CN114666361A - Fire-fighting Internet of things-based water system overall fault detection system and method - Google Patents

Fire-fighting Internet of things-based water system overall fault detection system and method Download PDF

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CN114666361A
CN114666361A CN202210113033.1A CN202210113033A CN114666361A CN 114666361 A CN114666361 A CN 114666361A CN 202210113033 A CN202210113033 A CN 202210113033A CN 114666361 A CN114666361 A CN 114666361A
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data
water system
fighting
things
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王毅杰
郑瑞祥
侯林早
李冕
宫爱科
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Shanghai Zhimian Weiye Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
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    • G—PHYSICS
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    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L67/01—Protocols
    • H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q2209/00—Arrangements in telecontrol or telemetry systems
    • H04Q2209/80—Arrangements in the sub-station, i.e. sensing device
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q2209/00—Arrangements in telecontrol or telemetry systems
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Abstract

The invention provides a fire-fighting Internet of things-based water system overall fault detection system and method, which can be used for monitoring real-time faults and health level of the whole fire-fighting water system in a building by a multivariable Shewhart Control Chart method, and the algorithm really considers the relation between historical data at single points of the Internet of things water system and can effectively and quickly judge the health state of the whole water system; according to the result feedback of the algorithm, the method can help the building units to quickly judge whether the whole fire-fighting water system is abnormal at each moment, help the social units to carry out self-check and self-change of the fire-fighting water system safety risk, help the fire-fighting supervision departments to supervise and enforce law, help to reduce the fault occurrence rate of the fire-fighting water system of each building unit when fire is extinguished, help to improve the fire-fighting self-rescue capacity of the social units, and help to improve the real-time supervision capacity of the government supervision departments to the fire-fighting water system safety level of each building unit.

Description

Fire-fighting Internet of things-based water system overall fault detection system and method
Technical Field
The invention relates to the field of fire fighting equipment detection and early warning, in particular to a system and a method for detecting overall faults of a water system based on a fire fighting Internet of things.
Background
Along with the improvement of the safety consciousness of residents, the fire safety level is also paid more attention. In order to improve the fire safety level of a region, a fire fighting water system and other fire fighting facilities are usually arranged on site, so that fire fighters or masses can quickly acquire fire fighting equipment and resources, and the fire extinguishing work can be completed in the first time. Due to the use specificity of the fire-fighting equipment, the fire-fighting equipment is required to be kept in a usable state, so that the fire-fighting equipment is extremely important for fault detection and early warning of the fire-fighting equipment. Wherein to the fire control facility of equipment such as fire extinguisher, can effectively avoid trouble scheduling problem through periodic replacement equipment, but to fire water system, be difficult to change, still have a large amount of hidden dangers, including pressure leakage, excessive pressure, weeping, body rupture etc. on the other hand, according to the data display of conflagration investigation report, the operation health condition of fire water system equipment has played extremely crucial effect to the fire extinguishing effect, consequently need detect it.
In the existing method, the fire protection maintenance worker usually performs fault detection and health condition supervision on the fire protection and protection system regularly, including modes of manual sampling, statistics, analysis and inspection and the like, and the method has the following disadvantages: firstly, regular sampling inspection of fire protection maintenance workers is difficult to ensure the real-time performance of a fire protection water system at each single point; secondly, the detection data is recorded and uploaded by personnel, the data volume is small, and systematic detection evaluation and early warning processes are lacked; thirdly, the efficiency of data collection is low by manual inspection, and the phenomenon of fraud is easy to occur.
