CN114664058B - Overall fault early warning system and method for fire fighting water system - Google Patents
Overall fault early warning system and method for fire fighting water system Download PDFInfo
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- A62C—FIRE-FIGHTING
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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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Abstract
The invention provides an integral fault early warning system and method of a fire water system, which realize real-time fault risk early warning of the integral fire water system in a building through a MCUSUM Control Chart method, and the algorithm considers the relation between historical data and real-time data at a single point of the water system of the Internet of things, so that the possible fault problem of the integral water system can be quickly and effectively found and early warning can be given; according to the result feedback of the algorithm, the building units can be helped to quickly evaluate the overall health condition of the fire-fighting water system, and give out early warning to the possible problems, so that the aspect that the fire-fighting water system needs to be lifted is locked, the self-checking and self-modifying of the fire-fighting water system safety risk are facilitated for the social units, the failure occurrence rate of the fire-fighting water system of each building unit in fire extinguishment is reduced, the fire-fighting self-rescue capability of the social units is improved, and the real-time supervision capability of the supervision department on the fire-fighting water system safety level of each building unit is improved.
Description
Technical Field
The invention relates to the field of fire-fighting equipment detection and early warning, in particular to an overall fault early warning system and method of a fire-fighting water system.
Background
With the improvement of the safety consciousness of residents, the fire safety level is also more and more important. In order to improve the fire safety level of the area, a fire-fighting water system and other fire-fighting facilities are usually arranged on site, so that fire-fighting personnel or masses can conveniently and quickly acquire fire-fighting equipment and resources, and fire-extinguishing work is completed in the first time. Because of the special use of the fire-fighting equipment, the fire-fighting equipment is required to be in a usable state, and therefore, fault detection and early warning of the fire-fighting equipment are extremely important. The fire-fighting facilities such as equipment such as fire extinguishers can be replaced regularly, so that the problems of faults and the like can be effectively avoided, but the fire-fighting water system is difficult to replace, a large number of hidden dangers including pressure leakage, overvoltage, liquid leakage, pipe body breakage and the like exist, and on the other hand, according to the data of a fire investigation report, the operation health condition of the fire-fighting water system equipment plays an extremely critical role in the fire extinguishing effect, so that the fire-fighting water system equipment needs to be detected.
In the existing method, related fire-fighting maintenance staff is generally adopted to regularly perform fault detection and health condition supervision on a fire-fighting waterproof system, and the method comprises the following modes of manual sampling, statistics, analysis and inspection and the like, and has the following defects: firstly, the fire-fighting maintenance staff can check regularly and in a sampling way, so that the real-time performance of the fire-fighting water system at each single point is difficult to ensure; secondly, the detection data is recorded and uploaded by personnel, the data volume is small, and a systematic detection evaluation and early warning process is absent; thirdly, by manual verification, the data collection efficiency is low, and fraud is easy to occur.
In addition, in the current fire water system, although a large number of internet of things devices for detecting the liquid level hydraulic pressure value of each important node are installed in the water system, the data are generally only used for assisting maintenance personnel in judging whether the water system is normal or not, and the information contained in the real-time data and the historical data of each device is not fully utilized. On the other hand, through the data of fire investigation reports, it is easy to find that the operation health condition of fire fighting water system equipment plays an extremely critical role on the fire extinguishing effect. Therefore, based on the fire-fighting Internet of things data, real-time and automatic fire-fighting water system fault detection and early warning are realized, and the method is very important for building units to improve the health level of the fire-fighting water system, enhance the fire extinguishing capability of the water system and help safety liability people to perform daily operation and maintenance.
Disclosure of Invention
The invention aims to solve the defects of the prior art and provides an integral fault early warning system and method of a fire-fighting water system.
