Turnout machine state analysis method and equipment based on gradient curve

Abstract

This application relates to a method and device for analyzing the status of a turnout switch machine based on a gradient curve. The method includes collecting the operating parameters of the turnout switch machine over a certain period of time, including but not limited to the power or current values ​​of the turnout switch machine; generating a gradient curve based on the operating parameters of the turnout switch machine; calculating the cumulative sum of the gradient curve; and determining whether the turnout switch machine is in an abnormal state based on the cumulative sum. This application captures the operating changes of the turnout switch machine from the perspective of gradient changes, thereby enabling automatic detection and analysis of abnormal turnout switch machine conditions.

Description

Technical Field

[0001] The present application relates to the field of rail transportation technology, and in particular to a method and device for analyzing the state of a turnout machine based on a gradient curve. Background Art

[0002] A turnout is a track connection device that allows rolling stock to transfer from one track to another. They are typically installed in large numbers at stations and marshalling yards. They maximize the throughput capacity of a track, and their proper movement and transfer are crucial for ensuring safe train operation. A turnout consists of a switch, connecting parts, frogs, and guardrails. Switching is controlled by a turnout machine.

[0003] Currently, the monitoring and analysis of turnout machine operational safety parameters primarily relies on manual indirect analysis of current, voltage, and power data collected by existing centralized signal monitoring systems. This is significantly influenced by factors such as human skill level, responsibility, and experience, lacks intelligent analysis capabilities, and the accuracy of analysis results varies widely. Therefore, monitoring and analyzing the operational safety parameters of turnout machines to achieve intelligent analysis and early warning has become a pressing technical issue in this field. Summary of the Invention

[0004] In view of the above, the present application provides a method and device for analyzing the state of a turnout machine based on a gradient curve, the purpose of which is to solve the above technical problems.

[0005] In a first aspect, the present application provides a method for analyzing a switch machine state based on a gradient curve, the method comprising:

[0006] Collect the working parameters of the turnout machine within a preset time period;

[0007] generating a gradient curve based on the working parameters of the turnout machine;

[0008] The cumulative sum of the gradient curves is calculated, and whether the turnout machine is in an abnormal state is determined based on the cumulative sum.

[0009] Preferably, after generating the gradient curve based on the operating parameters of the turnout machine, the method further includes:

[0010] A filtering operation is performed on the gradient curve.

[0011] Preferably, calculating the cumulative sum of the gradient curves and judging whether the turnout machine is in an abnormal state according to the cumulative sum includes:

[0012] According to the time sequence corresponding to the gradient curve, starting from the starting data of the gradient curve, the gradient values ​​with positive gradient values ​​are accumulated in sequence, and recorded as the accumulated sum of the gradient curve;

[0013] If the gradient value at a certain time point is a negative number, or the working parameter corresponding to the time point is the maximum value among the working parameters, the accumulated sum is cleared;

[0014] If a preset number of consecutive gradient values ​​are all zero, clearing the accumulated sum;

[0015] Determining in real time whether the accumulated sum is greater than or equal to a preset threshold;

[0016] If so, it is determined that the turnout machine is in an abnormal state.

[0017] Preferably, the method further comprises:

[0018] When it is determined that the turnout switch machine is in an abnormal state, the operation phase of the turnout switch machine in the abnormal state is determined according to the time point of the abnormal state.

[0019] Preferably, after determining the operation phase of the switch machine in the abnormal state according to the time point of the abnormal state, the method further includes:

[0020] The action of the turnout machine in an abnormal state is sent to a preset terminal.

[0021] Preferably, after collecting the operating parameters of the turnout machine within a preset time period, the method further includes:

[0022] Reading the maximum value of the working parameters of the turnout machine;

[0023] Determining whether the maximum value is greater than or equal to a preset value;

[0024] If so, it is determined that the turnout machine is in an abnormal state.

[0025] Preferably, after collecting the operating parameters of the turnout machine within a preset time period, the method further includes:

[0026] Determining whether the switch machine operating parameters have missing values ​​or invalid values;

[0027] If any, perform data filling operation on the missing value or invalid value.

[0028] Preferably, the operating parameters of the switch machine include but are not limited to the power value or current value of the switch machine.

[0029] Preferably, after collecting the operating parameters of the turnout machine within a preset time period, the method further includes:

[0030] Calculating the mean, median and / or mode of the operating parameters of the turnout machine;

[0031] Comparing the average, median and / or mode of the working parameters of the turnout machine with the average, median and / or mode of pre-stored normal working parameters to obtain a comparison result;

[0032] It is determined whether the working parameters of the turnout machine are abnormal based on the comparison result.

