Energy scheduling management and control method and system based on big data analysis

Abstract

The invention discloses an energy scheduling management and control method and system based on big data analysis, and relates to the technical field of energy management and control, and the method comprises the steps: if the environment characteristics located in different straight line sections or curved sections are unstable and change in historical environment characteristics, and belong to the relation of adjacent straight line sections or curved sections, determining that the historical environment characteristics are not stable; or if the environmental characteristics in different linear sections and bending sections are stable and belong to a non-adjacent linear section or bending section relationship, extracting a correlation coefficient among environmental change, line bending condition conversion and influence of line linear condition conversion on energy loss; and predicting an energy loss value of the target to-be-measured line, which is influenced by environment and line condition conversion in the energy transmission process, so as to perform energy scheduling on the original energy distribution amount. According to the big data analysis-based energy scheduling management and control method and system provided by the invention, the reasonability of energy scheduling distribution can be improved.

Description

Technical Field

[0001] The present application relates to the field of energy management and control technology, and in particular to an energy scheduling and control method and system based on big data analysis. Background Art

[0002] With the development of the global economy and the acceleration of industrialization, energy, as a key element supporting the operation of modern society, continues to grow in demand. Therefore, it is crucial to make energy dispatching and allocation more rational, reduce and improve the efficiency of energy distribution management and control.

[0003] In traditional technologies, the influencing factors considered in the detection and analysis of energy loss conditions caused by energy transmission during energy scheduling are single, and the correlation and interference effects between the influencing factors under diverse conditions are not considered, and the final detection results are not further verified, which leads to low efficiency of the final energy scheduling and control results and waste of resources.

[0004] Metal material quality supervision and assessment is a key step to ensure material quality and product safety. It involves comprehensive consideration of multiple aspects, including chemical composition, physical properties, mechanical properties, microstructure and processing performance. Strict quality supervision and assessment can not only improve product quality and safety, but also promote technological innovation, enhance market competitiveness, protect public safety and health, and support sustainable development.

[0005] In traditional technology, the inspection and evaluation of metal material quality is often carried out by comprehensively evaluating the data as a whole, without considering the influence of the correlation between multiple areas in different locations and adjacent conditions, so that the final quality evaluation result of the metal material is not accurate enough only in terms of the overall quality. Summary of the invention

[0006] In order to overcome the above-mentioned deficiencies of the prior art, the present application provides an energy scheduling and control method and system based on big data analysis.

[0007] In a first aspect, the present application provides an energy dispatching and control method based on big data analysis, the method comprising: Obtain a target line to be tested that requires energy transmission, predict the energy loss value of the target line to be tested under an ideal environment to obtain a first energy consumption prediction value, obtain all historical test lines and historical environmental characteristics, if there are environmental characteristics located in different straight sections or curved sections in the historical environmental characteristics that are unstable and belong to adjacent straight sections or curved sections, extract the correlation coefficient between the environmental change and the energy loss to obtain a first correlation influence coefficient; If there are environmental characteristics located in different straight sections and curved sections in the historical environmental characteristics, which are stable and belong to non-adjacent straight sections or curved sections, the correlation coefficient between the influence of line curvature conversion on energy loss is extracted to obtain the second correlation influence coefficient 1; And extract the correlation coefficient between the energy loss affected by the line straight line condition conversion to obtain the second correlation influence coefficient 2, and predict the energy degree value of the target line to be tested affected by the environment and line condition conversion during energy transmission according to the first correlation influence coefficient, the second correlation influence coefficient 1, and the second correlation influence coefficient 2, and output the second energy consumption prediction value; According to the position distribution of straight sections and curved sections in all historical detection lines, the comprehensive verification conditions to be compared and formed by the line combinations under different position distribution conditions of straight sections and curved sections are statistically calculated; A comparison verification condition that can be used to verify the energy consumption forecast value of the target line to be tested is selected from the comprehensive comparison verification conditions to perform error verification or adjustment on the second energy consumption forecast value, and based on the first energy consumption forecast value, an actual energy consumption forecast value one and an actual energy consumption forecast value two are obtained, and based on the actual energy consumption forecast value one or the actual energy consumption forecast value two, energy scheduling is performed on the original energy allocation, and the actual energy demand allocation is output.

[0008] Preferably, the target line to be tested that needs to transmit energy and the current operating parameter values ​​of the target line to be tested and the original energy allocation amount preset for the target line to be tested are obtained, and the damage condition of the target line to be tested is detected to obtain the damage characteristics of the target line; Obtain theoretical characteristic data of all lines for energy transmission under ideal conditions, and establish a simulation prediction model 1 based on the theoretical characteristic data; The target line to be tested, the original energy distribution, the target line damage characteristics and the current operating parameter values ​​are input into the simulation prediction model 1 for testing, so as to predict the energy loss value of the target line to be tested under an ideal environment to obtain a first energy consumption prediction value; Obtain all historical detection lines detected in the historical period and the historical operating parameter values ​​of all historical detection lines and the historical environmental characteristics of various lines in all historical detection lines. If there are environmental characteristics located in different straight sections or curved sections in the historical environmental characteristics that are unstable and belong to adjacent straight sections or curved sections, they are counted as environmental characteristics one; According to the environmental characteristic 1, a historical route 1 is selected from all historical detection routes, and energy loss values ​​1 of two adjacent straight sections or curved sections in the historical route 1 are obtained; According to the energy loss value 1 and the corresponding environmental change degree value in the environmental characteristic 1, the correlation coefficient between the environmental change and the energy loss is extracted to obtain the first correlation influence coefficient.