In order to solve the problems, the invention adopts the following technical scheme:
an overall fault early warning method of a fire fighting water system 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 one way;
step 2: the edge computing gateway receives data acquired by the detection sensor;
step 3: the edge computing gateway acquires data acquired by all detection sensors in the fire water system, analyzes the acquired data according to a Multivariate CUSUM Control Chart method, and completes risk early warning of the whole fire water system;
step 4: the risk early warning result is sent to the received equipment in a communication mode, wherein the received equipment comprises a mobile phone, a computer and an Internet of things data center;
step 5: and the data is received and stored by the data center of the Internet of things.
Further, in the step 3, the whole fire fighting water system needs to be pre-warned, and first, the history data X of the whole fire fighting water system in the normal state needs to be obtained IC ＝{X 1 ，X 2 ，X 3 ，…，X n Each data variable X i Is represented as X i ＝[X i1 X i2 …X im ] T M represents the vector dimension of the data variable, in this example m represents the number of detection sensors in the fire water system; calculating to obtain historical average dataExpressed as:
next, a sample variance S is obtained, expressed as:
for the new sensor group data acquired over a period of time, denoted Y 1 ，Y 2 ，Y 3 ，…，Y t Wherein each variable Y i Are all m-dimensional vectors, Y i ＝[Y i1 Y i2 …Y im ] T The method comprises the steps of carrying out a first treatment on the surface of the Acquiring a data average value of a sensor group in the period of time, wherein the finally acquired data average value is expressed as mu; and to propose the assumption:
in the MCUMSUM method, two variables s are set in an iterative fashion i and Ci Expressed as:
wherein the parameter k is set by the permissible deviation of each sensor and the mean value dataObtaining a sample variance S;
by the variable s i and Ci Obtaining a detection mean judgment position Z i The obtained detection mean value judgment position Z i And comparing the detected value with the set value h, and judging whether to send out early warning.
Further, the parameter k is obtained by the following formula:
wherein p is an m-dimensional vector, each value p in the vector i ∈[-1，1]，i＝1，2，…，m；100·p i % represents the maximum mean deviation percentage allowed for the ith sensor.
Further, the detection mean value judgment position Z i The acquisition method of (1) is expressed as follows:
substitution of s i Obtaining:
wherein ,representing mean data obtained from the historical acquisition data.
Further, in the step 3, if Z i If the value is more than h, the state change of the fire-fighting water system is considered to be larger in the detection time, and early warning is sent out when the fire-fighting water system is in the detection time; otherwise, the fire fighting water system is considered to work stably.
The system is based on the method and comprises a detection sensor, an edge computing gateway and an Internet of things data center; wherein the detection sensor is arranged on the fire 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.
Further, the fire-fighting water system comprises an automatic water-spraying fire-extinguishing system, a foam fire-extinguishing system, a fire-fighting water tank, a water tank and a pipeline, wherein the fire-fighting water tank 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 positions of the fire water tank 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 acquired by the detection sensor; the communication module is used for connecting an Internet of things data center and external equipment; the storage module is used for storing detection data acquired by the detection sensor.
The beneficial effects of the invention are as follows:
the data of the water pressure, the water level and the like at a single point in the fire fighting water system are collected, the collected data are transmitted to the edge computing gateway, the edge computing gateway uniformly uploads the data to form a systematic automatic detection layout, the real-time supervision and review level of the fire fighting water system are improved, and the normal operation of the fire fighting water system is effectively ensured;
performing fault detection on a single point of the cancellation and waterproof system by an X Control Chart method, performing early warning on the single point of the cancellation and waterproof system based on a CUSUM Control Chart method, timely feeding back possible faults in the water system to owners or related parts, performing early warning on potential problems in advance, improving the fire Control level, and guaranteeing social safety;
by means of Multivariate Shewhart Control Chart, the running condition of the whole fire fighting water system is predicted by combining data acquired by detection sensors in the whole fire fighting water system, and partial point position abnormality in the fire fighting water system can be accurately detected;
by means of Multivariate CUSUM Control Chart, the set value h used for comparison in the early warning process is iterated continuously by combining normal operation data collected continuously by detection sensors in the fire-fighting water system, and the early warning result is ensured to be accurate.
