Multi-source heterogeneous railway monitoring data management method, system, equipment and medium

Abstract

The invention discloses a multi-source heterogeneous railway monitoring data management method, system and device and a medium. The method comprises the following steps: configuring a data acquisition rule and a data cloud edge storage rule of railway monitoring data; performing data acquisition and data storage on the railway monitoring data according to the data acquisition rule and the data cloud edge storage rule; wherein the data cloud edge storage rule comprises an edge node cache elimination model and an elastic storage strategy; the data acquisition means that after railway monitoring data stored at the edge end are processed by a data acquisition rule and an edge node cache elimination model, obtained target monitoring data are synchronously acquired to an acquisition end; the data storage means that the target monitoring data is synchronously stored to the cloud according to an elastic storage strategy. According to the dynamic strategy of network fluctuation and different data priorities, required multi-source heterogeneous data can be elastically stored, the data acquisition efficiency is greatly improved, and the method is suitable for full-life-cycle management of railway monitoring data.

Description

Technical Field

[0001] This application relates to the technical field of rail transit data management, and particularly to a multi-source heterogeneous railway monitoring data management method, system, device and medium. Background Art

[0002] In complex operating environments such as high-speed railways, the full-life cycle management of corresponding monitoring data mainly includes the following processes: data collection, data transmission, and data storage. Among them, the collected data includes data such as optical fibers, radars, pictures, and videos. These data have large format differences and large data volumes, resulting in low collection efficiency. Furthermore, during the data transmission process, the network transmission of the full-volume data collection is prone to network congestion, and the transmission bandwidth within the railway is limited, making it difficult to guarantee the timeliness of key data. In data storage, the fixed storage period cannot adapt to the requirements of different business scenarios, and data units cannot dynamically adjust their own slicing rules according to policies.

[0003] Therefore, although the existing technologies have formed a relatively complete technical system in the aspects of data collection, transmission, storage, etc., there are still significant bottlenecks in aspects such as heterogeneous data compatibility, dynamic allocation of network resources, and self-adaptability of storage strategies. Summary of the Invention

[0004] The main technical problem to be solved by this application is to provide a multi-source heterogeneous railway monitoring data management method, system, device and medium, so as to solve the problem that there are still significant bottlenecks in aspects such as heterogeneous data compatibility, dynamic allocation of network resources, and self-adaptability of storage strategies in the full-life cycle management of existing railway monitoring data.

[0005] To solve the above technical problem, a technical solution adopted by this application is to provide a multi-source heterogeneous railway monitoring data management method. The method includes the steps of: configuring data collection rules and data cloud-edge storage rules for railway monitoring data; according to the data collection rules and data cloud-edge storage rules, performing data collection and data storage on the railway monitoring data; wherein, the data cloud-edge storage rules include an edge node cache elimination model and an elastic storage strategy; the data collection refers to synchronously collecting the target monitoring data obtained by processing the railway monitoring data stored at the edge end through the data collection rules and the edge node cache elimination model to the collection end; the data storage refers to synchronously storing the target monitoring data to the cloud according to the elastic storage strategy.

[0006] In some embodiments, the data collection rules include settings of the railway monitoring data corresponding to time range, line, data tag, and device dimension.

[0007] In some embodiments, the data collection and data storage of the railway monitoring data include: adaptively changing the data collection mode according to the real-time monitored network bandwidth; the data collection mode includes a key target data mode and a general data mode, the key target data mode is for a large amount of the railway monitoring data, and according to the network bandwidth, the target monitoring data of different slice sizes is collected; the general data mode collects the target monitoring data of a fixed slice size according to a custom rule dimension.

[0008] In some embodiments, the edge node cache elimination model includes a data value evaluation factor and a hybrid elimination strategy, the data value evaluation factor includes a timeliness coefficient and a service weight, and is used to evaluate the data value of the railway monitoring data; the hybrid elimination strategy means eliminating the railway monitoring data with low data value and long unaccessed time stored at the edge.