In addition, in the existing fire fighting water system, although a large number of internet of things devices for detecting liquid level hydraulic values of all important nodes are installed in the water system, the data are usually only used for assisting maintenance personnel to judge whether the water system is normal, and information contained in real-time data and historical data of all the devices is not fully utilized. On the other hand, through the data of the fire investigation report, the operation health condition of the fire-fighting water system equipment is easily discovered to play a very critical role in the fire-fighting effect. Therefore, based on the data of the fire-fighting internet of things, the real-time and automatic fire-fighting water system fault detection and early warning are very important for building units to improve the health level of the fire-fighting water system, enhance the fire extinguishing capacity of the water system and help people in safety responsibility to carry out daily operation and maintenance.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a system and a method for detecting the overall fault of a water system based on a fire-fighting Internet of things.
In order to solve the problems, the invention adopts the following technical scheme:
a method for detecting the overall fault of a water system based on the fire-fighting Internet of things comprises the following steps:
step 1: the detection sensor acquires analog quantity data acquired by the detection sensor and transmits the analog quantity data to the edge computing gateway in a one-way mode;
and 2, step: the edge computing gateway receives data collected by the detection sensor;
and step 3: the edge computing gateway integrates the detection data, and judges whether the whole waterproof system fails or not by a multivariable Shewhart Control Chart method according to the detection data of all the detection sensors;
and 4, step 4: sending the fault judgment result to a receiving device in a communication mode, wherein the receiving device comprises a mobile phone, a computer and an Internet of things data center;
and 5: and the data center of the Internet of things receives and stores the data.
Further, in the step 3, the fault judgment of the whole fire fighting and water preventing system firstly needs to acquire the historical data X of the whole fire fighting and water preventing system in a normal stateIC＝{X1,X2,X3,…,XnEach data variable XiIs represented as Xi＝[Xi1Xi2…Xim]TM represents the vector dimension of the data variable, in this case the number of test sensors in the fire fighting water system; historical mean data are obtained through calculation
Figure BDA0003495383100000021
Expressed as:
Figure BDA0003495383100000022
next, a sample variance S is obtained, expressed as:
Figure BDA0003495383100000023
for all newly acquired data sets X of detection sensors in the fire fighting water system, describing the distance T between the vector and the distribution formed by the IC data by using the Mahalanobis distance;
due to T2Approximately obeys F distribution, and therefore T can be obtained2Upper control limit UCL of (1)MSCCComprises the following steps:
Figure BDA0003495383100000024
wherein, Fα(m, n-m) represents the critical value of F distribution when the type I error is alpha and the degrees of freedom are m and n-m respectively;
according to T2And an upper control limit UCLMSCCAnd (3) judging the overall working condition of the fire-fighting water system.
Further, the setting range of the alpha is 1.5% -5%.
Further, the distance T has the following relationship:
Figure BDA0003495383100000031
wherein the content of the first and second substances,
Figure BDA0003495383100000032
representing the historical mean data of all detection sensors obtained by calculation; x represents the data collected by all the detection sensors at the detection moment; s represents the sample variance of the historically collected data.
Further, in the step 3, if T is obtained by calculation2>UCLMSCCIf the fire water system is in the normal working state, judging that the whole working state of the fire water system at the moment corresponding to the newly acquired data X is abnormal; otherwise, the working state is considered to be normal.
A fire-fighting Internet of things-based water system overall fault detection system is based on the method and comprises a detection sensor, an edge calculation gateway and an Internet of things data center; wherein the detection sensor is arranged in the fire-fighting water system; the edge computing gateway is connected with the detection sensors and can receive real-time data detected by the detection sensors; the edge computing gateway is also connected with the data center of the Internet of things.
Furthermore, the fire water system comprises an automatic water spraying fire extinguishing system, a foam fire extinguishing system, a fire pool, a water tank and a pipeline, wherein the fire pool and the water tank are connected with the automatic water spraying fire extinguishing system and the foam fire extinguishing system through the pipeline.
Further, the detection sensor comprises a hydraulic sensor and a liquid level sensor, wherein the hydraulic sensor is arranged at the junction and corner parts of the pipeline; the liquid level sensor is arranged at the fire pool and the water tank.