[0033] In a second aspect, the present application provides an electronic device, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0034] Memory for storing computer programs;

[0035] The processor is configured to implement the steps of the method for analyzing the state of a turnout machine based on a gradient curve according to any one of the embodiments of the first aspect when executing the program stored in the memory.

[0036] The above technical solution provided by the embodiment of the present application has the following advantages compared with the prior art:

[0037] The switch machine state analysis method and equipment based on gradient curves proposed in this application collect the operating parameters of the switch machine (for example, power value, current value), generate a gradient curve with the collected operating parameters, calculate the cumulative sum of the gradient curve, and judge whether the switch machine is in an abnormal state based on the cumulative sum. The changes in the operation of the switch machine are captured from the perspective of gradient changes, thereby accurately realizing automatic detection and automatic analysis of abnormal states of the switch machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] Figure 1 This is a flow chart diagram of a preferred embodiment of a method for analyzing a turnout machine state based on a gradient curve of the present application;

[0041] Figure 2 Schematic diagram of the switch machine operating parameters collected for this application;

[0042] Figure 3 A schematic diagram of the gradient curve of this application;

[0043] Figure 4 This is a module diagram of a preferred embodiment of a switch machine state analysis device based on a gradient curve of the present application;

[0044] Figure 5 A schematic diagram of a preferred embodiment of the electronic device of the present application;

[0045] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of this application more clear, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0047] It should be noted that the technical solutions between the various embodiments in this application can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0048] This application provides a method for analyzing the state of a turnout machine based on a gradient curve. Figure 1 The figure shows a flow chart of an embodiment of the method for analyzing the state of a turnout machine based on a gradient curve of the present application. The method can be executed by an electronic device (e.g., a computer device), which can be implemented by software and / or hardware. The method for analyzing the state of a turnout machine based on a gradient curve includes:

[0049] Step S10: collecting the working parameters of the turnout machine within a preset time period;

[0050] Step S20: generating a gradient curve based on the operating parameters of the turnout machine;

[0051] Step S30: Calculate the cumulative sum of the gradient curves, and determine whether the turnout machine is in an abnormal state based on the cumulative sum.

[0052] A turnout switch is an important signaling device used to reliably change turnout positions, alter turnout opening directions, lock turnout points, and indicate turnout position. It effectively ensures driving safety, improves transportation efficiency, and reduces operator workload. Since turnout switching is controlled by the turnout switch, monitoring and analyzing its operating parameters can determine if there are any abnormalities in turnout switching.

[0053] In this embodiment, the working parameters of the turnout machine within a certain period of time are first collected. The collected working parameters can be the working parameters of a complete action cycle of the turnout machine, or the working parameters of an incomplete action cycle. Assuming that the complete cycle includes three stages: unlocking, action, and locking, if only the process of the "unlocking" and "action" stages needs to be analyzed, then only the data of the "unlocking" and "action" stages can be collected, that is, the collected working parameters are usually for a complete action cycle, and in some special cases, the working parameters of an incomplete action cycle can also be collected. For example, the power value of the turnout machine within 5 minutes is collected, wherein the working parameter can be the power value of the turnout machine, or the current value of the turnout machine, that is, the working parameters include but are not limited to the power value or current of the switch machine. Figure 2 As shown, it is a schematic diagram of the working parameters (power values) of the turnout machine collected in this application, the horizontal axis represents time in seconds, and the vertical axis represents power value in kilowatts.

[0054] It should be noted that the preset time period described above depends on the type of switch machine. This time period is typically fixed, and only the data required for algorithm analysis or a complete cycle of the switch machine's operation is sufficient. Data loss or incomplete data due to a fault is considered a separate fault type, for example, a fault with a high proportion of missing values ​​is treated as such.

[0055] After collecting the operating parameters of the turnout machine within a preset time period, the method further includes:

[0056] Determining whether the switch machine operating parameters have missing values ​​or invalid values;

[0057] If any, perform data filling operation on the missing value or invalid value.

[0058] Since the collected working parameters may contain missing values, invalid values, or garbled characters, it is necessary to pre-process the collected working parameters to determine whether the collected working parameters of the turnout machine contain missing values, invalid values, or garbled characters. If it is determined that they do, a data filling operation is performed on the missing values ​​or invalid values. The data filling operation can be one of global constant automatic filling, central metric automatic filling, and group mean automatic filling. Preferably, in this embodiment, the missing values ​​or invalid values ​​are filled with the mean of the previous and next values. It should be noted that since a high proportion of missing values ​​or invalid values ​​has a significant impact on subsequent analysis, if the missing values ​​or invalid values ​​exceed a certain proportion (for example, 20%) in the collected working parameters of the turnout machine, an early warning message is generated to remind the user that the proportion of missing values ​​or invalid values ​​in the collected data is high. Furthermore, since the failure to collect data or serious data loss is a fault condition, if the working parameters of the turnout machine cannot be collected, or the collected data loss exceeds a certain proportion, an early warning message can be directly generated and sent to a preset terminal (for example, the terminal of the on-duty personnel or other staff).

[0059] In one embodiment, after collecting the operating parameters of the switch machine within a preset time period, the method further includes:

[0060] Calculating the mean, median and / or mode of the operating parameters of the turnout machine;

[0061] Comparing the average, median and / or mode of the working parameters of the turnout machine with the average, median and / or mode of pre-stored normal working parameters to obtain a comparison result;

[0062] It is determined whether the operating parameters of the turnout machine are abnormal based on the comparison result.

[0063] Since the collected working parameters (such as power values) may all or most of them be zero (or close to zero), that is, the collected working parameters of the turnout machine are abnormal, indicating that the operation process of the turnout machine is abnormal at this time. Therefore, after collecting the working parameters of the turnout machine, the average, median and / or mode of the collected working parameters of the turnout machine can be calculated, and the average, median and / or mode of the working parameters of the turnout machine can be compared with the average, median and / or mode of the pre-stored normal working parameters to obtain a comparison result. For example, the average of the normal working parameters is a, and the average of the actual collected data is b. The difference between a and b can be compared to determine whether the collected working parameters are abnormal, or the value of |ba| / a can be calculated. If the value is less than 10%, it is determined that the collected working parameters are normal, and the operation process of the turnout machine is normal at this time. The comparison of the median and mode is roughly the same as the comparison method of the average, and will not be repeated here. This type of fault can be identified by comparing the mean, median and / or mode of the collected operating parameters with the mean, median and / or mode of normal operating parameters.

[0064] Furthermore, after collecting the operating parameters of the turnout machine within a preset time period, the method further includes:

[0065] Reading the maximum value of the working parameters of the turnout machine;

[0066] Determining whether the maximum value is greater than or equal to a preset value;

[0067] If so, it is determined that the turnout machine is in an abnormal state.

[0068] The collected working parameters of the turnout and switch machine are sorted, and the maximum value is read from the sorting result to determine whether the maximum value is greater than or equal to a preset value. The preset value can be set according to the actual operating conditions, for example, twice the rated power of the turnout and switch machine. It is determined whether the maximum value of the working parameters of the turnout and switch machine is greater than or equal to the preset value. If so, it means that the instantaneous power of the turnout and switch machine far exceeds the rated power, that is, the turnout and switch machine is in an abnormal state.

[0069] After collecting the operating parameters of the turnout machine within a preset time period, if the collected power values ​​are all normal power values, it is also possible to determine whether the turnout is a single-link turnout or a multi-link turnout by the number of maximum power values ​​among the collected power values. The number of maximum power values ​​is equal to the number of turnouts. For example, if the maximum power value among the collected power values ​​is 0.5KW, and there is only one, then the turnout is a single-link turnout. If the maximum power value among the collected power values ​​is 0.5KW, and there are two 0.5KW values, then the turnout is a multi-link turnout. It should be noted that a single-link turnout is a one-way track, and the frog heart rail of its movable heart rail fits tightly with the wing rail in the same direction of opening. It consists of a switch, a switch, a guardrail, a connecting part, and a switch sleeper. A multi-link turnout contains multiple single-link turnouts, and the multi-link turnout is controlled and operated by a turnout machine.

[0070] According to the working parameters of the turnout machine, a gradient curve can be drawn, such as Figure 3 The figure shows a schematic diagram of the gradient curve of the present application. Furthermore, after the gradient curve is generated, a filtering operation can be performed on the generated gradient curve to smooth the gradient curve and eliminate interference. Since the gradient curve is a curve that reflects the change of parameters, the cumulative sum of the gradient curve can be calculated, and the cumulative sum of the gradient curve can be used to determine whether the turnout machine is in an abnormal state. Specifically,

[0071] Calculating the cumulative sum of the gradient curves and determining whether the turnout machine is in an abnormal state according to the cumulative sum includes:

[0072] According to the time sequence corresponding to the gradient curve, starting from the starting data of the gradient curve, the gradient values ​​with positive gradient values ​​are accumulated in sequence, and recorded as the accumulated sum of the gradient curve;

[0073] If the gradient value at a certain time point is a negative number, or the working parameter corresponding to the time point is the maximum value among the working parameters, the accumulated sum is cleared;

[0074] If a preset number of consecutive gradient values ​​are all zero, clearing the accumulated sum;

[0075] Determining in real time whether the accumulated sum is greater than or equal to a preset threshold;

[0076] If so, it is determined that the turnout machine is in an abnormal state.

[0077] The cumulative sum can characterize the amplitude of data changes. By calculating the cumulative sum, it is possible to count whether abnormal fluctuations exceed the limit. This embodiment analyzes the gradient as a positive number. Therefore, in the process of counting the cumulative sum, if the gradient at a certain time point is negative, the cumulative sum is cleared. If, in the process of counting the cumulative sum, the power value corresponding to a certain time point is the maximum value among the collected power values, it means that this is a normal fluctuation, so the cumulative sum also needs to be cleared at this time. If, in the process of counting the cumulative sum, the gradient values ​​of a preset number (for example, 2 consecutive times) are all zero, it means that this section of the curve is smooth and has no fluctuations. Even if there is a very small peak in front (for example, interference), it can be ignored. At this time, the cumulative sum is also cleared. It should be noted that the preset number can be set to other values ​​according to actual conditions. In the process of counting the cumulative sum, it is judged in real time whether the cumulative sum is greater than or equal to the preset threshold. If so, it is judged that the turnout machine is in an abnormal state.

[0078] In one embodiment, the method further comprises:

[0079] When it is determined that the turnout switch machine is in an abnormal state, the operation phase of the turnout switch machine in the abnormal state is determined according to the time point of the abnormal state.

[0080] The operating parameters of the switch machine are collected in chronological order, and the cumulative sum is also calculated in chronological order. The start and end data numbers involved in the cumulative sum calculation can be recorded during the calculation. If the cumulative sum is cleared, the starting data number can be updated. When it is determined that the switch machine is in an abnormal state, the operation stage of the switch machine during the abnormal state can be determined based on the time point of the abnormal state. The operation stages of the switch machine include unlocking, switching, and locking.

[0081] In one embodiment, after determining the operation phase of the switch machine in the abnormal state according to the time point of the abnormal state, the method further includes:

[0082] The action of the turnout machine in an abnormal state is sent to a preset terminal.

[0083] After determining the operational phase of the switch machine during an abnormal state, the operational phase (process) of the switch machine during the abnormal state can be sent to a maintenance terminal (e.g., a monitoring terminal of an equipment maintenance personnel), allowing maintenance personnel to promptly obtain the current switch machine operating parameters (curves), alarm information, possible fault causes, and repair suggestions, allowing personnel to promptly troubleshoot the switch machine based on the abnormal information. For example, if the operational phase of the switch machine during an abnormal state is the unlocking phase, it may be due to a foreign object being stuck.

[0084] The present application collects the working parameters of the turnout machine (for example, power value or current value), generates a gradient curve with the collected working parameters, calculates the cumulative sum of the gradient curve, determines whether the turnout machine is in an abnormal state based on the cumulative sum, captures the changes in the turnout machine's operation from the perspective of gradient changes, and realizes automatic detection and automatic analysis of abnormal states of the turnout machine.

[0085] Reference Figure 4 , which is a schematic diagram of the functional modules of the switch machine state analysis device 100 based on the gradient curve of the present application.

[0086] The gradient curve-based switch machine state analysis device 100 described herein can be installed in an electronic device. Depending on the functionality implemented, the gradient curve-based switch machine state analysis device 100 may include an acquisition module 110, a generation module 120, and a determination module 130. A module, also referred to herein as a unit, refers to a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and are stored in the electronic device's memory.

[0087] In this embodiment, the functions of each module / unit are as follows:

[0088] Collection module 110: used to collect the working parameters of the turnout machine within a preset time period;

[0089] Generating module 120: generating a gradient curve based on the working parameters of the turnout machine;

[0090] The judgment module 130 is configured to calculate the cumulative sum of the gradient curves and judge whether the turnout machine is in an abnormal state according to the cumulative sum.

[0091] In one embodiment, the switch machine state analysis device based on the gradient curve further includes a filtering module, which is used to:

[0092] A filtering operation is performed on the gradient curve.