Drawings
FIG. 1 is a schematic diagram of a system connection according to a first embodiment of the present invention;
FIG. 2 is a graph showing the latest data collected by four detection sensors according to the first embodiment of the present invention;
FIG. 3 shows a detection mean value determination position Z according to a first embodiment of the present invention i A change curve.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
Embodiment one:
as shown in FIG. 1, the overall fault early warning system of the fire water system comprises a detection sensor, an edge computing gateway and an Internet of things data center; wherein the detection sensor is arranged on the fire 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-spraying fire-extinguishing system, a foam fire-extinguishing system, a fire-fighting water tank and a pipeline, wherein the fire-fighting water tank and the water tank are connected with the automatic water-spraying fire-extinguishing system and the foam fire-extinguishing system through the pipeline, so that water storage in the fire-fighting water tank 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 junction, the corner and other parts of a pipeline, and the pipeline part has important significance for the whole fire water system and is easy to have hidden danger; the installation site of the hydraulic sensor in this example also includes a floor terminal water test device site. The liquid level sensor is arranged at the positions of the fire water tank 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 acquired by the detection sensor; the communication module is used for connecting an Internet of things data center and external equipment, such as a mobile phone, a computer, a tablet personal computer and the like; the storage module is used for storing detection data acquired by the detection sensor.
An overall fault early warning method of a fire fighting water system 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 one way;
step 2: the edge computing gateway receives data acquired by the detection sensor, including liquid level and hydraulic data;
step 3: the edge computing gateway acquires data acquired by all detection sensors in the fire water system, analyzes the acquired data according to a set algorithm, and completes risk early warning of the whole fire water system;
step 4: the risk early warning result is sent to the received equipment in a communication mode, wherein the received equipment comprises a mobile phone, a computer and an Internet of things data center; the communication modes comprise 4G/5G, wireless network and the like;
step 5: and the data is received and stored by the data center of the Internet of things.
The method for performing fault early warning on the whole cancellation and waterproof system in the step 3 is a Multivariate CUSUM Control Chart (MCUSUM) method, which implements the determination of whether the average properties of the entire fire fighting water system deviate. In the judging process, firstly, the history data X of the whole fire fighting water system in a normal state needs to be obtained I C＝{X 1 ，X 2 ，X 3 ，…，X n Each data variable X i Is represented as X i ＝[X i1 X i2 …X im ] T M represents the vector dimension of the data variable, in this case m represents the number of detection sensors in the fire water system, each data variable X i And (5) equating analog quantity data obtained by all the sensors at a certain moment. Calculating to obtain mean value dataExpressed as:
from the obtained mean value dataThe sample variance S is calculated and expressed as:
for the new sensor group data acquired over a period of time, denoted Y 1 ，Y 2 ，Y 3 ，…，Y t Wherein each variable Y i Are all m-dimensional vectors, Y i ＝[Y i1 Y i2 …Y im ] T The method comprises the steps of carrying out a first treatment on the surface of the And acquiring a data average value of the sensor group in the period, wherein the acquired data of each detection sensor needs to be respectively averaged, and the finally acquired data average value is expressed as mu. In order to determine whether there is a trend of variation in the analog average value, the following assumption is made:
in the MCUMSUM method, two variables s are set in an iterative fashion i and Ci Expressed as:
wherein the parameter k is obtained by:
wherein p is an m-dimensional vector, each value p in the vector i ∈[-1，1]，i＝1，2，…，m；100.p i % represents the maximum mean deviation percentage allowed for the ith sensor.
