Smart energy consumption management system applied to electrical equipment
By deploying monitoring equipment on electrical equipment and analyzing historical data, establishing characteristic energy consumption models, and correcting abnormalities and missing parts in the data, the abnormality of monitoring data caused by electromagnetic interference is solved, and more accurate energy consumption monitoring and management is achieved.
Patent Information
- Application Number
- CN202510131159.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-06
- Publication Date
- 2025-05-30
AI Technical Summary
When there is electromagnetic interference in the surrounding environment of electrical equipment, abnormalities or missing are prone to transmission of monitoring data through wireless communication, making it difficult for enterprises to understand the real environment of the equipment and cannot accurately monitor energy consumption.
Design an intelligent energy consumption management system, including monitoring equipment deployment module, feature data analysis module, feature energy consumption model establishment module and data correction module. By establishing a three-dimensional model of electrical equipment, deploying monitoring equipment at the target points in the thermal imaging image; analyzing historical monitoring data, extracting data to be corrected and abnormal data; establishing a characteristic energy consumption model to correct abnormalities and missing parts in the data.
It effectively corrects the abnormalities and missing parts in the electrical equipment monitoring data, provides more accurate energy consumption monitoring data, supports the enterprise's energy management and decision-making, and improves the intelligence level of energy consumption management.
Smart Images
Figure CN120069306A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of equipment energy consumption management, and specifically to a smart energy consumption management system applied to electrical equipment. Background Art
[0002] In today's highly competitive business environment that emphasizes energy conservation and emission reduction, as an important entity in energy consumption, enterprises should scientifically and correctly manage the energy consumption data of electrical equipment. Electrical equipment is usually equipped with built-in measurement modules for real-time monitoring and recording of electrical parameters such as operating power, voltage, and current. However, due to the complex and ever-changing production environment of enterprises, various monitoring devices are usually installed on electrical equipment, and the monitoring data on the monitoring devices is wirelessly transmitted to the host. Taking monitoring devices such as temperature sensors as an example, by deploying temperature sensors at key parts of electrical equipment for monitoring and analysis, to a certain extent, by understanding the temperature environment of electrical equipment, it is possible to determine whether the energy consumption of the electrical equipment increases, and whether there are abnormalities in the operating state and energy consumption of the electrical equipment.
[0003] However, when there is electromagnetic interference in the environment around the electrical equipment, it will cause problems such as abnormal or missing of some data during the wireless communication transmission of the monitoring data, which will make it difficult for enterprises to understand the real environment of the equipment and accurately conduct intelligent monitoring of energy consumption. Therefore, it is necessary to analyze the historical information of the electrical equipment to correct the abnormal or missing monitoring data, providing strong support for the enterprise's energy management and decision-making. Summary of the Invention
[0004] The purpose of the present invention is to provide a smart energy consumption management system applied to electrical equipment to solve the problems raised in the prior art.
[0005] To achieve the above purpose, the present invention provides the following technical solutions:
[0006] A smart energy consumption management system applied to electrical equipment, including: a monitoring device deployment module, a feature data analysis module, a feature energy consumption model establishment module, and a data correction module;
[0007] The monitoring device deployment module: used to establish a three-dimensional model of the electrical equipment, obtain all detection points in the three-dimensional model according to the positions of the components in the three-dimensional model; obtain a number of thermal imaging images of the electrical equipment during the operation time, obtain the target points according to the changes of the pixels at the corresponding positions of each detection point on the thermal imaging images, and deploy the monitoring devices at the target points;
[0008] Feature data analysis module: It is used to collect all historical monitoring data of the monitoring device, analyze the monitoring data to obtain all the feature data therein; and based on the feature data, obtain the data deviation range, and then extract the data to be corrected and abnormal data in the monitoring data according to the data deviation range.
[0009] Feature energy consumption model establishment module: It is used to collect the historical electrical parameters of the electrical equipment, and obtain the feature energy consumption model of the change of the feature data with the electrical parameters according to the electrical parameters at the moments corresponding to the respective feature data.
[0010] Data correction module: It is used to obtain the pre-correction values of the data to be corrected and abnormal data according to the feature data, and sequentially correct the data to be corrected and abnormal data according to the time sequence through the feature energy consumption model.
