A power equipment foundation settlement prediction method and system

Abstract

This invention discloses a method and system for predicting the settlement of power equipment foundations. The method includes: dividing at least one settlement observation data sequence according to various settlement correlations to obtain at least one set of settlement observation data sequences; selecting a target settlement observation data sequence from the set of settlement observation data sequences based on the location information of each monitoring point and using a preset sequence selection rule; inputting the target settlement observation data sequence into a preset settlement prediction model, which outputs target settlement prediction data corresponding to the target monitoring point; and correcting the target settlement prediction data based on other settlement observation data sequences in the set of settlement observation data sequences to obtain other settlement prediction data corresponding to other monitoring points. This efficient monitoring and early warning of the settlement status of power equipment foundations provides strong support for decision-making regarding the safe operation and maintenance of equipment.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power equipment settlement analysis, and particularly relates to a power equipment foundation settlement prediction method and system. BACKGROUND

[0002] The long-term stable and safe operation of power equipment foundations, such as transformer substation foundations, power transmission tower foundations, etc., is crucial to the reliability of the entire power grid. Foundation settlement is a key factor affecting the structural stability thereof, and uneven settlement can cause the equipment framework to tilt, redistribute internal stress, and even cause structural damage, seriously threatening the safety of the power grid. Therefore, continuous and accurate settlement prediction and monitoring of power equipment foundations is an important means to achieve condition-based maintenance and prevent accidents.

[0003] At present, common settlement prediction methods mainly rely on analyzing the settlement observation data sequences of each monitoring point independently, such as using regression analysis, time series models (such as ARIMA) or machine learning algorithms (such as support vector machines, neural networks) to establish independent prediction models for each point. However, this method has obvious limitations in practical application: first, a large transformer substation or a power transmission line often has dozens or even hundreds of monitoring points, and establishing and training a prediction model for each point requires a huge amount of computing resources, resulting in low prediction efficiency and making it difficult to meet the real-time or high-frequency prediction requirements. Second, this method ignores the internal correlation and spatial correlation of settlement deformation between different monitoring points under the same power equipment foundation. Due to the continuity of geological conditions and load distribution, adjacent or similar geological unit monitoring points often show certain synergy or proportional relationship in settlement trend, and independent prediction models cannot effectively utilize this information, which may lead to deviation of the prediction results from the actual overall deformation law. SUMMARY

[0004] The present application provides a power equipment foundation settlement prediction method and system to solve the technical problem of requiring a huge amount of computing resources to establish and train a prediction model for each point.

[0005] In a first aspect, the present application provides a power equipment foundation settlement prediction method, comprising:

[0006] Obtaining settlement observation data of at least one monitoring point within a preset time period, and sorting each settlement observation data of the same monitoring point to obtain at least one settlement observation data sequence;

[0007] Determining the settlement correlation degree of each settlement observation data sequence, and dividing the at least one settlement observation data sequence according to the settlement correlation degree to obtain at least one settlement observation data sequence set;

[0008] obtain monitoring point position information corresponding to each subsidence observation data sequence in a certain subsidence observation data sequence set, and select a target subsidence observation data sequence in the certain subsidence observation data sequence set according to each monitoring point position information and by using a preset sequence selection rule;

[0009] input the target subsidence observation data sequence into a preset subsidence prediction model, and obtain a target subsidence prediction data corresponding to a target monitoring point from the subsidence prediction model;

[0010] correct the target subsidence prediction data according to other subsidence observation data sequences in the certain subsidence observation data sequence set, and obtain other subsidence prediction data corresponding to other monitoring points.

[0011] In a second aspect, the present application provides a power equipment foundation subsidence prediction system, comprising:

[0012] an obtaining module configured to obtain subsidence observation data of at least one monitoring point in a preset time period, and sort each subsidence observation data of the same monitoring point to obtain at least one subsidence observation data sequence;

[0013] a dividing module configured to determine subsidence correlation degrees of each subsidence observation data sequence, and divide the at least one subsidence observation data sequence according to each subsidence correlation degree to obtain at least one subsidence observation data sequence set;

[0014] an selecting module configured to obtain monitoring point position information corresponding to each subsidence observation data sequence in a certain subsidence observation data sequence set, and select a target subsidence observation data sequence in the certain subsidence observation data sequence set according to each monitoring point position information and by using a preset sequence selection rule;

[0015] an outputting module configured to input the target subsidence observation data sequence into a preset subsidence prediction model, and obtain a target subsidence prediction data corresponding to a target monitoring point from the subsidence prediction model;

[0016] a correcting module configured to correct the target subsidence prediction data according to other subsidence observation data sequences in the certain subsidence observation data sequence set, and obtain other subsidence prediction data corresponding to other monitoring points.

