A centralized high-speed rail data security monitoring system based on smart contract big data learning

Abstract

The present disclosure provides a high-speed rail data security centralized monitoring system based on smart contract big data learning. The data monitoring module is used to collect real-time operating data corresponding to trackside equipment; the blockchain module is used to perform hash operations on the operating data and store the corresponding hash values; the big data machine learning module is used to verify data integrity based on the hash values ​​and determine corresponding equipment failure risk information based on the operating data using a preset machine learning algorithm. The data transmission structure between each module includes a signature, a header, and a trailer; the header carries the identity of the current module, and the trailer carries the identities of all sub-modules corresponding to the data source; based on the identity carried in the trailer corresponding to the operating data, trackside equipment with equipment failure risk information is located. This system can ensure the storage and execution security of railway equipment data and monitor the operating status of the underlying trackside equipment along the railway and provide system management.

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] Currently, the security of railway operation data is becoming increasingly prominent as the complexity and scale of railway systems increase rapidly. In particular, with the emergence of high-speed railways, the widespread use of networking, and the proliferation of intelligent devices, the amount of railway data generated, as well as the risks of tampering, leakage, and management, are all increasing dramatically. Risks exist in data storage, processing, and transmission. Therefore, the security and intelligent processing of railway data must receive significant attention. Summary of the Invention

[0003] The disclosed embodiments provide at least one 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 operating data corresponding to the trackside equipment in real time;

[0006] The blockchain module is configured 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 based on the hash value and determine corresponding equipment failure risk information based on the operating data using a preset machine learning algorithm;

[0008] 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 identities of all sub-modules corresponding to the data source;

[0010] The big data machine learning module is also used to locate the 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 embodiment, the data monitoring module includes a centralized monitoring host;

[0012] The centralized monitoring host is set up at the railway station and is used to collect the operating 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 a data packet.

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

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

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

[0019] In an optional embodiment, 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 configured to:

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

[0024] In an optional embodiment, the high-speed rail data security centralized monitoring system for smart contract big data learning further includes an alarm module, which 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 a 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 operating data corresponding to the trackside equipment in real time;

[0029] Controlling the blockchain module to perform a hash operation on the operating data and storing a hash value corresponding to the operating 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 corresponding equipment failure risk information based on 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. 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 security monitoring method of the above-mentioned smart contract big data learning are performed.

[0033] An embodiment of the present disclosure also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is run 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 embodiments of the present disclosure also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-mentioned method for centralized monitoring of high-speed rail data security based on smart contract big data learning.

[0035] The disclosed embodiments provide a high-speed rail data security centralized monitoring system based on smart contract big data learning, comprising: a data monitoring module, a blockchain module, and a big data machine learning module. The data monitoring module is configured to collect real-time operational data corresponding to trackside equipment; the blockchain module is configured to perform a hash operation on the operational data and store the hash value corresponding to the operational data; the big data machine learning module is configured to verify data integrity based on the hash value and determine corresponding equipment failure risk information based on the operational data using a preset machine learning algorithm. The data transmission structure between modules in the high-speed rail data security centralized monitoring system based on smart contract big data learning includes a signature, a header, and a trailer. The header carries an identity corresponding to the current module, and the trailer carries the identities of all sub-modules corresponding to the data source. The big data machine learning module is further configured to locate trackside equipment with the equipment failure risk information based on the identity carried in the trailer corresponding to the operational data. This ensures the storage and execution security of railway equipment data and enables monitoring, prediction, and system management of the operational status of 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 given below and described in detail with reference to the accompanying 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 briefly introduces the drawings required for use in the embodiments. The drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used 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 those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without inventive effort.

[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 solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions 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, not all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings herein 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 disclosure for which protection is sought, but merely represents 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 are within 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, it does not need to be further defined or explained in subsequent drawings.

[0045] The term "and / or" herein simply describes an association relationship, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, and the existence of B alone. In addition, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0046] Research has found that railway data security is becoming increasingly critical as the complexity and scale of railway systems increase. In particular, with the advent of high-speed rail, the widespread use of networking, and the proliferation of intelligent devices, the volume of railway data generated, along with the risks of tampering, leakage, and management, are increasing dramatically. Risks exist in data storage, processing, and transmission. Therefore, the security and intelligent processing of railway data must receive significant attention.

[0047] Based on the above research, the present disclosure provides a high-speed rail data security centralized monitoring system based on smart contract big data learning, comprising: a data monitoring module, a blockchain module, and a big data machine learning module; the data monitoring module is used to collect real-time operating data corresponding to trackside equipment; 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 data integrity based on the hash values ​​and determine corresponding equipment failure risk information based on the operating data using a preset machine learning algorithm; wherein the data transmission structure between each module in the high-speed rail data security centralized monitoring system based on smart contract big data learning includes a signature, a header, and a trailer; the header carries the identity of the current module, and the trailer carries the identities of all sub-modules of the corresponding data source; the big data machine learning module is further used to locate trackside equipment with equipment failure risk information based on the identity carried in the trailer corresponding to the operating data. This ensures the storage and execution security of railway equipment data and monitors, predicts, and manages the operating status of the underlying trackside equipment along the railway.

[0048] To facilitate understanding of this embodiment, first, a high-speed rail data security centralized monitoring system for smart contract big data learning disclosed in this embodiment is introduced in detail. 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 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 and 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 operating data is sent to the big data machine learning module, which verifies the data integrity based on the hash value calculated by the blockchain module, and uses a preset machine learning algorithm to process the operating data to determine the corresponding equipment failure risk information.

[0056] Here, the big data machine learning module is pre-configured with a variety of machine learning algorithms. The operating 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 equipment failure risk information 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 aggregated from the bottom-level trackside equipment to the top-level big data machine learning module. In essence, it is 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 in a step-by-step manner. 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] To ensure data is not tampered with during transmission and to ensure traceability when an alarm is generated, a secret key mechanism is implemented in each module, and a signature mechanism is included in the data packet. Each module's key consists of a private key and a public key. The private key is known only to the module and is not publicly disclosed, while the public key is publicly available. The public key also serves as the module's external identity.

[0061] Specifically, the data packet signature mechanism adds a signature, a header, and a trailer to the original data. This signature is generated by processing the data packet using the module's private key using the asymmetric RSA encryption algorithm. The signature verifies the integrity of the packet. The header contains the module's identity and the packet number, indicating the packet number generated by this computer. The trailer includes the headers of all subsequent packets that generated the original data in this packet, recording the source of the data generated by this packet for easy traceability. The trailer allows for tracing back to the lowest level of trackside equipment.

[0062] The disclosed embodiments provide a high-speed rail data security centralized monitoring system based on smart contract big data learning, comprising: a data monitoring module, a blockchain module, and a big data machine learning module; the data monitoring module is configured to collect real-time operating data corresponding to trackside equipment; the blockchain module is configured to perform a hash operation on the operating data and store the hash value corresponding to the operating data; the big data machine learning module is configured to verify data integrity based on the hash value and determine corresponding equipment failure risk information based on the operating data using a preset machine learning algorithm; wherein the data transmission structure between modules in the high-speed rail data security centralized monitoring system based on smart contract big data learning includes a signature, a header, and a trailer; the header carries an identity corresponding to the current module, and the trailer carries the identities of all sub-modules corresponding to the data source; the big data machine learning module is further configured to locate trackside equipment with the equipment failure risk information based on the identity carried in the trailer corresponding to the operating data. This ensures the storage and execution security of railway equipment data and enables monitoring, prediction, and system management of the operating status of 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 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 a specific implementation, a centralized monitoring host, located at a railway station, collects real-time operational data from the station's trackside equipment. It constructs a data packet with the trackside equipment's identity as the end of the packet and the centralized monitoring host's identity as the header. The centralized monitoring center receives operational data uploaded by all centralized monitoring hosts; for each operational data item, it constructs an original data packet with the corresponding centralized monitoring host's identity as the end of the packet and the centralized monitoring center's identity as the header. The original data packet is then sent to the blockchain module.

[0066] Specifically, a centralized monitoring host is typically deployed at each station, specifically to monitor the station and nearby trackside equipment. The collected monitoring data is sent to a centralized monitoring center for centralized processing and to the blockchain system for secure storage and big data processing. The centralized monitoring center primarily collects and aggregates monitoring data from centralized monitoring hosts at all stations along the railway line, centrally processes this data to obtain key data, and stores this key data on the blockchain for secure storage and big data processing. It also manages and configures commands sent to the centralized monitoring hosts. Real-time commands are executed immediately after being stored on the blockchain, while delayed commands are executed only after conditions are met in smart contracts.

[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 user management and 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; and according to the preset contract conditions, send the delayed execution command that meets the contract conditions to the corresponding centralized monitoring host. Based on the equipment failure risk information and the preset contract conditions, generate alarm information corresponding to the equipment failure risk information.

[0069] Smart contracts are programs built on top of blockchain modules that can be dynamically executed according to contractual conditions. In this system, they are primarily used to generate alarms after processing monitoring data according to pre-set contractual conditions, and to send commands from the centralized monitoring center to the centralized monitoring host that require delayed execution according to pre-set rules and conditions.

[0070] Furthermore, the data server is primarily used to store raw monitoring data after hash values ​​have been extracted, and is also the data source for the big data machine learning module. The integrity of the monitoring data is guaranteed by the blockchain module and the hash values ​​in the blocks.

[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 status 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 uses the aforementioned secret key mechanism and signature mechanism. With the addition of these mechanisms, the monitoring system ultimately establishes a bottom-up cascade signature data packet mechanism, which serves as a step-by-step transmission path for alarm information when problems occur in underlying trackside equipment. This cascade signature mechanism allows for the top-down tracing of the source and transmission path of the alarm packet, allowing for the identification and location of the specific trackside equipment experiencing the problem and analysis of the overall manifestations of the problem.

[0073] The disclosed embodiments provide a high-speed rail data security centralized monitoring system based on smart contract big data learning, comprising: a data monitoring module, a blockchain module, and a big data machine learning module; the data monitoring module is configured to collect real-time operating data corresponding to trackside equipment; the blockchain module is configured to perform a hash operation on the operating data and store the hash value corresponding to the operating data; the big data machine learning module is configured to verify data integrity based on the hash value and determine corresponding equipment failure risk information based on the operating data using a preset machine learning algorithm; wherein the data transmission structure between modules in the high-speed rail data security centralized monitoring system based on smart contract big data learning includes a signature, a header, and a trailer; the header carries an identity corresponding to the current module, and the trailer carries the identities of all sub-modules corresponding to the data source; the big data machine learning module is further configured to locate trackside equipment with the equipment failure risk information based on the identity carried in the trailer corresponding to the operating data. This ensures the storage and execution security of railway equipment data and enables monitoring, prediction, and system management of the operating status of underlying trackside equipment along the railway.

[0074] Afterwards, a method for centralized monitoring of high-speed rail data security based on smart 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 based on smart contract big data learning provided in the embodiment of the present disclosure is generally a computer device with certain computing capabilities. The computer device includes, for example: a terminal device or a server or other processing device. The terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a 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 based on smart contract big data learning can be implemented by a processor calling computer-readable instructions 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. Control the data monitoring module to collect operating data corresponding to the trackside equipment in real time.

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

[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 can be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0081] The disclosed embodiments provide a high-speed rail safety data monitoring method, comprising: controlling the data monitoring module to collect real-time operating data corresponding to trackside equipment; controlling the blockchain module to perform a hash operation on the operating data and store the hash value corresponding to the operating data; controlling the big data machine learning module to verify data integrity based on the hash value and determine corresponding equipment failure risk information based on the operating data using a preset machine learning algorithm; wherein the data transmission structure between modules in the high-speed rail data security centralized monitoring system based on smart contract big data learning includes a signature, a header, and a trailer; the header carries an identity corresponding to the current module, and the trailer carries the identities of all sub-modules corresponding to the data source; controlling the big data machine learning module to locate trackside equipment with the equipment failure risk information based on the identity carried in the trailer corresponding to the operating data. This ensures the storage and execution security of railway equipment data and monitors, predicts, and systematically manages the operating status of underlying trackside equipment along the railway.

[0082] Corresponding to Figure 4 The embodiment of the present disclosure also provides an electronic device 500, such as Figure 5 FIG. 5 is a schematic structural diagram 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; the memory 521 here is also called internal memory, which is used to temporarily store the operation data in the processor 51 and the data exchanged with the external memory 522 such as the hard disk. The processor 51 exchanges data with the external memory 522 through the memory 521. When the electronic device 500 is running, the processor 51 and the memory 52 communicate through the 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 having a computer program stored thereon. When executed by a processor, the computer program executes the steps of the method for centralized monitoring of high-speed rail data security using smart contract big data learning as described in the above method embodiment. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0085] The embodiments of the present disclosure also provide 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 embodiment, the computer program product is implemented as a computer storage medium. In another 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 brevity 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, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

[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 enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute 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 scope of protection of the present disclosure is not limited thereto. Although the present disclosure has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed in the present disclosure, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure shall be subject to the scope of protection of the claims.

Claims

1. A high-speed rail data security centralized monitoring system based on smart contract big data learning, characterized by: include: Data monitoring module, blockchain module, and big data machine learning module; The data monitoring module is used to collect the operating data corresponding to the trackside equipment in real time; The blockchain module is configured 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 based on the hash value and determine corresponding equipment failure risk information based on the operating data using a preset machine learning algorithm; 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 identities of all sub-modules corresponding to the data source; The big data machine learning module is further configured to locate the trackside equipment with the equipment failure risk information based on the identity identifier carried in the tail of the packet corresponding to the operating data; Among them, a secret key mechanism is added to each module, and a signature mechanism is added to the data packet. The keys in each module include private key and public key. The private key is only for this module and is not disclosed to the outside. The public key can be disclosed to the outside. The public key also serves as the external identity of this module. The data packet signature mechanism adds a signature, header, and 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 a signature. The integrity of this data packet is ensured by signature verification. The header contains the identity of this module and the data packet number. The tail contains the headers of all 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. The tail can be traced back to the lowest level trackside equipment.

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 at the railway station and is used to collect the operating 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 a data packet.

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 further 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 to: generating a real-time execution command and a delayed execution command in response to a user's management configuration operation on the centralized monitoring host; 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 for 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 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, wherein the method comprises: Control the data monitoring module to collect the operating data corresponding to the trackside equipment in real time; Controlling the blockchain module to perform a hash operation on the operating data and storing a hash value corresponding to the operating 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 corresponding equipment failure risk information based on 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; 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 of the packet corresponding to the operating data; Among them, a secret key mechanism is added to each module, and a signature mechanism is added to the data packet. The keys in each module include private key and public key. The private key is only for this module and is not disclosed to the outside. The public key can be disclosed to the outside. The public key also serves as the external identity of this module. The data packet signature mechanism adds a signature, header, and 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 a signature. The integrity of this data packet is ensured by signature verification. The header contains the identity of this module and the data packet number. 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. The tail can be traced back to the lowest level trackside equipment.

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 based on 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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