By the variable s i and Ci Obtaining a detection mean judgment position Z i ：
Substituting the above formula into the detection mean value judgment positionZ i In (1), obtaining:
the detection mean value obtained is judged to be Z i Comparing with a set value h, wherein if Z i If the value is more than h, the state change of the fire-fighting water system is considered to be larger in the detection time, and early warning is sent out when the fire-fighting water system is in the detection time; otherwise, the fire fighting water system is considered to work stably. It should be noted that the set value h is represented as the upper control limit of the fire fighting water system in the MCUSUM method; the parameter is affected by Average Run Length (ARL), which represents the average value of a number of experimental samples through a large number of groups, in which the number of samples increases with the continuous acquisition of data by the sensor, that is, the set value h changes with the acquisition of data by the sensor according to a set algorithm, and in which the magnitude of the set value h is related to the number m of detection sensors, the sample data amount, and the magnitude of the calculated value of the obtained parameter k. When the sample data is iteratively updated, that is, the sample data is ideally infinite, the value of the set value h is as follows:
in the implementation process, as shown in fig. 2, first, according to the process in step 3, acquired data of four detection sensors in the Multivariate CUSUM Control Chart method are obtained, as shown in fig. 2; obtaining historical mean value data according to the acquired data
The sample variance S is obtained, expressed as:
acquiring data newly acquired by a detection sensor, which is 25 groups of data in this example; and obtaining the detection mean value judgment position Z i And curves, as shown in FIG. 3, wherein the left graph in FIG. 3 is Z for 25 sets of data i The change curve, right image, is a partial enlarged image.
According to the above formula, the value of the parameter k is close to 0.5, and the number of the detection sensors is 4, so that the set value h is between 6.9 and 9.4 according to the table, and the larger value is 9.4. According to the partial enlargement, only the data before the second detected data point does not exceed 9.4, including the 0 point on the abscissa, so Z of the next 23 detected points is considered i The value must exceed the number of detection sensors by 4, the set value h when the parameter k is 0.5 indicates that the average state of the fire fighting water system at the moment corresponding to the subsequent 23 detection data points deviates, which accords with the situation shown in fig. 2, and the average value of the rest three sensor data is substantially lower than the historical average value except that the average value of the first detection sensor does not change substantially in fig. 2, and then early warning should be sent out.
Embodiment two:
the embodiment is obtained based on an improvement of the embodiment, wherein before the integral fault early warning is performed in the step 3, the fault judgment is completed at the single point position of the cancellation and waterproof system according to the data collected by the detection sensor arranged at the single point by the X Control Chart method, and specifically comprises the following steps:
firstly, a historical data set X of a single-point part in a normal state needs to be acquired IC ，X IC ＝{X 1 ，X 2 ，X 3 ，…，X n -a }; from historical dataset X IC Obtaining mean value dataExpressed as:
from mean dataHistorical data set X IC The standard deviation sigma of the sample is obtained as follows:
obtaining the upper bound UCL according to the standard deviation of the sample s Lower bound LCL s Upper bound UCL s And lower bound LCL s Expressed as:
wherein the parameter t n-1，α Representing 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 α is set to 2.5%.
UCL according to the obtained upper limit s Lower bound LCL s The current data X is analyzed: if LCL s ≤X≤UCL s The data of the analog quantity is considered to be normal, and then the working state of the fire water system at the point is inferred to be normal; if LCL s More than or equal to X, in statistical sense, considering that the data of the analog quantity is lower than the historical data, deducing that the fire fighting water system at the point has the conditions of pressure leakage, under-pressure, leakage and the like; if UCL s And less than or equal to X, and considering that the data of the analog quantity is higher than the historical data in a statistical sense, deducing that the fire fighting water system at the point has an overpressure condition.
After the single-point part fault detection of the fire fighting water system is completed, risk early warning is completed on the single-point part according to historical data and current data acquired by the detection sensor by combining a CUSUM Control Chart method. The risk early warning process comprises the following steps:
first, a history data set X of a normal state is obtained IC ＝{X 1 ，X 2 ，X 3 ，…，X n ' average dataIn order to determine whether there is a trend of increasing the average value of analog quantity data collected by the sensor, the following assumption is made:
H 0 : mean value of mu 0 ，
H 1 : mean value of mu 1 ，(μ 1 ＞μ 0 )；
For a new period of time t, the analog data set X acquired by all the detection sensors T ＝{X T1 ，X T2 ，X T3 ，…，X Tt In this example, for the analog data in the latest time period t acquired at the current time, a variable is definedTo measure analog data set X T Data and mean value mu in (a) 0 The gap between:
wherein ,Δ1 ＝μ 1 -μ 0 The method comprises the steps of carrying out a first treatment on the surface of the For detecting analog data sets X in a time period t T To predict future increasing trend, calculate upper bound two UCL CUsUM Lower bound two LCL CUsUM Expressed as:
wherein, alpha and beta are set values, alpha is an I-type error, and beta is an II-type error; UCL according to upper limit CUSUM Lower bound two LCL CUSUM Judging the analog data set X T Whether there is an increasing trend in the mean of (2), wherein: if it isJudging that the average value is not high, and not carrying out early warning; if->Judging that the average value has an increasing trend, considering that the risk of overvoltage possibly exists in the fire-fighting water system at the moment, and sending out early warning; if->More data is considered to be needed for judgment, the current data quantity is insufficient for making a conclusion in a statistical sense, and no early warning is made.
Similarly, in order to determine whether there is a tendency for the average value of analog quantity data collected by the sensor to decrease, the following assumption is made:
H 0 : mean value of mu 0 ，
H 2 : mean value of mu 2 ，(μ 2 ＜μ 0 )；
For the analog data set X in the new time period t T ＝{X T1 ，X T2 ，X T3 ，…，X Tt In this example, for analog data acquired at the current time during time period t, a variable is definedTo measure analog data set X T Data and mean value mu in (a) 0 The gap between:
wherein ,Δ2 ＝μ 2 -μ 0 The method comprises the steps of carrying out a first treatment on the surface of the For detecting analog data sets X in a time period t T To predict future decreasing trend, to calculate upper bound three UCL CUsUM Lower bound triple LCL CUsUM Expressed as:
wherein, alpha and beta are set values, alpha is an I-type error, and beta is an II-type error; according to the upper limit three UCL CUSUM Lower bound triple LCL CUSUM Judging the analog data set X T Whether there is a trend of decrease in the average value of (1), wherein: if it isJudging that the average value is not high, and not carrying out early warning; if->Judging that the average value has a reducing trend, considering that the risk of pressure leakage and pressure shortage possibly exists in the fire-fighting water system at the moment, and sending out early warning; if->More data is considered to be needed for judgment, the current data quantity is insufficient for making a conclusion in a statistical sense, and no early warning is made.
Embodiment III:
the embodiment is obtained based on an improvement of the embodiment, wherein in the step 3, before the overall fault early warning of the fire fighting water system, the overall fire fighting water system is detected and judged according to the overall data collected by the corresponding fire fighting water system, and the judging method is Multivariate Shewhart Control Chart. The judging process comprises the following steps: firstly, the history data X of the whole fire fighting water system in a normal state needs to be obtained Ic ＝{X 1 ，X 2 ，X 3 ，…，X n Each data variable X i Is represented as X i ＝[X i1 X i2 …X im ] T M represents the vector dimension of the data variable, in this example m represents the number of detection sensors in the fire water system; calculating to obtain mean value dataExpressed as:
the sample variance S is expressed as:
for newly obtained data X, the distance T between the vector and the distribution formed by the IC data is described by the Mahalanobis distance, where the distance T has the following relationship:
wherein T2 Approximately obeys the F distribution, and thus T can be obtained 2 Upper control limit UCL of (2) MSCC The method comprises the following steps:
wherein ,Fα (m, n-m) represents a critical value (critical value) of 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 T 2 And upper control limit UCL MSCC In relation to T 2 ＞UCL MSCC The whole working state of the fire fighting water system at the moment corresponding to the newly obtained data X is considered to be abnormal; otherwise, the working state is considered to be normal.
