Reservoir landslide creep deformation analysis method and device and electronic equipment

Abstract

The embodiment of the invention discloses a reservoir landslide creep deformation analysis method and device and electronic equipment. The method comprises the following steps: firstly, determining each target deformation identification thread unit in each deformation identification thread unit, and then respectively determining each knowledge fragment vector corresponding to landslide creep deformation data according to each target deformation identification thread unit. And then, determining a correlation coefficient between each preset type label and the knowledge fragment vector set, further, determining a target type label corresponding to the landslide creep deformation data based on each correlation coefficient, and finally, determining a landslide creep deformation analysis result. The knowledge fragment vectors are introduced through the deformation identification thread unit, and the category labels corresponding to the landslide creep deformation data are determined according to the correlation coefficients between the preset category labels and the knowledge fragment vector set, so that the purpose of automatically analyzing the landslide creep deformation data of the reservoir area is achieved, the accuracy of the analysis result is improved, and the analysis efficiency is improved. And the requirement of predicting the landslide risk in time is met.

Description

Technical Field

[0001] One or more embodiments of the present specification relate to the field of intelligent measurement and control technology, and in particular, to a method, device and electronic equipment for analyzing creep deformation of landslides in reservoir areas. Background Art

[0002] Landslide is a common geological disaster. Due to various factors, the strength of the rock and soil in the slope of the reservoir area gradually decreases or the shear stress inside the slope continues to increase, which destroys the stability of the slope. The weaker rock mass in the slope first deforms because the shear strength is less than the shear stress, and the creep deformation of the landslide in the reservoir area will lead to landslide. Therefore, to judge the possibility of landslide, the important reference indicator is the creep deformation. However, in the actual analysis process, manual identification methods are often used. Since the creep deformation of landslides in the reservoir area is a slow process, the manual identification method often leads to inaccurate analysis and judgment results due to visual fatigue, and it is impossible to predict landslide risks in a timely manner. Summary of the invention

[0003] The embodiments of this specification provide a method, device and electronic equipment for analyzing creep deformation of landslide in a reservoir area, and the technical solution thereof is as follows: In a first aspect, an embodiment of this specification provides a method for analyzing creep deformation of a landslide in a reservoir area, the method comprising: Determine at least one target deformation recognition thread unit among the deformation recognition thread units based on the analysis instruction; Determine the knowledge fragment vectors corresponding to the landslide creep deformation data according to the target deformation recognition thread units; Determine the correlation coefficient between each preset category label and the knowledge fragment vector set based on the mean algorithm, the preset category label includes a risk-free label, a low-risk label, a medium-risk label and a high-risk label, and the knowledge fragment vector set is composed of each of the knowledge fragment vectors; The target category label corresponding to the landslide creep deformation data is determined based on each of the correlation coefficients, and the landslide creep deformation analysis result is determined according to the target category label, and the landslide creep deformation analysis result includes no risk, low risk, medium risk and high risk.

[0004] In a second aspect, a reservoir area landslide creep deformation analysis device is provided, the device comprising: A determination module, configured to determine at least one target deformation recognition thread unit among the deformation recognition thread units based on the analysis instruction; A vector module, used for respectively determining each knowledge segment vector corresponding to the landslide creep deformation data according to each of the target deformation identification thread units; An association module, configured to determine the association coefficients between each preset type of label and the knowledge fragment vector set based on the mean algorithm, where the preset type of labels includes a risk-free label, a low-risk label, a medium-risk label, and a high-risk label, and the knowledge fragment vector set is composed of each of the knowledge fragment vectors; An analysis module, configured to determine the target type of label corresponding to the landslide creep deformation data based on each of the association coefficients, and determine the landslide creep deformation analysis result according to the target type of label, where the landslide creep deformation analysis result includes risk-free, low-risk, medium-risk, and high-risk.

[0005] In a third aspect, an electronic device is provided, including a device processor and a memory; The device processor is connected to the memory; The memory is configured to store executable program code; The device processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any possible implementation manner of the first aspect.

[0006] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer or a device processor, the computer or the device processor is caused to execute the method provided in the first aspect or any possible implementation manner of the first aspect.

