Locomotive toilet anti-freezing emptying intelligent control method and system

Abstract

The invention relates to the technical field of anti-freezing of locomotive sanitary facilities, in particular to an intelligent control method and system for anti-freezing and emptying of a locomotive toilet stool, and the method comprises the steps: dynamically distributing the weight of related data of a multi-source locomotive toilet stool through a dynamic weight fusion algorithm, and quantifying a dynamic risk index of the related data of the multi-source locomotive toilet stool in a current analysis time period; combining long-term risk trends and short-term risk abrupt changes of all the dynamic risk indexes calculated in all the collected analysis periods, and fusing the long-term risk trends and the short-term risk abrupt changes to construct long-term and short-term bimodal icing risk indexes; and setting a multi-level threshold anti-freezing mechanism strategy by using the bimodal icing risk index. According to the method, through dynamic long and short term risk analysis and graded anti-freezing treatment of the related data of the multi-source locomotive toilet stool, the locomotive toilet stool can dynamically adapt to the extremely cold environment, energy consumption is reduced, and misjudgment and failure caused by static weight distribution and single index evaluation of a traditional system are avoided.

Description

Technical Field

[0001] This application relates to the technical field of anti-freezing of locomotive sanitary facilities, and specifically relates to an intelligent control method and system for anti-freezing and emptying of locomotive toilets. Background Art

[0002] In a traditional locomotive sanitary system, frequent failures often occur due to problems such as sewage retention and pipeline freezing in a low-temperature environment, which not only affects the normal operation of the equipment, brings sanitation hazards and environmental pollution, but also seriously threatens the punctuality rate and operation safety of the locomotive.

[0003] In the existing anti-freezing and emptying system of locomotive toilets, traditional electric tracing or constant temperature heating modes rely on continuous electrical energy input to maintain the temperature of pipelines and liquid storage tanks. In extremely cold environments, these systems need to operate at full power for a long time, resulting in a sharp increase in energy consumption. Specifically, the traditional anti-freezing and emptying control system of toilets triggers heating or emptying operations by presetting temperature thresholds or fixed weight distributions, but ignores the dynamic coupling effects of locomotive toilet-related data such as pipeline temperature, sewage level, locomotive vibration, and environmental humidity. This design is prone to frequent misjudgments in extremely cold environments. To compensate for this misjudgment defect, the anti-freezing and emptying control system of toilets is forced to adopt a continuous full-power heating mode, which not only increases energy consumption but also easily induces microcracks in pipeline materials, exacerbates the penetration of cold air and the risk of icing, thus forming a vicious cycle of "high energy consumption - low reliability". Summary of the Invention

[0004] To solve the above technical problems, this application provides an intelligent control method and system for anti-freezing and emptying of locomotive toilets. The specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of this application provides an intelligent control method for anti-freezing and emptying of locomotive toilets. The method includes the following steps:

[0006] Collect multi-source locomotive toilet-related data in the locomotive toilet system and perform preprocessing; wherein, the multi-source locomotive toilet-related data is collected by a variety of sensors deployed on pipelines, sewage storage tanks, locomotive chassis, and the outside of the car body;

[0007] Use a dynamic weighted fusion algorithm to dynamically allocate the weights of multi-source locomotive toilet-related data and quantify the dynamic risk index of multi-source locomotive toilet-related data within the current analysis period;

[0008] Combine the long-term risk trend and short-term risk mutation of all dynamic risk indexes calculated for all analysis periods collected, and fuse the two to construct a long-short-term dual-modal icing risk index;

[0009] Set a multi-level threshold anti-freezing mechanism strategy using the dual-modal icing risk index.

[0010] Preferably, the multi-source locomotive toilet-related data includes: pipeline temperature, sewage level, locomotive vibration, and environmental humidity.

[0011] Preferably, the method for dynamically allocating the weights of multi-source locomotive toilet-related data and quantifying the dynamic risk index of multi-source locomotive toilet-related data within the current analysis period using the dynamic weighted fusion algorithm is as follows:

[0012] Use the entropy weight method and the least squares method to determine the weights of each type of locomotive toilet-related data and the slope of the fitting line within the current analysis period, respectively. The result of weighted fusion of the weights of all types of locomotive toilet-related data on the absolute value of the slope of the fitting line is used as the dynamic risk index of multi-source locomotive toilet-related data within the current analysis period.