[0017] In a third aspect, an electronic device is provided, comprising at least one processor, and a memory connected to the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the power equipment foundation settlement prediction method of any one of the embodiments of the present application.

[0018] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the program instructions are executed by a processor to enable the processor to perform the steps of the power equipment foundation settlement prediction method of any one of the embodiments of the present application.

[0019] The power equipment foundation settlement prediction method and system of the present application can identify monitoring areas with similar or related settlement trends by calculating the settlement correlation of the settlement observation data sequences of each monitoring point and clustering, laying a foundation for subsequent collaborative prediction. Furthermore, by combining the spatial position information of the monitoring points, a circle is drawn with the two most distant points as the diameter, and the core point with the largest settlement is selected as the prediction target, ensuring that the selected target sequence can represent the overall settlement characteristics of the area and has significance and centrality, thereby improving the accuracy and reliability of the initial prediction. Finally, by using the accurate prediction results of the target point and correcting based on the historical settlement proportion relationship between the other points in the set and the target point, the settlement prediction data of all points in the set can be efficiently and accurately obtained. This method reduces the computational resource consumption of independent complex modeling for each monitoring point as much as possible, greatly improves the overall efficiency of large-scale, multi-point settlement prediction, ensures the prediction accuracy, and realizes efficient monitoring and early warning of the settlement status of the power equipment foundation, providing strong support for safe operation and maintenance decision-making of the equipment. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0021] Figure 1 A flowchart of a power equipment foundation settlement prediction method provided by an embodiment of the present application;

[0022] Figure 2 A structural block diagram of a power equipment foundation settlement prediction system provided by an embodiment of the present application;

[0023] Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0025] Referring to Figure 1 , a flowchart of a power equipment foundation settlement prediction method is shown.

[0026] As Figure 1 shown, the power equipment foundation settlement prediction method specifically includes the following steps:

[0027] In step S101, settlement observation data of at least one monitoring point in a preset time period is obtained, and each settlement observation data of the same monitoring point is sorted to obtain at least one settlement observation data sequence.

[0028] In this step, the monitoring point refers to a settlement observation point arranged on a power equipment foundation (such as a transformer foundation, a power distribution device building column foundation, a tower foundation, etc.). These points are usually preset in the foundation construction stage, such as a settlement observation marker. After obtaining the settlement observation data in the preset time period, each settlement observation data of the same monitoring point is sorted based on the time sequence to obtain at least one settlement observation data sequence.

[0029] In step S102, the settlement correlation degree of each settlement observation data sequence is determined, and each settlement observation data sequence is divided according to the settlement correlation degree to obtain at least one settlement observation data sequence set.

[0030] In this step, the settlement difference between two adjacent settlement observation data in a certain settlement observation data sequence is obtained, and each settlement difference is sorted based on the chronological order to obtain a certain settlement difference sequence corresponding to a certain settlement observation data sequence; the first settlement difference in the certain settlement difference sequence is defined as an initial settlement difference, and whether the target difference between the other settlement differences in the certain settlement difference sequence and the initial settlement difference is greater than a preset threshold value is judged, wherein the other settlement differences are any settlement difference in the certain settlement difference sequence except the initial settlement difference; if the target difference between at least one settlement difference and the initial settlement difference is not greater than the preset threshold value, the target difference corresponding to the at least one settlement difference is added, and the addition result is defined as a certain settlement correlation degree of the certain settlement difference sequence; if the difference between all settlement differences and the initial settlement difference is greater than the preset threshold value, the certain settlement correlation degree of the certain settlement difference sequence is directly defined as zero.

[0031] It should be noted that the settlement observation data sequences corresponding to each settlement correlation degree in the certain settlement correlation degree set are divided into the same settlement observation data sequence set to obtain at least one settlement observation data sequence set.

