A power energy-saving detection and control system for intelligent power consumption

Abstract

The present invention relates to the field of energy-saving management, and discloses a power energy-saving detection and control system for intelligent power consumption, including a power consumption data detection module for collecting electrical data and temperature data; a communication transmission module for transmitting the collected electrical data and temperature data to a cloud platform; a power consumption data processing module for processing the electrical data and temperature data received in the cloud platform, respectively calculating the energy consumption coefficient and usage status coefficient of each electrical device, and calculating the energy consumption risk score of each electrical device through an electricity consumption analysis model; an energy consumption analysis module for comparing the energy consumption risk scores of each electrical device with a preset energy consumption risk score range in the system, and classifying each electrical device into a high energy consumption state, a medium energy consumption state, and a low energy consumption state according to the comparison result, and an energy-saving control module for performing energy-saving control on the electrical devices in the high energy consumption state according to the analysis result of the energy consumption analysis module.

Description

Technical Field

[0001] The present invention relates to the field of energy-saving management, and in particular to an electric power energy-saving detection and control system for smart electricity use. Background Art

[0002] As enterprises become more aware of the national energy conservation and emission reduction policies, combined with their own needs to reduce costs and increase efficiency, saving electricity has always been a mainstream topic. Factories and enterprises are always unable to rely on publicity and KPI assessment to turn off the power of employees at will, and it is easy to cause emotional fluctuations among employees.

[0003] Current energy-saving management solutions that use technologies such as the Internet of Things can achieve real-time monitoring, analysis, and management of energy consumption in various power-using places. However, there are still certain defects. For example, if the same energy-saving strategy is always implemented for different environments and usage needs, the usage needs may not be met. For example, for electrical equipment, including lighting, air conditioning, fans, etc., take air conditioning as an example. Air conditioning is used to adjust the ambient temperature. During actual operation, the air conditioning will not adjust the demand due to the number of people in the area where it is used. Only when there is no one in the area of ​​action, the air conditioning needs to be turned off. When there are people in the area of ​​action, even if there is only one person, the air conditioning needs to be kept running. This will result in the air conditioning being unable to meet actual usage needs after energy-saving measures are taken. Summary of the invention

[0004] The purpose of the present invention is to provide an electric power energy-saving detection and control system for smart electricity use to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An electric power energy-saving detection and control system for smart electricity use, comprising:

[0007] The power consumption data detection module is used to collect real-time electrical data from each power-consuming device and obtain the temperature data of each power-consuming device in real time based on the temperature sensor;

[0008] Communication transmission module, used to transmit the collected electrical data and temperature data to the cloud platform;

[0009] The power consumption data processing module is used to process the electrical data and temperature data received in the cloud platform, calculate the energy consumption coefficient and usage status coefficient of each power-consuming device, and calculate the energy consumption risk score of each power-consuming device through the power consumption analysis model;

[0010] An energy consumption analysis module is used to compare the energy consumption risk scores of each electrical device with the preset energy consumption risk score range of the system, and classify each electrical device into a high energy consumption state, a medium energy consumption state, and a low energy consumption state according to the comparison results;

[0011] An energy-saving control module, according to the analysis results of the energy consumption analysis module, performs energy-saving control on the electrical devices in the high energy consumption state; among them, the method of energy-saving control is:

[0012] Compare each operating parameter of the current electrical device in the high energy consumption state with each operating parameter of the electrical device in the normal energy consumption state one by one;

[0013] If at least one operating parameter of the electrical device in the high energy consumption state exceeds the operating parameter of the electrical device in the medium energy consumption state, enable the first-level energy-saving adjustment mode; otherwise, start the second-level energy-saving adjustment mode.

[0014] For a further solution, the establishment process of the electrical consumption analysis model is as follows:

[0015] Collect the current, voltage, power factor, and power consumption of the current electrical device through a detection sensor, and obtain the energy consumption coefficient Kn of the current electrical device after processing;

[0016] Collect the temperature of the current electrical device during operation through a temperature sensor, and obtain the usage status coefficient Tn of the current electrical device after processing;

[0017] Establish an electrical consumption analysis model according to the energy consumption coefficient Kn and the usage status coefficient Tn of the current electrical device, and the expression is:

[0018] In the formula, is a preset fixed coefficient, α and β are influencing factors, Wn is a fluctuation coefficient, P(Tn, Kn) is the energy consumption risk score, Tn0 is the usage status reference coefficient, and Kn0 is the energy consumption reference coefficient.

