Tunnel construction energy consumption data intelligent management method and system

Abstract

The invention relates to the technical field of data processing, in particular to a tunnel construction energy consumption data intelligent management method and system.The method comprises the steps that in the tunnel construction process, energy consumption data of at least three kinds of energy consumption equipment are obtained in real time, and a corresponding energy consumption data sequence is obtained; mapping all the energy consumption data sequences into the same two-dimensional coordinate system to obtain a curve corresponding to each energy consumption data sequence, and analyzing each curve to obtain an energy consumption difference index; all the energy consumption data sequences are mapped to the same N-dimensional coordinate system, an N-dimensional curve is obtained, N is larger than 2, all the energy consumption data sequences are subjected to curved surface fitting to obtain an energy consumption fitting curved surface, the energy consumption fitting curved surface and the N-dimensional curve are analyzed, and the energy consumption change abnormal degree is obtained; abnormality identification is performed on the energy consumption data in combination with the energy consumption difference index and the energy consumption change abnormality degree, so that the accuracy of performing abnormality management on the energy consumption data is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for intelligent management of tunnel construction energy consumption data. Background Art

[0002] Tunnels are engineering structures buried in the earth, representing a form of human utilization of underground space. Tunnel construction is divided into the excavation phase, lining phase, and restoration phase. The excavation phase, with the heaviest equipment investment and energy consumption, is the core phase of tunnel construction. The excavation phase primarily involves excavating the tunnel underground through mechanical or manual means. Key energy-consuming equipment includes shield machines, ventilators, and drainage pumps. These devices operate at high loads during this phase, accounting for the vast majority of total tunnel construction energy consumption. Therefore, real-time monitoring of energy consumption data during the excavation phase is crucial.

[0003] Traditionally, fixed thresholds are set for each energy-consuming device. When a particular energy-consuming device exceeds its corresponding fixed threshold, the energy consumption data is considered abnormal during tunnel construction. However, synergistic effects exist between various energy-consuming devices. For example, tunnel boring machines (TBMs), ventilators, and drainage pumps. In soft soil formations, the TBM advances faster, requiring higher ventilation and drainage. In hard rock formations, however, the TBM advances slower, requiring less ventilation and drainage. Traditional methods for determining abnormal energy consumption data ignore the synergistic effects between construction conditions and energy-consuming devices, lacking a systematic and holistic approach, resulting in inaccurate determinations of energy consumption data anomalies.

[0004] Therefore, how to improve the accuracy of abnormal management of energy consumption data based on the synergy between various energy-consuming devices has become an urgent problem that needs to be solved. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides a method and system for intelligent management of tunnel construction energy consumption data to solve the problem of how to improve the accuracy of abnormal management of energy consumption data based on the synergy between various energy-consuming devices.

[0006] In a first aspect, an embodiment of the present invention provides a method for intelligent management of tunnel construction energy consumption data, the method comprising the following steps: During the tunnel construction process, energy consumption data of at least three energy-consuming devices are acquired in real time to obtain corresponding time series, and the data in each time series are linearly normalized to obtain corresponding energy consumption data series; Mapping all energy consumption data sequences into the same two-dimensional coordinate system to obtain a curve corresponding to each energy consumption data sequence, and fitting all energy consumption data sequences to obtain an energy consumption fitting curve, wherein the abscissa of the two-dimensional coordinate system represents time, and the ordinate represents energy consumption data, and obtaining an energy consumption difference index between all energy consumption data sequences based on the similarity between the curve corresponding to each energy consumption data sequence and the energy consumption fitting curve, as well as the synergy difference between each energy consumption data sequence; Mapping all energy consumption data sequences to the same N-dimensional coordinate system, fitting the data in all energy consumption data sequences to obtain an N-dimensional curve, where N is greater than 2, and performing surface fitting on all energy consumption data sequences to obtain an energy consumption fitting surface, where the N coordinate axes of the N-dimensional coordinate system respectively represent the energy consumption data of each energy-consuming device, and obtaining the degree of abnormal energy consumption variation between all energy consumption data sequences based on the projected area of ​​the energy consumption fitting surface on each coordinate plane of the N-dimensional coordinate system and the curvature of each data point on the N-dimensional curve; In combination with the energy consumption difference index and the abnormal degree of energy consumption change, abnormal management is performed on the energy consumption data of the tunnel during construction.

[0007] Preferably, the energy consumption difference index between all energy consumption data sequences is obtained based on the similarity between the curve corresponding to each energy consumption data sequence and the energy consumption fitting curve, and the synergy difference between each energy consumption data sequence, including: Obtaining the degree of data difference between all energy consumption data sequences based on the similarity between the curve corresponding to each energy consumption data sequence and the energy consumption fitting curve, as well as abnormal data in all energy consumption data sequences; Obtaining intersection points between curves corresponding to each of the energy consumption data sequences, clustering all the intersection points to obtain at least two clusters, and obtaining a synergy difference index between all the energy consumption data sequences based on the distance between each two clusters; The average value between the data difference degree and the synergy difference index is calculated to obtain the energy consumption difference index between all energy consumption data sequences.

[0008] Preferably, obtaining the degree of data difference between all energy consumption data sequences based on the similarity between the curve corresponding to each energy consumption data sequence and the energy consumption fitting curve, as well as abnormal data in all energy consumption data sequences, includes: Calculating the DTW distance between the curve corresponding to each energy consumption data sequence and the energy consumption fitting curve respectively, performing a weighted summation on all DTW distances and then performing linear normalization to obtain a first variable; Obtain abnormal data in all energy consumption data sequences through a box plot, calculate the ratio between the number of all abnormal data and the number of all data in all energy consumption data sequences, and perform linear normalization on the ratio to obtain a second variable; A weighted sum is performed on the first variable and the second variable to obtain the degree of data difference between all energy consumption data sequences.

