New energy efficiency management data monitoring and analyzing system and method

Abstract

The invention discloses a new energy energy efficiency management data monitoring analysis system and method, and particularly relates to the field of new energy energy efficiency, and the system comprises a multi-dimensional data collection module, an equipment energy efficiency reference value calculation module, a real-time energy efficiency index calculation module, an abnormality recognition module, an energy efficiency influence factor analysis module, and an energy efficiency monitoring analysis optimization module. The multi-dimensional data acquisition module is used for acquiring equipment operation parameters, environment parameters and equipment state parameters in real time; the equipment energy efficiency reference value calculation module is used for calculating energy efficiency reference values of different new energy equipment under the standard working condition; according to the method, through multi-dimensional parameter acquisition, the limitation that a traditional method only depends on a single parameter is reduced, and a rich data basis is provided for accurate energy efficiency analysis; through formulating a targeted optimization strategy, the conversion from passive processing to active optimization is realized, the energy waste and the equipment loss are effectively reduced, and the energy efficiency utilization level of the new energy equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy energy efficiency, and more specifically, to a new energy energy efficiency management data monitoring and analysis system and method. Background Art

[0002] In the field of new energy, such as the development and utilization of energy sources like solar energy and wind energy, energy efficiency management is a key link to ensure the efficient conversion and utilization of energy. Currently, the energy efficiency monitoring of new energy devices mainly relies on traditional single-point data collection and simple threshold judgment, and there are the following problems: First, the existing monitoring methods can only obtain the basic operating parameters of the devices, such as voltage, current, etc., and lack comprehensive and accurate collection of key environmental factors and device status parameters that affect energy efficiency, resulting in insufficiently comprehensive and in-depth energy efficiency analysis and making it difficult to accurately identify the root causes of low energy efficiency; Second, in the data analysis process, fixed empirical formulas are mostly used for energy efficiency evaluation, without fully considering the characteristic differences of different new energy devices under different operating scenarios. The analysis method lacks flexibility and adaptability, and it is difficult to achieve refined energy efficiency management; Third, for the energy efficiency anomalies that occur during the operation of the devices, traditional methods often can only handle them after the problems are clearly manifested, lacking real-time dynamic monitoring and early warning mechanisms, and unable to adjust the device operation strategies in a timely manner, resulting in increased energy waste and device losses.

[0003] Therefore, there is an urgent need for a new energy energy efficiency management data monitoring and analysis system and method to improve the energy efficiency utilization level of new energy devices and reduce operating costs through a comprehensive, accurate, and dynamically adaptable new energy energy efficiency management data monitoring and analysis method. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a new energy energy efficiency management data monitoring and analysis system and method, through the following solutions, to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: A new energy energy efficiency management data monitoring and analysis system, characterized in that it includes a device status monitoring terminal, an environment perception terminal, a data analysis terminal, and a control center, specifically including:

[0006] A multi-dimensional data collection module: Using the device status monitoring terminal and the environment perception terminal to collect device operation parameters, environment parameters, and device status parameters in real time during the operation of new energy devices;

[0007] A device energy efficiency benchmark value calculation module: The data analysis terminal analyzes the collected data and calculates the energy efficiency benchmark values of different new energy devices under standard working conditions;

[0008] Real-time Energy Efficiency Index Calculation Module: During the operation of the device, the data analysis terminal calculates the real-time energy efficiency index of the device;

[0009] Abnormality Identification Module: The data analysis terminal calculates the energy efficiency deviation degree between the real-time energy efficiency index and the energy efficiency reference value, and conducts abnormality identification based on the energy efficiency deviation degree;

[0010] Energy Efficiency Influence Factor Analysis Module: The data analysis terminal conducts key factor analysis according to the abnormality identification result;

[0011] Energy Efficiency Monitoring, Analysis and Optimization Module: The control center monitors the energy efficiency status in real time. Once an abnormality occurs, corresponding optimization strategies are executed according to the results of the energy efficiency influence factor analysis.

