Dam monitoring data trend anomaly identification method and system and electronic equipment

Abstract

The invention relates to a dam monitoring data trend anomaly recognition method and system and electronic equipment, and the method comprises the steps: obtaining the monitoring data of a dam, and calculating a first deformation trend component of the dam based on the monitoring data through a statistical regression method; updating pre-acquired parameters of a residual neural network according to the monitoring data, and determining a second deformation trend component of the dam based on the updated parameters; and calculating the correlation between the first deformation trend component and the second deformation trend component, and determining the abnormal trend in the monitoring data based on the difference between the first deformation trend component and the second deformation trend component through a clustering algorithm in response to the fact that the correlation is smaller than a preset threshold value.

Description

Technical Field

[0001] The present application relates to the field of dam abnormality monitoring, and in particular to a method, system and electronic equipment for identifying abnormal trends in dam monitoring data. Background Art

[0002] Dam monitoring data is important information for diagnosing the health status of the dam. Identifying trend anomalies in monitoring data is key to preventing dam safety issues. However, dam monitoring data is usually affected by environmental changes.

[0003] At present, the trend analysis of dam monitoring data mainly identifies data anomalies by constructing regression models or reconstruction models and analyzing the residuals of measured data and estimated data results. The above two methods usually require that the measured data points meet a certain regression error or reconstruction residual size before they are identified as abnormal. However, when the data undergoes a slowly changing abnormal process, due to the small estimated residuals of the data in the initial stage, it is difficult to identify the abnormal trend process directly using traditional methods. It can only be identified when the data has a large anomaly, and the potential risks to dam safety cannot be discovered in time.

[0004] Existing technologies are unable to identify abnormal trends in dam monitoring data in a timely and accurate manner. Summary of the invention

[0005] The embodiments of the present application provide a method, system and electronic device for identifying abnormal trends in dam monitoring data, so as to at least solve the problem in the related art that abnormal trends in dam monitoring data cannot be identified in a timely and accurate manner.

[0006] In a first aspect, an embodiment of the present application provides a method for identifying abnormal trends in dam monitoring data, comprising:

[0007] Acquire monitoring data of the dam, and calculate a first deformation trend component of the dam based on the monitoring data by using a statistical regression method;

[0008] updating the parameters of the pre-acquired residual neural network according to the monitoring data, and determining the second deformation trend component of the dam based on the updated parameters;

[0009] The correlation between the first deformation trend component and the second deformation trend component is calculated, and in response to the correlation being less than a preset threshold, an abnormal trend in the monitoring data is determined based on a difference between the first deformation trend component and the second deformation trend component through a clustering algorithm.

[0010] In one embodiment, the monitoring data includes dam monitoring data, monitoring time and upstream water level, and updating the pre-acquired parameters of the residual neural network according to the monitoring data includes:

[0011] Determining a prediction result based on the monitoring time and the upstream water level by a pre-acquired residual neural network;

[0012] The parameters of the residual neural network are updated according to the dam monitoring data and the prediction results.

[0013] In one embodiment, determining the prediction result includes:

[0014] The prediction result is divided into a period component and a trend component, and the prediction result is calculated as follows:

[0015]

[0016] Among them, X output Indicates the preset result, W c represents the weight of the periodic component, represents the periodic component, W t represents the weight of the trend component, Represents the trend component.

[0017] In one embodiment, determining the second deformation trend component includes:

[0018]

[0019] Among them, δ θ2 represents the second deformation trend component, W t represents the weight of the trend component, represents the trend component, which is calculated as follows:

[0020]

[0021]

[0022] in, These are intermediate results. are weight matrices, are all deviation vectors, i = 1, 2, 3, j = 1, 2, 3, relu (*) represents the relu activation function, represents an input parameter, and the input parameter is determined based on the monitoring time, σ(*) represents the Sigmod activation function, and hardtanh(*) represents the hardtanh activation function.

[0023] In one embodiment, the calculating the correlation between the first deformation trend component and the second deformation trend component includes:

[0024]

[0025] Among them, δθ1 represents the first deformation trend component, δ θ2 represents the second deformation trend component, and E(*) represents the mean calculation.

[0026] In one embodiment, the determining the abnormal trend in the monitoring data based on the difference between the first deformation trend component and the second deformation trend component by using a clustering algorithm includes:

[0027] calculating a difference between the first deformation trend component and the second deformation trend component, and dividing the difference into two categories by a clustering algorithm;

[0028] Abnormal data is determined from the two categories according to the quantity of each category, and it is determined that the dam monitoring data corresponding to the abnormal data has an abnormal trend.

