LKJ operation record item point parameter configuration method, device, system and equipment

Abstract

The invention relates to the technical field of LKJ data processing, in particular to an LKJ operation record item point parameter configuration method, device, system and equipment, and the method comprises the steps: extracting line data and operation data from LKJ historical operation data, and carrying out the preprocessing of the extracted data; the method comprises the steps of preprocessing data, analyzing the preprocessed data, extracting data feature rule information, calculating LKJ operation record item point parameters according to the extracted data feature rule information, and automatically configuring the LKJ operation record item point parameters into an LKJ operation record analysis system. And objectivity and accuracy of parameter configuration are improved.

Description

Technical Field

[0001] The present invention relates to the field of LKJ data processing technology, and in particular to a method, device, system and equipment for configuring LKJ operation record item parameters. Background Art

[0002] At present, the configuration of LKJ operation record item parameters mainly relies on manual experience. The specific process is as follows:

[0003] Data collection: It requires cross-regional and cross-departmental manual collection of data such as railway line station spacing, track length, vehicle type formation, and train running time under signal control conditions. The workload is heavy and difficult to implement;

[0004] Data analysis and verification: Multiple manual checks of the collected data may even require on-site or commissioned verification, which is time-consuming, labor-intensive, and prone to errors;

[0005] Parameter configuration: Based on basic data, parameter tables are manually created and configured into the system. This work requires high professionalism, high threshold, and is difficult to operate.

[0006] This manual configuration method has the following significant defects: low efficiency: manual operation is time-consuming and labor-intensive, inefficient, and difficult to meet parameter update requirements; poor accuracy: easily affected by subjective factors, resulting in parameter deviations and affecting system reliability; difficult to update: as the railway transportation situation changes, parameter updates are difficult, restricting system development. Summary of the invention

[0007] The embodiments of the present invention provide a method, device, system and equipment for configuring LKJ operation record item parameters to solve the problems of low efficiency, poor accuracy and difficulty in updating in the prior art.

[0008] In a first aspect, the present invention provides a method for configuring parameters of an LKJ operation record item point, the method comprising:

[0009] Extract line data and operation data from LKJ historical operation data;

[0010] Preprocess the extracted data;

[0011] Analyze the preprocessed data, extract the data feature information, and build a point parameter calculation model;

[0012] Calculate the LKJ operation record item point parameters according to the item point parameter calculation model;

[0013] Automatically configure LKJ operation record item point parameters into the LKJ operation record analysis system.

[0014] In one embodiment, the step of analyzing the preprocessed data, extracting data feature regularity information, and constructing an item point parameter calculation model includes:

[0015] Identify the factors that affect the parameters of LKJ operation record items;

[0016] Extract the rules and relationships between item point parameters and influencing factors from the preprocessed data;

[0017] The item point parameter calculation model is established according to the extracted rules and relationships.

[0018] In one implementation, the line data includes: distance between stations and track length.

[0019] In one embodiment, the operation data includes: an interval operation time tensor.

[0020] In one embodiment, the dimensions of the interval running time tensor include vehicle type, formation, location, and signal control.

[0021] In one embodiment, the preprocessing of the extracted data includes: data cleaning, data conversion, and data interpolation of the extracted data.

[0022] In one embodiment, the step of automatically configuring the LKJ operation record item point parameters into the LKJ operation record analysis system includes:

[0023] Convert the format of LKJ operation record item parameters;

[0024] Update the LKJ operation record item point parameters after format conversion to the LKJ operation record analysis system;

[0025] The LKJ operation record item point parameters after format conversion are visualized and a verification and modification operation interface is provided.

[0026] In a second aspect, the present invention provides a device for configuring parameters of LKJ operation record items, comprising:

[0027] The acquisition module is used to extract line data and operation data from LKJ historical operation data;

[0028] A preprocessing module, used for preprocessing the extracted data;

[0029] The analysis module is used to analyze the preprocessed data, extract the data feature information, and build a point parameter calculation model;

[0030] A calculation module is used to calculate the LKJ operation record item point parameters according to the item point parameter calculation model;

[0031] Configuration module, used to automatically configure LKJ operation record item point parameters into the LKJ operation record analysis system.