[0012] Preferably, the device operation parameters include the output power P of the solar photovoltaic module, the power generation E of the wind turbine, the rotational speed n, the blade length r, and the fan rotation efficiency η; the environmental parameters include the light intensity S, the environmental temperature T, the wind speed v, and the wind direction θ; the device status parameters include the tilt angle α of the photovoltaic module, the surface fouling degree C, the vibration frequency f of the fan blade, and the bearing temperature Tb.

[0013] Preferably, the energy efficiency reference value includes the energy efficiency reference value of the solar photovoltaic module and the energy efficiency reference value of the wind turbine; the data analysis terminal establishes an energy efficiency reference value calculation model for the solar photovoltaic module, and imports the output power P of the photovoltaic module, the light intensity S, the tilt angle α of the photovoltaic module, and the environmental temperature T into the energy efficiency reference value calculation model for the solar photovoltaic module to obtain the energy efficiency reference value E r of the solar photovoltaic module, and the energy efficiency reference value calculation model for the solar photovoltaic module is specifically expressed as: The data analysis terminal establishes an energy efficiency reference value calculation model for the wind turbine, and imports the power generation E, the wind speed v, the blade length r, and the fan rotation efficiency η into the energy efficiency reference value calculation model for the wind turbine to obtain the energy efficiency reference value E w of the wind turbine, and the energy efficiency reference value calculation model for the wind turbine is specifically expressed as: where ρ represents the air density.

[0014] Preferably, the real-time energy efficiency index includes the real-time energy efficiency index of the solar photovoltaic module and the real-time energy efficiency index of the wind turbine. The data analysis terminal establishes a real-time energy efficiency index calculation model for the solar photovoltaic module, and imports the output power P of the photovoltaic module, the light intensity S, the tilt angle α of the photovoltaic module, the surface fouling degree C, and the environmental temperature T into the real-time energy efficiency index calculation model for the solar photovoltaic module to obtain the real-time energy efficiency index E i of the solar photovoltaic module, and the real-time energy efficiency index calculation model for the solar photovoltaic module is specifically expressed as: The data analysis terminal establishes a real-time energy efficiency index calculation model for a wind turbine, and imports the power generation E, wind speed v, blade length r, vibration frequency f, and fan rotation efficiency η into the real-time energy efficiency index calculation model of the wind turbine to obtain the real-time energy efficiency index E of the wind turbine. a , The real-time energy efficiency index calculation model of the wind turbine is specifically expressed as: Among them, f0 represents the natural vibration frequency of the blade under normal working conditions.

[0015] Preferably, the calculation of the energy efficiency deviation degree includes the calculation of the energy efficiency deviation degree of the solar photovoltaic module and the calculation of the energy efficiency deviation degree of the wind turbine. The data analysis terminal establishes an energy efficiency deviation degree calculation model for the solar photovoltaic module, and imports the energy efficiency reference value E of the solar photovoltaic module r and the real-time energy efficiency index E of the solar photovoltaic module i into the energy efficiency deviation degree calculation model of the solar photovoltaic module to obtain the energy efficiency deviation degree D of the solar photovoltaic module s , The energy efficiency deviation degree calculation model of the solar photovoltaic module is specifically expressed as: The data analysis terminal establishes an energy efficiency deviation degree calculation model for the wind turbine, and imports the energy efficiency reference value E of the wind turbine w and the real-time energy efficiency index E of the wind turbine a into the energy efficiency deviation degree calculation model of the wind turbine to obtain the energy efficiency deviation degree D of the wind turbine w , The calculation of the energy efficiency deviation degree of the wind turbine is specifically expressed as: The abnormal identification is specifically: when D s > 15% or D w > 20%, it is determined that the equipment has energy efficiency anomalies.

[0016] Preferably, the key factor analysis includes the key factor analysis of the photovoltaic module and the key factor analysis of the wind turbine; the key factor analysis of the photovoltaic module is used to establish the temperature influence factor F T = 0.004(T - 25), the pollution degree influence factor F c = 0.01C, the tilt angle deviation factor F α = 1 - cos(α - α0), where T represents the ambient temperature, C represents the surface pollution degree, α represents the tilt angle, and α0 represents the optimal tilt angle of the photovoltaic module; the key factor analysis of the wind turbine is used to establish the wind speed deviation vibration frequency deviation bearing temperature influence factor F Tb = 0.003(Tb - Tb0), where v represents the wind speed, v optV represents the optimal working wind speed of the fan, f represents the vibration frequency of the blade, f0 represents the natural vibration frequency of the blade under normal working conditions, Tb represents the bearing temperature, and Tb0 represents the upper limit of the normal working temperature of the bearing.

