High-speed rail data security centralized monitoring system for intelligent contract big data learning

Abstract

The invention provides a high-speed rail data security centralized monitoring system for intelligent contract big data learning. A data monitoring module is used for collecting operation data corresponding to trackside equipment in real time; the block chain module is used for performing hash operation on the operation data and storing a hash value corresponding to the operation data; the big data machine learning module is used for checking data integrity according to the hash value, and determining corresponding equipment fault risk information according to the operation data by adopting a preset machine learning algorithm; a data transmission structure among the modules comprises a signature, a packet header and a packet tail; the packet header carries an identity label corresponding to the current module, and the packet tail carries identity labels of all sub-level modules corresponding to the data source; and positioning the trackside equipment with the equipment fault risk information according to the identity label carried in the packet tail corresponding to the operation data. Storage safety and execution safety of railway equipment data can be guaranteed, and monitoring and system management are carried out on the running state of bottom layer trackside equipment along a railway.

Description

Technical Field

[0001] The present disclosure relates to the technical field of railway safety data processing, and in particular to a high-speed railway data security centralized monitoring system for smart contract big data learning. Background Art

[0002] At present, the data security of railway operation has become increasingly prominent as the complexity and scale of the railway system have increased rapidly. In particular, with the emergence of high-speed railways, the popularization of networking, and the increase in intelligent devices, the amount of railway data generated, the risk of tampering, the risk of leakage, and the risk of management will increase dramatically. There are risks in the storage, processing, and transmission of data. Therefore, the security and intelligent processing of railway data must be given sufficient attention. Summary of the invention

[0003] The disclosed embodiments at least provide a high-speed rail data security centralized monitoring system for smart contract big data learning, which can ensure the storage security and execution security of railway equipment data, and monitor, predict and manage the operating status of the underlying trackside equipment along the railway.

[0004] The disclosed embodiment provides a high-speed rail data security centralized monitoring system for smart contract big data learning, including: a data monitoring module, a blockchain module, and a big data machine learning module;

[0005] The data monitoring module is used to collect the operation data corresponding to the trackside equipment in real time;

[0006] The blockchain module is used to perform a hash operation on the operation data and store a hash value corresponding to the operation data;

[0007] The big data machine learning module is used to verify data integrity according to the hash value, and to determine corresponding equipment failure risk information according to the operating data using a preset machine learning algorithm;

[0008] Among them, the data transmission structure between modules in the high-speed rail data security centralized monitoring system of the smart contract big data learning includes a signature, a header and a tail;

[0009] The packet header carries the identity of the current module, and the packet tail carries the identity of all sub-modules corresponding to the data source;

[0010] The big data machine learning module is also used to locate trackside equipment that has the equipment failure risk information based on the identity identifier carried in the packet tail corresponding to the operating data.

[0011] In an optional implementation, the data monitoring module includes a centralized monitoring host;

[0012] The centralized monitoring host is set up in a railway station and is used to collect the operating data corresponding to the trackside equipment belonging to the railway station in real time, and construct a data packet with the identity identifier corresponding to the trackside equipment as the packet tail and the identity identifier corresponding to the centralized monitoring host as the packet header.

[0013] In an optional implementation, the data monitoring module further includes a centralized monitoring center, and the centralized monitoring center is used to:

[0014] Receiving all the operation data uploaded by the centralized monitoring host;

[0015] For each of the operating data, an original data packet is constructed with the identity identifier corresponding to the corresponding centralized monitoring host as the packet tail and the identity identifier of the centralized monitoring center as the packet header, and the original data packet is sent to the blockchain module.

[0016] In an optional implementation manner, the centralized monitoring center is also used for:

[0017] In response to a user's management configuration operation on the centralized monitoring host, generating a real-time execution command and a delayed execution command;

[0018] The real-time execution command is sent to the blockchain module and executed immediately.

[0019] In an optional implementation, the high-speed rail data security centralized monitoring system for smart contract big data learning further includes a smart contract module, and the smart contract module is used to:

[0020] Obtaining a delayed execution command sent by the centralized monitoring center to the centralized monitoring host;

[0021] According to the preset contract conditions, the delayed execution command that meets the contract conditions is sent to the corresponding centralized monitoring host.

[0022] In an optional implementation, the smart contract module is further used to:

[0023] According to the equipment failure risk information and the preset contract conditions, alarm information corresponding to the equipment failure risk information is generated.

