CN115620474A - Energy storage prefabricated cabin fire alarm optimization method and system based on AHP - Google Patents
Energy storage prefabricated cabin fire alarm optimization method and system based on AHP Download PDFInfo
- Publication number
- CN115620474A CN115620474A CN202211074620.0A CN202211074620A CN115620474A CN 115620474 A CN115620474 A CN 115620474A CN 202211074620 A CN202211074620 A CN 202211074620A CN 115620474 A CN115620474 A CN 115620474A
- Authority
- CN
- China
- Prior art keywords
- fire
- alarm
- signal
- detector
- energy storage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- 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
Abstract
The invention belongs to the technical field of energy storage power stations, and particularly relates to an energy storage prefabricated cabin fire alarm optimization method and system based on AHP (attitude and heading process), which comprehensively utilize BMS (battery management system) signals and fire detector signal coupling analysis to identify fire risks, and the specific implementation process comprises the following steps: BMS gathers the prefabricated cabin of battery multiple temperature signal, receives the fire detector signal that the fire control host computer transmitted simultaneously, carries out analysis and logic judgement processing to these two main kinds of signals, gives the fire alarm signal after the optimization and sends to fire extinguishing system, and this signal will be as the instruction of fire control warning and fire extinguishing system action. The invention can reduce the refusing action risk of the fire-fighting system, is beneficial to finding out the fire risk in advance and ensures that the fire alarm is more accurate and timely.
Description
Technical Field
The invention belongs to the technical field of energy storage power stations, and particularly relates to an energy storage prefabricated cabin fire alarm optimization method and system based on AHP.
Background
The energy storage is important for China to achieve the '3060 double-carbon' target and guarantee the safe operation of a novel power system which is mainly built by using new energy, and is an important support for guaranteeing the large-scale development of the new energy and the energy safety of China. In the process of rapid development of energy storage, safety accidents at home and abroad frequently occur. According to incomplete statistics, 50 accidents of fire and explosion of energy storage power stations occur in total in 2011-2021 in 10 years all over the world. The most causing energy storage power station accidents in China are the energy storage power station explosion of 16-day Beijing national Xuan Fuweisi optical storage and charging technology Limited company in 2021 year 4. This accident results in multiple deaths.
The safety accidents of the energy storage power station are mostly caused by the fact that the battery is short-circuited due to self or external reasons and then thermal runaway occurs under the condition of early warning loss or hysteresis. Existing fire protection systems are not specifically configured for battery fires and also lack coordination with other systems to collectively prevent fires. The fire-fighting system cannot timely and accurately give out fire alarm and fire extinguishing response, so that the initial fire of the battery cannot be effectively inhibited, and finally, the battery is converted into an out-of-control fire accident.
Fire detection of a battery prefabricated cabin of an energy storage power station is generally realized by a temperature-sensitive detector and a smoke-sensitive detector. When a single type of fire detector acts, a fire alarm is given, when two types of fire detectors act simultaneously, a fire alarm is given, the electrical loop of the prefabricated cabin is cut off, and a fire extinguishing system is started. The fire detector has the risks of false alarm, missed alarm, failure and the like, which can cause the misoperation or the refusal of a fire protection system, thereby causing huge fire accidents or economic losses.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide an energy storage prefabricated cabin fire alarm optimization method and system based on an AHP (analytic hierarchy process) for identifying fire risks by comprehensively utilizing BMS (battery management system) signals and fire detector signal coupling analysis.
The technical scheme of the invention is as follows: an energy storage prefabricated cabin fire alarm optimization method based on AHP comprehensively utilizes BMS signals and fire detector signal coupling analysis to identify fire risks, and the specific implementation process is as follows: BMS gathers the prefabricated indoor multiple temperature signal of battery, receives the fire detector signal that the fire control host computer transmitted simultaneously, carries out analysis and logic judgement processing to these two main kinds of signals, gives the fire alarm signal after the optimization and sends to fire extinguishing system, and this signal will be as the instruction of fire control warning and fire extinguishing system action, and the risk that the signal after the optimization can greatly reduced conflagration maloperation or refused to move.
Preferably, the AHP-based energy storage prefabricated cabin fire alarm optimization method is used for carrying out weight distribution on various switch state quantity signals by the AHP, and comprises the following specific steps:
1) Establishing a fire risk hierarchical structure chart;
2) Constructing a judgment matrix of the A → B level:
in the above formula, D AB Is an n-th order matrix, n is the number of B-level indexes and satisfies B ij ＝1/b ji (i，j＝1，2，…，n，)，b ij ＞0，b ii =1, wherein b ij The factor i is the ratio of relative importance of the factor j, and is qualitatively judged by a decision maker;
3) Determining a decision matrix D AB Eigenvector λ = [ λ ] corresponding to maximum eigenvalue λ max 1 λ 2 … λ n ] T Normalizing the lambda to obtain a weight vector omega AB ＝[ω 1 ω 2 … ω n ] T The attribute weight is obtained;
4) Carrying out consistency check on the judgment matrix;
5) Repeating steps 2 to 4 to obtain B 1 →C、B 2 Judgment matrix D of → C B1C 、D B2C And corresponding weight vector omega B1C 、ω B2C And carrying out consistency check;
6) According to omega AB 、ω B1C And ω B2C Calculating the weight of the C-level state quantity to the fire risk A; and weighting and summing all the state quantities to obtain a system fire risk value:
wherein R is the system fire risk value, alpha i The weighted value of the ith state quantity is Sij, the Boolean value of the jth ith state quantity of Sij is 0 or 1,1 represents alarm, and 0 represents no alarm.
Preferably, the step 4) performs a consistency check on the judgment matrix as follows:
4-1) calculating a consistency index:
wherein n is the order of the judgment matrix;
4-2) taking any random consistency index RI;
4-3) calculating the consistency ratio: CR = CI/RI, if CR <0.1, the decision matrix is considered to pass the consistency check, otherwise satisfactory consistency is not achieved.
Preferably, in the step 6), the system fire risk value is compared with a predetermined threshold value to obtain a system fire risk level, and the system makes a corresponding response according to the fire risk level;
T 1 ≤R<T 2 : judging that the fire risk grade of the system is level 1, and if an early fire or potential fire risk exists, informing the on-duty personnel by the fire host through acousto-optic alarm, and simultaneously sending an alarm signal to a fire-fighting master control room;
T 2 ≤R<T 3 : judging that the fire risk grade of the system is grade 2, and if small-scale fire (such as thermal runaway of a single battery) exists, cutting off an electric loop of the battery prefabricated cabin on the basis of grade 1 response;
R≥T 3 : and judging that the fire risk grade of the system is grade 3, and starting the automatic fire extinguishing system on the basis of grade 2 response if a large-scale fire occurs.
A system for an energy storage prefabricated cabin fire alarm optimization method based on AHP comprises a plurality of temperature measuring points, a plurality of fire detectors, a fire system and a BMS system, wherein the fire system receives signals of the fire detectors and sends the signals to the BMS system; the BMS system collects temperature measuring point signals and compares the temperature measuring point signals with a set high-temperature threshold value to generate a switching state quantity signal; and the BMS receives the fire detector switch state quantity signals, analyzes and logically judges the fire detector switch state quantity signals and the temperature measuring point switch state quantity signals together to obtain the fire risk level of the system, and makes different responses according to the fire risk level.
Preferably, the temperature measuring points comprise a battery module temperature measuring point, a high-pressure tank temperature measuring point, a header cabinet temperature measuring point and a prefabricated cabin temperature measuring point, and each temperature measuring point is provided with a plurality of temperature measuring points.
Preferably, the fire detector comprises a temperature-sensing sensor, a smoke detector and a combustible gas detector, wherein a plurality of fire detectors are provided, and when the temperature is detected to be out of limit or the temperature rising speed is detected to be out of limit, a state quantity signal of an action alarm switch of the temperature-sensing detector is given; when the smoke concentration is detected to be out of limit, a signal of the state quantity of the action alarm switch of the smoke detector is given; when the concentration of the combustible gas is detected to be out of limit, a signal of the state quantity of an action alarm switch of the combustible gas detector is given, the combustible gas detector generally alarms in two stages, the first-stage threshold value is 0.1% -5% of the lower limit of the explosion concentration of the combustible gas, the second-stage threshold value is 10% -50% of the lower limit of the explosion concentration of the combustible gas, 1 represents the action of the fire detector, and 0 represents the non-action of the fire detector.
Preferably, the fire fighting system comprises a fire fighting host, an alarm system and a fire extinguishing system, wherein the fire fighting host receives a signal of the fire detector and sends the signal to the BMS system.
Preferably, the BMS system includes three levels of a battery module management system, a battery cluster management system, and a battery stack management system; and the BMS system acquires a temperature measuring point signal and compares the temperature measuring point signal with a set high-temperature threshold value to generate a switch state quantity signal, wherein 1 represents that the temperature measuring point exceeds the limit, and 0 represents that the temperature measuring point does not exceed the limit.
Compared with the prior art, the invention has the following beneficial effects:
(1) The fire-fighting system refusal risk is reduced, under the condition of the prior art, if a fire disaster occurs in the prefabricated cabin but the detector fails and does not act, the fire-fighting system cannot be automatically started, and the BMS temperature signal measuring point is added as a factor for fire judgment, so that the refusal risk of the fire-fighting system is greatly reduced.
(2) The system is beneficial to discovering fire risks in advance, when the battery module generates early fire, the fire detector does not detect the fire, the system can generate a level 1 fire risk level alarm through the analysis of the battery module temperature measuring point signals of the BMS, and precious time is strived for discovering and processing the fire.
(3) The fire alarm is more accurate and timely, the BMS signal and the detector signal are integrated as fire judgment conditions, and the fire alarm of the system can be more accurate and timely through reasonable weight distribution and fire risk threshold setting.
Drawings
FIG. 1 is a system architecture diagram of the present invention;
FIG. 2 is a diagram of a fire risk hierarchy based on the AHP algorithm;
FIG. 3 is a system flow diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, but the present invention is not limited thereto.
Example 1
A fire alarm optimization method for a battery prefabricated cabin of an energy storage power station based on an AHP (analytic hierarchy process) comprehensively utilizes BMS (battery management system) signals and fire detector signals to carry out coupling analysis to identify fire risks, and the specific implementation process is as follows: BMS gathers the prefabricated cabin of battery multiple temperature signal, receives the fire detector signal that the fire control host computer transmitted simultaneously, carries out analysis and logic judgement processing to these two main kinds of signals, gives the fire alarm signal after the optimization and sends to fire extinguishing system, and this signal will be as the instruction of fire control warning and fire extinguishing system action. The optimized signal can greatly reduce the risk of false operation or failure of the fire.
Fig. 1 is a system architecture diagram of the present invention, and the main components are as follows:
1) And the temperature measuring points comprise a battery module temperature measuring point, a high-voltage box temperature measuring point, a confluence cabinet temperature measuring point, a prefabricated cabin temperature measuring point and the like. The number of each measuring point is several.
2) The fire detector comprises a temperature-sensing sensor, a smoke-sensing detector, a combustible gas detector and the like. The number of each detector is several, and depends on factors such as the protection range of the detector. When the temperature is detected to be out of limit or the temperature rising speed is detected to be out of limit, a state quantity signal of an action alarm switch of the temperature-sensitive detector is given; when the smoke concentration is detected to be out of limit, a signal of the state quantity of the action alarm switch of the smoke detector is given; when the concentration of the combustible gas is detected to be out of limit, a signal of the state quantity of an action alarm switch of the combustible gas detector is given, the combustible gas detector generally alarms in two stages, the first-stage threshold value is 0.1% -5% of the lower limit of the explosive concentration of the combustible gas, and the second-stage threshold value is 10% -50% of the lower limit of the explosive concentration of the combustible gas. 1 indicates detector action and 0 indicates no action.
3) The fire-fighting system comprises a fire-fighting host, an alarm system, a fire-fighting system and the like. The fire-fighting host receives the signal of the fire detector and sends the signal to the BMS system.
4) The BMS system comprises a battery module management system, a battery cluster management system and a battery stack management system. The BMS collects the temperature measuring point signals and compares the temperature measuring point signals with a set high-temperature threshold value to generate a switching state quantity signal. 1 indicates temperature measurement point overrun and 0 indicates no overrun. And the BMS receives the fire detector switching state quantity signals, and analyzes and logically judges the fire detector switching state quantity signals together with the temperature switching state quantity signals to obtain the fire risk grade of the system. The system will respond differently depending on the fire risk level.
The temperature measuring points and the fire detectors in the invention are not limited to those listed, and other conventional temperature measuring points and fire detectors are also applicable, so that the colon marks are omitted in the system architecture diagram.
The system flow of the invention is shown in figure 3, and the AHP (analytic hierarchy process) is used for carrying out weight distribution on various switch state quantity signals, and the method comprises the following steps:
1) Establishing a fire risk hierarchical structure chart, as shown in figure 2;
2) Construct a → B level decision (pair-wise comparison) matrix:
in the above formula, D AB Is an n-order matrix, n is the number of B-level indexes and satisfies B ij ＝1/b ji (i，j＝1，2，…，n，)，b ij ＞0，b ii =1, wherein b ij The ratio of the relative importance of the factor i to the factor j is judged by a decision maker according to the following table.
| Factor i to factor j | Quantized value |
| Of equal importance | 1 |
| Of slight importance | 3 |
| Of greater importance | 5 |
| Of strong importance | 7 |
| Of extreme importance | 9 |
| Intermediate value of two adjacent judgments | 2，4，6，8 |
3) Determining a decision matrix D AB Eigenvector λ = [ λ ] corresponding to maximum eigenvalue λ max 1 λ 2 … λ n ] T Normalizing lambda to obtain a weight vector omega AB ＝[ω 1 ω 2 … ω n ] T I.e. the weight of the attribute sought
4) And carrying out consistency check on the judgment matrix. The process is as follows:
4-1) calculating a consistency index:
where n is the order of the judgment matrix
4-2) looking up the following table to obtain the random consistency index RI
| Order of matrix | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 |
4-3) calculating the consistency ratio: CR = CI/RI. Generally, if CR <0.1, the decision matrix is deemed to pass the consistency check, otherwise it does not have satisfactory consistency.
5) Repeating steps 2 to 4 to obtain B 1 →C、B 2 Judgment matrix D of → C B1C 、D B2C And corresponding weight vector omega B1C 、ω B2C And a consistency check is performed.
6) According to omega AB 、ω B1C And ω B2C Calculating the weight of the C-level state quantity to the fire risk A;
and weighting and summing all the state quantities to obtain a system fire risk value:
And comparing the system fire risk value with a set threshold value to obtain a system fire risk grade, and making a corresponding response by the system according to the fire risk grade.
T 1 ≤R<T 2 : judging that the fire risk grade of the system is level 1, and if an early fire or potential fire risk exists, informing the on-duty personnel by the fire host through acousto-optic alarm, and simultaneously sending an alarm signal to a fire-fighting master control room;
T 2 ≤R<T 3 : judging that the fire risk grade of the system is grade 2, and if small-scale fire (such as thermal runaway of a single battery) exists, cutting off an electric loop of the battery prefabricated cabin on the basis of grade 1 response;
R≥T 3 : and judging that the fire risk grade of the system is grade 3, and starting the automatic fire extinguishing system on the basis of grade 2 response, wherein a large-scale fire exists.
Example 2
The state quantities in this example are as follows:
| numbering | Quantity of state |
| C1 | High temperature of battery module |
| C2 | High temperature of high pressure tank |
| C3 | The temperature of the collecting cabinet is high |
| C4 | High temperature in the cabin |
| C5 | Temperature-sensitive detector alarm |
| C6 | Smoke detector alarm |
| C7 | Combustible gas first-level alarm |
| C8 | Combustible gas two-stage alarm |
Defining the importance relative value between indexes of A → B level, and calculating to obtain a judgment matrix of A → B level:
calculating a weight matrix omega AB ＝[0.167 0.833] T ；
The second-order matrix has complete consistency;
by one-time examination, define B 1 Calculating relative importance value between indexes of the → C level to obtain B 1 Determination matrix for level → C:
calculating a weight matrix omega B1C ＝[0.1089 0.1887 0.3512 0.3512] T ；
And (3) checking consistency: CR = 0.0039-woven fabric 0.1;
by one-time examination, define B 2 Calculating relative importance value between indexes of the → C level to obtain B 2 Determination matrix for level → C:
calculating a weight matrix omega B2C ＝[0.3705 0.1852 0.0995 0.3448] T ；
And (3) checking consistency: CR = 0.0039-woven fabric 0.1;
through one-time check, the weight of the final 8 state quantity signals to the fire risk A is calculated: c i Weight to a = C i To B j Weight of (c) B j The weight for A is as follows:
| numbering | Quantity of state | Weight of |
| C1 | High temperature of battery module | 0.01815 |
| C2 | High temperature of the high pressure tank | 0.03145 |
| C3 | The temperature of the collecting cabinet is high | 0.05853 |
| C4 | High temperature in the cabin | 0.05853 |
| C5 | Temperature-sensitive detector alarm | 0.30875 |
| C6 | Smoke detector alarm | 0.15433 |
| C7 | Combustible gas first-level alarm | 0.08292 |
| C8 | Combustible gas two-stage alarm | 0.28733 |
And calculating a system fire risk value R according to the state values and the weights of all the state quantities. And responding according to the fire risk value R and a set threshold value.
R is more than or equal to 0.05 and less than 0.08, the fire disaster is judged to be a fire risk class of class 1, and a fire protection system gives an alarm.
And R is more than or equal to 0.08 and less than 0.4, judging the fire risk class as class 2, giving an alarm by a fire protection system, and cutting off an electrical loop of the container.
And R is more than or equal to 0.4, judging as 3-level fire risk level, alarming by a fire-fighting system, cutting off an electrical loop of the container, and starting the fire-fighting system.
Claims (9)
1. An energy storage prefabricated cabin fire alarm optimization method based on AHP is characterized in that: utilize BMS signal and fire detector signal coupling analysis comprehensively to discern the fire risk, concrete realization process is: BMS gathers the prefabricated indoor multiple temperature signal of battery, receives the fire detector signal that the fire control host computer transmitted simultaneously, carries out analysis and logic judgement processing to these two main kinds of signals, gives the fire alarm signal after the optimization and sends to fire extinguishing system, and this signal will be as the instruction of fire control warning and fire extinguishing system action, and the risk that the signal after the optimization can greatly reduced conflagration maloperation or refused to move.
2. The energy storage prefabricated cabin fire alarm optimization method based on the AHP as claimed in claim 1, wherein: the method comprises the following steps of carrying out weight distribution on various switch state quantity signals by using AHP, and specifically comprising the following steps:
1) Establishing a fire risk hierarchical structure chart;
2) Constructing a judgment matrix of the A → B level:
in the above formula, D AB Is an n-order matrix, n is the number of B-level indexes and satisfies B ij ＝1/b ji (i，j＝1，2，…，n，)，b ij ＞0，b ii =1, wherein b ij The factor i is the ratio of relative importance of the factor j, and is qualitatively judged by a decision maker;
3) Determining a decision matrix D AB Eigenvector λ = [ λ ] corresponding to maximum eigenvalue λ max 1 λ 2 …λ n ] T Normalizing the lambda to obtain a weight vector omega AB ＝[ω 1 ω 2 …ω n ] T The attribute weight is obtained;
4) Carrying out consistency check on the judgment matrix;
5) Repeating steps 2 to 4 to obtain B 1 →C、B 2 Judgment matrix D of → C B1C 、D B2C And corresponding weight vector omega B1C 、ω B2C And checking consistency;
6) According to omega AB 、ω B1C And ω B2C Calculating the weight of the C-level state quantity to the fire risk A; and weighting and summing all the state quantities to obtain a system fire risk value:
wherein R is the fire risk value of the system, alpha i The weighted value of the ith state quantity is Sij, the Boolean value of the jth ith state quantity of Sij is 0 or 1,1 represents alarm, and 0 represents no alarm.
3. The AHP-based energy storage prefabricated cabin fire alarm optimization method as recited in claim 2, wherein: the consistency check of the judgment matrix in the step 4) is carried out as follows:
4-1) calculating a consistency index:
wherein n is the order of the judgment matrix;
4-2) taking any random consistency index RI;
4-3) calculating the consistency ratio: CR = CI/RI, if CR <0.1, the decision matrix is considered to pass the consistency check, otherwise satisfactory consistency is not achieved.
4. The AHP-based energy storage prefabricated cabin fire alarm optimization method as recited in claim 2, wherein: step 6) comparing the system fire risk value with a set threshold value to obtain a system fire risk level, and making a corresponding response by the system according to the fire risk level;
T 1 ≤R<T 2 : judging that the fire risk grade of the system is grade 1, and early fire or potential fire risk exists, informing an on-duty person by a fire host through sound-light alarm, and sending an alarm signal to a fire-fighting master control room;
T 2 ≤R<T 3 : judging that the fire risk grade of the system is grade 2, and if small-scale fire (such as thermal runaway of a single battery) exists, cutting off an electric loop of the battery prefabricated cabin on the basis of grade 1 response;
R≥T 3 : and judging that the fire risk grade of the system is grade 3, and starting the automatic fire extinguishing system on the basis of grade 2 response if a large-scale fire occurs.
5. A system for the AHP-based energy storage prefabricated cabin fire-fighting alarm optimization method of claim 1, characterized in that: the fire-fighting system receives signals of the fire detectors and sends the signals to the BMS system; the BMS system collects temperature measuring point signals and compares the temperature measuring point signals with a set high-temperature threshold value to generate a switching state quantity signal; and the BMS receives the fire detector switch state quantity signals, analyzes and logically judges the fire detector switch state quantity signals and the temperature measuring point switch state quantity signals together to obtain the fire risk level of the system, and makes different responses according to the fire risk level.
6. The system of the energy storage prefabricated cabin fire alarm optimization method based on AHP of claim 5, wherein: the temperature measuring points comprise battery module temperature measuring points, high-pressure box temperature measuring points, confluence cabinet temperature measuring points and prefabricated cabin temperature measuring points, and the number of each temperature measuring point is several.
7. The system of the energy storage prefabricated cabin fire alarm optimization method based on AHP of claim 5, wherein: the fire detector comprises a temperature-sensing sensor, a smoke detector and a combustible gas detector, wherein a plurality of fire detectors are provided, and when the temperature is detected to be out of limit or the temperature rising speed is detected to be out of limit, a state quantity signal of an action alarm switch of the temperature-sensing detector is given; when the detected smoke concentration exceeds the limit, a signal of the state quantity of an action alarm switch of the smoke detector is given; when the concentration of the combustible gas is detected to be out of limit, a signal of the state quantity of an action alarm switch of the combustible gas detector is given, the combustible gas detector generally alarms in two stages, the first-stage threshold value is 0.1% -5% of the lower limit of the explosion concentration of the combustible gas, the second-stage threshold value is 10% -50% of the lower limit of the explosion concentration of the combustible gas, 1 represents the action of the fire detector, and 0 represents the non-action of the fire detector.
8. The system of the energy storage prefabricated cabin fire alarm optimization method based on AHP of claim 5, wherein: the fire extinguishing system comprises a fire extinguishing host, an alarm system and a fire extinguishing system, wherein the fire extinguishing host receives a signal of the fire detector and sends the signal to the BMS system.
9. The system of the AHP-based energy storage prefabricated cabin fire fighting alarm optimization method of claim 5, wherein: the BMS system comprises a battery module management system, a battery cluster management system and a battery stack management system; the BMS system collects temperature measuring point signals and compares the temperature measuring point signals with a set high-temperature threshold value to generate a switch state quantity signal, wherein 1 represents that the temperature measuring points exceed the limit, and 0 represents that the temperature measuring points do not exceed the limit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211074620.0A CN115620474A (en) | 2022-08-30 | 2022-08-30 | Energy storage prefabricated cabin fire alarm optimization method and system based on AHP |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211074620.0A CN115620474A (en) | 2022-08-30 | 2022-08-30 | Energy storage prefabricated cabin fire alarm optimization method and system based on AHP |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115620474A true CN115620474A (en) | 2023-01-17 |
Family
ID=84858312
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211074620.0A Pending CN115620474A (en) | 2022-08-30 | 2022-08-30 | Energy storage prefabricated cabin fire alarm optimization method and system based on AHP |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115620474A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116978203A (en) * | 2023-09-25 | 2023-10-31 | 四川天地宏华导航设备有限公司 | Intelligent system and method based on fire control |
-
2022
- 2022-08-30 CN CN202211074620.0A patent/CN115620474A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116978203A (en) * | 2023-09-25 | 2023-10-31 | 四川天地宏华导航设备有限公司 | Intelligent system and method based on fire control |
| CN116978203B (en) * | 2023-09-25 | 2023-12-26 | 四川天地宏华导航设备有限公司 | Intelligent system and method based on fire control |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113096343B (en) | Multi-sensor cooperative automobile battery fire prevention system | |
| CN111627181B (en) | Comprehensive pipe rack fire early warning method fusing multi-source parameters and gradient information thereof | |
| CN116246407B (en) | Agriculture and forestry area fire early warning supervisory systems based on artificial intelligence | |
| CN102708645B (en) | Ship-cabin chain fire-disaster alarming priority assessment method | |
| CN205680751U (en) | A kind of battery thermal runaway detecting system | |
| CN102682560B (en) | Device for assessing level of fire interlock alarming in ship cabin | |
| CN113053053B (en) | Interval demonstration multi-sensor fusion disaster cellular alarm linkage system based on particle swarm optimization | |
| CN115620474A (en) | Energy storage prefabricated cabin fire alarm optimization method and system based on AHP | |
| CN111047816A (en) | Fire monitoring method and system for battery charging cabinet | |
| CN106287225A (en) | A kind of combustion gas on-line monitoring early warning system and detection control method thereof | |
| CN112328961A (en) | On-line monitoring device quality evaluation system based on fault tree and Bayesian network | |
| CN116342999A (en) | Open space fire smoke detection and identification method for ultrahigh voltage transformer substation | |
| CN106296440A (en) | Based on transformer station's warning information analysis and decision system integrated for ANN and ES and method | |
| CN116951328A (en) | Intelligent drainage pipeline operation monitoring system based on big data | |
| CN116565354A (en) | Fault grading diagnosis early warning method, system and equipment for electrochemical energy storage system | |
| CN112224023B (en) | High-speed motor train unit storage battery charging protection method | |
| CN110031152B (en) | Thermal heating system and method for detecting temperature deviation accumulation leakage of adjacent days | |
| CN114997804A (en) | Method and system for cooperatively sensing running state of complex power distribution network information physical system | |
| CN104240432B (en) | Mn-rich slag production safety based on information fusion monitoring method | |
| CN110131784B (en) | Thermal heating system and all-day temperature deviation accumulation leakage detection method thereof | |
| CN115171318B (en) | Fire detection method for lithium battery in movable energy storage bin of energy storage power station | |
| CN103440728A (en) | Ship fire extinguishing system cascading failure evaluation method for distributed intelligence control | |
| Du et al. | Battery fault diagnosis and thermal runaway warning based on the Feature-Exponential-Function and Dynamic Time Warping method | |
| Li et al. | Exploration on path improvement of fire alarm system [J] | |
| CN114708702B (en) | Multi-equipment linkage monitoring platform for household safety of old people based on decision engine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |