Electric power energy-saving detection and control system for intelligent power utilization

Abstract

The invention relates to the field of energy-saving management, and discloses an electric power energy-saving detection and control system for intelligent power utilization, which comprises a power utilization data detection module for collecting electrical data and temperature data; the communication transmission module is used for transmitting the collected electrical data and temperature data to a cloud platform; the power consumption data processing module is used for processing the electrical data and the temperature data received in the cloud platform, respectively calculating the energy consumption coefficient and the use state coefficient of each electric device, and calculating the energy consumption risk score of each electric device through a power consumption analysis model; the energy consumption analysis module is used for comparing the energy consumption risk score of each electric device with an energy consumption risk score interval preset by the system and dividing each electric device into a high energy consumption state, a medium energy consumption state and a low energy consumption state according to a comparison result; and the energy-saving control module is used for performing energy-saving control on the electric equipment in the high-energy-consumption state.

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 by 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] 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;

[0011] 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:

[0012] 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;

[0013] 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.

[0014] In a further solution, the process of establishing the power consumption analysis model is as follows:

[0015] 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;

[0016] 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;

[0017] 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:

[0018] 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.

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

[0020] 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;

[0021] 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.

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

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

[0024] Obtain reference change curves of current power-consuming equipment in unit time based on big data;

[0025] 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;

[0026] 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;

[0027] 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;

[0028] Obtain the change in energy consumption coefficient Kn during peak electricity consumption And the change of the usage state coefficient Tn

[0029] Then obtain the change in energy consumption coefficient Kn during the low electricity consumption period And the change of the usage state coefficient Tn

[0030] 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 高 is the area below the actual change curve and above the reference change curve, S 低 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.

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

[0032] 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;

[0033] 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.

[0034] In 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] 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;

[0037] 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 ;

[0038] Monitor the changing trend of the adjusted energy consumption coefficient of the current electrical equipment within a detection period;

[0039] 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;

[0040] Otherwise, the energy-saving adjustment is judged to be successful and the operation continues according to the current operating parameters.

[0041] The process of the two-round adjustment is as follows:

[0042] 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.

[0043] In a further solution, the secondary energy-saving adjustment mode is:

[0044] 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 ;

[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] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 As shown, the present invention is an electric power energy-saving detection and control system for smart electricity use, comprising:

[0054] 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; the electrical data includes current, voltage, power factor and power consumption; the current, voltage, power factor and power consumption are respectively obtained by current sensor, voltage sensor, power factor sensor, power sensor or electric meter detection, and the specific detection process is the existing technology and will not be described here;

[0055] Communication transmission module, used to transmit the collected electrical data and temperature data to the cloud platform;

[0056] 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;

[0057] 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;

[0058] 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:

[0059] 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;

[0060] 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.

[0061] The process of establishing the power consumption analysis model is as follows:

[0062] 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;

[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] 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.

[0071] The present invention compares the energy consumption risk score of the electrical equipment with the energy consumption risk score range preset by the system, thereby accurately and quickly dividing the specific high and low energy consumption states of each electrical equipment, providing effective data support for subsequent energy-saving control.

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

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

[0074] Obtain reference change curves of current power-consuming equipment in unit time based on big data;

[0075] 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;

[0076] 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;

[0077] 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;

[0078] Obtain the change in energy consumption coefficient Kn during peak electricity consumption And the change of the usage state coefficient Tn

[0079] Then obtain the change in energy consumption coefficient Kn during the low electricity consumption period And the change of the usage state coefficient Tn

[0080] 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 dWhen ≤0, D(S h -S d )=0; where S 高 is the area below the actual change curve and above the reference change curve, S 低 is the area enclosed by the actual change curve and the reference change curve. is the reference value, μ and θ are weight coefficients. It should be noted that They are obtained by integrating the corresponding time periods, for example: Kn(t) is the actual change curve of the energy consumption coefficient, t0 and t1 are the start time and end time;

[0081] The present invention iteratively divides the peak power consumption period and the valley power consumption period of the current power-consuming equipment, so as to output the fluctuation coefficient more accurately according to the ratio of the change of the energy consumption coefficient in the peak power consumption period and the valley power consumption period to the use state coefficient. The difference between the reference value and the change in the state coefficient Tn The difference between the reference value and the overall ratio can be used to obtain the actual fluctuation of the electrical equipment. Obviously, if The smaller, The larger the value, the greater the fluctuation of the electrical equipment during the peak period of electricity consumption. The bigger, The smaller the temperature is, the smaller the fluctuation of the electrical equipment during the low electricity consumption period. On the contrary, because the higher the operating temperature is during the low electricity consumption period, the more abnormal it is, which is reflected in the usage state coefficient. If the usage state coefficient is The bigger, The smaller it is, the larger the overall fluctuation coefficient Wn is.

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

[0083] The detected current, voltage, power factor and power consumption are input into a pre-trained recurrent neural network model, and the energy consumption coefficient Kn is output; the establishment and training process of the recurrent neural network model is an existing technology and will not be described in detail here;

[0084] 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. The accurate temperature fluctuation is obtained by means of standard deviation, and the operating temperature fluctuation reflects the use status of the electrical equipment.

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

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

[0087] 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;

[0088] 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 ;

[0089] Monitor the changing trend of the adjusted energy consumption coefficient of the current electrical equipment within a detection period;

[0090] 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;

[0091] Otherwise, the energy-saving adjustment is judged to be successful and the operation continues according to the current operating parameters.

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

[0093] 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.

[0094] In the case where the operating parameters of the electrical equipment in a high energy consumption state exceed the control parameters, in order to avoid drastic changes in the electrical equipment caused by uniform adjustment of parameters, the present invention adopts a step-by-step adjustment method. First, the abnormal parameter items, that is, the items exceeding the control parameters, are adjusted. Then, through the monitoring after the 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 adjustment is performed in sequence according to the difference between each operating parameter and the control parameter. 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 adjustment of the electrical equipment is successful, so there is no need for subsequent adjustments to prevent the transition adjustment from failing to meet the actual use needs of the current electrical equipment. Through the above technical solution, the operating parameters of the electrical equipment are adjusted to the minimum while meeting the energy-saving control requirements, so as to achieve the purpose of taking into account the actual use needs of the electrical equipment.

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

[0096] 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 ;

[0097] 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;

[0098] 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.

[0099] The present invention analyzes the energy consumption coefficient of the current electrical equipment by analyzing the energy consumption coefficient change trend. When the energy consumption coefficient change trend is downward and the energy consumption coefficient corresponding to the end point of the detection period is Kn≤Kn c , it is judged that the high energy consumption state of the electrical equipment is due to actual usage needs, not that there is a problem with the operation of the electrical equipment itself, so no adjustment is made; otherwise, it is judged that the electrical equipment is in a deteriorated state, so the operating parameters need to be adjusted. Since all operating parameters are lower than the control parameters, the method of uniformly adjusting all operating parameters to the control parameters will not cause drastic changes in the electrical equipment, and the purpose of energy-saving control can be achieved. It should be noted that monitoring will also be carried out after the adjustment. If the energy consumption coefficient is still in an upward trend, it means that the electrical equipment has a fault and an early warning needs to be issued for timely maintenance without continuing to adopt energy-saving control.

[0100] It should be noted that the calculation formulas and various parameters involved in the calculation in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0101] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage 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 高 is the area below the actual change curve and above the reference change curve, S 低 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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