Method, device and computer equipment for determining abnormal low-pressure gas discharge device

Abstract

The present application discloses a method, device and computer equipment for determining a low-pressure gas abnormal emission device. The method includes: respectively obtaining the historical temperature data and the current temperature of the temperature detection points of multiple devices to be identified; respectively comparing the current temperature of each device to be identified with the historical temperature data to obtain a first comparison result, which is used to indicate whether the current temperature is consistent with its historical temperature change trend; comparing the current temperature difference of each device to be identified among the multiple devices to be identified with each other to obtain a second comparison result, which is used to indicate whether the current temperature change trend of each device to be identified is consistent; and determining the device with abnormal low-pressure gas emission among the multiple devices to be identified based on the first comparison result and the second comparison result.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and computer equipment for determining a low-pressure gas abnormal emission device. Background Art

[0002] In high-energy-consuming refining and chemical enterprises, gas, as one of the most important energy media, plays an important role in energy conservation, emission reduction, cost reduction and efficiency improvement. The gas system is mainly divided into high-pressure gas system and low-pressure gas system. The low-pressure gas system is mainly a mixed gas of C1-C4, such as exhaust gas, non-condensable gas, and low-fraction gas of coking, catalytic and other devices. The low-pressure gas emitted by all devices will be transported and transferred to the gas holder through pipelines (the gas in the gas holder is commonly known as gas holder gas in the refinery). The gas holder gas will then be pressurized by the gas holder compressor, and after desulfurization, it will enter the high-pressure pipeline network to form a high-pressure gas system, and finally be supplied to the heating furnace of each production device or the reactor of the hydrogen production system. From the gas process, it can be seen that the gas system is mainly composed of gas production devices, gas transmission units (including low-pressure gas pipelines, high-pressure gas pipelines, gas holder compressors), gas holders and flare systems, gas consumption devices, etc. The entire gas system is relatively complex, with many emission points and high monitoring difficulty. From the perspective of enterprise safety and environmental protection, abnormal emission of low-pressure gas will cause a series of problems to the gas system, mainly manifested in the following aspects: (1) Due to the lack of detection means for low-pressure gas emission, it is impossible to control the occurrence of disorderly emission from the source, and it is impossible to reasonably supervise abnormal emission; (2) Dispatchers locate the gas emission device based on experience, and often need to call each device to check, which is time-consuming and labor-intensive, and the dispatch adjustment of abnormal emission has a certain lag; (3) When a large amount of low-pressure gas is abnormally discharged, the gas system produces more than it consumes, which may cause the gas tank to exceed the limit, and it needs to be balanced by flaring, which inevitably causes environmental pollution and a large amount of resource waste. (4) Since the time and flow rate of low-pressure abnormal emission are extremely unstable, the flow meter range is difficult to determine, resulting in a range that is too large or too small. Therefore, there are few flow meters on the main pipe. If a flow meter is to be installed, it is necessary to open a hole under pressure. Low-pressure gas is flammable, explosive, toxic and harmful, and it is difficult to install a flow meter on a running device. Generally speaking, most companies will install a thermometer on the low-pressure gas main pipe of the device to monitor the temperature of the low-pressure gas discharged from the entire device. Even if there is no such thermometer, a patch thermometer can be installed on the pipe wall to measure the low-pressure gas discharge temperature. Summary of the invention

[0003] The embodiments of the present application provide a method, device and computer equipment for determining an abnormal low-pressure gas emission device, so as to at least solve the technical problem in the related art that the abnormal emission device is determined by manual investigation, resulting in low investigation efficiency.

[0004] According to one aspect of an embodiment of the present application, a method for determining a low-pressure gas abnormal emission device is provided, including: respectively obtaining historical temperature data and current temperatures of temperature detection points of multiple devices to be identified; respectively comparing the current temperature of each device to be identified with the historical temperature data to obtain a first comparison result, wherein the first comparison result is used to indicate whether the current temperature is consistent with its historical temperature change trend; comparing the current temperature differences of each device to be identified among the multiple devices to be identified with each other to obtain a second comparison result, wherein the second comparison result is used to indicate whether the current temperature change trend of each device to be identified is consistent; and determining the device with abnormal low-pressure gas emission among the multiple devices to be identified based on the first comparison result and the second comparison result.

[0005] Optionally, the current temperature of each device to be identified is compared with the historical temperature data to obtain a first comparison result, including: training an autoregressive differential moving average model based on the historical temperature data, and using the autoregressive differential moving average model to predict the temperature of the temperature detection point at the current moment to obtain a predicted temperature value; comparing the predicted temperature value with the current temperature to obtain a first comparison result; determining the first identification result based on the first comparison result, the first identification result is used to indicate the low-pressure gas emission state of the device to be identified, and the emission state includes: normal state, transition state and abnormal state.

[0006] Optionally, determining the first identification result based on the first comparison result includes: obtaining the root mean square error between the predicted temperature value and the current temperature; when the root mean square error is less than a first preset value, determining that the emission state is a normal state; when the root mean square error is greater than the first preset value and less than the second preset value, determining that the emission state is a transitional state; when the root mean square error is greater than the second preset value, determining that the emission state is an abnormal state, and taking the abnormal state as the first identification result.

[0007] Optionally, before obtaining an autoregressive differential moving average model based on the historical temperature data, the method further includes: obtaining the autocorrelation function and the partial autocorrelation function of the historical temperature data; determining whether the time series of the historical temperature data is stationary based on the autocorrelation function and the partial autocorrelation function; and if the time series of the historical temperature data is not stationary, performing a differential operation on the historical temperature data so that the time series of the historical temperature data reaches a stationary state.

[0008] Optionally, the method further includes: respectively obtaining the temperature of each device to be identified at a current moment and the temperature at a moment before the current moment; respectively determining the temperature difference between the temperature of each device to be identified at a current moment and the temperature at a moment before the current moment to obtain the second comparison result; obtaining the standard deviation corresponding to the temperature difference of all devices to be identified; when the second comparison result indicates that the temperature difference of the device to be identified is greater than three times the standard deviation, determining the second identification result as that the emission state of the device to be identified is an abnormal state.

[0009] Optionally, determining that there is a device with abnormal low-pressure gas emissions among the multiple devices to be identified based on the first identification result and the second identification result includes: when the first identification result and the second identification result both indicate that the emission state of the device to be identified is an abnormal state, determining that the device to be identified is a device with abnormal low-pressure gas emissions.

[0010] According to another aspect of an embodiment of the present application, a device for determining a low-pressure gas abnormal emission device is also provided, including: an acquisition module, used to respectively acquire historical temperature data and current temperature of temperature detection points of multiple devices to be identified; a first comparison module, used to respectively compare the current temperature of each device to be identified with the historical temperature data, to obtain a first comparison result, wherein the first comparison result is used to indicate whether the current temperature is consistent with its historical temperature change trend; a second comparison module, used to compare the current temperature differences of each device to be identified among the multiple devices to be identified, to obtain a second comparison result, wherein the second comparison result is used to indicate whether the current temperature change trend of each device to be identified is consistent; a determination module, used to determine whether there is a device with abnormal low-pressure gas emission among the multiple devices to be identified based on the first comparison result and the second comparison result.

[0011] According to another aspect of the embodiment of the present application, a computer device is also provided, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the above-mentioned method for determining the abnormal low-pressure gas emission device.

[0012] According to another aspect of the embodiment of the present application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned method for determining the abnormal low-pressure gas emission device by running the computer program.

[0013] According to another aspect of the embodiment of the present application, a computer program product is also provided, including computer instructions, which, when executed by a processor, implement the above-mentioned method for determining the low-pressure gas abnormal emission device.