[0093] In one embodiment, calculating the cumulative sum of the gradient curves and determining whether the turnout machine is in an abnormal state according to the cumulative sum includes:

[0094] According to the time sequence corresponding to the gradient curve, starting from the starting data of the gradient curve, the gradient values ​​with positive gradient values ​​are accumulated in sequence, and recorded as the accumulated sum of the gradient curve;

[0095] If the gradient value at a certain time point is a negative number, or the working parameter corresponding to the time point is the maximum value among the working parameters, the accumulated sum is cleared;

[0096] If a preset number of consecutive gradient values ​​are all zero, clearing the accumulated sum;

[0097] Determining in real time whether the accumulated sum is greater than or equal to a preset threshold;

[0098] If so, it is determined that the turnout machine is in an abnormal state.

[0099] In one embodiment, the switch machine state analysis device based on the gradient curve further includes a determination module, which is used to:

[0100] When it is determined that the turnout switch machine is in an abnormal state, the operation phase of the turnout switch machine in the abnormal state is determined according to the time point of the abnormal state.

[0101] In one embodiment, the switch machine state analysis device based on the gradient curve further includes a sending module, which is used to:

[0102] The action of the turnout machine in an abnormal state is sent to a preset terminal.

[0103] In one embodiment, the acquisition module is further configured to:

[0104] Reading the maximum value of the working parameters of the turnout machine;

[0105] Determining whether the maximum value is greater than or equal to a preset value;

[0106] If so, it is determined that the turnout machine is in an abnormal state.

[0107] In one embodiment, the switch machine state analysis device based on the gradient curve further includes a preprocessing module, which is used to:

[0108] Determining whether the switch machine operating parameters have missing values ​​or invalid values;

[0109] If any, perform data filling operation on the missing value or invalid value.

[0110] In one embodiment, the operating parameter of the switch machine includes a power value or a current value of the switch machine.

[0111] In one embodiment, the switch machine state analysis device based on the gradient curve further includes a comparison module, which is used to:

[0112] Calculating the mean, median and / or mode of the operating parameters of the turnout machine;

[0113] Comparing the average, median and / or mode of the working parameters of the turnout machine with the average, median and / or mode of pre-stored normal working parameters to obtain a comparison result;

[0114] It is determined whether the working parameters of the turnout machine are abnormal based on the comparison result.

[0115] Reference Figure 5 , which is a schematic diagram of a preferred embodiment of the electronic device 1 of the present application.

[0116] The electronic device 1 includes, but is not limited to, a memory 11, a processor 12, a display 13, and a communication interface 14. The electronic device 1 can be connected to a network via the communication interface 14. The network can be a wired communication network.

[0117] The memory 11 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as the hard disk or internal memory of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped with the electronic device 1. Of course, the memory 11 may also include both the internal storage unit of the electronic device 1 and its external storage device. In this embodiment, the memory 11 is generally used to store an operating system and computer programs installed on the electronic device 1, such as the program code of the gradient curve-based switch machine state analysis program 10. In addition, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.

[0118] In some embodiments, the processor 12 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 12 is generally used to control the overall operation of the electronic device 1, such as performing control and processing related to data interaction or communication. In this embodiment, the processor 12 is used to execute program code stored in the memory 11 or process data, such as executing the program code of the gradient curve-based switch machine state analysis program 10.

[0119] The display 13 can be referred to as a display screen or a display unit. In some embodiments, the display 13 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an organic light-emitting diode (OLED) touch screen. The display 13 is used to display information processed by the electronic device 1 and to display a visual work interface.

[0120] The communication interface 14 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface). The communication interface 14 is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0121] Figure 5 Only the electronic device 1 having the components 11 - 14 and the switch machine state analysis program 10 based on the gradient curve is shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0122] In the above embodiment, the processor 12 can implement the following steps when executing the gradient curve-based switch machine state analysis program 10 stored in the memory 11:

[0123] Collect the working parameters of the turnout machine within a preset time period;

[0124] generating a gradient curve based on the working parameters of the turnout machine;

[0125] The cumulative sum of the gradient curves is calculated, and whether the turnout machine is in an abnormal state is determined based on the cumulative sum.

[0126] The storage device may be the memory 11 of the electronic device 1 , or may be another storage device that is communicatively connected to the electronic device 1 .