[0009] Preferably, obtaining the synergy difference index between all energy consumption data sequences based on the distance between each two clusters includes: For any two clusters, respectively obtain the cluster centers of the arbitrary two clusters, calculate the Euclidean distance between the cluster centers of the arbitrary two clusters, and obtain the inter-cluster distance between the arbitrary two clusters; The inter-cluster distance between every two clusters is obtained, the coefficient of variation of all inter-cluster distances is calculated, and the coefficient of variation is linearly normalized to obtain the synergy difference index between all energy consumption data sequences.

[0010] Preferably, obtaining the abnormal degree of energy consumption variation among all energy consumption data sequences based on the projected area of ​​the energy consumption fitting surface on each coordinate plane of the N-dimensional coordinate system and the curvature of each data point on the N-dimensional curve includes: Obtaining non-differentiable points on the N-dimensional curve, calculating the ratio of the number of all non-differentiable points to the number of all data points on the N-dimensional curve, and obtaining an instantaneous abnormal characteristic value; Obtaining the curvature of each data point in the N-dimensional curve, calculating the absolute value of the difference between the curvatures of every two adjacent data points in the N-dimensional curve, and taking the average of all the absolute values ​​of the differences as the curvature change rate; Obtaining the projected area of ​​the energy consumption fitting surface on each coordinate plane in the N-dimensional coordinate system, calculating the absolute value of the difference between each two projected areas to obtain an area difference, performing weighted summation on all area differences and then performing linear normalization to obtain a data discreteness index; The instantaneous abnormal characteristic value, the curvature change rate and the data discreteness index are weightedly summed to obtain the abnormal degree of energy consumption change between all energy consumption data sequences.

[0011] Preferably, the abnormal management of the energy consumption data of the tunnel during construction is performed in combination with the energy consumption difference index and the abnormal degree of energy consumption change, including: Performing a weighted summation of the energy consumption difference index and the energy consumption change abnormality degree to obtain the comprehensive energy consumption abnormality degree of all energy consumption data sequences; The energy consumption data of the tunnel during construction is managed abnormally according to the degree of abnormal comprehensive energy consumption.

[0012] Preferably, performing abnormal management on the energy consumption data of the tunnel during construction according to the abnormal degree of the comprehensive energy consumption includes: If the comprehensive energy consumption abnormality level is greater than a preset comprehensive energy consumption abnormality level threshold, it is determined that the energy consumption data of the tunnel during construction is abnormal, and an abnormal energy consumption warning is issued for the at least three energy-consuming equipment.

[0013] In the second aspect, an embodiment of the present invention also provides an intelligent management system for tunnel construction energy consumption data, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements an intelligent management method for tunnel construction energy consumption data as described in the first aspect.

[0014] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In the process of tunnel construction, the present invention obtains energy consumption data of at least three energy-consuming devices in real time to obtain corresponding time series, and linearly normalizes the data in each time series to obtain corresponding energy consumption data series; all energy consumption data series are mapped to the same two-dimensional coordinate system to obtain curves corresponding to each energy consumption data series, and all energy consumption data series are fitted to obtain energy consumption fitting curves, wherein the horizontal axis of the two-dimensional coordinate system represents time, and the vertical axis represents energy consumption data; based on the similarity between the curve corresponding to each energy consumption data series and the energy consumption fitting curve, as well as the synergy difference between each energy consumption data series, the energy consumption between all energy consumption data series is obtained. energy consumption difference index; mapping all energy consumption data sequences to the same N-dimensional coordinate system, and fitting the data in all energy consumption data sequences to obtain an N-dimensional curve, where N is greater than 2, and performing surface fitting on all energy consumption data sequences to obtain an energy consumption fitting surface, wherein the N coordinate axes of the N-dimensional coordinate system respectively represent the energy consumption data of each of the energy-consuming devices, and according to the projected area of ​​the energy consumption fitting surface on each coordinate plane of the N-dimensional coordinate system and the curvature of each data point on the N-dimensional curve, the abnormal degree of energy consumption change between all energy consumption data sequences is obtained; combining the energy consumption difference index and the abnormal degree of energy consumption change, performing abnormal management on the energy consumption data of the tunnel during construction. Among them, taking into account the synergistic effect between the energy consumption data of multiple energy-consuming equipment during the tunnel construction process, the characteristics of the energy consumption data sequences corresponding to multiple energy-consuming equipment in the two-dimensional space (energy consumption difference index) and the characteristics in the N-dimensional space (the degree of abnormal energy consumption changes) are combined to perform abnormal management of the energy consumption data during the tunnel construction process. Compared with the early warning method based on whether the energy consumption data of each energy-consuming equipment exceeds the threshold, the probability of missed reports is reduced. At the same time, the probability of false alarm due to the increase of energy consumption data of a certain energy-consuming equipment due to environmental factors and other factors while there is no abnormality in the actual energy consumption data is reduced, thereby improving the accuracy of abnormal identification of energy consumption data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a method flow chart of a method for intelligent management of tunnel construction energy consumption data provided by the first embodiment of the present invention; Figure 2 A schematic diagram of a curve of energy consumption data sequences corresponding to a shield machine, a ventilator, and a drainage pump in a two-dimensional coordinate system provided by an embodiment of the present invention; Figure 3 An embodiment of the present invention provides a schematic diagram of a curve of energy consumption data sequences corresponding to a shield machine, a ventilator, and a drainage pump in a three-dimensional coordinate system. DETAILED DESCRIPTION

[0017] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0018] It should be noted that the terms "first," "second," and the like in the specification of the present disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0020] See also Figure 1 , is a method flow chart of a method for intelligent management of tunnel construction energy consumption data provided by the first embodiment of the present invention, such as Figure 1 As shown, the method may include: Step S101: During tunnel construction, energy consumption data of at least three energy-consuming devices are acquired in real time to obtain corresponding time series, and the data in each time series is linearly normalized to obtain a corresponding energy consumption data series.

