CN116451043B - Fault model building system based on user gas meter measurement data analysis - Google Patents
Fault model building system based on user gas meter measurement data analysis Download PDFInfo
- Publication number
- CN116451043B CN116451043B CN202310686541.3A CN202310686541A CN116451043B CN 116451043 B CN116451043 B CN 116451043B CN 202310686541 A CN202310686541 A CN 202310686541A CN 116451043 B CN116451043 B CN 116451043B
- Authority
- CN
- China
- Prior art keywords
- gas
- period
- rate
- fault
- daily
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005259 measurement Methods 0.000 title claims abstract description 38
- 238000007405 data analysis Methods 0.000 title claims abstract description 24
- 238000001514 detection method Methods 0.000 claims abstract description 18
- 239000007789 gas Substances 0.000 claims description 334
- 239000002737 fuel gas Substances 0.000 claims description 30
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 28
- 230000002159 abnormal effect Effects 0.000 claims description 25
- 238000004458 analytical method Methods 0.000 claims description 23
- 230000000737 periodic effect Effects 0.000 claims description 12
- 238000010411 cooking Methods 0.000 claims description 9
- 239000003345 natural gas Substances 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 6
- 238000004868 gas analysis Methods 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 17
- 230000035945 sensitivity Effects 0.000 abstract description 4
- 230000008569 process Effects 0.000 description 9
- 238000004891 communication Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/002—Investigating fluid-tightness of structures by using thermal means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Measuring Volume Flow (AREA)
Abstract
The application provides a fault model building system based on user gas meter measurement data analysis, which relates to the technical field of gas detection, and comprises the following components: the system comprises a daily data acquisition module, a daily supervision and division module, a data analysis module and a fault model establishment module; according to the application, the daily measurement data of the daily gas meter are analyzed to obtain the types of different use stages of a user, a plurality of comparison periods are divided according to the types of the different use stages, different fault comparison modes are established according to the data information when faults occur in the comparison periods, and finally the daily measurement data of the gas meter are compared according to the different fault comparison modes, so that the gas use condition is obtained; the method solves the problems of low sensitivity and timeliness of gas detection and dangerous hidden danger of life and property safety of users in the prior art.
Description
Technical Field
The application relates to the technical field of gas detection, in particular to a fault model building system based on user gas meter measurement data analysis.
Background
In recent years, with the increase of population and the development of economy, the demand of people for energy is gradually increased, and energy supply is under tremendous pressure, in this case, the production and living modes taking coal as a main energy structure cannot meet the demand of energy at present, the demand of environmental protection and the demand of sustainable development, in this case, gas is focused as clean energy, and in order to standardize the use standard of gas, the gas is used and monitored efficiently and safely, and the gas is required to be detected;
in the prior art, when detecting the fuel gas, the fuel gas concentration around the fuel gas pipeline is often detected in real time to determine whether the fuel gas leaks, and the method has low sensitivity and timeliness, and has better effect when the fuel gas concentration exceeds a certain range, so that the problem that dangerous hidden danger exists in the life and property safety of a user can be caused.
Disclosure of Invention
The application aims to solve at least one of the technical problems in the prior art to a certain extent, and aims to provide a fault model building system based on analysis of measurement data of a user gas meter, which is used for analyzing the daily measurement data of the daily gas meter to obtain types of different use stages of the user, dividing a plurality of comparison periods according to the types of the different use stages, building different fault comparison models according to data information when faults occur in the comparison periods, and finally comparing the daily measurement data of the gas meter according to the different fault comparison models so as to obtain the gas use condition; the method solves the problems of low sensitivity and timeliness of gas detection and dangerous hidden danger of life and property safety of users in the prior art.
In order to achieve the above object, the present application is realized by the following technical scheme: the application provides a fault model building system based on user gas meter measurement data analysis, which is characterized in that the building system comprises: the system comprises a daily data acquisition module, a daily supervision and division module, a data analysis module and a fault model establishment module;
the daily data acquisition module is used for acquiring daily measurement data of the user gas meter, wherein the daily measurement data comprise gas meter readings, and the daily measurement data of the user gas meter are transmitted to the daily supervision and division module;
the daily supervision dividing module obtains types of different use phases of the user based on daily measurement data of a daily gas meter of the user, and divides a plurality of comparison periods according to the types of the different use phases;
the data analysis module is used for acquiring data information when faults occur in different comparison periods;
the fault model building module is used for building different fault comparison models according to the data information when faults occur in different comparison periods.
Further, the daily data acquisition module is configured with a gas acquisition strategy comprising: acquiring a gas meter reading from a gas meter every first acquisition time, subtracting a previous gas meter reading from a next gas meter reading to obtain gas consumption, and setting the ratio of the gas consumption to the first acquisition time as a gas rate;
and acquiring the all-natural gas rate, acquiring an average value of the all-natural gas rate, and setting the average value as the solar gas rate.
