment. The operating status parameters at the first moment and the operating status parameters at the second moment refer to various status parameters of the equipment at startup and during operation, respectively. These parameters include power, voltage, current, temperature, pressure, etc. They can be used to analyze the energy consumption performance of the equipment at different stages.

[0087] Specifically, the power of the device can be calculated by measuring the current and voltage, and the running time of the device can be recorded by a timer. The power, voltage, current, temperature and pressure of the device when it starts in the first moment operating state parameters can be measured at the moment when the device starts. For example, the power of the device when it starts can be calculated by measuring the current and voltage at the moment of startup.

[0088] More specifically, the power, voltage, current, temperature and pressure of the equipment in operation in the operating state parameters at the second moment are measured during the normal operation of the equipment, and these parameters can reflect the energy consumption of the equipment in a stable operating state. For example, the power of the equipment in operation can be calculated by measuring the current and voltage of the equipment during operation.

[0089] Preferably, in order to more accurately reflect the energy consumption characteristics of the equipment, more measurement points and parameter types can be added during the startup and operation of the equipment. For example, in addition to measuring the power, voltage, current, temperature and pressure of the equipment, parameters such as vibration and noise of the equipment can also be measured. These parameters can provide more information for analyzing the energy consumption of the equipment.

[0090] Furthermore, different measurement methods and devices can be selected according to the specific type of equipment and application scenarios to improve the accuracy and reliability of data collection. For example, for equipment with high precision requirements, high-precision sensors and data acquisition devices can be used to ensure the accuracy of the measurement data.

[0091] In some embodiments, the step of constructing a data collection function and a corresponding energy consumption analysis model based on the operating state parameters and energy consumption characteristic classification includes:

[0092] According to the operating state parameters, a power model and a temperature model are constructed, wherein the power model and the temperature model correspond to one of the energy consumption feature categories respectively; the power model and the temperature model are both used to characterize the energy consumption components under the corresponding category;

[0093] constructing the data acquisition function according to the operating state parameters, the power model and the temperature model;

[0094] According to the data collection function, construct an energy consumption analysis index function;

[0095] Based on the energy consumption analysis index function, the energy consumption analysis model is constructed.

[0096] It should be noted that this method analyzes the energy consumption characteristics of the device by constructing a power model and a temperature model. The power model and the temperature model are mathematical models established based on the operating status parameters of the device, which are used to describe the power and temperature changes of the device at different time points. The power model is used to characterize the power change law of the device during operation, while the temperature model is used to describe the temperature change law of the device during operation. These models are the basis for constructing data acquisition functions and energy consumption analysis models, which can help us analyze and predict the energy consumption of the device more accurately.

[0097] Specifically, the construction of the power model and temperature model requires parameter setting based on the actual operating data of the equipment. For example, the initial power in the power model The power change can be obtained by measuring the power of the equipment at startup. The power change amplitude coefficient can be calculated by the power change of the equipment at different operating stages. and the time decay coefficient It can be obtained by fitting the power change curve of the equipment.

[0098] More specifically, the initial temperature in the temperature model The temperature change can be obtained by measuring the temperature of the device at startup. The temperature change amplitude coefficient can be calculated by the temperature change of the equipment at different operating stages. and angular frequency It can be obtained by fitting the temperature change curve of the equipment. The settings of these parameters need to be adjusted in combination with the specific type and operating conditions of the equipment to ensure the accuracy and applicability of the model.

[0099] Preferably, in order to improve the accuracy and applicability of the model, more influencing factors and parameters can be introduced into the model. For example, in the power model, the influence of factors such as the load change of the equipment and the ambient temperature on the power can be considered, and the corresponding parameters and function forms can be added. In the temperature model, the influence of factors such as the heat dissipation characteristics and material characteristics of the equipment on the temperature can be considered, and the corresponding parameters and function forms can be added.

[0100] Furthermore, the model can be verified and optimized through experiments and data analysis to improve the prediction accuracy and reliability of the model. For example, by comparing the energy consumption predicted by the model with the actual measured energy consumption, the model parameters can be adjusted and optimized to ensure the accuracy and applicability of the model.