[0019] For a further solution, the working process of the energy consumption analysis module includes:

[0020] Compare the calculated energy consumption risk score P i (Tn, Kn) of the i-th electrical device with the preset energy consumption risk score range of the system for comparison;

[0021] If then determine that the i-th electrical device is in a high energy consumption state; if then determine that the i-th electrical device is in a medium energy consumption state; if then determine that the i-th electrical device is in a low energy consumption state.

[0022] For a further solution, the method for obtaining the fluctuation coefficient Wn is as follows:

[0023] Obtain the actual change curve of the power consumption of the current electrical equipment within a unit time;

[0024] Based on big data, obtain the reference change curve of the current electrical equipment within a unit time;

[0025] Plot the actual change curve and the reference change curve in the same coordinate system, where the abscissa of this coordinate system is time and the ordinate is power consumption;

[0026] Mark the time period of the area enclosed by the lower part of the actual change curve and the upper part of the reference change curve as the peak power consumption period of the current electrical equipment;

[0027] Mark the time period of the area enclosed by the upper part of the actual change curve and the upper part of the reference change curve as the low power consumption period of the current electrical equipment;

[0028] Obtain the change amount of the energy consumption coefficient Kn during the peak power consumption period and the change amount of the usage status coefficient Tn

[0029] Then obtain the change amount of the energy consumption coefficient Kn during the low power consumption period and the change amount of the usage status coefficient Tn

[0030] According to the current electrical equipment, calculate to obtain the fluctuation coefficient Wn; the expression is: In the formula, D(S h -S d ) is a judgment function. When S h -S d >0, D(S h -S d )=(S h -S d )ρ. When S h -S d ≤0, D(S h -S d ) = 0; where S 高 is the area of the region enclosed by the lower part of the actual change curve and the upper part of the reference change curve, S 低 is the area of the region enclosed by the upper part of the actual change curve and the upper part of the reference change curve, is the reference value, and μ and θ are weight coefficients.

[0031] For a further solution, the process for obtaining the energy consumption coefficient Kn and the usage status coefficient Tn of the current electrical equipment is as follows:

[0032] Input the detected current, voltage, power factor, and power consumption into a pre-trained recurrent neural network model to output the energy consumption coefficient Kn;

[0033] Collect the temperature T0 during operation in sequence according to the set frequency, and calculate the usage status coefficient Tn. The expression is: In the formula, T0 j is the temperature collected for the jth time, n is the total number of collections, is the average temperature.

[0034] For a further solution, the first-level energy-saving adjustment mode is:

[0035] Mark the operating parameters of the current electrical equipment in sequence as A1, A2, AM,..., AN;

[0036] Take the operating parameters of the electrical equipment in the medium energy consumption state as reference parameters, and mark them in sequence as B1, B2, BM,..., BN according to the corresponding order;

[0037] After adjusting the operating parameter of the AMth item to BM when it exceeds the corresponding item, obtain the energy consumption coefficient Kn of the current electrical equipment, and take the energy consumption coefficient of the electrical equipment in the medium energy consumption state as the reference value Kn c ;

[0038] Monitor the change trend of the energy consumption coefficient of the current electrical equipment after adjustment within a detection period;

[0039] If the change trend of the energy consumption coefficient is upward and the energy consumption coefficient Kn corresponding to the end of the detection period > Kn c , it is determined that the energy-saving adjustment fails, and enter the second-round adjustment;

[0040] Otherwise, it is determined that the energy-saving adjustment is successful, and continue to operate according to the current operating parameters.

[0041] The process of the second-round adjustment is:

[0042] Obtain the difference between the operating parameters of the current electrical equipment and the reference parameters, and sort the differences in descending order. Adjust the corresponding operating parameters to the reference parameters in sequence until the change trend of the energy consumption coefficient is downward and the energy consumption coefficient Kn corresponding to the end of the detection period ≤ Kn c until.