Further, the daily supervision and division module includes a period division unit configured with a period division policy including: acquiring a solar gas rate of a first day, setting a difference value between the solar gas rate of a previous day and the solar gas rate of a later day as a solar gas fluctuation amount, obtaining an average value of the solar gas fluctuation amounts, and setting the average value as a fluctuation threshold; the first period is set to be the day before the fluctuation amount of the solar gas is smaller than the fluctuation threshold value, and the second period is set to be the day before the fluctuation amount of the solar gas is larger than the fluctuation threshold value.
Further, the daily supervision partitioning module further includes a periodic supervision unit configured with a periodic supervision policy, the periodic supervision policy including: acquiring a first number of first periods and a second number of periods, respectively acquiring an average value of the daily gas rate of the first period and an average value of the daily gas rate of the second period, and setting a difference value between the average value of the daily gas rate of the first period and the average value of the daily gas rate of the second period as a period difference value;
acquiring the fluctuation quantity of solar fuel gas in a first period, and obtaining an average value to obtain a difference value threshold;
when the period difference value is larger than the difference value threshold value, judging that the period is normal;
and when the period difference value is smaller than or equal to the difference value threshold value, judging that the period is abnormal, and then, carrying out a period division strategy again to update the first period and the second period.
Further, the data analysis module is configured with a gas usage analysis strategy comprising: and comparing the gas rates obtained each time, judging the idle period when the gas rate is smaller than or equal to 0, and judging the gas period when the gas rate is larger than 0.
Further, the data analysis module is further configured with an alignment period establishment policy, the alignment period establishment policy including: respectively counting the gas consumption period and the idle period of the first period and the second period, obtaining the average gas consumption period duration of the first period, setting the average gas consumption period duration as the first gas duration, obtaining the idle period duration of the first period, and setting the average gas consumption period duration as the first idle period duration; establishing a first comparison period according to the first air duration, the first idle period duration and the alternating sequence of the idle period and the air consumption period;
acquiring the average gas use period duration of the second period, setting the average gas use period duration as the second gas use period duration, acquiring the idle period duration of the second period, and setting the idle period duration as the second idle period duration; and establishing a second comparison period according to the second gas consumption period, the second idle period and the alternating sequence of the idle period and the gas consumption period.
Further, the data analysis module comprises a fault analysis unit configured with a gas analysis strategy comprising: acquiring a real-time gas rate and a comparison period; acquiring a time point at the moment, and acquiring the gas consumption condition of the corresponding time point on the comparison period;
when the fuel gas is in an idle period and the real-time fuel gas rate is less than or equal to 0, outputting a fuel gas normal signal;
when the gas leakage signal is in an idle period and the gas rate is more than 0 and less than or equal to a first rate threshold value, outputting the gas leakage signal;
outputting an abnormal use signal when the fuel gas rate is greater than the first rate threshold value and the fuel gas rate is in the idle period;
when the gas consumption period is the gas consumption period and the gas rate is smaller than or equal to a second rate threshold value, outputting a gas normal signal;
and outputting an abnormal use signal when the gas consumption period is the gas consumption period and the gas rate is greater than the second rate threshold.
Further, the fault analysis unit is configured with a fault analysis strategy, the fault analysis strategy comprising: when a gas leakage fault is output, acquiring a gas meter reading from a gas meter every time of fault acquisition, subtracting a previous gas meter reading from a next gas meter reading to obtain a leakage amount, and setting the ratio of the leakage amount to the fault acquisition time as a leakage rate;
calculating according to the leakage rate to obtain leakage quantity in the first time period, and judging that the leakage is general leakage fault when the leakage quantity is smaller than or equal to a safety threshold value;
when the leakage amount is greater than the safety threshold, it is judged that the leakage is serious.
Further, the fault analysis unit is further configured with a fault removal policy, the fault removal policy comprising: when the output gas leaks out, the gas kitchen range is detected at high temperature, the water temperature of the water heater is detected, the indoor temperature is detected at room temperature,
the high temperature detection includes: setting a temperature sensor near the gas cooking bench, wherein the temperature sensor acquires the temperature of the primary cooking bench every time of temperature acquisition;
the water temperature detection includes: acquiring water temperature from the water heater once every time the temperature is acquired;
the room temperature detection comprises: a temperature sensor is arranged indoors, and the temperature sensor acquires indoor temperature once every interval of temperature acquisition time;
when the difference value of the hearth temperature minus the room temperature is larger than or equal to a first temperature difference threshold value or the difference value of the water temperature minus the room temperature is larger than or equal to a second temperature difference threshold value, judging that the leakage is misjudged;
and judging that the fuel gas leaks when the difference of the hearth temperature minus the room temperature is smaller than the first temperature difference threshold value or the difference of the water temperature minus the room temperature is smaller than the second temperature difference threshold value.
Further, the fault model building module is configured with a fault model building policy, the fault model building policy comprising:
counting the types and occurrence time of gas signals in a first comparison period, wherein the gas signals comprise gas normal signals, general leakage faults, serious leakage faults, leakage misjudgment and abnormal use signals, and first gas state data are obtained;
screening out leakage misjudgment and normal gas signals in the first gas state data, and setting the gas rate when common gas is leaked, the gas rate when serious gas is leaked and the gas rate when abnormal use is carried out as first period fault data;
establishing a first fault comparison model, and taking first period fault data as comparison parameters;
counting the types and occurrence time of gas signals in a second comparison period, wherein the gas signals comprise gas normal signals, general leakage faults, serious leakage faults, leakage misjudgment and abnormal use signals, and second gas state data are obtained;
screening out leakage misjudgment and normal gas signals in the second gas state data, and setting the gas rate when common gas is leaked, the gas rate when serious gas is leaked and the gas rate when abnormal use is carried out as second period fault data;
establishing a second fault comparison model, and taking second period fault data as comparison parameters;
and acquiring a real-time gas rate, substituting the gas rate into the comparison model to obtain a gas fault signal, and using an abnormal signal or a normal signal.
The application has the beneficial effects that: the method comprises the steps of firstly collecting daily measurement data of a daily gas meter, analyzing the daily measurement data of the daily gas meter to obtain types of different use stages of a user, dividing a plurality of comparison periods according to the types of the different use stages, and obtaining the use habit of the user by acquiring the daily use data; the use habit is divided into a plurality of comparison periods, so that the accuracy of comparison is improved;
and then, different fault comparison models are established according to the data information when faults occur in the comparison period, and finally, the daily measurement data of the gas meter are compared according to the different fault comparison models, so that the gas service condition is obtained, the comparison models are established according to the different comparison periods, the daily measurement data are convenient to judge, and the gas detection convenience is improved.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
FIG. 1 is a schematic block diagram of the present application;
FIG. 2 is a schematic diagram of the periodic division of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application aims to provide a fault model building system based on user gas meter measurement data analysis, which is used for obtaining types of different use stages of a user by analyzing daily measurement data of a daily gas meter, dividing a plurality of comparison periods according to the types of the different use stages, building different fault comparison models according to data information when faults occur in the comparison periods, and finally comparing the daily measurement data of the gas meter according to the different fault comparison models so as to obtain gas use conditions; the method solves the problems that in the prior art, the sensitivity and timeliness of gas detection are low, a better effect is achieved only when the gas concentration exceeds a certain range, and dangerous hidden danger exists in life and property safety of users.
Referring to fig. 1, the present application provides a fault model building system based on analysis of measurement data of a user gas meter, wherein the building system comprises: the system comprises a daily data acquisition module, a daily supervision and division module, a data analysis module and a fault model establishment module;
the daily data acquisition module is used for acquiring daily measurement data of the user gas meter, wherein the daily measurement data comprise gas meter readings, and the daily measurement data of the user gas meter are transmitted to the daily supervision and division module; in the specific implementation process, the daily measurement data of the gas meter can well reflect the gas consumption change of the user, so that the gas consumption habit of the user can be collected conveniently;
the daily data acquisition module is configured with a gas acquisition strategy, and the gas acquisition strategy comprises: acquiring a gas meter reading from a gas meter every first acquisition time, subtracting a previous gas meter reading from a next gas meter reading to obtain gas consumption, and setting the ratio of the gas consumption to the first acquisition time as a gas rate; in the specific implementation process, the first acquisition time is 1 hour; if the acquisition interval time is too long, the acquired data cannot reflect the stepwise change of the air consumption of the user, and if the acquisition interval time is too short, the acquisition amount is too much, and the calculation is complicated;
acquiring an all-natural gas rate, acquiring an average value of the all-natural gas rate, and setting the average value as the solar gas rate;
the daily supervision dividing module obtains types of different use phases of the user based on daily measurement data of a daily gas meter of the user, and divides a plurality of comparison periods according to the types of the different use phases; in the specific implementation process, the use habits of the user can be obtained by acquiring daily use data due to different use habits of the user in different dates; if the user is not divided into a plurality of comparison periods by using habits, the accuracy of comparison can be improved;
referring to fig. 2, the daily supervision and division module includes a period division unit configured with a period division policy, where the period division policy includes: acquiring a solar gas rate of a first day, setting a difference value between the solar gas rate of a previous day and the solar gas rate of a later day as a solar gas fluctuation amount, obtaining an average value of the solar gas fluctuation amounts, and setting the average value as a fluctuation threshold; setting the previous day of which the fluctuation amount of the solar fuel gas is smaller than the fluctuation threshold value as a first period, and setting the previous day of which the fluctuation amount of the solar fuel gas is larger than the fluctuation threshold value as a second period; in the specific implementation process, the daily gas consumption rate can well reflect the daily gas consumption of the user, the use habit of the user can lead to certain difference of the daily gas consumption of the user, the daily gas consumption of the user is distinguished by setting the daily gas fluctuation quantity and the fluctuation threshold value, so that the purpose of distinguishing the gas consumption period is achieved, wherein the first day is set to be 30 days, a large amount of data are collected for taking an average value, extreme conditions caused by overlarge or undersize partial data are avoided, and the accuracy of a division result is ensured;
the daily supervision dividing module further comprises a periodic supervision unit, wherein the periodic supervision unit is configured with a periodic supervision strategy, and the periodic supervision strategy comprises the following steps: acquiring a first number of first periods and a second number of periods, respectively acquiring an average value of the daily gas rate of the first period and an average value of the daily gas rate of the second period, and setting a difference value between the average value of the daily gas rate of the first period and the average value of the daily gas rate of the second period as a period difference value; in the implementation process, the first number is set to be 5, the obvious difference between the first period and the second period is represented on daily gas consumption of a user, the average value of the daily gas rate can represent daily gas consumption in each period, the larger the period difference is, the larger the difference between the first period and the second period is, the problem of division between the first period and the second period is proved, the smaller the period difference is, the smaller the difference between the first period and the second period is, and the problem of division between the first period and the second period is proved;
acquiring the fluctuation quantity of solar fuel gas in a first period, and obtaining an average value to obtain a difference value threshold;
when the period difference value is larger than the difference value threshold value, judging that the period is normal;
when the period difference value is smaller than or equal to the difference value threshold value, judging that the period is abnormal, and carrying out a period division strategy again to update the first period and the second period;
the data analysis module is used for acquiring data information when faults occur in different comparison periods;
the data analysis module is configured with a gas consumption analysis strategy comprising: comparing the gas rates obtained each time, judging the idle period when the gas rate is smaller than or equal to 0, and judging the gas period when the gas rate is larger than 0;
the data analysis module is further configured with a comparison period establishment policy, the comparison period establishment policy comprising: respectively counting the gas consumption period and the idle period of the first period and the second period, obtaining the average gas consumption period duration of the first period, setting the average gas consumption period duration as the first gas duration, obtaining the idle period duration of the first period, and setting the average gas consumption period duration as the first idle period duration; establishing a first comparison period according to the first air duration, the first idle period duration and the alternating sequence of the idle period and the air consumption period; in the specific implementation process, the gas consumption habit of the user is mainly distinguished by the relative time length of the gas consumption period and the idle period of the user, the gas consumption period and the idle period of the user are counted and divided, the habit of the user in the period can be obtained, and an accurate comparison reference is provided;
acquiring the average gas use period duration of the second period, setting the average gas use period duration as the second gas use period duration, acquiring the idle period duration of the second period, and setting the idle period duration as the second idle period duration; establishing a second comparison period according to the second gas consumption time length, the second idle period time length and the alternating sequence of the idle period and the gas consumption period;
the data analysis module includes a fault analysis unit configured with a gas analysis strategy comprising: acquiring a real-time gas rate and a comparison period; acquiring a time point at the moment, and acquiring the gas consumption condition of the corresponding time point on the comparison period;
when the fuel gas is in an idle period and the real-time fuel gas rate is less than or equal to 0, outputting a fuel gas normal signal;
when the gas leakage signal is in an idle period and the gas rate is more than 0 and less than or equal to a first rate threshold value, outputting the gas leakage signal; specifically, the first rate threshold is a normal leak rate at which the gas leaks, the value being set manually; when the gas valve is in the idle period, under normal conditions, the gas rate is 0, and if the gas is used in a trace amount, the gas is judged to be leaked;
outputting an abnormal use signal when the fuel gas rate is greater than the first rate threshold value and the fuel gas rate is in the idle period; specifically, when the gas rate is greater than the first rate threshold, the gas is possibly caused by artificial use of gas, the gas leakage is not judged, and the abnormal gas use signal is recorded;