[0101] In some embodiments, the power model is generally expressed as:

[0102]

[0103] in, For the device at time The power, is the initial power of the device, is the power change of the equipment, is the amplitude coefficient of power change, is the time attenuation coefficient;

[0104] The general formula of the temperature model is:

[0105]

[0106] in, For the device at time The temperature, is the initial temperature of the device, is the temperature change of the device, is the amplitude coefficient of temperature change, is the angular frequency, is the phase angle.

[0107] It should be noted that the power model and temperature model mentioned in this method are mathematical expressions used to describe the power and temperature changes of the equipment at different time points. The power model characterizes the power change law of the equipment during operation by considering factors such as initial power, power change, amplitude coefficient of power change and time attenuation coefficient. The temperature model describes the temperature change law of the equipment during operation by considering factors such as initial temperature, temperature change, amplitude coefficient of temperature change, angular frequency and phase angle. The establishment of these models provides a basis for subsequent data collection and energy consumption analysis, making energy consumption monitoring and optimization more scientific and accurate.

[0108] Specifically, the parameters in the power model are set as follows: Initial power It is the power value of the device when it starts, which can be calculated by measuring the current and voltage when the device starts. It is the power variation of the equipment during operation, which can be calculated by measuring the power value of the equipment at different operation stages. and the time decay coefficient It is obtained by fitting the power change curve of the device and is used to describe the change trend of power over time. The parameters in the temperature model are set as follows: initial temperature It is the temperature value of the device when it starts, which can be obtained by measuring the temperature sensor reading when the device starts. It is the temperature variation of the equipment during operation, which can be calculated by measuring the temperature value of the equipment at different operation stages. It is obtained by fitting the temperature change curve of the device and is used to describe the amplitude of temperature change over time. and phase angle It is a parameter that describes the periodicity and phase characteristics of temperature change, and can usually be extracted from temperature change data through methods such as Fourier transform.

[0109] Preferably, in order to improve the accuracy and applicability of the model, more influencing factors and parameters can be introduced into the model. For example, in the power model, the influence of factors such as the load change of the equipment and the ambient temperature on the power can be considered, and the corresponding parameters and function forms can be added. In the temperature model, the influence of factors such as the heat dissipation characteristics and material characteristics of the equipment on the temperature can be considered, and the corresponding parameters and function forms can be added.

[0110] Furthermore, the model can be verified and optimized through experiments and data analysis. For example, by comparing the power and temperature values ​​predicted by the model with the actual measured values, the model parameters can be adjusted and optimized to ensure the accuracy and applicability of the model.

[0111] In some embodiments, constructing a data acquisition function according to the operating state parameter, the power model and the temperature model includes:

[0112] Based on time series analysis, the power model formula is decomposed into three power components;

[0113] Based on time series analysis, the general temperature model is decomposed into two temperature components;

[0114] Data acquisition functions are constructed respectively according to the three power components and the two temperature components.

[0115] It should be noted that this method decomposes the power model and temperature model into multiple components through time series analysis, so as to capture the energy consumption characteristics of the equipment at different time points in more detail. Time series analysis is a statistical method used to analyze the trends and patterns of data points over time. By decomposing complex power and temperature models into multiple components, the energy consumption of the equipment at different operating stages can be monitored and analyzed separately, thereby improving the accuracy and reliability of energy consumption monitoring. For example, the power model can be decomposed into the power component of the startup stage, the power component of the stable operation stage, etc., and the temperature model can be decomposed into the temperature component of the initial heating stage, the temperature component of the stable operation stage, etc.

[0116] Specifically, the power model can be decomposed by dividing the power change process of the equipment into different stages. For example, the power component of the startup stage can be determined by measuring the power change of the equipment at the initial startup stage, and the power component of the stable operation stage can be determined by measuring the power change of the equipment during normal operation.

[0117] More specifically, the disassembly of the temperature model can also be achieved through similar methods. For example, the temperature component of the initial heating stage can be determined by measuring the temperature change of the equipment at the initial startup, and the temperature component of the stable operation stage can be determined by measuring the temperature change of the equipment during normal operation. During the disassembly process, time series analysis methods such as moving average method, exponential smoothing method, etc. can be used to extract and analyze trends and patterns in the data.

[0118] Preferably, in order to further improve the accuracy and applicability of the model, more analysis methods and parameters can be introduced in the disassembly process. For example, autoregressive model, seasonal decomposition and other methods can be used to perform more complex disassembly and analysis on the power and temperature model.

[0119] Furthermore, different disassembly methods and parameter settings can be selected according to the specific type and operating conditions of the equipment. For example, the characteristics of power and temperature changes may be different for different types of industrial equipment, so when disassembling the model, it is necessary to make adjustments based on the specific conditions of the equipment. Through these refinement and optimization measures, the actual energy consumption characteristics of the equipment can be better reflected, providing more accurate data support for energy consumption monitoring and optimization.

[0120] In some embodiments, the data collection function is specifically:

[0121]

[0122]

[0123]

[0124] in, are the data acquisition gain coefficients respectively.

[0125] It should be noted that the data acquisition function mentioned in this method is constructed based on the disassembled power and temperature components, and is used to collect energy consumption data of the equipment in different operating stages in real time. The data acquisition gain coefficient is a parameter used to adjust the weights of each component in the data acquisition function to ensure that the collected data can accurately reflect the actual energy consumption of the equipment. By setting an appropriate data acquisition gain coefficient, the accuracy and reliability of data acquisition can be improved, thereby providing a reliable data basis for subsequent energy consumption analysis and optimization.

[0126] Specifically, the construction of the data acquisition function needs to be set according to the decomposed power and temperature components. For example, for the power model, it can be decomposed into three power components, each component corresponding to a data acquisition function. The first power component can be represented as the power change of the device at startup, the second power component can be represented as the power change of the device during stable operation, and the third power component can be represented as the power change of the device at the end of operation.

[0127] More specifically, for the temperature model, it can be decomposed into two temperature components, each corresponding to a data acquisition function. The first temperature component can be expressed as the temperature change of the device at startup, and the second temperature component can be expressed as the temperature change of the device during stable operation. Data acquisition gain coefficient Adjustments can be made based on the actual operating conditions and energy consumption characteristics of the equipment to ensure that the collected data can accurately reflect the actual energy consumption of the equipment.

[0128] Preferably, in order to further improve the accuracy and reliability of data collection, more parameters and adjustment mechanisms can be introduced into the data collection function. For example, a time window parameter can be introduced to adjust the time range and frequency of data collection to better capture the energy consumption changes of the device at different operating stages.

[0129] Furthermore, different methods for setting the data acquisition gain coefficient can be selected according to the specific type and operating conditions of the equipment. For example, different types of industrial equipment may have different energy consumption characteristics, so when setting the data acquisition gain coefficient, it is necessary to adjust it according to the specific conditions of the equipment. Through these refinement and optimization measures, the actual energy consumption characteristics of the equipment can be better reflected, providing more accurate data support for energy consumption monitoring and optimization.

[0130] In some embodiments, constructing an energy consumption analysis index function according to the data collection function includes:

[0131] According to the data collection function, construct an energy consumption analysis index function;

[0132] The energy consumption analysis index function is:

[0133]

[0134] The energy consumption analysis model is:

[0135]

[0136] in, For the device at time Energy consumption, For the device at time The total energy consumption in For the The data collection function is in time Energy consumption, is the total number of data collection functions.

[0137] It should be noted that the energy consumption analysis index function and energy consumption analysis model mentioned in this method are mathematical tools for calculating and analyzing the energy consumption of equipment at different time points. The energy consumption analysis index function obtains the energy consumption value of the equipment in a certain period of time by integrating the data acquisition function, while the energy consumption analysis model calculates the total energy consumption of the equipment in a certain period of time by accumulating the energy consumption values ​​of each data acquisition function. The establishment of these models and functions provides a quantitative analysis basis for energy consumption monitoring and optimization, making energy consumption management more scientific and accurate.

[0138] Specifically, the construction of the energy consumption analysis index function requires the integration operation based on the data collection function. For example, for the energy consumption of a device in a certain period of time, the data collection function can be used to calculate the energy consumption of the device. and The integration range can be set according to the actual monitoring time period, such as the entire operation process from the start to the stop of the equipment.

[0139] More specifically, the energy consumption analysis model calculates the total energy consumption of the device by accumulating the energy consumption values ​​of each data collection function in different time periods. For example, if the device has multiple data collection functions The total energy consumption can be obtained by summing the energy consumption values ​​of these functions.

[0140] Preferably, in order to improve the accuracy and applicability of energy consumption analysis, more parameters and adjustment mechanisms can be introduced into the energy consumption analysis index function and the energy consumption analysis model. For example, a weight coefficient can be introduced to adjust the importance of different data acquisition functions in energy consumption analysis to better reflect the actual energy consumption characteristics of the equipment.

[0141] Furthermore, different integration and accumulation methods can be selected according to the specific type and operating conditions of the equipment. For example, different types of industrial equipment may have different energy consumption characteristics, so when constructing energy consumption analysis index functions and energy consumption analysis models, they need to be adjusted according to the specific conditions of the equipment. Through these refinement and optimization measures, the actual energy consumption of the equipment can be better reflected, providing more accurate data support for energy consumption monitoring and optimization.

[0142] In some embodiments, the constructing of the energy consumption optimization model based on the operating state parameters, the data acquisition function and the energy consumption calculation function includes:

[0143] According to the data acquisition function, determining a data acquisition gain coefficient for building an energy consumption optimization model;

[0144] Determining an energy consumption calculation function according to the operating state parameters and the data acquisition gain coefficient;

[0145] Determining an energy consumption optimization model according to the energy consumption calculation function;

[0146] The energy consumption calculation function is specifically:

[0147]

[0148] in, For the device at time The optimized energy consumption is obtained by comprehensively calculating the energy consumption caused by power and temperature changes;

[0149] The energy consumption optimization model is:

[0150]

[0151] in, is the total energy consumption after optimization, For the The data collection function is in time Optimized energy consumption.

[0152] It should be noted that this method achieves the minimization control of equipment energy consumption by constructing an energy consumption optimization model. The energy consumption optimization model is established based on the energy consumption calculation function and the data acquisition gain coefficient, and is used to determine the optimal energy consumption configuration of the equipment under different operating conditions. The energy consumption calculation function is obtained by integrating the data acquisition function, and is used to calculate the energy consumption value of the equipment within a certain period of time. The data acquisition gain coefficient is used to adjust the weights of each component in the data acquisition function to ensure that the optimization model can accurately reflect the actual energy consumption of the equipment. Through the application of the optimization model, energy consumption can be minimized while ensuring the normal operation of the equipment, thereby improving energy utilization efficiency.

[0153] Specifically, the construction of the energy consumption calculation function requires the integration operation based on the data collection function. For example, to optimize the energy consumption of a device in a certain period of time, the data collection function can be used to calculate the energy consumption of the device. , and The integration is calculated. The integration range can be set according to the actual monitoring time period, such as the entire operation process from the start to the stop of the equipment. The data acquisition gain factor can be adjusted according to the actual operation and energy consumption characteristics of the equipment to ensure that the optimization model can accurately reflect the actual energy consumption of the equipment. For example, if the energy consumption of the equipment changes greatly during the startup phase, the data acquisition gain factor of the startup phase can be increased to better capture the energy consumption changes of the equipment.

[0154] Preferably, in order to improve the accuracy and applicability of the energy consumption optimization model, more parameters and adjustment mechanisms can be introduced into the model. For example, external factors such as equipment load and ambient temperature can be introduced as optimization variables to more comprehensively consider various factors affecting equipment energy consumption.

[0155] Furthermore, different optimization algorithms and parameter setting methods can be selected according to the specific type and operating conditions of the equipment. For example, for different types of industrial equipment, their energy consumption characteristics and optimization targets may be different, so when building the energy consumption optimization model, it is necessary to make adjustments according to the specific conditions of the equipment. Through these refinement and optimization measures, the energy consumption of the equipment can be minimized and the energy utilization efficiency can be improved.

[0156] In some embodiments, constructing an energy consumption optimization gain coefficient calculation model according to the energy consumption optimization model and the energy consumption analysis model includes:

[0157] Determining that the energy consumption analysis model and the energy consumption optimization model are dual-objective functions;

[0158] Utilizing preset boundary conditions, the dual objective function is optimized to obtain the energy consumption optimization gain coefficient calculation model;

[0159] The dual objective function is:

[0160]

[0161] The preset boundary conditions are:

[0162]

[0163]

[0164] in, is the dual objective function, is the weight coefficient, are the minimum and maximum values ​​of power, are the minimum and maximum values ​​of temperature.

[0165] It should be noted that this method further optimizes the energy consumption performance of the equipment by constructing an energy consumption optimization gain coefficient calculation model. The energy consumption optimization gain coefficient calculation model is established based on the energy consumption analysis model and the energy consumption optimization model, and is used to determine the optimal gain coefficient under different operating conditions to minimize energy consumption. The energy consumption analysis model is used to analyze the energy consumption of the equipment under the current operating state, while the energy consumption optimization model is used to calculate the energy consumption performance of the equipment under different gain coefficients. Through the optimization of the dual objective function and the application of preset boundary conditions, the optimal energy consumption optimization gain coefficient can be obtained, thereby improving the energy utilization efficiency of the equipment.

[0166] Specifically, the dual objective function includes the objective functions of the energy consumption analysis model and the energy consumption optimization model, which correspond to the current energy consumption of the equipment and the optimized energy consumption respectively. and It is used to adjust the importance of the two objective functions in the optimization process and can be set according to actual needs. For example, if you pay more attention to reducing the current energy consumption, you can increase If you are more concerned about the optimized energy consumption, you can increase The value of .

[0167] More specifically, the preset boundary conditions are used to limit the range of equipment operating parameters, such as the minimum and maximum values ​​of power. and , as well as the minimum and maximum temperatures and ,These boundary conditions can be set according to the performance and safety requirements of the equipment.

[0168] Preferably, in order to improve the accuracy and applicability of the energy consumption optimization gain coefficient calculation model, more optimization algorithms and parameter adjustment mechanisms can be introduced into the model. For example, intelligent optimization algorithms such as genetic algorithms and particle swarm optimization can be used to solve the optimal gain coefficient to improve the efficiency and accuracy of optimization.

[0169] Furthermore, different boundary conditions and weight coefficient setting methods can be selected according to the specific type and operating conditions of the equipment. For example, for different types of industrial equipment, their energy consumption characteristics and optimization goals may be different, so when constructing the energy consumption optimization gain coefficient calculation model, it is necessary to make adjustments according to the specific conditions of the equipment. Through these refinement and optimization measures, the energy consumption optimization of the equipment can be better achieved and the energy utilization efficiency can be improved.

[0170] The above-mentioned embodiments of the present invention have the following beneficial effects: The method of the present invention can realize comprehensive monitoring and accurate analysis of the energy consumption of industrial equipment by constructing data acquisition functions and energy consumption analysis models. Specifically, the method utilizes equipment operating status parameters and energy consumption characteristic classification to construct a power model and a temperature model, which can accurately capture the energy consumption changes of the equipment in different operating stages. Through the application of time series analysis and data acquisition gain coefficients, the method improves the efficiency and accuracy of data acquisition, making energy consumption monitoring more real-time and accurate. In addition, the method can also construct an energy consumption calculation function and an energy consumption optimization model based on the equipment's operating status parameters and data acquisition functions, thereby providing reliable data support and analysis tools for energy consumption management and optimization.

[0171] Furthermore, this method can effectively optimize the energy consumption of the equipment by constructing an energy consumption optimization gain coefficient calculation model. Through the optimization of the dual objective function and the application of preset boundary conditions, this method can achieve the minimum control of energy consumption while ensuring the normal operation of the equipment. This not only helps to reduce the energy consumption cost of the enterprise, but also improves the energy utilization efficiency and provides strong support for the sustainable development of the enterprise. In addition, the optimization model of this method can also dynamically adjust the operating parameters of the equipment according to the energy consumption analysis results, further improving the effect of energy consumption optimization.

[0172] like Figure 2 As shown, a remote-based real-time monitoring system 200 for energy consumption of industrial equipment in some embodiments includes:

[0173] The data acquisition module 201 is used to construct a data acquisition function and a corresponding energy consumption analysis model based on the operating status parameters and energy consumption characteristics of the industrial equipment;

[0174] An energy consumption calculation module 202, configured to determine an energy consumption calculation function based on the operating state parameters and the data acquisition function;

[0175] An energy consumption optimization module 203 is used to construct an energy consumption optimization model according to the energy consumption calculation function;

[0176] The energy consumption optimization gain module 204 is used to construct an energy consumption optimization gain coefficient calculation model according to the energy consumption optimization model and the energy consumption analysis model.

[0177] It is understandable that the modules recorded in the remote industrial equipment energy consumption real-time monitoring system 200 are similar to the reference Figure 1The steps in the remote-based real-time monitoring method for energy consumption of industrial equipment described above correspond to each other. Therefore, the operations, features and beneficial effects described above for the remote-based real-time monitoring method for energy consumption of industrial equipment are also applicable to the remote-based real-time monitoring system for energy consumption of industrial equipment 200 and the modules contained therein, and will not be repeated here.

[0178] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention 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), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0179] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0180] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0181] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0182] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A remote-based real-time monitoring method for energy consumption of industrial equipment, characterized in that: The method includes: Based on the operating status parameters and energy consumption characteristics of industrial equipment, construct data acquisition functions and corresponding energy consumption analysis models; Determining an energy consumption calculation function based on the operating state parameter and the data acquisition function; According to the energy consumption calculation function, an energy consumption optimization model is constructed; Constructing an energy consumption optimization gain coefficient calculation model according to the energy consumption optimization model and the energy consumption analysis model; Based on the operating state parameters and energy consumption characteristic classification, a data acquisition function and a corresponding energy consumption analysis model are constructed, including: According to the operating state parameters, a power model and a temperature model are constructed, wherein the power model and the temperature model correspond to one of the energy consumption feature categories respectively; the power model and the temperature model are both used to characterize the energy consumption components under the corresponding category; constructing the data acquisition function according to the operating state parameters, the power model and the temperature model; According to the data collection function, construct an energy consumption analysis index function; Based on the energy consumption analysis index function, construct the energy consumption analysis model; Based on the operating state parameters, the data acquisition function and the energy consumption calculation function, an energy consumption optimization model is constructed, including: According to the data acquisition function, determining a data acquisition gain coefficient for building an energy consumption optimization model; Determining an energy consumption calculation function according to the operating state parameters and the data acquisition gain coefficient; Determining an energy consumption optimization model according to the energy consumption calculation function; The energy consumption calculation function is specifically: ； in, For the device at time The optimized energy consumption is obtained by comprehensively calculating the energy consumption caused by power and temperature changes; The energy consumption optimization model is: ； in, is the total energy consumption after optimization, For the The data collection function is in time Optimized energy consumption; According to the energy consumption optimization model and the energy consumption analysis model, an energy consumption optimization gain coefficient calculation model is constructed, including: Determining that the energy consumption analysis model and the energy consumption optimization model are dual-objective functions; Utilizing preset boundary conditions, the dual objective function is optimized to obtain the energy consumption optimization gain coefficient calculation model; The dual objective function is: ； The preset boundary conditions are: ； ； in, is the dual objective function, is the weight coefficient, are the minimum and maximum values ​​of power, are the minimum and maximum values ​​of temperature.