[0021] Tunnel construction is divided into the excavation stage, lining stage, and restoration stage. The excavation stage is the stage with the largest equipment investment and the highest energy consumption. The energy-consuming equipment in this stage includes shield machines, ventilators, drainage pumps, air compressors, shotcrete machines, mobile substations, etc. These equipment all operate at high load during the excavation stage, and their energy consumption accounts for the vast majority of the total energy consumption of tunnel construction. Therefore, it is particularly important to effectively manage the construction energy consumption data of this stage.

[0022] Traditionally, fixed thresholds are set for each energy-consuming device. When a particular energy-consuming device exceeds its corresponding threshold, the energy consumption data is considered abnormal during tunnel construction. However, there are synergistic effects between various energy-consuming devices. For example, in soft soil, the shield machine advances faster, requiring more ventilation and drainage. In hard rock, the advance is slower, requiring less ventilation and drainage. Another example is the air compressor, shotcrete machine, anchor drill, and mobile substation. When the surrounding rock is unstable, the shotcrete machine and anchor drill have a heavy workload. The air compressor needs to provide compressed air to the shotcrete machine and anchor drill, increasing its energy consumption. Simultaneously, the mobile substation supplies power to various devices, increasing its energy consumption. Traditional methods for determining anomalies in energy consumption data ignore the synergistic effects between energy-consuming devices and lack a systematic and holistic approach, resulting in inaccurate determination of energy consumption anomalies. Therefore, in the embodiment of the present invention, the abnormal situation of each energy-consuming device is determined based on the coordination between the various energy-consuming devices, thereby improving the accuracy of abnormal management of energy consumption data.

[0023] First, during tunnel construction, energy consumption data for at least three types of energy-consuming equipment is acquired in real time to generate corresponding time series. In this embodiment of the present invention, shield machines, ventilators, and drainage pumps are used as examples, but this is not a limitation and can be customized by the implementer based on the specific scenario. The data in each time series is then linearly normalized to generate a corresponding energy consumption data series, unifying the dimensions and facilitating subsequent analysis and processing. Linear normalization is a conventional technique and will not be further described here.

[0024] In step S102, all energy consumption data sequences are mapped into the same two-dimensional coordinate system to obtain a curve corresponding to each of the energy consumption data sequences, and all energy consumption data sequences are fitted to obtain an energy consumption fitting curve. The horizontal axis of the two-dimensional coordinate system represents time, and the vertical axis represents energy consumption data. Based on the similarity between the curve corresponding to each of the energy consumption data sequences and the energy consumption fitting curve, as well as the synergy difference between each of the energy consumption data sequences, the energy consumption difference index between all energy consumption data sequences is obtained.

[0025] Taking into account the existence of a certain correlation among the energy consumption data of the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump, that is, the dust and heat generated by the shield machine will directly increase the ventilation load, and the energy consumption of the drainage pump will also increase significantly. The two-dimensional curve is a curve showing the energy consumption data of each energy-consuming equipment changing with time. Therefore, the energy consumption data sequences corresponding to the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump, can be mapped to the same two-dimensional coordinate system to obtain the curves corresponding to the three energy-consuming equipment, namely, the curve corresponding to each energy consumption data sequence. Among them, the horizontal axis of the two-dimensional coordinate system represents time, and the vertical axis represents energy consumption data. Figure 2 , which is a schematic diagram of energy consumption curves of energy consumption data sequences corresponding to shield machines, ventilators and drainage pumps in a two-dimensional coordinate system. Figure 2 In the figure, the curve indicated by the shield machine is the curve of the energy consumption data series corresponding to the shield machine, the curve indicated by the ventilator is the curve of the energy consumption data series corresponding to the ventilator, and the curve indicated by the drainage pump is the curve of the energy consumption data series corresponding to the drainage pump. At the same time, a polynomial curve fitting is performed on the energy consumption data series corresponding to the shield machine, the ventilator, and the drainage pump to obtain an energy consumption fitting curve, which is used to represent the average energy consumption between the energy consumption data series corresponding to these three energy-consuming devices. Polynomial curve fitting is a prior art and will not be repeated here. Furthermore, the degree of abnormality of the energy consumption data is preliminarily determined based on the similarity between the curve corresponding to each energy consumption data sequence and the energy consumption fitting curve, so as to reduce the probability of false alarm due to the increase of energy consumption data of a certain energy-consuming equipment caused by environmental factors and other factors while the actual energy consumption data does not have abnormalities: if the similarity between the curves corresponding to the three energy-consuming equipment and the energy consumption fitting curve is lower, the energy consumption data of at least one of the three energy-consuming equipment may be abnormal, which indirectly reflects that the degree of abnormality of the energy consumption data of the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump, is greater.

[0026] Since the box plot can reveal outliers between data, in an embodiment of the present invention, a box plot is constructed based on the data in all energy consumption data sequences (the energy consumption data sequences corresponding to the three energy-consuming devices, namely, the shield machine, the ventilator, and the drainage pump). The abnormal data in the energy consumption data sequences corresponding to the three energy-consuming devices are screened out by the box plot to improve the accuracy of the preliminary judgment on the degree of abnormality of the energy consumption data: the more abnormal data there are, the greater the possibility that the energy consumption data will be abnormal, which indirectly reflects that the degree of abnormality of the energy consumption data of the three energy-consuming devices, namely, the shield machine, the ventilator, and the drainage pump, is greater. Among them, the box plot is a prior art and will not be described in detail here.

