Rail transit signal equipment status assessment method, device, medium and electronic equipment

Abstract

This application discloses a method, apparatus, medium, and electronic device for evaluating the status of rail transit signal equipment. The method comprises: obtaining the equipment failure time corresponding to the equipment class to be evaluated, and determining the failure rate type of the equipment class to be evaluated based on the equipment failure time; determining a status evaluation index for the equipment class to be evaluated based on the failure rate type, and determining an evaluation index weight corresponding to the status evaluation index; and performing a status evaluation on the equipment class to be evaluated using the evaluation index weight and the status evaluation index. Implementation of the technical solution of this application can improve the accuracy of equipment operating status evaluation and enhance the operational stability of the rail transit system.

Description

Technical Field

[0001] The present application relates to the field of rail transit safety technology, and specifically to a rail transit signal equipment status assessment method, device, medium and electronic equipment. Background Art

[0002] During the operation of rail transit signal equipment, accurately grasping the changes in the failure rate of rail transit signal equipment is crucial for fully understanding the operating status of the equipment and ensuring the stable operation of the rail transit system.

[0003] Prior art failure rate analysis of rail transit signaling equipment often relies on general models or simple statistical data, such as the common bathtub curve model. However, in complex industrial production environments, rail transit signaling equipment may be affected by multiple factors, and its failure rate curve does not exhibit a typical bathtub shape. This makes it difficult to accurately assess the operating status of the equipment based on the bathtub curve model, which in turn affects the stable operation of the rail transit system. Summary of the Invention

[0004] The present application provides a rail transit signal equipment status assessment method, device, medium and electronic equipment, which can realize the quantitative assessment of the equipment operating status, and can achieve the purpose of improving the assessment accuracy of the equipment operating status and improving the stability of the rail transit system operation.

[0005] According to a first aspect of the present application, a method for evaluating the status of rail transit signal equipment is provided, the method comprising:

[0006] Obtaining a device failure time corresponding to a device class to be evaluated, and determining a failure rate type of the device class to be evaluated based on the device failure time;

[0007] Determining a status evaluation index for the device type to be evaluated based on the failure rate type, and determining an evaluation index weight corresponding to the status evaluation index;

[0008] The evaluation index weight and the status evaluation index are used to perform status evaluation on the device type to be evaluated.

[0009] According to a second aspect of the present application, a rail transit signal equipment status assessment device is provided, the device comprising:

[0010] a failure rate type determination module, configured to obtain a device failure time corresponding to a device class to be evaluated, and determine a failure rate type of the device class to be evaluated based on the device failure time;

[0011] An indicator weight determination module, configured to determine a status evaluation indicator for the device type to be evaluated based on the failure rate type, and determine an evaluation indicator weight corresponding to the status evaluation indicator;

[0012] The status evaluation module is used to perform status evaluation on the device type to be evaluated by using the evaluation indicator weight and the status evaluation indicator.

[0013] According to a third aspect of the present invention, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the rail transit signal equipment status assessment method as described in the embodiment of the present application.

[0014] According to the fourth aspect of the present invention, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the computer program, the rail transit signal equipment status assessment method as described in the embodiment of the present application is implemented.

[0015] According to a fifth aspect of the present application, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the rail transit signal equipment status assessment method as described in the embodiment of the present application.

[0016] The technical solution of this application takes the equipment failure time of the equipment type to be evaluated as the starting point, analyzes the equipment failure characteristics of the equipment type to be evaluated, and obtains the failure rate type of the equipment type to be evaluated. By using state evaluation indicators adapted to the failure rate type and combining the evaluation indicator weights, a scientific quantitative evaluation of the operating status of rail transit signal equipment can be effectively achieved. Compared with the use of general models such as the bathtub curve model to analyze the failure rate of rail transit signal equipment based on statistical assumptions, the embodiment of this application is based on the actual failure time of the equipment type to be evaluated, fully considering the complexity and differences of the industrial production environment in which different rail transit signal equipment is located, and realizing customized analysis of the equipment type to be evaluated, realizing quantitative evaluation of the equipment operating status, improving the evaluation accuracy of the equipment operating status, and helping to improve the stability of rail transit system operation.

[0017] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flow chart of a method for evaluating the status of rail transit signal equipment according to the first embodiment;

[0020] Figure 2 This is a flow chart of a method for evaluating the status of rail transit signal equipment provided in accordance with the second embodiment;

[0021] Figure 3 This is a schematic diagram of the structure of the rail transit signal equipment status evaluation device provided in Example 3 of the present application;

[0022] Figure 4 This is a structural diagram of an electronic device provided in Example 4 of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0024] It should be noted that the terms "first", "second", "target" and "candidate" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] Example 1

[0026] Figure 1This is a flowchart of a method for evaluating the status of rail transit signal equipment, according to Example 1. This example is applicable to evaluating the status of rail transit signal equipment in a rail transit system. This method can be performed by a rail transit signal equipment status evaluation device, which can be implemented in hardware and / or software and integrated into the electronic equipment running the system.

[0027] like Figure 1 As shown, the method includes:

[0028] S110: Obtain device failure time corresponding to the device class to be evaluated, and determine the failure rate type of the device class to be evaluated based on the device failure time.

[0029] S120: Determine a status evaluation index for the device type to be evaluated based on the failure rate type, and determine an evaluation index weight corresponding to the status evaluation index.

[0030] S130: Perform status evaluation on the device type to be evaluated using the evaluation indicator weight and the status evaluation indicator.