Further, the edge gateway comprises a storage module, a processing module and a communication module; the processing module is used for processing the data collected by the detection sensor; the communication module is used for connecting the data center of the Internet of things and external equipment; the storage module is used for storing detection data acquired by the detection sensor.
The invention has the beneficial effects that:
the water pressure, water level and other data at a single point in the fire-fighting and water-proofing system are collected, the collected data are transmitted to the edge computing gateway, and the data are uploaded uniformly by the edge computing gateway to form a systematic automatic detection layout, so that the real-time supervision and reexamination level of the fire-fighting and water-proofing system is improved, and the normal operation of the fire-fighting and water-proofing system is effectively guaranteed;
the method comprises the steps of carrying out fault detection on a single point of a fire-fighting and water-proofing system through an X Control Chart method, carrying out early warning on the single point of the fire-fighting and water-proofing system based on a CUSUM Control Chart method, feeding back possible faults in a water system to an owner or a related part in time, carrying out early warning on potential problems in advance, improving the fire-fighting level and guaranteeing social safety;
predicting the overall operation condition of the fire-fighting water system by a Multivariate Shewhart Control Chart method in combination with data acquired by a detection sensor in the whole fire-fighting water system, and accurately detecting partial point position abnormity in the fire-fighting water system;
the set value h used for comparison in the early warning process is continuously iterated by combining the normal operation data continuously collected by a detection sensor in the fire fighting water system through a multivariable CUSUM Control Chart method, and the accuracy of the early warning result is ensured.
Drawings
FIG. 1 is a system connection diagram according to a first embodiment of the present invention;
FIG. 2 is a diagram illustrating a variation of water pressure of four detecting sensors according to a first embodiment of the present invention;
FIG. 3 is a diagram illustrating T obtained by calculation according to a first embodiment of the present invention2A change in situation.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than being drawn according to the number, shape and size of the components in actual implementation, and the type, amount and proportion of each component in actual implementation can be changed freely, and the layout of the components can be more complicated.
The first embodiment is as follows:
as shown in fig. 1, a fire-fighting internet of things-based water system overall fault detection system comprises a detection sensor, an edge computing gateway and an internet of things data center; wherein the detection sensor is arranged in the fire-fighting water system; the edge computing gateway is connected with the detection sensors and can receive real-time data detected by the detection sensors; the edge computing gateway is also connected with the data center of the Internet of things.
The fire-fighting water system comprises an automatic water spray fire-fighting system, a foam fire-fighting system, a fire pool, a water tank and a pipeline, wherein the fire pool and the water tank are connected with the automatic water spray fire-fighting system and the foam fire-fighting system through the pipeline, and the stored water in the fire pool and the water tank is transported to all parts in a building.
The detection sensor comprises a hydraulic sensor and a liquid level sensor, wherein the hydraulic sensor is arranged at the position of a junction, a corner and the like of the pipeline, and the pipeline position has important significance for the whole fire water system and is easy to have hidden danger; in this example, the installation position of the hydraulic pressure sensor also comprises a position of a floor end water testing device. The liquid level sensor is arranged at the fire pool and the water tank.
The edge gateway comprises a storage module, a processing module and a communication module; the processing module is used for processing the data collected by the detection sensor; the communication module is used for connecting the data center of the internet of things and external equipment, such as a mobile phone, a computer, a tablet computer and the like; the storage module is used for storing detection data acquired by the detection sensor.
A method for detecting the overall fault of a water system based on the fire-fighting Internet of things comprises the following steps:
step 1: the detection sensor acquires analog quantity data acquired by the detection sensor and transmits the analog quantity data to the edge computing gateway in a one-way mode;
step 2: the edge computing gateway receives data collected by the detection sensor, wherein the data comprises liquid level data and hydraulic data;
and step 3: the edge computing gateway integrates the detection data, and judges whether the whole fire-fighting and water-proofing system fails or not through a set algorithm according to the detection data of all the detection sensors;
and 4, step 4: sending the fault judgment result to a receiving device in a communication mode, wherein the receiving device comprises a mobile phone, a computer and an Internet of things data center; the communication mode comprises 4G/5G, wireless network and the like;
and 5: and the data center of the Internet of things receives and stores the data.