[0027] Combining the similarity between the curves corresponding to the shield machine, ventilator, and drainage pump and the energy consumption fitting curve, as well as the abnormal data in the energy consumption data series corresponding to these three energy-consuming devices, the degree of data difference between the energy consumption data series corresponding to these three energy-consuming devices is obtained to preliminarily determine the degree of abnormality of the energy consumption data. Specifically: Calculating the DTW distance between the curve corresponding to each energy consumption data sequence and the energy consumption fitting curve respectively, performing a weighted summation on all DTW distances and then performing linear normalization to obtain a first variable, wherein the DTW distance is a prior art and will not be described in detail here; Obtain abnormal data in all energy consumption data sequences through a box plot, calculate the ratio between the number of all abnormal data and the number of all data in all energy consumption data sequences, and perform linear normalization on the ratio to obtain a second variable; A weighted sum is performed on the first variable and the second variable to obtain the degree of data difference between all energy consumption data sequences.

[0028] In one embodiment, the calculation formula for the degree of data difference between the energy consumption data sequences corresponding to the shield machine, the ventilator, and the drainage pump is:

[0029] in, Indicates the degree of data difference between the energy consumption data series corresponding to the shield machine, ventilator and drainage pump; The weight of the curve representing the energy consumption data sequence corresponding to the i-th energy-consuming device. In this embodiment of the present invention, the shield machine is recorded as the first energy-consuming device, the ventilator is recorded as the second energy-consuming device, and the drainage pump is recorded as the third energy-consuming device. This is not limited here and can be set by the implementer according to the specific scenario. The curve representing the energy consumption data sequence corresponding to the i-th energy-consuming device; represents the energy consumption fitting curve; represents the DTW distance between the curve of the energy consumption data sequence corresponding to the i-th energy-consuming device and the energy consumption fitting curve; m represents the number of abnormal data; M represents the number of all data in all energy consumption data sequences; represents weight; represents the linear normalization function.

[0030] It should be noted that It is used to characterize the similarity between the curve corresponding to the i-th energy-consuming device and the energy consumption fitting curve. The larger the value is, the lower the similarity between the curve corresponding to the i-th energy-consuming device and the energy consumption fitting curve is, that is, the difference between the energy consumption of the i-th energy-consuming device and the average energy consumption is large, and the energy consumption data of the i-th energy-consuming device is more likely to be abnormal, and thus The larger the value is, the greater the abnormality of the energy consumption data of the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump; the larger the value of m is, the more abnormal data there are in the energy consumption data sequence corresponding to the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump. The larger the value, the greater the abnormality of the energy consumption data of the three energy-consuming equipment, namely, shield machine, ventilator and drainage pump. Because the shield machine is the main equipment among the three energy-consuming equipment, and the ventilator and shield machine have stronger synergy than the drainage pump and shield machine, the weight corresponding to the shield machine is set to , set the weight corresponding to the ventilator to , set the weight corresponding to the drainage pump to , that is, set , , There is no restriction here, and implementers can set it according to specific scenarios.

[0031] Considering that in a two-dimensional coordinate system, the intersection points between the curves corresponding to the three energy-consuming devices (the shield machine, the ventilator, and the drainage pump) represent the intersection positions of the energy consumption data corresponding to these three energy-consuming devices during the change process, the more regular the distance between each two adjacent intersection points, the more regular the change of the energy consumption data corresponding to the three energy-consuming devices, and the stronger the synergy of the three energy-consuming devices during the data change process, that is, the stronger the correlation between the energy consumption data corresponding to the three energy-consuming devices, which indirectly reflects the smaller the degree of abnormality of the energy consumption data corresponding to the three energy-consuming devices; conversely, if the difference between each two adjacent intersection points is greater, it indirectly reflects the greater the degree of abnormality of the energy consumption data corresponding to the three energy-consuming devices. Therefore, in an embodiment of the present invention, the intersection points between the curves corresponding to the three energy-consuming devices (the shield machine, the ventilator, and the drainage pump) are clustered to obtain at least two clusters. Based on the distance between each two clusters, a synergy difference index between all energy consumption data sequences is obtained to indirectly reflect the degree of abnormality of the energy consumption data corresponding to the three energy-consuming devices (the shield machine, the ventilator, and the drainage pump). Considering that DBSCAN clustering can autonomously divide the data points to be clustered into multiple clusters without pre-specifying the number of clusters, in this embodiment of the present invention, the DBSCAN clustering algorithm is selected to cluster the intersections of the curves corresponding to the three energy-consuming equipment: the shield machine, the ventilator, and the drainage pump. The DBSCAN clustering algorithm is a prior art and will not be described in detail here. The specific process of obtaining the synergy difference index between the energy consumption data series corresponding to the shield machine, the ventilator, and the drainage pump is as follows: For any two clusters, respectively obtain the cluster centers of the arbitrary two clusters, calculate the Euclidean distance between the cluster centers of the arbitrary two clusters, and obtain the inter-cluster distance between the arbitrary two clusters; Obtain the inter-cluster distance between each two clusters, calculate the coefficient of variation of all inter-cluster distances, perform linear normalization on the coefficient of variation, and obtain the synergy difference index between all energy consumption data series, which is recorded as The larger the coefficient of variation, the more uneven the distribution of all inter-cluster distances, that is, among all the intersections between the curves corresponding to the three energy-consuming equipment, the shield machine, the ventilator and the drainage pump, the more irregular the distance between each two adjacent intersections, the smaller the synergy of the three energy-consuming equipment in the data change process, and the larger the synergy difference index, the greater the abnormality of the energy consumption data corresponding to the three energy-consuming equipment. Among them, the coefficient of variation is the prior art and will not be repeated here.