[0024] In an optional implementation, the high-speed rail data security centralized monitoring system for smart contract big data learning further includes an alarm module, and the alarm module is used to:

[0025] Obtaining the alarm information generated by the smart contract module and the equipment failure risk information generated by the big data machine learning module;

[0026] The alarm information and the equipment failure risk information are sent to the user.

[0027] The present disclosure also provides a method for centralized monitoring of high-speed rail data security for smart contract big data learning, which is applied to the centralized monitoring system for high-speed rail data security for smart contract big data learning as described in any of the above embodiments. The method includes:

[0028] Control the data monitoring module to collect the operation data corresponding to the trackside equipment in real time;

[0029] Control the blockchain module to perform a hash operation on the operation data, and store the hash value corresponding to the operation data;

[0030] Control the big data machine learning module to verify data integrity according to the hash value, and use a preset machine learning algorithm to determine the corresponding equipment failure risk information according to the operating data; wherein the data transmission structure between modules in the high-speed rail data security centralized monitoring system of the smart contract big data learning includes a signature, a header and a tail; the header carries the identity of the current module, and the tail carries the identity of all sub-modules of the corresponding data source;

[0031] The big data machine learning module is controlled to locate the trackside equipment that has the equipment failure risk information according to the identity carried in the tail of the packet corresponding to the operating data.

[0032] An embodiment of the present disclosure also provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps in the high-speed rail data centralized monitoring method for smart contract big data learning are performed.

[0033] The embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for centralized monitoring of high-speed rail data security based on the above-mentioned smart contract big data learning is executed.

[0034] The disclosed embodiments also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the high-speed rail data security centralized monitoring method for the above-mentioned smart contract big data learning.

[0035] The disclosed embodiment provides a high-speed rail data security centralized monitoring system for smart contract big data learning, including: a data monitoring module, a blockchain module and a big data machine learning module; the data monitoring module is used to collect the operation data corresponding to the trackside equipment in real time; the blockchain module is used to perform hash operations on the operation data and store the hash values ​​corresponding to the operation data; the big data machine learning module is used to verify the data integrity according to the hash value, and use a preset machine learning algorithm to determine the corresponding equipment failure risk information according to the operation data; wherein, the data transmission structure between each module in the high-speed rail data security centralized monitoring system for smart contract big data learning includes a signature, a header and a tail; the header carries the identity corresponding to the current module, and the tail carries the identity of all sub-modules of the corresponding data source; the big data machine learning module is also used to locate the trackside equipment with the equipment failure risk information according to the identity carried in the tail corresponding to the operation data. It can ensure the storage security and execution security of railway equipment data, and monitor, predict and manage the operation status of the underlying trackside equipment along the railway.

[0036] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following is a brief introduction to the drawings required for use in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can also be obtained based on these drawings without creative work.

[0038] Figure 1 A schematic diagram of centralized monitoring of high-speed rail data security for smart contract big data learning provided by an embodiment of the present disclosure is shown;

[0039] Figure 2 A schematic diagram of a data packet signature mechanism provided by an embodiment of the present disclosure is shown;

[0040] Figure 3 A schematic diagram of another high-speed rail data security centralized monitoring system for smart contract big data learning provided by an embodiment of the present disclosure is shown;

[0041] Figure 4A flowchart of a high-speed rail data security centralized monitoring method for smart contract big data learning provided by an embodiment of the present disclosure is shown;

[0042] Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0044] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0045] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0046] Research has found that the data security of railway operations has become increasingly prominent as the complexity and scale of the railway system have increased rapidly. In particular, with the emergence of high-speed railways, the popularization of networking, and the increase in intelligent devices, the amount of railway data generated, the risk of tampering, the risk of leakage, and the risk of management will increase dramatically. There are risks in the storage, processing, and transmission of data. Therefore, the security and intelligent processing of railway data must be given sufficient attention.

[0047] Based on the above research, the present disclosure provides a high-speed rail data security centralized monitoring system for smart contract big data learning, including: a data monitoring module, a blockchain module and a big data machine learning module; the data monitoring module is used to collect the operation data corresponding to the trackside equipment in real time; the blockchain module is used to perform hash operations on the operation data and store the hash values ​​corresponding to the operation data; the big data machine learning module is used to verify the data integrity according to the hash value, and use a preset machine learning algorithm to determine the corresponding equipment failure risk information according to the operation data; wherein, the data transmission structure between each module in the high-speed rail data security centralized monitoring system for smart contract big data learning includes a signature, a header and a tail; the header carries the identity corresponding to the current module, and the tail carries the identity of all sub-modules of the corresponding data source; the big data machine learning module is also used to locate the trackside equipment with the equipment failure risk information according to the identity carried in the tail corresponding to the operation data. It can ensure the storage security and execution security of railway equipment data, and monitor, predict and manage the operation status of the underlying trackside equipment along the railway.