In the step 3, the algorithm is set as a Multivariate Shewhart Control Chart method. The judging process comprises the following steps: firstly, historical data X of the whole fire fighting water system in a normal state needs to be acquiredIC＝{X1,X2,X3,…,XnEach data variable XiIs represented as Xi＝[Xi1Xi2…Xim]TM represents the vector dimension of the data variable, in this case the number of sensors in the fire-fighting water system; calculating to obtain mean data
Figure BDA0003495383100000051
Expressed as:
Figure BDA0003495383100000052
next, a sample variance S is obtained, expressed as:
Figure BDA0003495383100000053
for all the detecting sensors in the fire fighting water system to obtain a new data set X, the distance T between the vector and the distribution formed by the IC data is described by using the Mahalanobis distance, wherein the distance T has the following relation:
Figure BDA0003495383100000054
wherein T is2Approximately obeys F distribution, and therefore T can be obtained2Upper control limit UCL of (3)MSCCComprises the following steps:
Figure BDA0003495383100000055
wherein, Fα(m, n-m) represents the critical value (critical value) of the F distribution when the Type I error (Type I error) is alpha and the degrees of freedom are m, n-m respectively; in this example, α is 2.5%.
Judgment of T2And an upper control limit UCLMSCCIn relation to (c), if T2>UCLMSCCIf the fire water system is in the normal working state, judging that the whole working state of the fire water system at the moment corresponding to the newly acquired data X is abnormal; otherwise, the working state is considered to be normal.
In the implementation process, as shown in fig. 2 and 3, four detection sensors with the numbers of No.4510919C0195003, No.4510919C0195008, No.4510919C0195016 and No.4510919C0195021 collect the water pressure data of the corresponding arrangement parts of the fire fighting water system, and acquire the average value data
Figure BDA0003495383100000061
Expressed as:
Figure BDA0003495383100000062
and a sample variance S is obtained, expressed as:
Figure BDA0003495383100000063
according to the formula, calculating to obtain the upper control limit UCLMSCC15.74. And according to the latest time period, four detection sensors are arrangedRespectively collecting 25 groups of data, as shown in fig. 2, wherein the water pressure change curve of one detection sensor is normal in fluctuation, and the water pressure fluctuation curves of the other three detection sensors are abnormal; calculating and obtaining T in the time period according to data acquired by four sensors2As shown in fig. 3. As can be seen from fig. 3, the data value after the 10 th data point is higher than the set upper control limit UCLMSCCIt shows that the fire water system has abnormal conditions in the time period corresponding to the last 15 data points, which corresponds to the water pressure change curve corresponding to the single detection sensor in fig. 2, and can also accurately detect the abnormal conditions of one or more of the detection sensors.
Example two:
the embodiment is obtained based on a modification of the embodiment, wherein before step 3, the fault judgment is completed on a single-point part of the fire-fighting and water-proofing system according to an XControl Chart method in combination with data collected by a detection sensor arranged at the single point, and the method specifically includes the following steps:
firstly, a historical data set X of a single-point part in a normal state needs to be acquiredIC，XIC＝{X1,X2,X3,…,Xn}; from the historical data set XICObtaining mean data
Figure BDA0003495383100000064
Expressed as:
Figure BDA0003495383100000065
according to mean value data
Figure BDA0003495383100000066
And a historical data set XICObtaining a sample standard deviation sigma as:
Figure BDA0003495383100000071
according to the sampleStandard deviation, obtaining upper bound UCLsAnd lower bound LCLsUpper bound UCLsAnd a lower bound LCLsExpressed as:
Figure BDA0003495383100000072
Figure BDA0003495383100000073
wherein the parameter tn-1,αRepresenting the critical value of Student t distribution when the degree of freedom is n-1 and the Type I error (Type I error) is alpha; in this example a is set to 2.5%.