[0009] In some embodiments, the data collection and data storage of the railway monitoring data include: calculating the data value weight of the railway monitoring data according to the data value evaluation factor; obtaining the target monitoring data according to the hybrid elimination strategy; wherein, eliminating the railway monitoring data with the lowest data value weight accounting for the first ratio and the railway monitoring data that has not been accessed for the longest time accounting for the second ratio; the sum of the first ratio and the second ratio is 1.

[0010] In some embodiments, the data value weight is:

[0011] ;

[0012] Wherein, represents the data value weight, represents the timeliness coefficient, represents the current time minus the generation time of the railway monitoring data, represents the half-life, represents the service weight, represents the priority of the railway monitoring data.

[0013] In some embodiments, the elastic storage strategy means storing the target monitoring data in the cloud with a corresponding cloud storage period; when performing the data storage, it is necessary to calculate the cloud storage period according to the basic period, data importance level and data update frequency of the target monitoring data; the cloud storage period is:

[0014] ;

[0015] Wherein, represents the cloud storage period, represents the said basic period represents the said data importance level represents the said data update frequency

[0016] The present application also provides a multi-source heterogeneous railway monitoring data management system, which includes: a rule configuration unit for configuring data collection rules and data cloud-edge storage rules for railway monitoring data; a data management unit for collecting and storing the railway monitoring data according to the data collection rules and the data cloud-edge storage rules; wherein, the data cloud-edge storage rules include an edge node cache elimination model and an elastic storage policy; the data collection refers to synchronously collecting the target monitoring data obtained by processing the railway monitoring data stored at the edge end through the data collection rules and the edge node cache elimination model to the collection end; the data storage refers to synchronously storing the target monitoring data to the cloud according to the elastic storage policy.

[0017] The present application also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the method are implemented.

[0018] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the said method are implemented.

[0019] The beneficial effects of the present application are: The present application discloses a multi-source heterogeneous railway monitoring data management method, system, device, and medium; the method includes the steps of: configuring data collection rules and data cloud-edge storage rules for railway monitoring data; collecting and storing the railway monitoring data according to the data collection rules and the data cloud-edge storage rules; wherein, the data cloud-edge storage rules include an edge node cache elimination model and an elastic storage policy; the data collection refers to synchronously collecting the target monitoring data obtained by processing the railway monitoring data stored at the edge end through the data collection rules and the edge node cache elimination model to the collection end; the data storage refers to synchronously storing the target monitoring data to the cloud according to the elastic storage policy. The present invention can elastically store the required multi-source heterogeneous data according to the dynamic policy of network fluctuations and different data priorities, greatly improving the data collection efficiency and being applicable to the full life cycle management of railway monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flowchart of an embodiment of a multi-source heterogeneous railway monitoring data management method of the present application;

[0021] Figure 2It is a specific flowchart of step S2 in an embodiment of a multi-source heterogeneous railway monitoring data management method of the present application;

[0022] Figure 3 It is a block diagram of the composition of an embodiment of a multi-source heterogeneous railway monitoring data management system of the present application;

[0023] Figure 4 It is a schematic architecture diagram of an embodiment of an electronic device of the present application;

[0024] Figure 5 It is a schematic block diagram of an embodiment of a computer-readable storage medium of the present application. Detailed implementation manners

[0025] To facilitate the understanding of the present application, the following further describes the present application in detail with reference to the accompanying drawings and specific embodiments. The accompanying drawings show preferred embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described in this specification. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive.

[0026] It should be noted that unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not used to limit the present application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0027] To facilitate the understanding of the present application, the terms proposed in the present application are briefly described herein:

[0028] Data is generated at the edge and stored within the limited validity period of the hardware conditions. Alarm data will be reported to the cloud storage by default and supports configuring reporting rules.

[0029] The cloud defaults to receiving various alarm / warning / real-time data reported by the edge (it is also possible to configure rules to actively pull specified data from the edge within the validity period of the edge and store it in the cloud / collection end).