[0032] Furthermore, by combining the data difference degree and the synergy difference index, the energy consumption difference index between the energy consumption data series corresponding to the three energy-consuming equipment, namely, the shield machine, the ventilator, and the drainage pump, is obtained to preliminarily determine the abnormality of the energy consumption data. Specifically, the average value between the data difference degree and the synergy difference index is calculated to obtain the energy consumption difference index between all energy consumption data series. The calculation formula of the energy consumption difference index is:

[0033] in, It represents the energy consumption difference index between the energy consumption data series corresponding to the three energy-consuming equipment: shield machine, ventilator and drainage pump; Indicates the degree of data difference between the energy consumption data series corresponding to the shield machine, ventilator and drainage pump; It represents the synergy difference index between the energy consumption data series corresponding to the shield machine, ventilator and drainage pump.

[0034] It should be noted that The larger the value is, the greater the difference between the energy consumption of at least one of the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump, and the average energy consumption is, and the more abnormal data points there are in the energy consumption data series corresponding to these three energy-consuming equipment. The larger the value, the greater the abnormality of the energy consumption data of the three energy-consuming devices; The larger the value is, the smaller the coordination of the energy consumption data of the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump, is during the data change process. The larger the value is, the more abnormal the energy consumption data of these three energy-consuming devices are.

[0035] At this point, the energy consumption data sequences corresponding to the three energy-consuming equipment, namely, the shield machine, the ventilator, and the drainage pump, were analyzed through a two-dimensional coordinate system. The energy consumption difference indexes between the energy consumption data sequences corresponding to the three energy-consuming equipment, namely, the shield machine, the ventilator, and the drainage pump, were obtained, and a preliminary judgment was made on the abnormality of the energy consumption data.

[0036] Step S103: Map all energy consumption data sequences into the same N-dimensional coordinate system, and fit the data in all energy consumption data sequences to obtain an N-dimensional curve, where N is greater than 2. Surface fitting is performed on all energy consumption data sequences to obtain an energy consumption fitting surface. The N coordinate axes of the N-dimensional coordinate system respectively represent the energy consumption data of each of the energy-consuming devices. Based on the projected area of ​​the energy consumption fitting surface on each coordinate plane of the N-dimensional coordinate system and the curvature of each data point on the N-dimensional curve, the degree of abnormality of energy consumption changes between all energy consumption data sequences is obtained.

[0037] Considering that analyzing the energy consumption data sequences corresponding to the three energy-consuming devices (the shield machine, ventilator, and drainage pump) only in two-dimensional space, that is, analyzing the energy consumption data sequences corresponding to the three energy-consuming devices in a two-dimensional coordinate system, it is impossible to comprehensively determine whether the energy consumption data has abnormalities based on the coordination of these three energy-consuming devices. Therefore, in an embodiment of the present invention, an N-dimensional analysis (N greater than 2) is performed on the energy consumption data sequences corresponding to each energy-consuming device. That is, the energy consumption data sequences corresponding to each energy-consuming device are mapped to the same N-dimensional coordinate system. This presents potential abnormal data in the energy consumption data sequences corresponding to the three energy-consuming devices from multiple angles, making the determination of energy consumption data abnormalities more accurate. Curve fitting is performed on the energy consumption data sequences corresponding to each energy-consuming device.

[0038] Since the embodiment of the present invention takes the shield machine, the ventilator and the drainage pump as examples, the energy consumption data sequences corresponding to the three energy consuming devices are subjected to a three-dimensional analysis in the embodiment of the present invention, that is, the energy consumption data sequences corresponding to the three energy consuming devices are mapped to the same three-dimensional coordinate system, and the energy consumption data sequences corresponding to the three energy consuming devices are subjected to polynomial curve fitting to obtain a three-dimensional curve, wherein the three coordinate axes of the three-dimensional coordinate system respectively represent the energy consumption data of the three energy consuming devices, the shield machine, the ventilator and the drainage pump, with reference to FIG. Figure 3 , which is a curve diagram of the energy consumption data sequence corresponding to the shield machine, ventilator and drainage pump in a three-dimensional coordinate system, that is, a three-dimensional space curve diagram of energy consumption, Figure 3 In the 3D coordinate system, the curve is the 3D curve obtained by fitting the energy consumption data series corresponding to the shield machine, ventilator, and drainage pump through a polynomial curve. If the energy consumption data series corresponding to four energy-consuming devices are selected, a 4D analysis is required. This means that the energy consumption data series corresponding to the four selected energy-consuming devices are mapped to the same 4D coordinate system to obtain the 4D curve, and so on.

[0039] Since non-differentiable points may exist in an N-dimensional curve, these non-differentiable points are often cusps, breakpoints, or turning points on the N-dimensional curve. These non-differentiable points are likely to be outliers in the energy consumption data. The greater the number of non-differentiable points, the greater the likelihood of anomalies in the energy consumption data. At the same time, the difference in curvature between each two adjacent data points on the N-dimensional curve can reflect the regularity of energy consumption data changes. The greater the difference in curvature between each two adjacent data points on the N-dimensional curve, the more irregular the changes in the energy consumption data, and thus the greater the possibility of anomalies in the energy consumption data, that is, the greater the degree of anomaly in the energy consumption data.

[0040] In order to judge the degree of abnormality of energy consumption data according to the synergy between various energy-consuming devices in an N-dimensional coordinate system, in an embodiment of the present invention, a polynomial surface fitting is performed on the energy consumption data sequence corresponding to each energy-consuming device to obtain an energy consumption fitting surface. The synergy between various energy-consuming devices is reflected according to the projected area of ​​the energy consumption fitting surface on each coordinate plane of the N-dimensional coordinate system, thereby indirectly reflecting the degree of abnormality of the energy consumption data: if the difference between the projected areas on each two coordinate planes is large, it means that the greater the difference in the change of energy consumption data between the energy-consuming devices, the worse the synergy between the energy-consuming devices, and indirectly indicates that the energy consumption data is likely to be abnormal, that is, the greater the degree of abnormality of the energy consumption data. Among them, the polynomial fitting surface is a prior art and will not be described here.