[0011] Furthermore, the monitoring device deployment module includes a feature point judgment unit, a detection point judgment unit, and a monitoring device deployment unit:
[0012] Feature point judgment unit: It is used to establish the spatial coordinate system corresponding to the three-dimensional model, and obtain the component ranges of the components in the spatial coordinate system according to the positions and regions of the components in the three-dimensional model; obtain all the load components that consume energy during operation, where the component ranges corresponding to a certain load component a and a certain load component b are G a and G b , if there is a certain coordinate point P a on the component range G a and a certain coordinate point P b on the component range G b and the distance between them is less than the preset distance threshold, then randomly obtain M component coordinate points from the component ranges G a and G b , and take the coordinate point corresponding to the minimum sum of the distances from all component coordinate points in the spatial coordinate system as the feature point, and then obtain all the feature points in the three-dimensional model.
[0013] Furthermore, the detection point judgment unit: It is used to take the component range of the outermost component on the electrical equipment in the spatial coordinate system as the equipment range; obtain the coordinate P 0 corresponding to a certain feature point, and all the equipment coordinate points on the equipment range, and take the equipment coordinate point with the shortest distance to the coordinate P 0 as the detection point corresponding to a certain feature point, and then obtain all the detection points.
[0014] Further, a monitoring device deployment unit: starting from the moment when an electrical device starts to operate and ending at the moment when it stops operating, with an interval period of T, a number of operating moments are obtained; at each operating moment, a thermal imaging image of the electrical device is taken, and the thermal imaging image is converted into a grayscale image to obtain the grayscale value of each detection point at each operating moment. According to all the grayscale values corresponding to each detection point, the grayscale variance corresponding to each detection point is further obtained. The detection point with the maximum grayscale variance is taken as the target point, and the monitoring device is deployed at the position of the target point.
[0015] Generally, an electrical device needs to consume energy to operate, such as generators, transformers, control cabinets, distribution cabinets, etc. In this solution, the electrical device is a distribution cabinet, and the consumed energy is electrical energy; and the distribution cabinet includes various load components, including current transformers, capacitors, etc. These load components will generate heat when powered on, resulting in an increase in the component temperature. To ensure the safety of the electrical device during operation, usually the electrical device has a housing. In this solution, the device range is the range of the housing of the electrical device, and the detection points are all points on the housing of the electrical device. The target point is also a point on the housing of the electrical device, and the monitoring device is deployed on the housing, and the monitoring device is a temperature sensor.
[0016] Further, the feature data analysis module includes a feature data judgment unit, a feature value calculation unit, and a feature data analysis unit:
[0017] The feature data judgment unit: used to collect all the historical monitoring data of the monitoring device. The monitoring data includes the monitoring moment and the monitoring value. If the difference Vpq between two adjacent monitoring data p and q satisfies: V a <Vpq = V q -V p <V b , then both the monitoring data p and q are taken as the data to be detected. V p and V q are the monitoring values corresponding to the monitoring data p and q respectively. V a is the first monitoring difference, and V b is the second monitoring difference. V a <0, 0 < |V a | < V b ; if the total duration corresponding to the continuous data to be detected is greater than the preset duration threshold, then all the continuous data to be detected are aggregated into a feature set, and several feature sets are obtained, and all the data to be detected in the feature set are taken as feature data.
[0018] Furthermore, the feature value calculation unit is used to sort each feature set according to the time sequence of the first feature data in each feature set; obtain two feature sets A and B with adjacent sequence numbers, and the last feature data D of feature set A 1 The corresponding time T 1 , earlier than the first feature data D of feature set B 2 The corresponding time T 2 , then the time T 1 and time T 2 All monitoring data between are taken as target data; according to each monitoring value in feature set A, the average monitoring value V corresponding to feature set A is obtained A , according to each monitoring value in feature set B, the average monitoring value V corresponding to feature set B is obtained B , and then get the characteristic value: VA B = k*(V A +V B ) / 2, where k is the characteristic coefficient.