[0029] In one embodiment, the calculating the first deformation trend component of the dam based on the monitoring data by using a statistical regression method comprises:

[0030] δ=δ H +δ T +δ θ1

[0031]

[0032] δ θ1 =c1θ+c2lnθ

[0033]

[0034] Where, δ represents the dam monitoring data, δ H represents the deformation caused by water level change, δ T Indicates deformation caused by temperature change, δ θ1 represents the deformation caused by time change, H represents the upstream water level, a i represents the coefficient to be fitted, n represents the quantity, c1 and c2 are both time coefficients, t represents the monitoring time, and the monitoring time represents the number of days from the current time to the start time of the monitoring data, θ is equal to t / 100, and b 1i and b 2i are statistical coefficients, δ θ1 represents the first deformation trend component.

[0035] In one embodiment, the method further includes: in response to the correlation being greater than or equal to a preset threshold, determining that the trend of the monitoring data is normal.

[0036] In a second aspect, an embodiment of the present application provides a system for identifying abnormal trends in dam monitoring data, including:

[0037] The first trend module is used to obtain monitoring data of the dam and calculate the first deformation trend component of the dam based on the monitoring data by using a statistical regression method;

[0038] A second trend module: used for updating the parameters of the pre-acquired residual neural network according to the monitoring data, and determining the second deformation trend component of the dam based on the updated parameters;

[0039] Trend judgment module: used to calculate the correlation between the first deformation trend component and the second deformation trend component, and in response to the correlation being less than a preset threshold, determine the abnormal trend in the monitoring data based on the difference between the first deformation trend component and the second deformation trend component through a clustering algorithm.

[0040] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for identifying abnormal trends in dam monitoring data as described in the first aspect above is implemented.

[0041] The embodiments of the present application provide a method, system and electronic device for identifying abnormal trends in dam monitoring data, which have at least the following technical effects.

[0042] The present application extracts the first deformation trend component in the dam body deformation through a statistical model, extracts the second deformation trend component in the dam monitoring data through a residual neural network model, and then implements the screening of abnormal processes through correlation analysis between the first deformation trend component and the second deformation trend component. After obtaining the preliminary abnormal process, the trend component is clustered for further abnormal process identification, thereby more timely and accurately identifying the abnormal trend in the dam monitoring data, which is beneficial to ensuring the safety and stability of the dam.

[0043] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0045] Figure 1 It is a flow chart of a method for identifying abnormal trends in dam monitoring data provided by an embodiment of the present application;

[0046] Figure 2 is a schematic diagram of residual distribution using a traditional statistical model method according to an exemplary embodiment;

[0047] Figure 3 is a schematic diagram of anomaly identification results of monitoring data obtained by using the method of the present application according to an exemplary embodiment;

[0048] Figure 4 is a schematic diagram showing a comparison of trend component calculation results obtained by using the method of the present application and the statistical model method according to an exemplary embodiment;

[0049] Figure 5 It is a structural schematic diagram of a dam monitoring data trend anomaly identification system according to an embodiment of the present application;

[0050] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0052] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0053] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0054] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0055] In a first aspect, an embodiment of the present application provides a method for identifying abnormal trends in dam monitoring data. Figure 1 : is a flow chart of a method for identifying abnormal trends in dam monitoring data provided by an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0056] Step S101, acquiring monitoring data of the dam, and calculating a first deformation trend component of the dam based on the monitoring data by using a statistical regression method.

[0057] Optionally, the monitoring data includes dam monitoring data, water level upstream of the dam body and monitoring time. The dam monitoring data can be expressed as z = {t, δ}, where δ = (δ1, δ2, ...δ n ) represents the acquisition of the collected dam monitoring data vectors in chronological order, t=(t1, t2, ...t n ) represents the n moments corresponding to the dam monitoring data. The collected dam monitoring data are normalized to convert data of different ranges into the same standard range.

[0058] In one example, calculating the first deformation trend component in step S101 includes:

[0059] δ=δ H +δ T +δθ1

[0060] δ θ1 =c1θ+c2lnθ

[0061]

[0062] Among them, δ represents the dam monitoring data, δ H represents the deformation caused by water level change, δ T Indicates deformation caused by temperature change, δ θ1 represents the deformation caused by time change, H represents the upstream water level, a i represents the coefficient to be fitted, n represents the number, and according to experience, n is usually 3 or 4, c1 and c2 are both time coefficients, t represents the monitoring time, and the monitoring time represents the number of days from the current time to the start time of the monitoring data, θ is equal to t / 100, b 1i and b 2i are statistical coefficients, δ θ1 Represents the first deformation trend component.