[0017] Preferably, the optimization strategy includes the optimization of abnormal energy efficiency of photovoltaic modules and the optimization of abnormal energy efficiency of wind turbines; the optimization of abnormal energy efficiency of photovoltaic modules specifically includes: when the environmental temperature T > 25 °C and the temperature influence factor F T > 0.1, start the surface cooling system of the module to reduce the module temperature; when the pollution degree influence factor F c > 0.2, trigger the automatic cleaning program to clean the surface of the module; when the tilt angle deviation factor F α > 0.1, adjust the tilt angle of the module through the servo motor to make it close to the optimal tilt angle; the optimization of abnormal energy efficiency of the wind turbine specifically includes: when the wind speed deviation degree F v > 0.3, adjust the blade angle to make the fan in the optimal power capture state; when the vibration frequency deviation degree F f > 0.2 or the bearing temperature influence factor F Tb > 0.15, send a warning signal to remind relevant technical personnel to carry out maintenance.

[0018] Preferably, the new energy energy efficiency management data monitoring and analysis method includes an equipment status monitoring terminal, an environmental perception terminal, a data analysis terminal, and a control center, and specifically includes the following steps:

[0019] S1. Use the equipment status monitoring terminal and the environmental perception terminal to collect equipment operation parameters, environmental parameters, and equipment status parameters in real time during the operation of new energy equipment;

[0020] S2. The data analysis terminal analyzes the collected data and calculates the energy efficiency reference value of different new energy equipment under standard working conditions;

[0021] S3. During the operation of the equipment, the data analysis terminal calculates the real-time energy efficiency index of the equipment;

[0022] S4. The data analysis terminal calculates the energy efficiency deviation degree between the real-time energy efficiency index and the energy efficiency reference value, and performs abnormal identification according to the energy efficiency deviation degree;

[0023] S5. The data analysis terminal performs key factor analysis according to the abnormal identification results;

[0024] S6. The control center monitors the energy efficiency status in real time. Once an abnormality occurs, it executes the corresponding optimization strategy according to the analysis results of the energy efficiency influencing factors.

[0025] The technical effects and advantages of the present invention:

[0026] 1. Through multi-dimensional parameter collection, the present invention obtains key factors affecting the energy efficiency of new energy equipment from multiple perspectives, including equipment operation parameters, environmental parameters, and equipment status parameters, reducing the limitations of traditional methods that rely only on a single parameter and providing a rich data basis for accurate energy efficiency analysis;

[0027] 2. The energy efficiency analysis model constructed by the present invention combines the actual operating conditions of the equipment and environmental factors. Through targeted calculation formulas, it can accurately reflect the energy efficiency level of the equipment in different scenarios, solves the problem of poor adaptability of traditional fixed empirical formulas, and improves the scientificity and accuracy of energy efficiency evaluation;

[0028] 3. Through real-time calculation of energy efficiency deviation degree and anomaly identification mechanism, the present invention can timely detect energy efficiency anomalies during the operation of the equipment. By analyzing the influencing factors, it formulates targeted optimization strategies, realizes the transformation from passive processing to active optimization, effectively reduces energy waste and equipment loss, and improves the energy efficiency utilization level of new energy equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic structural diagram of the system of the present invention;

[0030] Figure 2 It is a schematic structural diagram of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0032] As shown in the attached Figure 1 The new energy efficiency management data monitoring and analysis system includes an equipment status monitoring terminal, an environmental perception terminal, a data analysis terminal, and a control center, specifically including:

[0033] Multi-dimensional data collection module: Using the equipment status monitoring terminal and the environmental perception terminal to collect equipment operation parameters, environmental parameters, and equipment status parameters in real time during the operation of new energy equipment;

[0034] In this embodiment, it should be specifically noted that: the device operation parameters include the output power P of the solar photovoltaic module, the power generation E of the wind turbine, the rotational speed n, the blade length r, and the fan rotation efficiency η; the environmental parameters include the light intensity S, the environmental temperature T, the wind speed v, and the wind direction θ; the device status parameters include the tilt angle α of the photovoltaic module, the surface fouling degree C, the vibration frequency f of the fan blade, and the bearing temperature Tb.