[0019] Furthermore, the characteristic data analysis unit is used to establish a two-dimensional coordinate system with the monitoring time as the horizontal coordinate and the monitoring value as the vertical coordinate, and according to the time T 1 Corresponding feature data D 1 , the feature data D 1 The monitoring value is used as V 1 , get the coordinates C 1 =(T 1 ,V 1 ), according to time T 2 Corresponding feature data D 2 , the feature data D 2 The monitoring value is used as V 2 , get the coordinates C 2 =(T 2 ,V 2 ), and then the center coordinates are Set the time T 1 and time T 2 The goal between
[0020] The data is sorted in chronological order; the data deviation range is obtained: Among them, T n is the monitoring time of a target data, V n is the monitoring value of a target data, T is the center coordinate C 0 As the starting point, along the coordinate C 2 direction, forward size is T 2 -T 0 The distance between the center coordinates C 0 As the starting point, along the center coordinate C0 With coordinate C 2 In the vertical direction of the line segment, forward by a distance of VA B; if a certain target data is within the data deviation range R, then the certain target data is regarded as the data to be corrected. If it is not within the data deviation range R, then the certain target data is regarded as abnormal data, and thus all the data to be corrected and abnormal data are obtained.
[0021] Due to the characteristic data D 1 and the characteristic data D 2 represent temperature, then under normal circumstances, the temperature in the corresponding time period between the characteristic data D 1 and the characteristic data D 2 basically changes around the characteristic data D 1 and the characteristic data D 2 data and does not differ much; among them, due to the distance D 1 or D 2 the monitoring data of the target data closer in time is closer to D 1 or D 2 , while the monitoring data closer to the middle time between the distance D 1 and D 2 may differ greatly from D 1 and D 2 Therefore, this scheme uses the ellipse formula, that is, the data range for representation, to determine whether the monitoring values of each target data meet the conditions; if a certain target data is not within the data range, then it is regarded as abnormal data, that is, data with a large deviation; if a certain target data is within the data range, then it is regarded as the data to be corrected, that is, data whose normality or deviation cannot be determined. And the characteristic data is normal data that does not need to be corrected, and the target data is the data that needs to be corrected in this scheme. The target data includes two types: the data to be corrected and abnormal data. In this scheme, the electrical parameter is the operating power of the electrical equipment.
[0022] Furthermore, the characteristic energy consumption model establishment module includes a characteristic energy consumption model establishment unit:
[0023] Characteristic energy consumption model establishment unit: used to establish a plane coordinate system with the electrical parameter as the abscissa and the monitoring value as the ordinate; if the electrical parameter at each moment in a certain characteristic set is X 1 , and the difference between the maximum monitoring value and the minimum monitoring value is less than the preset monitoring difference threshold, then according to each characteristic data in a certain characteristic set, the average monitoring value is obtained as the target value F 1 , and then a certain model coordinate point (X 1 , F 1), and then all model coordinate points are obtained; according to all model coordinate points, by the least square method, the characteristic energy consumption model F = k*(e x -1) is obtained, where F is the monitored value, X is the electrical parameter, e is the natural exponent, and k is the energy consumption model coefficient.
[0024] It should be noted that due to conditions such as heat dissipation, the relationship between temperature and operating power is not a simple linear relationship. Instead, it conforms to the situation that when the operating power is stable at a small value, due to heat dissipation, the temperature is difficult to rise. However, when the operating power is stable at a large value, since the heat dissipation rate is constant, the temperature of the electrical equipment will have a relatively large value at this time.
[0025] Furthermore, the data correction module includes a pre-correction numerical calculation unit and a data correction unit:
[0026] The pre-correction numerical calculation unit: is used to set the monitoring time interval of the monitoring data as g, and according to the duration DUR between time T 1 and time T 2 , the total number of target data H = DUR / g is obtained; all target data are sorted according to time, starting from the target data D at serial number 1, according to the difference V1 2 = V 1 between the monitoring values of time T 2 and time T 2 -V 1 , where V 1 and V 2 are the monitoring values corresponding to the characteristic data D 1 and the characteristic data D 2 respectively, and the pre-correction value of the target data D at serial number 1 is obtained as:
[0027] V 1 +V1 2 / H.
