Railway perimeter floating object risk source assessment method and device

Abstract

The invention relates to the technical field of railway perimeter risk source assessment, and relates to a railway perimeter floater risk source assessment method and device, and the method comprises the steps: obtaining a to-be-assessed hyperspectral remote sensing image; according to the to-be-evaluated hyperspectral remote sensing image, identifying the railway perimeter floating object risk source to obtain an identification result; determining first information according to the identification result, wherein the first information comprises the distance between the to-be-evaluated floating object risk source and the railway safety limit; dividing the risk area of the railway perimeter according to the railway safety clearance to obtain the divided risk area; acquiring second information, wherein the second information comprises an environment wind speed corresponding to a to-be-evaluated floating object risk source; and according to the first information and the second information, calculating the probability of the risk area after invasion division of the floating object risk source, and obtaining an evaluation result, the risk level of the floating object risk source is accurately evaluated, and on-site hidden danger improvement can be effectively guided.

Description

Technical Field

[0001] The present invention relates to the technical field of railway perimeter risk source assessment, and in particular to a railway perimeter floating object risk source assessment method and device. Background Art

[0002] In windy weather, light floating objects such as billboards, banners, plastic greenhouses, plastic sheets, dust nets, and hard floating objects such as color steel plates, color steel roofs, and color steel sheds on the railway perimeter are blown into the railway safety limit. In the least serious cases, they will hang on the contact network and damage railway equipment, causing train delays; in the most serious cases, they will collide with high-speed trains and endanger driving safety. The risk sources of various floating objects on the railway perimeter are prominent safety hazards to the safe operation of railways.

[0003] At present, there are few research results on the prevention and control of floating objects around railway perimeters at home and abroad, and the research content focuses on the drone image recognition of floating object risk sources and the image detection, recognition and early warning of intrusion floating objects. At present, there is no quantitative assessment method for the wind-induced intrusion risk of light and hard floating object risk sources from the perspective of the destructive intensity of risk sources and the wind-induced movement trajectory of disaster-causing factors. Summary of the invention

[0004] The purpose of the present invention is to provide a method and device for assessing risk sources of floating objects around railways, so as to improve the above-mentioned problems.

[0005] In order to achieve the above objectives, the present application provides the following technical solutions:

[0006] On the one hand, an embodiment of the present application provides a method for assessing risk sources of floating objects at the perimeter of a railway, the method comprising:

[0007] Acquire a hyperspectral remote sensing image to be evaluated, wherein the hyperspectral remote sensing image includes a hyperspectral remote sensing image of a railway perimeter;

[0008] Identify the risk source of floating objects around the railway according to the hyperspectral remote sensing image to be evaluated, and obtain an identification result;

[0009] Determining first information according to the identification result, the first information including the distance between the risk source of the floating object to be assessed and the railway safety clearance;

[0010] Dividing the risk area of ​​the railway perimeter according to the railway safety clearance to obtain the divided risk area;

[0011] Acquiring second information, where the second information includes an environmental wind speed corresponding to a floating object risk source to be assessed;

[0012] The probability of the floating object risk source invading the divided risk area is calculated according to the first information and the second information to obtain an assessment result, which includes a danger level of the floating object risk source.

[0013] In a second aspect, an embodiment of the present application provides a railway perimeter floating object risk source assessment device, the device comprising:

[0014] A first acquisition module is used to acquire a hyperspectral remote sensing image to be evaluated, wherein the hyperspectral remote sensing image includes a hyperspectral remote sensing image of a railway perimeter;

[0015] The first processing module is used to identify the risk source of floating objects around the railway according to the hyperspectral remote sensing image to be evaluated, and obtain an identification result;

[0016] A second processing module is used to determine first information according to the identification result, where the first information includes the distance between the risk source of the floating object to be assessed and the railway safety clearance;

[0017] A third processing module is used to divide the risk area of ​​the railway perimeter according to the railway safety clearance to obtain the divided risk area;

[0018] A second acquisition module is used to acquire second information, where the second information includes an environmental wind speed corresponding to a floating object risk source to be assessed;

[0019] The fourth processing module is used to calculate the probability of the floating object risk source invading the divided risk area according to the first information and the second information to obtain an evaluation result, wherein the evaluation result includes a danger level of the floating object risk source.