[0041] Therefore, in the embodiment of the present invention, based on the number of non-differentiable points and the curvature of each data point on the three-dimensional curves corresponding to the three energy-consuming devices, namely, the shield machine, the ventilator, and the drainage pump, as well as the projected area of ​​the energy consumption fitting surface on each coordinate plane of the three-dimensional coordinate system, the abnormal degree of energy consumption changes between the energy consumption data sequences corresponding to the three energy-consuming devices, namely, the shield machine, the ventilator, and the drainage pump, is obtained to reflect the abnormal degree of energy consumption data in the N-dimensional space. Specifically: Using SymPy to implement symbolic differentiation, obtaining non-differentiable points on the N-dimensional curve by the symbolic differentiation method, calculating the proportion of the number of all non-differentiable points in the number of all data points on the N-dimensional curve, and obtaining instantaneous abnormal characteristic values, wherein using SymPy to implement symbolic differentiation and obtaining non-differentiable points by the symbolic differentiation method are prior arts and will not be described in detail here; Obtaining the curvature of each data point in the N-dimensional curve, calculating the absolute value of the difference between the curvatures of every two adjacent data points in the N-dimensional curve, and taking the average of all the absolute values ​​of the differences as the curvature change rate; Obtaining the projected area of ​​the energy consumption fitting surface on each coordinate plane in the N-dimensional coordinate system, calculating the absolute value of the difference between each two projected areas to obtain an area difference, performing weighted summation on all area differences and then performing linear normalization to obtain a data discreteness index; The instantaneous abnormal characteristic value, the curvature change rate and the data discreteness index are weightedly summed to obtain the abnormal degree of energy consumption change between all energy consumption data sequences.

[0042] In one embodiment, the calculation formula for the abnormal degree of energy consumption variation among the energy consumption data sequences corresponding to the shield machine, the ventilator, and the drainage pump is:

[0043] in, represents the degree of abnormal energy consumption variation among the energy consumption data series corresponding to the shield machine, ventilator, and drainage pump; q represents the number of all non-differentiable points on the N-dimensional curve (a three-dimensional curve in the embodiment of the present invention); Q represents the number of all data points on the N-dimensional curve (a three-dimensional curve in the embodiment of the present invention); j represents the curvature of the j-th data point on the N-dimensional curve (a three-dimensional curve in the embodiment of the present invention); j+1 represents the curvature of the j+1-th data point on the N-dimensional curve (a three-dimensional curve in the embodiment of the present invention); represents the absolute value of the difference between the curvature of the jth data point and the curvature of the j+1th data point on an N-dimensional curve (a three-dimensional curve in the embodiment of the present invention); N represents the number of energy-consuming devices to be analyzed (the shield machine, ventilator, and drainage pump are selected as three energy-consuming devices in the embodiment of the present invention), that is, the dimension of the N-dimensional coordinate system; S represents the number of absolute values ​​of the difference between the projected areas on each two coordinate planes in the N-dimensional coordinate system, that is, the number of area differences. In the embodiment of the present invention, the three-dimensional coordinate system is used, and the number of area differences is 3; Indicates the Area difference; represents the first weight; represents the second weight; represents the third weight; Indicates the The weight corresponding to the area difference; represents the linear normalization function.

[0044] It should be noted that That is, the instantaneous abnormal characteristic value. The larger q is, the more non-differentiable points there are in the N-dimensional curve (a three-dimensional curve in the embodiment of the present invention), the more abnormal values ​​there are in the energy consumption data, and the greater the possibility that the energy consumption data is abnormal. Thus, the larger the instantaneous abnormal characteristic value is, The larger the value, the greater the abnormality of the energy consumption data; is the rate of change of curvature, The larger the value, the greater the difference between the jth data point and the j+1th data point on the N-dimensional curve (a three-dimensional curve in the embodiment of the present invention), the more irregular the change of energy consumption data, and the greater the curvature change rate. The larger the value is, the greater the abnormality of the energy consumption data of the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump; It is the data dispersion index. The larger the value, the larger the value is in the N-dimensional coordinate system (a three-dimensional coordinate system in the embodiment of the present invention). The greater the difference between the projected areas on the two coordinate planes corresponding to the area difference, the greater the difference in the energy consumption data between the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump, and the worse the coordination between the three energy-consuming equipment, and thus the greater the data discreteness index. The larger the value is, the greater the abnormality of the energy consumption data of the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump. Considering that the curvature change rate has a greater impact on the abnormality of energy consumption, it is given a larger weight, which is The value set is large, so set , , There is no restriction here, and implementers can set it according to specific scenarios.

[0045] At this point, the energy consumption data sequences corresponding to the three energy-consuming equipment, namely, the shield machine, the ventilator, and the drainage pump, are analyzed through the N-dimensional coordinate system to obtain the degree of abnormality in energy consumption changes between the energy consumption data sequences corresponding to the three energy-consuming equipment, namely, the shield machine, the ventilator, and the drainage pump. This is combined with the energy consumption difference index obtained by analyzing the energy consumption data sequences corresponding to the three energy-consuming equipment, namely, the shield machine, the ventilator, and the drainage pump, in two-dimensional space, to more comprehensively determine whether the energy consumption data has abnormalities, thereby improving the accuracy of abnormal management of energy consumption data.

[0046] Step S104 : performing abnormal management on the energy consumption data of the tunnel during construction in combination with the energy consumption difference index and the abnormal degree of energy consumption change.

[0047] Combined with the analysis of the energy consumption data series corresponding to the three energy-consuming equipment (shield machine, ventilator and drainage pump) in two-dimensional space and N-dimensional space, the comprehensive energy consumption anomaly degree of the energy consumption data series corresponding to the three energy-consuming equipment (shield machine, ventilator and drainage pump) is obtained. That is, the comprehensive energy consumption anomaly degree is obtained by combining the energy consumption change anomaly degree and the energy consumption difference index. Specifically: The energy consumption difference index and the energy consumption change abnormality degree are weighted and summed to obtain the comprehensive energy consumption abnormality degree of all energy consumption data sequences.