[0028] Furthermore, the data correction unit: is used to judge that if the target data D at serial number 1 is abnormal data, the pre-correction value is used as the corrected data of the target data D at serial number 1; if the target data D at serial number 1 is data to be corrected, and the variance S L of the electrical parameters within the previous time period L of the target data D at serial number 1 is less than the preset variance threshold S 0 , and the difference V L between the maximum monitoring value and the minimum monitoring value within the time period L is greater than the monitoring value threshold V,0, then the pre-correction value is used as the corrected data of the target data D at serial number 1; if the target data D at serial number 1 is data to be corrected, and the variance S L <S 0 , and V L≤V, 0, substitute the average electrical parameters within the time period L into the characteristic energy consumption model to obtain the first monitored value, and calculate the average value with the pre-corrected value corresponding to the target data D, 1 to obtain the average corrected value, which is used as the corrected data for the target data D, 1; if the target data D, 1 is the data to be corrected and the variance S L ≥S 0 , then use the pre-corrected value as the corrected data for the target data D, 1; then, based on the obtained target data D, 1, re-sort and re-obtain the pre-corrected value of the target data with the serial number 1 for judgment, and then correct all the target data, and replace the original monitored data with the corrected target data.
[0029] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides an intelligent energy consumption management system applied to electrical equipment, including: a monitoring device deployment module, a characteristic data analysis module, a characteristic energy consumption model establishment module, and a data correction module; the monitoring device deployment module includes a characteristic point judgment unit, a detection point judgment unit, and a monitoring device deployment unit; the characteristic data analysis module includes a characteristic data judgment unit, a characteristic value calculation unit, and a characteristic data analysis unit; the characteristic energy consumption model establishment module includes a characteristic energy consumption model establishment unit; the data correction module includes a pre-corrected value calculation unit and a data correction unit. By analyzing the historical data of electrical equipment in combination with monitoring devices, the present invention obtains the data to be corrected and abnormal data during the wireless communication transmission process, corrects them, provides strong support for the enterprise's energy management and decision-making, and provides strong data support for energy consumption analysis. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Figure 1 It is a structural diagram of an intelligent energy consumption management system applied to electrical equipment according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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 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] Embodiment: As Figure 1 shown, the present invention provides a technical solution for an intelligent energy consumption management system applied to electrical equipment, including: a monitoring device deployment module, a characteristic data analysis module, a characteristic energy consumption model establishment module, and a data correction module;
[0033] Monitoring device deployment module: used to establish a 3D model of electrical equipment, obtain all detection points in the 3D model according to the positions of each component in the 3D model; acquire several thermal imaging images of the electrical equipment during the operation time, obtain the target points therein according to the changes of the pixels at the corresponding positions of each detection point on the thermal imaging images, and deploy the monitoring device at the target points.
[0034] The monitoring device deployment module includes a feature point judgment unit, a detection point judgment unit, and a monitoring device deployment unit:
[0035] Feature point judgment unit: used to establish a spatial coordinate system corresponding to the 3D model, obtain the component ranges of each component in the spatial coordinate system according to the positions and regions of each component in the 3D model; acquire all load components that consume energy during operation, where the component ranges corresponding to a certain load component a and a certain load component b are G a and G b , if there exists a coordinate point P a on the component range G a and a coordinate point P b on the component range G b and the distance between them is less than the preset distance threshold, then randomly obtain M component coordinate points from the component ranges G a and G b , and take the coordinate point corresponding to the minimum sum of distances from all component coordinate points in the spatial coordinate system as the feature point, and then obtain all feature points in the 3D model.
[0036] Detection point judgment unit: used to take the component range of the outermost component on the electrical equipment in the spatial coordinate system as the equipment range; acquire the coordinate P 0 corresponding to a certain feature point, as well as all equipment coordinate points on the equipment range, and take the equipment coordinate point with the shortest distance from the coordinate P 0 as the detection point corresponding to a certain feature point, and then obtain all detection points.
[0037] Monitoring device deployment unit: used to take the start time of a certain operation of the electrical equipment as the starting point, the end time of the operation as the end point, and the time interval as T, to obtain several operation times; take thermal imaging images of the electrical equipment at each operation time, convert the thermal imaging images into grayscale images, obtain the grayscale values of each detection point at each operation time, obtain the grayscale variance corresponding to each detection point according to all the grayscale values corresponding to each detection point, take the detection point with the maximum grayscale variance as the target point, and deploy the monitoring device at the target point position.