[0020] In a third aspect, an embodiment of the present application provides a railway perimeter floating object risk source assessment device, the device comprising a memory and a processor. The memory is used to store a computer program; the processor is used to implement the steps of the railway perimeter floating object risk source assessment method when executing the computer program.

[0021] In a fourth aspect, an embodiment of the present application provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned railway perimeter floating object risk source assessment method are implemented.

[0022] The beneficial effects of the present invention are:

[0023] The present invention collects hyperspectral remote sensing images by regularly scanning the external environment of the railway through high-resolution remote sensing satellites, thereby identifying the risk sources of floating objects on the railway perimeter. Compared with manual inspection and drone inspection, the method has lower cost, higher efficiency and wider monitoring range. The risk areas of the railway perimeter are divided according to the identification results, the distribution space of potential risk sources is locked, the scope of hidden danger remediation is reduced, and the cost of hidden danger remediation is reduced. Finally, the probability of the floating object risk source invading the divided risk area is calculated according to the first information and the second information to obtain an evaluation result, thereby achieving an accurate evaluation of the risk level of the floating object risk source and effectively guiding the on-site hidden danger remediation.

[0024] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 The figure is a schematic flow chart of a method for assessing risk sources of floating objects on railway perimeters according to an embodiment of the present invention.

[0027] Figure 2 It is a schematic diagram of the structure of the railway perimeter floating object risk source assessment device described in an embodiment of the present invention.

[0028] Figure 3 It is a schematic diagram of the structure of the railway perimeter floating object risk source assessment device described in an embodiment of the present invention.

[0029] Figure 4 This is a schematic diagram of the risk area after division.

[0030] Labels in the figure: 800, railway perimeter floating object risk source assessment equipment; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, first acquisition module; 902, first processing module; 903, second processing module; 904, third processing module; 905, second acquisition module; 906, fourth processing module. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0032] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0033] Embodiment 1:

[0034] This embodiment provides a method for assessing risk sources of floating objects at the railway perimeter. It can be understood that in this embodiment, a scenario can be laid out, for example, a scenario for assessing the risk level of risk sources of floating objects at the railway perimeter.

[0035] See also Figure 1 , the figure shows that the method includes step S1, step S2, step S3, step S4, step S5 and step S6.

[0036] Step S1, obtaining a hyperspectral remote sensing image to be evaluated, wherein the hyperspectral remote sensing image includes a hyperspectral remote sensing image of a railway perimeter;

[0037] In this step, high-resolution remote sensing satellites are used to regularly scan the external environment of the railway to collect hyperspectral remote sensing images, thereby identifying the risk sources of floating objects around the railway. Compared with manual inspections and drone inspections, this method has lower costs, higher efficiency, and a wider monitoring range.

[0038] Step S2, identifying the risk source of floating objects around the railway according to the hyperspectral remote sensing image to be evaluated, and obtaining an identification result;

[0039] The step S2 also includes step S21, step S22, step S23 and step S24, which specifically include:

[0040] Step S21, extracting features from the hyperspectral remote sensing image to be evaluated to obtain feature information corresponding to each floating object risk source;

[0041] Step S22: constructing a semantic feature label of each floating object risk source based on the feature information corresponding to each floating object risk source;

[0042] Step S23, constructing a floating object risk source identification model based on a deep learning algorithm and the semantic feature labels of each floating object risk source;

[0043] Step S24: sending the hyperspectral remote sensing image to be evaluated to the floating object risk source identification model to obtain the identification result.

[0044] In this embodiment, because the spectral characteristics and texture characteristics of the two types of floating risk sources in remote sensing images are quite different, high-spectral remote sensing images of typical railway perimeters in typical landforms such as plains, mountains, and offshore are taken by remote sensing satellites, and the spectral curve characteristics, spectral transformation characteristics, spectral measurement characteristics and texture characteristics of various risk sources in typical landforms are statistically analyzed, and then the semantic feature labels of risk source remote sensing images are constructed, and the deep learning algorithm and the spectral and texture semantic labels of risk sources are used to establish a remote sensing image multimodal fusion framework model of risk sources, namely, a floating risk source identification model, and the accuracy of the model is improved through training and verification of a large number of remote sensing images. First, the remote sensing images of the railway perimeter under some typical landforms are sliced ​​to make the training set and verification set of the model; then, the training set is substituted into the model for training, and compared and analyzed with the verification set until the verification accuracy is not less than 95%; finally, the floating risk source identification model is obtained and used for the semantic segmentation of the remote sensing image of the risk source of the railway perimeter under typical landforms, and accurate identification. It should be noted that the two types of floating objects include (1) light floating objects: billboards, banners, plastic greenhouses, plastic sheets, and dust nets; and (2) hard floating objects: light steel buildings and the protective colored steel plates of color steel sheds.