[0009] Preferably, if there are environmental features in different straight sections and curved sections in the historical environmental features that are stable and belong to non-adjacent straight sections or curved sections, they are counted as environmental feature 2; According to the second environmental characteristic, a second historical route is selected from all historical detection routes, and two energy loss values ​​2 belonging to two adjacent sections in the second historical route, the front section of which is a straight section and the rear section of which is a curved section, are obtained; According to the energy loss value 2 and the corresponding environmental stability value in the environmental characteristic 2, the correlation coefficient between the influence of the line bending condition conversion on the energy loss is extracted to obtain the second correlation influence coefficient 1.

[0010] Preferably, obtaining energy loss values ​​3 of two adjacent sections in the historical route 2, the front section of which is a curved section and the rear section of which is a straight section; According to the energy loss value 3 and the corresponding environmental stability value in the environmental condition feature 2, extracting the correlation coefficient between the influence of the line straight line condition conversion on the energy loss to obtain the second correlation influence coefficient 2; The environmental feature 1 and the environmental feature 2 are combined into a feature set 1 to be trained; The first correlation influence coefficient, the second correlation influence coefficient one and the second correlation influence coefficient two are combined into a second feature set to be trained; Obtaining historical energy allocations of all historical detection lines, and establishing energy consumption prediction model 2 according to the historical operating parameter values, the historical energy allocations, the first feature set to be trained, and the second feature set to be trained; Detect the current environmental characteristics of the target line to be tested, input the current operating parameter value, current environmental characteristics, original energy allocation and target line to be tested into energy consumption prediction model 2 for testing, so as to predict the energy loss value of the target line to be tested due to the change of environment and line conditions during energy transmission, and output a second energy consumption prediction value.

[0011] Preferably, if there are straight sections and curved sections in all historical detection lines, and the straight section is only located at the head end and the rest are curved sections, then the statistics are counted as line condition feature 1; If the straight line segment is only located in the middle and the rest are curved segments, it is counted as line condition feature 2; If the straight line segment is only located at the end position and the rest is curved segments, it is counted as line condition feature 3; If the straight section and the curved section are in a position distribution intersection relationship, the statistics are line condition feature 4; Count all the historical detection lines that only have straight sections, and output line condition feature 5, and count all the historical detection lines that only have curved sections, and output line condition feature 6; The line condition feature 1 and the line condition feature 6 are combined into the verification condition 1 to be compared, and the line condition feature 2 and the line condition feature 5 are combined into the verification condition 2 to be compared; The line condition feature 3, the line condition feature 5 and the line condition feature 6 are combined into the verification condition 3 to be compared, and the line condition feature 4, the line condition feature 5 and the line condition feature 6 are combined into the verification condition 4 to be compared; The first verification condition to be compared, the second verification condition to be compared, the third verification condition to be compared and the fourth verification condition to be compared are combined to form a comprehensive verification condition to be compared.

[0012] Preferably, a to-be-compared verification condition that can be used to verify the energy consumption prediction value of the target circuit to be tested is selected from the comprehensive to-be-compared verification conditions, and a preprocessing condition is output; Input the lines, current operating parameter values, current environmental characteristics, and original energy allocation in the pre-processing conditions into the second energy consumption prediction model for testing, and output the energy consumption prediction value to be compared; A preset energy consumption difference interval value is set, and if the difference between the second energy consumption prediction value and the energy consumption prediction value to be compared is within the energy consumption difference interval value, the first energy consumption prediction value and the second energy consumption prediction value are integrated to obtain an actual energy consumption prediction value of one; If the difference between the second energy consumption prediction value and the energy consumption prediction value to be compared is outside the energy consumption difference interval, the second energy consumption prediction value is error-controlled according to the energy consumption prediction value to be compared, and a third energy consumption prediction value is output; Integrate the first energy consumption prediction value and the third energy consumption prediction value to obtain a second century energy consumption prediction value; According to the actual energy consumption prediction value 1 or the actual energy consumption prediction value 2, energy scheduling is performed on the original energy allocation amount to output the actual energy demand allocation amount.

[0013] Second, an energy dispatching and control system based on big data analysis includes: The first energy consumption correlation coefficient extraction unit is used to obtain the target line to be tested that needs to be energy transmitted, predict the energy loss value of the target line to be tested under an ideal environment to obtain a first energy consumption prediction value, obtain all historical detection lines and historical environmental characteristics, and if there are environmental characteristics located in different straight sections or curved sections in the historical environmental characteristics that are unstable and belong to adjacent straight sections or curved sections, extract the correlation coefficient between the environmental change and the energy loss to obtain a first correlation influence coefficient; The second energy consumption correlation coefficient extraction unit is used to extract the correlation coefficient between the influence of line bending condition conversion on energy loss to obtain the second correlation influence coefficient 1 if there are environmental characteristics located in different straight sections and curved sections in the historical environmental characteristics that are stable and belong to non-adjacent straight sections or curved sections; The energy consumption prediction unit is used to extract the correlation coefficient between the energy loss affected by the line straight line condition conversion to obtain the second correlation influence coefficient 2, and predict the energy degree value of the target line to be tested affected by the environment and line condition conversion during the energy transmission process according to the first correlation influence coefficient, the second correlation influence coefficient 1, and the second correlation influence coefficient 2, and output the second energy consumption prediction value; The verification condition statistics unit is used to calculate the comprehensive verification conditions to be compared for the combinations of the lines belonging to the straight sections and the curved sections under different position distribution conditions according to the position distribution conditions between the straight sections and the curved sections existing in all historical detection lines; The energy dispatching unit is used to select a comparison verification condition from the comprehensive comparison verification conditions that can be used to verify the energy consumption prediction value of the target line to be tested, so as to perform error verification or adjustment on the second energy consumption prediction value, and obtain the actual energy consumption prediction value one and the actual energy consumption prediction value two according to the first energy consumption prediction value, and perform energy dispatch on the original energy allocation according to the actual energy consumption prediction value one or the actual energy consumption prediction value two, and output the actual energy demand allocation.

