CN114743332A - Perception early warning method and device for intelligent fire fighting, storage medium and terminal - Google Patents
Perception early warning method and device for intelligent fire fighting, storage medium and terminal Download PDFInfo
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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
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- 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
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- G06Q10/0635—Risk analysis of enterprise or organisation activities
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- 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
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- G06Q50/10—Services
- G06Q50/26—Government or public services
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
Abstract
The invention discloses a perception early warning method, a perception early warning device, a storage medium and a terminal for intelligent fire fighting, wherein the method comprises the following steps: receiving real-time sensing data from a fire sensor; calculating the risk degree of the fire fighting system according to the real-time sensing data and by combining a pre-generated state hyperspace model; and carrying out visual grading early warning according to the risk degree of the fire-fighting system. Because this application is through the real-time data of the whole fire sensors who is used for data acquisition among the analysis fire extinguishing system to combine the super space model of the state that generates in advance to calculate fire extinguishing system's risk degree, combine the risk degree to confirm its state, begin to deviate from the normal value when the state and just carry out the early warning, can early, early warning fire incident more fast, kill the disaster in the bud state, thereby promoted early warning efficiency.
Description
Technical Field
The invention relates to the technical field of intelligent fire safety, in particular to a perception early warning method, a device, a storage medium and a terminal for intelligent fire protection.
Background
At present, the existing fire safety alarm is carried out or alarms when a fire disaster happens or after the fire disaster happens, if the alarm is carried out after the fire disaster happens, although rescue workers can be informed to control the fire disaster in time, the rescue workers need a certain time to arrive at the fire disaster site, and the fire disaster can also cause a certain loss in the time. With the development of the technology, emerging information technologies such as the Internet of things, cloud computing, big data, mobile internet, AI and the like are comprehensively utilized, a fire-fighting big data early warning, forecasting and monitoring platform is constructed, the deep integration of informatization and fire-fighting service work is comprehensively promoted, and the transformation from traditional fire fighting to modern fire fighting is realized for constructing a three-dimensional and full-coverage social fire prevention and control system.
In the prior art, an intelligent fire-fighting classification early warning method and system (application number: CN 110070690A) based on deep learning analyzes the environmental temperature of different monitoring points and the variation trend of the environmental temperature, smoke concentration, flame temperature and flame radiation energy wavelength of a place where a fire easily occurs, and can early warn whether a limit boundary value in the next time period reaches a pre-classification early warning threshold value condition or not in advance, and can early warn the fire in a classification way in advance by early warning the environmental temperature, smoke concentration, flame temperature and flame radiation energy wavelength of the place where the fire possibly occurs, so that rescue workers have time to reach the fire scene, and the loss caused by the fire is reduced. The prior art carries out early warning by analyzing the change trend of environmental temperature, smoke concentration, flame temperature and flame radiation energy wavelength, because the change of these factors usually waits until the conflagration has taken place and can only discover, therefore the early warning is lagged somewhat, and to some fire control facilities, like the fire control water pressure, the fire control hidden danger that the trouble of water level monitoring facility caused, can not carry out timely early warning.
Disclosure of Invention
The embodiment of the application provides a perception early warning method and device for intelligent fire fighting, a storage medium and a terminal. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a perception early warning method for intelligent fire protection, where the method includes:
receiving real-time sensing data from a fire sensor;
calculating the risk degree of the fire fighting system according to the real-time sensing data and by combining a pre-generated state hyperspace model;
and carrying out visual grading early warning according to the risk degree of the fire fighting system.
Optionally, before receiving the real-time sensing data from the fire sensor, the method further includes:
reading feedback information of each fire-fighting facility in the Internet of things after the cloud platform is cleaned and filed;
preprocessing the feedback information of each fire-fighting facility to obtain normal operation data of each fire-fighting facility;
and constructing a state hyperspace model according to the normal operation data of each fire fighting facility.