[0045] Step S3, determining first information according to the identification result, wherein the first information includes the distance between the risk source of the floating object to be assessed and the railway safety clearance;

[0046] The step S3 also includes step S31, step S32, step S33, step S34 and step S35, which specifically include:

[0047] Step S31, determining the center point of the floating object risk source spot according to the recognition result;

[0048] Step S32, obtaining coordinate data information of the railway line;

[0049] Step S33, generating a railway line centerline according to the coordinate data information of the railway line;

[0050] Step S34, generating a railway safety clearance according to a preset line rule and the railway line center line;

[0051] Step S35: Calculate the vertical distance between the railway safety clearance and the center point of the floating object risk source pattern to obtain first information.

[0052] In this embodiment, the floating object risk source identification model is connected to the geographic information platform, and the identification results are automatically imported into the geographic information platform. The geographic information platform uses stereo projection and computational geometry tools to calculate the area of ​​the risk source patch and the geodetic coordinates of its center point. The coordinate data of the railway line is imported into the geographic information platform. The geographic information platform draws the center line center of the line according to the coordinate data of the railway line, and then generates the railway safety limit according to the preset line rules. At the same time, the platform calculates the vertical distance from the center point of the risk source patch to the safety limit through the patch coordinates and the line coordinates, thereby realizing rapid distance measurement calculation of the risk source.

[0053] Step S4, dividing the risk area of ​​the railway perimeter according to the railway safety clearance to obtain the divided risk area;

[0054] The step S4 also includes step S41, step S42, step S43, step S44 and step S45, which specifically include:

[0055] Step S41, obtaining third information, wherein the third information includes a limit drift distance corresponding to a historical extreme wind field;

[0056] In this step, the drift distance of floating objects under different wind directions, wind forces and durations can be calculated by establishing a motion trajectory model, and then the limit drift distance of floating objects can be calculated based on historical extreme wind field information. It can be understood that the motion trajectory model is based on numerical simulation analysis and floating object intrusion limit measured data. The specific process of establishing the motion trajectory model of floating object disaster factors is as follows:

[0057] S=(k 1 , k 2 , k 3 , t)

[0058] In the above formula, S represents the three-dimensional distance of the floating object caused by wind; k 1 Indicates wind speed parameter; k 2 Indicates wind direction parameter; k 3 represents the starting height parameter; t represents the wind load action time.

[0059] It should be noted that k 1 Indicates wind speed parameter; k 2 Indicates wind direction parameter; k 3The starting height parameter is determined by establishing a wind field remote sensing prediction model. The specific construction process of the wind field remote sensing prediction model is as follows: the atmosphere around the railway is continuously imaged in multiple bands by meteorological remote sensing satellites, the displacement of characteristic targets such as clouds and water vapor in the atmosphere within a known time interval is tracked, and the atmospheric height layer where the target is located is inverted using the radiation information or three-dimensional geometric relationship of each spectral channel to obtain the atmospheric wind vector corresponding to the height layer, and then the wind vector field of the railway perimeter is obtained; the atmospheric images corresponding to the wind vector fields of different time series are input into a deep learning model based on a convolution-long short-term memory network combined with an attention mechanism, CNN is used to capture local patterns and features in the image, LSTM is used to process the image to capture medium- and long-term dependencies, and the Attention mechanism is used to provide the model with the ability to focus on key information, so as to accurately predict the wind vector field after a specified time step, thereby predicting the k-th wind vector field. 1 , k 2、 k 3 Make accurate predictions.

