Intelligent early warning system for line safety

Abstract

The invention relates to the field of line safety early warning, in particular to an intelligent early warning system for line safety, which is provided with an acquisition and detection module for acquiring real-time pictures of a train running line and detecting road condition data of the line; the feature extraction module is used for dividing a train running route into a plurality of road sections, extracting abnormal features of each road section according to a train running route real-time picture acquired by the image acquisition unit, and identifying noise intensity of road section detection points according to route road condition data acquired by the data detection unit; the feature analysis module is used for analyzing suitable road condition characterization parameters of train operation based on the abnormal features in combination with the noise intensity of the road section detection points, and distinguishing the operation stability condition of each road section based on the suitable road condition characterization parameters of train operation; and the monitoring identification module is used for adaptively selecting a monitoring mode based on the operation stability condition of each road section, so that abnormal conditions influencing the normal operation of the train are warned in time, and the normal operation of the train is ensured.

Description

Technical Field

[0001] The present invention relates to the field of line safety early warning, and in particular to a line safety intelligent early warning system. Background Art

[0002] With the large-scale construction of rail transit, the line safety warning system has gradually become a research hotspot in the field of rail transit. The line safety warning system detects relevant track data in real time by setting up sensors, and uses big data analysis to predict potential safety hazards; it monitors train operation in all directions through image acquisition, and promptly detects and handles abnormal situations; it processes and analyzes big data to improve the accuracy and timeliness of warnings.

[0003] Chinese Patent Publication No.: CN115187048A, discloses a method and system for identifying the status of foreign object intrusion events in multiple domains of rail lines, belonging to the field of railway disaster prevention risk warning technology. According to the monitoring requirements of the entire process of foreign object intrusion events, a track surface state acquisition sensor device is selected to collect track surface state data; the track surface limit area is divided into a focus area, a warning area, and a safety area, and foreign objects invading the limit are identified, tracked, and features are extracted; a limit intrusion event state description model is defined; based on the limit intrusion event state characteristics, the identified limit intrusion foreign object feature data is used as the limit intrusion event state description model dynamic feature parameter quantification risk value, and the limit intrusion foreign object event detection rate of the track surface limit safety area, warning area, and focus area is calculated.

[0004] However, the following problems still exist in the prior art: after the track has been put into use for a long time, the fasteners and connecting parts may be damaged due to metal wear or corrosion, affecting the stability of the track. At the same time, the track may be deformed. The degree of deformation of the track and the visibility of the train running section may affect the normal operation of the train. Summary of the invention

[0005] To this end, the present invention provides an intelligent line safety warning system to overcome the problem that after the track has been in use for a long time, the fasteners and connecting parts may be damaged due to metal wear or corrosion, affecting the stability of the track. At the same time, the track may be deformed. The degree of deformation of the track and the visibility of the train running section may affect the normal operation of the train.

[0006] To achieve the above object, the present invention provides a line safety intelligent early warning system, which includes: A line safety intelligent early warning system, comprising: An acquisition and detection module, comprising an image acquisition unit for acquiring real-time images of a train running line and a data detection unit for detecting line condition data, wherein the line condition data includes noise intensity and vibration frequency; a feature extraction module, which is connected to the acquisition and detection module, and is used to divide the train running route into a plurality of sections, extract abnormal features of each section according to the real-time picture of the train running route collected by the image acquisition unit, and identify the noise intensity of the section detection point according to the line road condition data collected by the data detection unit, wherein the abnormal features include the moving speed of the moving body and the coverage area of ​​the obstacle body; A feature analysis module, which is connected to the feature extraction module, is used to analyze the road condition suitability characterization parameters of the train operation based on the abnormal features combined with the noise intensity of the road section detection point, and distinguish the operation stability of each section based on the road condition suitability characterization parameters of the train operation; The monitoring and identification module is connected to the feature extraction module and the feature analysis module respectively, and is used to select a monitoring method based on the operation stability of each road section, including: The upper limit of the train running speed is adjusted according to the road condition suitability characterization parameter, and at the same time, the track deviation characteristics of each section are monitored, and the road condition safety level is determined according to the track deviation characteristics combined with the visibility of the section where the deviation is located, and whether the normal operation standard is met based on the road condition safety level, so as to determine whether to suspend the operation of the train at a predetermined distance from the section where the deviation is located; Or, adjust the upper limit of train speed according to abnormal characteristics; Wherein, the offset characteristics include the vibration frequency of the road section detection point and the deformation degree of the road section track; an early warning module, which is connected to the monitoring and identification module and is used to send early warning information to the train dispatching center and the train cab based on the analysis results of the monitoring and identification module, The warning information includes the operating stability and the location information of the track deviation section.

[0007] Furthermore, the characteristic analysis module is used to analyze the road condition suitable characterization parameters of train operation based on abnormal characteristics combined with the noise intensity of the road section detection point, including: (1) In formula (1), K represents the road condition suitability characterization parameter, V represents the moving speed of the moving object, S represents the coverage area of ​​the obstacle, N represents the noise intensity of the road section detection point, N0 represents the preset noise intensity threshold of the road section detection point, α is the weight coefficient of the moving speed of the moving object and the coverage area of ​​the obstacle, and β is the noise intensity weight coefficient of the road section detection point.

[0008] Furthermore, the feature analysis module is used to distinguish the operation stability of each section based on the road condition suitability characterization parameters of the train operation, including: If the road condition suitability characterization parameter is greater than or equal to the road condition suitability characterization parameter threshold, the operation condition of the road section is determined to be a weakly stable category; If the road condition suitability characterization parameter is less than the road condition suitability characterization parameter threshold, the operation condition of the road section is determined to be a strong stable category.

[0009] Further, the monitoring and identification module is used to select the operation stability based on each road section, and the selected monitoring method includes: If the operation condition of the section is of the weak stability category, the upper limit of the train speed is adjusted according to the road condition suitability characterization parameters. At the same time, the track deviation characteristics of each section are monitored, and the visibility of the section where the track deviation is located is combined with the track deviation characteristics to determine whether it meets the normal operation standards, so as to determine whether to suspend the operation of the train at a predetermined distance from the section where the track deviation is located; If the operating conditions of the section are of the strongly stable category, the upper limit of the train operating speed will be adjusted based on the abnormal characteristics.

[0010] Furthermore, the monitoring and identification module is used to adjust the upper limit of the train running speed according to the road condition suitability characterization parameter, including: The upper limit of train operating speed is lowered, and the speed upper limit reduction value is positively correlated with the road condition suitability characterization parameter.

[0011] Furthermore, the monitoring and identification module is used to determine the road safety level according to the track deviation characteristics combined with the visibility of the road section where the track deviation is located, including: Used to obtain the vibration frequency data of each road section detection point detected by the data detection unit; Used to extract track deformation data based on the real-time image data of the train running route collected by the image acquisition unit; Used to assign corresponding weight values ​​as the first weight to the vibration frequency of the road section detection point and the degree of track deformation of the road section where the offset is located; for assigning a corresponding weight value as a second weight to the visibility of the road section where the offset is located; Used to determine the road safety level according to the sum of the first weight and the second weight.

[0012] Furthermore, the monitoring and identification module is used to determine whether the road safety level meets the normal operation standard, including: If the road safety level is less than the road safety level threshold, it is determined to meet the normal operating standards.

[0013] Furthermore, the monitoring and identification module is used to determine whether to suspend the operation of the train at a predetermined distance from the section where the deviation is located, including: If the section where the deviation is located does not meet the normal operating standards, the operation of the train will be suspended at a predetermined distance from the section where the deviation is located.

[0014] Furthermore, the monitoring and identification module is used to adjust the upper limit of the train speed according to the abnormal characteristics, including: Lowering the upper limit of the train running speed, wherein the value of the speed upper limit reduction is positively correlated with the moving speed of the moving body; The upper limit of the train running speed is reduced, and the speed upper limit reduction value is positively correlated with the coverage area of ​​the obstacle.

[0015] Furthermore, it also includes an early warning information display module, which is connected to the early warning module and is used to display the content of the early warning information issued by the early warning module.

[0016] Compared with the prior art, the present invention sets up an acquisition and detection module for collecting real-time images of train running lines and detecting line road condition data; a feature extraction module is used to divide the train running line into a number of sections, extract abnormal features of each section according to the real-time images of the train running line collected by the image acquisition unit, and identify the noise intensity of the section detection point according to the line road condition data collected by the data detection unit; a feature analysis module is used to analyze the road condition suitable characterization parameters of the train running based on the abnormal features combined with the noise intensity of the section detection points, and distinguish the operation stability of each section based on the road condition suitable characterization parameters of the train running; a monitoring and identification module is used to adaptively select the monitoring method based on the operation stability of each section, and then timely warn of abnormal conditions that affect the normal operation of the train to ensure the normal operation of the train.