[0048] To facilitate understanding of this embodiment, firstly, a high-speed rail data security centralized monitoring system for smart contract big data learning disclosed in the embodiment of the present disclosure is introduced in detail, see Figure 1 As shown, it is a schematic diagram of a high-speed rail data security centralized monitoring system for smart contract big data learning provided by an embodiment of the present disclosure.

[0049] like Figure 1 As shown in the , the high-speed rail data security centralized monitoring system based on smart contract big data learning includes: data monitoring module, blockchain module and big data machine learning module.

[0050] Specifically, the data monitoring module is used to collect the operating data corresponding to the trackside equipment in real time; the blockchain module is used to perform hash operations on the operating data and store the hash values ​​corresponding to the operating data; the big data machine learning module is used to verify the data integrity based on the hash value, and use the preset machine learning algorithm to determine the corresponding equipment failure risk information based on the operating data.

[0051] In the specific implementation, the data monitoring module conducts real-time monitoring of the underlying trackside equipment along the railway, collects the operating data of the trackside equipment, and sends the operating data to the blockchain module, which centrally manages and stores the data in real time.

[0052] Here, the blockchain module uses a private chain, and the blocks on the chain only store the hash value of the running data. The hash value can be calculated using the SHA3 algorithm, which is implemented through the following formula:

[0053] H=SHA3(DATA)

[0054] Among them, H represents the hash value corresponding to the running data; SHA3 represents the hash operation algorithm; DATA represents the original running data.

[0055] Furthermore, the original operation data is sent to the big data machine learning module, which verifies the data integrity according to the hash value calculated by the blockchain module, and uses a preset machine learning algorithm to process the operation data to determine the corresponding equipment failure risk information.

[0056] Here, a variety of machine learning algorithms are pre-configured in the big data machine learning module. The operation data is processed through the machine learning algorithms, the historical operating status of the corresponding trackside equipment is analyzed, and early warnings are issued for the risk information of equipment failures that may occur.

[0057] Among them, machine learning algorithms can include mainstream algorithms such as BP, CNN, RNN, LSTM, and GPT.

[0058] In the specific implementation, the data transmission between modules in the high-speed rail data security centralized monitoring system of smart contract big data learning adopts the data packet format of signature + header + original data + tail. The header carries the identity of the current module, and the tail carries the identity of all sub-modules of the corresponding data source. Figure 2 , which is a schematic diagram of a data packet signature mechanism provided by an embodiment of the present disclosure.

[0059] Here, from the perspective of data transmission and processing, data is gradually gathered from the bottom-level trackside equipment to the top-level big data machine learning module. It is essentially an inverted tree topology, that is, the operating data is transmitted from the trackside equipment-data monitoring module-blockchain module-big data machine learning module, which is a step-by-step transmission form. The trackside equipment is a sub-module of the data monitoring module, the data monitoring module is a sub-module of the blockchain module, and the blockchain module is a sub-module of the big data machine learning module.

[0060] Here, in order to ensure that the data is not tampered with during transmission and that the alarm is traceable, a secret key mechanism is added to each module and a signature mechanism is added to the data packet. The key in each module includes a private key and a public key. The private key is only known to this module and is not disclosed to the outside world. The public key can be disclosed to the outside world. The public key also serves as the external identity of this module.

[0061] Specifically, the data packet signature mechanism adds a signature, a header, and a tail to the original data. The signature uses the private key of this module to process the data packet using the asymmetric encryption algorithm RSA to obtain the signature. The signature can ensure the integrity of this data packet through signature verification. The header contains the identity of this module and the data packet number, that is, the number of data packets generated by this computer. The tail contains the headers of all the next-level data packets that generated the original data in this data packet, which is used to record the data source that generated this data packet for easy traceability. The tail can be traced down to the lowest level trackside equipment.