[0060] Step S42, determining radius information according to the third information and a preset safety factor;

[0061] In this step, the limit drift distance corresponding to the historical extreme wind field is assumed to be S f , the preset safety factor is K, the radius information R = S f ·K.

[0062] Step S43: dividing the railway perimeter according to the radius information and the railway safety clearance to obtain a safe area and a risk area;

[0063] In this step, the external environment is divided into two areas with the railway safety limit as the starting point and R as the radius. It is impossible for the wind-induced drift of floating objects in the external area to invade the railway safety limit. Therefore, it is only necessary to conduct a risk assessment on the floating object risk source in the internal area of ​​R, that is, the area outside the R boundary is the floating object intrusion safety area, and the area inside is the floating object intrusion risk area.

[0064] Step S44, obtaining fourth information, wherein the fourth information includes the maximum value of the average wind speed information within a preset time and the instantaneous peak wind speed information;

[0065] In this step, a specific implementation method is to set the maximum value of the 2-minute average wind speed of the site in the past year as V 1 and the instantaneous peak wind speed is recorded as V 2 This is the fourth information.

[0066] Step S45: Divide the risk area according to the fourth information.

[0067] In this step, according to V 1 and V 2The corresponding wind-induced drift distance of floating objects can be determined as S 1 With S 2 , it can be understood that the limit drift distance is S f The corresponding limit wind speed is V f , V 1 <V 2 <V f , then under the same conditions, S 1 <S 2 <R. According to S1, S2, and R, the risk area can be divided into three areas from the inside to the outside, such as Figure 4 shown.

[0068] In this embodiment, the motion trajectory model is used to divide the site into risk areas and safe areas, which locks the distribution space of potential risk sources, shrinks the scope of hidden danger remediation, and reduces the cost of hidden danger remediation.

[0069] Step S5, obtaining second information, wherein the second information includes the environmental wind speed corresponding to the floating object risk source to be assessed;

[0070] Step S6: Calculate the probability of the floating object risk source invading the divided risk area according to the first information and the second information to obtain an assessment result, wherein the assessment result includes the danger level of the floating object risk source.

[0071] The step S6 also includes step S61, step S62, step S63, step S64 and step S65, which specifically include:

[0072] Step S61, calculating the critical wind speed corresponding to the floating object risk source to be evaluated based on the floating object risk source damage aging time series model to obtain fifth information;

[0073] In this step, the critical wind speed corresponding to the wind-induced damage of the risk source at any time can be inversely calculated through the damage aging time series model of the floating debris risk source. The specific calculation process is:

[0074] v t =G(F t )

[0075] In the above formula, v t represents the critical wind speed corresponding to the wind-induced damage of the risk source at any time; G(F t ) represents the critical wind speed function corresponding to Ft at any time.

[0076] The step S61 also includes step S611, step S612 and step S613, which specifically include:

[0077] Step S611, obtaining aging damage aging factors of at least five floating object risk sources;

[0078] Step S612, calculating the material damage aging intensity corresponding to the floating object risk source according to at least five of the risk source aging damage aging factors;

[0079] In this step, the specific calculation formula for the material damage aging strength corresponding to the floating object risk source is:

[0080] F=(f 1 , f 2 , f 3 , f 4 、f 5 )

[0081] In the above formula, F represents the material damage aging intensity corresponding to the floating object risk source, f 1 Represents the static strength factor of the floating material, f 2 represents the fatigue strength factor of the floating material, f 3 represents the wind damage intensity factor of floating material, f 4 Indicates the environmental corrosion intensity factor of the floating material, f 5 Represents the solar aging intensity factor of floating material.

[0082] Step S613: constructing a floating object risk source damage aging time series model based on the material damage aging intensity corresponding to the floating object risk source.

[0083] In this step, based on the strength aging time series characteristics of the risk source material and the strong wind damage strength characteristics, the Transformer-GRU time series prediction algorithm is introduced into the damage aging constitutive model to realize the aging failure time prediction of the risk source strength and the strong wind coupled damage strength prediction, thereby establishing a damage aging time series model of the risk source.

[0084] F t =(F, t)

[0085] In the above formula, F t It represents the damage aging time series intensity of the risk source material, and t represents the time interval between the calculation time and the new construction time.