[0013] Preferably, the weighted fusion is an operation of weighted summation of the absolute values of the slopes of the fitting lines by the weights of all types of data.

[0014] Preferably, the long-term risk trend is further determined by using a moving average algorithm to extract the long-term trend of all dynamic risk indices.

[0015] Preferably, the short-term risk mutation is further determined by using the CUSUM cumulative control algorithm to detect the short-term mutation of all dynamic risk indices.

[0016] Preferably, the construction method of the long-short-term bimodal icing risk index is further obtained from the normalized result after multiplying the long-term risk trend and the short-term risk mutation.

[0017] Preferably, the method for setting a multi-level threshold anti-freezing mechanism strategy by utilizing the bimodal icing risk includes:

[0018] Set multi-level thresholds, and trigger anti-freezing measures at low, medium, and high risk levels according to the bimodal icing risk index;

[0019] Maintain the basic monitoring mode at the low risk level;

[0020] Activate the dynamic electric tracing strategy at the medium risk level;

[0021] Activate the emergency drainage program and switch to the full-power electric tracing mode at the high risk level.

[0022] Preferably, the judgment method for setting multi-level thresholds and triggering anti-freezing measures at low, medium, and high risk levels according to the bimodal icing risk index is as follows:

[0023] Set a first threshold and a second threshold; where the first threshold is less than the second threshold;

[0024] When the bimodal icing risk is less than the first threshold, the locomotive toilet is at the low risk level;

[0025] When the dual - mode icing risk is greater than or equal to the first threshold and less than the second threshold, the locomotive toilet is at a medium - risk level;

[0026] When the dual - mode icing risk is greater than or equal to the second threshold, the locomotive toilet is at a high - risk level.

[0027] In a second aspect, another embodiment of the present application further provides an intelligent anti - freezing and emptying control system for a locomotive toilet, which implements the above - mentioned intelligent anti - freezing and emptying control method for a locomotive toilet. The system includes:

[0028] A multi - source locomotive - toilet - related data acquisition and pre - processing module, which is used to acquire multi - source locomotive - toilet - related data in the locomotive toilet system and perform pre - processing;

[0029] A dynamic risk modeling module, which is used to dynamically allocate weights to multi - source locomotive - toilet - related data using a dynamic weighted fusion algorithm and quantify the dynamic risk index of multi - source locomotive - toilet - related data within the current analysis period;

[0030] A long - term and short - term risk fusion module, which is used to combine the long - term risk trend and short - term risk mutation of all dynamic risk indexes calculated for all analysis periods collected, and fuse the two to construct a long - term and short - term dual - mode icing risk index;

[0031] A hierarchical anti - freezing control module, which is used to set a multi - level threshold anti - freezing mechanism strategy using the dual - mode icing risk index.

[0032] The present application has at least the following beneficial effects:

[0033] (1) Aiming at the problems that single - sensor data is difficult to comprehensively reflect the state of the locomotive toilet system and static weight allocation cannot adapt to the dynamic environment, a dynamic weighted fusion algorithm is used to dynamically allocate weights to multi - source locomotive - toilet - related data, and the least - squares method is combined to quantify the icing probability under the synergistic effect of multiple parameters, solving the problems of risk assessment deviation and anti - freezing control failure;

[0034] (2) Aiming at the problems that short - term fluctuations mask long - term hidden dangers or sudden mutations are not captured in time, a moving average algorithm is used to extract the long - term trend, and the CUSUM algorithm is combined to detect short - term cumulative deviations, balancing the persistence and suddenness of risks and excluding the interference of the one - sidedness of a single index on the control strategy;

[0035] (3) Through the hierarchical threshold control strategy, dynamically trigger progressive responses such as electric tracing heating and emergency drainage. Design to preferentially heat the areas with larger slopes of the locomotive toilet, alleviating local temperature differences and microcracks caused by differences in material thermal conductivity, and extending the pipeline life. Optimize the drainage timing in combination with locomotive vibration data, utilize mechanical vibration to assist sewage flow, improve drainage efficiency and reduce air pump energy consumption. The edge computing unit realizes real-time data processing and decision-making closed-loop, improves the system response speed, ensures reliability and stability in extreme environments, and achieves beneficial effects such as reduced energy consumption, reduced operation and maintenance costs, and improved anti-freezing reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0037] Figure 1 It is a flowchart of an intelligent control method for anti-freezing drainage of a locomotive toilet provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] An intelligent control method for anti-freezing drainage of a locomotive toilet provided by an embodiment of the present application is specifically referred to Figure 1 , and the method includes the following steps:

[0039] Step 1: Collect multi-source data related to the locomotive toilet system in the locomotive toilet and perform preprocessing.

[0040] In the intelligent control system for anti-freezing drainage of the locomotive toilet, to ensure the comprehensiveness and accuracy of data collection, high-precision temperature sensors are installed at the pipelines to monitor the temperature changes of the pipelines in real time; capacitive liquid level sensors are installed in the sewage storage tank to dynamically detect the sewage volume and judge the drainage requirements; vibration sensors are deployed at the locomotive chassis and connections to synchronously collect the influence of the locomotive operation status on the pipeline vibration; humidity sensors are installed outside the vehicle body to collect external environmental humidity data to evaluate the icing risk.

[0041] The above sensors synchronously transmit multi-dimensional data to the edge computing unit at a frequency of 10 Hz, providing a basis for subsequent analysis. The high-frequency noise interference in the sensor signals is eliminated through the wavelet denoising algorithm to ensure the authenticity of the data; the sliding window normalization method is used to map data with different dimensions to the interval [-1, 1], eliminating the negative impact of magnitude differences on model training, and sorting the mapped data in ascending order of time.

[0042] Among them, the time period within a preset duration before each acquisition moment is used as the analysis period corresponding to the acquisition moment. In this embodiment, the preset duration is set to 1 hour. This application only intercepts the time-series data within the analysis period of the current acquisition moment to construct the temperature sequence, liquid level sequence, vibration sequence, and humidity sequence of the current acquisition moment, and ensures the timeliness and dynamic relevance of the sequence through the time period constraint within the preset duration.

[0043] Step 2: Use the dynamic weighted fusion algorithm to dynamically allocate the weights of multi-source locomotive toilet-related data and quantify the dynamic risk index of multi-source locomotive toilet-related data within the current analysis period.

[0044] When the locomotive runs in an extremely cold environment, the problems of sewage retention and pipeline freezing in the extremely cold environment are particularly prominent. Due to working conditions such as frequent starting and stopping of the locomotive and long-term parking in cold regions, local low-temperature retention points are likely to form in the sewage pipeline of the locomotive toilet. Traditional electric tracing or constant-temperature heating modes need to rely on continuous high energy consumption to maintain the pipeline temperature, resulting in significant energy waste. For example, due to the difference in material thermal conductivity between the sewage storage tank and pipeline of the locomotive toilet, local temperature differences are likely to occur during continuous heating, inducing microcracks at the pipeline joints, and the infiltration of cold air through the cracks further exacerbates the internal icing risk.

[0045] In addition, the locomotive toilet system is significantly affected by dynamic factors such as locomotive vibration and sudden changes in external humidity. Traditional solutions relying on the data of a single temperature sensor (such as only monitoring the temperature of the sewage tank) are difficult to comprehensively reflect the true state of each section of the pipeline, elbows, and drain valves. The static weight allocation mechanism cannot adapt to dynamic scenarios such as sewage sloshing caused by vibration and surface condensation caused by sudden increase in humidity, resulting in risk assessment deviations. For example, when the locomotive passes through a blizzard area, the data of the humidity sensor on the outside of the car body changes drastically, but the static weight fails to timely increase the priority of the humidity parameter, resulting in the system underestimating the surface icing risk and ultimately causing the anti-freezing control to fail, threatening the normal emptying function of the locomotive toilet and the operation safety of the locomotive.

[0046] Therefore, this application uses the entropy weight method and the least squares method to determine the weight of each parameter and the slope of the fitting line within the current analysis period, and takes the result of weighted fusion of the weights of all parameters on the slope of the fitting line as the dynamic risk index of multi-source locomotive toilet-related data within the current analysis period.

[0047] It can be understood that the fusion can be divided into forward fusion and reverse fusion. This embodiment adopts the method of forward fusion. Forward fusion is a fusion method such as addition and multiplication between data. The specific forward fusion method is determined by the implementer according to the actual situation, and this application does not make special restrictions.

[0048] In this embodiment, the normalized temperature sequence, liquid level sequence, vibration sequence, and humidity sequence are used as inputs. The dynamic weighted fusion algorithm is used to calculate the weights of each parameter by the entropy weight method: First, the uncertainty of the parameters is quantified based on information entropy to achieve dynamic allocation of different parameter weights, and the weights ω1, ω2, ω3, and ω4 of the four parameters are output respectively. The weights adaptively adjust the contribution priority of each parameter to the ice formation risk of the locomotive toilet according to the real-time operating environment. For example, if the locomotive starts and stops frequently or passes through a bumpy track, the vibration causes the liquid level in the sewage storage tank of the locomotive toilet to fluctuate violently. At this time, the mutation of the liquid level sequence will trigger the entropy weight method to reduce ω2, avoiding misjudging the emptying demand of the locomotive toilet due to short-term liquid level fluctuations, and instead relying on the comprehensive decision-making of temperature and humidity parameters.

[0049] Subsequently, using the least squares method with the four data sequences as inputs respectively, the fitted straight lines of the four data sequences are output, and the slopes k1, k2, k3, and k4 of the fitted straight lines of the four data sequences are output. The slope of the fitted straight line of each parameter reflects the severity of the parameter change during the dynamic operation of the locomotive toilet, which directly affects the real-time performance of the anti-freezing control. Combining the actual operating state of the locomotive, the specific ice formation causes of the locomotive toilet system are analyzed.

[0050] Based on the above analysis, a dynamic risk index A for multi-source locomotive toilet-related data is constructed. The specific calculation formula is:

[0051]

[0052] Specifically, 4 is the type of parameters of multi-source locomotive toilet-related data, ω i is the weight dynamically allocated to the i-th data sequence. By quantifying the uncertainty of the parameters through information entropy, it reflects the impact of the dynamic change of the data on the weight allocation. The larger its value, the greater the impact of the change of the i-th data on the current ice formation risk of the locomotive toilet system, so as to automatically focus on the current most critical ice formation causes of the locomotive toilet (such as high humidity, strong vibration) during locomotive operation, and avoid misjudgment caused by static allocation; k i is the slope of the fitted straight line of the i-th data sequence, which reflects the change rate of the parameter within the historical moment. The absolute value |k i | represents the severity of the change, which is directly related to the actual change of the locomotive toilet parameters. The data slope is transformed into the quantification of the ice formation risk of the locomotive toilet to ensure that the algorithm response corresponds one-to-one with the physical phenomenon. The larger its value, the faster the change speed of the parameter, and the easier it is to trigger the risk critical value; The sum of the products of the above two parameters is calculated, combining the importance and change severity of the locomotive toilet parameters to avoid being dominated by a single parameter or ignoring weak signals.

[0053] It should be understood that the dynamic risk index A of the multi-source locomotive toilet-related data is used to quantify the comprehensive probability of the locomotive toilet icing in the current environment. The larger the value of A, the higher the risk state of the toilet experiencing the combined action of multiple parameters.

[0054] Step 3: Combine the long-term risk trend and short-term risk mutation of all the dynamic risk indexes calculated for all the analysis periods collected, and fuse the two to construct a long-short-term bimodal icing risk index.

[0055] Since solely relying on the dynamic risk index A of real-time multi-parameters may lead to misjudgment of the pipeline icing risk. For example, the short-term fluctuations of the A value may mask long-term hidden dangers, or sudden mutations may not be captured in time, making it difficult to balance the long-term trend and short-term anomalies, resulting in problems such as lag or over-response in anti-freezing control.

[0056] Therefore, this application combines the long-term risk trend and short-term risk mutation of all the dynamic risk indexes calculated for all the analysis periods collected and fuses them to construct a long-short-term bimodal icing risk index.

[0057] In this embodiment, the dynamic risk indexes A calculated at each historical collection moment are sorted in ascending order of time to obtain a dynamic risk sequence.

[0058] Taking the dynamic risk sequence as the input, first use the moving average algorithm. Set the observation time window to 5 minutes, corresponding to N = 3000 data points. Among them, at a sampling frequency of 10Hz, the noise suppression and trend sensitivity can be balanced, and the step size is 10s to smooth the short-term fluctuations. Output the moving average value SMA of the dynamic risk sequence, which represents the long-term stable trend of the dynamic risk sequence, can accurately capture the chronic risk accumulation of the locomotive toilet in a continuous low-temperature environment, and highlight the hidden danger of the sewage gradually freezing in the static retention scenario of the locomotive toilet.

[0059] Among them, the moving average algorithm is a well-known technology and will not be elaborated here. In other embodiments, methods such as exponentially weighted moving average, linear regression, and polynomial regression can also be used to determine the long-term risk trend of all dynamic risk indexes.

[0060] Subsequently, taking the dynamic risk sequence as the input, use the CUSUM cumulative control algorithm. Through the statistical mean and tolerance deviation threshold of the dynamic risk index value, output the cumulative deviation value CU, which represents the short-term mutation intensity of the A value and is used to monitor the short-term outbreak risk of the locomotive toilet in a sudden harsh environment. For example, when the locomotive suddenly encounters a snowstorm or passes through a high-humidity tunnel, the data of the external humidity sensor soars, and the dynamic risk index A rises rapidly due to the sudden increase in the weight of the humidity parameter. The CU algorithm quickly captures this mutation through the cumulative deviation value, marking the instantaneous surge in the risk of condensate icing on the surface of the locomotive toilet.

[0061] Among them, the statistical mean is used as a reference value in the CUSUM cumulative control algorithm to measure the difference between the current observed value and the expected value under normal conditions; the tolerance deviation threshold is set to 1.5 times the standard deviation of all dynamic risk indices and is used in the CUSUM cumulative control algorithm to determine whether the cumulative deviation exceeds the acceptable range, thereby determining whether a significant change or abnormal situation has occurred. Among them, the CUSUM cumulative control algorithm is a well-known technology and will not be elaborated here. In other embodiments, methods such as moving range, wavelet transform, and difference method can also be used to determine the short-term risk mutation of all dynamic risk indices.

[0062] Based on the above analysis, a long-term and short-term bimodal icing risk B is constructed. The specific calculation relationship is as follows:

[0063] B = Norm(SMA × CU)

[0064] Specifically, SMA is the sliding average of the dynamic risk sequence, which is used to smooth the short-term fluctuations of the dynamic risk index, extract long-term trend features. The locomotive toilet system identifies the continuous low-temperature state through the SMA value to prevent the static sewage in the locomotive toilet system from freezing due to long-term retention, avoid the hidden icing problem caused by the traditional system ignoring the long-term trend, and reflect the persistent state of the icing risk. The larger its value, the higher the risk state of the toilet within the observation window. CU is the cumulative deviation value of the dynamic risk sequence, which is used to detect the positive mutation of the dynamic risk index in the short term and capture sudden risk events. For dynamic events such as heavy snow and bumps faced by the locomotive, it can avoid the sudden freezing of the locomotive toilet. The larger its value, the more the recent dynamic risk index continuously exceeds the historical normal range, and the system is in a rapidly deteriorating state. Norm( ) is the normalization function.

[0065] It should be understood that the bimodal icing risk index B is essentially a comprehensive manifestation of long-term risk accumulation and short-term risk outbreak, taking into account both "chronic risks" and "acute risks", dynamically quantifying the combined icing threat faced by the locomotive toilet in combination with the locomotive operation state, avoiding the one-sidedness of a single indicator. The larger its value, the higher the icing risk currently faced by the toilet.

[0066] Step Four: Set a multi-level threshold anti-freezing mechanism strategy using the bimodal icing risk index.

[0067] Based on the bimodal icing risk B calculated in Step Three, set a multi-level threshold trigger mechanism to achieve hierarchical anti-freezing control.

[0068] Specifically, a first threshold B1 and a second threshold B2 are set, where the first threshold is less than the second threshold. The value range of B1 is from 0.2 to 0.4 to avoid frequent activation of heating or emptying, with energy conservation being the priority; the value range of B2 is from 0.6 to 0.8 to balance the emergency response speed and false alarm rate, ensuring rapid linkage of full-power anti-freezing measures under high risks. Specifically, in this embodiment, the specific values of B1 and B2 are 0.3 and 0.7.

[0069] When the dual-modal icing risk B < B1, the locomotive toilet is at a low risk level: maintaining the basic monitoring mode, operating periodic temperature inspections with the lowest energy consumption, continuously updating data through the edge computing unit, and not activating the active heating or emptying function;

[0070] When the dual-modal icing risk B1 ≤ B < B2, the locomotive toilet is at a medium risk level: starting the dynamic electric tracing heating strategy, adjusting the heating power according to the slope of the current temperature sequence, and preferentially locally heating the pipeline areas with a larger absolute value of the slope to inhibit the icing trend;

[0071] When the dual-modal icing risk B ≥ B2, the locomotive toilet is at a high risk level: immediately activating the emergency emptying program, linking the solenoid valve and air pump of the sewage storage tank, optimizing the emptying timing in combination with the vibration sensor data (for example, using the mechanical vibration during locomotive operation to assist sewage flow), simultaneously switching to the full-power electric tracing heating mode, and generating an alarm signal on the edge computing platform and pushing it to the on-vehicle monitoring system and remote operation and maintenance center to prompt manual intervention for verification.

[0072] The hierarchical threshold control strategy divides the low, medium, and high risk levels through double thresholds. On the premise of ensuring the basic monitoring function, it adopts a progressive response mechanism of dynamic electric tracing heating (medium risk) and emergency emptying + full-power heating (high risk), reducing energy consumption compared with the traditional continuous full-power heating mode. At the same time, through the design of preferentially heating areas with a larger slope, it effectively alleviates the local temperature difference and microcrack problems caused by the difference in material thermal conductivity. The data preprocessing and real-time analysis capabilities of the edge computing unit ensure the timing optimization of vibration data and emptying operations. Using the mechanical vibration during locomotive operation to assist sewage flow not only improves the emptying efficiency but also reduces the energy consumption of the air pump, significantly enhancing the average response speed of the anti-freezing system in extreme environments.

[0073] Based on the same inventive concept as the above method, another embodiment of the present application also provides an intelligent control system for anti-freezing and emptying of locomotive toilets, which includes:

[0074] The multi-source locomotive toilet-related data collection and preprocessing module is used to collect and preprocess the multi-source locomotive toilet-related data in the locomotive toilet system. That is, through the deployment of multiple sensors in the pipeline, sewage storage tank, locomotive chassis and the outside of the vehicle body, the environment and system status data are collected in real time, and the raw data is preprocessed to provide high-quality input for subsequent modules.

[0075] The dynamic risk modeling module is used to dynamically assign weights to multi-source locomotive toilet related data using a dynamic weighted fusion algorithm, and quantify the dynamic risk index of multi-source locomotive toilet related data in the current analysis period. That is, the comprehensive probability of the current icing risk is quantified based on the preprocessed multi-source locomotive toilet related data.

[0076] The long-term and short-term risk fusion module is used to combine the long-term risk trend and short-term risk mutation of all dynamic risk indices calculated in all analysis periods collected, and fuse the two to construct a long-term and short-term dual-modal icing risk index.

[0077] The hierarchical antifreeze control module is used to set a multi-level threshold antifreeze mechanism strategy using a dual-modal icing risk index.

[0078] The system adopts a layered modular design and consists of a multi-source locomotive toilet-related data collection and preprocessing module, a dynamic risk modeling module, a long-term and short-term risk fusion module, and a graded antifreeze control module. The modules are connected in series and work together to achieve a closed-loop process from data collection to risk analysis and intelligent control.

[0079] Each module realizes data interaction and real-time processing through the edge computing unit, forming a closed-loop logic of "data collection → risk modeling → risk fusion → intelligent control", ensuring the system's adaptability and efficient operation in extremely cold environments.

[0080] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not invented by the present application.

[0081] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. An intelligent control method for anti-freezing drainage of locomotive toilets, characterized in that, The method includes the following steps: Collect multi-source locomotive toilet-related data in the locomotive toilet system and perform preprocessing; wherein, the multi-source locomotive toilet-related data is collected by various sensors arranged on pipelines, sewage storage tanks, locomotive chassis and the outside of the vehicle body; Use the dynamic weighted fusion algorithm to dynamically allocate the weights of multi-source locomotive toilet-related data and quantify the dynamic risk index of multi-source locomotive toilet-related data within the current analysis period; Combine the long-term risk trend and short-term risk mutation of all dynamic risk indices calculated for all analysis periods collected, and fuse the two to construct a long-short-term bimodal icing risk index; Use the bimodal icing risk index to set a multi-level threshold anti-freezing mechanism strategy.

2. The intelligent control method for anti-freezing and emptying of locomotive toilets according to claim 1, wherein The multi-source locomotive toilet-related data includes: pipeline temperature, sewage level, locomotive vibration and environmental humidity.

3. The intelligent control method for anti-freezing and emptying of a locomotive toilet as claimed in claim 1, characterized in that, The method of using the dynamic weighted fusion algorithm to dynamically allocate the weights of multi-source locomotive toilet-related data and quantify the dynamic risk index of multi-source locomotive toilet-related data within the current analysis period is as follows: Use the entropy weight method and the least squares method to determine the weight of each locomotive toilet-related data and the slope of the fitting line within the current analysis period respectively, and take the result of weighted fusion of the weights of all kinds of locomotive toilet-related data on the absolute value of the slope of the fitting line as the dynamic risk index of multi-source locomotive toilet-related data within the current analysis period.

4. The intelligent control method for anti-freezing and emptying of a locomotive toilet according to claim 3, characterized in that, The weighted fusion is an operation of weighted summation of the weights of all kinds of data on the absolute value of the slope of the fitting line.

5. The intelligent control method for anti-freezing and emptying of a locomotive toilet according to claim 1, characterized in that, The long-term risk trend is further determined by using the moving average algorithm to extract the long-term trend of all dynamic risk indices.

6. The intelligent control method for anti-freezing and emptying of a locomotive toilet according to claim 1, characterized in that, The short-term risk mutation is further determined by using the CUSUM cumulative control algorithm to detect the short-term mutation of all dynamic risk indices.

7. The intelligent control method for preventing freezing and emptying of a locomotive toilet according to claim 1, characterized in that The construction method of the long-short-term bimodal icing risk index is further obtained from the normalized result after multiplying the long-term risk trend and the short-term risk mutation.

8. The intelligent control method for anti-freezing and emptying of a locomotive toilet as claimed in claim 1, wherein, The method of using the bimodal icing risk to set a multi-level threshold anti-freezing mechanism strategy includes: Set multi-level thresholds, and trigger anti-freezing measures at low, medium and high risk levels according to the bimodal icing risk index; Maintain the basic monitoring mode at the low risk level; Start the dynamic electric tracing strategy at the medium risk level; Activate the emergency evacuation procedure and switch to the full-power electric tracing mode at the high risk level.

9. The intelligent control method for anti-freezing and emptying of a locomotive toilet as claimed in claim 8, characterized in that, The judgment method of setting multi-level thresholds and triggering anti-freezing measures at low, medium and high risk levels according to the bimodal icing risk index is: Set a first threshold and a second threshold; where the first threshold is less than the second threshold; When the bimodal icing risk is less than the first threshold, the locomotive toilet is at a low risk level; When the bimodal icing risk is greater than or equal to the first threshold and less than the second threshold, the locomotive toilet is at a medium risk level; When the bimodal icing risk is greater than or equal to the second threshold, the locomotive toilet is at a high risk level.

10. An intelligent control system for anti-freezing drainage of locomotive toilets, characterized in that, Implement an intelligent control method for anti-freezing and evacuation of a locomotive toilet as described in any one of claims 1-9. The system includes: A multi-source locomotive toilet-related data acquisition and preprocessing module, which is used to collect multi-source locomotive toilet-related data in the locomotive toilet system and perform preprocessing; A dynamic risk modeling module, which is used to dynamically allocate the weights of multi-source locomotive toilet-related data by using a dynamic weighted fusion algorithm, and quantify the dynamic risk index of multi-source locomotive toilet-related data within the current analysis period; A long-term and short-term risk fusion module, which is used to combine the long-term risk trend and short-term risk mutation of all dynamic risk indexes calculated in all analysis periods collected, and fuse the two to construct a long-term and short-term bimodal icing risk index; A hierarchical anti-freezing control module, which is used to set a multi-level threshold anti-freezing mechanism strategy by using the bimodal icing risk index.

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