[0007] The beneficial effects brought by the technical solutions provided in some embodiments of this specification at least include: First, determine each target deformation recognition thread unit in each deformation recognition thread unit based on the analysis instruction, and then determine each knowledge fragment vector corresponding to the landslide creep deformation data according to each target deformation recognition thread unit. Next, determine the association coefficients between each preset type of label and the knowledge fragment vector set based on the mean algorithm, and further, determine the target type of label corresponding to the landslide creep deformation data based on each association coefficient. Finally, determine the landslide creep deformation analysis result according to the target type of label. By introducing knowledge fragment vectors through the deformation recognition thread unit and determining the type of label corresponding to the landslide creep deformation data according to the association coefficients between each preset type of label and the knowledge fragment vector set, the purpose of automatically analyzing the landslide creep deformation data of the reservoir area is achieved, the accuracy of the analysis result is improved, and the requirement of timely predicting the landslide risk is met. Description of the Drawings

[0008] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0009] Figure 1 It is a flowchart of a method for analyzing the creep deformation of a landslide in a reservoir area provided by an embodiment of this specification; Figure 2 It is a schematic structural diagram of a device for analyzing the creep deformation of a landslide in a reservoir area provided by an embodiment of this specification; Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of this specification. Detailed implementation manners

[0010] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application.

[0011] The terms "first", "second", "third", etc. in the description, claims and accompanying drawings of this specification are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0012] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes can be made to the functions and arrangements of the described elements without departing from the scope of the content of this specification. Each example can appropriately omit, substitute or add various processes or components. For example, the described method can be executed in a different order from the described order, and various steps can be added, omitted or combined. In addition, the features described in some examples can be combined into other examples.

[0013] Please refer to Figure 1 , Figure 1 which shows an overall flowchart of a method for analyzing the creep deformation of a landslide in a reservoir area provided by an embodiment of this specification.

[0014] As Figure 1 shown, the method for analyzing the creep deformation of a landslide in a reservoir area can at least include the following steps: Step 101: Determine at least one target deformation recognition thread unit in each deformation recognition thread unit based on the analysis instruction.

[0015] In the embodiments of this specification, the server can receive the analysis instruction sent by the target terminal, continuously receive the collected landslide creep deformation data, and analyze the landslide creep deformation in the reservoir area.

[0016] Among them, landslide creep deformation is caused by the influence of various factors, which gradually reduces the strength of the slope rock and soil mass or continuously increases the shear stress inside the slope, resulting in the destruction of the slope's stable state. The relatively weak rock and soil mass inside the slope first deforms because its shear strength is less than the shear stress. When the deformed slide shape develops to the slope surface, discontinuous tensile cracks are formed. The appearance of the cracks strengthens the infiltration of surface water, and the deformation further develops. The trailing edge cracks widen, and small offsets appear. Shear cracks also appear on both sides of the sliding mass, and the rock and soil near the toe of the slope are extruded. At this time, the sliding surface is basically formed but not fully penetrated. The speed of this deformation is relatively slow and is generally easy to be ignored, but once a landslide occurs, the risk weight is very large. Therefore, long-term monitoring is required for landslide creep deformation to obtain landslide creep deformation data.

[0017] After the server receives the landslide creep deformation data and the analysis instruction collected by the sensor, it needs to determine each target deformation recognition thread unit in each deformation recognition thread unit according to the analysis instruction.

[0018] Among them, the deformation recognition thread unit essentially belongs to data recognition. Inputting data into it can obtain the required output features. Each deformation recognition thread unit can be set by those skilled in the art or can self-learn according to the set data recognition criteria. In order to reduce the calculation amount in the data recognition process while ensuring sufficient feature output, it is necessary to extract in each deformation recognition thread unit to obtain each target deformation recognition thread unit. Subsequently, only analyze the landslide creep deformation data through each target deformation recognition thread unit.

[0019] Optionally, when extracting the target deformation recognition thread unit, random extraction or regular extraction methods can be selected, which depends on the analysis instruction received by the server.

[0020] In an implementable manner, determining at least one target deformation recognition thread unit in each deformation recognition thread unit based on the analysis instruction includes: Receiving the analysis instruction and determining the extraction rule corresponding to the analysis instruction; Extracting in each of the deformation recognition thread units based on the extraction rule to obtain at least one target deformation recognition thread unit.