[0014] Compared with the prior art, the present invention has the following characteristics and beneficial effects: By predicting the energy loss value of the target line under ideal environment and the energy loss value induced by the different position distribution between the straight sections and the curved sections in the line under non-ideal environment and the diverse conditions of whether the environmental characteristics are stable or unstable, the energy loss value under non-ideal environment is judged and predicted differently. According to the correlation influence and sustainable development characteristics between the data, the correlation influence coefficients between the environmental change influence, the line bending condition conversion influence and the line straight condition conversion influence and the energy loss value of the target line under test are extracted, so as to realize the prediction of the energy loss value of the target line under non-ideal environment. In order to reduce the prediction If the result has a large error, further verification processing is carried out, that is, by matching the lines with the same distribution conditions between the straight section and the curved section from all historical detection lines, and predicting the energy consumption value with the same characteristic information as the target line to be tested, and comparing the energy consumption value predicted by the target line to be tested to determine whether the energy consumption value predicted by the target line to be tested is accurate. If the error is large, error control is carried out. Through the above processing method, the large error of the prediction result is reduced to a large extent, the correlation between the diverse influencing factors is fully considered, and corresponding verification processing is carried out to enhance the rigor of the entire data processing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flowchart of the steps of an energy scheduling and control method based on big data analysis mainly embodied in this embodiment.

[0016] Figure 2 It is a structural block diagram of an energy dispatching and control system based on big data analysis mainly embodied in this embodiment. DETAILED DESCRIPTION

[0017] The present invention is further described in detail below in conjunction with the following examples.

[0018] Reference Figure 1 , an energy dispatching and control method based on big data analysis, the method comprises the following steps: S1. Obtain the target line to be tested that requires energy transmission, predict the energy loss value of the target line to be tested under an ideal environment to obtain a first energy consumption prediction value, obtain all historical detection lines and historical environmental characteristics, if there are environmental characteristics located in different straight sections or curved sections in the historical environmental characteristics that show unstable changes and belong to adjacent straight sections or curved sections, extract the correlation coefficient between the impact of environmental changes on energy loss to obtain a first correlation influence coefficient.

[0019] S2. If there are environmental characteristics located in different straight sections and curved sections in the historical environmental characteristics that are stable and belong to non-adjacent straight sections or curved sections, the correlation coefficient between the impact of line bending condition conversion on energy loss is extracted to obtain the second correlation influence coefficient one.

[0020] S3. And extract the correlation coefficient between the impact of line straight line condition conversion on energy loss to obtain the second correlation influence coefficient two, and predict the energy loss level value affected by the environment and line condition conversion during the energy transmission process of the target line to be tested based on the first correlation influence coefficient, the second correlation influence coefficient one, and the second correlation influence coefficient two, and output the second energy consumption prediction value.

[0021] S4. According to the position distribution of the straight sections and curved sections existing in all historical detection lines, the comprehensive verification conditions to be compared and formed by the line combinations under different position distribution conditions of the straight sections and curved sections are statistically calculated.

[0022] S5. Filter out a comparison verification condition from the comprehensive comparison verification conditions that can be used to verify the energy consumption forecast value of the target line to be tested, so as to perform error verification or adjustment on the second energy consumption forecast value, and obtain an actual energy consumption forecast value one and an actual energy consumption forecast value two based on the first energy consumption forecast value, and perform energy scheduling on the original energy allocation according to the actual energy consumption forecast value one or the actual energy consumption forecast value two, and output the actual energy demand allocation.

[0023] Specifically, by predicting the energy loss level of the target line to be tested under an ideal environment and the energy loss level induced by the different position distribution conditions between the straight sections and the curved sections in the line under a non-ideal environment and the diverse conditions of whether the environmental characteristics are stable or unstable, the energy loss level values ​​under non-ideal conditions are distinguished and predicted, so as to extract the correlation influence coefficients between the environmental change influence, the line bending condition conversion influence and the line straight condition conversion influence and the energy loss level value of the target line to be tested respectively according to the correlation influence and sustainable development characteristics between the data, so as to realize the prediction of the energy loss level value of the target line to be tested under a non-ideal environment, in order to reduce If there is a large error in the prediction result, further verification processing is carried out, that is, by matching the lines with the same distribution conditions between the straight section and the curved section from all historical detection lines, and predicting the energy consumption value with the same characteristic information as the target line to be tested, and comparing the energy consumption value predicted by the target line to be tested to determine whether the energy consumption value predicted by the target line to be tested is accurate. If the error is large, error control is carried out. Through the above processing method, the large error of the prediction result is reduced to a large extent, the correlation between the diverse influencing factors is fully considered, and corresponding verification processing is carried out to enhance the rigor of the entire data processing process.

[0024] The specific step S1 includes the following sub-steps: The target line to be tested that needs energy transmission and the current operating parameter values ​​of the target line to be tested and the original energy allocation preset for the target line to be tested are obtained, and the damage condition of the target line to be tested is detected to obtain the damage characteristics of the target line.

[0025] The theoretical characteristic data of all lines for energy transmission under ideal conditions are obtained, and a simulation prediction model is established based on the theoretical characteristic data.