[0032] In one specific embodiment, assuming that there is a settlement observation data sequence Si= (s(t1), s(t2), s(t3),..., s(tn)) of the i-th monitoring point, the difference between two adjacent observation data in the sequence is calculated: Δdk=s(tk+1)-s(tk), where k =1, 2,..., n-1; the settlement difference sequence Di= (Δd1, Δd2, Δd3,..., Δdn-1) of the i-th monitoring point is obtained. This sequence reflects the settlement change amount of the point at different time intervals.

[0033] The first difference Δd1 in the settlement difference sequence Di is defined as the initial settlement difference. It represents the settlement rate in the early observation stage.

[0034] A threshold value ε is set. The threshold value is a key parameter, which can be determined according to historical data, engineering experience or settlement sensitivity (for example, ε=0.5mm). Its significance is to judge whether the subsequent settlement rate has deviated “significantly”.

[0035] All other differences Δdj (j=2, 3,..., n-1) in the settlement difference sequence Di except Δd1 are traversed, the absolute difference between each Δdj and the initial settlement difference Δd1, i.e. the target difference: δj=|Δdj-Δd1| is calculated, and each target difference δj is compared with the preset threshold value ε.

[0036] If there is at least one target difference δj not greater than ε (i.e. the settlement difference does not significantly deviate from the initial rate), then all δj satisfying this condition are added. The larger the added sum, the closer the settlement rate of the point is to the initial rate at most times, the more stable the settlement process, and the higher the correlation degree.

[0037] If all target differences δj are greater than ε, then the settlement correlation degree of the point is directly defined as 0. This indicates that the settlement rate of the point has undergone a drastic or continuous change in the later period, which is completely inconsistent with the initial trend, and the stability is poor, so it is considered that the correlation degree is zero.

[0038] In this embodiment, the settlement behaviors of multiple monitoring points under a large power equipment foundation (such as a substation) are not completely independent. They are subject to common geological conditions, foundation load distribution, and overall structural stiffness. Therefore, the settlement trends of the points naturally form several sets with similar patterns. By dividing by the settlement correlation degree, the data-driven method is used to automatically identify these sets with similar settlement behaviors, rather than subjectively grouping them by geographical distance. And when at least one settlement difference is not greater than a preset threshold, the target difference corresponding to at least one settlement difference is added, and the added result is defined as a settlement correlation degree of a certain settlement difference sequence, which means that even if there are individual mutation points (whose target difference will be much larger than ε) in the sequence caused by measurement errors or temporary external disturbances, as long as the settlement is stable in most time periods, these mutation points will not be counted in the correlation degree cumulative value, thereby avoiding the decisive influence of accidental noise on the evaluation result of the correlation degree, making the evaluation result more stable and reliable. By clustering by the settlement correlation degree, the entire process from the beginning of settlement to now of the points in the set can be as similar as possible (stable settlement or synchronous acceleration / deceleration). Then, their future settlement development is also likely to maintain this proportional relationship. Therefore, it is reasonable and reliable to correct the future prediction value with the historical proportion.

[0039] It should be noted that the settlement observation data sequences corresponding to the settlement correlation degrees of zero can be input into the subsequent settlement prediction model one by one for prediction, which can improve the prediction accuracy compared to the subsequent correction method.

[0040] In step S103, the position information of each monitoring point corresponding to each settlement observation data sequence in a certain settlement observation data sequence set is obtained, and a preset sequence selection rule is used to select a target settlement observation data sequence in the certain settlement observation data sequence set according to the position information of each monitoring point.

[0041] In this step, the monitoring point position information corresponding to each settlement observation data sequence in a certain settlement observation data sequence set is mapped into a preset two-dimensional coordinate system, and a circle is drawn with the distance between the first monitoring point and the second monitoring point as the diameter to obtain a certain closed area, wherein the first monitoring point and the second monitoring point are the two monitoring points farthest away from each other in the monitoring points corresponding to each settlement observation data sequence in a certain settlement observation data sequence set; taking the center of the certain closed area as a center point, it is judged whether the number of monitoring points of the to-be-determined monitoring points within a preset distance threshold from the center point is less than a preset number threshold; if not less than the preset number threshold, the to-be-determined settlement observation data sequence corresponding to at least one to-be-determined monitoring point is obtained, and the settlement difference sequences corresponding to each to-be-determined settlement observation data sequence are added to obtain each settlement amount; the to-be-determined settlement observation data sequence corresponding to the maximum settlement amount is defined as a certain target settlement observation data sequence.