[0062] A high-speed rail data security centralized monitoring system for smart contract big data learning provided by the embodiment of the present disclosure includes: a data monitoring module, a blockchain module and a big data machine learning module; the data monitoring module is used to collect the operation data corresponding to the trackside equipment in real time; the blockchain module is used to perform hash operation on the operation data and store the hash value corresponding to the operation data; the big data machine learning module is used to verify the data integrity according to the hash value, and use a preset machine learning algorithm to determine the corresponding equipment failure risk information according to the operation data; wherein, the data transmission structure between each module in the high-speed rail data security centralized monitoring system for smart contract big data learning includes a signature, a header and a tail; the header carries the identity corresponding to the current module, and the tail carries the identity of all sub-modules of the corresponding data source; the big data machine learning module is also used to locate the trackside equipment with the equipment failure risk information according to the identity carried in the tail corresponding to the operation data. It can ensure the storage security and execution security of railway equipment data, and monitor, predict and manage the operation status of the underlying trackside equipment along the railway.

[0063] For further information, see Figure 3 As shown, it is a schematic diagram of another high-speed rail data security centralized monitoring system for smart contract big data learning provided by an embodiment of the present disclosure.

[0064] like Figure 3 As shown in the figure, the data monitoring module includes: a centralized monitoring host and a centralized monitoring center; the high-speed rail data security centralized monitoring system based on smart contract big data learning also includes: a smart contract module, an alarm module and a data server.

[0065] In the specific implementation, the centralized monitoring host is set up at the railway station to collect the operation data corresponding to the trackside equipment belonging to the railway station in real time, and use the identity identifier corresponding to the trackside equipment as the packet tail and the identity identifier corresponding to the centralized monitoring host as the packet header to construct the data packet. The centralized monitoring center is used to: receive the operation data uploaded by all centralized monitoring hosts; for each operation data, use the identity identifier corresponding to the corresponding centralized monitoring host as the packet tail and the identity identifier of the centralized monitoring center as the packet header to construct the original data packet, and send the original data packet to the blockchain module.

[0066] Specifically, a centralized monitoring host is usually configured at each station to monitor the trackside equipment in and around the station, and send the collected monitoring data to the centralized monitoring center for centralized processing and to the blockchain system for secure storage and big data processing. The centralized monitoring center is mainly used to collect and summarize the monitoring data of the centralized monitoring hosts at all stations along the railway, perform centralized processing of the monitoring data to obtain key data, and store the key data in the blockchain for secure storage and big data processing. At the same time, it is used to manage and configure the commands sent to the centralized monitoring host. Real-time execution commands are executed immediately after being stored in the blockchain, and delayed execution commands are written into smart contracts and can only be executed when conditions are met.

[0067] As a possible implementation method, the centralized monitoring center is also used to: generate real-time execution commands and delayed execution commands in response to the user's management configuration operations on the centralized monitoring host; and execute the real-time execution commands immediately after sending them to the blockchain module.

[0068] Furthermore, the smart contract module is used to: obtain the delayed execution command sent by the centralized monitoring center to the centralized monitoring host; according to the preset contract conditions, send the delayed execution command that meets the contract conditions to the corresponding centralized monitoring host. According to the equipment failure risk information, according to the preset contract conditions, generate alarm information corresponding to the equipment failure risk information.

[0069] Here, smart contracts are programs built on the blockchain module that can be dynamically executed according to contract conditions. In this system, it is mainly used to generate alarms after processing monitoring data according to pre-set contract conditions, and to send commands that need to be delayed in the commands sent by the centralized monitoring center to the centralized monitoring host to the centralized monitoring host according to the set rules and conditions.

[0070] Furthermore, the data server is mainly used to store the original monitoring data after the hash value is extracted, and is also the data source of the big data machine learning module. The integrity of the monitoring data is guaranteed by the blockchain module and the hash value in the block.

[0071] Furthermore, the alarm module is mainly used to generate alarms or early warning prompts based on the processing results sent by the smart contract and big data machine learning module, and convey them to railway staff in a rich form, so that the staff can grasp and handle the operation of the railway system in a timely manner.

[0072] It should be noted that the data transmission format between the centralized monitoring host, centralized monitoring center, smart contract module, alarm module and data server also adopts the above-mentioned secret key mechanism and signature mechanism. After adding the secret key mechanism and signature mechanism, the monitoring system finally constructs a bottom-up cascade signature data packet mechanism, that is, the step-by-step transmission path of the alarm information when a problem occurs in the underlying trackside equipment. Through the cascade signature mechanism, the source and transmission path of the data packet that generates the alarm can be traced from top to bottom through the alarm, that is, the specific trackside equipment with the problem can be found and located and the comprehensive manifestation of the problem can be analyzed.