[0043] For a further solution, the second-level energy-saving adjustment mode is:

[0044] Obtain the energy consumption coefficient Kn of the current electrical equipment, and take the energy consumption coefficient of the electrical equipment in the medium energy consumption state as the reference value Kn c ;

[0045] Monitor the change trend of the energy consumption coefficient of the current electrical equipment within a detection period. If the energy consumption coefficient change trend is downward and the energy consumption coefficient corresponding to the end of the detection period Kn≤Kn c , then it is determined that the operating parameters of the current equipment do not need to be adjusted;

[0046] Otherwise, all operating parameter values ​​of the current electrical equipment are adjusted to operating parameter values ​​of the electrical equipment in the medium energy consumption state.

[0047] Beneficial effects of the present invention:

[0048] (1) The power consumption data processing module accurately analyzes the energy consumption and usage of each power-consuming device. The power consumption analysis model can evaluate the actual energy consumption risk of the power-consuming device from the two aspects of energy consumption and operating temperature, thereby dividing the power-consuming device into three energy consumption states: high, medium, and low. The corresponding energy-saving control module performs targeted energy-saving control on the power-consuming device in the high energy consumption state. Compared with the prior art in which a unified energy-saving strategy is implemented for all power-consuming devices, resulting in the situation that some power consumption cannot meet the usage demand, energy-saving control is only performed on the power-consuming device in the high energy consumption state, which can take into account the actual usage demand of the power-consuming device while reducing the energy consumption state as much as possible, thereby achieving the purpose of high efficiency and energy saving;

[0049] (2) For high-energy-consuming electrical equipment with operating parameters exceeding the control parameters, in order to avoid drastic changes in the electrical equipment caused by unified parameter adjustment, a step-by-step approach is adopted to adjust the parameters. First, the abnormal parameter items, that is, the items exceeding the control parameters, are adjusted. Then, through monitoring after adjustment, if the energy consumption is still on an upward trend, it means that the energy consumption of the electrical equipment has not been improved. Therefore, a second round of adjustment is performed, that is, the difference between each operating parameter and the control parameter is adjusted in turn. After each adjustment of an operating parameter, a monitoring period is performed. As long as the energy consumption coefficient change trend is downward and the energy consumption coefficient Kn corresponding to the end point of the detection period is ≤Kn c , it means that the electrical equipment has been adjusted successfully, so no subsequent adjustments are needed to prevent the transition adjustment from failing to meet the actual usage needs of the current electrical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] Figure 1 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0052] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0053] Please refer to Figure 1 As shown, the present invention is a power energy-saving detection and control system for intelligent power consumption, including:

[0054] An electricity consumption data detection module, configured to collect real-time electrical data from each electrical device and obtain the temperature data of each electrical device during operation in real time based on a temperature sensor; the electrical data includes current, voltage, power factor, and electricity consumption; the current, voltage, power factor, and electricity consumption are respectively detected by a current sensor, a voltage sensor, a power factor sensor, an electricity quantity sensor, or an electric meter. The specific detection process is prior art and will not be described in detail here;

[0055] A communication transmission module, configured to transmit the collected electrical data and temperature data to a cloud platform;

[0056] An electricity consumption data processing module, configured to process the electrical data and temperature data received in the cloud platform, calculate the energy consumption coefficient and usage status coefficient of each electrical device respectively, and calculate the energy consumption risk score of each electrical device through an electricity consumption analysis model;

[0057] An energy consumption analysis module, configured to compare the energy consumption risk scores of each electrical device with the preset energy consumption risk score range of the system, and classify each electrical device into a high energy consumption state, a medium energy consumption state, and a low energy consumption state according to the comparison result;

[0058] An energy-saving control module, according to the analysis result of the energy consumption analysis module, performs energy-saving control on the electrical devices in the high energy consumption state; wherein, the energy-saving control method is:

[0059] Compare each operating parameter of the current electrical device in the high energy consumption state with each operating parameter of the electrical device in the normal energy consumption state one by one;

[0060] If at least one operating parameter of the electrical device in the high energy consumption state exceeds the operating parameter of the electrical device in the medium energy consumption state, enable the first-level energy-saving adjustment mode; otherwise, start the second-level energy-saving adjustment mode.

[0061] The establishment process of the electricity consumption analysis model is as follows:

[0062] Collect the current, voltage, power factor, and electricity consumption of the current electrical device through a detection sensor, and obtain the energy consumption coefficient Kn of the current electrical device after processing;

[0063] The temperature of the current electrical equipment during operation is collected by a temperature sensor, and the current operating state coefficient Tn of the electrical equipment is obtained after processing;

[0064] The power consumption analysis model is established based on the energy consumption coefficient Kn and the usage state coefficient Tn of the current power equipment. The expression is:

[0065] In the formula, is a preset fixed coefficient, α and β are influencing factors, Wn is the fluctuation coefficient, P(Tn, Kn) is the energy consumption risk score, Tn0 is the usage status reference coefficient, and Kn0 is the energy consumption reference coefficient.