[0026] The target line to be tested, the original energy distribution, the target line damage characteristics and the current operating parameter values ​​are input into the simulation prediction model 1 for testing to predict the energy loss value of the target line to be tested under ideal conditions to obtain the first energy consumption prediction value.

[0027] Obtain all historical detection lines detected in historical periods, historical operating parameter values ​​of all historical detection lines, and historical environmental characteristics of various lines in all historical detection lines. If there are environmental characteristics in different straight sections or curved sections in the historical environmental characteristics that show unstable changes and belong to adjacent straight sections or curved sections, they are counted as environmental characteristic one.

[0028] According to environmental characteristic one, historical route one is selected from all historical detection routes, and energy loss value one belonging to two adjacent sections in historical route one and both of which are straight sections or curved sections is obtained.

[0029] According to the energy loss value 1 and the corresponding environmental change degree value in the environmental characteristic 1, the correlation coefficient between the environmental change and the energy loss is extracted to obtain the first correlation influence coefficient.

[0030] Specifically, such as the current operating parameter values ​​(power, capacitance, resistance, voltage and other parameter values ​​of the line operation), simulation prediction model 1 (such as the energy loss value of all lines that have been tested for energy transmission under ideal conditions (referring to the case where the influence of temperature and humidity in the environment is not considered), line length, line width radius, line material, comprehensive line damage degree and line transmission operation parameter values ​​and other characteristic data, such as using the artificial intelligence model in the existing technology: using machine learning algorithms, such as neural networks, support vector machines, etc., to learn and train a large amount of historical data, and establish a mapping relationship between input (such as electrical parameters of the line, operating status, environmental factors, etc.) and output (line loss value). The trained model can predict the corresponding line loss value based on the new input data.For example, the neural network model can automatically learn the complex nonlinear relationship in the data and has good adaptability to the changing power operation conditions), the first energy consumption prediction value (if it is Z1), all historical detection lines (if there are A, B, C, D, E, F), environmental characteristics one (such as A for example, the historical environmental characteristics here refer to the temperature and humidity in the external environment, if the temperature is used as an example here, if there is a straight line segment in A, and the ambient temperature value to which the straight line segment belongs is unstable, such as the sub-segments a1, a2, a3 in the straight line segment, where a1 and a2 are between are adjacent, a2 and a3 are adjacent, the temperature of a1 is w1, the temperature of a2 is w2, and the temperature of a3 is w3. The adjacent values ​​of w1, w2, and w3 are different. If there is a curved section in A, the explanation is the same as that of a straight section. The straight section or curved section here refers to the existence of one of the straight section and the curved section, not both. Historical route 1 (if A is selected, because A has environmental characteristic 1), and obtain two adjacent straight sections in historical route 1 The energy loss value of each segment or curved segment belongs to one (for example, if the energy loss values ​​of a1 and a2 are s1 and s2 respectively, a1 and a2 are both straight segments), the first correlation influence coefficient (such as (w2-w1): (s2-s1) if it is r0, in order to improve the accuracy of the data, statistics of multiple different historical periods can be performed and the average value can be calculated to obtain the average coefficient R1. It should be noted that the average value condition here is the same unstable and changing ambient temperature value in different historical periods. For different straight segments or curved segments in A The corresponding correlation influence coefficients for unstable and changing ambient temperature and humidity values ​​are also different. For example, in A, a1, a2, and a3, a1 and a2 are unstable and changing ambient temperature and humidity conditions, and a2 and a3 are unstable and changing ambient temperature and humidity conditions. The correlation influence coefficient obtained for a1 and a2 is a value, and the correlation influence coefficient obtained for a2 and a3 is a value. Finally, the second correlation influence coefficient one is obtained, that is, the second correlation influence coefficient one includes multiple statistically obtained data sets, that is, the first correlation influence coefficient, and so on).

[0031] The specific step S2 includes the following sub-steps: If the environmental characteristics located in different straight sections and curved sections in the historical environmental characteristics are stable and belong to non-adjacent straight sections or curved sections, they will be counted as environmental characteristic 2.

[0032] According to environmental characteristic 2, historical route 2 is selected from all historical detection routes, and energy loss value 2 belonging to two adjacent sections in historical route 2 with the front section being a straight section and the rear section being a curved section is obtained.

[0033] According to the energy loss value 2 and the corresponding environmental stability value in the environmental characteristic 2, the correlation coefficient between the influence of the line bending condition conversion on the energy loss is extracted to obtain the second correlation influence coefficient 1.

[0034] Specifically, such as environmental characteristic 2 (taking B as an example, if there is a straight line segment in B, and the ambient temperature value of the straight line segment is stable, such as the straight line segments are b1 and b3, and the curved segment is b2, where b1 and b2 are adjacent, b2 and b3 are adjacent, and the temperature of b1, b2, and b3 is w4, this distribution is the environmental characteristic 2), historical route 2 (if B is selected, because there is environmental characteristic 2 in B), energy loss value 2 (such as obtaining the energy loss values ​​of the respective segments of b1 and b2 if they are s3 and s4 respectively), the second correlation influence coefficient 1 (such as statistically calculating the curvature q1 of b2, if q1:(s4-s3) is r1, in order to improve the accuracy of the data, statistics of multiple different historical periods can be performed, and the average value can be calculated to obtain the average coefficient R2, that is, the second correlation influence coefficient 1).

[0035] The specific step S3 includes the following sub-steps: Obtain energy loss value three of two adjacent sections in historical route two, with the front section being a curved section and the rear section being a straight section.