[0031] The "equipment category to be evaluated" refers to a type of rail transit signaling equipment that requires a condition assessment. The "equipment failure time" refers to the specific time point at which the safety and effectiveness of rail transit signaling equipment belonging to the "equipment category to be evaluated" can no longer be guaranteed after reaching the end of its service life.

[0032] Optionally, the device failure times corresponding to the device type to be evaluated can be collected from the operation logs and fault records of rail transit signaling equipment. The raw failure data collected from the operation logs and fault records requires further data cleaning. For example, after removing duplicate values, processing missing values, correcting erroneous values, and normalizing the raw failure data, the device failure times for the device type to be evaluated are obtained.

[0033] The device failure time is used to determine the failure rate type of the device being evaluated. Optionally, a failure rate curve is plotted by determining a probability density function of the device failure time. The failure rate type of the device being evaluated is determined based on the increasing and decreasing trends of the probability density function and the failure rate curve.

[0034] Among them, the failure rate type is used to measure the change in failure rate over the entire life cycle of the equipment type to be evaluated. Optional failure rate types include increasing, decreasing, and balanced types, which correspond to the three failure stages of the bathtub curve model, namely, early failure, accidental failure, and wear-out failure. The early failure period is characterized by an initial high failure rate that decreases rapidly over time, with the curve showing a decreasing type. The accidental failure period is characterized by a stable failure rate at a low level, with the curve approximating a horizontal line, which is a constant type. The wear-out failure period is characterized by a sharp increase in the failure rate, with an increasing curve, marking the end of the product life.

[0035] The failure rate type is used to determine the status evaluation index of the device class to be evaluated. The status evaluation index is related to the failure rate type of the device class to be evaluated and is used to evaluate the operating status of the device class to be evaluated.

[0036] Different failure rate types correspond to different failure rate levels, and the status assessment indicators used for the equipment types to be assessed with different failure rate types are different. Optionally, based on a preset correspondence between the failure rate type and the status assessment indicator, the status assessment indicator corresponding to the failure rate type is determined.

[0037] The evaluation indicator weights corresponding to the status assessment indicators are used to coordinate the relative importance of different indicators and ensure the scientificity and objectivity of the evaluation results. Each status assessment indicator has a corresponding evaluation indicator weight.

[0038] Optionally, evaluation indicator weights are derived through a combination of subjective and objective weighting. Subjective weighting incorporates prior industry knowledge, while objective weighting is determined solely by the degree of data variation. Optionally, evaluation indicator weights include both expert weighting and entropy weighting. Expert weighting is subjective, while entropy weighting is objective.

[0039] Optionally, the status assessment indicators are weighted using evaluation indicator weights, and the status assessment of the equipment type to be assessed is performed based on the obtained weighted results.

[0040] The technical solution of this application takes the equipment failure time of the equipment type to be evaluated as the starting point, analyzes the equipment failure characteristics of the equipment type to be evaluated, and obtains the failure rate type of the equipment type to be evaluated. By using state evaluation indicators adapted to the failure rate type and combining the evaluation indicator weights, a scientific quantitative evaluation of the operating status of rail transit signal equipment can be effectively achieved. Compared with the use of general models such as the bathtub curve model to analyze the failure rate of rail transit signal equipment based on statistical assumptions, the embodiment of this application is based on the actual failure time of the equipment type to be evaluated, fully considering the complexity and differences of the industrial production environment in which different rail transit signal equipment is located, and realizing customized analysis of the equipment type to be evaluated, realizing quantitative evaluation of the equipment operating status, improving the evaluation accuracy of the equipment operating status, and helping to improve the stability of rail transit system operation.

[0041] In an optional embodiment, determining the status evaluation index for the device class to be evaluated based on the failure rate type includes: if the failure rate type is a balanced type, determining the number of failures as the status evaluation index of the device class to be evaluated; if the failure rate type is an increasing type, determining at least two of the wavelet total energy, the last moment failure rate value, the average failure time, the median failure time and the characteristic failure time as the status evaluation index of the device class to be evaluated; if the failure rate type is a decreasing type, determining at least two of the wavelet total energy, the first failure time, the 20,000-hour failure rate value, the average failure time, the median failure time and the characteristic failure time as the status evaluation index of the device class to be evaluated.

[0042] The balanced failure rate corresponds to the occasional failure period of the bathtub curve model, characterized by a stable low failure rate and a constant failure rate. The number of failures is used as a status assessment indicator for the device class being evaluated. Optionally, the number of failures can be calculated by counting the number of failures experienced by the device class being evaluated. When the failure rate is balanced, the number of failures is negatively correlated with the device's operating status: a higher number of failures indicates a worse operating status.

[0043] The increasing type corresponds to the wear-out failure period of the bathtub curve model, characterized by a sharp increase in the failure rate, marking the end of the product life. This indicates that the device type to be evaluated has experienced a period of accidental failure, and at least two of the wavelet total energy, last-minute failure rate, mean failure time, median failure time, and characteristic failure time are determined as condition assessment indicators for the device type to be evaluated. The decreasing type corresponds to the early failure period of the bathtub curve model, characterized by an initially high failure rate that rapidly decreases over time. This indicates that the device type to be evaluated has not yet reached the period of accidental failure, and at least two of the wavelet total energy, first failure time, 20,000-hour failure rate, mean failure time, median failure time, and characteristic failure time are determined as condition assessment indicators for the device type to be evaluated.