Optionally, constructing a state hyperspace model according to normal operation data of each fire fighting device, including:
acquiring the acquisition time of the normal operation data of each fire-fighting facility;
sequencing the normal operation data of each fire fighting facility according to the sequence of the acquisition time to obtain sequenced normal operation data;
equidistant sampling is carried out on the sorted normal operation data to obtain sample data;
and carrying out normalization processing on the sample data, and generating a state hyperspace model according to a normalized result.
Optionally, the pre-generated state hyperspace model includes a spatial position calculation module and a distance calculation module;
calculating the risk degree of the fire fighting system according to the real-time sensing data and by combining a pre-generated state hyperspace model, wherein the risk degree comprises the following steps:
calculating the spatial position of the real-time sensing data in a pre-generated state hyperspace model according to a spatial position calculation module;
calculating a target distance between the spatial position and a normal state point existing in a pre-generated state hyperspace model according to a distance calculation module;
and converting the target distance into the risk degree of the fire fighting system.
Optionally, the method further comprises:
calculating the state contribution degree corresponding to the target distance;
drawing a risk degree curve according to the target distance;
and sending the state contribution degree and risk degree curve to a client for displaying.
Optionally, the visual grading early warning is performed according to the risk degree of the fire fighting system, and the method comprises the following steps:
when the risk degree of the fire fighting system is greater than a preset early warning line and the early warning duration and the early warning times are greater than preset parameter values, generating early warning information;
and carrying out visual early warning according to the early warning information.
Optionally, performing visual early warning according to the early warning information, including:
counting the total amount of the early warning information;
when the total amount is larger than a preset threshold value, generating an early warning record;
when the number of the early warning records is larger than the preset number, generating warning information;
acquiring grade color rendering parameters corresponding to the number of the early warning records;
and performing color rendering on the alarm information based on the grade color rendering parameters, and sending the rendered alarm information to the client for display.
In a second aspect, an embodiment of the present application provides a perception early warning device for intelligent fire protection, and the device includes:
the sensing data receiving module is used for receiving real-time sensing data from the fire sensor;
the risk degree calculation module is used for calculating the risk degree of the fire fighting system according to the real-time sensing data and by combining a pre-generated state hyperspace model;
and the grading early warning module is used for carrying out visual grading early warning according to the risk degree of the fire-fighting system.
In a third aspect, embodiments of the present application provide a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, the intelligent fire-fighting perception early warning device firstly receives real-time perception data from a fire-fighting sensor, then calculates the risk degree of a fire-fighting system according to the real-time perception data and by combining a pre-generated state hyperspace model, and finally carries out visual grading early warning according to the risk degree of the fire-fighting system. Because this application is through the real-time data of the whole fire sensors who is used for data acquisition among the analysis fire extinguishing system to combine the super space model of the state that generates in advance to calculate fire extinguishing system's risk degree, combine the risk degree to confirm its state, begin to deviate from the normal value when the state and just carry out the early warning, can early, early warning fire incident more fast, kill the disaster in the bud state, thereby promoted early warning efficiency.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flowchart of a sensory warning method for intelligent fire protection according to an embodiment of the present application;
FIG. 2 is a process diagram of a sensory warning process for intelligent fire protection provided herein;
FIG. 3 is a schematic flow chart of a method for constructing a state hyperspace model according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a sensory warning device for intelligent fire protection according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The application provides a perception early warning method, a perception early warning device, a storage medium and a terminal for intelligent fire fighting, and aims to solve the problems in the related technical problems. Among the technical scheme that this application provided, because this application is through the real-time data of the whole fire sensors who is used for data acquisition among the analysis fire extinguishing system, and combine the super space model of state that generates in advance to calculate fire extinguishing system's risk degree, combine the risk degree to confirm its state, just carry out the early warning when the state begins to deviate from the normal value, can be earlier, early warning fire incident sooner, kill the disaster in the bud state, thereby early warning efficiency has been promoted, adopt the exemplary embodiment to explain in detail below.
The following describes in detail the perception early warning method for intelligent fire protection provided by the embodiment of the present application with reference to fig. 1 to 3. The method can be realized by relying on a computer program and can run on a sensing and early warning device of intelligent fire protection based on a Von Neumann system. The computer program may be integrated into the application or may run as a separate tool-like application.
Referring to fig. 1, a schematic flow chart of a perception early warning method for intelligent fire protection is provided in an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application may include the following steps:
s101, receiving real-time sensing data from a fire sensor;
the fire fighting sensors are a large number of fire fighting components arranged in the actual environment, such as fire fighting related acquisition sensors, fire extinguishing devices, water taps or video information. This large number of fire fighting components may constitute the internet of things of a fire fighting system.
Generally, information of fire fighting facilities is collected through the internet of things of a fire fighting system and stored on a cloud platform to become historical sensing data.
In the embodiment of the application, before receiving real-time sensing data from a fire sensor, a state hyperspace model needs to be generated, when the model is generated, feedback information of each fire protection facility in the internet of things after being cleaned and filed by a cloud platform is read at first, then the feedback information of each fire protection facility is preprocessed, normal operation data of each fire protection facility is obtained, and finally the state hyperspace model is constructed according to the normal operation data of each fire protection facility.
Specifically, when the state hyperspace model is constructed according to the normal operation data of each fire fighting facility, the acquisition time of the normal operation data of each fire fighting facility is firstly acquired, then the normal operation data of each fire fighting facility is sequenced according to the sequence of the acquisition time to obtain sequenced normal operation data, then the sequenced normal operation data is sampled at equal intervals to obtain sample data, and finally the sample data is normalized to generate the state hyperspace model according to the normalized result.
In one possible implementation, after the state hyperspace model is generated, real-time sensing data from the fire sensor can be received for early warning analysis.
S102, calculating the risk degree of the fire fighting system according to the real-time sensing data and by combining a pre-generated state hyperspace model;
the real-time perception data is the latest acquired data at the current moment.
Typically, the pre-generated state hyperspace model includes a spatial location computation module and a distance computation module.
In the embodiment of the application, the spatial position of real-time sensing data in the pre-generated state hyperspace model is calculated according to a spatial position calculating module, then the target distance between the spatial position and the existing normal state point in the pre-generated state hyperspace model is calculated according to a distance calculating module, and finally the target distance is converted into the risk degree of a fire fighting system.
Further, after the risk degree of the fire fighting system is obtained, the state contribution degree corresponding to the target distance is calculated firstly, then a risk degree curve is drawn according to the target distance, and finally the state contribution degree and the risk degree curve are sent to the client side for displaying.
In a possible implementation manner, the newly entered real-time sensing data first needs to calculate a position in the state hyperspace model, then calculates a distance from the position of the normal state point according to the position, and converts the distance into a risk degree. Assuming that the new one second of sensing data is transmitted back, the distance between the new data and the normal data can be measured based on the spatial position according to the sample set. The distance calculation may be performed using the euclidean distance, or other distance, and the euclidean distance calculation formula is:
And further, according to the calculated distance, calculating the positions of the distance in all normal distances, normalizing the positions into the risk degree, and generating a time sequence of the risk degree of the fire fighting system. For a single risk value, if the value is closer to 0, the more normal the state is, the lower the risk of the system is; if the numerical value is closer to 1, the more abnormal the state is, the higher the risk of the system is. In addition, the state contribution degree corresponding to the distance is calculated through an AI algorithm, so that the reason causing the distance change is visually known, maintenance personnel of the fire fighting system are further guided to conduct investigation, and the efficiency of finding the potential risk is improved.
It should be noted that each real-time data of the fire fighting system can be calculated to obtain a risk value through distance calculation. And (3) continuously inputting real-time data into the model along with the operation of the system, thereby obtaining a risk degree curve of the operation state of the system. The risk degree curve is displayed in a visual mode in real time in a Web page, and the instant risk condition of the fire fighting system is depicted.
And S103, carrying out visual grading early warning according to the risk degree of the fire-fighting system.