[0032] In a third aspect, the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for configuring the LKJ operation record item point parameters as described in the first aspect above is implemented.

[0033] In a fourth aspect, the present invention provides a LKJ operation record item point parameter configuration system, comprising: an item point parameter configurator, a middleware, a database, and a client;

[0034] The item point parameter configurator is used to extract line data and operation data from LKJ historical operation data, preprocess the extracted data, analyze the preprocessed data, extract data feature regularity information, build an item point parameter calculation model, calculate LKJ operation record item point parameters according to the item point parameter calculation model, and automatically configure the LKJ operation record item point parameters into the LKJ operation record analysis system;

[0035] The middleware is connected to the item point parameter configurator, and the item point parameter configurator reads the cache from the middleware, obtains the processing message, and writes the cache to the middleware and sends the message;

[0036] The database is connected to the item point parameter configurator, and the item point parameter configurator calls the LKJ operation record item point parameters from the database and obtains the data returned by the database;

[0037] The client is connected to the database, and the client calls the LKJ operation record item point parameters from the database and obtains the data returned by the database.

[0038] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0039] The LKJ operation record item point parameter configuration method of the present invention extracts line data and operation data from LKJ historical operation data; pre-processes the extracted data; analyzes the pre-processed data, extracts data feature regularity information, and constructs an item point parameter calculation model; calculates LKJ operation record item point parameters according to the item point parameter calculation model; and automatically configures the LKJ operation record item point parameters into the LKJ operation record analysis system, thereby realizing intelligent calculation of item point parameters and self-updating of parameters, and improving the objectivity and accuracy of parameter configuration.

[0040] 1. The present invention is based on historical operation data and obtains necessary information from actual operation record files through professional algorithms, which overcomes the limitation of traditional manual configuration methods that rely on subjective experience and improves the objectivity and accuracy of parameter configuration.

[0041] 2. The present invention realizes intelligent computing, adopts big data analysis technology, conducts large-scale analysis of historical operation data, extracts patterns and features in the data, and combines statistics, operations research and other methods to establish a parameter calculation model, thereby realizing the automation and intelligence of parameter configuration.

[0042] 3. The present invention realizes self-update of parameters, supports automatic update and maintenance of parameters, can automatically adjust parameters according to changes in railway transportation situation, reduce manual maintenance costs, and improve system operation efficiency and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0044] Figure 1 A schematic diagram of an application environment of a method provided in Embodiment 1 of the present invention;

[0045] Figure 2 A flowchart of a method for configuring LKJ operation record item parameters according to an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of the structure of a device for configuring LKJ operation record item parameters provided by an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present invention;

[0048] Figure 5 A schematic diagram of the structure of an LKJ operation record item point parameter configuration system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0049] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0050] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0051] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0052] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0053] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0054] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0055] The embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0056] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0057] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0058] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0059] The full name of LKJ is the train operation control recording device, which is an important control device used to control the two risks and one overrun (risk-on, risk-off, and overspeed) of trains. It is a comprehensive data information table for railway management, which is convenient for railway managers to dispatch vehicles within the station area and maintain the normal travel order of trains. LKJ data is the data of the train operation monitoring and recording device, which can accurately record the train operation status, signal equipment status and crew operation status. It is a train speed monitoring device developed by my country's railways with the main purpose of ensuring train operation safety.

[0060] A method for configuring LKJ operation record item parameters provided in the first embodiment of the present invention can be applied in the following cases: Figure 1 In an application environment, a client communicates with a server. The client includes but is not limited to a PDA, a desktop computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a cloud terminal device, a personal digital assistant (PDA), and other terminal devices. The server can be implemented by an independent server or a server cluster consisting of multiple servers.

[0061] See also Figure 2 , is a flow chart of a method for configuring LKJ operation record item parameters provided in the first embodiment of the present invention. The above method for configuring LKJ operation record item parameters can be applied to Figure 1 The client in the client, the terminal device corresponding to the client connects to the target database through a preset application programming interface (API). When the target data is driven to execute the corresponding task, a corresponding task log will be generated, and the above task log can be collected through the API. Figure 2As shown, the LKJ operation record item point parameter configuration method includes the following steps:

[0062] S101: extracting line data and operation data from LKJ historical operation data.