[0030] The collection end can be understood as the download end. In the case of network access, as long as the IP of the platform can be accessed, an account with data download permission can be used to download data from the cloud / edge.

[0031] Figure 1 Shows an embodiment of the multi-source heterogeneous railway monitoring data management method of the present application, including the steps:

[0032] Step S1: Configure the data collection rules and data cloud-edge storage rules of railway monitoring data.

[0033] It should be noted that the data acquisition rules include the settings of railway monitoring data corresponding to time range, line, data label, and device dimension.

[0034] Among them, for the time range dimension, the periodic acquisition rule is used, and the data acquisition period is set according to the time granularity of minutes / hours / days. For example, the data acquisition task is executed every 15 minutes; at the same time, it supports configuring preset time periods, such as 22:00:00 - 03:00:00 every day.

[0035] Secondly, for the line dimension, according to the device subordination relationship, the line, monitoring point, and device range for collecting corresponding railway monitoring data can be custom-selected according to the subordination relationship of line - monitoring point - device.

[0036] Furthermore, for the data label dimension, the structured data labels corresponding to the required railway monitoring data to be collected can be configured. For sensor data, standardized acquisition fields such as weather parameters (temperature, humidity, wind speed, etc.) can be defined; for device logs, abnormal event type labels (alarm / fault, etc.) can be defined. Unstructured data labels corresponding to the required railway monitoring data to be collected can also be configured. For the image recognition type, data categories such as large animals, people, mechanical operations, trains, rockfalls, debris flows, climbing, etc. can be identified and marked through algorithms; for multi-modal fusion data, composite labels can be generated by combining lidar point cloud data, high-definition video streams, and fiber optic signal data.

[0037] Finally, for the device dimension, the device grouping strategy is adopted. It can be dynamically grouped according to device types (sentinels, optical fibers, etc.) and supports batch configuration for acquisition; it can also divide device clusters (line - monitoring point - device) based on geographical location subordination relationships.

[0038] The above rule dimensions support flexible combinations. For example, it supports configuring to collect data with the label of "heavy rain + alarm" for "radar + video + picture" generated by the device foreign object sentinel at monitoring points B + C under line A at 22:00:00 - 04:00:00 every day; it also supports various data acquisitions of other device products under the same monitoring point after the device triggers an event, etc.

[0039] Taking the railway acoustic fiber DAS system as an example, a single DAS host covers 50 kilometers of line, and the daily raw data volume generated by the dual-channel optical fiber reaches 3.6TB. After compression processing (calculated at a compression rate of 80%), 2.8TB of effective data still needs to be stored. Through the event point dynamic slicing compression technology, about 90GB of key analysis data can be extracted from the single-day data (calculated based on 1000 typical event samples), and the data volume compression ratio reaches 31:1.

[0040] In the dedicated railway 100 - megabit network environment, there are significant differences in the multi - thread transmission efficiency of full - volume data (data obtained after ordinary compression processing) and sliced data (data obtained by the event - point dynamic slicing compression technology). In the ideal state (continuous transmission at 12MB / s), the full - volume data transmission takes 75 hours and 13 minutes (about 3 days and 3 hours), while the sliced data transmission only takes 2 hours. In the actual working conditions (considering network fluctuations and equipment performance, calculated at 10MB / s), the full - volume data transmission takes 96 hours and 51 minutes (about 4 days), and the sliced data transmission delay is controlled within 2 hours and 15 minutes. Among them, the event - point dynamic slicing compression technology, through the event - driven data acquisition mechanism, reduces the daily data transmission volume by 96.7% while ensuring the integrity of key information, significantly alleviating the bandwidth pressure of the dedicated railway transmission network and providing basic support for the implementation of the railway monitoring data management method of this application.