[0063] Optionally, the dam deformation represented by the dam monitoring data includes deformation caused by water level change, deformation caused by temperature change and deformation caused by time change. The present application calculates the deformation caused by time change, i.e., the first deformation trend component, by a regression method. The coefficients in the above formula are calculated by the least squares method according to the monitoring data, thereby calculating the first deformation trend component of the dam monitoring data at different times. The trend component in the regression method is an irreversible component, which is used to extract the long-term change trend in the dam body deformation for subsequent analysis.

[0064] Step S102, updating the parameters of the residual neural network acquired in advance according to the monitoring data, and determining the second deformation trend component of the dam based on the updated parameters.

[0065] Optionally, the residual neural network includes but is not limited to any one of ResNet, ResNeXt and DenseNet. The residual neural network is trained according to the historical monitoring data to obtain a pre-acquired residual neural network. The parameters of the residual neural network are updated by the back propagation method for the real-time monitoring data, so as to calculate the second deformation trend component according to the updated parameters.

[0066] In one example, the monitoring data includes dam monitoring data, monitoring time, and upstream water level, and step S102 includes:

[0067] Step S201, determine the prediction result based on the monitoring time and the upstream water level through the pre-acquired residual neural network. Optionally, decompose the prediction result into a period component and a trend component to facilitate the calculation of the second deformation trend component. The second deformation trend component is a reversible trend component.

[0068] In one example, determining the prediction result in step S201 includes: dividing the prediction result into a period component and a trend component, and the prediction result is calculated as follows:

[0069]

[0070] Among them, X output Indicates the preset result, W c represents the weight of the periodic component, represents the periodic component, W t represents the weight of the trend component, Represents the trend component.

[0071] The periodic component is calculated as follows:

[0072]

[0073] in, These are intermediate results. are weight matrices, are all deviation vectors, i = 1, 2, 3, j = 1, 2, 3, relu (*) represents the relu activation function, σ (*) represents the sigmoid activation function, Represents the periodic component. Represents input parameters. The input parameters are input matrices determined based on the upstream water level and monitoring time, including H, H2, H 3 , and The input matrix is ​​constructed, where H represents the upstream water level, t represents the monitoring time, the monitoring time represents the number of days from the current time to the start time of the monitoring data, and a=1,2.

[0074] Trend Component The calculation method is as follows:

[0075]

[0076] in, These are intermediate results. are weight matrices, are all deviation vectors, i=1,2,3, j=1,2,3, relu(*) represents the relu activation function, σ(*) represents the sigmoid activation function, and hardtanh(*) represents the hardtanh activation function. represents input parameters, which are determined based on the monitoring time, and specifically include θ and ln(θ), where θ=t / 100, and t represents the number of days from the current time to the start time of the monitoring data.

[0077] In this way, the Sigmod and Hardtan activation functions are used simultaneously to extract the reversible trend components in the dam monitoring data, making the residual network model more sensitive to both the slow-changing and sudden-changing characteristics of the data, thereby better discovering the changing trend of the data.

[0078] Step S202, updating the parameters of the residual neural network according to the dam monitoring data and the prediction results. Optionally, updating the parameters of the residual neural network by back propagation according to the error between the dam monitoring data and the prediction results.

[0079] In one example, determining the second deformation trend component in step S102 includes:

[0080]

[0081] Among them, δ θ2 represents the second deformation trend component, W t In this way, the second deformation trend component is determined according to the updated residual neural network parameters. Compared with the traditional algorithm, the residual network used in this application can better discover the change trend of the data and is more sensitive to the slow-changing and sudden-changing characteristics of the data.

[0082] Step S103, calculating the correlation between the first deformation trend component and the second deformation trend component, and in response to the correlation being less than a preset threshold, determining the abnormal trend in the monitoring data based on the difference between the first deformation trend component and the second deformation trend component through a clustering algorithm.

[0083] Optionally, in response to the correlation between the first deformation trend component and the second deformation trend component being greater than or equal to a preset threshold, the trend of the monitoring data is determined to be normal. When the correlation between the first deformation trend component and the second deformation trend component is less than a preset threshold. The preset threshold can be set according to actual needs, and the preset threshold in this application is usually between 0.8 and 0.9. The clustering algorithm includes but is not limited to any one of mean clustering, hierarchical clustering and DBSCAN clustering algorithms.

