Fault identification method and system for monitoring equipment based on communicating pipe principle

Abstract

The invention relates to the technical field of monitoring equipment, in particular to a fault identification method and system for monitoring equipment based on a communicating pipe principle, and the method comprises the steps: obtaining historical pressure data monitored by the monitoring equipment, carrying out the preprocessing of the historical pressure data, and obtaining a feature data set; acquiring the pressure data of the monitoring equipment on the current day to update the feature data set, and calculating the feature data set based on a secondary moving average method to obtain the trend slope of the pressure change of the monitoring equipment; repeating the steps to obtain a trend slope set; if a plurality of continuous elements in the trend slope set are negative, and the mean value of the continuous negative elements is less than or equal to a preset trend slope threshold value, judging that the monitoring equipment is leaking; and constructing a trend prediction model, and predicting the date number required for reducing the pressure intensity of the monitoring equipment from the current value to zero according to the trend prediction model. Whether the monitoring equipment leaks liquid or not can be identified according to the trend slope set, and the number of days when the pressure intensity of the monitoring equipment is reduced to 0 is predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring equipment, and in particular to a fault identification method and system for monitoring equipment based on a connecting pipe principle. Background Art

[0002] Leakage in the waterways of connected-tube monitoring equipment (such as hydrostatic levels) can cause the pressure to drop to negative values ​​after the liquid is completely released, affecting the accuracy of the equipment's monitoring of relative settlement of various structures. To address this issue, time series analysis methods are currently used to analyze equipment pressure data and identify leaking devices. However, conventional time series analysis methods suffer from a certain lag, resulting in low identification accuracy.

[0003] At the same time, because the monitoring equipment uses the connecting pipe principle, it measures the vertical displacement of all measuring points relative to a single point (also called the reference point), thereby obtaining relative settlement values ​​at multiple points. When a water leak occurs in the equipment, but the liquid has not yet completely leaked out, the connecting pipe principle shows that the pressure at each point and the liquid level at the reference point will drop simultaneously. The pressure difference is not affected by the leak and can still reflect the actual data on site. The static level can still measure the actual settlement on site through this pressure difference. Therefore, the problem of how to predict the number of days between the occurrence of a leak in the equipment and the complete leakage of the liquid needs to be solved urgently. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a fault identification method and system for monitoring equipment based on the connecting pipe principle.

[0005] The present invention adopts the following technical solution: a fault identification method for monitoring equipment based on the connecting pipe principle, the method comprising: Step 1: Acquire historical pressure data monitored by the monitoring device, and pre-process the historical pressure data to obtain a feature data set consisting of multiple feature data; Step 2: Obtain the pressure data of the monitoring device on the same day, update the characteristic data set according to the pressure data on the same day, and calculate the trend slope of the pressure change of the monitoring device by using the quadratic moving average method on the updated characteristic data set; Step 3: Repeat step 2 to obtain a trend slope set including a plurality of trend slopes, wherein the elements in the trend slope set are sorted according to the number of days on which the pressure data of the day is obtained; Step 4: If multiple elements in the trend slope set are negative consecutively, and the average of the consecutive negative elements is less than or equal to a preset trend slope threshold, it is determined that the monitoring device is leaking; Step 5: construct a trend prediction model based on the quadratic moving average method and the current feature data set, and predict the number of days required for the pressure of the monitoring device to drop from the current value to zero according to the trend prediction model.

[0006] A fault identification method for monitoring equipment based on the connecting pipe principle in one embodiment of the present invention can reduce the interference of data by external factors and improve the accuracy of data by preprocessing historical pressure data; by using the quadratic moving average method to process the feature data set, it can correct the lag deviation that occurs when using conventional time series methods for prediction, and obtain the linear trend of the data, that is, the trend slope, and identify whether the monitoring equipment is leaking based on the obtained multiple consecutive trend slopes to remind staff to perform maintenance, and construct a trend prediction model to predict the number of days when the pressure of the monitoring equipment drops to 0, which facilitates the work planning and arrangement of maintenance personnel and improves maintenance efficiency.