[0127] For a detailed description of the above steps, please refer to the Figure 4 Functional module diagram of the embodiment of the switch machine state analysis device 100 based on the gradient curve and Figure 1 Description of a flowchart of an embodiment of a method for analyzing the state of a turnout machine based on a gradient curve.

[0128] In addition, the embodiment of the present application also proposes a computer-readable storage medium, which can be non-volatile or volatile. The computer-readable storage medium can be any one of a hard disk, a multimedia card, an SD card, a flash memory card, an SMC, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, etc., or any combination of several of them. The computer-readable storage medium includes a data storage area and a program storage area. The program storage area stores a switch machine state analysis program 10 based on a gradient curve. When the switch machine state analysis program 10 based on a gradient curve is executed by a processor, the following operations are implemented:

[0129] Collect the working parameters of the turnout machine within a preset time period;

[0130] generating a gradient curve based on the working parameters of the turnout machine;

[0131] The cumulative sum of the gradient curves is calculated, and whether the turnout machine is in an abnormal state is determined based on the cumulative sum.

[0132] The specific implementation of the computer-readable storage medium of the present application is roughly the same as the specific implementation of the above-mentioned gradient curve-based turnout machine state analysis method, and will not be repeated here.

[0133] It should be noted that the serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. In addition, the terms "including", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0134] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, electronic device, or network equipment, etc.) to execute the methods described in each embodiment of the present application.

[0135] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for analyzing the state of a turnout machine based on a gradient curve, characterized in that: The method comprises: Collect the working parameters of the turnout machine within a preset time period; generating a gradient curve based on the working parameters of the turnout machine; Calculating a cumulative sum of the gradient curves, and judging whether the turnout machine is in an abnormal state according to the cumulative sum; Calculating the cumulative sum of the gradient curves and determining whether the turnout machine is in an abnormal state according to the cumulative sum includes: According to the time sequence corresponding to the gradient curve, starting from the starting data of the gradient curve, the gradient values ​​with positive gradient values ​​are accumulated in sequence, and recorded as the accumulated sum of the gradient curve; If the gradient value at a certain time point is a negative number, or the working parameter corresponding to the time point is the maximum value among the working parameters, the accumulated sum is cleared; If a preset number of consecutive gradient values ​​are all zero, clearing the accumulated sum; Determining in real time whether the accumulated sum is greater than or equal to a preset threshold; If so, it is determined that the turnout machine is in an abnormal state.

2. The method for analyzing the state of a turnout machine based on a gradient curve according to claim 1, characterized in that: After generating the gradient curve based on the operating parameters of the turnout machine, the method further includes: A filtering operation is performed on the gradient curve.

3. The method for analyzing the state of a turnout machine based on a gradient curve according to claim 1 or 2, characterized in that: The method further comprises: When it is determined that the turnout switch machine is in an abnormal state, the operation phase of the turnout switch machine in the abnormal state is determined according to the time point of the abnormal state.

4. The method for analyzing the state of a turnout machine based on a gradient curve according to claim 3, characterized in that: After determining the operation phase of the turnout machine in the abnormal state according to the time point of the abnormal state, the method further includes: The action of the turnout machine in an abnormal state is sent to a preset terminal.

5. The method for analyzing the state of a turnout machine based on a gradient curve according to claim 1, wherein: After collecting the operating parameters of the turnout machine within a preset time period, the method further includes: Reading the maximum value of the working parameters of the turnout machine; Determining whether the maximum value is greater than or equal to a preset value; If so, it is determined that the turnout machine is in an abnormal state.

6. The method for analyzing the state of a turnout machine based on a gradient curve according to claim 1, wherein: After collecting the operating parameters of the turnout machine within a preset time period, the method further includes: Determining whether the switch machine operating parameters have missing values ​​or invalid values; If any, perform data filling operation on the missing value or invalid value.

7. The method for analyzing the state of a turnout machine based on a gradient curve according to claim 1, wherein: The switch machine operating parameters include the power value or current value of the switch machine.

8. The method for analyzing the state of a turnout machine based on a gradient curve according to claim 1, wherein: After collecting the operating parameters of the turnout machine within a preset time period, the method further includes: Calculating the mean, median and / or mode of the operating parameters of the turnout machine; Comparing the average, median and / or mode of the working parameters of the turnout machine with the average, median and / or mode of pre-stored normal working parameters to obtain a comparison result; It is determined whether the working parameters of the turnout machine are abnormal based on the comparison result.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; The processor is configured to implement the switch machine state analysis method based on a gradient curve as described in any one of claims 1 to 8 when executing the program stored in the memory.

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