when the gas consumption period is the gas consumption period and the gas rate is smaller than or equal to a second rate threshold value, outputting a gas normal signal;
outputting an abnormal use signal when the gas consumption period is the gas consumption period and the gas rate is greater than the second rate threshold; in a specific implementation process, the second rate threshold is the gas rate of the gas peak; if the gas using rate of the user in the gas using period is higher than the second rate threshold, recording the gas using rate as a gas using signal;
the fault analysis unit is configured with a fault analysis strategy comprising: when a gas leakage signal is output, acquiring a gas meter reading from a gas meter every time of fault acquisition time, subtracting a previous gas meter reading from a next gas meter reading to obtain a leakage amount, setting the ratio of the leakage amount to the fault acquisition time as a leakage rate, and setting the fault acquisition time as 0.1h when the gas meter is in specific implementation;
calculating according to the leakage rate to obtain leakage quantity in the first time period, and judging that the leakage is general leakage fault when the leakage quantity is smaller than or equal to a safety threshold value; the first time length is set to 0.5h; for example when the leak rate is 0.1m 3 The leakage amount in the first time period is the leakage rate multiplied by the first time period is equal to 0.05m 3 ；
When the leakage quantity is larger than the safety threshold value, judging that the leakage is serious; in the specific implementation process, the safety threshold is 25% of the lower limit of the gas explosion; for example, in a unit space, the safety threshold is 1.5m 3 The unit space is set according to the space size of the existing kitchen, and is set to 15m 3 The method comprises the steps of carrying out a first treatment on the surface of the When the leakage amount in the first time period is larger than the safety threshold, danger may be caused to a user, and the processing priority value is higher;
the fault analysis unit is further configured with a fault removal policy, the fault removal policy comprising: when the gas leakage fault is output, the gas stove is subjected to high-temperature detection, the water temperature of the water heater is subjected to water temperature detection, and the indoor temperature is subjected to room temperature detection;
the high temperature detection includes: setting a temperature sensor near the gas cooking bench, wherein the temperature sensor acquires the temperature of the primary cooking bench every time of temperature acquisition;
the water temperature detection includes: acquiring water temperature from the water heater once every time the temperature is acquired;
the room temperature detection comprises: a temperature sensor is arranged indoors, and the temperature sensor acquires indoor temperature once every interval of temperature acquisition time;
when the difference value of the hearth temperature minus the room temperature is larger than or equal to a first temperature difference threshold value or the difference value of the water temperature minus the room temperature is larger than or equal to a second temperature difference threshold value, judging that the leakage is misjudged;
when the difference of the hearth temperature minus the room temperature is smaller than a first temperature difference threshold or the difference of the water temperature minus the room temperature is smaller than a second temperature difference threshold, judging that the gas is leaked, and when the gas is in specific implementation, setting the first temperature difference threshold to be 35 ℃ and setting the second temperature difference threshold to be 15 ℃; under the condition of no use of fuel gas, the water temperature of the water heater is similar to the room temperature, and the cooking bench temperature is similar to the room temperature; under the condition of using the gas, the room temperature and the water temperature of the water heater or the temperature of the cooking bench are obviously different, and whether a user is using the gas can be judged through the difference between the room temperature and the water temperature of the water heater or the temperature of the cooking bench;
the fault model building module is used for building different fault comparison models according to the data information when faults occur in different comparison periods;
the fault model establishing module is configured with a fault model establishing strategy, and the fault model establishing strategy comprises the following steps:
counting the types and occurrence time of gas signals in a first comparison period, wherein the gas signals comprise gas normal signals, general leakage faults, serious leakage faults, leakage misjudgment and abnormal use signals, and first gas state data are obtained;
screening out leakage misjudgment and normal gas signals in the first gas state data, and setting the gas rate when common gas is leaked, the gas rate when serious gas is leaked and the gas rate when abnormal use is carried out as first period fault data;
establishing a first fault comparison model, and taking first period fault data as comparison parameters;
counting the types and occurrence time of gas signals in a second comparison period, wherein the gas signals comprise gas normal signals, general leakage faults, serious leakage faults, leakage misjudgment and abnormal use signals, and second gas state data are obtained;
screening out leakage misjudgment and normal gas signals in the second gas state data, and setting the gas rate when common gas is leaked, the gas rate when serious gas is leaked and the gas rate when abnormal use is carried out as second period fault data;
establishing a second fault comparison model, and taking second period fault data as comparison parameters;
in the specific implementation process, the use conditions of users in different periods are different, so that different fault comparison models are required to be established;
and acquiring a real-time gas rate, substituting the gas rate into the comparison model to obtain a gas fault signal, and using an abnormal signal or a normal signal.