[0086] In this embodiment, the damage aging time series model of floating object risk sources can accurately calculate the real strength of the risk source at the current moment through historical wind field, rainfall, and temperature information, and can also predict its strength aging failure time, wind-induced damage intensity and its corresponding wind speed; and then, the wind-induced damage level of the risk source can be quantitatively evaluated by predicting the wind speed.

[0087] Step S62: Calculate the drift distance corresponding to the risk source of the floating object to be assessed based on the motion trajectory model to obtain sixth information;

[0088] Step S63: determining whether the first information is less than or equal to the sixth information and recording it as a first event;

[0089] In this step, the first event is recorded as L≤D, where L represents the distance between the risk source of the floating object to be assessed and the railway safety clearance, and D represents the drift distance corresponding to the risk source of the floating object to be assessed.

[0090] Step S64, determining whether the second information is greater than or equal to the fifth information is recorded as a second event;

[0091] In this step, the second event is recorded as v 0 ≥v t , where v 0 represents the ambient wind speed corresponding to the floating debris risk source to be assessed, v t Indicates the critical wind speed corresponding to wind-induced damage of risk source.

[0092] Step S65: Evaluate the danger level of the floating object risk source according to the probability of occurrence of the first event and the second event.

[0093] In this step, the probability of the first event occurring is multiplied by the probability of the second event occurring to obtain P C =P A ·P B , where P A and P B They represent the probability of the first event occurring and the probability of the second event occurring, respectively. C It indicates the probability of wind-induced intrusion of floating objects. It should be noted that the wind speed statistics adopt the extreme value type I probability distribution, and the wind speed is v 0 The probability distribution function is as follows:

[0094]

[0095] In the above formula, P(v 0 ) indicates that the wind speed is less than or equal to v 0 α is the scale parameter of the distribution; u is the mode of the distribution.

[0096] (1) The probability of the second event is:

[0097] P B =1-P(v t )

[0098] The probability of wind-induced intrusion of floating objects in different areas is:

[0099]

[0100] In the above formula, P B It represents the critical wind speed probability of floating material destruction, when v0 <v t When P B =0; P(v t ) indicates that the wind speed is less than or equal to [v t ]; P A1 represents the probability of floating debris intrusion in high-risk areas; P(v 1 ) indicates that the wind speed is less than or equal to v 1 The probability of A2 represents the probability of floating debris intrusion in the medium risk area; P(v 2 ) indicates that the wind speed is less than or equal to v 2 The probability of A3 represents the probability of floating debris intrusion in low-risk areas; P(v f ) indicates that the wind speed is less than or equal to v f The probability of time.

[0101] In summary, the probability of wind-induced intrusion of floating objects can be quantitatively calculated as follows:

[0102] When P C =0, that is, v 0 <vt, the risk source will not cause material damage and cannot intrude into the railway safety limit;

[0103] When 0<P C When <0.25, that is, v 0 ≥v t , the risk source has material damage, but it is far away from the safety limit, and floating objects need a very high wind speed to invade the railway safety limit. This type of risk source can be assessed as a low risk source;

[0104] When 0.25≤P C When <0.5, that is, v 0 ≥v t , the risk source has material damage, but it is far away from the safety limit, and floating objects need a large wind speed to invade the railway safety limit. This type of risk source can be assessed as a medium risk source;

[0105] When P C ≥0.5, that is, v 0 ≥v t , material damage occurs at the risk source, but it is close to the safety limit. Floating objects can invade the railway safety limit at a lower wind speed, so this type of risk source is assessed as a high-risk risk source.

[0106] Embodiment 2:

[0107] like Figure 2As shown, this embodiment provides a railway perimeter floating object risk source assessment device, the device includes a first acquisition module 901, a first processing module 902, a second processing module 903, a third processing module 904, a second acquisition module 905 and a fourth processing module 906, which specifically include:

[0108] A first acquisition module 901 is used to acquire a hyperspectral remote sensing image to be evaluated, wherein the hyperspectral remote sensing image includes a hyperspectral remote sensing image of a railway perimeter;

[0109] The first processing module 902 is used to identify the risk source of floating objects around the railway according to the hyperspectral remote sensing image to be evaluated, and obtain an identification result;

[0110] The second processing module 903 is used to determine first information according to the identification result, where the first information includes the distance between the risk source of the floating object to be assessed and the railway safety clearance;

[0111] The third processing module 904 is used to divide the risk area of ​​the railway perimeter according to the railway safety clearance to obtain the divided risk area;

[0112] A second acquisition module 905 is used to acquire second information, where the second information includes an environmental wind speed corresponding to a floating object risk source to be assessed;

[0113] The fourth processing module 906 is used to calculate the probability of the floating object risk source invading the divided risk area according to the first information and the second information to obtain an evaluation result, and the evaluation result includes the danger level of the floating object risk source.

[0114] In a specific embodiment of the present disclosure, the first processing module further includes a first processing unit, a second processing unit, a third processing unit and a fourth processing unit, which specifically include:

[0115] A first processing unit is used to extract features from the hyperspectral remote sensing image to be evaluated to obtain feature information corresponding to each floating object risk source;

[0116] A second processing unit is used to construct a semantic feature label of each floating object risk source based on the feature information corresponding to each floating object risk source;

[0117] A third processing unit is used to build a floating object risk source identification model based on a deep learning algorithm and a semantic feature label of each floating object risk source;

[0118] The fourth processing unit is used to send the hyperspectral remote sensing image to be evaluated to the floating object risk source identification model to obtain the identification result.

[0119] In a specific embodiment of the present disclosure, the second processing module further includes a fifth processing unit, a first acquisition unit, a sixth processing unit, a seventh processing unit and an eighth processing unit, which specifically include:

[0120] A fifth processing unit, configured to determine a center point of a floating object risk source pattern according to the identification result;

[0121] A first acquisition unit, used to acquire coordinate data information of a railway line;

[0122] A sixth processing unit, configured to generate a railway line centerline according to the coordinate data information of the railway line;

[0123] A seventh processing unit, configured to generate a railway safety clearance according to a preset line rule and the center line of the railway line;

[0124] The eighth processing unit is used to calculate the vertical distance between the railway safety clearance and the center point of the floating object risk source pattern to obtain the first information.

[0125] In a specific implementation of the present disclosure, the third processing module further includes a second acquisition unit, a ninth processing unit, a tenth processing unit, a third acquisition unit and an eleventh processing unit, which specifically include:

[0126] A second acquisition unit is used to acquire third information, where the third information includes a limit drift distance corresponding to a historical extreme wind field;

[0127] a ninth processing unit, configured to determine radius information according to the third information and a preset safety factor;

[0128] a tenth processing unit, configured to divide the railway perimeter according to the radius information and the railway safety clearance to obtain a safe area and a risk area;

[0129] A third acquisition unit, used to acquire fourth information, wherein the fourth information includes a maximum value of the average wind speed information within a preset time and instantaneous peak wind speed information;

[0130] An eleventh processing unit is used to divide the risk area according to the fourth information.

[0131] In a specific implementation of the present disclosure, the fourth processing module further includes a first calculation unit, a second calculation unit, a first judgment unit, a second judgment unit and an evaluation unit, which specifically include:

[0132] The first calculation unit is used to calculate the critical wind speed corresponding to the floating object risk source to be evaluated based on the floating object risk source damage aging time series model to obtain fifth information;

[0133] A second calculation unit is used to calculate the drift distance corresponding to the risk source of the floating object to be evaluated based on the motion trajectory model to obtain sixth information;

[0134] A first judging unit, configured to judge whether the first information is less than or equal to the sixth information as a first event;

[0135] A second judging unit, configured to judge whether the second information is greater than or equal to the fifth information as a second event;

[0136] An evaluation unit is used to evaluate the danger level of the floating object risk source according to the probability of occurrence of the first event and the second event.

[0137] In a specific implementation of the present disclosure, the first computing unit further includes a fourth acquiring unit, a third computing unit, and a twelfth processing unit, which specifically include:

[0138] A fourth acquisition unit is used to acquire aging damage aging factors of at least five floating object risk sources;

[0139] A third calculation unit is used to calculate the material damage aging intensity corresponding to the floating object risk source according to at least five of the risk source aging damage aging factors;

[0140] The twelfth processing unit is used to construct a floating object risk source damage aging time series model based on the material damage aging intensity corresponding to the floating object risk source.