[0084] In this way, the correlation analysis of the first deformation trend component and the second deformation trend component is performed to identify abnormal trends and quantify the degree of abnormality in the trend process, so as to timely discover data anomalies and ensure the safety and stability of the dam.

[0085] In one example, step S103 includes:

[0086]

[0087] Among them, δ θ1 represents the first deformation trend component, δ θ2 represents the second deformation trend component, E(*) represents mean calculation, and optionally, in this example, K-means mean clustering method is used to perform correlation calculation.

[0088] In one example, step S103 includes: calculating the difference between the first deformation trend component and the second deformation trend component, dividing the difference into two categories by a clustering algorithm, determining abnormal data from the two categories according to the number of each category, and determining that the dam monitoring data corresponding to the abnormal data has an abnormal trend.

[0089] Alternatively, the difference may be expressed as: where δ θ1 represents the first deformation trend component, δ θ2 Represents the second deformation trend component. θ Clustering is performed, and d θ All data points in the dataset are divided into two categories, and the category with less data is determined as abnormal data. The clustering algorithm includes but is not limited to any one of mean clustering, hierarchical clustering and DBSCAN clustering algorithm.

[0090] In this way, the abnormal process screening is achieved through the correlation analysis of the first deformation trend component and the second deformation trend component. After obtaining the preliminary abnormal process, further abnormal process identification can be carried out through clustering analysis of the trend components, which makes it more sensitive to the slowly changing abnormal process of the data, thereby identifying the abnormal trend in the dam monitoring data more timely and accurately.

[0091] The dam monitoring data trend anomaly identification method proposed in this application is compared with the traditional statistical model method. Figure 2 is a schematic diagram of residual distribution using a traditional statistical model method according to an exemplary embodiment, such as Figure 2 As shown in the figure, although the traditional statistical model method has a large residual near the abnormal process, it fails to identify the abnormal data process due to the small overall residual. Figure 3 is a schematic diagram of anomaly identification results of monitoring data obtained by using the method of the present application according to an exemplary embodiment, such as Figure 3 As shown, the dam monitoring data trend anomaly identification method proposed in this application can better identify the abnormal process of the data, especially the method effectively identifies the stage of rapid increase of the monitoring data in the second half. Figure 4 FIG. 1 is a schematic diagram showing a comparison of trend component calculation results obtained by using the method of the present application and the statistical model method according to an exemplary embodiment. Figure 4As shown, the dam monitoring data trend anomaly identification method proposed in this application can identify more trend anomaly data and has a better identification effect, which is conducive to timely warning dam safety management personnel of anomalies in the data, thereby ensuring the safety and stability of the dam.

[0092] In summary, the present application extracts the long-term change trend (first deformation trend component) in the dam body deformation through a statistical model, and uses Sigmod and Hardtan activation functions simultaneously in the residual neural network model to extract the reversible trend component (second deformation trend component) in the dam monitoring data, so that the residual network model can better discover the change trend of the data, and is more sensitive to the slow-changing characteristics and mutation characteristics of the data. Then, the abnormal process is screened through the correlation analysis of the first deformation trend component and the second deformation trend component. After obtaining the preliminary abnormal process, the trend component is clustered for further abnormal process identification, so as to more timely and accurately identify the abnormal trend in the dam monitoring data, which is conducive to ensuring the safety and stability of the dam.

[0093] In a second aspect, the embodiment of the present application provides a system for identifying abnormal trends in dam monitoring data. Figure 5 is a structural diagram of a dam monitoring data trend anomaly identification system according to an embodiment of the present application, such as Figure 5 As shown, the system includes:

[0094] The first trend module 100 is used to obtain monitoring data of the dam and calculate the first deformation trend component of the dam based on the monitoring data by using a statistical regression method.

[0095] The second trend module 200 is used to update the parameters of the pre-acquired residual neural network according to the monitoring data, and determine the second deformation trend component of the dam based on the updated parameters.

[0096] Trend judgment module 300: used to calculate the correlation between the first deformation trend component and the second deformation trend component, and in response to the correlation being less than a preset threshold, determine the abnormal trend in the monitoring data based on the difference between the first deformation trend component and the second deformation trend component through a clustering algorithm.