[0073] A high-speed rail data security centralized monitoring system for smart contract big data learning provided by the embodiment of the present disclosure includes: a data monitoring module, a blockchain module and a big data machine learning module; the data monitoring module is used to collect the operation data corresponding to the trackside equipment in real time; the blockchain module is used to perform hash operation on the operation data and store the hash value corresponding to the operation data; the big data machine learning module is used to verify the data integrity according to the hash value, and use a preset machine learning algorithm to determine the corresponding equipment failure risk information according to the operation data; wherein, the data transmission structure between each module in the high-speed rail data security centralized monitoring system for smart contract big data learning includes a signature, a header and a tail; the header carries the identity corresponding to the current module, and the tail carries the identity of all sub-modules of the corresponding data source; the big data machine learning module is also used to locate the trackside equipment with the equipment failure risk information according to the identity carried in the tail corresponding to the operation data. It can ensure the storage security and execution security of railway equipment data, and monitor, predict and manage the operation status of the underlying trackside equipment along the railway.

[0074] Afterwards, a method for centralized monitoring of high-speed rail data security for intelligent contract big data learning disclosed in an embodiment of the present disclosure is introduced in detail. The execution subject of the method for centralized monitoring of high-speed rail data security for intelligent contract big data learning provided in the embodiment of the present disclosure is generally a computer device with certain computing capabilities, and the computer device includes, for example: a terminal device or a server or other processing device, and the terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the method for centralized monitoring of high-speed rail data security for intelligent contract big data learning can be implemented by a processor calling a computer-readable instruction stored in a memory.

[0075] See also Figure 4 As shown, it is a flowchart of a high-speed rail data security centralized monitoring method for smart contract big data learning provided by an embodiment of the present disclosure, and the method is applied to Figure 1 or Figure 3 The high-speed rail data security centralized monitoring system for smart contract big data learning shown in the figure includes steps S401 to S104, wherein:

[0076] S401, controlling the data monitoring module to collect the operation data corresponding to the trackside equipment in real time.

[0077] S402: Control the blockchain module to perform a hash operation on the operation data, and store the hash value corresponding to the operation data.

[0078] S403, control the big data machine learning module to verify the data integrity according to the hash value, and use the preset machine learning algorithm to determine the corresponding equipment failure risk information according to the operating data; wherein, the data transmission structure between modules in the high-speed rail data security centralized monitoring system of the smart contract big data learning includes a signature, a header and a tail; the header carries the identity corresponding to the current module, and the tail carries the identity of all sub-modules of the corresponding data source.

[0079] S404: Control the big data machine learning module to locate the trackside equipment with the equipment failure risk information according to the identity carried in the tail of the packet corresponding to the operating data.

[0080] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference may be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0081] The disclosed embodiment provides a high-speed rail safety data monitoring method, the method comprising: controlling the data monitoring module to collect the operation data corresponding to the trackside equipment in real time; controlling the blockchain module to perform hash operation on the operation data and store the hash value corresponding to the operation data; controlling the big data machine learning module to verify the data integrity according to the hash value, and using a preset machine learning algorithm to determine the corresponding equipment failure risk information according to the operation data; wherein the data transmission structure between each module in the high-speed rail data security centralized monitoring system of the smart contract big data learning includes a signature, a header and a tail; the header carries the identity corresponding to the current module, and the tail carries the identity of all sub-modules of the corresponding data source; controlling the big data machine learning module to locate the trackside equipment with the equipment failure risk information according to the identity carried in the tail corresponding to the operation data. The storage security and execution security of railway equipment data can be guaranteed, and the operation status of the underlying trackside equipment along the railway can be monitored, predicted and system managed.

[0082] Corresponds to Figure 4 The embodiment of the present disclosure also provides an electronic device 500, such as Figure 5 FIG. 5 is a schematic diagram of the structure of an electronic device 500 provided in an embodiment of the present disclosure, including:

[0083] Processor 51, memory 52, and bus 53; memory 52 is used to store execution instructions, including memory 521 and external memory 522; memory 521 here is also called internal memory, which is used to temporarily store operation data in processor 51 and data exchanged with external memory 522 such as hard disk. Processor 51 exchanges data with external memory 522 through memory 521. When the electronic device 500 is running, the processor 51 communicates with the memory 52 through bus 53, so that the processor 51 executes Figure 4 Steps of the high-speed rail data security centralized monitoring method based on smart contract big data learning.

[0084] The present disclosure 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 high-speed rail data security centralized monitoring method for smart contract big data learning described in the above method embodiment are executed. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0085] The presently disclosed embodiment also provides a computer program product, which includes computer instructions. When the computer instructions are executed by a processor, the steps of the high-speed rail data security centralized monitoring method for smart contract big data learning described in the above method embodiment can be executed. For details, please refer to the above method embodiment, which will not be repeated here.

[0086] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0087] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed system and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0088] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0090] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0091] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A high-speed rail data security centralized monitoring system based on smart contract big data learning, characterized in that: include: Data monitoring module, blockchain module and big data machine learning module; The data monitoring module is used to collect the operation data corresponding to the trackside equipment in real time; The blockchain module is used to perform a hash operation on the operation data and store a hash value corresponding to the operation data; The big data machine learning module is used to verify data integrity according to the hash value, and to determine corresponding equipment failure risk information according to the operating data using a preset machine learning algorithm; Among them, the data transmission structure between modules in the high-speed rail data security centralized monitoring system of the smart contract big data learning includes a signature, a header and a tail; The packet header carries the identity of the current module, and the packet tail carries the identity of all sub-modules corresponding to the data source; The big data machine learning module is also used to locate trackside equipment that has the equipment failure risk information based on the identity identifier carried in the packet tail corresponding to the operating data.

2. The high-speed rail data security centralized monitoring system for smart contract big data learning according to claim 1 is characterized in that: The data monitoring module includes a centralized monitoring host; The centralized monitoring host is set up in a railway station and is used to collect the operating data corresponding to the trackside equipment belonging to the railway station in real time, and construct a data packet with the identity identifier corresponding to the trackside equipment as the packet tail and the identity identifier corresponding to the centralized monitoring host as the packet header.

3. The high-speed rail data security centralized monitoring system for smart contract big data learning according to claim 2 is characterized in that: The data monitoring module also includes a centralized monitoring center, which is used to: Receiving all the operation data uploaded by the centralized monitoring host; For each of the operating data, an original data packet is constructed with the identity identifier corresponding to the corresponding centralized monitoring host as the packet tail and the identity identifier of the centralized monitoring center as the packet header, and the original data packet is sent to the blockchain module.

4. The high-speed rail data security centralized monitoring system for smart contract big data learning according to claim 3 is characterized in that: The centralized monitoring center is also used for: In response to a user's management configuration operation on the centralized monitoring host, generating a real-time execution command and a delayed execution command; The real-time execution command is sent to the blockchain module and executed immediately.

5. The high-speed rail data security centralized monitoring system for smart contract big data learning according to claim 4 is characterized in that: The high-speed rail data security centralized monitoring system of smart contract big data learning also includes a smart contract module, which is used to: Obtaining a delayed execution command sent by the centralized monitoring center to the centralized monitoring host; According to the preset contract conditions, the delayed execution command that meets the contract conditions is sent to the corresponding centralized monitoring host.

6. The high-speed rail data security centralized monitoring system for smart contract big data learning according to claim 5 is characterized in that: The smart contract module is also used to: According to the equipment failure risk information and the preset contract conditions, alarm information corresponding to the equipment failure risk information is generated.

7. The high-speed rail data security centralized monitoring system for smart contract big data learning according to claim 6 is characterized in that: The high-speed rail data security centralized monitoring system for smart contract big data learning also includes an alarm module, which is used to: Obtaining the alarm information generated by the smart contract module and the equipment failure risk information generated by the big data machine learning module; The alarm information and the equipment failure risk information are sent to the user.

8. A high-speed rail data security centralized monitoring method based on smart contract big data learning, characterized in that: A high-speed rail data security centralized monitoring system applied to smart contract big data learning as described in any one of claims 1 to 7, the method comprising: Control the data monitoring module to collect the operation data corresponding to the trackside equipment in real time; Control the blockchain module to perform a hash operation on the operation data, and store the hash value corresponding to the operation data; Control the big data machine learning module to verify data integrity according to the hash value, and use a preset machine learning algorithm to determine the corresponding equipment failure risk information according to the operating data; wherein the data transmission structure between modules in the high-speed rail data security centralized monitoring system of the smart contract big data learning includes a signature, a header and a tail; the header carries the identity of the current module, and the tail carries the identity of all sub-modules of the corresponding data source; The big data machine learning module is controlled to locate the trackside equipment that has the equipment failure risk information according to the identity carried in the tail of the packet corresponding to the operating data.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate through the bus. When the machine-readable instructions are executed by the processor, the steps of the high-speed rail data security centralized monitoring method for smart contract big data learning as described in claim 8 are performed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the high-speed rail data security centralized monitoring method for smart contract big data learning as described in claim 8.

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