[0036] According to the energy loss value three and the corresponding environmental stability value in the environmental characteristic two, the correlation coefficient between the influence of the line straight line condition conversion on the energy loss is extracted to obtain the second correlation influence coefficient two.

[0037] Environmental feature 1 and environmental feature 2 are combined into feature set 1 to be trained.

[0038] The first correlation influence coefficient, the second correlation influence coefficient one and the second correlation influence coefficient two are combined into a second feature set to be trained.

[0039] The historical energy distribution of all historical detection lines is obtained, and the energy consumption prediction model 2 is established according to the historical operation parameter values, the historical energy distribution, the first feature set to be trained and the second feature set to be trained.

[0040] Detect the current environmental characteristics of the target line to be tested, input the current operating parameter values, current environmental characteristics, original energy allocation and the target line to be tested into the second energy consumption prediction model for testing, so as to predict the energy loss value affected by the environment and line condition conversion during the energy transmission process of the target line to be tested, and output the second energy consumption prediction value.

[0041] Specifically, such as energy loss value three (such as obtaining the energy loss values ​​of the respective sections of b2 and b3 if they are s4 and s5 respectively), the second correlation influence coefficient two (such as q1: (s5-s4) if it is r2, in order to improve the accuracy of the data, statistics of multiple different historical periods can be performed, and the average value can be calculated to obtain the average coefficient R3, that is, the second correlation influence coefficient two), energy consumption prediction model two (such as H=KX+Y+W0, H refers to the energy loss degree value, K refers to the historical energy allocation, Y refers to the energy consumption quota value corresponding to the historical operating parameter value, W0 refers to the energy consumption compensation value corresponding to the ambient temperature and humidity, X: which one of the feature sets to be trained is selected is determined by the feature set to be trained, that is, the feature set to be trained is used to match the distribution between the straight sections and the curved sections in the line to select the corresponding correlation influence coefficient in the feature set to be trained two, and it should be noted that: X is the corresponding correlation influence coefficient matched from the feature set to be trained two. and, such as b1, b2, b3, and b4 in B, if the distribution characteristics of b1 and b2 belong to environmental characteristic one, the selected correlation influence coefficient is R1, if the distribution characteristics of b2 and b3 belong to environmental characteristic two, b2 is a straight section and b3 is a curved section, then the selected correlation influence coefficient is R2, if the distribution characteristics of b3 and b4 belong to environmental characteristic two, b3 is a curved section and b4 is a straight section, then the selected correlation influence coefficient is R3, then X is R1+R2+R3), the second energy consumption prediction value (matching feature information according to the current operating parameter value, the current environmental characteristics and the historical operating parameter value in the energy consumption prediction model two and the feature set one to be trained to determine whether the distribution characteristics of the target line to be tested belong to environmental characteristic one or environmental characteristic two, so that the correlation influence coefficient can be matched from the feature set two to be trained, and substituted into H=KX+Y+W0 for testing, if the predicted second energy consumption prediction value is Z2).

[0042] The specific step S4 includes the following sub-steps: If there are straight sections and curved sections in all historical detection lines, and the straight section is only located at the head end and the rest are curved sections, it is counted as line condition feature one.

[0043] If the straight line segment is only located in the middle and the rest are curved segments, it is counted as line condition feature 2.

[0044] If the straight line segment is only located at the end position and the rest are curved segments, it is counted as line condition feature three.

[0045] If the straight section and the curved section are in a position distribution intersection relationship, the statistics are line condition feature 4; Count all the historical detection lines that only have straight sections, and output line condition feature five, and count all the historical detection lines that only have curved sections, and output line condition feature six.

[0046] Line condition feature 1 and line condition feature 6 are combined into condition 1 to be compared and verified, and line condition feature 2 and line condition feature 5 are combined into condition 2 to be compared and verified.

[0047] Line condition feature three, line condition feature five and line condition feature six are combined into verification condition three to be compared, and line condition feature four, line condition feature five and line condition feature six are combined into verification condition four to be compared.

[0048] The first verification condition to be compared, the second verification condition to be compared, the third verification condition to be compared and the fourth verification condition to be compared are combined to form the comprehensive verification condition to be compared.

[0049] Specifically, such as line condition feature 1 (if there are straight segments and curved segments in A, namely a1 and a2, a1 only exists at the beginning of A, and the rest are curved segments of a2, this distribution is line condition feature 1), line condition feature 2 (if there are b1, b2, and b3 in the neutron segments of B, b1 and b3 are straight segments, b2 is a curved segment, and b2 is located in the middle of B, this distribution is line condition feature 2), line condition feature 3 (if there are c1 and c2 in the neutron segments of C, c1 is a curved segment, c2 is a straight segment, and c2 only exists at the beginning of C, and the rest are curved segments of c1, this distribution is line condition feature 3), line condition feature 4 (if there are d1, d2, d3, d4, and d5 in the neutron segments of D, d1, d3, and d5 are are all curved sections, d2 and d4 are all straight sections, d1, d2, d3, d4, and d5 are adjacent to each other in sequence, and this distribution is line condition feature four), line condition feature five (such as E is a straight line and there is no curved section), line condition feature six (such as F is a curved line and there is no straight section), verification condition one to be compared (such as A and F are combined to form a conditional information for subsequent feature matching with the target line to be tested), verification condition two to be compared (such as B and E are combined to form a conditional information for subsequent feature matching with the target line to be tested), verification condition three to be compared (such as C, E and F are combined to form a conditional information for subsequent feature matching with the target line to be tested), verification condition four to be compared (such as D, E and F are combined to form a conditional information for subsequent feature matching with the target line to be tested).

[0050] The specific step S5 includes the following sub-steps: A verification condition to be compared that can verify the energy consumption prediction value of the target circuit to be tested is selected from the comprehensive verification conditions to be compared, and a preprocessing condition is output.

[0051] The lines in the preprocessing conditions, the current operating parameter values, the current environmental characteristics, and the original energy distribution are input into the energy consumption prediction model 2 for testing, and the energy consumption prediction value to be compared is output.

[0052] A preset energy consumption difference interval value is set. If the difference between the second energy consumption prediction value and the energy consumption prediction value to be compared is within the energy consumption difference interval value, the first energy consumption prediction value and the second energy consumption prediction value are integrated to obtain an actual energy consumption prediction value of one.

[0053] If the difference between the second energy consumption prediction value and the energy consumption prediction value to be compared is outside the energy consumption difference interval, the second energy consumption prediction value is error-controlled according to the energy consumption prediction value to be compared, and a third energy consumption prediction value is output.

[0054] The first energy consumption forecast value and the third energy consumption forecast value are integrated to obtain the century energy consumption forecast value 2.

[0055] According to the actual energy consumption forecast value 1 or the actual energy consumption forecast value 2, the original energy allocation is energy dispatched to output the actual energy demand allocation.

[0056] Specifically, such as pre-processing conditions (if the target line to be tested is L, and if the sub-segments in L include l1, l2, l3, and l4, where l1 and l4 are straight segments, l2 and l3 are curved segments, and if the environmental characteristics of l1, l2, l3, and l4 are unstable, such as w5, w6, w7, and w6 respectively, then this distribution meets C in the third condition to be compared and verified, so the third condition to be compared is selected as the condition information for subsequent verification and comparison processing, that is, the third condition to be compared is the pre-processing condition), the energy consumption prediction value to be compared (such as taking F in the third condition to be compared as an example, then F is divided into sub-segments f1, f2, f3, and f4 accordingly, and according to the current operating parameter value, the current environmental characteristics and F, and matching with the environmental characteristic one, the associated influence is selected from the first feature set to be trained The influence coefficient is calculated, and the selected multiple correlation influence coefficients are summed up, and finally substituted into H=KX+Y+W0 for testing. If the predicted energy consumption to be compared is Z3), the preset energy consumption difference interval value (if it is T1-T2, including T1 and T2, it is preset by the energy consumption error value detected and counted in the historical period, and can be updated in real time), the actual energy consumption prediction value one (if the difference between Z3-Z2 is between T1-T2, then Z1+Z2 is the actual energy consumption prediction value one), the third energy consumption prediction value (if the difference between Z3-Z2 is outside T1-T2, that is, greater than T1-T2, it means that the prediction error is large, and fine-tuning can be performed, such as Z2+(Z2+Z3) / 2, if it is Z4, Z1+Z4 is the third energy consumption prediction value), the actual energy demand allocation (that is, the original energy allocation + Z1+Z4).

[0057] An energy dispatching and control system based on big data analysis, by applying the above-mentioned energy dispatching and control method based on big data analysis, includes an energy consumption correlation coefficient extraction unit 1, an energy consumption correlation coefficient extraction unit 2, an energy consumption prediction unit, a verification condition statistics unit and an energy dispatching unit, with reference to Figure 2 , obtain the target line to be tested that needs to be energy transmitted through the energy consumption correlation coefficient extraction unit 1, predict the energy loss value of the target line to be tested under an ideal environment to obtain a first energy consumption prediction value, obtain all historical detection lines and historical environmental characteristics, if there are environmental characteristics located in different straight sections or curved sections in the historical environmental characteristics, which are unstable and belong to adjacent straight sections or curved sections, extract the correlation coefficient between the impact of environmental changes on energy loss to obtain a first correlation influence coefficient; through the energy consumption correlation coefficient extraction unit 2, if there are environmental characteristics located in different straight sections and curved sections in the historical environmental characteristics, which are stable and belong to non-adjacent straight sections or curved sections, extract the correlation coefficient between the impact of line bending condition conversion on energy loss to obtain a second correlation influence coefficient 1; through the energy consumption prediction unit and extract the correlation coefficient between the impact of line straight condition conversion on energy loss to obtain a second correlation influence coefficient Coefficient 2, according to the first correlation influence coefficient, the second correlation influence coefficient 1, and the second correlation influence coefficient 2, predict the energy loss value of the target line to be tested during energy transmission affected by the environment and line condition conversion, and output the second energy consumption prediction value; through the verification condition statistical unit, according to the position distribution between the straight sections and the curved sections existing in all historical detection lines, count the comprehensive comparison verification conditions composed of the line combinations under different position distribution conditions of the straight sections and the curved sections; through the energy scheduling unit, select a comparison verification condition that can be used to verify the energy consumption prediction value of the target line to be tested from the comprehensive comparison verification conditions, so as to perform error verification or adjustment on the second energy consumption prediction value, and according to the first energy consumption prediction value, obtain the actual energy consumption prediction value 1 and the actual energy consumption prediction value 2, according to the actual energy consumption prediction value 1 or the actual energy consumption prediction value 2, perform energy scheduling on the original energy allocation, and output the actual energy demand allocation.

[0058] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An energy dispatching and control method based on big data analysis, characterized in that: The following steps are involved: Obtain a target line to be tested that requires energy transmission, predict the energy loss value of the target line to be tested under an ideal environment to obtain a first energy consumption prediction value, obtain all historical test lines and historical environmental characteristics, if there are environmental characteristics located in different straight sections or curved sections in the historical environmental characteristics that are unstable and belong to adjacent straight sections or curved sections, extract the correlation coefficient between the environmental change and the energy loss to obtain a first correlation influence coefficient; If there are environmental characteristics located in different straight sections and curved sections in the historical environmental characteristics, which are stable and belong to non-adjacent straight sections or curved sections, the correlation coefficient between the influence of line curvature conversion on energy loss is extracted to obtain the second correlation influence coefficient 1; And extract the correlation coefficient between the energy loss affected by the line straight line condition conversion to obtain the second correlation influence coefficient 2, and predict the energy degree value of the target line to be tested affected by the environment and line condition conversion during energy transmission according to the first correlation influence coefficient, the second correlation influence coefficient 1, and the second correlation influence coefficient 2, and output the second energy consumption prediction value; According to the position distribution of straight sections and curved sections in all historical detection lines, the comprehensive verification conditions to be compared and formed by the line combinations under different position distribution conditions of straight sections and curved sections are statistically calculated; A comparison verification condition that can be used to verify the energy consumption forecast value of the target line to be tested is selected from the comprehensive comparison verification conditions to perform error verification or adjustment on the second energy consumption forecast value, and based on the first energy consumption forecast value, an actual energy consumption forecast value one and an actual energy consumption forecast value two are obtained, and based on the actual energy consumption forecast value one or the actual energy consumption forecast value two, energy scheduling is performed on the original energy allocation, and the actual energy demand allocation is output.

2. The energy dispatching and control method based on big data analysis according to claim 1 is characterized in that: The steps of obtaining a target line to be tested that needs to be energy transmitted, predicting the energy loss value of the target line to be tested under an ideal environment to obtain a first energy consumption prediction value, obtaining all historical test lines and historical environmental characteristics, and if there are environmental characteristics located in different straight sections or curved sections in the historical environmental characteristics that are unstable and belong to adjacent straight sections or curved sections, extracting the correlation coefficient between the environmental change affecting the energy loss to obtain the first correlation influence coefficient are specifically as follows: Obtaining the target line to be tested that needs to transmit energy and the current operating parameter value of the target line to be tested and the original energy allocation preset for the target line to be tested, and performing damage condition detection on the target line to be tested to obtain damage characteristics of the target line; Obtain theoretical characteristic data of all lines for energy transmission under ideal conditions, and establish a simulation prediction model 1 based on the theoretical characteristic data; The target line to be tested, the original energy distribution, the target line damage characteristics and the current operating parameter values ​​are input into the simulation prediction model 1 for testing, so as to predict the energy loss value of the target line to be tested under an ideal environment to obtain a first energy consumption prediction value; Obtain all historical detection lines detected in the historical period and the historical operating parameter values ​​of all historical detection lines and the historical environmental characteristics of various lines in all historical detection lines. If there are environmental characteristics located in different straight sections or curved sections in the historical environmental characteristics that are unstable and belong to adjacent straight sections or curved sections, they are counted as environmental characteristics one; According to the environmental characteristic 1, a historical route 1 is selected from all historical detection routes, and energy loss values ​​1 of two adjacent straight sections or curved sections in the historical route 1 are obtained; According to the energy loss value 1 and the corresponding environmental change degree value in the environmental characteristic 1, the correlation coefficient between the environmental change and the energy loss is extracted to obtain the first correlation influence coefficient.

3. The energy dispatching and control method based on big data analysis according to claim 2 is characterized in that: If there are environmental characteristics located in different straight sections or curved sections in the historical environmental characteristics, which are stable and belong to non-adjacent straight sections or curved sections, the step of extracting the correlation coefficient between the influence of line curvature conversion on energy loss to obtain the second correlation influence coefficient 1 is specifically as follows: If there are environmental characteristics in different straight sections and curved sections in the historical environmental characteristics that are stable and belong to non-adjacent straight sections or curved sections, they will be counted as environmental characteristics 2; According to the second environmental characteristic, a second historical route is selected from all historical detection routes, and two energy loss values ​​2 belonging to two adjacent sections in the second historical route, the front section of which is a straight section and the rear section of which is a curved section, are obtained; According to the energy loss value 2 and the corresponding environmental stability value in the environmental characteristic 2, the correlation coefficient between the influence of the line bending condition conversion on the energy loss is extracted to obtain the second correlation influence coefficient 1.

4. The energy dispatching and control method based on big data analysis according to claim 3 is characterized in that: The steps of extracting the correlation coefficient between the energy loss affected by the line straight line condition conversion to obtain the second correlation influence coefficient 2, predicting the energy degree value of the target line to be tested affected by the environment and line condition conversion during energy transmission according to the first correlation influence coefficient, the second correlation influence coefficient 1, and the second correlation influence coefficient 2, and outputting the second energy consumption prediction value are specifically as follows: Obtaining energy loss values ​​3 of two adjacent sections in the historical route 2, the front section of which is a curved section and the rear section of which is a straight section; According to the energy loss value 3 and the corresponding environmental stability value in the environmental characteristic 2, extracting the correlation coefficient between the influence of the line straight line condition conversion on the energy loss to obtain the second correlation influence coefficient 2; The environmental feature 1 and the environmental feature 2 are combined into a feature set 1 to be trained; The first correlation influence coefficient, the second correlation influence coefficient one and the second correlation influence coefficient two are combined into a second feature set to be trained; Obtaining historical energy allocations of all historical detection lines, and establishing energy consumption prediction model 2 according to the historical operating parameter values, the historical energy allocations, the first feature set to be trained, and the second feature set to be trained; Detect the current environmental characteristics of the target line to be tested, input the current operating parameter value, current environmental characteristics, original energy allocation and target line to be tested into energy consumption prediction model 2 for testing, so as to predict the energy loss value of the target line to be tested due to the change of environment and line conditions during energy transmission, and output a second energy consumption prediction value.

5. The energy dispatching and control method based on big data analysis according to claim 4 is characterized in that: According to the position distribution of straight sections and curved sections in all historical detection lines, the steps of calculating the comprehensive verification conditions to be compared and formed by the line combinations under different position distribution conditions of straight sections and curved sections are as follows: If there are straight sections and curved sections in all historical detection lines, and the straight section is only located at the head end and the rest are curved sections, it is counted as line condition feature 1; If the straight line segment is only located in the middle and the rest are curved segments, it is counted as line condition feature 2; If the straight line segment is only located at the end position and the rest is curved segments, it is counted as line condition feature 3; If the straight section and the curved section are in a position distribution intersection relationship, the statistics are line condition feature 4; Count all the historical detection lines that only have straight sections, and output line condition feature 5, and count all the historical detection lines that only have curved sections, and output line condition feature 6; The line condition feature 1 and the line condition feature 6 are combined into the verification condition 1 to be compared, and the line condition feature 2 and the line condition feature 5 are combined into the verification condition 2 to be compared; The line condition feature 3, the line condition feature 5 and the line condition feature 6 are combined into the verification condition 3 to be compared, and the line condition feature 4, the line condition feature 5 and the line condition feature 6 are combined into the verification condition 4 to be compared; The first verification condition to be compared, the second verification condition to be compared, the third verification condition to be compared and the fourth verification condition to be compared are combined to form a comprehensive verification condition to be compared.

6. The energy dispatching and control method based on big data analysis according to claim 5 is characterized in that: A step of selecting a comparison verification condition that can be used to verify the energy consumption prediction value of the target circuit to be tested from the comprehensive comparison verification conditions, performing error verification or adjustment on the second energy consumption prediction value, and obtaining an actual energy consumption prediction value 1 and an actual energy consumption prediction value 2 according to the first energy consumption prediction value, performing energy scheduling on the original energy allocation according to the actual energy consumption prediction value 1 or the actual energy consumption prediction value 2, and outputting the actual energy demand allocation, specifically comprises: Selecting a to-be-compared verification condition from the comprehensive to-be-compared verification conditions that can verify the energy consumption prediction value of the target to-be-tested circuit, and outputting a preprocessing condition; Input the lines, current operating parameter values, current environmental characteristics, and original energy allocation in the pre-processing conditions into the second energy consumption prediction model for testing, and output the energy consumption prediction value to be compared; A preset energy consumption difference interval value is set, and if the difference between the second energy consumption prediction value and the energy consumption prediction value to be compared is within the energy consumption difference interval value, the first energy consumption prediction value and the second energy consumption prediction value are integrated to obtain an actual energy consumption prediction value of one; If the difference between the second energy consumption prediction value and the energy consumption prediction value to be compared is outside the energy consumption difference interval, the second energy consumption prediction value is error-controlled according to the energy consumption prediction value to be compared, and a third energy consumption prediction value is output; Integrate the first energy consumption prediction value and the third energy consumption prediction value to obtain a second century energy consumption prediction value; According to the actual energy consumption prediction value 1 or the actual energy consumption prediction value 2, energy scheduling is performed on the original energy allocation amount to output the actual energy demand allocation amount.

7. An energy dispatching and control system based on big data analysis, characterized in that: The system is used to implement an energy dispatching and control method based on big data analysis as described in any one of claims 1 to 6, comprising: The first energy consumption correlation coefficient extraction unit is used to obtain the target line to be tested that needs to be energy transmitted, predict the energy loss value of the target line to be tested under an ideal environment to obtain a first energy consumption prediction value, obtain all historical detection lines and historical environmental characteristics, and if there are environmental characteristics located in different straight sections or curved sections in the historical environmental characteristics that are unstable and belong to adjacent straight sections or curved sections, extract the correlation coefficient between the environmental change and the energy loss to obtain a first correlation influence coefficient; The second energy consumption correlation coefficient extraction unit is used to extract the correlation coefficient between the influence of line bending condition conversion on energy loss to obtain the second correlation influence coefficient 1 if there are environmental characteristics located in different straight sections and curved sections in the historical environmental characteristics that are stable and belong to non-adjacent straight sections or curved sections; The energy consumption prediction unit is used to extract the correlation coefficient between the energy loss affected by the line straight line condition conversion to obtain the second correlation influence coefficient 2, and predict the energy degree value of the target line to be tested affected by the environment and line condition conversion during the energy transmission process according to the first correlation influence coefficient, the second correlation influence coefficient 1, and the second correlation influence coefficient 2, and output the second energy consumption prediction value; The verification condition statistics unit is used to calculate the comprehensive verification conditions to be compared for the combinations of the lines belonging to the straight sections and the curved sections under different position distribution conditions according to the position distribution conditions between the straight sections and the curved sections existing in all historical detection lines; The energy dispatching unit is used to select a comparison verification condition from the comprehensive comparison verification conditions that can be used to verify the energy consumption prediction value of the target line to be tested, so as to perform error verification or adjustment on the second energy consumption prediction value, and obtain the actual energy consumption prediction value one and the actual energy consumption prediction value two according to the first energy consumption prediction value, and perform energy dispatch on the original energy allocation according to the actual energy consumption prediction value one or the actual energy consumption prediction value two, and output the actual energy demand allocation.

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