In which the fire fighting system often has occasional transient abnormal conditions, but then no abnormal symptoms occur. The situation does not affect normal work, and is a normal state of a fire fighting system, and the phenomenon is called abnormal normal state in a model. When the risk degree of the fire protection system is monitored, the abnormal normality needs to be filtered, otherwise, the false alarm rate of the system is greatly increased.
Normally, the setting is performed by an abnormal normality rule setting page. The filtering rule for normalizing normality is: the user can input parameters such as an early warning line, an early warning accumulation frequency, an early warning interval duration and the like according to the maintenance experience of the fire fighting system on the Web page. Only when the risk degree (the risk degree calculated by the model) is lower than an early warning line, the early warning interval duration is less than M time, and the early warning cumulative frequency is more than N times, the system can generate an early warning record. The early warning record comprises variables such as early warning starting time, early warning level, early warning duration and the like.
In the embodiment of the application, when visual hierarchical early warning is performed according to the risk degree of a fire protection system, firstly, when the risk degree of the fire protection system is larger than a preset early warning line and the early warning time and the early warning frequency are larger than preset parameter values, early warning information is generated, and then visual early warning is performed according to the early warning information.
Specifically, when carrying out visual early warning according to early warning information, at first count the total amount of early warning information, then when the total amount is greater than and predetermines the threshold value, generate the early warning record, secondly when the quantity of early warning record is greater than and predetermines a number, generate alarm information, acquire the grade color rendering parameter that the quantity of early warning record corresponds at last, and render the parameter and carry out the color rendering to alarm information based on grade color, and send the alarm information after rendering to the customer end and demonstrate.
In one possible implementation manner, the early warning of the overall health degree of the fire fighting system can be performed in three levels, which are respectively: risk moderate warning (yellow), risk severe warning (orange), transmitter failure warning (red). The early warnings of the three levels are respectively set corresponding to different rules, namely parameters such as 'early warning lines', 'early warning accumulated times', 'early warning interval duration' and the like of different levels are set. The adjustable grading early warning meets the business requirements of early warning management work of the fire fighting system faults.
For example, as shown in fig. 2, fig. 2 is a schematic process diagram of the intelligent fire-fighting sensing and early-warning process provided by the present application, and firstly, abnormal state data cleaning is performed, and processing can be performed through a set data cleaning rule, and the function of this part is to delete all interference data and fault data, so that all data entering a subsequent algorithm are normal and healthy system operation data. Secondly, a state risk degree algorithm is adopted, space sampling and normalization processing are carried out according to normal state data input in the previous step, a state hyperspace model is generated, the position of the newly input data in the state hyperspace model is firstly calculated, then the distance between the newly input data and a normal state point is calculated, and the distance is converted into a risk degree; and finally, carrying out grading early warning, drawing a risk curve of the system according to the distance reading calculated in real time, and giving three levels of early warning after filtering abnormal normality.
In the embodiment of the application, the intelligent fire-fighting perception early warning device firstly receives real-time perception data from a fire-fighting sensor, then calculates the risk degree of a fire-fighting system according to the real-time perception data and by combining a pre-generated state hyperspace model, and finally carries out visual grading early warning according to the risk degree of the fire-fighting system. Because this application is through the real-time data of the whole fire sensors who is arranged in the analysis fire extinguishing system for data acquisition to combine the super space model of state that generates in advance to calculate fire extinguishing system's risk degree, combine the risk degree to confirm its state, just carry out the early warning when the state begins to deviate from the normal value, can early, more quickly early warning fire incident, kill the disaster in the bud state, thereby promoted early warning efficiency.
Referring to fig. 3, a schematic flow chart of a method for constructing a state hyperspace model according to an embodiment of the present application is provided. As shown in fig. 3, the method of the embodiment of the present application may include the following steps:
s201, reading feedback information of each fire fighting facility in the Internet of things after the cloud platform is cleaned and filed;
s202, preprocessing the feedback information of each fire-fighting facility to obtain normal operation data of each fire-fighting facility;
in a possible implementation mode, the feedback information of each fire fighting facility in the internet of things after being cleaned and filed by the cloud platform is read, and then the interference data and the fault data are deleted according to the data cleaning rule so as to ensure that all the data are healthy operation data.