[0066] In order to meet the energy-saving needs of electrical equipment under different power demands, the present invention accurately analyzes the energy consumption and usage of each electrical equipment through the power data processing module, and can evaluate the actual energy consumption risk of the electrical equipment from the two aspects of energy consumption and operating temperature through the power analysis model, so as to divide the electrical equipment into three energy consumption states: high, medium and low, and perform targeted energy-saving control on the electrical equipment in the high energy consumption state through the corresponding energy-saving control module. Compared with the prior art in which a unified energy-saving strategy is implemented for all electrical equipment, resulting in the situation that some electricity consumption cannot meet the usage demand, energy-saving control is only performed on the electrical equipment in the high energy consumption state, which can take into account the actual usage demand of the electrical equipment while reducing the energy consumption state as much as possible, thereby achieving the purpose of high efficiency and energy saving;

[0067] At the same time, a specific method for establishing an electricity consumption analysis model is given. The electricity consumption analysis model is established based on the energy consumption coefficient Kn and the usage status coefficient Tn of the current electrical equipment. The expression is: The influencing factors α and β in the formula are constants, and their numerical values ​​can be changed according to actual needs, thereby changing the data sensitivity of the corresponding items, so as to achieve the purpose of accurately calculating the energy consumption risk score P(Tn, Kn). |Tn-Tn0| and |Kn-Kn0| respectively show the deviation between the actual energy consumption coefficient and the usage status coefficient and their respective reference coefficients. Obviously, the greater the deviation, the greater the energy consumption score, indicating that the probability that the current electrical equipment is in a high energy consumption state is greater, and the fluctuation coefficient reflects the overall change state of the current electrical equipment in unit time. Obviously, the greater the change, the more unstable the current electrical equipment is, and therefore the greater the possibility of being in a high energy consumption state.

[0068] The working process of the energy consumption analysis module includes:

[0069] The calculated energy consumption risk score P of the i-th electrical equipment i (Tn, Kn) and the energy consumption risk score range preset by the system Make a comparison;

[0070] If then it is determined that the i-th electrical equipment is in a high energy consumption state; if then it is determined that the i-th electrical equipment is in a medium energy consumption state; if then it is determined that the i-th electrical equipment is in a low energy consumption state.

[0071] The present invention realizes accurate and rapid classification of the specific energy consumption level of each electrical equipment by comparing the energy consumption risk score of the electrical equipment with the preset energy consumption risk score range, providing effective data support for subsequent energy-saving control.

[0072] The method for obtaining the fluctuation coefficient Wn is as follows:

[0073] Obtain the actual change curve of the power consumption of the current electrical equipment within a unit time;

[0074] Based on big data, obtain the reference change curve of the current electrical equipment within a unit time;

[0075] Plot the actual change curve and the reference change curve in the same coordinate system, with time as the abscissa and power consumption as the ordinate;

[0076] Mark the time period of the area enclosed by the lower part of the actual change curve and the upper part of the reference change curve as the peak power consumption period of the current electrical equipment;

[0077] Mark the time period of the area enclosed by the upper part of the actual change curve and the upper part of the reference change curve as the low power consumption period of the current electrical equipment;

[0078] Obtain the change amount of the energy consumption coefficient Kn during the peak power consumption period and the change amount of the usage state coefficient Tn

[0079] Then obtain the change amount of the energy consumption coefficient Kn during the low power consumption period and the change amount of the usage state coefficient Tn

[0080] According to the Calculate to obtain the fluctuation coefficient Wn; the expression is: In the formula, D(S h -S d ) is a judgment function. When S h -S d >0, D(S h -S d )=(S h -S d )ρ. When S h -S dWhen ≤ 0, D(S h -S d ) = 0; where S 高 is the area of the region enclosed by the actual change curve below and the reference change curve, and S 低 is the area of the region enclosed by the actual change curve above and the reference change curve above, is the reference value, and μ and θ are weight coefficients. It should be noted that are all obtained by integrating over the corresponding time period. For example: Kn(t) is the actual change curve of the energy consumption coefficient, and t0 and t1 are the start time and end time;