According to the obtained upper bound UCLsAnd lower bound LCLsAnalyzing the current data X: if LCLs≤X≤UCLsIf the analog quantity data are normal, the working state of the fire water system at the point is inferred to be normal; if LCLsThe analog quantity data is considered to be lower than the historical data in the statistical sense, and the conditions of pressure leakage, undervoltage, leakage and the like of the fire water system at the point are inferred; if UCLsX, and if the data of the analog quantity is considered to be higher than the historical data in a statistical sense, the fire water system at the point is inferred to be in an overpressure condition.
After the single-point part fault detection of the fire fighting water system is completed, the risk early warning is completed on the single-point part by combining the CUSUM Control Chart method according to the historical data and the current data collected by the detection sensor. Wherein the process of risk early warning includes:
first, a history data set X of a normal state is obtainedIC＝{X1,X2,X3,…,Xn}, and mean data
Figure BDA0003495383100000074
In order to determine whether there is a trend of increasing the average value of the analog quantity data collected by the sensor, the following assumptions are made:
H0: mean value of μ0，
Figure BDA0003495383100000075
H1: mean value of μ1，(μ1>μ0)；
For a new time period t, the analog data set X collected by all the detection sensorsT＝{XT1,XT2,XT3,…,XTtDefine the variables for the analog data collected at the current time within the latest time period t in this example
Figure BDA0003495383100000076
To measure the analog quantity data set XTData in (1) and mean value μ0The difference between them:
Figure BDA0003495383100000077
wherein, Delta1＝μ1-μ0(ii) a For detecting the analog data set X in the time period tTTo predict the future trend of mean increase, calculating the upper bound of the two UCLsCUSUMAnd a lower bound of two LCLsCUSUMExpressed as:
Figure BDA0003495383100000081
Figure BDA0003495383100000082
wherein alpha and beta are set values, alpha is a type I error, and beta is a type II error; two UCLs according to an upper boundCUSUMAnd a lower bound of two LCLsCUSUMJudging the simulation data set XTWhether there is an increasing trend in the mean of (a), wherein: if it is
Figure BDA0003495383100000083
Judging that the average value does not become high, and not performing early warning; if it is
Figure BDA0003495383100000084
Judging that the average value has an increasing trend, considering that the fire fighting water system has a risk of overpressure at the moment, and sending out an early warning; if it is
Figure BDA0003495383100000085
And judging that more data are needed, judging that the current data quantity is not enough to make a statistically meaningful conclusion, and not giving early warning.
Similarly, in order to determine whether there is a tendency for the average value of the analog quantity data collected by the sensor to decrease, the following assumption is made:
H0: mean value of μ0，
Figure BDA0003495383100000086
H2: mean value of μ2，(μ2<μ0)；
For a new time period t, the analog data set X collected by all the detection sensorsT＝{XT1,XT2,XT3,…,XTtDefine the variables for the analog data collected at the current time within the latest time period t in this example
Figure BDA0003495383100000087
To measure the analog quantity data set XTData in (1) and mean value μ0The difference between them:
Figure BDA0003495383100000088
wherein, Delta2＝μ2-μ0(ii) a For detecting the analog data set X in the time period tTTo predict future trends in mean decrease, computing a upper bound of three UCLsCUSUMAnd a lower bound of three LCLsCUSUMExpressed as:
Figure BDA0003495383100000089
Figure BDA00034953831000000810
wherein alpha and beta are set values, alpha is a type I error, and beta is a type II error; three UCL according to upper boundCUSUMAnd a lower bound of three LCLsCUSUMJudging the simulation data set XTWhether there is a decreasing trend in the mean of (a), wherein: if it is
Figure BDA00034953831000000811
Judging that the average value does not become high, and not performing early warning; if it is
Figure BDA00034953831000000812
Judging that the average value has a decreasing trend, considering that the risk of pressure leakage and underpressure possibly exists in the fire water system at the moment, and sending out an early warning; if it is
Figure BDA00034953831000000813
And judging that more data are needed, judging that the current data quantity is not enough to make a statistically meaningful conclusion, and not giving early warning.