The abnormal state data is set according to the regulations and rules of the fire protection system. The data cleansing rule setting is specifically as follows:
(1) the fire fighting system returns data in seconds according to the regulations, but occasionally, the data cannot be returned, or the data is cut off due to various reasons. If fire fighting equipment fails during data truncation, the data model can be distorted. Therefore, if the truncation time is more than 5 minutes, that is, 300 seconds in the normal working cycle, the data of one state cycle before the truncation point is deleted according to the preset rule.
(2) Only the data with the state of working or failure in the returned data of the fire fighting system are reserved.
(3) In order to ensure the reliability of the operation data, the data of the state operation for less than 5 minutes is also deleted.
(4) To ensure the speed of the data platform operation, only the latest 60 days of data are reserved.
(5) In order to ensure the speed and accuracy of model establishment, duplicate removal processing is carried out, only one record in the same running state is reserved, and the rest records are deleted.
(6) If a fault occurs, the data is deleted from the fault point to the earliest operation period (day) of the time, and normal data is guaranteed to be kept as much as possible.
And S203, constructing a state hyperspace model according to the normal operation data of each fire-fighting facility.
In a possible implementation mode, after the normal data are obtained, the screened normal data are sorted according to the time sequence, the sorted data are sampled at equal intervals, and all data in a certain proportion are extracted as sample data, so that the samples are rich enough to represent the whole state space. And then all sample data are normalized, and the dimension relation among variables is unified. If the values of some variables in the data may not change, it indicates that the corresponding sensors may fail or malfunction, and thus the data normalization will fail, so that the data needs to be further deleted.
It should be noted that, considering that the model algorithm is distance-based measurement and the fire fighting system operation data is mostly numerical, zero-mean normalization is adopted.
Assuming that the fire protection system has m status points in a period of time, each status point is composed of n sensor measuring points, and an observation vector defining a certain status point tj (j is 1, …, m) is x (tj) [ x1(tj), x2(tj), …, xn (tj) ], wherein xi (tj) is a measured value of the ith measuring point at the status point tj and represents an operation state of the fire protection device at the moment of tj. The expression for the state hyperspace model D can then be expressed as:
in the embodiment of the application, the intelligent fire-fighting perception early warning device firstly receives real-time perception data from a fire-fighting sensor, then calculates the risk degree of a fire-fighting system according to the real-time perception data and by combining a pre-generated state hyperspace model, and finally carries out visual grading early warning according to the risk degree of the fire-fighting system. Because this application is through the real-time data of the whole fire sensors who is used for data acquisition among the analysis fire extinguishing system to combine the super space model of the state that generates in advance to calculate fire extinguishing system's risk degree, combine the risk degree to confirm its state, begin to deviate from the normal value when the state and just carry out the early warning, can early, early warning fire incident more fast, kill the disaster in the bud state, thereby promoted early warning efficiency.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Referring to fig. 4, a schematic structural diagram of an intelligent fire-fighting sensing and warning device according to an exemplary embodiment of the present invention is shown. The intelligent fire-fighting perception early warning device can be realized to be all or part of the terminal through software, hardware or the combination of the software and the hardware. The device 1 comprises a perception data receiving module 10, a risk degree calculating module 20 and a grading early warning module 30.
The sensing data receiving module 10 is used for receiving real-time sensing data from the fire sensor;
the risk degree calculation module 20 is used for calculating the risk degree of the fire fighting system according to the real-time sensing data and by combining a pre-generated state hyperspace model;
and the grading early warning module 30 is used for carrying out visual grading early warning according to the risk degree of the fire-fighting system.
It should be noted that, when the sensing and early warning apparatus for intelligent fire protection provided in the above embodiment executes the sensing and early warning method for intelligent fire protection, the above division of each functional module is only used for illustration, and in practical application, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the above described functions. In addition, the above embodiment provides the intelligent fire-fighting perception early warning device and the intelligent fire-fighting perception early warning method embodiment, which belong to the same concept, and the embodiment of the method for implementing the perception early warning device and the perception early warning method embodiment is detailed in the embodiment, and is not described again here.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the embodiment of the application, the intelligent fire-fighting perception early warning device firstly receives real-time perception data from a fire-fighting sensor, then calculates the risk degree of a fire-fighting system according to the real-time perception data and by combining a pre-generated state hyperspace model, and finally carries out visual grading early warning according to the risk degree of the fire-fighting system. Because this application is through the real-time data of the whole fire sensors who is used for data acquisition among the analysis fire extinguishing system to combine the super space model of the state that generates in advance to calculate fire extinguishing system's risk degree, combine the risk degree to confirm its state, begin to deviate from the normal value when the state and just carry out the early warning, can early, early warning fire incident more fast, kill the disaster in the bud state, thereby promoted early warning efficiency.
The invention also provides a computer readable medium, on which program instructions are stored, and the program instructions, when executed by a processor, implement the perception early warning method for intelligent fire protection provided by the above method embodiments. The invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to execute the method for intelligent fire fighting perception early warning of the above method embodiments.
Please refer to fig. 5, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 5, terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may further include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may alternatively be at least one memory device located remotely from the processor 1001. As shown in fig. 5, the memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and an intelligent fire fighting perceptual warning application program.
In the terminal 1000 shown in fig. 5, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be configured to call the intelligent fire fighting perceptual alert application stored in the memory 1005, and specifically perform the following operations:
receiving real-time sensing data from a fire sensor;
calculating the risk degree of the fire fighting system according to the real-time sensing data and by combining a pre-generated state hyperspace model;
and carrying out visual grading early warning according to the risk degree of the fire-fighting system.
In one embodiment, the processor 1001, when executing the receiving of the real-time perception data from the fire sensor, further performs the following operations:
reading feedback information of each fire-fighting facility in the Internet of things after the cloud platform is cleaned and filed;
preprocessing the feedback information of each fire-fighting facility to obtain normal operation data of each fire-fighting facility;
and constructing a state hyperspace model according to the normal operation data of each fire fighting facility.
In one embodiment, processor 1001, in performing the building of the state hyperspace model from the normal operating data of each fire protection installation, specifically performs the following operations:
acquiring the acquisition time of the normal operation data of each fire-fighting facility;
sequencing the normal operation data of each fire fighting facility according to the sequence of the acquisition time to obtain sequenced normal operation data;
equidistant sampling is carried out on the sorted normal operation data to obtain sample data;
and carrying out normalization processing on the sample data, and generating a state hyperspace model according to a normalized result.
In one embodiment, when the processor 1001 calculates the risk level of the fire fighting system according to the real-time sensing data and by combining the state hyperspace model generated in advance, the following operations are specifically performed:
calculating the spatial position of the real-time sensing data in a pre-generated state hyperspace model according to a spatial position calculation module;
calculating a target distance between the spatial position and a normal state point existing in a pre-generated state hyperspace model according to a distance calculation module;
and converting the target distance into the risk degree of the fire fighting system.
In one embodiment, the processor 1001 also performs the following operations:
calculating the state contribution degree corresponding to the target distance;
drawing a risk degree curve according to the target distance;
and sending the state contribution degree and risk degree curve to a client for displaying.
In one embodiment, the processor 1001 performs the following operations when performing the visual grading pre-warning according to the risk degree of the fire protection system:
when the risk degree of the fire fighting system is greater than a preset early warning line and the early warning duration and the early warning times are greater than preset parameter values, generating early warning information;
and carrying out visual early warning according to the early warning information.
In an embodiment, when performing the visual warning according to the warning information, the processor 1001 specifically performs the following operations:
counting the total amount of the early warning information;
when the total amount is larger than a preset threshold value, generating an early warning record;
when the number of the early warning records is larger than the preset number, generating warning information;
acquiring grade color rendering parameters corresponding to the number of the early warning records;
and performing color rendering on the alarm information based on the grade color rendering parameters, and sending the rendered alarm information to the client for display.