[0021] In the embodiments of this specification, when the extraction of the target deformation recognition thread unit is rule-based extraction, the server needs to first receive the analysis instruction sent by the target terminal, and then determine the extraction rule included in the analysis instruction. Then, according to the determined extraction rule, extraction is performed among all the deformation recognition thread units to obtain all the target deformation recognition thread units. The fixed extraction rule makes the intervals between the extracted target deformation recognition thread units regular, strengthening the connection between the knowledge fragment vectors output by each subsequent target deformation recognition thread unit.

[0022] As an example, when there are 24 deformation recognition thread units, the extraction rule corresponding to the analysis instruction is to select a target deformation recognition thread unit every 5 deformation recognition thread units. If the 2nd deformation recognition thread unit is determined as the target deformation recognition thread unit, then the 8th, 14th, and 20th deformation recognition thread units are correspondingly determined as the target deformation recognition thread units.

[0023] Optionally, when there are 24 deformation recognition thread units, the extraction rule corresponding to the analysis instruction is to select a target deformation recognition thread unit every 7 deformation recognition thread units. If the 8th deformation recognition thread unit is determined as the target deformation recognition thread unit, then the 16th and 24th deformation recognition thread units are correspondingly determined as the target deformation recognition thread units.

[0024] In an implementable manner, before determining the knowledge fragment vectors corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units, it further includes: Determining the target training termination evaluation index values of each of the target deformation recognition thread units based on the example training set, where the example training set includes example deformation data and the example knowledge fragment vectors corresponding to the example deformation data; Training each of the target deformation recognition thread units according to each of the target training termination evaluation index values to obtain the trained target deformation recognition thread units; The determining the knowledge fragment vectors corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units: Determining the knowledge fragment vectors corresponding to the landslide creep deformation data according to each of the trained target deformation recognition thread units.

[0025] In the embodiments of this specification, before using the target deformation recognition thread unit to determine the knowledge fragment vectors corresponding to the landslide creep deformation data, if the deformation recognition thread units are not preset by those skilled in the art, it is necessary to train each target deformation recognition thread unit. First, an example training set can be obtained through historical analysis records. The example training set can include each example deformation data and its corresponding example knowledge fragment vector. Then, the target training termination evaluation index values of each target deformation recognition thread unit are determined according to the example output and recognition output in the example training set. Further, each target deformation recognition thread unit is trained according to the target training termination evaluation index values of each target to obtain the trained target deformation recognition thread unit. When subsequently determining the knowledge fragment vectors corresponding to the landslide creep deformation data according to each target deformation recognition thread unit respectively, it can be directly obtained through each trained target deformation recognition thread unit.

[0026] In one implementable manner, determining the target training termination evaluation index values of each of the target deformation recognition thread units based on the example training set includes: For any target deformation recognition thread unit, input each example deformation data in the example training set into the target deformation recognition thread unit to obtain each recognition knowledge fragment vector; Determine each recognition evaluation index value based on each recognition knowledge fragment vector and the example knowledge fragment vector corresponding to each example deformation data; Integrate each recognition evaluation index value based on the weight fusion method to obtain the target training termination evaluation index value of the target deformation recognition thread unit.

[0027] In the embodiments of this specification, when determining the target training termination evaluation index values of each target deformation recognition thread unit based on the example training set, for any target deformation recognition thread unit, first input each example deformation data in the example training set into the target deformation recognition thread unit to obtain each recognition knowledge fragment vector that has not been trained. Then, calculate the ratio of each recognition knowledge fragment vector and the example knowledge fragment vector corresponding to each example deformation data, and determine each recognition evaluation index value according to the ratio calculation result. Further, first determine the weights corresponding to each recognition evaluation index value according to the preset weight rule, and then integrate each recognition evaluation index value based on the weight fusion method, that is, multiply each recognition evaluation index value by the weight and then add them up to obtain the target training termination evaluation index value of the target deformation recognition thread unit.

[0028] Step 102: Determine the knowledge fragment vectors corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units.