[0081] The present invention iteratively divides the peak power consumption period and the off-peak power consumption period of the current electrical equipment, so that the result output of the more accurate fluctuation coefficient can be carried out according to the ratio of the change amount of the energy consumption coefficient and the usage state coefficient in the peak power consumption period and the off-peak power consumption period. By calculating the difference between the change amount of the energy consumption coefficient Kn in the peak power consumption period and the reference value, and the change amount of the usage state coefficient Tn and the difference calculation of the reference value, the overall ratio can obtain the actual fluctuation situation of the electrical equipment. Obviously, if is smaller, is larger, it means that the fluctuation of the electrical equipment in the peak power consumption period is larger, while is larger, is smaller, it means that the fluctuation of the electrical equipment in the off-peak power consumption period is smaller. On the contrary, because the higher the operating temperature in the off-peak power consumption period indicates the more abnormal, which is reflected in the usage state coefficient. If the usage state coefficient is larger, is smaller, the overall fluctuation coefficient Wn is larger.

[0082] The process of obtaining the energy consumption coefficient Kn and the usage state coefficient Tn of the current electrical equipment is as follows:

[0083] Input the detected current, voltage, power factor and power consumption into the pre-trained recurrent neural network model to output the energy consumption coefficient Kn; the establishment and training process of the recurrent neural network model is prior art and will not be elaborated here;

[0084] According to the set frequency, collect the temperature T0 during operation in sequence, and calculate the usage state coefficient Tn. The expression is: In the formula, T0 j is the temperature collected for the jth time, n is the total number of collections, is the average temperature. The accurate temperature fluctuation situation is obtained by the standard deviation method, and the usage state of the electrical equipment is reflected by the temperature fluctuation situation during operation.

[0085] The first-level energy-saving adjustment mode is as follows:

[0086] Mark the operating parameters of the current electrical equipment in sequence as A1, A2, AM,..., AN;

[0087] Take the operating parameters of the electrical equipment in the medium energy consumption state as the reference parameters, and mark them in sequence as B1, B2, BM,..., BN according to the corresponding order;

[0088] After adjusting the AMth item that exceeds the corresponding item's operating parameter to BM, obtain the energy consumption coefficient Kn of the current electrical equipment, and take the energy consumption coefficient of the electrical equipment in the medium energy consumption state as the reference value Kn c ;

[0089] Monitor the change trend of the energy consumption coefficient of the current electrical equipment after adjustment within a detection period;

[0090] If the change trend of the energy consumption coefficient is upward and the energy consumption coefficient Kn corresponding to the end of the detection period > Kn c , it is determined that the energy-saving adjustment fails, and enter the second-round adjustment;

[0091] Otherwise, it is determined that the energy-saving adjustment is successful, and continue to operate according to the current operating parameters.

[0092] The process of the second-round adjustment is as follows:

[0093] Obtain the differences between the operating parameters of the current electrical equipment and the reference parameters, arrange the differences in descending order, and adjust the corresponding operating parameters to the reference parameters in sequence until the change trend of the energy consumption coefficient is downward and the energy consumption coefficient Kn corresponding to the end of the detection period ≤ Kn c until.

[0094] In the case where the operating parameters of the electrical equipment in the high energy consumption state exceed the reference parameters, in order to avoid drastic changes in the electrical equipment caused by unified parameter adjustment, the present invention adopts a step-by-step method for adjustment. First, adjust the abnormal parameter items, that is, the items that exceed the reference parameters. Then, through the monitoring after adjustment, if the energy consumption is still in an upward trend, it indicates that the energy consumption situation of the electrical equipment has not been improved. Therefore, a second-round adjustment is carried out, that is, adjust in sequence according to the magnitude of the differences between the operating parameters and the reference parameters. After each adjustment of an operating parameter, monitor for a detection period. As long as it satisfies that the change trend of the energy consumption coefficient is downward and the energy consumption coefficient Kn corresponding to the end of the detection period ≤ Kn c, it indicates that the adjustment of the electrical equipment is successful, so there is no need to perform subsequent adjustments to prevent over-adjustment from not meeting the actual usage requirements of the current electrical equipment. Through the above technical solution, while meeting the energy-saving control requirements, the operating parameters of the electrical equipment are minimized to achieve the purpose of taking into account the actual usage requirements of the electrical equipment.