[0063] Obtain LKJ historical operation data, then analyze and process the historical operation data to extract the necessary data for parameter setting. The necessary data includes line data and operation data.

[0064] One implementation method is to extract line data and operation data based on keywords. Specifically, the data extraction method is not limited to this in actual application, and it can be determined according to the specific application environment, all of which are within the protection scope of this application.

[0065] Line data includes: station distance, track length. Operation data includes: interval operation time tensor. The dimensions of interval operation time tensor include vehicle type, marshaling, location, and signal control. The interval operation time tensor contains the interval operation time under various circumstances, including the interval operation time under different vehicle types, different marshaling, different locations, and different signal controls.

[0066] S102: Preprocess the extracted data.

[0067] The preprocessing of the extracted data includes: data cleaning, data conversion, and data interpolation.

[0068] Data cleaning: remove invalid values ​​and outliers, such as null values, duplicate values, extreme values, etc.

[0069] Data conversion: unify the data format, such as unifying the time format into a standard time-minute format.

[0070] Data interpolation: Reasonable interpolation of missing values, such as using mean value, median, etc.

[0071] S103: Analyze the preprocessed data, extract data feature regularity information, and construct an item point parameter calculation model.

[0072] S104: Calculate the LKJ operation record item point parameters according to the item point parameter calculation model.

[0073] S105: Automatically configure the LKJ operation record item point parameters into the LKJ operation record analysis system.

[0074] The LKJ operation record item point parameter configuration method of the present embodiment extracts line data and operation data from LKJ historical operation data; preprocesses the extracted data; analyzes the preprocessed data, extracts data feature regularity information, and constructs an item point parameter calculation model; calculates LKJ operation record item point parameters according to the item point parameter calculation model; and automatically configures LKJ operation record item point parameters into the LKJ operation record analysis system, thereby realizing intelligent calculation of item point parameters and self-updating of parameters, and improving the objectivity and accuracy of parameter configuration.

[0075] Specifically, first, the present invention is based on historical operation data and obtains necessary information from actual operation record files through professional algorithms, which overcomes the limitations of traditional manual configuration methods that rely on subjective experience and improves the objectivity and accuracy of parameter configuration. Second, the present invention realizes intelligent computing, uses big data analysis technology to conduct large-scale analysis of historical operation data, extracts patterns and features from the data, and combines statistics, operations research and other methods to establish parameter calculation models to achieve automation and intelligence of parameter configuration. Third, the present invention realizes parameter self-update, supports automatic update and maintenance of parameters, can automatically adjust parameters according to changes in the railway transportation situation, reduce manual maintenance costs, and improve system operation efficiency and reliability.

[0076] Based on the above embodiment, optionally, step S103 includes:

[0077] S201, identifying factors that affect LKJ operation record item point parameters.

[0078] Identify factors that affect the parameters of LKJ operation record items, such as railway lines, train types, signal control conditions, etc.

[0079] S202, extracting the rules and relationships between item point parameters and influencing factors from the preprocessed data.

[0080] The rules and relationships between the item point parameters and the influencing factors include: the correlation between the influencing factors and the item point parameters. For example, the correlation between the signal trend and the item point parameter trend; the correlation between the signal mean and the item point parameter mean in the time window; the changes in the item point parameters corresponding to different train types, etc. In practical applications, it is not limited to this, and it can be determined according to the specific application environment, which is within the protection scope of this application.

[0081] S203, establishing an item point parameter calculation model according to the extracted rules and relationships.

[0082] The parameter calculation model is established by using statistics, operations research and other methods, which can automatically calculate the LKJ operation record item parameters based on the input data.

[0083] One implementation method is to use multivariate statistical analysis to obtain the relationship between influencing factors and item point parameters.