Example three:
the embodiment is obtained based on the first improvement of the embodiment, wherein in step 3, after the fault detection of the whole fire-fighting water system is completed, whether the average attribute of the whole fire-fighting water system deviates or not is judged based on a multivariable CUSUM Control Chart (MCUSUM) method according to historical data collected by the whole fire-fighting water system, and early warning is realized. In the judgment process, firstly, the historical data X of the whole fire fighting water system in a normal state needs to be acquiredIC＝{X1,X2,X3,…,XnEach data variable XiIs represented as Xi＝[Xi1Xi2…Xim]TM represents the vector dimension of the data variable, m in this case represents the number of sensors detected in the fire fighting water system, and the data variable XiThe analog data obtained by all sensors at a certain time is equal. Calculating to obtain mean data
Figure BDA0003495383100000091
Expressed as:
Figure BDA0003495383100000092
the sample variance S is expressed as:
Figure BDA0003495383100000093
for sensor group data obtained over a new period of time, denoted as Y1,Y2,Y3,…,YtWherein each variable YiAre all m-dimensional vectors Yi＝[Yi1Yi2…Yim]T(ii) a And acquiring a mean value of the sensor group data in the period, wherein the acquired data of each detection sensor needs to be respectively averaged, and the finally obtained mean value of the data is expressed as mu. To determine whether there is a trend of change in the analog mean value, the following assumptions are made:
Figure BDA0003495383100000094
Figure BDA0003495383100000095
in the MCUMSUM method, two variables s are set in an iterative manneriAnd CiRespectively expressed as:
Figure BDA0003495383100000096
Figure BDA0003495383100000097
wherein the parameter k is obtained by:
Figure BDA0003495383100000098
Figure BDA0003495383100000099
Figure BDA00034953831000000910
wherein p is m-dimensional vector, and each value p in the vectori∈[-1,1],i＝1,2,…,m；100.pi% represents the maximum mean deviation percentage allowed by the ith sensor.
By a variable siAnd CiObtaining a detection mean judgment bit Zi：
Figure BDA0003495383100000101
Substituting the formula into the detection mean judgment position ZiIn (1), obtaining:
Figure BDA0003495383100000102
judging the obtained detection mean value ZiComparing with a set value h, wherein if Zi>h, considering that the state change of the fire fighting water system is large in the detection time, and sending early warning; otherwise, the fire-fighting water system is considered to work stably. It should be noted that the method for setting the value h as MCUSUMThe upper control limit of the fire fighting water system; the parameter is affected by Average Run Length (ARL), which represents the Average value of a large number of experimental samples, in this example the number of samples increases with the continuous data acquisition of the sensor, i.e. the set value h changes with the data acquired by the sensor and according to the set algorithm.
The above description is only a specific example of the present invention and does not constitute any limitation of the present invention. It will be apparent to persons skilled in the relevant art that various modifications and changes in form and detail can be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (9)

1. A method for detecting the overall fault of a water system based on the fire-fighting Internet of things is characterized by comprising the following steps:
step 1: the detection sensor acquires analog quantity data acquired by the detection sensor and transmits the analog quantity data to the edge computing gateway in a one-way mode;
step 2: the edge computing gateway receives data collected by the detection sensor;
and step 3: the edge computing gateway integrates the detection data, and judges whether the whole waterproof system fails or not by a multivariable Shewhart Control Chart method according to the detection data of all the detection sensors;
and 4, step 4: sending the fault judgment result to a receiving device in a communication mode, wherein the receiving device comprises a mobile phone, a computer and an Internet of things data center;
and 5: and the data center of the Internet of things receives and stores the data.