[0038] In general, electrical equipment needs to consume energy to operate, such as generators, transformers, control cabinets, distribution cabinets, etc. In this solution, the electrical equipment is a distribution cabinet, and the consumed energy is electric energy; and the distribution cabinet includes various load components, including current transformers, capacitors, etc. These load components generate heat when energized, resulting in an increase in the component temperature. To ensure the safety of electrical equipment during operation, usually electrical equipment has a housing. In this solution, the equipment scope is the scope of the housing of the electrical equipment, and the detection points are all points on the housing of the electrical equipment. The target point is also a point on the housing of the electrical equipment. The monitoring equipment is deployed on the housing, and the monitoring equipment is a temperature sensor.
[0039] Feature data analysis module: It is used to collect all historical monitoring data of the monitoring equipment, analyze the monitoring data, and obtain all the feature data therein; and according to the feature data, obtain the data deviation range, and then according to the data deviation range, extract the data to be corrected and abnormal data in the monitoring data.
[0040] The feature data analysis module includes a feature data judgment unit, a feature value calculation unit, and a feature data analysis unit:
[0041] Feature data judgment unit: It is used to collect all historical monitoring data of the monitoring equipment. The monitoring data includes the monitoring time and the monitoring value. If the difference Vpq between two adjacent monitoring data p and q at two times satisfies: V a <Vpq = V q -V p <V b , then both the monitoring data p and q are regarded as data to be detected. V p and V q are the monitoring values corresponding to the monitoring data p and q respectively. V a is the first monitoring difference, and V b is the second monitoring difference. V a <0, 0 < |V a | < V b ; if the total duration corresponding to the continuous data to be detected is greater than the preset duration threshold, then all the continuous data to be detected are aggregated into a feature set, and then several feature sets are obtained, and all the data to be detected in the feature set are regarded as feature data.
[0042] Feature value calculation unit: It is used to sort each feature set according to the chronological order of the first feature data in each feature set; obtain two adjacent feature sets A and B in sequence numbers, and the moment T 1 corresponding to the last feature data D 1 in the feature set A is earlier than the moment T 2 corresponding to the first feature data D 2, then all the monitoring data between time T 1 and time T 2 are used as target data; according to each monitoring value in feature set A, the average monitoring value V A corresponding to feature set A is obtained, and according to each monitoring value in feature set B, the average monitoring value V B corresponding to feature set B is obtained, and then the feature value is obtained: VA B = k * (V A +V B ) / 2, where k is the feature coefficient.
[0043] Feature data analysis unit: used to establish a two-dimensional coordinate system with the monitoring time as the abscissa and the monitoring value as the ordinate, and according to the feature data D 1 corresponding to time T 1 , taking the monitoring value of feature data D 1 as V 1 , the coordinate C 1 =(T 1 ,V 1 ) is obtained. According to the feature data D 2 corresponding to time T 2 , taking the monitoring value of feature data D 2 as V 2 , the coordinate C 2 =(T 2 ,V 2 ) is obtained, and then the center coordinate is Sort the target data between time T 1 and time T 2 in chronological order; obtain the data deviation range: where T n is the monitoring time of a certain target data, V n is the monitoring value of a certain target data, T is the starting point with the center coordinate C 0 , along the direction of coordinate C 2 , forward with a distance of T 2 -T 0 , VA B is the starting point with the center coordinate C 0 , along the vertical direction of the line segment between the center coordinate C 0 and coordinate C 2 , forward with a distance of VA B; if a certain target data is within the data deviation range R, then the certain target data is used as the data to be corrected, if it is not within the data deviation range R, then the certain target data is used as the abnormal data, and then all the data to be corrected and abnormal data are obtained.
[0044] Since feature data D 1 and feature data D 2If what is represented is temperature, then under normal circumstances, the characteristic data D 1 and the characteristic data D 2 The temperature in the corresponding time period between them is basically around the characteristic data D 1 and the characteristic data D 2 The data changes, and the difference is not too much; among them, due to the distance D 1 or D 2 The monitoring data of the target data closer in time is closer to D 1 or D 2 , and the monitoring data closer to the middle time between the distances D 1 and D 2 may be quite different from D 1 and D 2 . Therefore, this solution uses the ellipse formula, that is, the data range for representation, to determine whether the monitoring values of each target data meet the conditions; if a certain target data is not within the data range, it is regarded as abnormal data, that is, data with a large deviation; if a certain target data is within the data range, it is regarded as data to be corrected, that is, data whose normality cannot be determined, or data with a deviation. And the characteristic data is normal and does not need to be corrected. The target data is the data that needs to be corrected in this solution. The target data includes two types: data to be corrected and abnormal data. In this solution, the electrical parameter is the operating power of the electrical equipment.