[0017] In particular, the present invention analyzes the road condition suitable characterization parameters for train operation based on abnormal characteristics combined with the noise intensity of the section detection point. During the track operation process, the route traveled by the train is pre-planned. Therefore, some abnormal conditions may occur in the section where the train needs to run. For example, animals may migrate in the section ahead of the train, or soil may slide beside the section, causing soil accumulation on the track surface. Depending on the severity of the abnormal situation, the time required for the staff to handle and solve it is also different. Therefore, it may affect the normal operation of the train. In addition, the track is spliced ​​with a number of fasteners and connecting parts. The above parts may be damaged due to metal wear or corrosion, affecting the stability of the track, and then, it will also affect the normal operation of the train. Therefore, the present application analyzes the road condition suitable characterization parameters for train operation by combining the moving speed of the moving body on the track of the train operation route and the coverage area of ​​the obstacle with the noise intensity of the section detection point to characterize the degree of normal operation of the train, and provide data support for the subsequent differentiation of the operation stability of each section, so as to adaptively select the monitoring method, and then, timely warn of abnormal conditions that affect the normal operation of the train to ensure the normal operation of the train.

[0018] In particular, when the operation condition of the road section is of the weak stability category, the present invention pre-adjusts the upper limit of the train speed so that the train runs at a slower speed, giving the staff more time to deal with the abnormal conditions of the road section ahead, and reducing the possibility of taking measures to suspend the operation of the train. At the same time, the present application considers that during the long-term use of the track, the track will produce different degrees of deformation, such as track deformation, height deformation, horizontal deformation and twisting deformation. The above deformations gradually deteriorate over time, which may affect the smoothness of the train operation and the stability of the track. In addition, the vibration frequency generated by the track section with the above deformation is relatively higher than that of the normal track. The normal section is higher, which may generate higher frequency noise and affect the stability of train operation. This application determines the offset characteristics according to the deformation degree and vibration frequency of the section track, and determines the section with the offset characteristics as the section where the offset is located. The visibility of the section where the offset is located is a reference factor for the normal operation of the train. Therefore, the safety level of the road condition is comprehensively characterized by the offset characteristics combined with the visibility of the section where the offset is located, so as to determine whether the section where the offset is located meets the normal operating standards, and further determine whether to suspend the train operation at a predetermined distance from the section where the offset is located, and then, timely warn of abnormal conditions that affect the normal operation of the train to ensure the normal operation of the train. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a functional module diagram of the line safety intelligent early warning system of the embodiment of the invention; Figure 2 A logic decision diagram for distinguishing the operational stability of each road section according to an embodiment of the invention; Figure 3 A logic decision diagram for determining whether a deviation section meets normal operation standards according to an embodiment of the invention; Figure 4 A logical decision diagram for determining whether to suspend train operation at a predetermined distance from an offset section according to an embodiment of the invention. DETAILED DESCRIPTION

[0020] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0022] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] See also Figures 1 to 4 As shown, Figure 1 is a functional module diagram of the line safety intelligent early warning system according to an embodiment of the present invention, Figure 2 A logic decision diagram for distinguishing the operational stability of each road section according to an embodiment of the invention, Figure 3 This is a logic decision diagram for determining whether the deviation section meets the normal operation standard according to the embodiment of the invention. Figure 4 This is a logic decision diagram for determining to suspend the train operation at a predetermined distance from the offset section according to an embodiment of the invention. The line safety intelligent early warning system according to an embodiment of the invention comprises: An acquisition and detection module, comprising an image acquisition unit for acquiring real-time images of a train running line and a data detection unit for detecting line condition data, wherein the line condition data includes noise intensity and vibration frequency; a feature extraction module, which is connected to the acquisition and detection module, and is used to divide the train running route into a plurality of sections, extract abnormal features of each section according to the real-time picture of the train running route collected by the image acquisition unit, and identify the noise intensity of the section detection point according to the line road condition data collected by the data detection unit, wherein the abnormal features include the moving speed of the moving body and the coverage area of ​​the obstacle body; A feature analysis module, which is connected to the feature extraction module, is used to analyze the road condition suitability characterization parameters of the train operation based on the abnormal features combined with the noise intensity of the road section detection point, and distinguish the operation stability of each section based on the road condition suitability characterization parameters of the train operation; The monitoring and identification module is connected to the feature extraction module and the feature analysis module respectively, and is used to select a monitoring method based on the operation stability of each road section, including: The upper limit of the train running speed is adjusted according to the road condition suitability characterization parameter, and at the same time, the track deviation characteristics of each section are monitored, and the road condition safety level is determined according to the track deviation characteristics combined with the visibility of the section where the deviation is located, and whether the normal operation standard is met based on the road condition safety level, so as to determine whether to suspend the operation of the train at a predetermined distance from the section where the deviation is located; Or, adjust the upper limit of train speed according to abnormal characteristics; Wherein, the offset characteristics include the vibration frequency of the road section detection point and the deformation degree of the road section track; an early warning module, which is connected to the monitoring and identification module and is used to send early warning information to the train dispatching center and the train cab based on the analysis results of the monitoring and identification module, The warning information includes the operating stability and the location information of the track deviation section.