2. The method for detecting the overall fault of the water system based on the internet of things for fire fighting according to claim 1, wherein in the step 3, the fault judgment of the overall fire fighting water system needs to obtain the historical data X of the overall fire fighting water system in the normal stateIC＝{X1,X2,X3,…,XnEach data variable XiIs represented as Xi＝[Xi1Xi2…Xim]TM represents the vector dimension of the data variable, in this case the number of sensors in the fire-fighting water system; historical mean data are obtained through calculation
Figure FDA0003495383090000011
Expressed as:
Figure FDA0003495383090000012
next, a sample variance S is obtained, expressed as:
Figure FDA0003495383090000013
for all newly acquired data sets X of detection sensors in the fire fighting water system, describing the distance T between the vector and the distribution formed by the IC data by using the Mahalanobis distance;
due to T2Approximately obeys F distribution, and therefore T can be obtained2Upper control limit UCL of (1)MSCCComprises the following steps:
Figure FDA0003495383090000014
wherein, Fα(m, n-m) represents the critical value of F distribution when the type I error is alpha and the degrees of freedom are m and n-m respectively;
according to T2And an upper control limit UCLMSCCAnd (4) judging the overall working condition of the fire-fighting water system.
3. The method for detecting the overall fault of the water system based on the fire-fighting internet of things as claimed in claim 2, wherein the setting range of the alpha is 1.5% -5%.
4. The method for detecting the overall fault of the water system based on the fire-fighting internet of things as claimed in claim 2, wherein the distance T has the following relationship:
Figure FDA0003495383090000021
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003495383090000022
representing the historical mean data of all detection sensors obtained by calculation; x represents the data collected by all the detection sensors at the detection moment; s represents the sample variance of the historically collected data.
5. The method as claimed in claim 2, wherein in step 3, if T is obtained through calculation, T is obtained2＞UCLMSCCIf the fire water system is in the normal working state, judging that the whole working state of the fire water system at the moment corresponding to the newly acquired data X is abnormal; otherwise, the working state is considered to be normal.
6. A fire-fighting Internet of things-based water system overall fault detection system is based on the method of any one of claims 1-5, and comprises a detection sensor, an edge computing gateway and an Internet of things data center; wherein the detection sensor is arranged in the fire-fighting water system; the edge computing gateway is connected with the detection sensors and can receive real-time data detected by the detection sensors; the edge computing gateway is also connected with the data center of the Internet of things.
7. The system of claim 6, wherein the fire fighting water system comprises an automatic water spray fire extinguishing system, a foam fire extinguishing system, a fire pool and water tank and a pipeline, and the fire pool and the water tank are connected with the automatic water spray fire extinguishing system and the foam fire extinguishing system through the pipeline.
8. The system for detecting the overall fault of the water system based on the fire-fighting internet of things as claimed in claim 7, wherein the detection sensor comprises a hydraulic sensor and a liquid level sensor, wherein the hydraulic sensor is arranged at the junction and corner position of a pipeline; the liquid level sensor is arranged at the fire pool and the water tank.
9. The system for detecting the overall fault of the water system based on the fire-fighting internet of things as claimed in claim 8, wherein the edge gateway comprises a storage module, a processing module and a communication module; the processing module is used for processing the data collected by the detection sensor; the communication module is used for connecting the data center of the Internet of things and external equipment; the storage module is used for storing detection data acquired by the detection sensor.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116405273A (en) * 2023-03-27 2023-07-07 苏州慧至智能科技有限公司 Internet of things-oriented network attack detection and state estimation method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116405273A (en) * 2023-03-27 2023-07-07 苏州慧至智能科技有限公司 Internet of things-oriented network attack detection and state estimation method
CN116405273B (en) * 2023-03-27 2023-10-20 苏州慧至智能科技有限公司 Internet of things-oriented network attack detection and state estimation method

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