[0044] The total wavelet energy is used to quantify the steepness of the failure rate change. The larger the total wavelet energy, the more dramatic the failure rate change and the higher the steepness. Conversely, the smaller the total wavelet energy, the smoother the failure rate change and the lower the steepness. The failure rate curve obtained by fitting the equipment failure time is decomposed by wavelet packets to divide the signal energy into frequency bands. The sum of the energy of each sub-band is the total wavelet energy. The last moment failure rate value refers to the failure rate at the last moment recorded by the equipment failure time. The average failure time is based on the probability density function of the equipment type to be evaluated. Determine, optionally, mean time to failure use Calculated. Mean time to failure is used to reflect the overall reliability level. Median time to failure is the time when 50% of the devices in the device category being evaluated fail. Characteristic failure time is the time when 63.2% of the devices in the device category being evaluated fail, and is used to describe the concentrated failure characteristics of the wear-out failure period.

[0045] The first failure time refers to the time when the equipment to be evaluated first fails after it is put into use. The 20,000-hour failure rate refers to the failure rate of the equipment to be evaluated 20,000 hours after it is put into use.

[0046] When the failure rate type is increasing, the mean failure time, median failure time, and characteristic failure time are positive indicators; that is, higher values ​​indicate better equipment operating status. The final moment failure rate and total wavelet energy are negative indicators; that is, higher values ​​indicate worse equipment operating status.

[0047] For a decreasing failure rate, the first failure time, mean failure time, median failure time, characteristic failure time, and total wavelet energy are positive indicators. Higher values ​​for these positive indicators indicate better equipment performance. A 20,000-hour failure rate is a negative indicator. Higher values ​​for these negative indicators indicate worse equipment performance. For a decreasing failure rate, a larger total wavelet energy indicates a more dramatic change in the failure rate and a higher steepness, indicating that the device being evaluated will reach the stage of accidental failure sooner. Conversely, a smaller total wavelet energy indicates a more gradual change in the failure rate and a lower steepness, indicating that the device being evaluated will reach the stage of accidental failure later.

[0048] The above technical solution provides a practical scheme for determining status evaluation indicators, which is used to adapt the failure mechanism and reliability characteristics of the failure stage corresponding to the failure rate type. The status evaluation of the equipment type to be evaluated is performed using status evaluation indicators that match the failure rate type, thereby realizing a multi-dimensional evaluation of the equipment operating status, avoiding the one-sidedness of single-indicator evaluation, realizing a scientific and quantitative evaluation of the operating status of rail transit signal equipment, and improving the accuracy of the evaluation of the equipment operating status.

[0049] In an optional embodiment, determining the evaluation indicator weight corresponding to the state evaluation indicator includes: obtaining the expert weight of the state evaluation indicator; using the entropy weight method to determine the entropy weight of the state evaluation indicator based on the evaluation indicator value corresponding to the state evaluation indicator; and determining the evaluation indicator weight corresponding to the state evaluation indicator based on the expert weight and the entropy weight.

[0050] Expert weights are subjective and incorporate prior industry knowledge. Entropy weights are objective and determined by the degree of data variation. Entropy weights are determined using the entropy weight method and are based on the numerical value of the evaluation indicator corresponding to the state assessment indicator. The greater the degree of data variation and the higher the information content, the greater the entropy weight. Optionally, a weighted result can be obtained by combining the expert weights and the entropy weights, and this weighted result is used as the evaluation indicator weight corresponding to the state assessment indicator.

[0051] The above technical solution determines the weights of evaluation indicators by combining expert weights and entropy weights, so that the weights of evaluation indicators not only retain expert knowledge and take into account the actual business meaning of the indicators but also reduce subjective arbitrariness. It is both scientific and explainable, and solves the problem that the weights of evaluation indicators were fixed in the past and difficult to adapt to complex and changing operating environments.

[0052] In some optional embodiments, the evaluation indicator weight and the status evaluation indicator are used to perform a status evaluation on the device class to be evaluated, including: using the evaluation indicator value corresponding to the status evaluation indicator to query the scoring interval to obtain the target scoring interval corresponding to the evaluation indicator value; determining the status indicator score corresponding to the status evaluation indicator based on the target scoring interval; using the evaluation indicator weight to weight the status indicator score to obtain the device status score of the device class to be evaluated; and using the device status score to perform a status evaluation on the device class to be evaluated.

[0053] The scoring intervals are derived based on a large amount of real-world data, standards, and expert experience. By querying the scoring interval using the evaluation indicator values ​​corresponding to the state assessment indicators, we can obtain the target scoring interval. The target scoring interval is aligned with the evaluation indicator values.

[0054] The condition assessment indicator is related to the failure rate type. Optionally, an initial scoring interval is determined for each failure rate type. Then, based on the evaluation indicator value corresponding to the condition assessment indicator, a scoring interval query is performed to determine the target scoring interval corresponding to the evaluation indicator value. For example, the initial scoring interval for the balanced failure rate is set to [80, 100], the initial scoring interval for the decreasing failure rate is set to [70, 80], and the initial scoring interval for the increasing failure rate is set to [60, 70].

[0055] Once the target scoring interval is determined, the corresponding status indicator score can be further obtained. This way, the corresponding status indicator score and evaluation indicator weight are determined. The status indicator score is then weighted using the evaluation indicator weight. The resulting weighted evaluation result is the device status score for the device type being evaluated.

[0056] The device status score is used to quantify the operational status of the device type being evaluated. The device status score is positively correlated with the operational status of the device type being evaluated; a higher device status score indicates a better operational status for the device type being evaluated.

[0057] The above technical solution provides a practical and feasible equipment status scoring determination scheme, which can be used to quantify the operating status of rail transit signal equipment, improve the accuracy of equipment operating status assessment, and is conducive to improving the stability of rail transit system operation.

[0058] Example 2

[0059] Figure 2 This is a flow chart of a method for evaluating the status of rail transit signal equipment provided in accordance with Example 2. This embodiment is further optimized based on the above embodiment.

[0060] like Figure 2 As shown, the method includes:

[0061] S210: Obtain the device failure time corresponding to the device type to be evaluated.

[0062] S220: Determine the number of failures of the device type to be evaluated based on the device failure time.

[0063] Optionally, by counting the number of device failure times of the device class to be evaluated, the number of device failure times is determined as the number of failures. The number of failures can be used to determine the failure rate type of the device class to be evaluated.

[0064] S230: Determine a probability density function of the device failure time, and based on the probability density function, determine a failure rate function of the device class to be evaluated.

[0065] The probability density function (PDF) of equipment failure time is a core tool in reliability engineering for quantifying the distribution of failure times. Different distribution types correspond to different failure mechanisms and stage characteristics. The PDF can be used to determine the failure rate type of the equipment being evaluated. Alternatively, the probability distribution function (PDF) can be determined based on the PDF. The failure rate function for the equipment being evaluated can be determined based on the PDF and the PDF.

[0066] The increasing and decreasing trend of the failure rate function is used to determine the failure rate type of the equipment class to be evaluated.

[0067] S240: Determine the failure rate type of the device class to be evaluated based on the number of failures, the increase and decrease trend of the failure rate function, and the probability density function.

[0068] Among them, the number of failures provides statistical laws, the failure rate function reveals the internal change mechanism, and the probability density function describes the time distribution form. The combination of these three can uniquely correspond to the different failure stages of the bathtub curve model, and then determine the failure rate type of the equipment to be evaluated.

[0069] S250: Determine a status evaluation index for the device type to be evaluated based on the failure rate type, and determine an evaluation index weight corresponding to the status evaluation index.

[0070] S260: Perform status evaluation on the device type to be evaluated using the evaluation indicator weight and the status evaluation indicator.

[0071] The technical solution of the present application determines the number of failures of the equipment class to be evaluated based on the equipment failure time. Determine the probability density function of the equipment failure time. Based on the probability density function, determine the failure rate function of the equipment class to be evaluated. Based on the statistical laws provided by the number of failures, the inherent change mechanism revealed by the failure rate function, and the time distribution form described by the probability density function, the three can be combined to uniquely correspond to the different failure stages of the bathtub curve model, and jointly determine the failure rate type of the equipment class to be evaluated. A practical and feasible failure rate type determination scheme is provided to ensure the accuracy of the failure rate type determination. The failure rate type is used to evaluate the operating status of the equipment class to be evaluated, which is conducive to improving the accuracy of the evaluation of the equipment operating status.

[0072] In an optional embodiment, the failure rate type of the device class to be evaluated is determined based on the number of failures, the increase and decrease trend of the failure rate function and the probability density function, including: if the number of failures is less than or equal to the reliability loss coefficient of the device class to be evaluated, the failure rate type of the device class to be evaluated is determined to be a balanced type; if the number of failures is greater than the reliability loss coefficient of the device class to be evaluated, and the probability density function is in the form of an exponential function, the failure rate type of the device class to be evaluated is determined to be a balanced type; if the number of failures is greater than the reliability loss coefficient of the device class to be evaluated, and the probability density function is in the form of a function other than an exponential function, the failure rate type of the device class to be evaluated is determined based on the increase and decrease trend of the failure rate function.

[0073] Among them, the reliability loss coefficient is used to quantify the degree of equipment performance degradation. Determine the reliability loss coefficient of the equipment type to be evaluated. is the operating time of the device class to be evaluated, is the availability of the equipment class to be evaluated, is the average fault repair time for the device type being evaluated. Availability can be obtained by consulting the device manual.

[0074] If the number of failures is less than or equal to the reliability loss coefficient of the device type to be evaluated, it means that the device type to be evaluated has not experienced abnormal degradation within the statistical period of the device failure time and meets the reliability design expectations. In this case, the failure rate type of the device type to be evaluated is determined to be balanced.

[0075] If the number of failures is greater than the reliability loss coefficient of the device type to be evaluated, it means that the device type to be evaluated has experienced abnormal degradation within the statistical period of the device failure time. It is necessary to further analyze the failure rate type in combination with the probability density function of the device failure time.

[0076] The probability density function of the exponential distribution strictly corresponds to a constant failure rate. It is the only continuous distribution with the "memoryless property." This property indicates that the probability of equipment failure is dependent only on the current moment and is independent of historical operating time, consistent with the physical nature of random failure. In the bathtub curve model, the exponential distribution corresponds to occasional failure periods. If the probability density function of the equipment failure time is exponential, the failure rate type of the equipment being evaluated is determined to be balanced.

[0077] If the probability density function is in a form other than exponential, further analysis of the failure rate function's increasing or decreasing trend is necessary. Alternatively, the Mann-Kendall trend test can be used to determine whether the failure rate function is increasing or decreasing.

[0078] The above technical solution provides a practical solution for determining the failure rate type. It integrates the number of failures, the increase and decrease trend of the failure rate function and the probability density function, and provides technical and data support for determining the failure rate type of the equipment to be evaluated.

[0079] In an optional embodiment, determining the failure rate type of the device class to be evaluated based on the increasing or decreasing trend of the failure rate function includes: if the failure rate function shows an increasing trend, determining the failure rate type of the device class to be evaluated as an increasing type; if the failure rate function shows a decreasing trend, determining the failure rate type of the device class to be evaluated as a decreasing type.

[0080] If the failure rate function shows an increasing trend, it indicates a gradual increase in the failure rate, marking the end of the product lifespan. This corresponds to the wear-out failure period of the bathtub curve model, and the failure rate type of the device being evaluated is determined to be increasing. If the failure rate function shows a decreasing trend, it indicates a gradual decrease in the failure rate, corresponding to the early failure period of the bathtub curve model, and the failure rate type of the device being evaluated is determined to be decreasing.

[0081] The above technical solution determines the failure rate type according to the increasing and decreasing trend of the failure rate curve, provides a practical and feasible solution for determining the failure rate type, and provides technical and data support for determining the failure rate type.

[0082] In an optional embodiment, determining the probability density function of the device failure time and determining the failure rate function of the device class to be evaluated based on the probability density function includes: using at least two preset probability distributions to perform probability distribution fitting on the device failure time, and determining whether the preset probability distribution includes a target probability distribution that the device failure time conforms to; if included, using maximum likelihood estimation to determine the probability density function of the device failure time based on the target probability distribution; otherwise, using kernel density estimation to determine the probability density function of the device failure time; determining a probability distribution function based on the probability density function; and determining the failure rate function of the device class to be evaluated based on the probability density function and the probability distribution function.

[0083] The specific type of the preset probability distribution is not limited here and is determined according to actual business needs. The preset probability distribution is generally a common probability distribution. For example, the preset probability distribution can include at least two of the exponential distribution, Weibull distribution, normal distribution, lognormal distribution and gamma distribution.

[0084] Optionally, a Kolmogorov-Smirnov test is used to perform a goodness-of-fit test on the device failure time for the preset probability distribution, thereby determining a target probability distribution that the device failure time conforms to within the preset probability distribution. The target probability distribution is the probability distribution that the device failure time conforms to within the preset probability distribution.

[0085] When the target probability distribution is determined, the maximum likelihood estimation is used to estimate the parameters of the equipment failure time and obtain the probability density function of the equipment failure time. The probability distribution function of the equipment failure time is determined based on the probability density function. Determine the failure rate function, where represents the probability density function, represents the probability distribution function, is the likelihood function The maximum likelihood estimate of the parameter to be estimated in , represents the failure rate function.

[0086] If there is no probability distribution that matches the equipment failure time in the preset probability distribution, kernel density estimation is used to determine the probability density function of the equipment failure time. Specifically, 1) select the kernel function. Common kernel functions include Gaussian kernel function and Epanechnikov kernel function. 2) Determine the bandwidth. The choice of bandwidth affects the estimation results. Common selection methods include Silverman's empirical rule. 3) Calculate the kernel density estimate value. For a given equipment failure time, , the probability density function of the kernel density estimate is .

[0087] Probability density function based on kernel density estimation , calculate the distribution function by integrating .use Determine the failure rate function.

[0088] This technical solution uses distribution fitting and nonparametric estimation to determine the probability density function of device failure time. Based on this probability density function, the probability distribution function is then used to determine the failure rate function. This provides a reliable method for determining the failure rate function, providing both data and technical support for the subsequent use of the failure rate function to determine the failure rate type.

[0089] In a specific embodiment, the operating status of four types of rail transit signal equipment, namely, Class A, Class B, Class C, and Class D, is evaluated. Class A, Class B, Class C, and Class D are the types of equipment to be evaluated.

[0090] Original failure data of Class A, Class B, Class C, and Class D were collected from the operation logs and fault records of rail transit signal equipment. After deduplication, missing value processing, error value correction, and data standardization, the failure time of Class A, Class B, Class C, and Class D equipment was obtained.

[0091] Based on the formula Determine the reliability loss coefficients of Class A, Class B, Class C and Class D respectively, where is the running time, is availability, For the above mentioned four types of equipment, A, B, C and D, all run uninterrupted from 2016 to 2023, so , obtain availability by consulting the device manual , , we can calculate About 14.

[0092] Based on the device failure times for Class A, Class B, Class C, and Class D equipment, the failure rates for these four categories are 12, 59, 28, and 37, respectively. Since Class A equipment has fewer than 14 failures, the failure rate type for Class A equipment is considered balanced. The failure rates for Classes B, C, and D all exceed 14, requiring further analysis based on the probability density distribution of device failure times and the failure rate function to determine the failure rate types for these three categories.

[0093] First, an appropriate target probability distribution was selected from a set of predefined probability distributions to fit the failure times of devices in categories B, C, and D. The Kolmogorov-Smirnov test was used to verify the goodness of fit. The predefined probability distributions included at least two of the following: exponential, Weibull, normal, lognormal, and gamma distributions. Distribution fitting confirmed that the failure rates of devices in category B followed an exponential distribution, and that the failure rates of these devices were balanced.

[0094] Because the failure times of Class C and Class D devices do not have a consistent probability distribution within the pre-defined probability distribution, kernel density estimation (KDE) was used to estimate the probability density function from the device failure times, thereby deriving the failure rate function. The Mann-Kendall trend test was then used to determine the increasing or decreasing trend of the failure rate function. The failure rate function for Class C was determined to be decreasing, while the failure rate function for Class D was increasing, thus determining the failure rate types of Class C and Class D to be decreasing and increasing, respectively.

[0095] For balanced failure rate categories A and B, the number of failures Determined as the state evaluation index. For the C type of decreasing failure rate, the total wavelet energy , first failure time , 20,000-hour failure rate , mean time to failure , median failure time and characteristic failure time At least two of them are determined as the status evaluation indicators of the equipment type to be evaluated. For the class D with increasing failure rate, the total wavelet energy , final moment failure rate , mean time to failure , median failure time and characteristic failure time At least two of the above are determined as status evaluation indicators of the device class to be evaluated.

[0096] For example, the four types of equipment status evaluation indicators, namely Class A, Class B, Class C and Class D, and the corresponding evaluation indicator values ​​are shown in Table 1.

[0097] Table 1

[0098]

[0099] For categories C and D, the weights of the evaluation indicators corresponding to the status assessment indicators are shown in Table 2.

[0100] Table 2

[0101]

[0102] Among them, the evaluation index weight is obtained by weighting the expert weight and entropy weight.

[0103] For a balanced failure rate, the number of failures is the only condition assessment indicator, and the fewer failures, the higher the score. For example, the scoring rule may be: 1 failure corresponds to 100 points, 2 failures to 99 points, ... 20 failures to 81 points. If the number of failures exceeds 20 and the distribution is determined to be exponential, the score is 80.

[0104] For the decreasing failure rate, the first failure time , mean time to failure , median failure time , characteristic expiration time , total wavelet energy It is a positive indicator, with a failure rate of 20,000 hours. It is a reverse indicator.

[0105] For the failure rate determined to be decreasing, the mean failure time , median failure time , characteristic expiration time It is a positive indicator. The failure rate value at the last moment , total wavelet energy It is a reverse indicator.

[0106] The state assessment indicator is related to the failure rate type. Optionally, an initial scoring interval is determined for each failure rate type. Then, based on the evaluation indicator value corresponding to the state assessment indicator, a scoring interval query is performed to determine the target scoring interval corresponding to the evaluation indicator value. For example, the initial scoring interval for the balanced failure rate is set to [80, 100], the initial scoring interval for the decreasing failure rate is set to [70, 80], and the initial scoring interval for the increasing failure rate is set to [60, 70]. Once the target scoring interval is determined, the state indicator score corresponding to the target scoring interval can be further obtained.

[0107] Specifically, the device status scores for devices A, B, C, and D are calculated by weighted average based on the status indicator scores and evaluation indicator weights corresponding to each status evaluation indicator, as shown in Table 3 below. The cell corresponding to each status evaluation indicator column in Table 3 contains the status indicator score corresponding to the evaluation indicator. The device status score is obtained by weighting the status indicator scores using the evaluation indicator weights.

[0108] Table 3

[0109] Example 3

[0110] Figure 3This is a schematic diagram of the structure of a rail transit signal equipment status assessment device provided in Example 3 of this application. This embodiment is applicable to the situation where the status of rail transit signal equipment in a rail transit system is assessed. The device can be implemented by software and / or hardware and can be integrated into electronic devices such as smart terminals.

[0111] like Figure 3 As shown, the rail transit signal equipment status assessment device 300 may include:

[0112] The failure rate type determination module 310 is configured to obtain a device failure time corresponding to a device type to be evaluated, and determine a failure rate type of the device type to be evaluated based on the device failure time;

[0113] An indicator weight determination module 320 is configured to determine a status evaluation indicator for the device type to be evaluated based on the failure rate type, and determine an evaluation indicator weight corresponding to the status evaluation indicator;

[0114] The status evaluation module 330 is configured to perform status evaluation on the device type to be evaluated using the evaluation indicator weights and the status evaluation indicators.

[0115] The technical solution of this application takes the equipment failure time of the equipment type to be evaluated as the starting point, analyzes the equipment failure characteristics of the equipment type to be evaluated, and obtains the failure rate type of the equipment type to be evaluated. By using state evaluation indicators adapted to the failure rate type and combining the evaluation indicator weights, a scientific quantitative evaluation of the operating status of rail transit signal equipment can be effectively achieved. Compared with the use of general models such as the bathtub curve model to analyze the failure rate of rail transit signal equipment based on statistical assumptions, the embodiment of this application is based on the actual failure time of the equipment type to be evaluated, fully considering the complexity and differences of the industrial production environment in which different rail transit signal equipment is located, and realizing customized analysis of the equipment type to be evaluated, realizing quantitative evaluation of the equipment operating status, improving the evaluation accuracy of the equipment operating status, and helping to improve the stability of rail transit system operation.

[0116] Optionally, the failure rate type determination module 310 includes: a failure count determination submodule, used to determine the failure count of the device class to be evaluated based on the device failure time; a failure rate function determination submodule, used to determine the probability density function of the device failure time, and determine the failure rate function of the device class to be evaluated based on the probability density function; a failure rate type determination submodule, used to determine the failure rate type of the device class to be evaluated based on the failure count, the increase and decrease trend of the failure rate function and the probability density function.

[0117] Optionally, the failure rate type determination submodule includes: a first type determination unit, used to determine the failure rate type of the equipment class to be evaluated as a balanced type if the number of failures is less than or equal to the reliability loss coefficient of the equipment class to be evaluated; a second type determination unit, used to determine the failure rate type of the equipment class to be evaluated as a balanced type if the number of failures is greater than the reliability loss coefficient of the equipment class to be evaluated and the probability density function is in the form of an exponential function; a third type determination unit, used to determine the failure rate type of the equipment class to be evaluated based on the increase or decrease trend of the failure rate function if the number of failures is greater than the reliability loss coefficient of the equipment class to be evaluated and the probability density function is in the form of a function other than an exponential function.

[0118] Optionally, the third type determination unit includes: an increasing type determination subunit, which is used to determine the failure rate type of the device class to be evaluated as increasing if the failure rate function shows an increasing trend; and a decreasing type determination subunit, which is used to determine the failure rate type of the device class to be evaluated as decreasing if the failure rate function shows a decreasing trend.

[0119] Optionally, the failure rate function determination submodule includes: a probability distribution fitting unit, used to use at least two preset probability distributions to perform probability distribution fitting on the equipment failure time, and determine whether the preset probability distribution includes a target probability distribution that the equipment failure time conforms to; a first function determination unit, used to use maximum likelihood estimation to determine the probability density function of the equipment failure time based on the target probability distribution if included; a second function determination unit, used to use kernel density estimation to determine the probability density function of the equipment failure time otherwise; a distribution function determination unit, used to determine the probability distribution function based on the probability density function; and a failure rate function determination unit, used to determine the failure rate function of the equipment class to be evaluated based on the probability density function and the probability distribution function.

[0120] Optionally, the indicator weight determination module 320 includes: a first indicator determination submodule, which is used to determine the number of failures as the status evaluation indicator of the equipment class to be evaluated if the failure rate type is a balanced type; a second indicator determination submodule, which is used to determine at least two of the wavelet total energy, the last moment failure rate value, the average failure time, the median failure time and the characteristic failure time as the status evaluation indicators of the equipment class to be evaluated if the failure rate type is an increasing type; and a third indicator determination submodule, which is used to determine at least two of the wavelet total energy, the first failure time, the 20,000-hour failure rate value, the average failure time, the median failure time and the characteristic failure time as the status evaluation indicators of the equipment class to be evaluated if the failure rate type is a decreasing type.

[0121] Optionally, the indicator weight determination module 320 includes: an expert weight acquisition submodule, used to obtain the expert weight of the state evaluation indicator; an entropy weight determination submodule, used to determine the entropy weight of the state evaluation indicator based on the evaluation indicator value corresponding to the state evaluation indicator using the entropy weight method; and an indicator weight determination submodule, used to determine the evaluation indicator weight corresponding to the state evaluation indicator based on the expert weight and the entropy weight.

[0122] Optionally, the status assessment module 330 includes: a scoring interval query submodule, which is used to use the evaluation indicator value corresponding to the status assessment indicator to perform a scoring interval query to obtain a target scoring interval corresponding to the evaluation indicator value; an indicator score determination submodule, which is used to determine the status indicator score corresponding to the status assessment indicator based on the target scoring interval; a status score determination submodule, which is used to use the evaluation indicator weight to weight the status indicator score to obtain the equipment status score of the equipment class to be assessed; and a status assessment submodule, which is used to use the equipment status score to perform a status assessment on the equipment class to be assessed.

[0123] The rail transit signal equipment status evaluation device provided in the embodiment of the invention can execute the rail transit signal equipment status evaluation method provided in any embodiment of the present application, and has the corresponding performance modules and beneficial effects for executing the rail transit signal equipment status evaluation method.

[0124] Example 4

[0125] According to embodiments of the present application, the present application also provides an electronic device, a readable storage medium, and a computer program product.

[0126] Figure 4 The schematic diagram shows the structure of an electronic device 410 that can be used to implement an embodiment. Electronic device 410 includes at least one processor 411 and memory, such as a read-only memory (ROM) 412 and a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. Processor 411 can perform various appropriate actions and processes based on the computer programs stored in ROM 412 or loaded from storage unit 418 into RAM 413. RAM 413 can also store various programs and data required for the operation of electronic device 410. Processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to bus 414.

[0127] Multiple components in electronic device 410 are connected to I / O interface 415, including an input unit 416, such as a keyboard, mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a magnetic disk, optical disk, etc.; and a communication unit 419, such as a network card, modem, wireless communication transceiver, etc. The communication unit 419 allows electronic device 410 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0128] Processor 411 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processors, controllers, microcontrollers, etc. Processor 411 executes the various methods and processes described above, such as the rail transit signal equipment status assessment method.

[0129] In some embodiments, the rail transit signal equipment status assessment method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the rail transit signal equipment status assessment method described above can be performed. Alternatively, in other embodiments, processor 411 can be configured to execute the rail transit signal equipment status assessment method in any other appropriate manner (e.g., via firmware).

[0130] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable rail transit signaling equipment condition assessment device, so that when executed by the processor, the computer programs implement the functions / operations specified in the flowcharts and / or block diagrams. The computer programs can be executed entirely on the machine, partially on the machine, as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of the present application, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0134] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a rail transit signal equipment status assessment server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0135] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0136] The present application also discloses a computer program product, comprising a computer program that, when executed by a processor, implements the rail transit signal equipment status assessment method provided in any of the embodiments of the present application. This program product and the rail transit signal equipment status assessment method disclosed in each embodiment of the present application embody the same inventive concept and are therefore not further described here.

[0137] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

[0138] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for evaluating the status of rail transit signal equipment, characterized in that: The method comprises: Obtaining a device failure time corresponding to a device class to be evaluated, and determining a failure rate type of the device class to be evaluated based on the device failure time; Determining a status evaluation index for the device type to be evaluated based on the failure rate type, and determining an evaluation index weight corresponding to the status evaluation index; Performing a status assessment on the device type to be assessed using the evaluation indicator weights and the status assessment indicators; Wherein, determining the failure rate type of the device class to be evaluated based on the device failure time includes: determining the number of failures of the device class to be evaluated based on the device failure time; determining a probability density function of the device failure time, and determining the failure rate function of the device class to be evaluated based on the probability density function; determining the failure rate type of the device class to be evaluated based on the number of failures, the increase and decrease trend of the failure rate function, and the probability density function; Among them, determining the probability density function of the equipment failure time and determining the failure rate function of the equipment class to be evaluated based on the probability density function includes: using at least two preset probability distributions to perform probability distribution fitting on the equipment failure time, and determining whether the preset probability distribution includes a target probability distribution that the equipment failure time conforms to; if included, using maximum likelihood estimation to determine the probability density function of the equipment failure time based on the target probability distribution; otherwise, using kernel density estimation to determine the probability density function of the equipment failure time; determining a probability distribution function based on the probability density function; and determining the failure rate function of the equipment class to be evaluated based on the probability density function and the probability distribution function.

2. The method according to claim 1, characterized in that The determining of the failure rate type of the device class to be evaluated based on the number of failures, the increase and decrease trend of the failure rate function, and the probability density function includes: If the number of failures is less than or equal to the reliability loss coefficient of the device type to be evaluated, the failure rate type of the device type to be evaluated is determined to be balanced; If the number of failures is greater than the reliability loss coefficient of the device type to be evaluated, and the probability density function is in the form of an exponential function, the failure rate type of the device type to be evaluated is determined to be a balanced type; If the number of failures is greater than the reliability loss coefficient of the device class to be evaluated, and the probability density function is a function form other than an exponential function, the failure rate type of the device class to be evaluated is determined based on the increase or decrease trend of the failure rate function.

3. The method according to claim 2, characterized in that The determining the failure rate type of the device class to be evaluated based on the increase and decrease trend of the failure rate function includes: If the failure rate function shows an increasing trend, the failure rate type of the device to be evaluated is determined to be an increasing type; If the failure rate function shows a decreasing trend, the failure rate type of the device to be evaluated is determined to be a decreasing type.

4. The method according to claim 1, wherein The determining of a status evaluation index for the device type to be evaluated based on the failure rate type includes: If the failure rate type is balanced, the number of failures is determined as the status evaluation indicator of the device type to be evaluated; If the failure rate type is an increasing type, at least two of the wavelet total energy, the last moment failure rate value, the mean failure time, the median failure time, and the characteristic failure time are determined as the status evaluation indicators of the device type to be evaluated; If the failure rate type is a decreasing type, at least two of the wavelet total energy, the first failure time, the 20,000-hour failure rate value, the average failure time, the median failure time, and the characteristic failure time are determined as status evaluation indicators of the equipment type to be evaluated.

5. The method according to claim 1, wherein Determining the evaluation indicator weight corresponding to the status evaluation indicator includes: Obtaining expert weights of the status assessment indicators; Determine the entropy weight of the state evaluation indicator based on the evaluation indicator value corresponding to the state evaluation indicator using the entropy weight method; Based on the expert weight and the entropy weight, the evaluation index weight corresponding to the state evaluation index is determined.

6. The method according to claim 1, characterized in that The step of using the evaluation index weight and the status evaluation index to perform status evaluation on the device type to be evaluated includes: Using the evaluation index value corresponding to the state evaluation index to perform a scoring interval query, and obtain a target scoring interval corresponding to the evaluation index value; Determining a status indicator score corresponding to the status assessment indicator based on the target scoring interval; The status indicator score is weighted by using the evaluation indicator weight to obtain the device status score of the device class to be evaluated; The device status score is used to perform a status evaluation on the device type to be evaluated.

7. A rail transit signal equipment status assessment device, characterized in that: The device comprises: a failure rate type determination module, configured to obtain a device failure time corresponding to a device class to be evaluated, and determine a failure rate type of the device class to be evaluated based on the device failure time; An indicator weight determination module, configured to determine a status evaluation indicator for the device type to be evaluated based on the failure rate type, and determine an evaluation indicator weight corresponding to the status evaluation indicator; A status evaluation module, configured to perform a status evaluation on the device class to be evaluated using the evaluation indicator weights and the status evaluation indicators; The failure rate type determination module includes: a failure count determination submodule for determining the failure count of the device class to be evaluated based on the device failure time; a failure rate function determination submodule for determining the probability density function of the device failure time and, based on the probability density function, determining the failure rate function of the device class to be evaluated; and a failure rate type determination submodule for determining the failure rate type of the device class to be evaluated based on the failure count, the increase and decrease trend of the failure rate function, and the probability density function. Among them, the failure rate function determination submodule includes: a probability distribution fitting unit, which is used to use at least two preset probability distributions to perform probability distribution fitting on the equipment failure time, and determine whether the preset probability distribution includes the target probability distribution that the equipment failure time conforms to; a first function determination unit, which is used to use maximum likelihood estimation to determine the probability density function of the equipment failure time based on the target probability distribution if it is included; a second function determination unit, which is used to use kernel density estimation to determine the probability density function of the equipment failure time otherwise; a distribution function determination unit, which is used to determine the probability distribution function based on the probability density function; and a failure rate function determination unit, which is used to determine the failure rate function of the equipment class to be evaluated based on the probability density function and the probability distribution function.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for evaluating the state of rail transit signal equipment according to any one of claims 1 to 6 is implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein: When the processor executes the computer program, the rail transit signal equipment status assessment method according to any one of claims 1 to 6 is implemented.

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