[0095] The secondary energy-saving adjustment mode is as follows:

[0096] Obtain the energy consumption coefficient Kn of the current electrical equipment, and use the energy consumption coefficient of the electrical equipment in the medium energy consumption state as the reference value Kn c ;

[0097] Monitor the change trend of the energy consumption coefficient of the current electrical equipment within a detection period. If the change trend of the energy consumption coefficient is downward and the energy consumption coefficient Kn corresponding to the end of the detection period ≤ Kn c , it is determined that the operating parameters of the current equipment do not need to be adjusted;

[0098] Otherwise, adjust the values of all operating parameters of the current electrical equipment to the operating parameter values of the electrical equipment in the medium energy consumption state.

[0099] Through the analysis of the change trend of the energy consumption coefficient of the current electrical equipment and the specific energy consumption coefficient, when the change trend of the energy consumption coefficient is downward and the energy consumption coefficient Kn corresponding to the end of the detection period ≤ Kn c , it is determined that the high energy consumption state of the electrical equipment is due to actual usage requirements, rather than problems with the operation of the electrical equipment itself. Therefore, no adjustment is made; otherwise, it is determined that the electrical equipment is in a deteriorated state. Therefore, adjustment of the operating parameters is required. Since all operating parameters are lower than the reference parameters, the method of uniformly adjusting all operating parameters to the reference parameters will not cause drastic changes in the electrical equipment and can still achieve the purpose of energy-saving control. It should be noted that monitoring will also be performed after adjustment. If it is still in the state where the change trend of the energy consumption coefficient is upward, it indicates that there is a fault in the electrical equipment, and a warning needs to be issued for timely maintenance, rather than continuing to use energy-saving control.

[0100] It should be noted that: The calculation formulas and all parameters participating in the operation in the present invention have been pre-processed dimensionless, and the process of dimensionless processing is well-known in the industry and will not be described here.

[0101] The above has described a detailed description of an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. An electric power energy-saving detection and control system for smart electricity use, characterized in that: include: The power consumption data detection module is used to collect real-time electrical data from each power-consuming device and obtain the temperature data of each power-consuming device in real time based on the temperature sensor; Communication transmission module, used to transmit the collected electrical data and temperature data to the cloud platform; The power consumption data processing module is used to process the electrical data and temperature data received in the cloud platform, calculate the energy consumption coefficient and usage status coefficient of each power-consuming device, and calculate the energy consumption risk score of each power-consuming device through the power consumption analysis model; The energy consumption analysis module is used to compare the energy consumption risk score of each electrical device with the energy consumption risk score range preset by the system, and classify each electrical device into a high energy consumption state, a medium energy consumption state, and a low energy consumption state according to the comparison result; The energy-saving control module performs energy-saving control on the electrical equipment in a high energy consumption state according to the analysis result of the energy consumption analysis module; wherein the energy-saving control method is: Compare the various operating parameters of the electrical equipment in the current high energy consumption state with the various operating parameters of the electrical equipment in the normal energy consumption state one by one; If the electric device in the high energy consumption state has at least one operating parameter that exceeds the electric device in the medium energy consumption state, the first-level energy-saving adjustment mode is enabled; otherwise, the second-level energy-saving adjustment mode is started.

2. The power energy-saving detection and control system for smart electricity use according to claim 1 is characterized in that: The process of establishing the power consumption analysis model is as follows: The current, voltage, power factor and power consumption of the current electrical equipment are collected by the detection sensor, and the energy consumption coefficient Kn of the current electrical equipment is obtained after processing; The temperature of the current electrical equipment during operation is collected by a temperature sensor, and the current operating state coefficient Tn of the electrical equipment is obtained after processing; The power consumption analysis model is established based on the energy consumption coefficient Kn and the usage state coefficient Tn of the current power equipment. The expression is: In the formula, is a preset fixed coefficient, α and β are influencing factors, Wn is the fluctuation coefficient, P(Tn, Kn) is the energy consumption risk score, Tn0 is the usage status reference coefficient, and Kn0 is the energy consumption reference coefficient.