[0141] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0142] Embodiment 3:

[0143] Corresponding to the above method embodiment, this embodiment also provides a railway perimeter floating object risk source assessment device. The railway perimeter floating object risk source assessment device described below and the railway perimeter floating object risk source assessment method described above can refer to each other.

[0144] Figure 3 FIG. 8 is a block diagram of a railway perimeter floating object risk source assessment device 800 according to an exemplary embodiment. Figure 3 As shown, the railway perimeter floating object risk source assessment device 800 may include: a processor 801 and a memory 802. The railway perimeter floating object risk source assessment device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0145] The processor 801 is used to control the overall operation of the railway perimeter floating object risk source assessment device 800 to complete all or part of the steps in the above-mentioned railway perimeter floating object risk source assessment method. The memory 802 is used to store various types of data to support the operation of the railway perimeter floating object risk source assessment device 800, and these data may include, for example, instructions for any application or method operated on the railway perimeter floating object risk source assessment device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the railway perimeter floating object risk source assessment device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 may include: Wi-Fi module, Bluetooth module, NFC module.

[0146] In an exemplary embodiment, the railway perimeter floating object risk source assessment device 800 can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processing Device, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components, and are used to execute the above-mentioned railway perimeter floating object risk source assessment method.

[0147] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned railway perimeter floating object risk source assessment method are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions may be executed by the processor 801 of the railway perimeter floating object risk source assessment device 800 to complete the above-mentioned railway perimeter floating object risk source assessment method.

[0148] Embodiment 4:

[0149] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the railway perimeter floating object risk source assessment method described above can refer to each other.

[0150] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for assessing risk sources of floating objects around railways in the above method embodiment are implemented.

[0151] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0153] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for assessing risk sources of floating objects around railways, characterized in that: include: Acquire a hyperspectral remote sensing image to be evaluated, wherein the hyperspectral remote sensing image includes a hyperspectral remote sensing image of a railway perimeter; Identify the risk source of floating objects around the railway according to the hyperspectral remote sensing image to be evaluated, and obtain an identification result; Determining first information according to the identification result, the first information including the distance between the risk source of the floating object to be assessed and the railway safety clearance; Dividing the risk area of ​​the railway perimeter according to the railway safety clearance to obtain the divided risk area; Acquiring second information, where the second information includes an environmental wind speed corresponding to a floating object risk source to be assessed; The probability of the floating object risk source invading the divided risk area is calculated according to the first information and the second information to obtain an assessment result, wherein the assessment result includes a danger level of the floating object risk source.

2. The railway perimeter floating object risk source assessment method according to claim 1 is characterized in that: The risk sources of floating objects on the railway perimeter are identified based on the hyperspectral remote sensing image to be evaluated, and the identification results are obtained, including: Extracting features from the hyperspectral remote sensing image to be evaluated to obtain feature information corresponding to each floating object risk source; Constructing a semantic feature label for each floating object risk source based on the feature information corresponding to each floating object risk source; Building a floating object risk source identification model based on a deep learning algorithm and the semantic feature labels of each floating object risk source; The hyperspectral remote sensing image to be evaluated is sent to the floating object risk source identification model to obtain the identification result.

3. The railway perimeter floating object risk source assessment method according to claim 1 is characterized in that: Determining first information according to the recognition result includes: Determine the center point of the floating object risk source map according to the identification result; Obtain the coordinate data information of the railway line; Generating a railway line centerline according to the coordinate data information of the railway line; Generating railway safety clearance according to preset route rules and the railway route centerline; The vertical distance between the railway safety clearance and the center point of the floating object risk source pattern is calculated to obtain first information.

4. The railway perimeter floating object risk source assessment method according to claim 1 is characterized in that: The risk areas of the railway perimeter are divided according to the railway safety clearance, and the divided risk areas include: Acquiring third information, wherein the third information includes a limit drift distance corresponding to a historical extreme wind field; Determine radius information according to the third information and a preset safety factor; Dividing the railway perimeter according to the radius information and the railway safety clearance to obtain a safe area and a risk area; Acquire fourth information, wherein the fourth information includes a maximum value of average wind speed information and instantaneous peak wind speed information within a preset time; The risk areas are divided according to the fourth information.