[0029] In the embodiments of this specification, since each target deformation recognition thread unit essentially belongs to data recognition, inputting data into it can obtain the required output features. Each deformation recognition thread unit can be set by those skilled in the art or can self-learn according to the set data recognition criteria. Therefore, the landslide creep deformation data can be used as input and transmitted to each target deformation recognition thread unit respectively, and each knowledge fragment vector as output can be obtained. Among them, each knowledge fragment vector can be characterized as a feature vector, including landslide influencing factors and creep deformation descriptions.

[0030] Step 103: Determine the correlation coefficient between each preset category label and the knowledge fragment vector set based on the mean algorithm.

[0031] Among them, the preset category labels include a risk-free label, a low-risk label, a medium-risk label, and a high-risk label, and the knowledge fragment vector set is composed of each of the knowledge fragment vectors.

[0032] In the embodiments of this specification, after determining each knowledge fragment vector corresponding to the landslide creep deformation data, the correlation coefficient between each pre-set category label and the knowledge fragment vector set composed of all the knowledge fragment vectors can be further determined. Subsequently, only by analyzing the correlation coefficient can the category label corresponding to the knowledge fragment vector set be judged, and the determination of the landslide creep deformation analysis result can be realized.

[0033] Among them, the preset category labels include a risk-free label, a low-risk label, a medium-risk label, and a high-risk label. Therefore, it is necessary to determine the correlation coefficient between each pre-set category label and each knowledge fragment vector in the knowledge fragment vector set, which is defined as the correlation coefficient. If the correlation coefficient is larger, it indicates that the landslide creep deformation data to be analyzed has a higher degree of association with this category label. Then, the mean value of each correlation coefficient is calculated to obtain the correlation coefficient between each preset category label and the knowledge fragment vector set.

[0034] In an implementable manner, the determining the correlation coefficient between each preset category label and the knowledge fragment vector set based on the mean algorithm includes: For any preset category label, determine the preset knowledge fragment vector corresponding to the preset category label; Respectively determine the ratio of each knowledge fragment vector in the knowledge fragment vector set to the preset knowledge fragment vector to obtain each ratio result; Perform a mean calculation on each of the ratio results to obtain the correlation coefficient between the category label and the knowledge fragment vector set.

[0035] In the embodiments of this specification, when determining the correlation coefficient between each preset type of label and the knowledge fragment vector set based on the mean algorithm, for any preset type of label, the preset knowledge fragment vector corresponding to the preset type of label can be determined first. Then, the ratio of each knowledge fragment vector in the knowledge fragment vector set to the preset knowledge fragment vector is calculated respectively to obtain each ratio result. Further, the mean value of all the obtained ratio results is calculated, and the mean value calculation result is used as the correlation coefficient between the type of label and the knowledge fragment vector set.

[0036] In an implementable manner, after respectively determining the ratio of each knowledge fragment vector in the knowledge fragment vector set to the preset knowledge fragment vector to obtain each ratio result, it further includes: Calculating the standard deviation of each ratio result to obtain a standard deviation result; Based on the standard deviation result and the standard deviation threshold, screening each ratio result to obtain each target ratio result; Calculating the mean value of each ratio result to obtain the correlation coefficient between the type of label and the knowledge fragment vector set: Calculating the mean value of each target ratio result to obtain the correlation coefficient between the type of label and the knowledge fragment vector set.

[0037] In the embodiments of this specification, in order to improve the accuracy of subsequent analysis results, after obtaining each ratio result, large deviation data caused by accidental errors or other factors can be removed. First, the standard deviation of all ratio results can be calculated to obtain a standard deviation result. Then, the calculated standard deviation result is compared with a preset standard deviation threshold. If the standard deviation result exceeds the standard deviation threshold, the ratio result with the largest difference from the average value among each ratio result is screened out until the standard deviation result meets the requirements of the standard deviation threshold, and each target ratio result after screening is obtained. When calculating the mean value of each ratio result subsequently, only the screened target ratio results need to be calculated for the mean value.

[0038] Step 104: Determine the target type of label corresponding to the landslide creep deformation data based on each correlation coefficient, and determine the landslide creep deformation analysis result according to the target type of label.

[0039] Among them, the landslide creep deformation analysis results include no risk, low risk, medium risk, and high risk.

[0040] In the embodiments of this specification, after obtaining each correlation coefficient, the maximum correlation coefficient can be determined therefrom, and further, the target type of label corresponding to the landslide creep deformation data can be queried according to the maximum correlation coefficient, and finally, the landslide creep deformation analysis result can be determined according to the target type of label.

[0041] As an example, the preset category labels include a risk-free label, a low-risk label, a medium-risk label, and a high-risk label. When the target category label is determined to be the medium-risk label, the corresponding landslide creep deformation analysis result is determined to be that there is a medium landslide risk in this reservoir area.

[0042] Optionally, the low risk can be further divided into 1 - 20 levels in detail, the medium risk into 21 - 40 levels, and the high risk into 41 - 60 levels. The higher the level, the greater the probability of a landslide occurring.

[0043] In an implementable manner, determining the target category label corresponding to the landslide creep deformation data based on each of the correlation coefficients includes: Constructing a possibility distribution queue based on each of the correlation coefficients; Determining the category label corresponding to the maximum correlation coefficient in the possibility distribution queue according to the extreme value method to obtain the target category label corresponding to the landslide creep deformation data.

[0044] In the embodiments of this specification, when determining the target category label corresponding to the landslide creep deformation data through each of the correlation coefficients, a possibility distribution queue can be constructed first through each of the correlation coefficients. Through the possibility distribution queue, it can be clearly known the degree of association between the landslide creep deformation data to be processed and each preset category label, making the subsequent process of determining the target category label more convenient and fast. Then, after determining the maximum correlation coefficient in this possibility distribution queue according to the extreme value method and further querying the category label corresponding to this maximum correlation coefficient, the target category label corresponding to the landslide creep deformation data can be obtained.

[0045] As an example, the possibility distribution queue is (0.02, 0.45, 0.9, 0.12), where 0.02, 0.45, 0.9, and 0.12 are the correlation coefficients corresponding to the risk-free label, the low-risk label, the medium-risk label, and the high-risk label respectively. Then, it can be determined that 0.9 is the maximum correlation coefficient, and the category label corresponding to it is the medium-risk label, so there is a medium landslide risk in this reservoir area.

[0046] The specific embodiments of this specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the specific order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0047] Next, please refer to Figure 2 , Figure 2The structural schematic diagram of a reservoir area landslide creep deformation analysis device provided by an embodiment of this specification is shown. It should be noted that Figure 2 The shown reservoir area landslide creep deformation analysis device is used to execute the method of this application Figure 1 of the shown embodiment. For the sake of convenience of description, only the parts related to the embodiment of this application are shown. For the specific technical details not disclosed, please refer to the embodiment Figure 1 shown in this application.

[0048] As Figure 2 shown, the reservoir area landslide creep deformation analysis device can at least include: A determination module 201, configured to determine at least one target deformation recognition thread unit in each deformation recognition thread unit based on an analysis instruction; A vector module 202, configured to respectively determine each knowledge fragment vector corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units; An association module 203, configured to determine the correlation coefficient between each preset type label and the knowledge fragment vector set based on the mean algorithm, where the preset type labels include a risk-free label, a low-risk label, a medium-risk label, and a high-risk label, and the knowledge fragment vector set is composed of each of the knowledge fragment vectors; An analysis module 204, configured to determine the target type label corresponding to the landslide creep deformation data based on each of the correlation coefficients, and determine the landslide creep deformation analysis result according to the target type label, where the landslide creep deformation analysis result includes risk-free, low-risk, medium-risk, and high-risk.

[0049] In an implementable manner, the determination module 201 is specifically configured to: Receive an analysis instruction, and determine the extraction rule corresponding to the analysis instruction; Perform extraction in each of the deformation recognition thread units based on the extraction rule to obtain at least one target deformation recognition thread unit.

[0050] In an implementable manner, the determination module 201 is specifically further configured to: Determine the target training termination evaluation index value of each of the target deformation recognition thread units based on an example training set, where the example training set includes example deformation data and the example knowledge fragment vector corresponding to the example deformation data; Train each of the target deformation recognition thread units according to each of the target training termination evaluation index values to obtain the trained target deformation recognition thread units; The step of respectively determining each knowledge fragment vector corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units: Determine each knowledge fragment vector corresponding to the landslide creep deformation data according to each of the trained target deformation recognition thread units.

[0051] In an implementable manner, the determining module 201 is further specifically configured to: For any target deformation recognition thread unit, input each example deformation data in the example training set into the target deformation recognition thread unit to obtain each recognition knowledge fragment vector; Determine each recognition evaluation index value based on each recognition knowledge fragment vector and the example knowledge fragment vector corresponding to each of the example deformation data; Integrate each of the recognition evaluation index values based on the weight fusion method to obtain the target training termination evaluation index value of the target deformation recognition thread unit.

[0052] In an implementable manner, the association module 203 is specifically configured to: For any preset category label, determine the preset knowledge fragment vector corresponding to the preset category label; Respectively determine the ratio of each knowledge fragment vector in the knowledge fragment vector set to the preset knowledge fragment vector to obtain each ratio result; Perform a mean calculation on each of the ratio results to obtain the correlation coefficient between the category label and the knowledge fragment vector set.

[0053] In an implementable manner, the association module 203 is further specifically configured to: Perform a standard deviation calculation on each of the ratio results to obtain a standard deviation result; Based on the standard deviation result and the standard deviation threshold, screen each of the ratio results to obtain each target ratio result; The performing a mean calculation on each of the ratio results to obtain the correlation coefficient between the category label and the knowledge fragment vector set: Perform a mean calculation on each of the target ratio results to obtain the correlation coefficient between the category label and the knowledge fragment vector set.

[0054] In an implementable manner, the analysis module 204 is specifically configured to: Construct a possibility distribution queue based on each correlation coefficient; Determine the category label corresponding to the maximum correlation coefficient in the possibility distribution queue according to the maximum value method to obtain the target category label corresponding to the landslide creep deformation data.

[0055] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented by means of software and / or hardware. The "units" and "modules" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), etc.

[0056] Each processing unit and / or module of the embodiments of the present application can be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or can be implemented by software that executes the functions described in the embodiments of the present application.

[0057] Next, please refer to Figure 3 , Figure 3 which shows a schematic structural diagram of an electronic device provided by an embodiment of this specification.

[0058] As Figure 3 shown, the electronic device 300 may include: at least one device processor 301, at least one network interface 303, a user interface 303, a memory 305, and at least one communication bus 302.

[0059] Among them, the communication bus 302 can be used to realize the connection and communication of the above-mentioned various components.

[0060] Among them, the user interface 303 may include buttons, and the optional user interface may further include a standard wired interface and a wireless interface.

[0061] Among them, the network interface 304 may, but is not limited to, include a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0062] Among them, the device processor 301 may include one or more processing cores. The device processor 301 uses various interfaces and lines to connect various parts within the entire electronic device 300, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305, it executes various functions of the electronic device 300 and processes data. Optionally, the device processor 301 may be implemented in at least one of the hardware forms of DSP, FPGA, and PLA. The device processor 301 may integrate one or several combinations of a CPU, a GPU, and a modem, etc. Among them, the CPU mainly processes the operating system, the user interface, and application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the device processor 301 and may be implemented separately by a single chip.

[0063] Among them, the memory 305 may include RAM or ROM. Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned device processor 301. As Figure 3 shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0064] Specifically, the device processor 301 can be used to call the reservoir landslide creep deformation analysis application program stored in the memory 305 and specifically perform the following operations: Determine at least one target deformation recognition thread unit in each deformation recognition thread unit based on the analysis instruction; Determine each knowledge fragment vector corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units; Determine the correlation coefficient between each preset category label and the knowledge fragment vector set based on the mean algorithm. The preset category labels include a risk-free label, a low-risk label, a medium-risk label, and a high-risk label. The knowledge fragment vector set is composed of each of the knowledge fragment vectors; Determine the target category label corresponding to the landslide creep deformation data based on each of the correlation coefficients, and determine the landslide creep deformation analysis result according to the target category label. The landslide creep deformation analysis result includes risk-free, low-risk, medium-risk, and high-risk.

[0065] As an option of the embodiment of this specification, the determining at least one target deformation recognition thread unit in each deformation recognition thread unit based on the analysis instruction includes: Receive the analysis instruction and determine the extraction rule corresponding to the analysis instruction; Extract in each of the deformation recognition thread units based on the extraction rule to obtain at least one target deformation recognition thread unit.

[0066] As an option of the embodiment of this specification, before determining each knowledge fragment vector corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units, it further includes: Determine the target training termination evaluation index value of each of the target deformation recognition thread units based on the example training set, where the example training set includes example deformation data and example knowledge fragment vectors corresponding to the example deformation data; Train each of the target deformation recognition thread units according to the target training termination evaluation index value of each of the target deformation recognition thread units to obtain trained target deformation recognition thread units; The step of respectively determining, according to each of the target deformation recognition thread units, knowledge fragment vectors corresponding to the landslide creep deformation data: Respectively determine, according to each of the trained target deformation recognition thread units, knowledge fragment vectors corresponding to the landslide creep deformation data.

[0067] As an option of the embodiment of the present specification, the step of determining the target training termination evaluation index value of each of the target deformation recognition thread units based on the example training set includes: For any one of the target deformation recognition thread units, input each of the example deformation data in the example training set into the target deformation recognition thread unit to obtain each recognition knowledge fragment vector; Determine each recognition evaluation index value based on each recognition knowledge fragment vector and the example knowledge fragment vectors corresponding to each of the example deformation data; Integrate each of the recognition evaluation index values by using a weight fusion method to obtain the target training termination evaluation index value of the target deformation recognition thread unit.

[0068] As an option of the embodiment of the present specification, the step of determining the correlation coefficient between each preset type label and the knowledge fragment vector set based on the mean algorithm includes: For any one of the preset type labels, determine the preset knowledge fragment vector corresponding to the preset type label; Respectively determine the ratios of each of the knowledge fragment vectors in the knowledge fragment vector set to the preset knowledge fragment vector to obtain each ratio result; Perform a mean calculation on each of the ratio results to obtain the correlation coefficient between the type label and the knowledge fragment vector set.

[0069] As an option of the embodiment of the present specification, after respectively determining the ratios of each of the knowledge fragment vectors in the knowledge fragment vector set to the preset knowledge fragment vector to obtain each ratio result, it further includes: Perform a standard deviation calculation on each of the ratio results to obtain a standard deviation result; Screen each of the ratio results based on the standard deviation result and a standard deviation threshold to obtain each target ratio result; The step of performing a mean calculation on each of the ratio results to obtain the correlation coefficient between the type label and the knowledge fragment vector set: Calculate the mean value of each of the target ratio results to obtain the correlation coefficient between the category label and the knowledge fragment vector set.

[0070] As an option of the embodiments of this specification, the determining the target category label corresponding to the landslide creep deformation data based on each of the correlation coefficients includes: Construct a possibility distribution queue based on each correlation coefficient; Determine the category label corresponding to the maximum correlation coefficient in the possibility distribution queue according to the extreme value method to obtain the target category label corresponding to the landslide creep deformation data.

[0071] The embodiments of this specification also provide a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the above method are implemented. Among them, the computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical discs, DVDs, CD-ROMs, microdrives, and magneto-optical discs, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nano-systems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0072] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0073] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0074] In several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0075] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] In addition, in each embodiment of the present application, each functional unit may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0077] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned memory includes: USB flash drives, read-only memories (ROM), random access memories (RAM), mobile hard disks, magnetic disks, or optical discs and other media that can store program codes.

[0078] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable memory. The memory may include: flash drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs, etc.

[0079] The specific embodiments of this specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be executed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for analyzing creep deformation of landslides in a reservoir area, characterized in that, The method includes: Determining at least one target deformation recognition thread unit in each deformation recognition thread unit based on an analysis instruction; Respectively determining each knowledge fragment vector corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units; Determining the correlation coefficients between each preset type label and the knowledge fragment vector set based on the mean algorithm, where the preset type labels include a risk-free label, a low-risk label, a medium-risk label, and a high-risk label, and the knowledge fragment vector set is composed of each of the knowledge fragment vectors; Determining the target type label corresponding to the landslide creep deformation data based on each of the correlation coefficients, and determining the landslide creep deformation analysis result according to the target type label, where the landslide creep deformation analysis result includes risk-free, low-risk, medium-risk, and high-risk.

2. The method according to claim 1, characterized in that, The determining at least one target deformation recognition thread unit in each deformation recognition thread unit based on an analysis instruction includes: Receiving an analysis instruction and determining the extraction rule corresponding to the analysis instruction; Performing extraction in each of the deformation recognition thread units based on the extraction rule to obtain at least one target deformation recognition thread unit.

3. The method according to claim 1, characterized in that, Before respectively determining each knowledge fragment vector corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units, it further includes: Determining the target training termination evaluation index value of each of the target deformation recognition thread units based on an example training set, where the example training set includes example deformation data and the example knowledge fragment vectors corresponding to the example deformation data; Training each of the target deformation recognition thread units according to each of the target training termination evaluation index values to obtain the trained target deformation recognition thread units; The respectively determining each knowledge fragment vector corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units: Respectively determining each knowledge fragment vector corresponding to the landslide creep deformation data according to each of the trained target deformation recognition thread units.

4. The method according to claim 3, characterized in that, The determining the target training termination evaluation index value of each of the target deformation recognition thread units based on an example training set includes: For any target deformation recognition thread unit, inputting each example deformation data in the example training set into the target deformation recognition thread unit to obtain each recognition knowledge fragment vector; Determining each recognition evaluation index value based on each recognition knowledge fragment vector and the example knowledge fragment vectors corresponding to each of the example deformation data; Integrating each of the recognition evaluation index values based on the weight fusion method to obtain the target training termination evaluation index value of the target deformation recognition thread unit.

5. The method according to claim 1, characterized in that, The determining the correlation coefficients between each preset type label and the knowledge fragment vector set based on the mean algorithm includes: For any preset type label, determining the preset knowledge fragment vector corresponding to the preset type label; Respectively determining the ratios of each of the knowledge fragment vectors in the knowledge fragment vector set to the preset knowledge fragment vector to obtain each ratio result; Performing a mean calculation on each of the ratio results to obtain the correlation coefficient between the type label and the knowledge fragment vector set.

6. The method according to claim 5, characterized in that, After respectively determining the ratios of each of the knowledge fragment vectors in the knowledge fragment vector set to the preset knowledge fragment vector and obtaining each ratio result, the method further includes: Calculating the standard deviation of each of the ratio results to obtain a standard deviation result; Based on the standard deviation result and a standard deviation threshold, screening each of the ratio results to obtain each target ratio result; Calculating the mean of each of the ratio results to obtain the correlation coefficient between the category label and the knowledge fragment vector set: Calculating the mean to obtain the correlation coefficient between the category label and the knowledge fragment vector set.

7. The method according to claim 1, characterized in that, The determining the target category label corresponding to the landslide creep deformation data based on each of the correlation coefficients includes: Constructing a possibility distribution queue based on each of the correlation coefficients; Determining, according to the maximum value method, the category label corresponding to the maximum correlation coefficient in the possibility distribution queue to obtain the target category label corresponding to the landslide creep deformation data.

8. An apparatus for analyzing creep deformation of landslides in a reservoir area, characterized in that, The apparatus includes: A determination module, configured to determine at least one target deformation recognition thread unit in each of the deformation recognition thread units based on an analysis instruction; A vector module, configured to respectively determine each knowledge fragment vector corresponding to the landslide creep deformation data according to each of the target deformation recognition thread units; An association module, configured to determine the correlation coefficient between each preset category label and the knowledge fragment vector set based on an average value algorithm, where the preset category labels include a risk-free label, a low-risk label, a medium-risk label, and a high-risk label, and the knowledge fragment vector set is composed of each of the knowledge fragment vectors; An analysis module, configured to determine the target category label corresponding to the landslide creep deformation data based on each of the correlation coefficients, and determine a landslide creep deformation analysis result according to the target category label, where the landslide creep deformation analysis result includes risk-free, low-risk, medium-risk, and high-risk.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, When the processor executes the computer program, the steps of the method according to any one of claims 1-7 are implemented.

10. A computer-readable storage medium, on which a computer program is stored, wherein instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer or a processor, the computer or the processor is caused to execute the steps of the method according to any one of claims 1-7.

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