2. The method according to claim 1, characterized in that: The operating status parameters include: equipment power, equipment operating time, operating status parameters at the first moment and operating status parameters at the second moment; The operating state parameter at the first moment includes: one or more of: power when the device is started, voltage when the device is started, current when the device is started, temperature when the device is started, and pressure when the device is started; The operating status parameter at the second moment includes: one or more of: power during equipment operation, voltage during equipment operation, current during equipment operation, temperature during equipment operation, and pressure during equipment operation.

3. The method according to claim 1, characterized in that The power model general formula is: ； in, For the device at time The power, is the initial power of the device, is the power change of the equipment, is the amplitude coefficient of power change, is the time attenuation coefficient; The general formula of the temperature model is: ； in, For the device at time The temperature, is the initial temperature of the device, is the temperature change of the device, is the amplitude coefficient of temperature change, is the angular frequency, is the phase angle.

4. The method according to claim 3, characterized in that The constructing of a data acquisition function according to the operating state parameter, the power model and the temperature model comprises: Based on time series analysis, the power model is decomposed into three power components; Based on time series analysis, the general temperature model is decomposed into two temperature components; Data acquisition functions are constructed respectively according to the three power components and the two temperature components.

5. The method according to claim 4, characterized in that The data acquisition function is specifically: ； ； ； in, are the data acquisition gain coefficients respectively.

6. The method according to claim 5, characterized in that The step of constructing an energy consumption analysis index function according to the data collection function includes: According to the data collection function, construct an energy consumption analysis index function; The energy consumption analysis index function is: ； The energy consumption analysis model is: ； in, For the device at time Energy consumption, For the device at time The total energy consumption in For the The data collection function is in time Energy consumption, is the total number of data collection functions.

7. A remote-based real-time monitoring system for energy consumption of industrial equipment, characterized in that: The system includes: The data acquisition module is used to construct data acquisition functions and corresponding energy consumption analysis models based on the operating status parameters and energy consumption characteristics of industrial equipment; An energy consumption calculation module, used for determining an energy consumption calculation function based on the operating state parameters and the data acquisition function; An energy consumption optimization module, used to construct an energy consumption optimization model according to the energy consumption calculation function; An energy consumption optimization gain module is used to construct an energy consumption optimization gain coefficient calculation model according to the energy consumption optimization model and the energy consumption analysis model; Based on the operating state parameters and energy consumption characteristic classification, a data acquisition function and a corresponding energy consumption analysis model are constructed, including: According to the operating state parameters, a power model and a temperature model are constructed, wherein the power model and the temperature model correspond to one of the energy consumption feature categories respectively; the power model and the temperature model are both used to characterize the energy consumption components under the corresponding category; constructing the data acquisition function according to the operating state parameters, the power model and the temperature model; According to the data collection function, construct an energy consumption analysis index function; Based on the energy consumption analysis index function, construct the energy consumption analysis model; Based on the operating state parameters, the data acquisition function and the energy consumption calculation function, an energy consumption optimization model is constructed, including: According to the data acquisition function, determining a data acquisition gain coefficient for building an energy consumption optimization model; Determining an energy consumption calculation function according to the operating state parameters and the data acquisition gain coefficient; Determining an energy consumption optimization model according to the energy consumption calculation function; The energy consumption calculation function is specifically: ； in, For the device at time The optimized energy consumption is obtained by comprehensively calculating the energy consumption caused by power and temperature changes; The energy consumption optimization model is: ； in, is the total energy consumption after optimization, For the The data collection function is in time Optimized energy consumption; According to the energy consumption optimization model and the energy consumption analysis model, an energy consumption optimization gain coefficient calculation model is constructed, including: Determining that the energy consumption analysis model and the energy consumption optimization model are dual-objective functions; Utilizing preset boundary conditions, the dual objective function is optimized to obtain the energy consumption optimization gain coefficient calculation model; The dual objective function is: ； The preset boundary conditions are: ； ； in, is the dual objective function, is the weight coefficient, are the minimum and maximum values ​​of power, are the minimum and maximum values ​​of temperature.

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