[0097] In one example, the second trend module 200 includes:

[0098] Prediction unit: used to determine the prediction result based on the monitoring time and upstream water level through the pre-acquired residual neural network.

[0099] Update unit: used to update the parameters of the residual neural network according to the dam monitoring data and prediction results.

[0100] In one example, the prediction unit includes: dividing the prediction result into a period component and a trend component, and the prediction result is calculated as follows:

[0101]

[0102] Among them, X output Indicates the preset result, W c represents the weight of the periodic component, represents the periodic component, W t represents the weight of the trend component, Represents the trend component.

[0103] In one example, determining the second deformation trend component in the second trend module 200 includes:

[0104]

[0105] Among them, δ θ2 represents the second deformation trend component, W t represents the weight of the trend component, Represents the trend component, which is calculated as follows:

[0106]

[0107] in, These are intermediate results. are weight matrices, are all deviation vectors, i = 1, 2, 3, j = 1, 2, 3, relu (*) represents the relu activation function, Represents input parameters, which are determined based on the monitoring time. σ(*) represents the Sigmod activation function, and hardtanh(*) represents the hardtanh activation function.

[0108] In one example, the trend determination module 300 includes:

[0109]

[0110] Among them, δ θ1 represents the first deformation trend component, δ θ2 represents the second deformation trend component, and E(*) represents the mean calculation.

[0111] In one example, the trend determination module 300 includes:

[0112] It is used to calculate the difference between the first deformation trend component and the second deformation trend component, and divide the difference into two categories through a clustering algorithm; determine abnormal data from the two categories according to the number of each category, and determine whether the dam monitoring data corresponding to the abnormal data has an abnormal trend.

[0113] In one example, the first trend module 100 includes:

[0114] δ=δ H +δ T +δ θ1

[0115] δ θ1 =c1θ+c2lnθ

[0116]

[0117] Among them, δ represents the dam monitoring data, δ H represents the deformation caused by water level change, δ T Indicates deformation caused by temperature change, δ θ1 represents the deformation caused by time change, H represents the upstream water level, a i represents the coefficient to be fitted, n represents the quantity, c1 and c2 are both time coefficients, t represents the monitoring time, which represents the number of days from the current time to the start time of the monitoring data, θ is equal to t / 100, b 1i and b 2i are statistical coefficients, δ θ1 Represents the first deformation trend component.

[0118] In one example, the system further includes: in response to the correlation being greater than or equal to a preset threshold, determining that the trend of the monitoring data is normal.

[0119] In summary, the present application extracts the long-term change trend (first deformation trend component) in the dam body deformation through a statistical model, and uses Sigmod and Hardtan activation functions simultaneously in the residual neural network model to extract the reversible trend component (second deformation trend component) in the dam monitoring data, so that the residual network model can better discover the change trend of the data, and is more sensitive to the slow-changing characteristics and mutation characteristics of the data. Then, the abnormal process is screened through the correlation analysis of the first deformation trend component and the second deformation trend component. After obtaining the preliminary abnormal process, the trend component is clustered for further abnormal process identification, so as to more timely and accurately identify the abnormal trend in the dam monitoring data, which is conducive to ensuring the safety and stability of the dam.

[0120] In a third aspect, an embodiment of the present application provides an electronic device, Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for identifying abnormal trend of dam monitoring data provided in the first aspect is implemented. Figure 6 The electronic device 60 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0121] The electronic device 60 may be in the form of a general-purpose computing device, for example, it may be a server device. The components of the electronic device 60 may include but are not limited to: at least one processor 61, at least one memory 62, and a bus 63 connecting different system components (including the memory 62 and the processor 61).

[0122] The bus 63 includes a data bus, an address bus, and a control bus.

[0123] The memory 62 may include a volatile memory, such as a random access memory (RAM) 621 and / or a cache memory 622 , and may further include a read-only memory (ROM) 623 .

[0124] The memory 62 may also include a program / utility 625 having a set (at least one) of program modules 624, such program modules 624 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0125] The processor 61 executes various functional applications and data processing by running the computer programs stored in the memory 62, such as the method for identifying abnormal trends in dam monitoring data provided in the first aspect of the present application.