[0041] In this embodiment, the data cloud - edge storage rule includes an edge - node cache elimination model, and the edge - node cache elimination model includes a data value evaluation factor and a hybrid elimination strategy. The data value evaluation factor is introduced based on an improved LRU (Least Recently Used) algorithm, including a timeliness coefficient and a service weight, and is used to evaluate the data value of railway monitoring data. The hybrid elimination strategy retains the basic structure of the LRU linked list, adds a data value field to the node, and eliminates the railway monitoring data with low data value and long - time unaccessed data at the edge (along - line computer rooms) to obtain the target monitoring data.

[0042] It should be noted that the LRU algorithm is a commonly used cache elimination strategy. It assumes that the data accessed recently may still be useful in the future, while the data accessed earlier may no longer be needed. Therefore, when the cache is full, the LRU algorithm will choose to delete the data that has not been used for a long time.

[0043] Furthermore, the data cloud - edge storage rule also includes an elastic storage strategy. The elastic storage strategy means storing the target monitoring data in the cloud with the corresponding cloud storage period. Among them, the cloud storage period is related to the basic period, data importance level, and data update frequency of the target monitoring data.

[0044] In this embodiment, the target monitoring data can be divided into four levels of data, namely level - I, level - II, level - III, and level - IV data; and the basic period, data importance level, and data update frequency of the target monitoring data at different levels are different.

[0045] Among them, the basic period adopts the business benchmark value. The basic period of general business data (raw data without events, which can be regarded as level-IV data) is defaulted to 3 months, the basic period of warning data (level-IV data) is set to 1 year, and the basic period of alarm data (level-I, level-II, and level-III data) is set to 3 years.

[0046] Furthermore, the data importance level adopts the level weight mapping. For level-I data, the level weight is the level coefficient + 1.0, that is, the data importance level of level-I data is 2; for level-II data, the level weight is the level coefficient + 0.6, that is, the data importance level of level-II data is 2.6; for level-III data, the level weight is the level coefficient + 0.3, that is, the data importance level of level-III data is 3.3; for level-IV data, the level weight is the level coefficient + 0.1, that is, the data importance level of level-IV data is 4.1.

[0047] In this embodiment, for the data update frequency, the data update frequencies corresponding to different acquisition modes are different. If real-time monitoring data (>1 time / minute) is adopted, the data update frequency can adopt the first coefficient 0.9; if regular monitoring data (1 time / day) is adopted, the data update frequency can adopt the second coefficient 0.3. Among them, for the railway monitoring data that has not been collected since the generation time of the railway monitoring data, attenuation compensation is required; for example, the corresponding data update frequency can be reduced by 0.02 per day.

[0048] In some other embodiments, the first coefficient and the second coefficient can be configured and defined according to the actual acquisition mode and the edge storage capacity, as long as the cloud storage requirements are met.

[0049] It should be noted that when storing data, there is also a mandatory retention clause, that is, for the warning data saved as a case, it will be permanently saved and not affected by the above rules.

[0050] Step S2: According to the data acquisition rules and the data cloud-edge storage rules, perform data acquisition and data storage on the railway monitoring data.

[0051] Among them, data acquisition refers to synchronously acquiring the target monitoring data obtained by processing the railway monitoring data stored at the edge end through the data acquisition rules and the edge node cache elimination model to the acquisition end; data storage refers to synchronously storing the target monitoring data to the cloud according to the elastic storage strategy.

[0052] Specifically, as Figure 2 shown, step S2 includes the following sub-steps:

[0053] Step S21: Calculate the data value weight of the railway monitoring data according to the data value evaluation factor.

[0054] In this embodiment, by using the timeliness coefficient, service weight, as well as the half-life and priority corresponding to the railway monitoring data, the data value weight of the railway monitoring data is calculated as follows:

[0055] ;

[0056] Among them, represents the data value weight, represents the timeliness coefficient, represents the current time minus the generation time of the railway monitoring data, represents the half-life, represents the service weight, represents the priority of the railway monitoring data.