[0084] Multivariate statistical analysis is a branch of classical statistics. It is a multi-index (also called multivariate) comprehensive analysis method that can analyze the statistical laws of multiple indicators when they are interrelated. It is widely used in the study of economic and social phenomena. Multivariate statistical analysis methods can be roughly divided into two categories according to the analysis characteristics: one is the descriptive method, which mainly extracts important information from the original data and studies the main characteristics of the system, including principal component analysis, factor analysis, cluster analysis, correspondence analysis, etc. The second is the analytical method, which mainly studies the correlation and causal relationship between variables, and quantifies the relationship between variables by establishing a model, including multivariate regression analysis, discriminant analysis, canonical correlation analysis, etc. Multivariate regression analysis studies the interdependence between a dependent variable and multiple explanatory variables, reveals the quantitative relationship between the dependent variable and the explanatory variable, and thus estimates or predicts the dependent variable.

[0085] In the field of data analysis and predictive modeling, multiple linear regression is a basic and widely used statistical method for studying the linear relationship between a dependent variable and multiple independent variables. However, when faced with complex data relationships and nonlinear effects, the predictive ability of traditional linear regression models may be limited. At this time, neural networks, with their powerful nonlinear fitting ability and adaptive learning ability, have become a powerful choice for optimizing multiple linear regression predictions.

[0086] First, prepare the data. Collect data: Make sure the dataset contains enough observations of independent and dependent variables. Data preprocessing: Including missing value processing, outlier detection and processing, feature scaling, etc. Next, build the model. Then, choose the network structure: For multivariate linear regression, choose a network that only contains input and output layers, or use a single hidden layer network with a linear activation function. Initialize parameters: Randomly initialize weights and biases. Define the loss function: Select the mean square error (MSE) as the loss function to measure the difference between the predicted value and the actual value. Select the optimization algorithm: One implementation method is to use the gradient descent method to optimize the network parameters. Then train the model. Forward propagation: Calculate the network output. Calculate the loss: Use the loss function to evaluate the accuracy of the current prediction. Backward propagation: Update the network parameters according to the loss gradient. Iterative training: Repeat forward propagation, loss calculation, and backpropagation until the stopping condition is met (such as loss convergence, reaching the maximum number of iterations, etc.). Finally, evaluate and tune the model. Evaluate the model: Use the test set to evaluate the generalization ability of the model. Hyperparameter tuning: such as learning rate, number of neurons in the hidden layer, etc., can be optimized through methods such as cross-validation.

[0087] Based on the above embodiment, optionally, step S105 includes:

[0088] Step 301, convert the format of the LKJ operation record item point parameters.

[0089] The purpose of the above steps is to convert the parameters into a format that the system can recognize.

[0090] Step 302, updating the LKJ operation record item point parameters after format conversion into the LKJ operation record analysis system.

[0091] Step 303, visualize the LKJ operation record item point parameters after format conversion and provide manual verification and modification operations.

[0092] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0093] Corresponding to the method of the above embodiment, Figure 3 The structural block diagram of the LKJ operation record item parameter configuration device provided by the second embodiment of the present invention is shown. The above LKJ operation record item parameter configuration device is applied to a computer device, and the computer device is connected to the target database through a preset application program interface. When the target database is driven to execute a corresponding task, a corresponding task log will be generated, and the above task log can be collected through the API. For the convenience of explanation, only the part related to the embodiment of the present invention is shown.

[0094] See also Figure 3 , a LKJ operation record item parameter configuration device includes:

[0095] The acquisition module 401 is used to extract line data and operation data from LKJ historical operation data.

[0096] The preprocessing module 402 is used to preprocess the extracted data.

[0097] The analysis module 403 is used to analyze the preprocessed data, extract data feature regularity information, and construct an item point parameter calculation model.

[0098] The calculation module 404 is used to calculate the LKJ operation record item point parameters according to the item point parameter calculation model.

[0099] Configuration module 405 is used to automatically configure LKJ operation record item point parameters into the LKJ operation record analysis system.

[0100] The LKJ operation record item point parameter configuration device of the present invention extracts line data and operation data from LKJ historical operation data through an acquisition module; a preprocessing module preprocesses the extracted data; an analysis module analyzes the preprocessed data, extracts data feature regularity information, and constructs an item point parameter calculation model; a calculation module calculates the LKJ operation record item point parameters according to the item point parameter calculation model; and a configuration module automatically configures the LKJ operation record item point parameters into the LKJ operation record analysis system, thereby realizing intelligent calculation of item point parameters and self-updating of parameters, and improving the objectivity and accuracy of parameter configuration.