[0014] In an embodiment of the present application, historical temperature data and current temperatures of temperature detection points of multiple devices to be identified are obtained respectively; the current temperature of each device to be identified is compared with the historical temperature data to obtain a first comparison result, and the first comparison result is used to indicate whether the current temperature conforms to its historical temperature change trend; the current temperature differences of each device to be identified among the multiple devices to be identified are compared with each other to obtain a second comparison result, and the second comparison result is used to indicate whether the current temperature change trends of each device to be identified are consistent; based on the first comparison result and the second comparison result, it is determined that there is a device with abnormal low-pressure gas emission among the multiple devices to be identified, thereby achieving the purpose of quickly locating the abnormal emission device only through the temperature measuring instrument data, thereby achieving the technical effect of improving the efficiency of troubleshooting of abnormal emission devices, and further solving the technical problem of low troubleshooting efficiency due to the use of manual troubleshooting to determine abnormal emission devices in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0016] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing a method for determining a low-pressure gas abnormal discharge device according to an embodiment of the present application;

[0017] Figure 2 is a flow chart of a method for determining a low-pressure gas abnormal discharge device according to an embodiment of the present application;

[0018] Figure 3 It is a schematic diagram comparing the temperature of a low-pressure gas detection point and the ambient temperature of a device according to an embodiment of the present application (normal state);

[0019] Figure 4 It is a schematic diagram comparing the temperature of a low-pressure gas detection point and the ambient temperature of a device according to an embodiment of the present application (abnormal state);

[0020] Figure 5 It is a schematic diagram of temperature variation curves of low-pressure gas temperature measurement points of multiple devices according to an embodiment of the present application (normal state);

[0021] Figure 6 It is a schematic diagram of temperature variation curves of low-pressure gas temperature measurement points of multiple devices according to an embodiment of the present application (abnormal state);

[0022] Figure 7 is a schematic diagram of comparison between a predicted temperature value and an actual temperature value under a normal state according to an embodiment of the present application;

[0023] Figure 8 is a schematic diagram of comparison between a predicted temperature value and an actual temperature value in a transition state according to an embodiment of the present application;

[0024] Fig. 9 is a schematic diagram of comparison between a predicted temperature value and an actual temperature value under an abnormal state according to an embodiment of the present application;

[0025] Fig.10 It is a structural diagram of a determination device of a low-pressure gas abnormal emission device according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second", etc. 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 sequence. 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 of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising 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.

[0028] The information collected in the embodiments of the present application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or reject automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0029] In order to solve the problems existing in the related art, the embodiment of the present application provides a method for determining a low-pressure gas abnormal discharge device, which can be operated on Figure 1 In the computer terminal shown, the computer terminal is explained below.

[0030] The method for determining a low-pressure gas abnormal discharge device provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing a method for determining a low-pressure gas abnormal discharge device is shown. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0031] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0032] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for determining the low-pressure gas abnormal discharge device in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the above-mentioned method for determining the low-pressure gas abnormal discharge device is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0033] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0034] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0035] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0036] Under the above-mentioned operating environment, an embodiment of the present application provides an embodiment of a method for determining a low-pressure gas abnormal emission device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] Figure 2is a flow chart of a method for determining a low-pressure gas abnormal discharge device according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0038] Step S202, respectively obtaining historical temperature data and current temperature of temperature detection points of multiple devices to be identified;

[0039] In step S202, the temperature data can be collected by using a temperature meter installed on the low-pressure gas main or a patch thermometer installed on the pipe wall, and the obtained data is cleaned, for example, missing value processing and data smoothing are performed on the historical data. In addition, the detection point data within the time point that has been determined to be abnormal emission in history is removed, and data is filled according to the trend of ambient temperature changes.

[0040] Step S204, respectively comparing the current temperature of each device to be identified with the historical temperature data to obtain a first comparison result, where the first comparison result is used to indicate whether the current temperature conforms to its historical temperature change trend;

[0041] Step S206, comparing the temperature differences of the multiple devices to be identified at the current moment to obtain a second comparison result, where the second comparison result is used to indicate whether the current temperature change trends of each device to be identified are consistent;

[0042] Step S208: determining, based on the first comparison result and the second comparison result, which device among the multiple devices to be identified has abnormal low-pressure gas emission.

[0043] Through the above steps S202 to S208, by respectively obtaining the historical temperature data and the current temperature of the temperature detection points of multiple devices to be identified; respectively comparing the current temperature of each device to be identified with the historical temperature data, to obtain a first comparison result, the first comparison result is used to indicate whether the current temperature is consistent with its historical temperature change trend; comparing the current temperature differences of each device to be identified among the multiple devices to be identified, to obtain a second comparison result, the second comparison result is used to indicate whether the current temperature change trend of each device to be identified is consistent; based on the first comparison result and the second comparison result, it is determined that there is a device with abnormal low-pressure gas emission among the multiple devices to be identified, thereby achieving the purpose of quickly locating the abnormal emission device only through the temperature measurement instrument data, thereby achieving the technical effect of improving the efficiency of abnormal emission device troubleshooting, and then solving the technical problem of low troubleshooting efficiency due to the use of manual troubleshooting to determine abnormal emission devices in the related technology. The following is a detailed description.

[0044] It should be noted that when there is no abnormal discharge of low-pressure gas, the temperature at the detection point is only affected by the ambient temperature. Therefore, the temperature at the detection point will change with the change of the ambient temperature. Figure 3 As shown, it can be seen that the changing trends of the discharged low-pressure gas (low-pressure gas, red curve) and the ambient temperature (purple curve) are roughly the same. However, there will be a certain small range of fluctuations. This is because the on-site environment of each device is complex, and the temperature of the detection point is not only affected by the ambient temperature, but also by the surrounding equipment. In addition, there are generally fewer ambient temperature detection points in the factory area, and the distance from each low-pressure gas temperature detection point is different, so there will inevitably be a certain error between the two.

[0045] like Figure 4 As shown in the figure, when the discharged low-pressure gas (low-pressure gas, red curve) is abnormally discharged, the degree to which the detection point temperature is affected by the ambient temperature (purple curve) is reduced, and it is mainly affected by the abnormally discharged low-pressure gas in the pipeline. Therefore, the correlation between the detection point temperature and the ambient temperature is reduced, and the trends between the two will be quite different.

[0046] like Figure 5 As shown, under normal circumstances, the temperature of the low-pressure gas detection point will change with the change of ambient temperature, while in abnormal emissions, the trends between the two will be quite different. Based on this argument, when the temperature and time change curves of all device detection points over a period of time are plotted on the same graph, it can be found that all curve trends are roughly the same.

[0047] like Figure 6 As shown in Figure 2, if an abnormal emission occurs at a detection point, this point will tend to deviate from the group.

[0048] It is understandable that in Figure 5 and Figure 6 In the figure, curves of different colors represent the temperature change curves of the temperature detection points of different devices.

[0049] Based on the above analysis, the present application also provides another method for determining a low-pressure gas abnormal discharge device, including:

[0050] Step 1: Based on the temperature measuring instruments on the low-pressure gas mains of each device and the surrounding environment, collect real-time and historical data, and clean the obtained data;

[0051] Step 2: Compare the data with the historical data of the detection point itself, and use the autoregressive difference moving average model for prediction and identification;

[0052] Step 3: Compare the temperature difference horizontally with the real-time temperature difference of the main pipes of each device, and use three times the standard deviation to identify abnormalities;

[0053] Step 4: Combine the horizontal and vertical identification results to identify whether the current temperature is abnormal emission.

[0054] In some embodiments of the present application, the specific method of comparing the current temperature of each device to be identified with the historical temperature data to obtain a first comparison result is as follows: an autoregressive differential moving average model is trained according to the historical temperature data, and the autoregressive differential moving average model is used to predict the temperature of the temperature detection point at the current moment to obtain a predicted temperature value; the predicted temperature value is compared with the current temperature to obtain a first comparison result; the first identification result is determined according to the first comparison result, and the first identification result is used to indicate the low-pressure gas emission state of the device to be identified, and the emission state includes: normal state, transition state and abnormal state.

[0055] Specifically, based on the historical values ​​and current values ​​of the detection point over a period of time, a prediction comparison (vertical comparison) is performed based on its own historical trend, and the prediction method used is the autoregressive difference moving average model. Under normal conditions, the temperature of the detection point changes slowly due to the influence of the ambient temperature, and the current temperature will continue to change in the trend of the historical temperature, with a small change range.

[0056] Optionally, the first identification result can be determined based on the first comparison result, and the specific steps are as follows: obtain the root mean square error (RMSE) between the predicted temperature value and the current temperature; when the root mean square error is less than a first preset value, determine that the emission state is a normal state; when the root mean square error is greater than the first preset value and less than the second preset value, determine that the emission state is a transitional state; when the root mean square error is greater than the second preset value, determine that the emission state is an abnormal state, and use the abnormal state as the first identification result.

[0057] It should be noted that the first preset value and the second preset value can be set according to actual conditions.

[0058] In actual application scenarios, on the one hand, the continuity of the development of things is taken into consideration, and past time series data are used for statistical analysis to infer the development trend of things; on the other hand, the randomness caused by accidental factors is fully taken into consideration. In order to eliminate the impact of random fluctuations, historical data are used for statistical analysis, and the data is properly processed to perform trend prediction. Before the autoregressive differential moving average model is trained based on the historical temperature data, the autocorrelation function and partial autocorrelation function of the historical temperature data need to be obtained; whether the time series of the historical temperature data is stable is determined based on the autocorrelation function and the partial autocorrelation function; if the time series of the historical temperature data is not stable, a differential operation is performed on the historical temperature data to make the time series of the historical temperature data reach a stable state.

[0059] like Figure 7 As shown in Figure 2, when the prediction value (Forecast Data) is in a normal state, the deviation between the prediction value (Forecast Data) and the current value (Original Data) is small, and the RMSE value is small (for example: RMSE = 0.016). Figure 8 As shown in Figure 2, when in the transition state, the deviation between the predicted value and the actual observed value begins to increase, but has not yet reached the clear standard of abnormal emission (for example: RMSE = 0.15), as shown in Figure 2. Fig. 9 As shown, since the current value no longer continues the previous trend, and the predicted value is still predicted according to the previous historical data trend, the deviation between the predicted value and the current value is large, and the RMSE value is relatively large (for example: RMSE = 0.62).

[0060] It should be noted that Figures 3 to 9 The horizontal axis represents time, and the vertical axis represents temperature.

[0061] In some embodiments of the present application, the temperature difference at the current moment of each device to be identified is compared with each other, and the specific steps for obtaining the second identification result are as follows: respectively obtain the temperature at the current moment of each device to be identified and the temperature at the previous moment of the current moment; respectively determine the temperature difference corresponding to the temperature at the current moment of each device to be identified and the temperature at the previous moment, to obtain the second comparison result; obtain the standard deviation corresponding to the temperature difference of all devices to be identified; when the second comparison result indicates that the temperature difference of the device to be identified is greater than three times the standard deviation, determine that the second identification result is that the emission state of the device to be identified is an abnormal state.

[0062] Specifically, the temperature difference of the detection point is compared with the temperature difference of all detection points in the same time period (the difference between the current temperature value and the temperature in the previous period, such as the previous minute, which can be determined according to the frequency of database collection) (horizontal comparison). Since all detection points are mainly affected by the ambient temperature under normal conditions, the temperature difference changes are roughly the same. The temperature difference is used for diagnosis instead of the current temperature to eliminate the problem of different temperature benchmarks between each detection point, and no normalization is required.

[0063] The method used for horizontal comparison is three times the standard deviation in statistics to diagnose outliers. The temperature difference of all detection points in the same time period conforms to the normal distribution. According to the definition of normal distribution, the probability of being 3σ away from the average value is P(|x-μ|>3σ)<=0.003, which is an extremely low probability event. Under normal circumstances, it can be determined that samples with a distance exceeding 3σ from the average value do not exist. Therefore, when the sample is greater than 3σ from the average value, the sample is identified as an outlier. That is: when the temperature difference of the detection point exceeds three times the standard deviation of the temperature difference of all detection points, it is determined that the current detection point has abnormal low-pressure gas discharge, where x represents the temperature difference between the current moment and the previous moment.

[0064] In an optional manner, the abnormal discharge device can be determined by the following steps: Real-time temperature data collection: First, the low-pressure gas temperature data of all detection points is obtained in real time, including the temperature value of the current detection point and the temperature values ​​of all other detection points. Calculate the temperature difference: For each detection point, calculate the difference between its current temperature value and the temperature value at the previous moment (such as the previous minute) to obtain the temperature difference data of each detection point. Calculate the overall average temperature difference: Summarize the temperature difference values ​​of all detection points at the current time and calculate their average value (μ), which represents the average trend of the temperature change of all detection points. Calculate the standard deviation: Next, calculate the standard deviation (σ) of the temperature difference values ​​of all detection points to quantify the degree of fluctuation of the temperature difference change. The larger the standard deviation, the higher the degree of dispersion of the temperature difference value. Determine the three times standard deviation range: According to the average temperature difference (μ) and the standard deviation (σ), calculate the upper and lower limits of the three times standard deviation, that is, [μ3σ,μ+3σ]. The temperature difference within this range is considered to be within the normal fluctuation range, while the temperature difference outside this range is considered abnormal. Compare the temperature difference of the current detection point: compare the temperature difference of the current detection point with the calculated three times standard deviation range. If the temperature difference of the current detection point falls outside this range, that is, the temperature difference of the current detection point deviates significantly from the average temperature difference of other detection points, then it is determined that the current detection point is undergoing abnormal low-pressure gas discharge.

[0065] In some embodiments of the present application, determining that there is a device with abnormal low-pressure gas emissions among the multiple devices to be identified based on the first identification result and the second identification result includes: when the first identification result and the second identification result both indicate that the emission state of the device to be identified is an abnormal state, determining that the device to be identified is a device with abnormal low-pressure gas emissions.

[0066] Fig.10 A device for determining a low-pressure gas abnormal discharge device according to an embodiment of the present application, the device comprising:

[0067] An acquisition module 110 is used to respectively acquire historical temperature data and current temperature of temperature detection points of a plurality of devices to be identified;

[0068] A first comparison module 120, for comparing the current temperature of each device to be identified with the historical temperature data, to obtain a first comparison result, wherein the first comparison result is used to indicate whether the current temperature conforms to its historical temperature change trend;

[0069] A second comparison module 140 is used to compare the temperature differences of each of the multiple devices to be identified at the current moment to obtain a second comparison result, wherein the second comparison result is used to indicate whether the current temperature change trends of each device to be identified are consistent;

[0070] The determination module 160 is used to determine, based on the first comparison result and the second comparison result, whether there is a device with abnormal low-pressure gas emission among the multiple devices to be identified.

[0071] Through the above-mentioned low-pressure gas abnormal emission device determination device, by respectively obtaining the historical temperature data and the current temperature of the temperature detection points of multiple devices to be identified; respectively comparing the current temperature of each device to be identified with the historical temperature data to obtain a first comparison result, the first comparison result is used to indicate whether the current temperature conforms to its historical temperature change trend; the current temperature differences of each device to be identified among the multiple devices to be identified are compared with each other to obtain a second comparison result, the second comparison result is used to indicate whether the current temperature change trend of each device to be identified is consistent; based on the first comparison result and the second comparison result, it is determined that there is a device with abnormal low-pressure gas emission among the multiple devices to be identified, thereby achieving the purpose of quickly locating the abnormal emission device only through the temperature measuring instrument data, thereby achieving the technical effect of improving the efficiency of troubleshooting of abnormal emission devices, and further solving the technical problem of low troubleshooting efficiency due to the use of manual troubleshooting to determine abnormal emission devices in the related technology.

[0072] The first comparison module 120 includes: a first comparison submodule, which is used to compare the current temperature of each device to be identified with the historical temperature data to obtain a first comparison result, including: training an autoregressive differential moving average model based on the historical temperature data, and using the autoregressive differential moving average model to predict the temperature of the temperature detection point at the current moment to obtain a predicted temperature value; comparing the predicted temperature value with the current temperature to obtain a first comparison result; determining the first identification result based on the first comparison result, the first identification result is used to indicate the low-pressure gas emission state of the device to be identified, and the emission state includes: normal state, transition state and abnormal state.

[0073] The first comparison submodule includes: a first determination unit, which is used to determine the first recognition result according to the first comparison result, including: obtaining the root mean square error between the predicted temperature value and the current temperature; when the root mean square error is less than a first preset value, determining that the emission state is a normal state; when the root mean square error is greater than the first preset value and less than the second preset value, determining that the emission state is a transitional state; when the root mean square error is greater than the second preset value, determining that the emission state is an abnormal state, and taking the abnormal state as the first recognition result.

[0074] The first comparison submodule also includes: a stabilization unit, which is used to obtain the autocorrelation function and partial autocorrelation function of the historical temperature data before training the autoregressive differential moving average model based on the historical temperature data; determine whether the time series of the historical temperature data is stable based on the autocorrelation function and the partial autocorrelation function; when the time series of the historical temperature data is not stable, perform a differential operation on the historical temperature data to make the time series of the historical temperature data reach a stable state.

[0075] The second comparison module 140 includes: a second comparison submodule, which is used to compare the current temperature difference of each device to be identified with each other to obtain a second identification result, including: respectively obtaining the current temperature of each device to be identified and the temperature at the previous moment of the current moment; respectively determining the temperature difference between the current temperature of each device to be identified and the temperature at the previous moment to obtain the second comparison result; obtaining the standard deviation corresponding to the temperature difference of all devices to be identified; when the second comparison result indicates that the temperature difference of the device to be identified is greater than three times the standard deviation, determining that the second identification result is that the emission state of the device to be identified is an abnormal state.

[0076] The determination module 160 includes: a determination submodule, which is used to determine whether there is a device with abnormal low-pressure gas emissions among the multiple devices to be identified based on the first recognition result and the second recognition result, including: when the first recognition result and the second recognition result both indicate that the emission state of the device to be identified is an abnormal state, determining that the device to be identified is a device with abnormal low-pressure gas emissions.

[0077] It should be noted that Fig.10 The low-pressure gas abnormal discharge device determination device shown is used to perform Figure 2 The method for determining the low-pressure gas abnormal emission device is shown, so the relevant explanations in the above-mentioned method for determining the low-pressure gas abnormal emission device are also applicable to the determination device of the low-pressure gas abnormal emission device, and will not be repeated here.

[0078] An embodiment of the present application also provides a computer device, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the above-mentioned method for determining the abnormal low-pressure gas emission device.

[0079] An embodiment of the present application also provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned method for determining the low-pressure gas abnormal emission device by running the computer program.

[0080] An embodiment of the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the method for determining a low-pressure gas abnormal emission device in the present application.

[0081] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0082] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0085] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0087] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining a low-pressure gas abnormal discharge device, characterized in that: include: Respectively obtain historical temperature data and current temperature of temperature detection points of multiple devices to be identified; Comparing the current temperature of each device to be identified with the historical temperature data respectively to obtain a first comparison result, wherein the first comparison result is used to indicate whether the current temperature conforms to its historical temperature change trend; Comparing the temperature differences of the devices to be identified at the current moment among the multiple devices to be identified to obtain a second comparison result, wherein the second comparison result is used to indicate whether the current temperature change trends of the devices to be identified are consistent; Based on the first comparison result and the second comparison result, it is determined that there is a device with abnormal low-pressure gas emission among the multiple devices to be identified.

2. The method according to claim 1, characterized in that The current temperature of each device to be identified is compared with the historical temperature data to obtain a first comparison result, including: An autoregressive difference moving average model is obtained by training the historical temperature data, and the autoregressive difference moving average model is used to predict the temperature of the temperature detection point at the current moment to obtain a predicted temperature value; Comparing the predicted temperature value with the current temperature to obtain a first comparison result; A first identification result is determined according to the first comparison result, and the first identification result is used to indicate the low-pressure gas emission state of the device to be identified, and the emission state includes: a normal state, a transition state and an abnormal state.

3. The method according to claim 2, characterized in that Determining the first recognition result according to the first comparison result includes: Obtaining a root mean square error between the predicted temperature value and the current temperature; When the root mean square error is less than a first preset value, determining that the emission state is a normal state; When the root mean square error is greater than the first preset value and less than the second preset value, determining that the emission state is a transition state; When the root mean square error is greater than a second preset value, the emission state is determined to be an abnormal state, and the abnormal state is used as the first recognition result.

4. The method according to claim 2, characterized in that: Before obtaining the autoregressive difference moving average model through training according to the historical temperature data, the method further includes: Obtaining an autocorrelation function and a partial autocorrelation function of the historical temperature data; Determine whether the time series of the historical temperature data is stable according to the autocorrelation function and the partial autocorrelation function; In the case that the time series of the historical temperature data is not stable, a differential operation is performed on the historical temperature data to make the time series of the historical temperature data reach a stable state.

5. The method according to claim 3, characterized in that: The method further comprises: Respectively obtain the current temperature of each device to be identified and the temperature at a moment before the current moment; respectively determining the temperature difference between the current temperature of each device to be identified and the temperature of the previous temperature to obtain the second comparison result; Obtain the standard deviation of the temperature difference of all devices to be identified; When the second comparison result indicates that the temperature difference of the device to be identified is greater than three times the standard deviation, the second identification result is determined as the discharge state of the device to be identified is an abnormal state.

6. The method according to claim 5, characterized in that Determining, based on the first comparison result and the second comparison result, which device among the multiple devices to be identified has abnormal low-pressure gas emission, includes: When both the first recognition result and the second recognition result indicate that the emission state of the device to be identified is abnormal, it is determined that the device to be identified is a device that emits abnormal low-pressure gas.

7. A low-pressure gas abnormal emission identification device, characterized in that: include: An acquisition module, used to respectively acquire historical temperature data and current temperature of temperature detection points of multiple devices to be identified; A first comparison module, used to compare the current temperature of each device to be identified with the historical temperature data, to obtain a first comparison result, wherein the first comparison result is used to indicate whether the current temperature conforms to its historical temperature change trend; A second comparison module is used to compare the temperature differences of each device to be identified at a current moment among the multiple devices to be identified to obtain a second comparison result, wherein the second comparison result is used to indicate whether the current temperature change trends of each device to be identified are consistent; A determination module is used to determine, based on the first comparison result and the second comparison result, which device among the multiple devices to be identified has abnormal low-pressure gas emissions.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory is used to store program instructions; The processor is connected to the memory and is used to execute the method for determining the low-pressure gas abnormal emission device as described in any one of claims 1-6.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the method for determining the low-pressure gas abnormal emission device according to any one of claims 1 to 6 by running the computer program.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the method for determining the low-pressure gas abnormal emission device described in any one of claims 1 to 6 is implemented.

Images