[0045] Characteristic energy consumption model establishment module: It is used to collect the historical electrical parameters of the electrical equipment, and obtain the characteristic energy consumption model of the change of characteristic data with electrical parameters according to the electrical parameters at the moments corresponding to each characteristic data.
[0046] The characteristic energy consumption model establishment module includes a characteristic energy consumption model establishment unit:
[0047] Characteristic energy consumption model establishment unit: It is used to establish a plane coordinate system with electrical parameters as the abscissa and monitoring values as the ordinate; if the electrical parameters at each moment in a certain characteristic set are all X 1 , and the difference between the maximum monitoring value and the minimum monitoring value among them is less than the preset monitoring difference threshold, then according to each characteristic data in a certain characteristic set, the average monitoring value is obtained as the target value F 1 , and then a certain model coordinate point (X 1 , F 1 ) in the plane coordinate system is obtained, and then all model coordinate points are obtained; according to all model coordinate points, through the least square method, the characteristic energy consumption model F = k*(e x -1) is obtained, where F is the monitoring value, X is the electrical parameter, e is the natural exponent, and k is the energy consumption model coefficient.
[0048] It should be noted that due to conditions such as heat dissipation, the relationship between temperature and operating power is not a simple linear one. Instead, it conforms to the situation that when the operating power is stable at a relatively small value, due to heat dissipation, the temperature is difficult to rise. However, when the operating power is stable at a relatively large value, since the heat dissipation rate is constant, the temperature of the electrical equipment will reach a relatively large value at this time.
[0049] Data correction module: It is used to obtain the pre-correction values of each data to be corrected and abnormal data according to the characteristic data, and sequentially correct each data to be corrected and abnormal data according to the time sequence through the characteristic energy consumption model.
[0050] The data correction module includes a pre-correction value calculation unit and a data correction unit:
[0051] Pre-correction value calculation unit: It is used to set the monitoring time interval of the monitoring data as g, and according to the time duration DUR between time T 1 and time T 2 , obtain the total number of target data therein as H = DUR / g; sort all the target data according to time, starting from the target data D,1 with serial number 1, and according to the difference V1 2 = V 1 between the monitoring values of time T 2 and time T 2 , V 1 and V 1 are the monitoring values corresponding to the characteristic data D 2 and the characteristic data D 1 respectively, and obtain the pre-correction value of the target data D,1 as: 2 V
[0052] + V1 2 / H. 1
[0053] Data correction unit: It is used to judge that if the target data D,1 is abnormal data, take the pre-correction value as the corrected data of the target data D,1; if the target data D,1 is data to be corrected, and the variance S L of the electrical parameters within the previous time period L of the target data D,1 is less than the preset variance threshold S 0 , and the difference V L between the maximum monitoring value and the minimum monitoring value within the time period L is greater than the monitoring value threshold V,0, then take the pre-correction value as the corrected data of the target data D,1; if the target data D,1 is data to be corrected, and the variance S L < S 0 , and V L ≤V, 0, substitute the average electrical parameters within the time period L into the characteristic energy consumption model to obtain the first monitored value, and take the average with the pre-corrected value corresponding to the target data D, 1 to obtain the average corrected value as the corrected data of the target data D, 1; if the target data D, 1 is the data to be corrected and the variance S L ≥S 0 , then take the pre-corrected value as the corrected data of the target data D, 1; furthermore, according to the obtained target data D, 1, re-sort and re-obtain the pre-corrected value of the target data with the serial number 1 for judgment, and then correct all the target data, and replace each original monitored data with the corrected target data.
[0054] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.
Claims
1. An intelligent energy consumption management system applied to electrical equipment, characterized in that: include: Monitoring equipment deployment module, characteristic data analysis module, characteristic energy consumption model establishment module and data correction module; Monitoring equipment deployment module: used to establish a three-dimensional model of electrical equipment, obtain all detection points in the three-dimensional model according to the location of each component in the three-dimensional model; obtain several thermal imaging images of the electrical equipment during operation, obtain the target point in the thermal imaging image according to the change of the pixels at the corresponding position of each detection point, and deploy the monitoring equipment at the target point; Characteristic data analysis module: used to collect all historical monitoring data of the monitoring equipment, analyze the monitoring data, and obtain all characteristic data therein; and obtain the data deviation range based on the characteristic data, and then extract the data to be corrected and abnormal data in the monitoring data based on the data deviation range; Characteristic energy consumption model building module: used to collect the historical electrical parameters of electrical equipment, and obtain the characteristic energy consumption model of characteristic data changing with the electrical parameters according to the electrical parameters at the time corresponding to each characteristic data; Data correction module: used to obtain the pre-correction value of each data to be corrected and abnormal data according to the characteristic data, and correct each data to be corrected and abnormal data in chronological order according to the characteristic energy consumption model.
2. The intelligent energy consumption management system for electrical equipment according to claim 1, characterized in that: The monitoring equipment deployment module includes a feature point judgment unit, a detection point judgment unit and a monitoring equipment deployment unit: Feature point judgment unit: used to establish the spatial coordinate system corresponding to the three-dimensional model, and obtain the component range of each component in the spatial coordinate system according to the location and area of each component in the three-dimensional model; obtain all the load components that consume energy during operation, where the component ranges corresponding to a load component a and a load component b are G respectively. a and G b , if there is a component range G a A coordinate point P on a With component range G b A coordinate point P on b If the distance between them is less than the preset distance threshold, then the component range G a and G b M component coordinate points are randomly obtained in each component, and the coordinate point corresponding to the minimum sum of the distances to all component coordinate points in the spatial coordinate system is taken as the feature point, thereby obtaining all the feature points in the three-dimensional model.
3. The intelligent energy consumption management system for electrical equipment according to claim 2, characterized in that: The detection point judgment unit is used to take the component range of the outermost component on the electrical equipment in the spatial coordinate system as the equipment range; obtain the coordinate P0 corresponding to a certain feature point, and all equipment coordinate points on the equipment range, and take the equipment coordinate point with the shortest distance to the coordinate P0 as the detection point corresponding to the certain feature point, thereby obtaining all detection points.
4. The intelligent energy consumption management system for electrical equipment according to claim 3 is characterized in that: The monitoring equipment deployment unit is used to obtain a number of operating times with the start time of the electrical equipment as the starting point, the end time of the electrical equipment as the end point, and the interval time period as T; capture the thermal imaging image of the electrical equipment at each operating time, and convert the thermal imaging image into a grayscale image to obtain the grayscale value of each detection point at each operating time, and obtain the grayscale variance corresponding to each detection point based on all the grayscale values corresponding to each detection point, take the detection point with the maximum grayscale variance as the target point, and deploy the monitoring equipment at the target point.
5. The intelligent energy consumption management system for electrical equipment according to claim 1, characterized in that: The characteristic data analysis module includes a characteristic data judgment unit, a characteristic value calculation unit and a characteristic data analysis unit: Characteristic data judgment unit: used to collect all monitoring data of the monitoring equipment history, the monitoring data includes monitoring time and monitoring value, if there is a difference Vp q between two adjacent monitoring data p and q at two times that satisfies: V a <Vp q=V q -V p <V b , then the monitoring data p and q are both used as the data to be detected, V p and V q are the monitoring values corresponding to the monitoring data p and q, respectively, V a is the first monitoring difference, V b is the second monitoring difference, V a <0,0<|V a | <V b If there is a total duration corresponding to continuous data to be detected that is greater than a preset duration threshold, all continuous data to be detected are aggregated into a feature set, and then several feature sets are obtained, and all the data to be detected in the feature set are used as feature data.
6. The intelligent energy consumption management system for electrical equipment according to claim 5, characterized in that: The feature value calculation unit is used to sort each feature set according to the time sequence of the first feature data in each feature set; obtain two feature sets A and B with adjacent sequence numbers, and the time T1 corresponding to the last feature data D1 of feature set A is earlier than the time T2 corresponding to the first feature data D2 of feature set B, then all monitoring data between time T1 and time T2 are used as target data; according to each monitoring value in feature set A, the average monitoring value V corresponding to feature set A is obtained. A , according to each monitoring value in feature set B, the average monitoring value V corresponding to feature set B is obtained B , and then get the characteristic value: VA B = k*(V A +V B ) / 2, where k is the characteristic coefficient.
7. The intelligent energy consumption management system for electrical equipment according to claim 6, characterized in that: The characteristic data analysis unit is used to establish a two-dimensional coordinate system with the monitoring time as the horizontal coordinate and the monitoring value as the vertical coordinate, and according to the characteristic data D1 corresponding to the time T1, the monitoring value of the characteristic data D1 is used as V1 to obtain the coordinate C1 = (T1, V1), according to the characteristic data D2 corresponding to the time T2, the monitoring value of the characteristic data D2 is used as V2, and the coordinate C2 = (T2, V2) is obtained, and then the center coordinate is obtained. Sort the target data between time T1 and time T2 in chronological order; get the data deviation range: Among them, T n is the monitoring time of a target data, V n is the monitoring value of a certain target data, T is the distance T2-T0 from the center coordinate C0 along the direction of coordinate C2, and VAB is the distance VAB from the center coordinate C0 along the perpendicular direction of the line segment between the center coordinate C0 and coordinate C2; if a certain target data is within the data deviation range R, the target data is taken as the data to be corrected; if it is not within the data deviation range R, the target data is taken as abnormal data, and then all the data to be corrected and the abnormal data are obtained.
8. The intelligent energy consumption management system for electrical equipment according to claim 7, characterized in that: The characteristic energy consumption model establishment module includes a characteristic energy consumption model establishment unit: Characteristic energy consumption model establishment unit: used to establish a plane coordinate system with electrical parameters as horizontal coordinates and monitoring values as vertical coordinates; if the electrical parameters at each moment in a certain feature set are X1, and the difference between the maximum monitoring value and the minimum monitoring value is less than the preset monitoring difference threshold, then according to each characteristic data in the certain feature set, the average monitoring value is obtained as the target value F1, and then a certain model coordinate point (X1, F1) in the plane coordinate system is obtained, and then all model coordinate points are obtained; according to all model coordinate points, the characteristic energy consumption model F=k*(e x -1), where F is the monitoring value, X is the electrical parameter, e is the natural index, and k is the energy consumption model coefficient.
9. The intelligent energy consumption management system for electrical equipment according to claim 8, characterized in that: The data correction module includes a pre-correction value calculation unit and a data correction unit: Pre-correction value calculation unit: used to set the monitoring time interval of the monitoring data to g, and obtain the total number of target data therein as H=DUR / g according to the duration DUR between time T1 and time T2; sort all target data according to time, starting with target data D, 1 with sequence number 1, and according to the difference V1 2=V2-V1 between the monitoring values at time T1 and time T2, V1 and V2 are the monitoring values corresponding to the characteristic data D1 and the characteristic data D2 respectively, and obtain the pre-correction value of the target data D, 1 as: V1+V1 2 / H.
10. The intelligent energy consumption management system for electrical equipment according to claim 9, characterized in that: The data correction unit is used to determine if the target data D, 1 is abnormal data and use the pre-corrected value as the corrected data of the target data D, 1; If the target data D, 1 is the data to be corrected, and the variance S of the electrical parameters in the time period L before the target data D, 1 L is less than the preset variance threshold S0, and the difference V between the maximum monitored value and the minimum monitored value in the time period L L ,is greater than the monitoring value threshold V,0, the pre-corrected value is used as the corrected data of the target data D,1; If the target data D1 is the data to be corrected, and the variance S L <S0, and V L ≤ V0, substitute the average electrical parameters within the time period L into the characteristic energy consumption model to obtain the first monitoring value, and calculate the average value with the pre-corrected value corresponding to the target data D1 to obtain the average correction value, which is used as the corrected data of the target data D1; If the target data D, 1 is the data to be corrected, and the variance S L ≥S0, the pre-corrected value is used as the corrected data of the target data D, 1; then the target data D, 1 are re-sorted, and the pre-corrected value of the target data with sequence number 1 is re-obtained for judgment, and then all the target data are corrected, and the corrected target data replaces the original monitoring data.