[0024] Specifically, there is no specific limitation on the image acquisition unit, which only needs to have the function of acquiring real-time images of the train running line, for example, by scanning the track image of the running train through a movable laser imaging device. This will not be elaborated here.

[0025] Specifically, there is no specific limitation on the data detection unit, which only needs to have the function of detecting the noise intensity and vibration frequency of the detection points of each section. For example, by setting detection points on the side track walls at the connection of the parts of each section, sound level meters and vibration sensors are placed at the detection points to detect the noise intensity and vibration frequency of the detection points of each section. Of course, other forms can also be used, which will not be repeated here.

[0026] Specifically, there is no limitation on the way of dividing the train route. The stations that the train passes through can be used as the basis for division, and the distance between two adjacent stations is determined as a section, which will not be elaborated here.

[0027] Specifically, the appearance of moving objects and obstacles are situations that hinder the passage of trains. Moving objects include migrating animal groups and soil sliding beside the road section, and obstacles include soil layers accumulated on the track surface after the soil slides, etc., which will not be elaborated here.

[0028] Specifically, the predetermined distance from the offset section is determined based on the corresponding station of the offset section, and the train is suspended at the station before the corresponding station. Therefore, the predetermined distance is the distance between the corresponding station of the offset section and the previous station.

[0029] Specifically, there is no limitation on the feature extraction module, feature analysis module, monitoring and identification module, early warning module and the specific structure of the module, and they themselves or each unit therein can be composed of logical components or a combination of logical components, and the logical components include a field programmable processor, a computer or a microprocessor in a computer.

[0030] Specifically, the characteristic analysis module is used to analyze the road condition suitable characterization parameters of train operation based on abnormal characteristics combined with the noise intensity of the road section detection point, including: (1) In formula (1), K represents the road condition suitability characterization parameter, V represents the moving speed of the moving object, S represents the coverage area of ​​the obstacle, N represents the noise intensity of the road section detection point, N0 represents the preset noise intensity threshold of the road section detection point, α is the weight coefficient of the moving speed of the moving object and the coverage area of ​​the obstacle, and β is the noise intensity weight coefficient of the road section detection point.

[0031] In this embodiment, the moving speed V of the moving body and the coverage area S of the obstacle are obtained through real-time detection, and the noise intensity threshold value N0 of the section detection point is obtained in advance. The relevant data of the line safety monitoring completed by the same train for several times are obtained, and the noise intensity N data of the detection points of each section of the line are extracted to solve the noise intensity mean ΔN. It is set that N0=r×ΔN, r is the accuracy coefficient, 0.85<r<1.05, α is 0.55, and β is 0.45.

[0032] The road condition suitability characterization parameter threshold K0 is selected in the interval [1.16,1.23].

[0033] The present invention analyzes the road condition suitable characterization parameters of train operation based on abnormal characteristics combined with the noise intensity of the section detection point. During the track operation process, the route traveled by the train is pre-planned. Therefore, some abnormal situations may occur in the section where the train needs to run. For example, animals may migrate in the section ahead of the train, or soil may slide beside the section, causing soil accumulation on the track surface. Depending on the severity of the abnormal situation, the time required for the staff to handle and solve it is also different. Therefore, it may affect the normal operation of the train. In addition, the track is spliced ​​with a number of fasteners and connecting parts. The above parts may be damaged due to metal wear or corrosion, affecting the stability of the track, and then, it will also affect the normal operation of the train. Therefore, the present application analyzes the road condition suitable characterization parameters of the train operation through the moving speed of the moving body on the track of the train operation route and the coverage area of ​​the obstacle body combined with the noise intensity of the section detection point to characterize the degree of normal operation of the train, and provide data support for the subsequent differentiation of the operation stability of each section, so as to adaptively select the monitoring method, and then, timely warn of abnormal conditions that affect the normal operation of the train to ensure the normal operation of the train.

[0034] Specifically, the feature analysis module is used to distinguish the operation stability of each section based on the road condition suitability characterization parameters of the train operation, including: If the road condition suitability characterization parameter is greater than or equal to the road condition suitability characterization parameter threshold, the operation condition of the road section is determined to be a weakly stable category; If the road condition suitability characterization parameter is less than the road condition suitability characterization parameter threshold, the operation condition of the road section is determined to be a strong stable category.

[0035] Specifically, the monitoring and identification module is used to select the operation stability of each road section, and the selected monitoring method includes: If the operation condition of the section is of the weak stability category, the upper limit of the train speed is adjusted according to the road condition suitability characterization parameters. At the same time, the track deviation characteristics of each section are monitored, and the visibility of the section where the track deviation is located is combined with the track deviation characteristics to determine whether it meets the normal operation standards, so as to determine whether to suspend the operation of the train at a predetermined distance from the section where the track deviation is located; If the operating conditions of the section are of the strongly stable category, the upper limit of the train operating speed will be adjusted based on the abnormal characteristics.

[0036] Specifically, the monitoring and identification module is used to adjust the upper limit of the train speed according to the road condition suitability characterization parameter, including: The upper limit of train operating speed is lowered, and the speed upper limit reduction value is positively correlated with the road condition suitability characterization parameter.

[0037] In this embodiment, optionally, The road condition suitability characterization parameter K is compared with a preset first road condition suitability characterization parameter comparison threshold value K1 and a second road condition suitability characterization parameter comparison threshold value K2. If K>K2, the speed upper limit reduction value is determined to be the first speed upper limit reduction value c1, and c1=0.45c0 is set; If K1≤K≤K2, the speed upper limit reduction value is determined to be the second speed upper limit reduction value c2, and c2=0.37c0 is set; If K<K1, the speed upper limit reduction value is determined to be the third speed upper limit reduction value c3, and c3=0.3c0 is set; Among them, c0 represents the upper limit of initial speed, K1=1.2K0, K2=1.3K0.

[0038] Specifically, for determining the upper limit of the initial speed, those skilled in the art can make specific determinations based on the track conditions along which the train line passes and the train design, etc., which will not be elaborated here.

[0039] Specifically, the monitoring and identification module is used to determine the road safety level according to the track deviation characteristics combined with the visibility of the road section where the track deviation is located, including: Used to obtain the vibration frequency data of each road section detection point detected by the data detection unit; Used to extract track deformation data based on the real-time image data of the train running route collected by the image acquisition unit; To assign corresponding weight values ​​as the first weight Q1 to the vibration frequency of the road section detection point and the track deformation degree of the road section where the offset is located; Used to assign a corresponding weight value to the visibility of the road section where the offset is located as the second weight Q2; Used to determine the road safety level according to the sum of the first weight and the second weight.

[0040] Q1 is 0.57, Q2 is 0.43, and the road safety threshold L0 is selected in the interval [1.58, 1.64].

[0041] Specifically, the degree of track deformation of the offset section is compared with the track image collected by the acquisition and detection module and the track sample image that has not been put into use, and the similarity between the track image and the track sample image is obtained. The degree of track deformation of the offset section is determined based on the similarity, wherein the track sample image data information is pre-stored in the acquisition and detection module.

[0042] Specifically, the monitoring and identification module is used to determine whether the road safety level meets the normal operation standard, including: If the road safety level is less than the road safety level threshold, it is determined to meet the normal operating standards.

[0043] Specifically, the monitoring and identification module is used to determine whether to suspend the operation of the train at a predetermined distance from the section where the offset is located, including: If the section where the deviation is located does not meet the normal operating standards, the operation of the train will be suspended at a predetermined distance from the section where the deviation is located.

[0044] When the operation of the section is in the weak stability category, the present invention pre-adjusts the upper limit of the train speed so that the train runs at a slower speed, giving the staff more time to deal with the abnormal situation of the section ahead, and reducing the possibility of taking measures to suspend the operation of the train. At the same time, the present application considers that during the long-term use of the track, the track will produce different degrees of deformation, such as track deformation, height deformation, horizontal deformation and twisting deformation. The above deformation gradually deteriorates over time, which may affect the smoothness of the train operation and the stability of the track. In addition, the vibration frequency generated by the track section with the above deformation is higher than that of the normal track. The higher the section, the more likely it is to generate higher-frequency noise, affecting the stability of the train operation. This application determines the offset characteristics based on the degree of deformation of the section track and the vibration frequency, and determines the section with the offset characteristics as the section where the offset is located. The visibility of the section where the offset is located is a reference factor for the normal operation of the train. Therefore, the offset characteristics are combined with the visibility of the section where the offset is located to comprehensively characterize the safety level of the road condition, so as to determine whether the section where the offset is located meets the normal operating standards, and further determine whether to suspend the train operation at a predetermined distance from the section where the offset is located, and then, timely warn of abnormal conditions that affect the normal operation of the train to ensure the normal operation of the train.

[0045] Specifically, the monitoring and identification module is used to adjust the upper limit of the train speed according to the abnormal characteristics, including: Lowering the upper limit of the train running speed, wherein the value of the speed upper limit reduction is positively correlated with the moving speed V of the moving body; The upper limit of the train running speed is reduced, and the speed upper limit reduction value is positively correlated with the coverage area S of the obstacle.

[0046] In this embodiment, optionally, The moving speed V of the moving object is compared with a preset first moving object moving speed comparison threshold V1 and a second moving object moving speed comparison threshold V2, If V>V2, the speed upper limit reduction value is determined to be the first speed upper limit reduction value c1, and c1=0.36c0 is set; If V1≤V≤V2, the speed upper limit reduction value is determined to be the second speed upper limit reduction value c2, and c2=0.3c0 is set; If V<V1, the speed upper limit reduction value is determined to be the third speed upper limit reduction value c3, and c3=0.25c0 is set; Among them, c0 represents the upper limit of initial speed, V1=1.17V0, V2=1.21V0.

[0047] The coverage area S of the obstacle is compared with a preset comparison threshold S1 of the coverage area of ​​the first obstacle and a preset comparison threshold S2 of the coverage area of ​​the second obstacle. If S>S2, the speed upper limit reduction value is determined to be the first speed upper limit reduction value c1, and c1=0.4c0 is set; If S1≤S≤S2, the speed upper limit reduction value is determined to be the second speed upper limit reduction value c2, and c2=0.35c0 is set; If S<S1, the speed upper limit reduction value is determined to be the third speed upper limit reduction value c3, and c3=0.3c0 is set; Among them, c0 represents the upper limit of initial speed, S1=1.2S0, V2=1.4S0.

[0048] Specifically, it also includes an early warning information display module, which is connected to the early warning module and is used to display the content of the early warning information issued by the early warning module.

[0049] Specifically, there is no limitation on the specific structure of the warning display module, which only needs to have the function of displaying the warning information content issued by the warning module, such as an electronic display screen, which will not be elaborated here.

[0050] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A line safety intelligent early warning system, characterized in that: include: An acquisition and detection module, comprising an image acquisition unit for acquiring real-time images of a train running line and a data detection unit for detecting line condition data, wherein the line condition data includes noise intensity and vibration frequency; A feature extraction module, which is connected to the acquisition and detection module, is used to divide the train running route into a plurality of sections, extract abnormal features of each section according to the real-time picture of the train running route collected by the image acquisition unit, and identify the noise intensity of the section detection point according to the line road condition data collected by the data detection unit, wherein the abnormal features include the moving speed of the moving body and the coverage area of ​​the obstacle body; A feature analysis module, which is connected to the feature extraction module, is used to analyze the road condition suitability characterization parameters of the train operation based on the abnormal features combined with the noise intensity of the road section detection point, and distinguish the operation stability of each section based on the road condition suitability characterization parameters of the train operation; The monitoring and identification module is connected to the feature extraction module and the feature analysis module respectively, and is used to select a monitoring method based on the operation stability of each road section, including: The upper limit of the train running speed is adjusted according to the road condition suitability characterization parameter, and at the same time, the track deviation characteristics of each section are monitored, and the road condition safety level is determined according to the track deviation characteristics combined with the visibility of the section where the deviation is located, and whether the normal operation standard is met based on the road condition safety level, so as to determine whether to suspend the operation of the train at a predetermined distance from the section where the deviation is located; Or, adjust the upper limit of train speed according to abnormal characteristics; Wherein, the offset characteristics include the vibration frequency of the road section detection point and the deformation degree of the road section track; an early warning module, which is connected to the monitoring and identification module and is used to send early warning information to the train dispatching center and the train cab based on the analysis results of the monitoring and identification module, The warning information includes the operating stability and the location information of the track deviation section.

2. The line safety intelligent early warning system according to claim 1 is characterized in that: The characteristic analysis module is used to analyze the road condition suitable characterization parameters of train operation based on abnormal characteristics combined with the noise intensity of the road section detection point, including: Among them, K represents the road condition suitability characterization parameter, V represents the moving speed of the moving object, S represents the coverage area of ​​the obstacle, N represents the noise intensity of the road section detection point, N0 represents the preset noise intensity threshold of the road section detection point, α is the weight coefficient of the moving speed of the moving object and the coverage area of ​​the obstacle, and β is the noise intensity weight coefficient of the road section detection point.

3. The line safety intelligent early warning system according to claim 1 is characterized in that: The feature analysis module is used to distinguish the operation stability of each section based on the road condition suitability characterization parameters of the train operation, including: If the road condition suitability characterization parameter is greater than or equal to the road condition suitability characterization parameter threshold, the operation condition of the road section is determined to be a weakly stable category; If the road condition suitability characterization parameter is less than the road condition suitability characterization parameter threshold, the operation condition of the road section is determined to be a strong stable category.

4. The line safety intelligent early warning system according to claim 1 is characterized in that: The monitoring and identification module is used to select the operation stability of each road section, and the selected monitoring method includes: If the operation condition of the section is of the weak stability category, the upper limit of the train speed is adjusted according to the road condition suitability characterization parameters. At the same time, the track deviation characteristics of each section are monitored, and the visibility of the section where the track deviation is located is combined with the track deviation characteristics to determine whether it meets the normal operation standards, so as to determine whether to suspend the operation of the train at a predetermined distance from the section where the track deviation is located; If the operating conditions of the section are of the strongly stable category, the upper limit of the train operating speed will be adjusted based on the abnormal characteristics.

5. The line safety intelligent early warning system according to claim 1 is characterized in that: The monitoring and identification module is used to adjust the upper limit of the train running speed according to the road condition suitability characterization parameter, including: The upper limit of train operating speed is lowered, and the speed upper limit reduction value is positively correlated with the road condition suitability characterization parameter.

6. The line safety intelligent early warning system according to claim 1 is characterized in that: The monitoring and identification module is used to determine the road safety level according to the track deviation characteristics combined with the visibility of the road section where the track deviation is located, including: Used to obtain the vibration frequency data of each road section detection point detected by the data detection unit; Used to extract track deformation data based on the real-time image data of the train running route collected by the image acquisition unit; Used to assign corresponding weight values ​​as the first weight to the vibration frequency of the road section detection point and the degree of track deformation of the road section where the offset is located; for assigning a corresponding weight value as a second weight to the visibility of the road section where the offset is located; Used to determine the road safety level according to the sum of the first weight and the second weight.

7. The line safety intelligent early warning system according to claim 1 is characterized in that: The monitoring and identification module is used to determine whether the road condition meets the normal operation standard based on the road condition safety level, including: If the road safety level is less than the road safety level threshold, it is determined to meet the normal operating standards.

8. The line safety intelligent early warning system according to claim 1 is characterized in that: The monitoring and identification module is used to determine whether to suspend the operation of the train at a predetermined distance from the offset section, including: If the section where the deviation is located does not meet the normal operating standards, the operation of the train will be suspended at a predetermined distance from the section where the deviation is located.

9. The line safety intelligent early warning system according to claim 1 is characterized in that: The monitoring and identification module is used to adjust the upper limit of the train speed according to the abnormal characteristics, including: Lowering the upper limit of the train running speed, wherein the value of the speed upper limit reduction is positively correlated with the moving speed of the moving body; The upper limit of the train running speed is reduced, and the speed upper limit reduction value is positively correlated with the coverage area of ​​the obstacle.

10. The line safety intelligent early warning system according to claim 1, characterized in that: It also includes an early warning information display module, which is connected to the early warning module and is used to display the content of the early warning information issued by the early warning module.

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