3. The power energy-saving detection and control system for smart electricity use according to claim 2 is characterized in that: The working process of the energy consumption analysis module includes: The calculated energy consumption risk score P of the i-th electrical equipment i (Tn, Kn) and the energy consumption risk score range preset by the system Make a comparison; like Then the i-th electrical equipment is judged to be in a high energy consumption state; if Then the i-th electrical equipment is judged to be in medium energy consumption state; if Then it is judged that the i-th electrical equipment is in a low energy consumption state.

4. The power energy-saving detection and control system for smart electricity use according to claim 3 is characterized in that: The method for obtaining the fluctuation coefficient Wn is: Obtain the actual change curve of the power consumption of the current power-consuming equipment within a unit time; Obtain reference change curves of current power-consuming equipment in unit time based on big data; The actual change curve and the reference change curve are plotted in the same coordinate system, where time is the horizontal axis and power consumption is the vertical axis; The time period where the area below the actual change curve and the area above the reference change curve are located is marked as the peak power consumption period of the current power-consuming equipment; The time period where the area above the actual change curve and the area above the reference change curve are located is marked as the low power consumption period of the current power-consuming equipment; Obtain the change in energy consumption coefficient Kn during peak electricity consumption And the change of the usage state coefficient Tn Then obtain the change in energy consumption coefficient Kn during the low electricity consumption period And the change of the usage state coefficient Tn According to the current electrical equipment The fluctuation coefficient Wn is calculated; the expression is: In the formula, D(S h -S d ) is the judgment function, when S h -S d >0, D(S h -S d )=(S h -S d )ρ, when S h -S d When ≤0, D(S h -S d )=0; where S h is the area below the actual change curve and above the reference change curve, S d is the area enclosed by the actual change curve and the reference change curve. is the reference value, μ and θ are the weight coefficients.

5. The power energy-saving detection and control system for smart electricity use according to claim 4 is characterized in that: The process of obtaining the energy consumption coefficient Kn and the usage status coefficient Tn of the current electrical equipment is as follows: The detected current, voltage, power factor and power consumption are input into the pre-trained recurrent neural network model, and the energy consumption coefficient Kn is output; According to the set frequency, the temperature T0 during operation is collected in sequence, and the use state coefficient Tn is calculated. The expression is: Where T0 j is the temperature collected for the jth time, n is the total number of collections, is the mean temperature.

6. The power energy-saving detection and control system for smart electricity use according to claim 1 is characterized in that: The first-level energy-saving adjustment mode is: Mark the operating parameters of the current electrical equipment in sequence as A1, A2, AM, ..., AN; The operating parameters of the electrical equipment in the medium energy consumption state are used as reference parameters and are marked as B1, B2, BM, ..., BN in the corresponding order; After adjusting the operating parameters of the AM item that exceeds the corresponding item to BM, the energy consumption coefficient Kn of the current electrical equipment is obtained, and the energy consumption coefficient of the electrical equipment in the medium energy consumption state is used as the reference value Kn c ; Monitor the changing trend of the adjusted energy consumption coefficient of the current electrical equipment within a detection period; If the energy consumption coefficient changes in an upward trend and the energy consumption coefficient corresponding to the end point of the detection period Kn>Kn c , it is judged that the energy-saving adjustment has failed and the second round of adjustment is entered; Otherwise, the energy-saving adjustment is judged to be successful and the operation continues according to the current operating parameters.

7. The power energy-saving detection and control system for smart electricity use according to claim 6 is characterized in that: The process of the two-round adjustment is as follows: Obtain the difference between the current operating parameters of the electrical equipment and the reference parameters, and arrange the differences in descending order, and adjust the corresponding operating parameters to the reference parameters in sequence until the energy consumption coefficient changes in a downward trend and the energy consumption coefficient corresponding to the end of the detection period Kn≤Kn c until.

8. The power energy-saving detection and control system for smart electricity use according to claim 1 is characterized in that: The secondary energy-saving adjustment mode is: Get the energy consumption coefficient Kn of the current electrical equipment, and use the energy consumption coefficient of the electrical equipment in the medium energy consumption state as the reference value Kn c ; Monitor the change trend of the energy consumption coefficient of the current electrical equipment within a detection period. If the energy consumption coefficient change trend is downward and the energy consumption coefficient corresponding to the end of the detection period Kn≤Kn c , then it is determined that the operating parameters of the current equipment do not need to be adjusted; Otherwise, all operating parameter values ​​of the current electrical equipment are adjusted to operating parameter values ​​of the electrical equipment in the medium energy consumption state.

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