A snowmaking machine reliable state assessment method and system

Abstract

The present invention discloses a method for evaluating the reliable state of a snowmaking machine, comprising: obtaining a number of data samples of the snowmaking machine, the data samples comprising result samples and factor samples, the result samples representing fault conditions, the factor samples representing temperature data, humidity data, wind speed and direction data, and water supply data; performing data interval statistics under the condition of a single factor sample, comprising: performing statistics on the temperature data interval, humidity data interval, wind speed and direction data interval, and water supply data interval under a fault condition, that is, a risk interval, performing statistics on the temperature data interval, humidity data interval, wind speed and direction data interval, and water supply data interval under a fault-free condition, that is, a safety interval; performing statistics on the influencing factors when various factor samples interact in the safety interval; the method and system for evaluating the reliable state of a snowmaking machine can confirm the state of the snowmaking machine in real time to ensure the normal operation of the snowmaking machine.

Description

Technical Field

[0001] The invention relates to a reliable state evaluation method and system for a snowmaking machine. Background Art

[0002] Snowmaking machines are the main equipment for indoor and outdoor snowmaking in modern ski resorts, ice and snow parks, etc. When natural snowfall cannot meet the increasing demand for tourism and skiing, snowmaking machines came into being.

[0003] Snowmaking machines may fail during the snowmaking process, and the main reasons for the failure are:

[0004] Temperature reasons: The snowmaking environment temperature is low, causing ice to form, which blocks the nozzles and reduces the snowmaking capacity. More seriously, it may also cause the water pump to become blocked and damaged.

[0005] Due to humidity, snowmaking machines spray water mist, which turns into snowflakes in the air and falls down, so it is more difficult to make snow when the ambient humidity is greater than 80%;

[0006] Water supply: The principle of snowmaking machine is to mix water and air to make snow. Insufficient water supply will lead to lack of water source during snowmaking and failure to make snow.

[0007] Due to wind direction, the distance at which the snow is sprayed by the snow gun nozzle is affected by wind direction. The snow is sprayed at the farthest distance in a downwind environment. In an upwind environment, the sprayed snow is prone to flow back or get clogged at the nozzle, affecting snowmaking.

[0008] When a snowmaking machine malfunctions, it is often not caused by a single factor, but rather by the combined influence of multiple factors.

[0009] Based on this, in order to ensure that snowmaking machine operators can confirm the status of the snowmaking machine in real time and ensure the normal operation of the snowmaking machine, a method is needed to correctly evaluate the reliability of the snowmaking machine in real time. Summary of the invention

[0010] In view of the deficiencies in the prior art, an object of the present invention is to provide a snowmaking machine reliable status assessment method and system that can confirm the status of the snowmaking machine in real time and ensure the normal operation of the snowmaking machine.

[0011] The technical solution adopted by the present invention to solve its technical problem is:

[0012] A snowmaking machine reliable state assessment method, comprising:

[0013] Acquire a number of data samples of the snowmaking machine, the data samples including result samples and factor samples, the result samples represent the fault conditions, and the factor samples represent temperature data, humidity data, wind speed and direction data, and water supply data;

[0014] Under the condition of single factor sample, data interval statistics are performed, including: statistical temperature data interval, humidity data interval, wind speed and direction data interval and water supply data interval under fault conditions, that is, risk interval; statistical temperature data interval, humidity data interval, wind speed and direction data interval and water supply data interval under fault-free conditions, that is, safety interval;

[0015] The influencing factors when various factor samples interact in the statistical safety interval;

[0016] Obtain the temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine, and assign risk values ​​to them based on the data interval;

[0017] Weight coefficients are assigned to the temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine, and the reliability of the snowmaking machine is comprehensively evaluated based on the current risk value of the snowmaking machine and the weight coefficient.

[0018] Preferably, the characteristic value of the result sample without failure during the predetermined time period of the snowmaking machine operation is set to 0, and the characteristic value of the result sample with failure is set to N, wherein N is the number of failures.

[0019] As a preferred method, the method for performing data interval statistics under the condition of a single factor sample is:

[0020] Set the absolute safety interval of the factor samples, that is, the optimal data interval of temperature data, humidity data, wind speed and direction data, and water supply data, and when three of the factor samples are in the absolute safety interval, perform data interval statistics on the other factor sample.

[0021] As a preferred method, the method of statistically analyzing the influencing factors when various factor samples interact in the safety interval is:

[0022] Set two of the factor samples in the absolute safety interval, and set the other two factor samples in the safety interval, and determine whether the characteristic value of the result sample changes. When the characteristic value of the result sample changes, the data interval of the statistical factor sample is the first influencing factor interval;

[0023] Set one of the factor samples in the absolute safety interval, and set the other three factor samples in the safety interval, and determine whether the characteristic value of the result sample changes. When the characteristic value of the result sample changes, the data interval of the statistical factor sample is the second influencing factor interval;

[0024] The four factor samples in the factor sample are set within the safety interval to determine whether the characteristic value of the result sample changes. When the characteristic value of the result sample changes, the data interval of the statistical factor sample is the third influencing factor interval.

[0025] As a preferred method, the method of assigning risk value is:

[0026] The temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine are collected as factor samples, and the data interval to which each data belongs is determined. Different risk values ​​are assigned to them based on the differences in the safety interval, the first influencing factor interval, the second influencing factor interval, the third influencing factor interval, and the risk interval of the temperature data, humidity data, wind speed and direction data, and water supply data.

[0027] Preferably, the method of assigning weight coefficients to the temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine is:

[0028] Based on the impact of various data of the snowmaking machine in the risk range on the operating status of the snowmaking machine, weight coefficients are assigned to various data of the snowmaking machine. The operating status refers to the number of failures of the snowmaking machine within a given time period of operation.

[0029] As a preferred method, the method for comprehensively evaluating the reliability of the snowmaking machine based on the current risk value and weight coefficient of the snowmaking machine is:

[0030] The real-time temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine are matched with the safety interval, the first influencing factor interval, the second influencing factor interval, the third influencing factor interval, and the risk interval. The risk value of each data is obtained based on the matching result, and the risk value corresponding to each data is multiplied by its corresponding weight coefficient and then summed up. The current reliability status of the snowmaking machine is evaluated based on the summed result.

[0031] Another technical problem to be solved by the present invention is to provide a snowmaking machine reliable state assessment system, comprising:

[0032] A temperature detection module is arranged at the nozzle position or water pump position of the snowmaking machine, and is used to collect temperature data of the nozzle or water pump and transmit it;

[0033] Humidity detection modules are evenly distributed in the snowmaking area to collect humidity data of the snowmaking environment and transmit it;

[0034] A wind speed and direction detection module is provided at the nozzle position of the snowmaking machine, and is used to collect and transmit the wind speed and direction data at the nozzle position of the snowmaking machine;

[0035] A water supply detection module is arranged in the water supply pipe of the snowmaking machine and is used to collect and transmit the water supply data of the snowmaking machine;

[0036] A PLC control module is used to receive temperature data, humidity data, wind speed and direction data, and water supply data, and to perform data analysis, and to evaluate the reliability of the snowmaking machine after the analysis;

[0037] The display module is connected to the PLC control module and is used for displaying the reliable status evaluation results in real time.

[0038] Preferably, the wind speed and direction detection module includes an independently installed wind speed sensor, which measures the wind speed based on V=0.1F, wherein F is the pulse frequency of the wind speed sensor input to the PLC control module, and T0 is used as a timer and T1 as a counter, and the wind speed value is determined based on the count value of T1 when T0 is timed for 1 second.

[0039] Preferably, the wind speed and direction detection module includes an independently installed wind direction sensor, which first measures the 7-bit Gray code input, calculates the Gray code through the 7-bit input value, and then converts it into binary code to finally obtain the wind direction angle.

[0040] The beneficial effects of the present invention are:

[0041] Data samples corresponding to snowmaking machine failures are collected, and the numerical ranges of each snowmaking machine data are divided according to the data samples, so as to obtain the risk range corresponding to each data in the data sample and the influencing factors between each data, so as to determine the reliability status of the snowmaking machine when various factors are superimposed, and then the reliability status of the snowmaking machine is evaluated, which solves the gap in the current snowmaking machine reliability status evaluation technology on the market, and has high accuracy, which is convenient for staff to confirm the reliability status of the snowmaking machine in real time and shut down the machine for maintenance in time. DETAILED DESCRIPTION

[0042] The principles and features of the present invention are described below in conjunction with embodiments. The examples are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described in more detail by way of example in the following paragraphs. The advantages and features of the present invention will become clearer according to the following description and claims. It should be noted that a very simplified form and non-precise proportions are used only to facilitate and clearly assist in explaining the purpose of the embodiments of the present invention.

[0043] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may also be a component centered. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may also be a component centered. When a component is considered to be "set on" another component, it may be directly set on the other component or there may also be a component centered. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0045] Example

[0046] A snowmaking machine reliable state assessment method, comprising:

[0047] Acquire a number of data samples of the snowmaking machine, the data samples including result samples and factor samples, the result samples represent the fault conditions, and the factor samples represent temperature data, humidity data, wind speed and direction data, and water supply data;

[0048] Under the condition of single factor sample, data interval statistics are performed, including: statistical temperature data interval, humidity data interval, wind speed and direction data interval and water supply data interval under fault conditions, that is, risk interval; statistical temperature data interval, humidity data interval, wind speed and direction data interval and water supply data interval under fault-free conditions, that is, safety interval;

[0049] The influencing factors when various factor samples interact in the statistical safety interval;

[0050] Obtain the temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine, and assign risk values ​​to them based on the data interval;

[0051] Weight coefficients are assigned to the temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine, and the reliability of the snowmaking machine is comprehensively evaluated based on the current risk value of the snowmaking machine and the weight coefficient.

[0052] The characteristic value of the result sample without failure during the predetermined time period of the snowmaking machine operation is set to 0, and the characteristic value of the result sample with failure is set to N, where N is the number of failures.

[0053] In this embodiment, the predetermined time period is 1000 hours. When a fault occurs within the time period, the system is repaired and the original data interval state is maintained until the 1000-hour running time is completed. Of course, the running time can be reduced or increased as needed. The longer the running time, the more accurate the data result.

[0054] The method for performing data interval statistics under the condition of a single factor sample is:

[0055] Set the absolute safety interval of the factor samples, that is, the optimal data interval of temperature data, humidity data, wind speed and direction data, and water supply data, and when three of the factor samples are in the absolute safety interval, perform data interval statistics on the other factor sample.

[0056] The reason for the snowmaking machine's failure based on temperature is that the temperature is too low and ice forms, causing freezing and clogging the nozzle. Therefore, the absolute safety interval of the temperature data can be set above -5°C; the reason for the snowmaking machine's failure based on humidity is that when the ambient humidity is greater than 80%, the sprayed ice and snow will liquefy due to excessive humidity and form sleet. Therefore, the absolute safety interval of the humidity data can be set within 60%; the reason for the snowmaking machine's failure based on water supply is that there is a lack of water source during snowmaking and snowmaking cannot be made. The water supply of the snowmaking machine is determined by the size of the ski resort project and the size of the cubic volume of snow made. Therefore, the absolute safety interval of the water supply data can be set to more than 90% of the preset water supply; the reason for the snowmaking machine's failure based on wind direction is that excessive wind force in headwind blows the sprayed ice and snow back to the nozzle. Therefore, the absolute safety interval of the wind speed and direction data can be tailwind or no wind.

[0057] The method for statistically analyzing the influencing factors when various factor samples interact in the safety interval is:

[0058] Set two of the factor samples in the absolute safety interval, and set the other two factor samples in the safety interval, and determine whether the characteristic value of the result sample changes. When the characteristic value of the result sample changes, the data interval of the statistical factor sample is the first influencing factor interval;

[0059] Set one of the factor samples in the absolute safety interval, and set the other three factor samples in the safety interval, and determine whether the characteristic value of the result sample changes. When the characteristic value of the result sample changes, the data interval of the statistical factor sample is the second influencing factor interval;

[0060] The four factor samples in the factor sample are set within the safety interval to determine whether the characteristic value of the result sample changes. When the characteristic value of the result sample changes, the data interval of the statistical factor sample is the third influencing factor interval.

[0061] Among them, when the third influencing factor interval overlaps with the second influencing factor interval, the second influencing factor interval covers the overlapping part; when the second influencing factor interval overlaps with the first influencing factor interval, the first influencing factor interval covers the overlapping part; when the middle interval overlaps with the first influencing factor interval, the middle interval covers the overlapping part.

[0062] Since the relationship between the factor samples and the result samples of the snowmaking machine is a proportional relationship, that is to say, the worse the data bias of the factor sample, the higher the value of the result sample N. Based on this, this scheme adopts a progressive influencing factor interval to determine the risk value. The third influencing factor interval is interfered by four factor samples, so its value interval range is farthest from the risk interval, and the second influencing factor interval is interfered by three factor samples, so its value interval range is in the middle of the risk interval, and the first influencing factor interval is interfered by only two factor samples, so its value interval range is closest to the risk interval.

[0063] The method of assigning risk value is:

[0064] The temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine are collected as factor samples, and the data interval to which each data belongs is determined. Different risk values ​​are assigned to them based on the differences in the safety interval, the first influencing factor interval, the second influencing factor interval, the third influencing factor interval, and the risk interval of the temperature data, humidity data, wind speed and direction data, and water supply data.

[0065] The method for assigning weight coefficients to the temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine is as follows:

[0066] Based on the impact of various data of the snowmaking machine in the risk range on the operating status of the snowmaking machine, weight coefficients are assigned to various data of the snowmaking machine. The operating status refers to the mean time between failures, that is, the number of failures within a given time period of the snowmaking machine's operation.

[0067] The method for comprehensively evaluating the reliability of a snowmaking machine based on its current risk value and weight coefficient is as follows:

[0068] The real-time temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine are matched with the safety interval, the first influencing factor interval, the second influencing factor interval, the third influencing factor interval, and the risk interval. The risk value of each data is obtained based on the matching result, and the risk value corresponding to each data is multiplied by its corresponding weight coefficient and then summed up. The current reliability status of the snowmaking machine is evaluated based on the summed result.

[0069] A snowmaking machine reliable state assessment system, comprising:

[0070] A temperature detection module is arranged at the nozzle position or water pump position of the snowmaking machine, and is used to collect temperature data of the nozzle or water pump and transmit it;

[0071] Humidity detection modules are evenly distributed in the snowmaking area to collect humidity data of the snowmaking environment and transmit it;

[0072] A wind speed and direction detection module is provided at the nozzle position of the snowmaking machine, and is used to collect and transmit the wind speed and direction data at the nozzle position of the snowmaking machine;

[0073] A water supply detection module is arranged in the water supply pipe of the snowmaking machine and is used to collect and transmit the water supply data of the snowmaking machine;

[0074] A PLC control module is used to receive temperature data, humidity data, wind speed and direction data, and water supply data, and to perform data analysis, and to evaluate the reliability of the snowmaking machine after the analysis;

[0075] The display module is connected to the PLC control module and is used for displaying the reliable status evaluation results in real time.

[0076] The PLC control module receives data from the temperature detection module, humidity detection module, wind speed and direction detection module, and water supply detection module to determine which data range it is in, and evaluates the risk value. Finally, the evaluation result is sent to the display module, thereby providing staff with real-time warning and evaluating the reliability of the current operating status of the snowmaking machine.

[0077] In this solution, a color gamut display method is used to display the reliability status in real time. That is, different color gamuts are matched based on the total result of multiplying the risk value corresponding to each data by its corresponding weight coefficient, and real-time display is performed in the form of green high reliability status, yellow medium reliability status and red low reliability status.

[0078] The wind speed and direction detection module includes an independently installed wind speed sensor, which measures the wind speed based on V=0.1F, where F is the pulse frequency of the wind speed sensor input to the PLC control module. T0 is used as a timer and T1 is used as a counter. The wind speed value is determined based on the count value of T1 when T0 is timed for 1 second.

[0079] The wind speed and direction detection module includes an independently installed wind direction sensor. The wind direction sensor first measures the 7-bit Gray code input, calculates the Gray code through the 7-bit input value, and then converts it into binary code to finally obtain the wind direction angle.

[0080] The above embodiments of the present invention are not intended to limit the protection scope of the present invention, and the implementation modes of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, should fall within the protection scope of the present invention.

Claims

1. A method for evaluating the reliability of a snowmaking machine, characterized in that: Included are: Acquire a number of data samples of the snowmaking machine, the data samples including result samples and factor samples, the result samples represent the fault conditions, and the factor samples represent temperature data, humidity data, wind speed and direction data, and water supply data; Under the condition of single factor sample, data interval statistics are performed, including: statistical temperature data interval, humidity data interval, wind speed and direction data interval and water supply data interval under fault conditions, that is, risk interval; statistical temperature data interval, humidity data interval, wind speed and direction data interval and water supply data interval under fault-free conditions, that is, safety interval; The influencing factors when various factor samples interact in the statistical safety interval; Obtain the temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine, and assign risk values ​​to them based on the data interval; Assign weight coefficients to the temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine, and comprehensively evaluate the reliability of the snowmaking machine based on the current risk value of the snowmaking machine and the weight coefficients; The method for statistically analyzing the influencing factors when various factor samples interact in the safety interval is: Set two of the factor samples in the absolute safety interval, and set the other two factor samples in the safety interval, and determine whether the characteristic value of the result sample changes. When the characteristic value of the result sample changes, the data interval of the statistical factor sample is the first influencing factor interval; Set one of the factor samples in the absolute safety interval, and set the other three factor samples in the safety interval, and determine whether the characteristic value of the result sample changes. When the characteristic value of the result sample changes, the data interval of the statistical factor sample is the second influencing factor interval; Set the four factor samples in the factor sample within the safety interval, and determine whether the characteristic value of the result sample changes. When the characteristic value of the result sample changes, the data interval of the statistical factor sample is the third influencing factor interval; The method for performing data interval statistics under the condition of a single factor sample is: Set the absolute safety interval of the factor samples, that is, the optimal data interval of temperature data, humidity data, wind speed and direction data, and water supply data, and when three of the factor samples are in the absolute safety interval, perform data interval statistics on another factor sample; The method of assigning risk value is: The temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine are collected as factor samples, and the data intervals to which each data belongs are determined. Different risk values ​​are assigned to the temperature data, humidity data, wind speed and direction data, and water supply data based on the differences in the safety interval, the first influencing factor interval, the second influencing factor interval, the third influencing factor interval, and the risk interval. The method for assigning weight coefficients to the temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine is as follows: Based on the impact of various data of the snowmaking machine on the operating status of the snowmaking machine when the data are in the risk range, a weight coefficient is assigned to each data of the snowmaking machine. The operating status refers to the number of failures within a given time period of the snowmaking machine; The method for comprehensively evaluating the reliability of a snowmaking machine based on its current risk value and weight coefficient is as follows: The real-time temperature data, humidity data, wind speed and direction data, and water supply data of the snowmaking machine are matched with the safety interval, the first influencing factor interval, the second influencing factor interval, the third influencing factor interval, and the risk interval. The risk value of each data is obtained based on the matching result, and the risk value corresponding to each data is multiplied by its corresponding weight coefficient and then summed up. The current reliability status of the snowmaking machine is evaluated based on the summed result.

2. The snowmaking machine reliability status assessment method according to claim 1, characterized in that: The characteristic value of the result sample without failure during the predetermined time period of the snowmaking machine operation is set to 0, and the characteristic value of the result sample with failure is set to N, where N is the number of failures.

3. A snowmaking machine reliability status assessment system, the snowmaking machine reliability status assessment method is based on the snowmaking machine reliability status assessment method in claim 1, characterized in that: Included are: A temperature detection module is arranged at the nozzle position or water pump position of the snowmaking machine, and is used to collect temperature data of the nozzle or water pump and transmit it; Humidity detection modules are evenly distributed in the snowmaking area to collect humidity data of the snowmaking environment and transmit it; A wind speed and direction detection module is provided at the nozzle position of the snowmaking machine, and is used to collect and transmit the wind speed and direction data at the nozzle position of the snowmaking machine; A water supply detection module is arranged in the water supply pipe of the snowmaking machine and is used to collect and transmit the water supply data of the snowmaking machine; A PLC control module is used to receive temperature data, humidity data, wind speed and direction data, and water supply data, and to perform data analysis, and to evaluate the reliability of the snowmaking machine after the analysis; The display module is connected to the PLC control module and is used for displaying the reliable status evaluation results in real time.

4. The snowmaking machine reliability status assessment system according to claim 3, characterized in that: The wind speed and direction detection module includes an independently installed wind speed sensor. The wind speed sensor measures the wind speed based on V=0.1F, where F is the pulse frequency of the wind speed sensor input to the PLC control module. T0 is used as a timer and T1 is used as a counter. The wind speed value is determined based on the count value of T1 when T0 is timing for 1 second.

5. The snowmaking machine reliability status assessment system according to claim 4, characterized in that: The wind speed and direction detection module includes an independently installed wind direction sensor. The wind direction sensor first measures the 7-bit Gray code input, calculates the Gray code through the 7-bit input value, and then converts it into binary code to finally obtain the wind direction angle.