[0035] Device energy efficiency benchmark value calculation module: The data analysis terminal analyzes the collected data and calculates the energy efficiency benchmark values of different new energy devices under standard working conditions;

[0036] In this embodiment, it should be specifically noted that: the energy efficiency benchmark values include the energy efficiency benchmark value of the solar photovoltaic module and the energy efficiency benchmark value of the wind turbine; the data analysis terminal establishes an energy efficiency benchmark value calculation model for the solar photovoltaic module, and imports the output power P of the photovoltaic module, the light intensity S, the tilt angle α of the photovoltaic module, and the environmental temperature T into the energy efficiency benchmark value calculation model of the solar photovoltaic module to obtain the energy efficiency benchmark value E of the solar photovoltaic module r , the energy efficiency benchmark value calculation model of the solar photovoltaic module is specifically expressed as: The data analysis terminal establishes an energy efficiency benchmark value calculation model for the wind turbine, and imports the power generation E, the wind speed v, the blade length r, and the fan rotation efficiency η into the energy efficiency benchmark value calculation model of the wind turbine to obtain the energy efficiency benchmark value E of the wind turbine w , the energy efficiency benchmark value calculation of the wind turbine, the model is specifically expressed as: Among them, ρ represents the air density.

[0037] Real-time energy efficiency index calculation module: During the operation of the device, the data analysis terminal calculates the real-time energy efficiency index of the device;

[0038] In this embodiment, it should be specifically noted that: the real-time energy efficiency index includes the real-time energy efficiency index of the solar photovoltaic module and the real-time energy efficiency index of the wind turbine. The data analysis terminal establishes a real-time energy efficiency index calculation model for the solar photovoltaic module, and imports the output power P of the photovoltaic module, the light intensity S, the tilt angle α of the photovoltaic module, the surface fouling degree C, and the environmental temperature T into the real-time energy efficiency index calculation model of the solar photovoltaic module to obtain the real-time energy efficiency index E of the solar photovoltaic module i , the real-time energy efficiency index calculation model of the solar photovoltaic module is specifically expressed as: The data analysis terminal establishes a real-time energy efficiency index calculation model for the wind turbine, and imports the power generation E, the wind speed v, the blade length r, the vibration frequency f, and the fan rotation efficiency η into the real-time energy efficiency index calculation model of the wind turbine to obtain the real-time energy efficiency index E of the wind turbine a , the real-time energy efficiency index calculation model of the wind turbine is specifically expressed as: Among them, f0 represents the natural vibration frequency of the blade under normal operating conditions.

[0039] Abnormality identification module: The data analysis terminal calculates the energy efficiency deviation degree between the real-time energy efficiency index and the energy efficiency reference value, and conducts abnormality identification based on the energy efficiency deviation degree;

[0040] In this embodiment, specifically, it should be noted that: the calculation of the energy efficiency deviation degree includes the calculation of the energy efficiency deviation degree of the solar photovoltaic module and the energy efficiency deviation degree of the wind turbine. The data analysis terminal establishes a calculation model for the energy efficiency deviation degree of the solar photovoltaic module, and imports the energy efficiency reference value E r and the real-time energy efficiency index E i of the solar photovoltaic module into the calculation model for the energy efficiency deviation degree of the solar photovoltaic module to obtain the energy efficiency deviation degree D s of the solar photovoltaic module. The calculation model for the energy efficiency deviation degree of the solar photovoltaic module is specifically expressed as: The data analysis terminal establishes a calculation model for the energy efficiency deviation degree of the wind turbine, and imports the energy efficiency reference value E w and the real-time energy efficiency index E a of the wind turbine into the calculation model for the energy efficiency deviation degree of the wind turbine to obtain the energy efficiency deviation degree D w of the wind turbine. The calculation of the energy efficiency deviation degree of the wind turbine is specifically expressed as: The specific abnormality identification is: when D s > 15% or D w > 20%, it is determined that there is an energy efficiency abnormality in the equipment.

[0041] Energy efficiency influencing factor analysis module: The data analysis terminal conducts key factor analysis based on the abnormality identification result;

[0042] In this embodiment, specifically, it should be noted that: the key factor analysis includes the key factor analysis of the photovoltaic module and the key factor analysis of the wind turbine; the key factor analysis of the photovoltaic module is used to establish the temperature influence factor F T = 0.004(T - 25), the pollution degree influence factor F c = 0.01C, the tilt angle deviation factor F α = 1 - cos(α - α0), where T represents the ambient temperature, C represents the surface pollution degree, α represents the tilt angle, and α0 represents the optimal tilt angle of the photovoltaic module; the key factor analysis of the wind turbine is used to establish the wind speed deviation vibration frequency deviation bearing temperature influence factor F Tb = 0.003(Tb - Tb0), where v represents the wind speed, v optV represents the optimal working wind speed of the fan, f represents the vibration frequency of the blade, f0 represents the natural vibration frequency of the blade under normal working conditions, Tb represents the bearing temperature, and Tb0 represents the upper limit of the normal working temperature of the bearing.

[0043] Energy efficiency monitoring, analysis and optimization module: The control center monitors the energy efficiency status in real time. Once an abnormality occurs, corresponding optimization strategies are executed according to the analysis results of the energy efficiency influencing factors.

[0044] In this embodiment, it should be specifically noted that: the optimization strategies include the optimization of abnormal energy efficiency of photovoltaic modules and the optimization of abnormal energy efficiency of wind turbines; the optimization of abnormal energy efficiency of photovoltaic modules specifically includes: when the environmental temperature T > 25 °C and the temperature influence factor F T > 0.1, start the surface cooling system of the module to reduce the module temperature; when the fouling degree influence factor F c > 0.2, trigger the automatic cleaning program to clean the surface of the module; when the tilt angle deviation factor F α > 0.1, adjust the tilt angle of the module through the servo motor to make it close to the optimal tilt angle; the optimization of abnormal energy efficiency of the wind turbine specifically includes: when the wind speed deviation degree F v > 0.3, adjust the blade angle to make the fan in the optimal power capture state; when the vibration frequency deviation degree F f > 0.2 or the bearing temperature influence factor F Tb > 0.15, send a warning signal to remind relevant technical personnel to carry out maintenance.

[0045] Based on the above solution and the attached Figure 2 , this application embodiment also discloses a new energy energy efficiency management data monitoring and analysis method, including an equipment status monitoring terminal, an environment perception terminal, a data analysis terminal and a control center, specifically including the following steps:

[0046] S1. Use the equipment status monitoring terminal and the environment perception terminal to collect equipment operation parameters, environment parameters and equipment status parameters in real time during the operation of new energy equipment;

[0047] S2. The data analysis terminal analyzes the collected data and calculates the energy efficiency reference value of different new energy equipment under standard working conditions;

[0048] S3. During the operation of the equipment, the data analysis terminal calculates the real-time energy efficiency index of the equipment;

[0049] S4. The data analysis terminal calculates the energy efficiency deviation degree between the real-time energy efficiency index and the energy efficiency reference value, and performs abnormal identification according to the energy efficiency deviation degree;

[0050] S5. The data analysis terminal performs key factor analysis according to the abnormal identification result;

[0051] S6. The control center monitors the energy efficiency status in real time. Once an abnormality occurs, corresponding optimization strategies are executed according to the analysis results of the energy efficiency influencing factors.

[0052] Secondly: In the accompanying drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. For other structures, reference can be made to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0053] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A new energy efficiency management data monitoring and analysis system, characterized in that, It includes a device status monitoring terminal, an environment perception terminal, a data analysis terminal and a control center, specifically including: Multi-dimensional data acquisition module: Using the device status monitoring terminal and the environment perception terminal to collect device operation parameters, environment parameters and device status parameters in real time during the operation of new energy devices; Device energy efficiency baseline value calculation module: The data analysis terminal analyzes the collected data and calculates the energy efficiency baseline values of different new energy devices under standard working conditions; Real-time energy efficiency index calculation module: During the operation of the device, the data analysis terminal calculates the real-time energy efficiency index of the device; Abnormality identification module: The data analysis terminal calculates the energy efficiency deviation degree between the real-time energy efficiency index and the energy efficiency baseline value, and performs abnormality identification based on the energy efficiency deviation degree; Energy efficiency influencing factor analysis module: The data analysis terminal conducts key factor analysis based on the abnormality identification results; Energy efficiency monitoring, analysis and optimization module: The control center monitors the energy efficiency status in real time. Once an abnormality occurs, corresponding optimization strategies are executed according to the results of the energy efficiency influencing factor analysis.

2. The new energy efficiency management data monitoring and analysis system according to claim 1, characterized in that: The device operation parameters include the output power P of the solar photovoltaic module, the power generation E of the wind turbine, the rotational speed n, the blade length r, and the fan rotation efficiency η; the environment parameters include the light intensity S, the ambient temperature T, the wind speed v, and the wind direction θ; the device status parameters include the tilt angle α of the photovoltaic module, the surface pollution degree C, the vibration frequency f of the wind turbine blade, and the bearing temperature Tb.

3. The new energy efficiency management data monitoring and analysis system according to claim 1, wherein: The energy efficiency benchmark values include the energy efficiency benchmark value of the solar photovoltaic module and the energy efficiency benchmark value of the wind turbine; the data analysis terminal establishes an energy efficiency benchmark value calculation model for the solar photovoltaic module, and imports the output power P of the photovoltaic module, the light intensity S, the tilt angle α of the photovoltaic module, and the ambient temperature T into the energy efficiency benchmark value calculation model for the solar photovoltaic module to obtain the energy efficiency benchmark value E of the solar photovoltaic module r , the energy efficiency benchmark value calculation model for the solar photovoltaic module is specifically expressed as: The data analysis terminal establishes an energy efficiency benchmark value calculation model for the wind turbine, and imports the generated electricity E, the wind speed v, the blade length r, and the fan rotation efficiency η into the energy efficiency benchmark value calculation model for the wind turbine to obtain the energy efficiency benchmark value E of the wind turbine w , the energy efficiency benchmark value calculation for the wind turbine, the model is specifically expressed as: Among them, ρ represents the air density.

4. The new energy efficiency management data monitoring and analysis system according to claim 1, wherein: The real-time energy efficiency index includes the real-time energy efficiency index of the solar photovoltaic module and the real-time energy efficiency index of the wind turbine. The data analysis terminal establishes a calculation model for the real-time energy efficiency index of the solar photovoltaic module, and imports the output power P of the photovoltaic module, the light intensity S, the tilt angle α of the photovoltaic module, the surface pollution degree C, and the ambient temperature T into the calculation model for the real-time energy efficiency index of the solar photovoltaic module to obtain the real-time energy efficiency index E of the solar photovoltaic module i , and the calculation model for the real-time energy efficiency index of the solar photovoltaic module is specifically expressed as: The data analysis terminal establishes a calculation model for the real-time energy efficiency index of the wind turbine, and imports the power generation E, the wind speed v, the blade length r, the vibration frequency f, and the fan rotation efficiency η into the calculation model for the real-time energy efficiency index of the wind turbine to obtain the real-time energy efficiency index E of the wind turbine a , and the calculation model for the real-time energy efficiency index of the wind turbine is specifically expressed as: where f0 represents the natural vibration frequency of the blade under normal operating conditions.

5. The new energy energy efficiency management data monitoring and analysis system according to claim 1, characterized in that: The calculation of the energy efficiency deviation degree includes the calculation of the energy efficiency deviation degree of solar photovoltaic modules and the calculation of the energy efficiency deviation degree of wind turbines. The data analysis terminal establishes a calculation model for the energy efficiency deviation degree of solar photovoltaic modules, and takes the energy efficiency reference value E of the solar photovoltaic module r and the real-time energy efficiency index E of the solar photovoltaic module i and imports them into the calculation model for the energy efficiency deviation degree of solar photovoltaic modules to obtain the energy efficiency deviation degree D of the solar photovoltaic module s . The calculation model for the energy efficiency deviation degree of solar photovoltaic modules is specifically expressed as: The data analysis terminal establishes a calculation model for the energy efficiency deviation degree of wind turbines, and takes the energy efficiency reference value E of the wind turbine w and the real-time energy efficiency index E of the wind turbine a and imports them into the calculation model for the energy efficiency deviation degree of wind turbines to obtain the energy efficiency deviation degree D of the wind turbine w . The calculation of the energy efficiency deviation degree of the wind turbine is specifically expressed as: The specific abnormal identification is as follows: when D s > 15% or D w > 20%, it is determined that there is an energy efficiency abnormality in the equipment.

6. The new energy efficiency management data monitoring and analysis system according to claim 1, characterized in that: The key factor analysis includes the key factor analysis of photovoltaic modules and the key factor analysis of wind turbines; the key factor analysis of photovoltaic modules is used to establish the temperature influence factor F T = 0.004(T - 25), the fouling degree influence factor F c = 0.01C, the tilt angle deviation factor F α = 1 - cos(α - α0), where T represents the ambient temperature, C represents the surface fouling degree, α represents the tilt angle, and α0 represents the optimal tilt angle of the photovoltaic module; the key factor analysis of wind turbines is used to establish the wind speed deviation vibration frequency deviation bearing temperature influence factor F Tb = 0.003(Tb - Tb0), where v represents the wind speed, v opt represents the optimal operating wind speed of the fan, f represents the vibration frequency of the blade, f0 represents the natural vibration frequency of the blade under normal operating conditions, Tb represents the bearing temperature, and Tb0 represents the upper limit of the normal operating temperature of the bearing.

7. The new energy energy efficiency management data monitoring and analysis system according to claim 1, characterized in that: The optimization strategies include energy efficiency abnormality optimization of photovoltaic modules and energy efficiency abnormality optimization of wind turbines; The abnormal optimization of the energy efficiency of the photovoltaic module specifically includes: when the ambient temperature T > 25 °C and the temperature influence factor F T > 0.1, start the surface cooling system of the module to reduce the temperature of the module; when the fouling degree influence factor F c > 0.2, trigger the automatic cleaning program to clean the surface of the module; when the tilt angle deviation factor F α > 0.1, adjust the tilt angle of the module through the servo motor to make it close to the optimal tilt angle; the abnormal optimization of the energy efficiency of the wind turbine specifically includes: when the wind speed deviation degree F v > 0.3, adjust the blade angle to make the wind turbine in the optimal power capture state; when the vibration frequency deviation degree F f > 0.2 or the bearing temperature influence factor F Tb > 0.15, send out a warning signal to remind relevant technical personnel to carry out maintenance.

8. A method for monitoring and analyzing new energy energy efficiency management data, which is used to implement the new energy energy efficiency management data monitoring and analyzing system described in any one of claims 1 to 7 above, and is characterized in that: It includes a device status monitoring terminal, an environment perception terminal, a data analysis terminal and a control center, specifically including the following steps: S1. Using the device status monitoring terminal and the environment perception terminal to collect device operation parameters, environment parameters and device status parameters in real time during the operation of new energy devices; S2. The data analysis terminal analyzes the collected data and calculates the energy efficiency baseline values of different new energy devices under standard working conditions; S3. During the operation of the device, the data analysis terminal calculates the real-time energy efficiency index of the device; S4. The data analysis terminal calculates the energy efficiency deviation degree between the real-time energy efficiency index and the energy efficiency baseline value, and performs abnormality identification based on the energy efficiency deviation degree; S5. The data analysis terminal conducts key factor analysis based on the abnormality identification results; S6. The control center monitors the energy efficiency status in real time. Once an abnormality occurs, corresponding optimization strategies are executed according to the results of the energy efficiency influencing factor analysis.

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