5. The railway perimeter floating object risk source assessment method according to claim 1 is characterized in that: The probability of the floating object risk source invading the divided risk area is calculated according to the first information and the second information, including: Based on the damage aging time series model of floating object risk source, the critical wind speed corresponding to the floating object risk source to be evaluated is calculated to obtain the fifth information; Calculate the drift distance corresponding to the risk source of the floating object to be assessed based on the motion trajectory model to obtain sixth information; Determining whether the first information is less than or equal to the sixth information is recorded as a first event; Determining whether the second information is greater than or equal to the fifth information is recorded as a second event; The danger level of the floating object risk source is evaluated according to the probability of occurrence of the first event and the second event.

6. A railway perimeter floating object risk source assessment device, characterized in that: include: A first acquisition module is used to acquire a hyperspectral remote sensing image to be evaluated, wherein the hyperspectral remote sensing image includes a hyperspectral remote sensing image of a railway perimeter; The first processing module is used to identify the risk source of floating objects around the railway according to the hyperspectral remote sensing image to be evaluated, and obtain an identification result; A second processing module is used to determine first information according to the identification result, where the first information includes the distance between the risk source of the floating object to be assessed and the railway safety clearance; A third processing module is used to divide the risk area of ​​the railway perimeter according to the railway safety clearance to obtain the divided risk area; A second acquisition module is used to acquire second information, where the second information includes an environmental wind speed corresponding to a floating object risk source to be assessed; The fourth processing module is used to calculate the probability of the floating object risk source invading the divided risk area according to the first information and the second information to obtain an evaluation result, wherein the evaluation result includes a danger level of the floating object risk source.

7. The railway perimeter floating object risk source assessment device according to claim 6, characterized in that: The first processing module comprises: A first processing unit is used to extract features from the hyperspectral remote sensing image to be evaluated to obtain feature information corresponding to each floating object risk source; A second processing unit is used to construct a semantic feature label of each floating object risk source based on the feature information corresponding to each floating object risk source; A third processing unit is used to build a floating object risk source identification model based on a deep learning algorithm and a semantic feature label of each floating object risk source; The fourth processing unit is used to send the hyperspectral remote sensing image to be evaluated to the floating object risk source identification model to obtain the identification result.

8. The railway perimeter floating object risk source assessment device according to claim 6, characterized in that: The second processing module comprises: A fifth processing unit, configured to determine a center point of a floating object risk source pattern according to the identification result; A first acquisition unit, used to acquire coordinate data information of a railway line; A sixth processing unit, configured to generate a railway line centerline according to the coordinate data information of the railway line; A seventh processing unit, configured to generate a railway safety clearance according to a preset line rule and the center line of the railway line; The eighth processing unit is used to calculate the vertical distance between the railway safety clearance and the center point of the floating object risk source pattern to obtain the first information.

9. The railway perimeter floating object risk source assessment device according to claim 6, characterized in that: The third processing module comprises: A second acquisition unit is used to acquire third information, where the third information includes a limit drift distance corresponding to a historical extreme wind field; a ninth processing unit, configured to determine radius information according to the third information and a preset safety factor; a tenth processing unit, configured to divide the railway perimeter according to the radius information and the railway safety clearance to obtain a safe area and a risk area; A third acquisition unit, used to acquire fourth information, wherein the fourth information includes a maximum value of the average wind speed information within a preset time and instantaneous peak wind speed information; An eleventh processing unit is used to divide the risk area according to the fourth information.

10. The railway perimeter floating object risk source assessment device according to claim 6, characterized in that: The fourth processing module comprises: The first calculation unit is used to calculate the critical wind speed corresponding to the floating object risk source to be evaluated based on the floating object risk source damage aging time series model to obtain fifth information; A second calculation unit is used to calculate the drift distance corresponding to the risk source of the floating object to be evaluated based on the motion trajectory model to obtain sixth information; A first judging unit, configured to judge whether the first information is less than or equal to the sixth information as a first event; A second judging unit, configured to judge whether the second information is greater than or equal to the fifth information as a second event; An evaluation unit is used to evaluate the danger level of the floating object risk source according to the probability of occurrence of the first event and the second event.

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