Working principle: according to the application, firstly, through collecting gas meter readings, the resident gas velocity is obtained according to the difference value of the gas meter readings, the resident gas velocity on all days is obtained, the daily gas velocity is obtained by taking the average value, the difference value between the daily gas velocity on the previous day and the daily gas velocity on the next day is set as the gas fluctuation quantity, and the gas fluctuation quantity is divided to obtain a first period and a second period; comparing the gas rate of the user to obtain a gas consumption period and an idle period, obtaining the average duration of the gas consumption period and the idle period of the first period to obtain a first comparison period, and obtaining the average duration of the gas consumption period and the idle period of the second period to obtain a second comparison period; and acquiring the real-time gas rate of the user, and comparing according to the gas consumption condition of the comparison period.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied therein. The storage medium may be implemented by any type or combination of volatile or nonvolatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
Claims (7)
1. A fault model building system based on user gas meter measurement data analysis is characterized in that the building system comprises: the system comprises a daily data acquisition module, a daily supervision and division module, a data analysis module and a fault model establishment module;
the daily data acquisition module is used for acquiring daily measurement data of the user gas meter, wherein the daily measurement data comprise gas meter readings, and the daily measurement data of the user gas meter are transmitted to the daily supervision and division module;
the daily supervision dividing module obtains types of different use phases of the user based on daily measurement data of a daily gas meter of the user, and divides a plurality of comparison periods according to the types of the different use phases;
the data analysis module is used for acquiring data information when faults occur in different comparison periods;
the fault model building module is used for building different fault comparison models according to the data information when faults occur in different comparison periods;
the daily data acquisition module is configured with a gas acquisition strategy, and the gas acquisition strategy comprises: acquiring a gas meter reading from a gas meter every first acquisition time, subtracting a previous gas meter reading from a next gas meter reading to obtain gas consumption, and setting the ratio of the gas consumption to the first acquisition time as a gas rate;
acquiring an all-natural gas rate, acquiring an average value of the all-natural gas rate, and setting the average value as the solar gas rate;
the daily supervision and division module comprises a period division unit, wherein the period division unit is configured with a period division strategy, and the period division strategy comprises the following steps: acquiring a solar gas rate of a first day, setting a difference value between the solar gas rate of a previous day and the solar gas rate of a later day as a solar gas fluctuation amount, obtaining an average value of the solar gas fluctuation amounts, and setting the average value as a fluctuation threshold; setting the previous day of which the fluctuation amount of the solar fuel gas is smaller than the fluctuation threshold value as a first period, and setting the previous day of which the fluctuation amount of the solar fuel gas is larger than the fluctuation threshold value as a second period;
the daily supervision dividing module further comprises a periodic supervision unit, wherein the periodic supervision unit is configured with a periodic supervision strategy, and the periodic supervision strategy comprises the following steps: acquiring a first number of first periods and a second number of periods, respectively acquiring an average value of the daily gas rate of the first period and an average value of the daily gas rate of the second period, and setting a difference value between the average value of the daily gas rate of the first period and the average value of the daily gas rate of the second period as a period difference value;
acquiring the fluctuation quantity of solar fuel gas in a first period, and obtaining an average value to obtain a difference value threshold;
when the period difference value is larger than the difference value threshold value, judging that the period is normal;
and when the period difference value is smaller than or equal to the difference value threshold value, judging that the period is abnormal, and then, carrying out a period division strategy again to update the first period and the second period.
2. The fault model building system based on user gas meter measurement data analysis of claim 1, wherein the data analysis module is configured with a gas usage analysis strategy comprising: and comparing the gas rates obtained each time, judging the idle period when the gas rate is smaller than or equal to 0, and judging the gas period when the gas rate is larger than 0.
3. The fault model building system based on analysis of user gas meter measurement data according to claim 2, wherein the data analysis module is further configured with an alignment period building policy, the alignment period building policy comprising: respectively counting the gas consumption period and the idle period of the first period and the second period, obtaining the average gas consumption period duration of the first period, setting the average gas consumption period duration as the first gas duration, obtaining the idle period duration of the first period, and setting the average gas consumption period duration as the first idle period duration; establishing a first comparison period according to the first air duration, the first idle period duration and the alternating sequence of the idle period and the air consumption period;
acquiring the average gas use period duration of the second period, setting the average gas use period duration as the second gas use period duration, acquiring the idle period duration of the second period, and setting the idle period duration as the second idle period duration; and establishing a second comparison period according to the second gas consumption period, the second idle period and the alternating sequence of the idle period and the gas consumption period.
4. A fault model building system based on analysis of user gas meter measurement data according to claim 3, wherein the data analysis module comprises a fault analysis unit configured with a gas analysis strategy comprising: acquiring a real-time gas rate and a comparison period; acquiring a time point at the moment, and acquiring the gas consumption condition of the corresponding time point on the comparison period;
when the fuel gas is in an idle period and the real-time fuel gas rate is less than or equal to 0, outputting a fuel gas normal signal;
when the gas leakage signal is in an idle period and the gas rate is more than 0 and less than or equal to a first rate threshold value, outputting the gas leakage signal;
outputting an abnormal use signal when the fuel gas rate is greater than the first rate threshold value and the fuel gas rate is in the idle period;
when the gas consumption period is the gas consumption period and the gas rate is smaller than or equal to a second rate threshold value, outputting a gas normal signal;
and outputting an abnormal use signal when the gas consumption period is the gas consumption period and the gas rate is greater than the second rate threshold.
5. The system for building a fault model based on analysis of measurement data of a user gas meter according to claim 4, wherein the fault analysis unit is configured with a fault analysis strategy comprising: when a gas leakage signal is output, acquiring a gas meter reading from a gas meter every time of fault acquisition, subtracting a previous gas meter reading from a next gas meter reading to obtain a leakage amount, and setting the ratio of the leakage amount to the fault acquisition time as a leakage rate;
calculating according to the leakage rate to obtain leakage quantity in the first time period, and judging that the leakage is general leakage fault when the leakage quantity is smaller than or equal to a safety threshold value;
when the leakage amount is greater than the safety threshold, it is judged that the leakage is serious.
6. The system for building a fault model based on analysis of measurement data of a user gas meter according to claim 5, wherein the fault analysis unit is further configured with a fault removal policy, the fault removal policy comprising: when the output gas leaks out, the gas kitchen range is detected at high temperature, the water temperature of the water heater is detected, the indoor temperature is detected at room temperature,
the high temperature detection includes: setting a temperature sensor near the gas cooking bench, wherein the temperature sensor acquires the temperature of the primary cooking bench every time of temperature acquisition;
the water temperature detection includes: acquiring water temperature from the water heater once every time the temperature is acquired;
the room temperature detection comprises: a temperature sensor is arranged indoors, and the temperature sensor acquires indoor temperature once every interval of temperature acquisition time;
when the difference value of the hearth temperature minus the room temperature is larger than or equal to a first temperature difference threshold value or the difference value of the water temperature minus the room temperature is larger than or equal to a second temperature difference threshold value, judging that the leakage is misjudged;
and judging that the fuel gas leaks when the difference of the hearth temperature minus the room temperature is smaller than the first temperature difference threshold value or the difference of the water temperature minus the room temperature is smaller than the second temperature difference threshold value.
7. The system for fault model building based on analysis of user gas meter measurement data according to claim 6, wherein the fault model building module is configured with a fault model building policy, the fault model building policy comprising:
counting the types and occurrence time of gas signals in a first comparison period, wherein the gas signals comprise gas normal signals, general leakage faults, serious leakage faults, leakage misjudgment and abnormal use signals, and first gas state data are obtained;
screening out leakage misjudgment and normal gas signals in the first gas state data, and setting the gas rate when common gas is leaked, the gas rate when serious gas is leaked and the gas rate when abnormal use is carried out as first period fault data;
establishing a first fault comparison model, and taking first period fault data as comparison parameters;
counting the types and occurrence time of gas signals in a second comparison period, wherein the gas signals comprise gas normal signals, general leakage faults, serious leakage faults, leakage misjudgment and abnormal use signals, and second gas state data are obtained;
screening out leakage misjudgment and normal gas signals in the second gas state data, and setting the gas rate when common gas is leaked, the gas rate when serious gas is leaked and the gas rate when abnormal use is carried out as second period fault data;
establishing a second fault comparison model, and taking second period fault data as comparison parameters;
and acquiring a real-time gas rate, substituting the gas rate into the comparison model to obtain a gas fault signal, and using an abnormal signal or a normal signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310686541.3A CN116451043B (en) | 2023-06-12 | 2023-06-12 | Fault model building system based on user gas meter measurement data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310686541.3A CN116451043B (en) | 2023-06-12 | 2023-06-12 | Fault model building system based on user gas meter measurement data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116451043A CN116451043A (en) | 2023-07-18 |
| CN116451043B true CN116451043B (en) | 2023-09-05 |
Family
ID=87122216
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310686541.3A Active CN116451043B (en) | 2023-06-12 | 2023-06-12 | Fault model building system based on user gas meter measurement data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116451043B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107846446A (en) * | 2017-09-20 | 2018-03-27 | 成都秦川物联网科技股份有限公司 | Gas safe monitoring method and Internet of things system based on compound Internet of Things |
| US20200088121A1 (en) * | 2018-09-18 | 2020-03-19 | Denso Wave Incorporated | Controller for air-fuel ratio sensor, and program for detecting failure of air-fuel ratio sensor |
| CN112504356A (en) * | 2019-11-01 | 2021-03-16 | 金卡智能集团股份有限公司 | Gas anomaly detection method, device, equipment and storage medium |
| CN113506430A (en) * | 2021-09-09 | 2021-10-15 | 杭州先锋电子技术股份有限公司 | Intelligent gas meter safety detection method |
| CN113538170A (en) * | 2021-07-28 | 2021-10-22 | 江苏科技大学 | Gas supply system economic performance analysis method based on life cycle |
| CN113819493A (en) * | 2017-03-10 | 2021-12-21 | 李明守 | Intelligent gas stove and pot overflow control method |
| CN114328444A (en) * | 2021-11-09 | 2022-04-12 | 广州金燃智能系统有限公司 | Gas data modeling method and system based on dimension analysis and electronic equipment |
| US20230041634A1 (en) * | 2021-08-05 | 2023-02-09 | Itron Global Sarl | Cost Effective Pressure Sensors for Gas Meters |
| CN115773797A (en) * | 2023-02-10 | 2023-03-10 | 成都秦川物联网科技股份有限公司 | Intelligent gas flow correction method, internet of things system, device and medium |
-
2023
- 2023-06-12 CN CN202310686541.3A patent/CN116451043B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113819493A (en) * | 2017-03-10 | 2021-12-21 | 李明守 | Intelligent gas stove and pot overflow control method |
| CN107846446A (en) * | 2017-09-20 | 2018-03-27 | 成都秦川物联网科技股份有限公司 | Gas safe monitoring method and Internet of things system based on compound Internet of Things |
| US20200088121A1 (en) * | 2018-09-18 | 2020-03-19 | Denso Wave Incorporated | Controller for air-fuel ratio sensor, and program for detecting failure of air-fuel ratio sensor |
| CN112504356A (en) * | 2019-11-01 | 2021-03-16 | 金卡智能集团股份有限公司 | Gas anomaly detection method, device, equipment and storage medium |
| CN113538170A (en) * | 2021-07-28 | 2021-10-22 | 江苏科技大学 | Gas supply system economic performance analysis method based on life cycle |
| US20230041634A1 (en) * | 2021-08-05 | 2023-02-09 | Itron Global Sarl | Cost Effective Pressure Sensors for Gas Meters |
| CN113506430A (en) * | 2021-09-09 | 2021-10-15 | 杭州先锋电子技术股份有限公司 | Intelligent gas meter safety detection method |
| CN114328444A (en) * | 2021-11-09 | 2022-04-12 | 广州金燃智能系统有限公司 | Gas data modeling method and system based on dimension analysis and electronic equipment |
| CN115773797A (en) * | 2023-02-10 | 2023-03-10 | 成都秦川物联网科技股份有限公司 | Intelligent gas flow correction method, internet of things system, device and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116451043A (en) | 2023-07-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Buchberger et al. | Leak estimation in water distribution systems by statistical analysis of flow readings | |
| CN116257811B (en) | Abnormality processing method based on gas flow detection deep learning | |
| CN106527327A (en) | Civil gas safety monitoring method | |
| CN110702437B (en) | Evaluation method for health condition of hydraulic system of hydroelectric generating set | |
| CN114240086A (en) | Carbon emission monitoring method and device, storage medium and processor | |
| CN110094251A (en) | SCR catalyst performance deterioration analysis method based on multi-model modeling at times | |
| CN110837933A (en) | Leakage identification method, device, equipment and storage medium based on neural network | |
| CN112283593A (en) | Internet of things system for closing valve and detecting leakage of pipe network and leakage detection method thereof | |
| CN111197517B (en) | Fault diagnosis method and device for urea tank, vehicle and storage medium | |
| CN116256017A (en) | Carbon asset monitoring device and carbon asset monitoring system | |
| CN116451043B (en) | Fault model building system based on user gas meter measurement data analysis | |
| CN109543993B (en) | Method for analyzing photovoltaic power station, computer storage medium and computer device | |
| CN109145257A (en) | A kind of dam safety monitoring analysis method based on the public abnormal time of multi-measuring point | |
| CN116052406A (en) | Remote intelligent meter reading system | |
| Meng et al. | A data-driven approach to investigate the impact of air temperature on the efficiencies of coal and natural gas generators | |
| CN115526275B (en) | Remote multi-source data monitoring and analyzing method and system for electricity utilization safety | |
| CN111520871A (en) | Energy saving rate testing method and system for energy saving modification of central air conditioning system | |
| CN115034454A (en) | Method and device for predicting carbon emission based on electric quantity data | |
| CN115733258A (en) | Control method of all-indoor intelligent substation system based on Internet of things technology | |
| CN115165253A (en) | Water valve water leakage detection method and device, water valve and storage medium | |
| CN113947504A (en) | Electricity stealing analysis method and system based on random forest method | |
| CN204514555U (en) | A kind of leak detection system being applied to low-pressure coal saver | |
| CN112734211A (en) | Coal quality consistency evaluation method, device, equipment and readable storage medium | |
| CN113944801B (en) | Gas pressure regulator performance detection method and device based on data analysis | |
| CN211827428U (en) | Natural gas intelligent gas meter |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |