CN111862558A - Intelligent processing method of fire detection signal - Google Patents
Intelligent processing method of fire detection signal Download PDFInfo
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- CN111862558A CN111862558A CN202010011828.2A CN202010011828A CN111862558A CN 111862558 A CN111862558 A CN 111862558A CN 202010011828 A CN202010011828 A CN 202010011828A CN 111862558 A CN111862558 A CN 111862558A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
- G08B29/18—Prevention or correction of operating errors
- G08B29/185—Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
- G08B29/186—Fuzzy logic; neural networks
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/06—Electric actuation of the alarm, e.g. using a thermally-operated switch
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/10—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
- G08B29/02—Monitoring continuously signalling or alarm systems
- G08B29/04—Monitoring of the detection circuits
- G08B29/043—Monitoring of the detection circuits of fire detection circuits
Abstract
The invention relates to the field of fire fighting equipment induction, in particular to an intelligent processing method of a fire detection signal, which is characterized by comprising the following steps: s1, detecting the acquisition signal in real time through a detector; s2, preprocessing the signal; s3, converting the preprocessed signals into analog-digital signals to obtain digital quantity; s4, respectively establishing fuzzy sets of signals according to different signals, and establishing fuzzy rule relation libraries corresponding to input and output; and S5, carrying out fuzzy processing on the input quantity, and carrying out fuzzy logic judgment to obtain the output quantity, namely the fire occurrence probability. The invention greatly improves the accuracy and reliability of the system for judging the fire.
Description
Technical Field
The invention relates to the field of fire fighting equipment induction, in particular to an intelligent processing method of a fire detection signal.
Background
The traditional large-scale fire alarm system generally adopts a plurality of sensors to improve the reliability of the system, and the processing of sensor information also adopts a simple and or not relation or a simple set threshold value as a standard for judging whether a fire disaster occurs or not. The above methods for processing data lack flexibility, and sensor information is not comprehensively processed and analyzed to a certain extent, so that the contradiction between system sensitivity and false alarm rate cannot be solved.
In an actual alarm control system, detectors are installed in various environments, environmental factors have large influence on data, and the alarm and related linkage control of the whole system are directly influenced by the result of data processing, so that the system is a very critical link in the system. Therefore, how to process the data is very important and is a part with considerable difficulty. The real-time performance, correctness and rationality of the processing algorithm are directly related to the reliability and intelligent characteristics of the whole intelligent fire monitoring system.
In view of the above, it is an urgent need in the art to provide an intelligent processing method for fire detection signals to overcome the above technical drawbacks.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an intelligent processing method of fire detection signals, which greatly improves the accuracy and reliability of system judgment of fire.
In order to solve the technical problems, the technical scheme of the invention is as follows: an intelligent processing method of fire detection signals is characterized in that the method comprises the following steps:
s1, detecting the acquisition signal in real time through a detector;
s2, preprocessing the signal;
s3, converting the preprocessed signals into analog-digital signals to obtain digital quantity;
S4, respectively establishing fuzzy sets of signals according to different signals, and establishing fuzzy rule relation libraries corresponding to input and output;
and S5, carrying out fuzzy processing on the input quantity, and carrying out fuzzy logic judgment to obtain the output quantity, namely the fire occurrence probability.
According to the scheme, the input quantity is a signal after the sensor fuzzifies, and the output quantity is the probability of fire occurrence.
According to the scheme, the fuzzification grades of the input quantity and the output quantity are divided into four grades: MP with extremely high possibility of fire, NP with low possibility of fire, LP with no possibility of fire and IP with no possibility.
According to the scheme, the fuzzy processing is to perform fuzzy matrix operation by establishing fuzzy rules.
According to the above scheme, the step S5 specifically includes:
A. normalizing the input quantity;
B. fuzzifying the normalized input quantity;
C. judging through fuzzy logic reasoning to obtain a distribution function of output quantity;
D. the distribution function of the output quantity is clarified, namely, the output quantity is converted into a normalized output quantity;
E. the normalized output is converted to actual output.
According to the scheme, the normalization means that input or output is limited within a specified range, and the upper limit and the lower limit are determined as error domains.
According to the scheme, the fuzzification process is to convert the input quantity into the fuzzy quantity and construct a fuzzy set on an error domain according to the fuzzification level.
According to the scheme, the membership function of the fuzzy set adopts a Gaussian membership function.
According to the scheme, the signals comprise three-phase current, leakage current, smoke signals, temperature signals and gas signals.
According to the scheme, the detector comprises a smoke detector, a gas detector, a temperature detector, a current transformer and a leakage current transformer.
Compared with the prior art, the invention has the beneficial characteristics that: the invention uses the smoke and gas concentration, temperature and the like output by the smoke sensor and the gas detector as input signals of the fuzzy system, in order to fully extract the characteristic information of the three signals, the three signals are processed by the fuzzy system and then sent to fuzzy logic judgment, the output of the fuzzy logic judgment is not simple threshold judgment, but fuzzy logic judgment, and the precision of the system is greatly improved.
Drawings
FIG. 1 is a schematic diagram of a fire fighting control flow according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a fuzzy processing according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a Gaussian membership function according to an embodiment of the present invention;
Fig. 4 is a preview of a fuzzy simulation result according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Many aspects of the invention are better understood with reference to the following drawings. The components in the drawings are not necessarily to scale. Instead, emphasis is placed upon clearly illustrating the components of the present invention. Moreover, in the several views of the drawings, like reference numerals designate corresponding parts.
The word "exemplary" or "illustrative" as used herein means serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" or "illustrative" is not necessarily to be construed as preferred or advantageous over other embodiments. All of the embodiments described below are exemplary embodiments provided to enable persons skilled in the art to make and use the examples of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. In other instances, well-known features and methods are described in detail so as not to obscure the invention. For purposes of the description herein, the terms "upper," "lower," "left," "right," "front," "rear," "vertical," "horizontal," and derivatives thereof shall relate to the invention as oriented in fig. 1. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.
Referring to fig. 1 to 3, the invention discloses an intelligent processing method of fire detection signals based on a fire monitoring system, wherein a traditional switching value detector is changed into an analog detector, and a controller judges whether a fire occurs according to the received analog signals. Because the analog quantity detector is adopted, the intelligent fire monitoring system can continuously and accurately monitor the actual condition of the environment. The analog quantity detector transmits an analog quantity signal to the controller, and the controller judges whether a fire disaster occurs according to the received signal. In order to embody the intelligent characteristics well, the computer of the controller stores enough real fire characteristic parameters, false-alarm fire simulation parameters and interference parameters under different environments, and has a proper algorithm for processing the detector signals to distinguish false or real fire alarms. Thus, the controller can effectively process the input signal representing the current environmental condition, and make a correct judgment according to the development degree of the fire and the polluted state of the detector.
In the embodiment of the invention, considering that most of the floor-type fire is caused by electrical faults, the market demand and the system cost are considered, and the signals mainly detected by the system are three-phase current, leakage current, temperature signals and gas signals. The detector mainly adopts a smoke detector, a gas detector, a temperature detector, a current transformer and a leakage current transformer. Meanwhile, the fire signal is processed by using a fuzzy control intelligent algorithm, so that the accuracy and the reliability of the system for judging the fire are greatly improved.
The fire detection technology based on fuzzy control is a rule-based control that can fully utilize various recognitions of people about fire and the knowledge accumulation of experts. Because for fire, it is a nonlinear, unsteady, time-varying multiple-input multiple-output model, we cannot build an accurate input-output control model, and fuzzy algorithms do not need to build an accurate mathematical model of the controlled object in the design, thus making the control mechanism and strategy easy to accept and understand. Fuzzy control is very suitable for objects with difficult acquisition of mathematical models, difficult grasp of dynamic characteristics or very obvious change. In addition, the fuzzy control is designed based on heuristic knowledge and language occupation decision rules, which is beneficial to simulating the process and method of manual control, and enhancing the adaptability of the control system to ensure that the control system has certain intelligence level.
The invention adopts a fuzzy controller, and the smoke, the gas concentration and the temperature output by a smoke sensor and a gas detector are used as input signals of a fuzzy system, and the input signals are processed by the fuzzy system and then are sent to fuzzy logic for judgment.
The embodiment of the invention adopts a smoke sensor and a smoke concentration S detected by a gas detector, a gas concentration G detected by the gas detector and a gas concentration T detected by a temperature detector; shaping, filtering and amplifying the deviation signals; the processed signal is sent to A/D to convert analog-digital signal, and the obtained digital quantity can be used by system. Respectively establishing fuzzy sets of the smoke concentration change rate signal, the environment temperature change rate signal and the gas concentration signal as follows: fire Maximum (MP), fire medium (NP), fire Low (LP) and no possibility (IP). And finally obtaining the fire occurrence probability Pi according to fuzzy processing. The membership functions of the input and output variables of the fuzzy controller both select gaussian membership functions. And (5) simulating a result preview by using an MATLAB fuzzy control toolbox.
As shown in fig. 1, the fire alarm system provided by the present invention has a block diagram, wherein a fuzzy processing module is the key point of the present invention.
A schematic block diagram of the blurring process is shown in fig. 2. The input and output normalization means that the input and output are limited within a specified range so as to be convenient for design and implementation. Because the input value of the sensor is not a fuzzy number, the process of blurring is to convert the input value into a fuzzy quantity. Fuzzy logic reasoning determines a distribution function of the output quantities. The clarification process is to convert the distribution function of the output quantity into a normalized output quantity. Finally, the identification system converts the normalized output quantity into an actual output value.
Normalization is to limit the input or output to a specified range. Taking the input quantity signal as an example, the upper and lower limits of the input quantity signal are determined to be used as an error discourse field u, then fuzzy grades are given, the more grades are divided, the more accurate description is given to objects, the better the possible effect is, but at the same time, the more fine division makes fuzzy operation and reasoning complicated. Considering the actual situation, the temperature, smoke and gas signals are divided into 4 grades: fire Maximum (MP), fire medium (NP), fire Low (LP) and no possibility (IP). Which are fuzzy sets on the domain of discourse U, respectively.
Membership functions for these fuzzy sets need to be established, and gaussian functions are used in this example.
Although the system has three sensor signal inputs, the membership functions used are the same, all taking the temperature signal as an example, giving a membership function of the temperature signal that is a gaussian function, as shown in fig. 3. The membership functions chosen here are continuous, but in practice the domain of discourse is discretized, each membership function representing a set of vectors over a discrete domain of discourse. The above is an example given for temperature signals, and the same can be done for smoke and gas signals. Three sets of ambiguity sets { Ti }, { Si } and { Gi } can thus be constructed, representing the ambiguity quantization levels of the temperature signal, smoke signal and gas concentration signal, respectively.
Establishing the language rule is the core of the sensor information fuzzification processing. Fuzzy rules always appear in the form of "IF... THEN …". The fuzzy rule of the system is expressed in the form of: "if the temperature is Ti, and the smoke is Si, and the gas concentration is Gi, the probability of occurrence of a fire is Pi. Where Ti, Si, Gi are the fuzzy quantization levels of the temperature signal, smoke signal, and gas concentration signal mentioned above, respectively, and Pi is the quantization level of the occurrence probability of a fire alarm. For example, "IF (temperature is LP) AND (smoke is IP) AND (gas is NP) THEN (fire is NP)" is a fuzzy rule. More than one rule is not suitable, but many. The same rules may be merged but conflicting rules are not allowed. If the input variables are graded according to the above method, the fuzzy rule of the system has 64 pieces in total, as shown below;
(1) IF (temperature IP) AND (smoke IP) AND (gas IP) THEN (fire IP)
(2) IF (temperature LP) AND (smoke IP) AND (gas IP) THEN (fire LP)
(3) IF (temperature NP) AND (smoke IP) AND (gas IP) THEN (fire NP)
(4) IF (temperature MP) AND (smoke IP) AND (gas IP) THEN (fire MP)
(5) IF (temperature IP) AND (smoke LP) AND (gas IP) THEN (fire LP)
(6) IF (temperature LP) AND (smoke LP) AND (gas IP) THEN (fire LP)
(7) IF (temperature NP) AND (smoke LP) AND (gas IP) THEN (fire NP)
(8) IF (temperature MP) AND (smoke LP) AND (gas IP) THEN (fire MP)
(9) IF (temperature IP) AND (smoke NP) AND (gas IP) THEN (fire LP)
(10) IF (temperature LP) AND (smoke NP) AND (gas IP) THEN (fire NP)
(11) IF (temperature NP) AND (smoke NP) AND (gas IP) THEN (fire MP)
(12) IF (temperature MP) AND (smoke NP) AND (gas IP) THEN (fire MP)
(13) IF (temperature IP) AND (smoke MP) AND (gas IP) THEN (fire NP)
(14) IF (temperature LP) AND (smoke MP) AND (gas IP) THEN (fire NP)
(15) IF (temperature NP) AND (smoke MP) AND (gas IP) THEN (fire MP)
(16) IF (temperature MP) AND (smoke MP) AND (gas IP) THEN (fire MP)
(17) IF (temperature IP) AND (smoke IP) AND (gas LP) THEN (fire IP)
(18) IF (temperature LP) AND (smoke IP) AND (gas LP) THEN (fire LP)
(19) IF (temperature NP) AND (smoke IP) AND (gas LP) THEN (fire NP)
(20) IF (temperature MP) AND (smoke IP) AND (gas LP) THEN (fire MP)
(21) IF (temperature IP) AND (smoke LP) AND (gas LP) THEN (fire LP)
(22) IF (temperature LP) AND (smoke LP) AND (gas LP) THEN (fire LP)
(23) IF (temperature NP) AND (smoke LP) AND (gas LP) THEN (fire NP)
(24) IF (temperature MP) AND (smoke LP) AND (gas LP) THEN (fire MP)
(25) IF (temperature IP) AND (smoke LP) AND (gas LP) THEN (fire LP)
(26) IF (temperature LP) AND (smoke NP) AND (gas LP) THEN (fire NP)
(27) IF (temperature NP) AND (smoke NP) AND (gas LP) THEN (fire NP)
(28) IF (temperature MP) AND (smoke NP) AND (gas LP) THEN (fire MP)
(29) IF (temperature IP) AND (smoke MP) AND (gas LP) THEN (fire NP)
(30) IF (temperature LP) AND (smoke MP) AND (gas LP) THEN (fire NP)
(31) IF (temperature NP) AND (smoke MP) AND (gas LP) THEN (fire MP)
(32) IF (temperature MP) AND (smoke MP) AND (gas LP) THEN (fire MP)
(33) IF (temperature IP) AND (smoke IP) AND (gas NP) THEN (fire LP)
(34) IF (temperature LP) AND (smoke IP) AND (gas NP) THEN (fire LP)
(35) IF (temperature NP) AND (smoke IP) AND (gas NP) THEN (fire NP)
(36) IF (temperature MP) AND (smoke IP) AND (gas NP) THEN (fire MP)
(37) IF (temperature IP) AND (smoke LP) AND (gas NP) THEN (fire LP)
(38) IF (temperature LP) AND (smoke LP) AND (gas NP) THEN (fire LP)
(39) IF (temperature NP) AND (smoke LP) AND (gas NP) THEN (fire MP)
(40) IF (temperature MP) AND (smoke LP) AND (gas NP) THEN (fire NP)
(41) IF (temperature IP) AND (Smoke NP) AND (gas NP) THEN (fire NP)
(42) IF (temperature LP) AND (Smoke NP) AND (gas NP) THEN (fire NP)
(43) IF (temperature NP) AND (smoke NP) AND (gas NP) THEN (fire MP)
(44) IF (temperature MP) AND (smoke NP) AND (gas NP) THEN (fire MP)
(45) Positive (temperature IP) AND (smoke MP) AND (gas PN0THEN (fire NP)
(46) IF (temperature LP) AND (smoke MP) AND (gas NP) THEN (fire NP)
(47) IF (temperature NP) AND (smoke MP) AND (gas NP) THEN (fire MP)
(48) IF (temperature MP) AND (smoke MP) AND (gas NP) THEN (fire MP)
(49) IF (temperature IP) AND (smoke IP) AND (gas MP) THEN (fire LP)
(50) IF (temperature LP) AND (smoke IP) AND (gas MP) THEN (fire LP)
(51) IF (temperature NP) AND (smoke IP) AND (gas MP) THEN (fire NP)
(52) IF (temperature MP) AND (smoke IP) AND (gas MP) THEN (fire NP)
(53) IF (temperature IP) AND (smoke LP) AND (gas MP) THEN (fire LP)
(54) IF (temperature LP) AND (smoke LP) AND (gas MP) THEN (fire NP)
(55) IF (temperature NP) AND (smoke LP) AND (gas MP) THEN (fire NP)
(56) IF (temperature MP) AND (smoke LP) AND (gas MP) THEN (fire MP)
(57) IF (temperature IP) AND (smoke NP) AND (gas MP) THEN (fire LP)
(58) IF (temperature LP) AND (smoke NP) AND (gas MP) THEN (fire NP)
(59) IF (temperature NP) AND (smoke NP) AND (gas MP) THEN (fire NP)
(60) IF (temperature MP) AND (smoke NP) AND (gas MP) THEN (fire MP)
(61) IF (temperature IP) AND (smoke MP) AND (gas MP) THEN (fire NP)
(62) IF (temperature LP) AND (smoke MP) AND (gas MP) THEN (fire NP)
(63) IF (temperature NP) AND (smoke MP) AND (gas MP) THEN (fire MP)
(64) IF (temperature MP) AND (smoke MP) AND (gas MP) THEN (fire MP)
In the embodiment of the invention, fuzzy simulation is carried out through MATLAB. MATLAB provides a fuzzy control Toolbox (fuzzy logic Toolbox) that users can use to perform fuzzy inference and simulation of fuzzy controllers. The fuzzy control tool box integrates visualization tools such as a FIS editor, a membership function editor, a fuzzy rule editor, a rule browser and an output previewer, and a user can develop and design the fuzzy controller quickly.
After entering the FIS editor, designing input and output variable parameters: including variable number, membership function shape, range, fuzzy domain, etc. From the above discussion, it is understood that the input variables include temperature, smoke, and gas, the output variables are fire probabilities, and the membership functions are gaussian functions.
Defining fuzzy control rules: the fuzzy rule editor is actually a text editor, and as long as the fuzzy rule is written according to the specified fuzzy rule writing format, the computer can carry out fuzzy matrix operation according to the fuzzy inference synthesis rule. The fuzzy rule is built by inputting the 64 fuzzy rules listed above into the fuzzy rule editor in turn.
After the above work is completed, the preview of the fuzzy simulation result can be viewed, as shown in fig. 4. Respectively, smoke and temperature, temperature and gas concentration, and the relationship between smoke and gas concentration and the probability of fire.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. An intelligent processing method for fire detection signals is characterized by comprising the following steps:
s1, detecting the acquisition signal in real time through a detector;
s2, preprocessing the signal;
s3, converting the preprocessed signals into analog-digital signals to obtain digital quantity;
s4, respectively establishing fuzzy sets of signals according to different signals, and establishing fuzzy rule relation libraries corresponding to input and output;
and S5, carrying out fuzzy processing on the input quantity, and carrying out fuzzy logic judgment to obtain the output quantity, namely the fire occurrence probability.
2. A method for intelligently processing a fire detection signal according to claim 1, wherein: the input quantity is a signal after the fuzzification of the sensor, and the output quantity is the probability of fire occurrence.
3. A method for intelligently processing a fire detection signal according to claim 2, wherein: the fuzzification grades of the input quantity and the output quantity are divided into four grades: MP with extremely high possibility of fire, NP with low possibility of fire, LP with no possibility of fire and IP with no possibility.
4. A method for intelligently processing a fire detection signal according to claim 1, wherein: the fuzzy processing is to perform fuzzy matrix operation by establishing fuzzy rules.
5. A method for intelligently processing a fire detection signal according to claim 1, wherein: the step S5 specifically includes:
A. normalizing the input quantity;
B. fuzzifying the normalized input quantity;
C. judging through fuzzy logic reasoning to obtain a distribution function of output quantity;
D. the distribution function of the output quantity is clarified, namely, the output quantity is converted into a normalized output quantity;
E. the normalized output is converted to actual output.
6. A method for intelligently processing fire detection signals according to claim 5, wherein: the normalization means that the input or output is limited within a predetermined range, and the upper and lower limits thereof are determined as the error domain.
7. A method for intelligently processing a fire detection signal as recited in claim 6, wherein: the fuzzification process is to convert the input quantity into fuzzy quantity and construct a fuzzy set on an error domain according to the fuzzification level.
8. A method for intelligently processing a fire detection signal as recited in claim 7, wherein: and the membership function of the fuzzy set adopts a Gaussian membership function.
9. A method for intelligently processing a fire detection signal according to claim 1, wherein: the signals include three phase current, leakage current, smoke signals, temperature signals, and gas signals.
10. A method for intelligently processing a fire detection signal according to claim 1, wherein: the detector comprises a smoke detector, a gas detector, a temperature detector, a current transformer and a leakage current transformer.
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| CN113487848A (en) * | 2021-07-16 | 2021-10-08 | 云南电网有限责任公司保山供电局 | Fuzzy control's intelligent fire alarm system |
| CN114027822A (en) * | 2021-04-19 | 2022-02-11 | 北京超思电子技术有限责任公司 | Respiration rate measuring method and device based on PPG signal |
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