[0042] It should be noted that the to-be-determined monitoring point is a monitoring point within a preset distance threshold from the center point, the to-be-determined settlement observation data sequence is a settlement observation data sequence obtained at the to-be-determined monitoring point, and the maximum settlement amount is the settlement amount with the largest value among the settlement amounts.

[0043] Further, if less than the preset number threshold, a certain target monitoring point closest to the center point is directly obtained, and a settlement observation data sequence corresponding to the certain target monitoring point is directly defined as a certain target settlement observation data sequence.

[0044] In one specific embodiment, a preset two-dimensional coordinate system is established. Typically, this is a plane rectangular coordinate system (for example, an independent coordinate system based on the construction site is adopted). The longitude and latitude or construction grid coordinates of all monitoring points in the current sequence set are accurately mapped into the coordinate system to obtain the two-dimensional coordinates of each monitoring point.

[0045] The Euclidean distance between all points in the set is calculated, and the two points farthest apart are found, denoted as the first monitoring point P1 and the second monitoring point P2.

[0046] A circular region is drawn with the line connecting P1 and P2 as the diameter. The center O of the circular region is the midpoint of the line connecting P1 and P2, and the radius R is half the distance between P1 and P2. And this circular region is a minimum "minimum enclosing circle" approximation that can cover all points. It defines the spatial distribution range of the current point group, and the center O represents the geometric center of the distribution range.

[0047] A preset distance threshold and a preset quantity threshold are set, the preset distance threshold is used to define a "core area", for example, a circular radius R of 1 / 3 or a fixed value (such as 50 meters) can be set. It indicates how far from the center of the circle is "central area", the preset quantity threshold is used to determine whether there is a monitoring point in the core area, for example, set to 1.

[0048] In step S104, the certain target settlement observation data sequence is input into a preset settlement prediction model, and the settlement prediction model outputs a certain target settlement prediction data corresponding to a certain target monitoring point.

[0049] In this step, the settlement prediction model can be obtained by training the LSTM network or the GRU network through a large amount of settlement monitoring data of historical projects, and the training process is a conventional iterative training method, so it will not be described here.

[0050] Specifically, it is assumed that in step S103, the sequence of monitoring point P3 is selected as the target sequence. The sequence is the cumulative settlement data of the past 100 days.

[0051] A model capable of effectively learning long-term dependencies is selected, such as LSTM. It is assumed that a pre-trained LSTM model has been saved on the server, and the structure is: input layer (receiving sequence of the past 30 days), 2 LSTM layers and fully connected output layer. The system loads this trained LSTM model (including its network structure and weight parameters) from storage.

[0052] Get the 100-day settlement sequence SP3 of point P3.

[0053] Normalization: Calculate the minimum and maximum values of SP3, and normalize the entire sequence to the [0, 1] interval.

[0054] Build input samples: Use the sliding window method. Use data from days 1-30 to predict day 31, data from days 2-31 to predict day 32,..., and finally generate 70 training samples. The last sample (days 71-100 data) will be used to truly predict the future (i.e. day 101).

[0055] Perform prediction: input the last sample (normalized days 71-100 data, shape (1, 30, 1)) into the loaded LSTM model.

[0056] The model performs forward propagation calculation and outputs a normalized predicted value (corresponding to the settlement of day 101).

[0057] Result post-processing and output: the normalized prediction value output by the model is denormalized using the previously recorded min and max values to obtain the real settlement prediction value (unit: millimeter).

[0058] Step S105, according to other settlement observation data sequences in the certain settlement observation data sequence set, the certain target settlement prediction data is corrected to obtain other settlement prediction data corresponding to other monitoring points.

[0059] In this step, a first settlement amount corresponding to a first settlement observation data sequence and a certain target settlement amount corresponding to the certain target settlement observation data sequence are obtained, wherein the first settlement observation data sequence is a settlement observation data sequence corresponding to a first monitoring point, and the first monitoring point is any monitoring point in the other monitoring points; a first ratio is obtained by calculating the ratio between the first settlement amount and the certain target settlement amount; the first ratio is multiplied by the settlement prediction value in the certain target settlement prediction data to obtain first settlement prediction data corresponding to the first monitoring point.

[0060] In summary, the method of the present application can identify monitoring areas with similar or related settlement trends by calculating the settlement correlation degree of each monitoring point settlement observation data sequence and clustering, laying a foundation for subsequent collaborative prediction. Furthermore, by combining the spatial position information of the monitoring points, a circle is drawn with the two most distant points as the diameter, and the core point with the largest settlement amount is selected as the prediction target, ensuring that the selected target sequence can represent the overall settlement characteristics of the area and has significance and centrality, thereby improving the accuracy and reliability of the initial prediction. Finally, by using the accurate prediction result of the target point and correcting based on the historical settlement amount proportion relationship between the other points in the set and the target point, the settlement prediction data of all points in the group can be efficiently and accurately obtained. This method reduces the computational resource consumption of independent complex modeling for each monitoring point as much as possible, greatly improves the overall efficiency of large-scale, multi-point settlement prediction, ensures the prediction accuracy, and realizes efficient monitoring and early warning of the settlement status of the power equipment foundation, providing strong support for equipment safe operation and maintenance decision-making.

[0061] Please refer to Figure 2 , which shows the structure block diagram of a power equipment foundation settlement prediction system of the present application.

[0062] As Figure 2 shown, the power equipment foundation settlement prediction system 200 includes an acquisition module 210, a division module 220, a selection module 230, an output module 240, and a correction module 250.

[0063] The obtaining module 210 is configured to obtain settlement observation data of at least one monitoring point within a preset time period, and sort each settlement observation data of the same monitoring point to obtain at least one settlement observation data sequence; the dividing module 220 is configured to determine a settlement correlation degree of each settlement observation data sequence, and divide the at least one settlement observation data sequence according to each settlement correlation degree to obtain at least one settlement observation data sequence set; the selecting module 230 is configured to obtain monitoring point position information corresponding to each settlement observation data sequence in a certain settlement observation data sequence set, and select a target settlement observation data sequence in the certain settlement observation data sequence set according to each monitoring point position information by using a preset sequence selection rule; and the output module 240 is configured to input the certain target settlement observation data sequence into a preset settlement prediction model, and the settlement prediction model outputs a certain target settlement prediction data corresponding to a certain target monitoring point; and the correction module 250 is configured to correct the certain target settlement prediction data according to other settlement observation data sequences in the certain settlement observation data sequence set to obtain other settlement prediction data corresponding to other monitoring points.

[0064] It should be understood that Figure 2 the modules described in the Figure 1 correspond to the respective steps in the methods described with reference to Figure 2 . Thus, the operations and features described above for the methods, and the corresponding technical effects, apply equally to the modules in , and will not be described again here.

[0065] In some embodiments, the present application also provides a computer readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the power equipment foundation settlement prediction method in any of the method embodiments described above.

[0066] As an implementation form, the computer readable storage medium of the present application stores computer executable instructions, which are configured to:

[0067] obtain settlement observation data of at least one monitoring point within a preset time period, and sort each settlement observation data of the same monitoring point to obtain at least one settlement observation data sequence;

[0068] determine a settlement correlation degree of each settlement observation data sequence, and divide the at least one settlement observation data sequence according to each settlement correlation degree to obtain at least one settlement observation data sequence set;

[0069] Obtain the location information of monitoring points corresponding to each settlement observation data sequence in a certain settlement observation data sequence set, and select a target settlement observation data sequence in the certain settlement observation data sequence set according to the location information of each monitoring point and a preset sequence selection rule.

[0070] The settlement observation data sequence of a certain target is input into a preset settlement prediction model, and the settlement prediction model outputs the settlement prediction data of a certain target corresponding to the monitoring point of the target.

[0071] The settlement prediction data for a certain target is corrected based on other settlement observation data sequences in the set of settlement observation data sequences to obtain other settlement prediction data corresponding to other monitoring points.

[0072] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the power equipment foundation settlement prediction system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely located relative to a processor, and this remote memory may be connected to the power equipment foundation settlement prediction system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0073] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the power equipment foundation settlement prediction method described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the power equipment foundation settlement prediction system. The output device 340 may include a display screen or other display device.

[0074] The electronic device can execute the method provided by the embodiments of the application, has the function modules and beneficial effects corresponding to the execution method. Technical details not described in detail in the embodiments can be referred to the method provided by the embodiments of the application.

[0075] As an implementation form, the electronic device is applied to a power equipment foundation settlement prediction system, and is used for a client, and includes at least one processor and a memory connected with the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0076] Obtain settlement observation data of at least one monitoring point in a preset time period, and sort each settlement observation data of the same monitoring point to obtain at least one settlement observation data sequence;

[0077] Determine a settlement correlation degree of each settlement observation data sequence, and divide the at least one settlement observation data sequence according to each settlement correlation degree to obtain at least one settlement observation data sequence set;

[0078] Obtain monitoring point position information corresponding to each settlement observation data sequence in a certain settlement observation data sequence set, and select a target settlement observation data sequence in the certain settlement observation data sequence set according to each monitoring point position information by using a preset sequence selection rule;

[0079] Input the certain target settlement observation data sequence into a preset settlement prediction model, and the settlement prediction model outputs a certain target settlement prediction data corresponding to a certain target monitoring point;

[0080] According to other settlement observation data sequences in the certain settlement observation data sequence set, the certain target settlement prediction data is corrected to obtain other settlement prediction data corresponding to other monitoring points.

[0081] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and the necessary general hardware platform, and of course, it can also be realized by hardware. Based on such understanding, the above technical solutions essentially or say the part of the prior art that makes a contribution can be embodied in the form of a software product, which can be stored in a computer readable storage medium such as ROM / RAM, magnetic disk, optical disc, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the method of each embodiment or some parts of the embodiment.

[0082] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A power equipment foundation settlement prediction method characterized by, The method comprises the following steps: obtaining settlement observation data of at least one monitoring point within a preset time period, and sorting each settlement observation data of the same monitoring point to obtain at least one settlement observation data sequence; determining the settlement correlation degree of each settlement observation data sequence, and dividing the at least one settlement observation data sequence according to the settlement correlation degree to obtain at least one settlement observation data sequence set, wherein the settlement correlation degree of each settlement observation data sequence comprises: obtaining the settlement difference between adjacent two settlement observation data in a certain settlement observation data sequence, and sorting each settlement difference according to the time sequence to obtain a certain settlement difference sequence corresponding to the certain settlement observation data sequence; defining the first settlement difference in the certain settlement difference sequence as an initial settlement difference, and determining whether the target difference between the other settlement differences in the certain settlement difference sequence and the initial settlement difference is greater than a preset threshold, wherein the other settlement differences are any settlement differences in the certain settlement difference sequence except the initial settlement difference; if the target difference between at least one settlement difference and the initial settlement difference is not greater than the preset threshold, the target difference corresponding to the at least one settlement difference is added, and the addition result is defined as a certain settlement correlation degree of the certain settlement difference sequence; if the difference between all settlement differences and the initial settlement difference is greater than the preset threshold, the certain settlement correlation degree of the certain settlement difference sequence is directly defined as zero; the division of the at least one settlement observation data sequence according to the settlement correlation degree to obtain the at least one settlement observation data sequence set comprises: dividing at least one settlement correlation degree in the same range into the same settlement correlation degree set to obtain at least one settlement correlation degree set; dividing the settlement observation data sequence corresponding to each settlement correlation degree in a certain settlement correlation degree set into the same settlement observation data sequence set to obtain at least one settlement observation data sequence set; obtaining the monitoring point position information corresponding to each settlement observation data sequence in a certain settlement observation data sequence set, and selecting a target settlement observation data sequence in the certain settlement observation data sequence set according to the preset sequence selection rule and the monitoring point position information; inputting the certain target settlement observation data sequence into a preset settlement prediction model, and the settlement prediction model outputs a certain target settlement prediction data corresponding to a certain target monitoring point; correcting the certain target settlement prediction data according to other settlement observation data sequences in the certain settlement observation data sequence set to obtain other settlement prediction data corresponding to other monitoring points.

2. The power equipment foundation settlement prediction method of claim 1, wherein the selection of a target settlement observation data sequence in the certain settlement observation data sequence set according to the preset sequence selection rule and the monitoring point position information comprises: Map the monitoring point position information corresponding to each subsidence observation data sequence in a certain subsidence observation data sequence set to a preset two-dimensional coordinate system, and draw a circle with the distance between the first monitoring point and the second monitoring point as the diameter to obtain a certain closed area, wherein the first monitoring point and the second monitoring point are the two monitoring points farthest away from each other among the monitoring points corresponding to each subsidence observation data sequence in the certain subsidence observation data sequence set; Take the center of the certain closed area as a center point, and determine whether the number of monitoring points of the to-be-determined monitoring points within a distance less than a preset distance threshold from the center point is less than a preset number threshold; If not, obtain the to-be-determined subsidence observation data sequence corresponding to the at least one to-be-determined monitoring point, and add the subsidence difference sequences corresponding to each to-be-determined subsidence observation data sequence to obtain each subsidence amount; Define the to-be-determined subsidence observation data sequence corresponding to the maximum subsidence amount as the certain target subsidence observation data sequence.

3. The method of claim 2, wherein, The certain target subsidence prediction data is corrected according to other subsidence observation data sequences in the certain subsidence observation data sequence set to obtain other subsidence prediction data corresponding to other monitoring points, including: Obtain a first subsidence amount corresponding to a first subsidence observation data sequence and a certain target subsidence amount corresponding to the certain target subsidence observation data sequence, wherein the first subsidence observation data sequence is a subsidence observation data sequence corresponding to a first monitoring point, and the first monitoring point is any monitoring point in the other monitoring points; Calculate the ratio between the first subsidence amount and the certain target subsidence amount to obtain a first ratio; Multiply the first ratio by the subsidence prediction value in the certain target subsidence prediction data to obtain first subsidence prediction data corresponding to the first monitoring point.

4. A power equipment foundation settlement prediction system characterized by, Including: The obtaining module is configured to obtain subsidence observation data of at least one monitoring point in a preset time period, and sort each subsidence observation data of the same monitoring point to obtain at least one subsidence observation data sequence; The division module is configured to determine the subsidence correlation degree of each subsidence observation data sequence, and divide the at least one subsidence observation data sequence according to each subsidence correlation degree to obtain at least one subsidence observation data sequence set, wherein the determination of the subsidence correlation degree of each subsidence observation data sequence includes: Obtain the subsidence difference between adjacent two subsidence observation data in a certain subsidence observation data sequence, and sort each subsidence difference based on the time sequence to obtain a certain subsidence difference sequence corresponding to the certain subsidence observation data sequence; Define the first subsidence difference in the certain subsidence difference sequence as an initial subsidence difference, and determine whether the target difference between the other subsidence difference in the certain subsidence difference sequence and the initial subsidence difference is greater than a preset threshold, wherein the other subsidence difference is any subsidence difference in the certain subsidence difference sequence except the initial subsidence difference; if a target difference value between at least one settlement difference and the initial settlement difference is not greater than a preset threshold value, a target difference value corresponding to the at least one settlement difference is added, and an addition result is defined as a certain settlement correlation degree of the certain settlement difference sequence; if all the difference values between the settlement differences and the initial settlement difference are greater than the preset threshold value, the certain settlement correlation degree of the certain settlement difference sequence is directly defined as zero; the dividing the at least one settlement observation data sequence according to the respective settlement correlation degrees to obtain the at least one settlement observation data sequence set comprises: dividing the at least one settlement correlation degree in the same range to the same settlement correlation degree set to obtain the at least one settlement correlation degree set; dividing the settlement observation data sequence corresponding to the respective settlement correlation degrees in the certain settlement correlation degree set to the same settlement observation data sequence set to obtain the at least one settlement observation data sequence set; the selecting module is configured to obtain monitoring point position information corresponding to the respective settlement observation data sequences in the certain settlement observation data sequence set, and select a certain target settlement observation data sequence in the certain settlement observation data sequence set according to the respective monitoring point position information and a preset sequence selection rule; the output module is configured to input the certain target settlement observation data sequence into a preset settlement prediction model, and the settlement prediction model outputs a certain target settlement prediction data corresponding to a certain target monitoring point; the correction module is configured to correct the certain target settlement prediction data according to other settlement observation data sequences in the certain settlement observation data sequence set to obtain other settlement prediction data corresponding to other monitoring points.

5. An electronic device, comprising: comprise: at least one processor and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 3.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1 to 3.

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