[0126] The electronic device 60 may also communicate with one or more external devices 64 (e.g., keyboards, pointing devices, etc.). Such communication may be performed via an input / output (I / O) interface 65. Furthermore, the model-generated device 60 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 66. As shown, the network adapter 66 communicates with other modules of the model-generated device 60 via a bus 63. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the model-generated device 60, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0127] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0128] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0129] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for identifying abnormal trends in dam monitoring data, characterized in that: include: Acquire monitoring data of the dam, and calculate a first deformation trend component of the dam based on the monitoring data by using a statistical regression method; updating the parameters of the pre-acquired residual neural network according to the monitoring data, and determining the second deformation trend component of the dam based on the updated parameters; The correlation between the first deformation trend component and the second deformation trend component is calculated, and in response to the correlation being less than a preset threshold, an abnormal trend in the monitoring data is determined based on a difference between the first deformation trend component and the second deformation trend component through a clustering algorithm.

2. The method for identifying abnormal trend of dam monitoring data according to claim 1, characterized in that: The monitoring data includes dam monitoring data, monitoring time and upstream water level, and the updating of the pre-acquired parameters of the residual neural network according to the monitoring data includes: Determining a prediction result based on the monitoring time and the upstream water level by a pre-acquired residual neural network; The parameters of the residual neural network are updated according to the dam monitoring data and the prediction results.

3. The method for identifying abnormal trend of dam monitoring data according to claim 2, characterized in that: Determining the prediction result includes: The prediction result is divided into a period component and a trend component, and the prediction result is calculated as follows: Among them, X output Indicates the preset result, W c represents the weight of the periodic component, represents the periodic component, W t represents the weight of the trend component, Represents the trend component.

4. The method for identifying abnormal trend of dam monitoring data according to claim 3, characterized in that: Determining the second deformation trend component includes: Among them, δ θ2 represents the second deformation trend component, W t represents the weight of the trend component, represents the trend component, which is calculated as follows: in, These are intermediate results. are weight matrices, are all deviation vectors, i = 1, 2, 3, j = 1, 2, 3, relu (*) represents the relu activation function, represents an input parameter, and the input parameter is determined based on the monitoring time, σ(*) represents the Sigmod activation function, and hardtanh(*) represents the hardtanh activation function.

5. The method for identifying abnormal trend of dam monitoring data according to claim 1, characterized in that: The calculating the correlation between the first deformation trend component and the second deformation trend component includes: Among them, δ θ1 represents the first deformation trend component, δ θ2 represents the second deformation trend component, and E(*) represents the mean calculation.

6. The method for identifying abnormal trend of dam monitoring data according to claim 2, characterized in that: The determining the abnormal trend in the monitoring data based on the difference between the first deformation trend component and the second deformation trend component by using a clustering algorithm includes: calculating a difference between the first deformation trend component and the second deformation trend component, and dividing the difference into two categories by a clustering algorithm; Abnormal data is determined from the two categories according to the quantity of each category, and it is determined that the dam monitoring data corresponding to the abnormal data has an abnormal trend.

7. The method for identifying abnormal trend of dam monitoring data according to claim 2, characterized in that: The calculating the first deformation trend component of the dam based on the monitoring data by using a statistical regression method comprises: d=d H +d T +d θ1 d θ1 =c1θ+c2lnθ Among them, δ represents the dam monitoring data, δ H represents the deformation caused by water level change, δ T Indicates deformation caused by temperature change, δ θ1 represents the deformation caused by time change, H represents the upstream water level, a i represents the coefficient to be fitted, n represents the quantity, c1 and c2 are both time coefficients, t represents the monitoring time, and the monitoring time represents the number of days from the current time to the start time of the monitoring data, θ is equal to t / 100, and b 1i and b 2i are statistical coefficients, δ θ1 represents the first deformation trend component.

8. The method for identifying abnormal trend of dam monitoring data according to claim 1, characterized in that: The method further comprises: In response to the correlation being greater than or equal to a preset threshold, it is determined that the trend of the monitoring data is normal.

9. A dam monitoring data trend anomaly identification system, characterized in that: include: The first trend module is used to obtain monitoring data of the dam and calculate the first deformation trend component of the dam based on the monitoring data by using a statistical regression method; A second trend module: used for updating the parameters of the pre-acquired residual neural network according to the monitoring data, and determining the second deformation trend component of the dam based on the updated parameters; Trend judgment module: used to calculate the correlation between the first deformation trend component and the second deformation trend component, and in response to the correlation being less than a preset threshold, determine the abnormal trend in the monitoring data based on the difference between the first deformation trend component and the second deformation trend component through a clustering algorithm.

10. An electronic device, characterized in that: include Memory, processor, and A computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for identifying abnormal trends in dam monitoring data as described in any one of claims 1 to 8 is implemented.

Images