[0048] In one embodiment, the calculation formula for the comprehensive energy consumption abnormality degree of the energy consumption data series corresponding to the three energy-consuming equipment, namely, the shield machine, the ventilator, and the drainage pump, is:

[0049] in, It represents the comprehensive energy consumption abnormality of the energy consumption data series corresponding to the three energy-consuming equipment: shield machine, ventilator and drainage pump. It represents the energy consumption difference index of the energy consumption data series corresponding to the three energy-consuming equipment: shield machine, ventilator and drainage pump. Indicates the abnormal degree of energy consumption change of the energy consumption data series corresponding to the three energy-consuming equipment: shield machine, ventilator and drainage pump. Indicates the weight corresponding to the energy consumption difference index, Indicates the weight corresponding to the abnormal degree of energy consumption change.

[0050] It should be noted that The larger the value is, the more abnormal data points there are in the energy consumption data sequence corresponding to the three energy-consuming devices, namely, the shield machine, the ventilator and the drainage pump, and the worse the correlation between the energy consumption data of the three energy-consuming devices in the data change process. The larger the value is, the greater the abnormality of the energy consumption data of the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump; The larger the value is, the more abnormal values ​​there are in the energy consumption data of the three energy-consuming devices, namely, the shield machine, the ventilator and the drainage pump, in the N-dimensional space (three-dimensional space in the embodiment of the present invention), and the more irregular the changes in the energy consumption data of the three energy-consuming devices are. The larger the value is, the greater the abnormality of the energy consumption data of the three energy-consuming equipment, namely, the shield machine, the ventilator and the drainage pump. , There is no restriction here, and implementers can set it according to specific scenarios.

[0051] So far, the comprehensive energy consumption abnormality of the energy consumption data series corresponding to the three energy-consuming equipment: shield machine, ventilator and drainage pump has been obtained. , further, according to the abnormal degree of comprehensive energy consumption The abnormal management of energy consumption data during the construction of the tunnel is carried out. Compared with the early warning method based on whether the energy consumption data of each energy-consuming equipment exceeds the threshold, the probability of missed reports is reduced and the accuracy of abnormal identification of energy consumption data is improved: the preset comprehensive energy consumption abnormality threshold is set to 0.6. There is no restriction here. The implementer can set it according to the specific scenario. If the comprehensive energy consumption abnormality of the energy consumption data sequence corresponding to the three energy-consuming equipment of shield machine, ventilator and drainage pump is , it is determined that during the tunnel construction process, the energy consumption data of the three energy-consuming equipment, namely the shield machine, the ventilator and the drainage pump, have been abnormal. At the same time, an abnormal energy consumption warning is issued for the three energy-consuming equipment, and relevant staff are notified to inspect and repair the three energy-consuming equipment, namely the shield machine, the ventilator and the drainage pump.

[0052] In summary, the present invention obtains energy consumption data of at least three energy-consuming devices in real time during tunnel construction to obtain corresponding time series, and linearly normalizes the data in each time series to obtain corresponding energy consumption data series; maps all energy consumption data series to the same two-dimensional coordinate system to obtain a curve corresponding to each energy consumption data series, and fits all energy consumption data series to obtain an energy consumption fitting curve, wherein the horizontal axis of the two-dimensional coordinate system represents time, and the vertical axis represents energy consumption data; based on the similarity between the curve corresponding to each energy consumption data series and the energy consumption fitting curve, and the synergy difference between each energy consumption data series, the energy consumption data series are obtained. energy consumption difference index; mapping all energy consumption data sequences to the same N-dimensional coordinate system, fitting the data in all energy consumption data sequences to obtain an N-dimensional curve, where N is greater than 2, and performing surface fitting on all energy consumption data sequences to obtain an energy consumption fitting surface, where the N coordinate axes of the N-dimensional coordinate system respectively represent the energy consumption data of each of the energy-consuming devices, and obtaining the degree of abnormality of energy consumption changes between all energy consumption data sequences based on the projected area of ​​the energy consumption fitting surface on each coordinate plane of the N-dimensional coordinate system and the curvature of each data point on the N-dimensional curve; and performing abnormal management on the energy consumption data of the tunnel during construction in combination with the energy consumption difference index and the degree of abnormality of energy consumption changes. Among them, taking into account the synergistic effect between the energy consumption data of multiple energy-consuming equipment during the tunnel construction process, the characteristics of the energy consumption data sequences corresponding to multiple energy-consuming equipment in the two-dimensional space (energy consumption difference index) and the characteristics in the N-dimensional space (the degree of abnormal energy consumption changes) are combined to perform abnormal management of the energy consumption data during the tunnel construction process. Compared with the early warning method based on whether the energy consumption data of each energy-consuming equipment exceeds the threshold, the probability of missed reports is reduced. At the same time, the probability of false alarm due to the increase of energy consumption data of a certain energy-consuming equipment due to environmental factors and other factors while there is no abnormality in the actual energy consumption data is reduced, thereby improving the accuracy of abnormal identification of energy consumption data.

[0053] Based on the same inventive concept as the above method, an embodiment of the present invention also provides an intelligent management system for tunnel construction energy consumption data, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned methods for intelligent management of tunnel construction energy consumption data are implemented.

[0054] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for intelligent management of tunnel construction energy consumption data, characterized in that: The method for intelligent management of tunnel construction energy consumption data comprises: During the tunnel construction process, energy consumption data of at least three energy-consuming devices are acquired in real time to obtain corresponding time series, and the data in each time series is linearly normalized to obtain corresponding energy consumption data series; Mapping all energy consumption data sequences to the same two-dimensional coordinate system, obtaining a curve corresponding to each of the energy consumption data sequences, and fitting all of the energy consumption data sequences to obtain an energy consumption fitting curve, wherein the abscissa of the two-dimensional coordinate system represents time, and the ordinate represents energy consumption data, and obtaining energy consumption difference indicators between all energy consumption data sequences based on the similarity between the curve corresponding to each of the energy consumption data sequences and the energy consumption fitting curve, and the synergy difference between each of the energy consumption data sequences; Map all energy consumption data sequences to the same N-dimensional coordinate system, and fit the data in all energy consumption data sequences to obtain an N-dimensional curve, where N is greater than 2, and perform surface fitting on all energy consumption data sequences to obtain an energy consumption fitting surface, where the N coordinate axes of the N-dimensional coordinate system respectively represent the energy consumption data of each of the energy consuming devices, and obtain the degree of abnormal energy consumption variation between all energy consumption data sequences according to the projected area of ​​the energy consumption fitting surface on each coordinate plane of the N-dimensional coordinate system and the curvature of each data point on the N-dimensional curve; In combination with the energy consumption difference index and the abnormal degree of energy consumption change, abnormal management is performed on the energy consumption data of the tunnel during the construction process.

2. A method for intelligent management of tunnel construction energy consumption data according to claim 1, characterized in that: The energy consumption difference index between all energy consumption data sequences is obtained based on the similarity between the curve corresponding to each energy consumption data sequence and the energy consumption fitting curve, and the synergy difference between each energy consumption data sequence, including: According to the similarity between the curve corresponding to each of the energy consumption data sequences and the energy consumption fitting curve, and the abnormal data in all the energy consumption data sequences, the degree of data difference between all the energy consumption data sequences is obtained; Obtaining the intersection points between the curves corresponding to each of the energy consumption data sequences, clustering all the intersection points to obtain at least two clusters, and obtaining the synergy difference index between all the energy consumption data sequences according to the distance between each two of the clusters; The average value between the data difference degree and the synergy difference index is calculated to obtain the energy consumption difference index between all energy consumption data sequences.

3. A method for intelligent management of tunnel construction energy consumption data according to claim 2, characterized in that: The obtaining of the degree of data difference between all energy consumption data sequences according to the similarity between the curve corresponding to each energy consumption data sequence and the energy consumption fitting curve, and the abnormal data in all energy consumption data sequences, includes: Calculating the DTW distance between the curve corresponding to each of the energy consumption data sequences and the energy consumption fitting curve respectively, performing linear normalization after weighted summation on all DTW distances, and obtaining a first variable; Obtain abnormal data in all energy consumption data sequences through a box plot, calculate the ratio between the number of all abnormal data and the number of all data in all energy consumption data sequences, and linearly normalize the ratio to obtain a second variable; A weighted sum is performed on the first variable and the second variable to obtain the degree of data difference between all energy consumption data sequences.

4. A method for intelligent management of tunnel construction energy consumption data according to claim 2, characterized in that: The synergy difference index between all energy consumption data sequences is obtained according to the distance between each two clusters, including: For any two clusters, respectively obtain the cluster centers of the any two clusters, calculate the Euclidean distance between the cluster centers of the any two clusters, and obtain the inter-cluster distance between the any two clusters; The inter-cluster distance between every two of the clusters is obtained, the coefficient of variation of all inter-cluster distances is calculated, and the coefficient of variation is linearly normalized to obtain the synergy difference index between all energy consumption data sequences.

5. The method for intelligent management of tunnel construction energy consumption data according to claim 1, characterized in that: The abnormal degree of energy consumption variation among all energy consumption data sequences is obtained according to the projection area of ​​the energy consumption fitting surface on each coordinate plane of the N-dimensional coordinate system and the curvature of each data point on the N-dimensional curve, including: Obtaining non-differentiable points on the N-dimensional curve, calculating the proportion of the number of all non-differentiable points in the number of all data points on the N-dimensional curve, and obtaining an instantaneous abnormal characteristic value; Obtaining the curvature of each data point in the N-dimensional curve, calculating the absolute value of the difference between the curvatures of every two adjacent data points in the N-dimensional curve, and taking the average value of all the absolute values ​​of the differences as the curvature change rate; Obtaining the projection area of ​​the energy consumption fitting surface on each coordinate plane in the N-dimensional coordinate system, calculating the absolute value of the difference between every two projection areas to obtain the area difference, performing weighted summation on all area differences and performing linear normalization to obtain a data discreteness index; The instantaneous abnormal characteristic value, the curvature change rate and the data discreteness index are weightedly summed to obtain the abnormal degree of energy consumption change between all energy consumption data sequences.

6. The method for intelligent management of tunnel construction energy consumption data according to claim 1, characterized in that: The combining the energy consumption difference index and the abnormal degree of energy consumption change to perform abnormal management on the energy consumption data of the tunnel during the construction process includes: Performing a weighted summation on the energy consumption difference index and the energy consumption change abnormality degree to obtain the comprehensive energy consumption abnormality degree of all energy consumption data sequences; The energy consumption data of the tunnel during construction is managed abnormally according to the degree of abnormal comprehensive energy consumption.

7. A method for intelligent management of tunnel construction energy consumption data according to claim 6, characterized in that: The abnormal management of the energy consumption data of the tunnel during the construction process according to the abnormal degree of the comprehensive energy consumption includes: If the comprehensive energy consumption abnormality level is greater than a preset comprehensive energy consumption abnormality level threshold, it is determined that the energy consumption data of the tunnel during construction is abnormal, and abnormal energy consumption warnings are issued for the at least three energy-consuming devices.

8. A tunnel construction energy consumption data intelligent management system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for intelligent management of tunnel construction energy consumption data as described in any one of claims 1-7 are implemented.

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