[0007] Furthermore, the steps of obtaining historical pressure data monitored by the monitoring device and preprocessing the historical pressure data to obtain a feature data set consisting of a plurality of feature data specifically include: Before obtaining monitoring equipment The historical pressure data monitored on the date, wherein the historical pressure data is A set of single-day pressure data is composed of the monitoring data of the monitoring device for one day; Take the median data from 0 o'clock to 4 o'clock in the single-day pressure data as the characteristic data of the monitoring device, so as to obtain The feature data set is composed of the feature data, and the feature data set is recorded as ,in, is the current date number, The number of days for the monitoring equipment is The characteristic data at the time of The number of days for the monitoring equipment is The characteristic data at that time.

[0008] Furthermore, the step of obtaining the pressure data of the monitoring device on the current day and updating the characteristic data set according to the pressure data on the current day specifically includes: Obtain the pressure data of the monitoring device on the day, and take the median data from 0 to 4 o'clock in the pressure data on the day as the characteristic data of the monitoring device on the day, recorded as ,in, The current date number; The feature data Replace the feature data with the smallest date number in the feature data set to obtain , to complete the update of the feature dataset.

[0009] Furthermore, the trend slope is: ; in, The date number is The trend slope of the pressure change of the monitoring device when The date number is The moving average value of the feature data set is taken when The date number is The value of the quadratic moving average of the feature data set is obtained when .

[0010] Furthermore, a trend prediction model is constructed based on the quadratic moving average method and the current feature data set, and the step of predicting the number of days required for the pressure of the monitoring device to drop from the current value to zero according to the trend prediction model specifically includes: Construct a trend prediction model based on the quadratic moving average method and the current feature data set: ; ; in, is the number of days to be predicted, For the date number is Time-forward prediction The predicted pressure value of the monitoring device for the number of days, The number of days for the monitoring equipment is Smoothing coefficient when ; make , what we get That is, the number of days required for the pressure of the monitoring device to drop from the current value to zero.

[0011] The present invention also proposes a fault identification system for monitoring equipment based on the connecting pipe principle, the system comprising: An acquisition module is used to acquire historical pressure data monitored by the monitoring device, and pre-process the historical pressure data to obtain a feature data set consisting of multiple feature data; a first calculation module, configured to obtain pressure data of the monitoring device on the current day, update the characteristic data set according to the pressure data of the current day, and calculate the trend slope of the pressure change of the monitoring device by calculating the updated characteristic data set based on a quadratic moving average method; a second calculation module, configured to repeatedly execute the first calculation module to obtain a trend slope set comprising a plurality of trend slopes, wherein the elements in the trend slope set are sorted according to the number of days on which the pressure data of the current day is obtained; a judgment module, configured to judge that the monitoring device is leaking if a plurality of elements in the trend slope set are consecutively negative and the average of the consecutively negative elements is less than or equal to a preset trend slope threshold; The prediction module is used to build a trend prediction model based on the quadratic moving average method and the current feature data set, and predict the number of days required for the pressure of the monitoring device to drop from the current value to zero according to the trend prediction model.

[0012] A fault identification system for monitoring equipment based on the connecting pipe principle in one embodiment of the present invention can reduce the interference of data by external factors and improve the accuracy of data by preprocessing historical pressure data; by using the quadratic moving average method to process the feature data set, it can correct the lag deviation that occurs when using conventional time series methods for prediction, and obtain the linear trend of the data, that is, the trend slope, and identify whether the monitoring equipment is leaking based on the obtained multiple consecutive trend slopes to remind staff to perform maintenance, and construct a trend prediction model to predict the number of days when the pressure of the monitoring equipment drops to 0, which facilitates the work planning and arrangement of maintenance personnel and improves maintenance efficiency.

[0013] Furthermore, the acquisition module is specifically used to: Before obtaining monitoring equipment The historical pressure data monitored on the date, wherein the historical pressure data is A set of single-day pressure data is composed of the monitoring data of the monitoring device for one day; Take the median data from 0 o'clock to 4 o'clock in the single-day pressure data as the characteristic data of the monitoring device, so as to obtain The feature data set is composed of the feature data, and the feature data set is recorded as ,in, is the current date number, The number of days for the monitoring equipment is The characteristic data at the time of The number of days for the monitoring equipment is The characteristic data at that time.

[0014] Furthermore, the first calculation module is specifically configured to: Obtain the pressure data of the monitoring device on the day, and take the median data from 0 to 4 o'clock in the pressure data on the day as the characteristic data of the monitoring device on the day, recorded as ,in, The current date number; The feature data Replace the feature data with the smallest date number in the feature data set to obtain , to complete the update of the feature dataset.

[0015] Furthermore, the trend slope is: ; in, The date number is The trend slope of the pressure change of the monitoring device when The date number is The moving average value of the feature data set is taken when The date number is The value of the quadratic moving average of the feature data set is obtained when .

[0016] Furthermore, the prediction module is specifically used to: Construct a trend prediction model based on the quadratic moving average method and the current feature data set: ; ; in, is the number of days to be predicted, For the date number is Time-forward prediction The predicted pressure value of the monitoring device for the number of days, The number of days for the monitoring equipment is Smoothing coefficient when ; make , what we get That is, the number of days required for the pressure of the monitoring device to drop from the current value to zero. BRIEF DESCRIPTION OF THE DRAWINGS In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a flow chart of a fault identification method for a monitoring device based on the communicating pipe principle according to a first embodiment of the present invention; Figure 2 This is a structural block diagram of a fault identification system for monitoring equipment based on the communicating pipe principle according to the second embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0019] In the description of the embodiments of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0020] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0021] In the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "installed," "connected," "connected," "fixed," etc. should be understood in a broad sense. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on specific circumstances.

[0022] Example 1 Reference Figure 1 The first embodiment of the present invention is a fault identification method for monitoring equipment based on the connecting pipe principle, the method comprising: S1: Obtaining historical pressure data monitored by the monitoring device, preprocessing the historical pressure data, and obtaining a feature data set consisting of multiple feature data; further, the steps of obtaining historical pressure data monitored by the monitoring device, preprocessing the historical pressure data, and obtaining a feature data set consisting of multiple feature data specifically include: Before obtaining monitoring equipment The historical pressure data monitored on the date, where the historical pressure data is The single-day pressure data is composed of a group of single-day pressure data, which is the monitoring data of the monitoring equipment for one day; Take the median data from 0 o'clock to 4 o'clock in the single-day pressure data as the characteristic data of the monitoring equipment, and get The feature data set is composed of feature data, and the feature data set is recorded as ,in, is the current date number, For monitoring equipment on the date number is The characteristic data of For monitoring equipment on the date number is characteristic data at that time.

[0023] S2: Obtaining the pressure data of the monitoring device on the current day, updating the characteristic data set based on the pressure data of the current day, and calculating the trend slope of the pressure change of the monitoring device based on the updated characteristic data set based on the quadratic moving average method; further, the steps of obtaining the pressure data of the monitoring device on the current day and updating the characteristic data set based on the pressure data of the current day specifically include: Obtain the pressure data of the monitoring device on the day, and take the median data from 0 to 4 o'clock in the pressure data on the day as the characteristic data of the monitoring device on the day, recorded as ,in, The current date number; The feature data Replace the feature data with the smallest date in the feature data set to obtain , to complete the update of the feature dataset.

[0024] Furthermore, the trend slope is: ; in, The date number is The trend slope of the pressure change of the monitoring equipment, The date number is The moving average value of the feature data set is The date number is The value of the quadratic moving average of the feature data set when .

[0025] S3: Repeat S2 to obtain a trend slope set including multiple trend slopes, wherein the elements in the trend slope set are sorted according to the date number of the pressure data of the current day; the pressure data of the current day changes synchronously with the change of the date number, so that the feature data set can be continuously updated according to the time series change, so that the obtained trend slope has a small deviation and high accuracy.

[0026] S4: If multiple elements in the trend slope set are negative consecutively, and the mean of the consecutive negative elements is less than or equal to the preset trend slope threshold, it is judged that the monitoring device is leaking; further, if 5-10 elements in the trend slope set are negative consecutively, and the mean of the consecutive negative elements is less than or equal to -0.01, it is judged that the monitoring device is leaking; in this embodiment, if 7 elements in the trend slope set are negative consecutively, and the mean of the consecutive negative elements is less than or equal to -0.01, it is judged that the monitoring device is leaking.