[0101] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0102] Figure 4 This is a schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. Figure 4 As shown, the computer device of this embodiment includes: at least one processor ( Figure 4 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor. When the processor executes the computer program, the steps in any of the above-mentioned LKJ operation record item point parameter configuration method embodiments are implemented.

[0103] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 4 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0104] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0105] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments may also be an external storage device of a computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on a computer device. Further, the memory may also include both an internal storage unit of a computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of computer programs, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0106] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0107] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing.

[0108] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0109] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0110] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0111] Figure 5 A structural diagram of an LKJ operation record item point parameter configuration system provided in Example 5 of the present invention.

[0112] like Figure 5 As shown, the LKJ operation record item point parameter configuration system of this embodiment includes: an item point parameter configurator, middleware, a database, and a client.

[0113] The item point parameter configurator is used to extract line data and operation data from LKJ historical operation data, preprocess the extracted data, analyze the preprocessed data, extract data feature regularity information, build an item point parameter calculation model, calculate the LKJ operation record item point parameters according to the item point parameter calculation model, and automatically configure the LKJ operation record item point parameters into the LKJ operation record analysis system.

[0114] The middleware is connected to the item point parameter configurator, and the item point parameter configurator reads the cache from the middleware, obtains the processing message, writes the cache to the middleware, and sends the message.

[0115] Middleware includes Workerman and Redis.

[0116] Workerman is an open source, high-performance PHP application container developed in pure PHP. Workerman is an open source, high-performance PHP application container that has greatly broken through the scope of traditional PHP applications and is widely used in the development of the Internet, instant messaging, APP development, hardware communication, smart home, Internet of Things and other fields. Workerman is not a reinvent the wheel. It is not an MVC framework, but a more basic and general service framework. You can use it to develop TCP proxy, ladder proxy, game server, mail server, FTP server, and even develop a PHP version of Redis, a PHP version of database, a PHP version of nginx, a PHP version of php-fpm, etc. Workerman can be said to be an innovation in the PHP field, allowing developers to completely get rid of the constraints of PHP only being able to do WEB.

[0117] Redis (full name: Remote Dictionary Server) is an open source, high-performance key-value storage system that is fast, flexible, and scalable. It is a memory-based data structure storage system that can be used as a database, cache, and message broker.

[0118] Some of the main features and uses of Redis: High performance: Redis data is stored in memory, so it can provide extremely fast read and write operations. It adopts a single-threaded model and asynchronous I / O to avoid multi-threaded competition and blocking, thereby achieving very high performance. Diverse data structures: Redis supports a variety of data structures, including strings, hashes, lists, sets, and sorted sets. These data structures provide a wealth of operation commands, allowing developers to easily handle various data requirements. Persistence support: Redis provides two persistence methods, namely snapshotting and log appending (Append-only file, AOF). The snapshot method writes Redis memory data to disk in binary format, while AOF achieves persistence by appending Redis operation commands. Publish / Subscribe: Redis supports the publish / subscribe mode and can be used as a message broker. The publisher sends messages to the specified channel, and the subscriber can receive and process these messages. This mode is very useful in scenarios such as building real-time communications, event-driven systems, and message queues. Distributed Cache: Redis can achieve distributed storage and high availability of data through master-slave replication and sharding. Master-slave replication can copy data to multiple slave nodes to achieve read-write separation and data backup. Sharding can distribute data on multiple Redis nodes to achieve horizontal expansion and load balancing. Transaction support: Redis supports transactions. Developers can combine multiple operations into an atomic sequence of operations to ensure that these operations are either all executed successfully or not executed at all. Feature-rich: Redis is not just a simple cache, it also provides many other features, such as transaction support, Lua script execution, scheduled tasks, atomic operations, etc. This allows developers to implement more complex application logic in Redis. Redis is a feature-rich storage system suitable for a variety of scenarios, including caching, session storage, rankings, real-time analysis, etc. It has a wide range of applications and has active community support.

[0119] The database is connected to the item point parameter configurator, and the item point parameter configurator calls the LKJ operation record item point parameters from the database and obtains the data returned by the database. In actual applications, the data is the LKJ operation record item point parameter library.

[0120] The client is connected to the database, and the client calls the LKJ operation record item point parameters from the database and obtains the data returned by the database. The client includes a Web management system.

[0121] The LKJ operation record item point parameter configuration system of this embodiment extracts line data and operation data from LKJ historical operation data through an item point parameter configurator; preprocesses the extracted data; analyzes the preprocessed data, extracts data feature regularity information, and constructs an item point parameter calculation model; calculates LKJ operation record item point parameters according to the item point parameter calculation model; and automatically configures LKJ operation record item point parameters into the LKJ operation record analysis system, thereby realizing intelligent calculation of item point parameters and self-updating of parameters, and improving the objectivity and accuracy of parameter configuration.

[0122] 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.

[0123] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for configuring LKJ operation record item parameters, characterized in that: The LKJ operation record item point parameter configuration method includes: Extract line data and operation data from LKJ historical operation data; Preprocess the extracted data; Analyze the preprocessed data, extract the data feature information, and build a point parameter calculation model; Calculate the LKJ operation record item point parameters according to the item point parameter calculation model; Automatically configure LKJ operation record item point parameters into the LKJ operation record analysis system.

2. A method for configuring LKJ operation record item parameters as claimed in claim 1, characterized in that: The steps of analyzing the preprocessed data, extracting data feature regularity information, and constructing an item point parameter calculation model include: Identify the factors that affect the parameters of LKJ operation record items; Extract the rules and relationships between item point parameters and influencing factors from the preprocessed data; The item point parameter calculation model is established according to the extracted rules and relationships.

3. A method for configuring LKJ operation record item parameters as claimed in claim 1, characterized in that: The line data include: distance between stations and track length.

4. A method for configuring LKJ operation record item parameters as claimed in claim 1, characterized in that: The operation data includes: an interval operation time tensor.

5. A method for configuring LKJ operation record item parameters as claimed in claim 4, characterized in that: The dimensions of the interval running time tensor include vehicle type, formation, location, and signal control.

6. A method for configuring LKJ operation record item parameters as claimed in claim 1, characterized in that: The preprocessing of the extracted data includes: data cleaning, data conversion, and data interpolation of the extracted data.

7. A method for configuring LKJ operation record item parameters as claimed in claim 1, characterized in that: The step of automatically configuring the LKJ operation record item point parameters into the LKJ operation record analysis system comprises: Convert the format of LKJ operation record item parameters; Update the LKJ operation record item point parameters after format conversion to the LKJ operation record analysis system; The LKJ operation record item point parameters after format conversion are visualized and a verification and modification operation interface is provided.

8. A device for configuring parameters of LKJ operation record items, characterized in that: The LKJ operation record item point parameter configuration device comprises: The acquisition module is used to extract line data and operation data from LKJ historical operation data; A preprocessing module, used for preprocessing the extracted data; The analysis module is used to analyze the preprocessed data, extract the data feature information, and build a point parameter calculation model; A calculation module is used to calculate the LKJ operation record item point parameters according to the item point parameter calculation model; Configuration module, used to automatically configure LKJ operation record item point parameters into the LKJ operation record analysis system.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the LKJ operation record item point parameter configuration method as described in claims 1 to 7 is implemented.

10. A LKJ operation record item parameter configuration system, characterized in that: include: Item parameter configurator, middleware, database, client; The item point parameter configurator is used to extract line data and operation data from LKJ historical operation data, preprocess the extracted data, analyze the preprocessed data, extract data feature regularity information, build an item point parameter calculation model, calculate LKJ operation record item point parameters according to the item point parameter calculation model, and automatically configure the LKJ operation record item point parameters into the LKJ operation record analysis system; The middleware is connected to the item point parameter configurator, and the item point parameter configurator reads the cache from the middleware, obtains the processing message, and writes the cache to the middleware and sends the message; The database is connected to the item point parameter configurator, and the item point parameter configurator calls the LKJ operation record item point parameters from the database and obtains the data returned by the database; The client is connected to the database, and the client calls the LKJ operation record item point parameters from the database and obtains the data returned by the database.

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