[0057] It should be noted that the half-life is related to the data valid period. According to the time window matching rule, the data valid period is set to W (window length), and then the half-life is determined according to the empirical formula: T = W×ln2 ≈ 0.693W. Among them, if the service requires the data to expire within 30 days (W = 30), then T ≈ 21 days. At the same time, the higher the priority of the railway monitoring data, the higher the data value weight, and the priorities of different types of railway monitoring data are different. For example, the priority of the foreign object intrusion alarm data is 10, and the priority of the weather label data is 3.

[0058] In this embodiment, in the case of short acquisition time and large amount of acquired data, the data self-screening and elimination mode can be selected for data acquisition. During the acquisition process, the data value weight of the railway monitoring data will be automatically calculated, and the railway monitoring data with the lowest data value weight and the longest unaccessed time will be automatically eliminated, and the railway monitoring data with high data value weight will be preferentially acquired.

[0059] Step S22: Obtain the target monitoring data according to the hybrid elimination strategy.

[0060] It should be noted that the current acquisition scenario is to synchronize and acquire the target monitoring data stored at the edge to the acquisition end. Elimination means that during the acquisition synchronization process and local storage at the edge, the data sets with lower data value weights will be automatically eliminated.

[0061] In this embodiment, after calculating the data value weights of all the railway monitoring data stored at the edge, the railway monitoring data is compared, and the railway monitoring data with high data value weight (i.e., the target monitoring data) is preferentially acquired to the acquisition end, and the railway monitoring data with the lowest data value weight accounting for the first ratio and the railway monitoring data that has not been accessed for the longest time accounting for the second ratio are eliminated, and then the target monitoring data can be obtained; among them, the sum of the first ratio and the second ratio is 1.

[0062] In this embodiment, the first proportion is 20% and the second proportion is 80%.

[0063] In some other embodiments, the first proportion and the second proportion can also take other values as long as their sum is 1.

[0064] In this embodiment, in the case of short acquisition time and large amount of acquired data, for example, when it is necessary to collect data from a test case dataset and a severe weather dataset in a short period of time, and the edge storage is limited, by centrally collecting the data with high data value weights, the acquisition time can be shortened and the efficiency of data acquisition can be ensured.

[0065] Furthermore, for other data acquisition situations, fixed acquisition is performed according to the railway monitoring data stored at the edge according to custom conditions; such as full-volume acquisition in a fixed time period, etc.

[0066] In some embodiments, while steps S21 and S22 are being executed, data acquisition is also carried out, including: adaptively changing the data acquisition mode according to the real-time monitored network bandwidth.

[0067] Among them, the data acquisition mode includes a key target data mode and a normal data mode. The key target data mode is for a large amount of railway monitoring data, and according to the network bandwidth, target monitoring data of different slice sizes is acquired. The normal data mode acquires target monitoring data of a fixed slice size according to custom rule dimensions.

[0068] In this application, the nload / bmon technology is used to monitor the network bandwidth in real time. When collecting a large amount of railway monitoring data, it can be automatically switched to the key target data mode to reduce the slice size, including shortening the time before and after the optical fiber data event and shortening the video capture time for a certain number of seconds before and after the event. Such rules also need to be configured in advance in step S1. For example, when the standard bandwidth is 0 - 10M / s, the acquired slice size is 30s before and after the identified event; when the fluctuating bandwidth is 10 - 20M / s, the acquired slice size is 15s before and after the identified event, etc.; the cursor capture is the same. When collecting the railway monitoring data in other situations, the normal data mode is adopted, and it is automatically downloaded according to custom rule dimensions without any optimization processing, and it can be configured in advance in step S1 to default to downloading all the data 30s before and after the event.

[0069] It should be noted that nload is a command-line tool for monitoring network traffic, which displays real-time network usage in a graphical way. By using this command, the network traffic, including data such as upload and download rates and total traffic, can be displayed in real time in a graphical way on the command-line interface. bmon (Bandwidth Monitor) is a real-time bandwidth monitoring and rate estimation tool, similar to nload, but providing more detailed information at the packet level.

[0070] In this application, adopting this data acquisition mode can effectively improve the data acquisition efficiency.

[0071] Step S23: Calculate the cloud storage period according to the base period, data importance level, and data update frequency of the target monitoring data.

[0072] In this embodiment, when storing data, it is necessary to calculate the cloud storage period corresponding to the target monitoring data according to the base period, data importance level, and data update frequency of the target monitoring data as follows:

[0073] ;

[0074] Among them, represents the cloud storage period, represents the base period, represents the data importance level, represents the data update frequency.

[0075] Then, the target monitoring data can be stored in the cloud with the corresponding cloud storage period to complete the data storage.

[0076] In this application, a dynamic elastic storage strategy is adopted, and at the same time, the data acquisition priority is automatically adjusted according to network quality fluctuations. Compared with the traditional full-volume acquisition mode, the data acquisition efficiency has been improved by an order of magnitude. In addition to acoustic fiber data, this technical system has been successfully extended to data processing technologies such as intelligent slicing processing of stress fiber data, key frame extraction of video streaming media, and collaborative acquisition optimization of multi-modal data to ensure the intelligent management of railway monitoring data.

[0077] Based on the same inventive concept, as Figure 3 shown, the present invention also provides a multi-source heterogeneous railway monitoring data management system, which includes:

[0078] A rule configuration unit 101, configured to configure data acquisition rules and data cloud-edge storage rules for railway monitoring data.

[0079] A data management unit 102 is configured to perform data acquisition and data storage on railway monitoring data according to data acquisition rules and data cloud-edge storage rules. Among them, the data cloud-edge storage rules include an edge node cache elimination model and an elastic storage policy. Data acquisition refers to synchronously acquiring the target monitoring data obtained by processing the railway monitoring data stored at the edge end through the data acquisition rules and the edge node cache elimination model to the acquisition end. Data storage refers to synchronously storing the target monitoring data to the cloud according to the elastic storage policy.

[0080] In this application, other technical features in the above-mentioned multi-source heterogeneous railway monitoring data management system are the same as those disclosed in the above method embodiment, and will not be elaborated here.

[0081] Based on the same inventive concept, this application also provides an electronic device, which includes a processor, a memory, and a communication circuit. The processor is respectively connected to the memory and the communication circuit. Among them, the communication circuit is used for communication connection, the memory is used to store a computer program, and the processor is used to execute the computer program to implement the above method.

[0082] Please refer to Figure 4 , the electronic device described in the embodiment of this application may specifically include a processor 210 and a memory 220. The memory 220 is coupled to the processor 210.

[0083] The processor 210 is used to control the operation of the electronic device. The processor 210 may also be referred to as a CPU (Central Processing Unit, central processing unit). The processor 210 may be an integrated circuit chip with signal processing capabilities. The processor 210 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor, or the processor 210 may also be any conventional processor, etc.

[0084] The memory 220 is used to store a computer program, which may be RAM, ROM, or other types of storage terminals. Specifically, the memory 220 may include one or more computer-readable storage media, which may be non-transitory or transitory. The memory 220 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage terminals and flash storage terminals. In some embodiments, the non-transitory computer-readable storage medium in the memory 220 is used to store at least one program code.

[0085] The processor 210 is configured to execute the computer programs stored in the memory 220 to implement the methods described in the method embodiments of this application.

[0086] In some embodiments, the electronic device may further include: a peripheral terminal interface 230 and at least one peripheral terminal. The processor 210, the memory 220, and the peripheral terminal interface 230 may be connected through a bus or signal lines. Each peripheral terminal may be connected to the peripheral terminal interface 230 through a bus, signal lines, or a circuit board. Specifically, the peripheral terminal includes at least one of a radio frequency circuit 240, a display screen 250, an audio circuit 260, and a power supply 270.

[0087] The peripheral terminal interface 230 can be used to connect at least one peripheral terminal related to I / O (Input / Output) to the processor 210 and the memory 220. In some embodiments, the processor 210, the memory 220, and the peripheral terminal interface 230 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 210, the memory 220, and the peripheral terminal interface 230 may be implemented on a separate chip or circuit board, and this embodiment does not limit this.

[0088] The radio frequency circuit 240 is configured to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 240 communicates with a communication network and other Internet of Things devices through electromagnetic signals, and the radio frequency circuit 240 is the communication circuit of the electronic device. The radio frequency circuit 240 converts an electrical signal into an electromagnetic signal for transmission, or converts the received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 240 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so on. The radio frequency circuit 240 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area network, and / or WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 240 may further include a circuit related to NFC (Near Field Communication), and this application does not limit this.

[0089] The display screen 250 is used to display the UI (User Interface, operator interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 250 is a touch display screen, the display screen 250 also has the ability to collect touch signals on or above the surface of the display screen 250. The touch signals can be input to the processor 210 for processing as control signals. At this time, the display screen 250 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, there can be one display screen 250, which is provided on the front panel of the electronic device; in other embodiments, there can be at least two display screens 250, which are respectively provided on different surfaces of the electronic device or are in a folding design; in other embodiments, the display screen 250 can be a flexible display screen, which is provided on the curved surface or folding surface of the electronic device. Even, the display screen 250 can be set to an irregular non-rectangular shape, that is, a special-shaped screen. The display screen 250 can be prepared from materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0090] The audio circuit 260 may include a microphone and a speaker. The microphone is used to collect sound waves of the operator and the environment, and convert the sound waves into electrical signals and input them to the processor 210 for processing, or input them to the radio frequency circuit 240 to achieve voice communication. For the purpose of stereo collection or noise reduction, there can be multiple microphones, which are respectively provided at different parts of the electronic device. The microphone can also be an array microphone or an omnidirectional collection type microphone. The speaker is used to convert the electrical signals from the processor 210 or the radio frequency circuit 240 into sound waves. The speaker can be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert electrical signals into sound waves audible to humans, but also convert electrical signals into sound waves inaudible to humans for uses such as ranging. In some embodiments, the audio circuit 260 may further include a headphone jack.

[0091] The power supply 270 is used to supply power to each component in the electronic device. The power supply 270 can be alternating current, direct current, a disposable battery, or a rechargeable battery. When the power supply 270 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0092] For the detailed description of the functions and execution processes of each functional module or component in the embodiment of the electronic device of the present application, reference can be made to the description in the above method embodiments of the present application, and details will not be repeated here.

[0093] In several embodiments provided by the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the various embodiments of the electronic devices described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, in each embodiment of the present application, the various functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0096] Based on the same inventive concept, the present application also provides a computer-readable storage medium storing a computer program that can be executed by a processor to implement the above method.

[0097] Please refer to Figure 5 , if the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in the computer-readable storage medium 300. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions / computer programs to enable an Internet of Things device (which can be a personal computer, a server, or a network terminal, etc.) or a processor to execute all or part of the steps of the methods of each embodiment of the present application. And the aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical discs and other media, as well as electronic terminals such as computers, mobile phones, laptop computers, tablet computers, cameras, etc. with the above storage media.

[0098] The description of the execution process of the program data in the computer-readable storage medium can refer to the descriptions in the foregoing method embodiments of the present application, and will not be elaborated herein.

[0099] As can be seen, the present invention discloses a multi-source heterogeneous railway monitoring data management method, system, device and medium. The method includes the following steps: configuring data acquisition rules and data cloud-edge storage rules for railway monitoring data; according to the data acquisition rules and data cloud-edge storage rules, performing data acquisition and data storage on railway monitoring data; wherein, the data cloud-edge storage rules include an edge node cache elimination model and an elastic storage policy; data acquisition refers to synchronously collecting the target monitoring data obtained by processing the railway monitoring data stored at the edge end through the data acquisition rules and the edge node cache elimination model to the acquisition end; data storage refers to synchronously storing the target monitoring data to the cloud according to the elastic storage policy. The present invention can elastically store the required multi-source heterogeneous data according to the dynamic policy of network fluctuations and different data priorities, greatly improving the data acquisition efficiency, and is applicable to the full life cycle management of monitoring data in complex operation environments such as high-speed railways.

[0100] The above are only the embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A multi-source heterogeneous railway monitoring data management method, characterized in that, The method includes the steps of: Configuring the data acquisition rules and data cloud-edge storage rules for railway monitoring data; According to the data acquisition rules and data cloud-edge storage rules, performing data acquisition and data storage on the railway monitoring data; Among them, the data cloud-edge storage rules include an edge node cache elimination model and an elastic storage strategy; the data acquisition refers to synchronously acquiring the target monitoring data obtained by processing the railway monitoring data stored at the edge end through the data acquisition rules and the edge node cache elimination model to the acquisition end; the data storage refers to synchronously storing the target monitoring data to the cloud according to the elastic storage strategy.

2. The railway monitoring data management method according to claim 1, wherein The data acquisition rules include the settings of the railway monitoring data corresponding to the time range, line, data label, and device dimension.

3. The railway monitoring data management method according to claim 1, wherein Performing data acquisition and data storage on the railway monitoring data includes: Adapting to change the data acquisition mode according to the real-time monitored network bandwidth; the data acquisition mode includes a key target data mode and a general data mode. The key target data mode is for a large amount of the railway monitoring data, and according to the network bandwidth, acquires the target monitoring data with different slice sizes; the general data mode acquires the target monitoring data with a fixed slice size according to the custom rule dimension.

4. The railway monitoring data management method according to claim 1, characterized in that The edge node cache elimination model includes a data value evaluation factor and a hybrid elimination strategy. The data value evaluation factor includes a timeliness coefficient and a service weight, and is used to evaluate the data value of the railway monitoring data; the hybrid elimination strategy means eliminating the railway monitoring data with low data value and long unaccessed time stored at the edge end.

5. The railway monitoring data management method according to claim 4, characterized in that Performing data acquisition and data storage on the railway monitoring data includes: Calculating the data value weight of the railway monitoring data according to the data value evaluation factor; Obtaining the target monitoring data according to the hybrid elimination strategy; among them, eliminating the railway monitoring data with the lowest data value weight accounting for the first proportion and the railway monitoring data that has not been accessed for the longest time accounting for the second proportion; the sum of the first proportion and the second proportion is 1.

6. The railway monitoring data management method according to claim 5, characterized in that The data value weight is: ； Among them, represents the data value weight, represents the timeliness coefficient, represents the current time minus the generation time of the railway monitoring data, represents the half-life, represents the service weight, represents the priority of the railway monitoring data.

7. The railway monitoring data management method according to claim 1, wherein The elastic storage strategy means storing the target monitoring data in the cloud with the corresponding cloud storage period; when performing the data storage, it is necessary to calculate the cloud storage period according to the basic period, data importance level, and data update frequency of the target monitoring data; the cloud storage period is: ； Among them, represents the cloud storage period, represents the basic period, represents the data importance level, represents the data update frequency.

8. A multi-source heterogeneous railway monitoring data management system, characterized in that The system includes: A rule configuration unit for configuring the data acquisition rules and data cloud-edge storage rules for railway monitoring data; A data management unit, configured to perform data acquisition and data storage on the railway monitoring data according to the data acquisition rule and the data cloud-edge storage rule; wherein, the data cloud-edge storage rule includes an edge node cache elimination model and an elastic storage policy; the data acquisition refers to synchronously acquiring the target monitoring data obtained by processing the railway monitoring data stored at the edge end through the data acquisition rule and the edge node cache elimination model to the acquisition end; the data storage refers to synchronously storing the target monitoring data to the cloud according to the elastic storage policy.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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