[0027] S5: Constructing a trend prediction model based on the quadratic moving average method and the current feature data set, and predicting the number of days required for the pressure of the monitoring device to drop from the current value to zero according to the trend prediction model. Further, constructing a trend prediction model based on the quadratic moving average method and the current feature data set, and predicting the number of days required for the pressure of the monitoring device to drop from the current value to zero according to the trend prediction model specifically includes: Construct a trend prediction model based on the quadratic moving average method and the current feature data set: ; ; in, is the number of days to be predicted, For the date number is Time-forward prediction The predicted pressure value of the monitoring equipment for a number of days, For monitoring equipment on the date number is Smoothing coefficient when ; make , what we get This is the number of days required for the pressure of the monitoring device to drop from the current value to zero.

[0028] A fault identification method for monitoring equipment based on the connecting pipe principle in one embodiment of the present invention can reduce the interference of data by external factors and improve the accuracy of data by preprocessing historical pressure data; by using the quadratic moving average method to process the feature data set, it can correct the lag deviation that occurs when using conventional time series methods for prediction, and obtain the linear trend of the data, that is, the trend slope, and identify whether the monitoring equipment is leaking based on the obtained multiple consecutive trend slopes to remind staff to perform maintenance, and construct a trend prediction model to predict the number of days when the pressure of the monitoring equipment drops to 0, which facilitates the work planning and arrangement of maintenance personnel and improves maintenance efficiency.

[0029] Example 2 Reference Figure 2 The present invention also proposes a fault identification system for monitoring equipment based on the connecting pipe principle, the system comprising: An acquisition module is used to acquire historical pressure data monitored by the monitoring device, and pre-process the historical pressure data to obtain a feature data set consisting of multiple feature data; a first calculation module, configured to obtain pressure data of the monitoring device on the current day, update the characteristic data set according to the pressure data of the current day, and calculate the trend slope of the pressure change of the monitoring device by calculating the updated characteristic data set based on a quadratic moving average method; a second calculation module, configured to repeatedly execute the first calculation module to obtain a trend slope set comprising a plurality of trend slopes, wherein the elements in the trend slope set are sorted according to the number of days on which the pressure data of the current day is obtained; a judgment module, configured to judge that the monitoring device is leaking if a plurality of elements in the trend slope set are consecutively negative and the average of the consecutively negative elements is less than or equal to a preset trend slope threshold; The prediction module is used to build a trend prediction model based on the quadratic moving average method and the current feature data set, and predict the number of days required for the pressure of the monitoring device to drop from the current value to zero according to the trend prediction model.

[0030] Furthermore, the acquisition module is specifically used to: Before obtaining monitoring equipment The historical pressure data monitored on the date, wherein the historical pressure data is A set of single-day pressure data is composed of the monitoring data of the monitoring device for one day; Take the median data from 0 o'clock to 4 o'clock in the single-day pressure data as the characteristic data of the monitoring device, so as to obtain The feature data set is composed of the feature data, and the feature data set is recorded as ,in, is the current date number, The number of days for the monitoring equipment is The characteristic data at the time of The number of days for the monitoring equipment is The characteristic data at that time.

[0031] Furthermore, the first calculation module is specifically configured to: Obtain the pressure data of the monitoring device on the day, and take the median data from 0 to 4 o'clock in the pressure data on the day as the characteristic data of the monitoring device on the day, recorded as ,in, The current date number; The feature data Replace the feature data with the smallest date number in the feature data set to obtain , to complete the update of the feature dataset.

[0032] Furthermore, the trend slope is: ; in, The date number is The trend slope of the pressure change of the monitoring device when The date number is The moving average value of the feature data set is taken when The date number is The value of the quadratic moving average of the feature data set is obtained when .