In the embodiment of the application, the intelligent fire-fighting perception early warning device firstly receives real-time perception data from a fire-fighting sensor, then calculates the risk degree of a fire-fighting system according to the real-time perception data and by combining a pre-generated state hyperspace model, and finally carries out visual grading early warning according to the risk degree of the fire-fighting system. Because this application is through the real-time data of the whole fire sensors who is arranged in the analysis fire extinguishing system for data acquisition to combine the super space model of state that generates in advance to calculate fire extinguishing system's risk degree, combine the risk degree to confirm its state, just carry out the early warning when the state begins to deviate from the normal value, can early, more quickly early warning fire incident, kill the disaster in the bud state, thereby promoted early warning efficiency.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program to instruct related hardware, and the program for the sensory warning of intelligent fire protection can be stored in a computer-readable storage medium, and when executed, the program can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. A perception early warning method for intelligent fire fighting is characterized by comprising the following steps:
receiving real-time sensing data from a fire sensor;
calculating the risk degree of the fire fighting system according to the real-time sensing data and by combining a pre-generated state hyperspace model;
and carrying out visual grading early warning according to the risk degree of the fire fighting system.
2. The method of claim 1, wherein prior to receiving real-time sensory data from a fire sensor, further comprising:
reading feedback information of each fire-fighting facility in the Internet of things after the cloud platform is cleaned and filed;
preprocessing the feedback information of each fire-fighting facility to obtain normal operation data of each fire-fighting facility;
and constructing a state hyperspace model according to the normal operation data of each fire fighting facility.
3. The method of claim 2, wherein constructing the state hyperspace model from normal operating data for each fire protection facility comprises:
acquiring the acquisition time of the normal operation data of each fire-fighting facility;
sequencing the normal operation data of each fire fighting facility according to the sequence of the acquisition time to obtain sequenced normal operation data;
equidistant sampling is carried out on the sorted normal operation data to obtain sample data;
and carrying out normalization processing on the sample data, and generating a state hyperspace model according to a result after normalization.
4. The method of claim 1, wherein the pre-generated state hyperspace model comprises a spatial location computation module and a distance computation module;
the method for calculating the risk degree of the fire fighting system according to the real-time perception data and by combining a pre-generated state hyperspace model comprises the following steps:
calculating the spatial position of the real-time perception data in a pre-generated state hyperspace model according to the spatial position calculation module;
calculating a target distance between the spatial position and a normal state point existing in the pre-generated state hyperspace model according to the distance calculation module;
and converting the target distance into the risk degree of a fire fighting system.
5. The method of claim 4, further comprising:
calculating the state contribution degree corresponding to the target distance;
drawing a risk degree curve according to the target distance;
and sending the state contribution degree and the risk degree curve to a client for displaying.
6. The method of claim 1, wherein the visually grading pre-warning according to the risk level of the fire protection system comprises:
when the risk degree of the fire fighting system is larger than a preset early warning line and the early warning duration and the early warning times are larger than preset parameter values, generating early warning information;
and carrying out visual early warning according to the early warning information.
7. The method of claim 6, wherein the performing the visual warning according to the warning information comprises:
counting the total amount of the early warning information;
when the total number is larger than a preset threshold value, generating an early warning record;
when the number of the early warning records is larger than the preset number, generating warning information;
acquiring grade color rendering parameters corresponding to the number of the early warning records;
and performing color rendering on the alarm information based on the grade color rendering parameters, and sending the rendered alarm information to a client for display.
8. The utility model provides a perception early warning device of intelligence fire control which characterized in that, the device includes:
the sensing data receiving module is used for receiving real-time sensing data from the fire sensor;
the risk degree calculation module is used for calculating the risk degree of the fire fighting system according to the real-time perception data and by combining a pre-generated state hyperspace model;
and the grading early warning module is used for carrying out visual grading early warning according to the risk degree of the fire fighting system.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to perform the method steps according to any of claims 1-7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-7.
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