The above description is only a specific example of the invention and is not to be construed as limiting the invention in any way. It will be apparent to those skilled in the art that various modifications and changes in form and details may be made without departing from the principles and construction of the invention, but these modifications and changes based on the inventive concept are still within the scope of the appended claims.
Claims (6)
1. The integral fault early warning method of the fire fighting water system is characterized by comprising the following steps of:
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 one way;
step 2: the edge computing gateway receives data acquired by the detection sensor;
step 3: the edge computing gateway acquires data acquired by all detection sensors in the fire water system, analyzes the acquired data according to a Multivariate CUSUM Control Chart method, and completes risk early warning of the whole fire water system;
step 4: the risk early warning result is sent to the received equipment in a communication mode, wherein the received equipment comprises a mobile phone, a computer and an Internet of things data center;
step 5: the data center of the Internet of things receives and stores data;
in the step 3, the whole fire fighting water system is pre-warned, and firstly, the history data X of the whole fire fighting water system in a normal state needs to be obtained IC ＝{X 1 ,X 2 ,X 3 ,…,X n Each data variable X i Is represented as X i ＝]X i1 X i2 …X im ] T M represents the vector dimension of the data variable, in this example m represents the number of detection sensors in the fire water system; calculating to obtain historical average dataExpressed as:
next, a sample variance S is obtained, expressed as:
for the new sensor group data acquired over a period of time, denoted Y 1 ,Y 2 ,Y 3 ,…,Y t Wherein each variable Y i Are all m-dimensional vectors, Y i ＝[Y i1 Y i2 …Y im ] T The method comprises the steps of carrying out a first treatment on the surface of the Acquiring a data average value of a sensor group in the period of time, wherein the finally acquired data average value is expressed as mu; and to propose the assumption:
in the MCUMSUM method, two variables s are set in an iterative fashion i and Ci Expressed as:
wherein the parameter k is set by the permissible deviation of each sensor and the mean value dataObtaining a sample variance S; by the variable s i and Ci Obtaining a detection mean judgment position Z i The obtained detection mean value judgment position Z i Comparing the detected value with a set value h, and judging whether an early warning is sent out;
the parameter k is obtained by:
wherein p is an m-dimensional vector, each value p in the vector i ∈[-1,1],i＝1,2,…,m；p i Representing the maximum percentage of deviation allowed by the ith sensor;
the detection mean value judgment position Z i The acquisition method of (1) is expressed as follows:
substitution of s i Obtaining:
wherein ,representing mean data obtained from the historical acquisition data.
2. The method for overall fault warning of fire fighting water system according to claim 1, wherein in step 3, if Z i >h, sending out early warning; otherwise, the fire fighting water system is considered to work stably.
3. An overall fault early warning system of a fire water system, characterized in that the system is based on the method as claimed in claim 1 or 2, the system comprising a detection sensor, an edge computing gateway and an internet of things data center; wherein the detection sensor is arranged on the fire 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.
4. A fire protection system according to claim 3, wherein the fire protection system comprises a sprinkler system, a foam fire protection system, a fire pond and a water tank and a pipeline, wherein the fire pond and the water tank are connected with the sprinkler system and the foam fire protection system through the pipeline.
5. The system for early warning of overall failure of fire fighting water system according to claim 4, wherein 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 positions of the fire water tank and the water tank.
6. The overall fault early warning system of a fire water system according to claim 5, wherein the edge computing gateway comprises a storage module, a processing module and a communication module; the processing module is used for processing the data acquired by the detection sensor; the communication module is used for connecting an Internet of things data center and external equipment;
the storage module is used for storing detection data acquired by the detection sensor.
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