[0033] Furthermore, the prediction module is specifically used to: Construct a trend prediction model based on the quadratic moving average method and the current feature data set: ; ; in, is the number of days to be predicted, For the date number is Time-forward prediction The predicted pressure value of the monitoring device for the number of days, The number of days for the monitoring equipment is Smoothing coefficient when ; make , what we get That is, the number of days required for the pressure of the monitoring device to drop from the current value to zero. A fault identification system for monitoring equipment based on the connecting pipe principle in one embodiment of the present invention can reduce the interference of data by external factors and improve the accuracy of data by preprocessing historical pressure data; by using the quadratic moving average method to process the feature data set, it can correct the lag deviation that occurs when using conventional time series methods for prediction, and obtain the linear trend of the data, that is, the trend slope, and identify whether the monitoring equipment is leaking based on the obtained multiple consecutive trend slopes to remind staff to perform maintenance, and construct a trend prediction model to predict the number of days when the pressure of the monitoring equipment drops to 0, which facilitates the work planning and arrangement of maintenance personnel and improves maintenance efficiency.

[0034] Example 3 The third embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the fault identification method of the monitoring equipment based on the connecting pipe principle of the above embodiment.

[0035] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, "computer-readable medium" can be any device that stores, communicates, propagates, or transmits a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0036] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0037] The memory may include a large-capacity memory for data or instructions. By way of example, and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include removable or non-removable (or fixed) media. Where appropriate, the memory may be internal or external to the data processing device. In a specific embodiment, the memory is non-volatile memory. In a specific embodiment, the memory includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0038] Example 4 The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a terminal, which includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory, and execute the fault identification method of the monitoring equipment based on the communicating pipe principle of the above embodiment.

[0039] It should be understood that various aspects of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in other embodiments, it can be implemented using any one or a combination of the following technologies known in the art: discrete logic circuits having logic gates for performing logic functions on data signals, application-specific integrated circuits having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. Throughout this specification, references to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" indicate that the specific features, structures, materials, or characteristics described in connection with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0040] Under the premise that no conflict occurs, those skilled in the art may freely combine and superimpose the above-mentioned additional technical features.

[0041] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A fault identification method for monitoring equipment based on the connecting pipe principle, characterized in that: The method comprises: Step 1: obtaining historical pressure data monitored by the monitoring device, and preprocessing the historical pressure data to obtain a feature data set consisting of multiple feature data; Step 2: obtaining the pressure data of the monitoring device on the current day, and updating the characteristic data set according to the pressure data of the current day, and calculating the trend slope of the pressure change of the monitoring device by using the quadratic moving average method on the updated characteristic data set; Step 3, repeating step 2 to obtain a trend slope set including a plurality of the trend slopes, wherein the elements in the trend slope set are sorted according to the date number for obtaining the pressure data of the day; Step 4: if multiple elements in the trend slope set are negative continuously, and the average of the consecutive negative elements is less than or equal to a preset trend slope threshold, it is determined that the monitoring device is leaking; Step 5: construct a trend prediction model based on the quadratic moving average method and the current feature data set, and predict the number of days required for the pressure of the monitoring device to drop from the current value to zero according to the trend prediction model.

2. The fault identification method of the monitoring device based on the connecting pipe principle according to claim 1 is characterized in that: The steps of obtaining historical pressure data monitored by the monitoring device and preprocessing the historical pressure data to obtain a feature data set composed of a plurality of feature data specifically include: Before obtaining monitoring equipment The historical pressure data monitored on the date number, wherein the historical pressure data is A set of single-day pressure data is composed of monitoring data of the monitoring device for one day; Take the median data from 0 to 4 o'clock in the single-day pressure data as the characteristic data of the monitoring device, so as to obtain The feature data set is composed of the feature data, and the feature data set is recorded as ,in, is the current date number, The number of days for the monitoring device is The characteristic data at the time The number of days for the monitoring device is The characteristic data at that time.

3. The fault identification method of monitoring equipment based on the connecting pipe principle according to claim 2 is characterized in that: The step of obtaining the pressure data of the monitoring device on the current day and updating the characteristic data set according to the pressure data of the current day specifically includes: Obtain the pressure data of the monitoring device on the day, and take the median data from 0 to 4 in the pressure data on the day as the characteristic data of the monitoring device on the day, recorded as ,in, The current date number; The characteristic data Replace the feature data with the smallest date number in the feature data set to obtain , to complete the update of the feature dataset.

4. The fault identification method of monitoring equipment based on the connecting pipe principle according to claim 3 is characterized in that: The trend slope is: ； in, The date number is The trend slope of the pressure change of the monitoring device when The date number is The moving average value of the feature data set is taken as The date number is The value of the second moving average of the feature data set is obtained when .

5. The fault identification method of monitoring equipment based on the connecting pipe principle according to claim 4 is characterized in that: The steps of constructing a trend prediction model based on the quadratic moving average method and the current feature data set and predicting the number of days required for the pressure of the monitoring device to drop from the current value to zero according to the trend prediction model specifically include: Construct a trend prediction model based on the quadratic moving average method and the current feature data set: ； ； in, is the number of days to be predicted, For the date number is Forecasting The predicted pressure value of the monitoring device for the number of days, The number of days for the monitoring device is The smoothing coefficient when ; make , the obtained That is, the number of days required for the pressure of the monitoring device to drop from the current value to zero.

6. A fault identification system for monitoring equipment based on the connecting pipe principle, characterized in that: The system comprises: An acquisition module, used for acquiring historical pressure data monitored by the monitoring device, and preprocessing the historical pressure data to obtain a feature data set consisting of a plurality of feature data; A first calculation module is used to obtain the pressure data of the monitoring device on the same day, and update the characteristic data set according to the pressure data of the same day, and calculate the trend slope of the pressure change of the monitoring device by calculating the updated characteristic data set based on the quadratic moving average method; A second calculation module, configured to repeatedly execute the first calculation module to obtain a trend slope set including a plurality of the trend slopes, wherein the elements in the trend slope set are sorted according to the number of dates for obtaining the pressure data of the day; A judgment module, configured to judge that the monitoring device is leaking if a plurality of elements in the trend slope set are consecutively negative and the average of the consecutively negative elements is less than or equal to a preset trend slope threshold; The prediction module is used to construct a trend prediction model based on the quadratic moving average method and the current feature data set, and predict the number of days required for the pressure of the monitoring device to drop from the current value to zero according to the trend prediction model.

7. The fault identification system of monitoring equipment based on the connecting pipe principle according to claim 6 is characterized in that: The acquisition module is specifically used for: Before obtaining monitoring equipment The historical pressure data monitored on the date number, wherein the historical pressure data is A set of single-day pressure data is composed of monitoring data of the monitoring device for one day; Take the median data from 0 to 4 o'clock in the single-day pressure data as the characteristic data of the monitoring device, so as to obtain The feature data set is composed of the feature data, and the feature data set is recorded as ,in, is the current date number, The number of days for the monitoring device is The characteristic data at the time The number of days for the monitoring device is The characteristic data at that time.

8. The fault identification system of monitoring equipment based on the connecting pipe principle according to claim 7 is characterized in that: The first calculation module is specifically used for: Obtain the pressure data of the monitoring device on the day, and take the median data from 0 to 4 in the pressure data on the day as the characteristic data of the monitoring device on the day, recorded as ,in, The current date number; The characteristic data Replace the feature data with the smallest date number in the feature data set to obtain , to complete the update of the feature dataset.

9. The fault identification system of monitoring equipment based on the connecting pipe principle according to claim 8 is characterized in that: The trend slope is: ； in, The date number is The trend slope of the pressure change of the monitoring device when The date number is The moving average value of the feature data set is taken as The date number is The value of the second moving average of the feature data set is obtained when .

10. The fault identification system of monitoring equipment based on the connecting pipe principle according to claim 9 is characterized in that: The prediction module is specifically used for: Construct a trend prediction model based on the quadratic moving average method and the current feature data set: ； ； in, is the number of days to be predicted, For the date number is Forecasting The predicted pressure value of the monitoring device for the number of days, The number of days for the monitoring device is The smoothing coefficient when ; make , the obtained That is, the number of days required for the pressure of the monitoring device to drop from the current value to zero.

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