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

Abstract

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 the principle of a connecting pipe. The method comprises: obtaining historical pressure data monitored by the monitoring equipment, preprocessing the historical pressure data, and obtaining a feature data set; obtaining the pressure data of the monitoring equipment on the same day to update the feature data set, and calculating the feature data set based on the quadratic moving average method to obtain the trend slope of the pressure change of the monitoring equipment; repeating the above steps to obtain a trend slope set; if multiple elements in the trend slope set are negative continuously, and the mean of the consecutive negative elements is less than or equal to a preset trend slope threshold, it is judged that the monitoring equipment is leaking; constructing a trend prediction model, and predicting the number of days required for the pressure of the monitoring equipment to drop from the current value to zero according to the trend prediction model. The present invention can identify whether the monitoring equipment is leaking according to the trend slope set, and predict the number of days for the pressure of the monitoring equipment to drop to 0.

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] The leakage of the water channel of the monitoring equipment based on the principle of connecting pipes (such as the static level) will cause the pressure to drop to a negative value after the liquid leaks out, affecting the accuracy of the relative settlement data of each structure monitored by the equipment. In order to solve the problem of equipment leakage, the existing time series analysis method is used to analyze the equipment pressure data and identify the leakage fault equipment, but the ordinary time series method analysis has a certain lag deviation, resulting in low recognition accuracy.

[0003] At the same time, since the monitoring equipment adopts the principle of connecting pipes, the vertical displacement of all measuring points is measured relative to one point (also called the reference point), thereby obtaining the relative settlement values ​​of multiple points. When the equipment has a water leakage, but the liquid has not yet leaked out, it can be seen from the principle of connecting pipes that at this time, the pressure at each point and the base point liquid level will drop at the same time, and the pressure difference will not be affected by the leakage, and it can still reflect the real data on site. The static level can still measure the real settlement on site through the pressure difference. Therefore, the problem of how to predict the number of days between the occurrence of a leakage failure 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 of a monitoring device based on the connecting pipe principle, the method comprising:

[0006] 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;

[0007] 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;

[0008] 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;

[0009] 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;

[0010] 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.

[0011] 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 the 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 is convenient for the work planning and arrangement of maintenance personnel and improves maintenance efficiency.

[0012] 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 composed of a plurality of feature data specifically include:

[0013] 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;

[0014] Take the median data from 0 to 4 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.

[0015] 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:

[0016] Obtain the pressure data of the monitoring device on the day, and take the median data from 0 to 4 of the pressure data on the day as the characteristic data of the monitoring device on the day, recorded as ,in, The current date number;

[0017] 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.

[0018] Furthermore, the trend slope is:

[0019] ;

[0020] 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 .

[0021] 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:

[0022] Construct a trend prediction model based on the quadratic moving average method and the current feature data set:

[0023] ;

[0024] ;

[0025] 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 ;

[0026] 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.

[0027] The present invention also proposes a fault identification system for monitoring equipment based on the connecting pipe principle, the system comprising:

[0028] 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;

[0029] 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;

[0030] 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;

[0031] 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;

[0032] 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.

[0033] 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 the 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 is convenient for the work planning and arrangement of maintenance personnel and improves maintenance efficiency.

[0034] Furthermore, the acquisition module is specifically used for:

[0035] 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 monitoring data of the monitoring device for one day;

[0036] 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.

[0037] Furthermore, the first calculation module is specifically used for:

[0038] Obtain the pressure data of the monitoring device on the day, and take the median data from 0 to 4 of the pressure data on the day as the characteristic data of the monitoring device on the day, recorded as ,in, The current date number;

[0039] 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.

[0040] Furthermore, the trend slope is:

[0041] ;

[0042] in, The date number is The trend slope of the pressure change of the monitoring device at the time 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 .

[0043] Furthermore, the prediction module is specifically used for:

[0044] Construct a trend prediction model based on the quadratic moving average method and the current feature data set:

[0045] ;

[0046] ;

[0047] 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 ;

[0048] 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative labor.

[0050] Figure 1 It is a flow chart of a fault identification method of a monitoring device based on the connecting pipe principle according to the first embodiment of the present invention;

[0051] Figure 2 This is a structural block diagram of a fault identification system of a monitoring device based on the connecting pipe principle according to the second embodiment of the present invention. DETAILED DESCRIPTION

[0052] Embodiments of the present invention are described in detail below, 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.

[0053] 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" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0054] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. 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, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0055] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0056] Embodiment 1

[0057] 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:

[0058] 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:

[0059] 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;

[0060] Take the median data from 0 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, The number of days for monitoring equipment is The characteristic data at The number of days for monitoring equipment is characteristic data at that time.

[0061] S2: Obtain the pressure data of the monitoring device on the day, and update the characteristic data set according to the pressure data of the day, and calculate the trend slope of the pressure change of the monitoring device by 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 day, and updating the characteristic data set according to the pressure data of the day specifically include:

[0062] Obtain the pressure data of the monitoring device on the day, and take the median data from 0 to 4 in the pressure data of the day as the characteristic data of the monitoring device on the day, recorded as ,in, The current date number;

[0063] 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.

[0064] Furthermore, the trend slope is:

[0065] ;

[0066] 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 second moving average of the feature data set.

[0067] 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 day's pressure data; the day's pressure data 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.

[0068] 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.

[0069] 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:

[0070] Construct a trend prediction model based on the quadratic moving average method and the current feature data set:

[0071] ;

[0072] ;

[0073] 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 monitoring equipment is The smoothing coefficient when ;

[0074] make , the obtained This is the number of days required for the pressure of the monitoring device to drop from the current value to zero.

[0075] 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 the 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 is convenient for the work planning and arrangement of maintenance personnel and improves maintenance efficiency.

[0076] Embodiment 2

[0077] Reference Figure 2 The present invention also proposes a fault identification system for monitoring equipment based on the connecting pipe principle, the system comprising:

[0078] 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;

[0079] 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;

[0080] 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;

[0081] 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;

[0082] 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.

[0083] Furthermore, the acquisition module is specifically used for:

[0084] 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;

[0085] Take the median data from 0 to 4 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.

[0086] Furthermore, the first calculation module is specifically used for:

[0087] Obtain the pressure data of the monitoring device on the day, and take the median data from 0 to 4 of the pressure data on the day as the characteristic data of the monitoring device on the day, recorded as ,in, The current date number;

[0088] 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.

[0089] Furthermore, the trend slope is:

[0090] ;

[0091] in, The date number is The trend slope of the pressure change of the monitoring device at the time 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 .

[0092] Furthermore, the prediction module is specifically used for:

[0093] Construct a trend prediction model based on the quadratic moving average method and the current feature data set:

[0094] ;

[0095] ;

[0096] 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 ;

[0097] 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.

[0098] 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 the 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 is convenient for the work planning and arrangement of maintenance personnel and improves maintenance efficiency.

[0099] Embodiment 3

[0100] 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.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution system, device or apparatus. For purposes of this specification, "computer-readable medium" may be any device that contains storage, communication, propagation or transmission of a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution system, device or apparatus.

[0102] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a 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, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0103] Among them, 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), a 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 a removable or non-removable (or fixed) medium. Where appropriate, the memory may be inside or outside the data processing device. In a specific embodiment, the memory is a non-volatile memory. In a specific embodiment, the memory includes a read-only memory (ROM) and a 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), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0104] Embodiment 4

[0105] 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 connecting pipe principle of the above embodiment.

[0106] It should be understood that the various parts of the present invention can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented with hardware, as in another embodiment, any one of the following technologies known in the art or their combination can be used to implement: a discrete logic circuit with a logic gate circuit for implementing a logical function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0107] Under the premise that no conflict occurs, those skilled in the art may freely combine and superimpose the above-mentioned additional technical features.

[0108] 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 protection scope 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: Before obtaining monitoring equipment The historical pressure data monitored on the date number, wherein the historical pressure data is A group of single-day pressure data is composed of the monitoring data of the monitoring device for one day; the median data from 0 to 4 o'clock in the single-day pressure data is taken 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 the time; Step 2: 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, is the current date; 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 characteristic data set, and calculate the trend slope of the pressure change of the monitoring device based on 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 trend slope is: ； in, The date number is The trend slope of the pressure change of the monitoring device at the time The date number is The moving average value of the feature data set is taken at The date number is The value of the second moving average of the feature data set is obtained when .

3. The fault identification method of monitoring equipment based on the connecting pipe principle according to claim 2 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.

4. 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; A prediction module, used for building a trend prediction model based on a 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 a current value to zero according to the trend prediction model; Wherein, 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 the time; 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.

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

6. The fault identification system of monitoring equipment based on the connecting pipe principle according to claim 5 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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