CN110475223A - A kind of method for safety monitoring, device and terminal device - Google Patents
A kind of method for safety monitoring, device and terminal device Download PDFInfo
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Abstract
The present invention is suitable for safety monitoring technology field, provide a kind of method for safety monitoring, device and terminal device, applied to server, the server and sensing node communicate to connect, the described method includes: obtaining the environmental data that sensing node is sent, the environmental data is analyzed, and risk class is judged based on the analysis results, corresponding work order is generated according to the risk class, and the work order is sent to the sensing node, corresponding operation is executed to control the sensing node according to the work order.The present invention adjusts operating mode and the sampling period of sensing node by dynamic, so that safety monitoring system is when data occur abnormal, the working condition of sensing node can quickly be adjusted, improve sample frequency, system is enabled to quickly detect exception in fire early period of origination, safety monitoring system is improved to the efficiency and safety of fire hazard monitoring, while saving electric energy.
Description
Technical field
The invention belongs to safety monitoring technology field more particularly to a kind of method for safety monitoring, device and terminal device.
Background technique
In existing multihop self-organizing network, sensing node mostly uses battery to power.
In order to meet the requirement of battery life, the sensing node most of the time works in dormant state；Under normal circumstances, electric
Battery-powered wireless low-power consumption temperature sensor may wake up primary, acquisition data and transmission data at interval of certain time interval
Time continue preset time period after, sensing node enters dormant state, until wake up next time.
However, occurring from fire non-to the time interval of vigorous combustion in environment, security monitoring field (such as fire-fighting domain)
Often short, therefore, the sleep time of sensing node is too long, will lead to safety monitoring system and is difficult to carry out early warning in Initial Stage of Fire.If
The dormancy time of sensing node is too short, then not can guarantee the service life of battery.
Summary of the invention
In view of this, the embodiment of the invention provides a kind of method for safety monitoring, device and terminal device, it is existing to solve
In technology in environment, security monitoring field, the sleep time of sensing node is too long, will lead to safety monitoring system and is difficult in fire
Calamity initial stage carries out early warning.If the dormancy time of sensing node is too short, the problem of not can guarantee the service life of battery.
The first aspect of the embodiment of the present invention provides a kind of method for safety monitoring, is applied to server, the server
It is communicated to connect with sensing node；
The described method includes:
Obtain the environmental data that sensing node is sent；
The environmental data is analyzed, and judges risk class based on the analysis results；
Corresponding work order is generated according to the risk class, and the work order is sent to the sensing and is saved
Point executes corresponding operation to control the sensing node according to the work order.
Optionally, the environmental data is analyzed, and judges risk class based on the analysis results, comprising:
Pre-establish the standard deviation of the slope of the sequence of environmental data and the corresponding relationship of risk class or environmental data
With the corresponding relationship of risk class；
Calculate the slope of the sequence of environmental data or the standard deviation of environmental data；
Corresponding risk class is searched according to the slope or the standard deviation.
Optionally, the environmental data is analyzed, and judges risk class based on the analysis results, further includes:
Establish machine learning model；
The machine learning model is inputted using environmental data as input data, obtains the risk class of output.
Optionally, described the step of establishing machine learning model, includes:
Obtain history fire sample data；
Machine learning model is trained according to the history fire sample data, obtains machine learning after training
Model.
Optionally, corresponding work order is generated according to the risk class, and the work order is sent to described
Sensing node executes corresponding operation to control the sensing node according to the work order, comprising:
If the risk class is low-risk, the first work order is generated；Wherein, first work order is for controlling
The sensing node is made into low sample frequency and low power loss communication mode；
If the risk class is medium risk, the second work order is generated；Wherein, second work order is used for
It controls the sensing node and enters moderate sampling frequency and medium power consumption communication pattern；
If the risk class is high risk, third work order is generated；Wherein, the third work order is for controlling
It makes the sensing node and enters high sample frequency and high power consumption communication pattern.
The second aspect of the embodiment of the present invention provides a kind of safety monitoring device, is applied to server, the server
It is communicated to connect with sensing node；
Described device includes:
Module is obtained, for obtaining the environmental data of sensing node transmission；
Analysis module for analyzing the environmental data, and judges risk class based on the analysis results；
Generation module for generating corresponding work order according to the risk class, and the work order is sent
To the sensing node, corresponding operation is executed to control the sensing node according to the work order.
Optionally, the analysis module, comprising:
First establishing unit, for pre-establishing the slope of the sequence of environmental data and the corresponding relationship of risk class, or
The standard deviation of person's environmental data and the corresponding relationship of risk class；
Computing unit, for calculating the slope of the sequence of environmental data or the standard deviation of environmental data；
Searching unit, for searching corresponding risk class according to the slope or the standard deviation.
Optionally, the analysis module, further includes:
Second establishes unit, for establishing machine learning model；
Input unit obtains the wind of output for inputting the machine learning model for environmental data as input data
Dangerous grade.
Optionally, described second unit is established, comprising:
Subelement is obtained, for obtaining history fire sample data；
Training subelement obtains instruction for being trained according to the history fire sample data to machine learning model
Machine learning model after white silk.
Optionally, the generation module, comprising:
First generation unit generates the first work order if being low-risk for the risk class；Wherein, described
First work order is for controlling the sensing node into low sample frequency and low power loss communication mode；
Second generation unit generates the second work order if being medium risk for the risk class；Wherein, institute
It states the second work order and enters moderate sampling frequency and medium power consumption communication pattern for controlling the sensing node；
Third generation unit generates third work order if being high risk for the risk class；Wherein, described
Third work order enters high sample frequency and high power consumption communication pattern for controlling the sensing node.
The third aspect of the embodiment of the present invention provides a kind of terminal device, comprising: memory, processor and is stored in
In the memory and the computer program that can run on the processor, when the processor executes the computer program
It realizes such as the step of the above method.
The fourth aspect of the embodiment of the present invention provides a kind of computer readable storage medium, the computer-readable storage
Media storage has computer program, realizes when the computer program is executed by processor such as the step of the above method.
The embodiment of the present invention adjusts operating mode and the sampling period of sensing node by dynamic, so that safety monitoring system
When data occur abnormal, the working condition of sensing node can be quickly adjusted, improves sample frequency, so that system is sent out in fire
Raw initial stage can quickly detect exception, improve safety monitoring system to the efficiency and safety of fire hazard monitoring, save simultaneously
Electric energy.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only of the invention some
Embodiment for those of ordinary skill in the art without any creative labor, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is the flow diagram for the method for safety monitoring that the embodiment of the present invention one provides；
Fig. 2 is the structural schematic diagram for the low-power consumption multihop self-organizing network that the embodiment of the present invention one provides；
Fig. 3 is the flow diagram of method for safety monitoring provided by Embodiment 2 of the present invention；
Fig. 4 is the flow diagram for the method for safety monitoring that the embodiment of the present invention three provides；
Fig. 5 is the flow diagram for the method for safety monitoring that the embodiment of the present invention four provides；
Fig. 6 is the structural schematic diagram for the safety monitoring device that the embodiment of the present invention five provides；
Fig. 7 is the schematic diagram for the terminal device that the embodiment of the present invention six provides.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical solution in the embodiment of the present invention are explicitly described, it is clear that described embodiment is the present invention one
The embodiment divided, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not doing
Every other embodiment obtained under the premise of creative work out, should fall within the scope of the present invention.
Description and claims of this specification and term " includes " and their any deformations in above-mentioned attached drawing, meaning
Figure, which is to cover, non-exclusive includes.Such as process, method or system comprising a series of steps or units, product or equipment do not have
It is defined in listed step or unit, but optionally further comprising the step of not listing or unit, or optionally also wrap
Include the other step or units intrinsic for these process, methods, product or equipment.In addition, term " first ", " second " and
" third " etc. is for distinguishing different objects, not for description particular order.
In order to illustrate technical solutions according to the invention, the following is a description of specific embodiments.
Embodiment one
As shown in Figure 1, this method can be applied to such as mobile phone, PC, plate the present embodiment provides a kind of method for safety monitoring
The terminal devices such as computer or server.Method for safety monitoring provided by the present embodiment, comprising:
S101, the environmental data that sensing node is sent is obtained.
In a particular application, the environmental data that each sensing node is sent in multihop self-organizing network is obtained；Wherein, environment
Data refer to the environmental parameter that each sensing node is obtained by a variety of different types of multiple sensors.Environmental data include but
It is not limited to: smokescope, CO concentration, temperature and humidity.
In one embodiment, this method is applied to the server in low-power consumption multihop self-organizing network, wherein low-power consumption
Multi-hop wireless sensing network includes: server and more than one sensing node, routing node, edge router；Server with
More than one edge router communication connection, an edge router and a routing node communicate to connect, a routing section
Point is communicated to connect with more than one sensing node.
Fig. 2 illustratively shows a kind of structure chart of low-power consumption multihop self-organizing network.
In one embodiment, after sensing node gets environmental data, environmental data is sent to routing node, is routed
Environmental data is forwarded to edge router by node, and environmental data is forwarded to server by edge router.
In one embodiment, sensing node includes sensing module, radio receiving transmitting module, network control module and power consumption control
Molding block；
Wherein, sensing module is for detecting environmental data.
Radio receiving transmitting module includes receiving module and sending module；Wherein, it when radio receiving transmitting module is in wake-up states, connects
It receives module and sending module is in active state；
When radio receiving transmitting module is in listening state, receiving module is in active state, and sending module is in a dormant state；
Radio receiving transmitting module in a dormant state when, receiving module and sending module are in dormant state.
Network control module includes monitoring control module and data transmission module；Wherein, control module is monitored for controlling
Radio receiving transmitting module obtains the control instruction from server that routing node is sent；Data transmission module is for controlling wireless receipts
It sends out module and environmental data is sent to routing node.
Power consumption control module includes sleep block, monitors module and wake-up module；Wherein, sleep block, monitor module and
Wake-up module was respectively used to according to the sampling period, and the working condition for controlling network control module and radio receiving transmitting module switches to not
Dormancy state, listening state or wake-up states.Using real-time listening mode, the communication delay in network can reduce, so that system
Exception can be quickly detected in fire early period of origination.
Specifically, wireless sensor network (Wireless Sensor Networks, WSN) is a kind of distributed sensing net
Network, tip are the sensors that can perceive and check the external world.Sensor in WSN wirelessly communicates, network
Setting is flexible, and sensing equipment position can be changed at any time, can also pass through wired or wireless way with internet and connect.
Wherein, it is multihop self-organizing network that mode, which forms wireless sensor network, by wireless communication." multi-hop ad hoc net
For network " by multiple joint structures, node includes but is not limited to such as computer and mobile phone terminal device, above-mentioned terminal device equipment
By being wirelessly connected to each other, forward data can be passed through mutually.Data jump to another from a node in a network
Node, until reaching the destination.When all nodes all break down, data are unavailable.Therefore, multihop self-organizing network
Network topology structure is reliable and expansible.
S102, the environmental data is analyzed, and judges risk class based on the analysis results.
In a particular application, environmental data is analyzed, whether judges sensing node region based on the analysis results
There are risk and the grades of risk.Wherein, risk class includes but is not limited to low-risk, medium risk and high risk.
S103, corresponding work order is generated according to the risk class, and the work order is sent to the biography
Feel node, executes corresponding operation to control the sensing node according to the work order.
In a particular application, corresponding work order is generated according to risk class, and work order is sent to sensing section
Point executes corresponding operation to control sensing node according to work order.Wherein, work order includes but is not limited to be used to control
Sensing node enter low sample frequency and low power loss communication mode the first work order, for control sensing node enter it is medium
Second work order of sample frequency and medium power consumption communication pattern, and enter high sample frequency and height for controlling sensing node
The third work order of power communications mode.
The present embodiment adjusts operating mode and the sampling period of sensing node by dynamic, so that safety monitoring system is in number
When according to occurring abnormal, the working condition of sensing node can be quickly adjusted, improving sample frequency, and system is occurred in fire
Initial stage can quickly detect exception, improve safety monitoring system to the efficiency and safety of fire hazard monitoring, while saving electricity
Energy.
Embodiment two
As shown in Fig. 2, the present embodiment is the further explanation to the method and step in embodiment one.In the present embodiment,
Step S102, comprising:
S1021, the slope of sequence for pre-establishing environmental data and the corresponding relationship of risk class or environmental data
The corresponding relationship of standard deviation and risk class.
In a particular application, the sequence for obtaining environmental data, the slope and risk of the sequence of the environmental data pre-established
The corresponding relationship of grade；Alternatively, obtaining the standard deviation of environmental data, the standard deviation and risk class of environmental data are pre-established
Corresponding relationship.
The slope of the sequence of S1022, calculating environmental data or the standard deviation of environmental data.
In a particular application, the slope of the sequence of environmental data is calculated, or calculates the standard deviation of environmental data.
S1023, corresponding risk class is searched according to the slope or the standard deviation.
In a particular application, corresponding risk class is obtained according to the slope of the sequence of environmental data, alternatively, according to environment
The standard deviation of data obtains corresponding risk class.
The present embodiment is calculated by the change rate of environmental data and obtains corresponding risk class, and the reliability of data is improved
And safety.
Embodiment three
As shown in figure 3, the present embodiment is the further explanation to the method and step in embodiment one.In the present embodiment,
Step S102, further includes:
S1024, machine learning model is established；
In a particular application, machine learning model is established, wherein machine learning model can include but is not limited to BP nerve
One of network model, Random Forest model and supporting vector machine model.
S1025, the machine learning model is inputted using environmental data as input data, obtains the risk class of output.
In a particular application, machine learning model is inputted using environmental data as input data, obtains the risk etc. of output
Grade is as a result, risk class to determine sensing node region.
In one embodiment, the step of establishing machine learning model include:
Obtain history fire sample data；
Machine learning model is trained according to the history fire sample data, obtains machine learning after training
Model.
In a particular application, history fire sample data is obtained, according to history fire sample data to machine learning model
It is trained, obtains machine learning model after training.Wherein, history fire sample data is target within a preset period of time
The data for the fire that place occurred.For example, be set as 1 year in preset time period, obtain in June, 2018 in June, 2019 it
Between the data of fire that occurred of target place, as history fire sample data.
The present embodiment is trained by establishing machine learning model, obtains machine learning model exports and environment
The corresponding risk class of data improves efficiency and accuracy rate to fire hazard monitoring.
Example IV
As shown in figure 4, the present embodiment is the further explanation to the method and step in embodiment one.In the present embodiment,
Step S103, comprising:
If S1031, the risk class are low-risk, the first work order is generated；Wherein, first work order
For controlling the sensing node into low sample frequency and low power loss communication mode.
In a particular application, if risk class is low-risk, the first work order is generated；Wherein, the first work order
For controlling sensing node into low sample frequency and low power loss communication mode.Low sample frequency refers to lower than the first predeterminated frequency
Sample frequency.First predeterminated frequency can specifically be set according to the actual situation.Low power loss communication mode refers to control sensing
The radio receiving transmitting module of node enters dormant state.
If S1032, the risk class are medium risk, the second work order is generated；Wherein, second work refers to
It enables and enters moderate sampling frequency and medium power consumption communication pattern for controlling the sensing node.
In a particular application, if risk class is medium risk, the second work order is generated；Wherein, the second work refers to
It enables and enters moderate sampling frequency and medium power consumption communication pattern for controlling sensing node.Moderate sampling frequency refers to higher than first
Predeterminated frequency and the sample frequency for being lower than the second predeterminated frequency.Second predeterminated frequency can specifically be set according to the actual situation,
It should be noted that the second predeterminated frequency is higher than the first predeterminated frequency.Medium power consumption communication pattern refers to control sensing node
Radio receiving transmitting module enters listening state.
If S1033, the risk class are high risk, third work order is generated；Wherein, the third work order
Enter high sample frequency and high power consumption communication pattern for controlling the sensing node.
In a particular application, if risk class is high risk, third work order is generated；Wherein, third work order
Enter high sample frequency and high power consumption communication pattern for controlling sensing node.High sample frequency refers to higher than the second predeterminated frequency
Sample frequency.High power consumption communication pattern refers to that the radio receiving transmitting module of control sensing node enters wake-up states.
The present embodiment according to the risk class got come the operating mode for dynamically adjusting sensing node and sampling by adopting
Sample frequency enables safety monitoring system to quickly detect exception in fire early period of origination, improves safety monitoring system to fire
The efficiency and safety of calamity monitoring.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each process
Execution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present invention constitutes any limit
It is fixed.
Embodiment five
As shown in figure 5, the present embodiment provides a kind of safety monitoring device 100, for executing the step of the method in embodiment one
Suddenly.Safety monitoring device 100 provided in this embodiment is integrated in server, and safety monitoring device 100 includes:
Module 101 is obtained, for obtaining the environmental data of sensing node transmission；
Analysis module 102 for analyzing the environmental data, and judges risk class based on the analysis results；
Generation module 103 for generating corresponding work order according to the risk class, and the work order is sent out
It send to the sensing node, executes corresponding operation to control the sensing node according to the work order.
In one embodiment, the analysis module 102, comprising:
First establishing unit, for pre-establishing the slope of the sequence of environmental data and the corresponding relationship of risk class, or
The standard deviation of person's environmental data and the corresponding relationship of risk class；
Computing unit, for calculating the slope of the sequence of environmental data or the standard deviation of environmental data；
Searching unit, for searching corresponding risk class according to the slope or the standard deviation.
In one embodiment, the analysis module 102, further includes:
Second establishes unit, for establishing machine learning model；
Input unit obtains the wind of output for inputting the machine learning model for environmental data as input data
Dangerous grade.
In one embodiment, which is characterized in that described second establishes unit, comprising:
Subelement is obtained, for obtaining history fire sample data；
Training subelement obtains instruction for being trained according to the history fire sample data to machine learning model
Machine learning model after white silk.
In one embodiment, the generation module 103, comprising:
First generation unit generates the first work order if being low-risk for the risk class；Wherein, described
First work order is for controlling the sensing node into low sample frequency and low power loss communication mode；
Second generation unit generates the second work order if being medium risk for the risk class；Wherein, institute
It states the second work order and enters moderate sampling frequency and medium power consumption communication pattern for controlling the sensing node；
Third generation unit generates third work order if being high risk for the risk class；Wherein, described
Third work order enters high sample frequency and high power consumption communication pattern for controlling the sensing node.
The present embodiment adjusts operating mode and the sampling period of sensing node by dynamic, so that safety monitoring system is in number
When according to occurring abnormal, the working condition of sensing node can be quickly adjusted, improving sample frequency, and system is occurred in fire
Initial stage can quickly detect exception, improve safety monitoring system to the efficiency and safety of fire hazard monitoring, while saving electricity
Energy.
Embodiment six
Fig. 7 is the schematic diagram of terminal device provided in this embodiment.As shown in fig. 7, the terminal device 7 of the embodiment wraps
It includes: processor 70, memory 71 and being stored in the computer that can be run in the memory 71 and on the processor 70
Program 72, such as security monitor program.The processor 70 realizes above-mentioned each safety prison when executing the computer program 72
Control the step in embodiment of the method, such as step S101 to S103 shown in FIG. 1.Alternatively, the processor 70 executes the meter
The function of each module/unit in above-mentioned each Installation practice, such as module 101 to 103 shown in Fig. 5 are realized when calculation machine program 72
Function.
Illustratively, the computer program 72 can be divided into one or more module/units, it is one or
Multiple module/units are stored in the memory 71, and are executed by the processor 70, to complete the present invention.Described one
A or multiple module/units can be the series of computation machine program instruction section that can complete specific function, which is used for
Implementation procedure of the computer program 72 in the terminal device 7 is described.For example, the computer program 72 can be divided
It is as follows to be cut into acquisition module, analysis module and generation module, each module concrete function:
Module is obtained, for obtaining the environmental data of sensing node transmission；
Analysis module for analyzing the environmental data, and judges risk class based on the analysis results；
Generation module for generating corresponding work order according to the risk class, and the work order is sent
To the sensing node, corresponding operation is executed to control the sensing node according to the work order.
The terminal device 7 can be the calculating such as desktop PC, notebook, palm PC and cloud server and set
It is standby.The terminal device may include, but be not limited only to, processor 70, memory 71.It will be understood by those skilled in the art that Fig. 7
The only example of terminal device 7 does not constitute the restriction to terminal device 7, may include than illustrating more or fewer portions
Part perhaps combines certain components or different components, such as the terminal device can also include input-output equipment, net
Network access device, bus etc..
Alleged processor 70 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng.
The memory 71 can be the internal storage unit of the terminal device 7, such as the hard disk or interior of terminal device 7
It deposits.The memory 71 is also possible to the External memory equipment of the terminal device 7, such as be equipped on the terminal device 7
Plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), safe digital card (Secure Digital, SD) dodge
Deposit card (Flash Card) etc..Further, the memory 71 can also both include the storage inside list of the terminal device 7
Member also includes External memory equipment.The memory 71 is for storing needed for the computer program and the terminal device
Other programs and data.The memory 71 can be also used for temporarily storing the data that has exported or will export.
It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each function
Can unit, module division progress for example, in practical application, can according to need and by above-mentioned function distribution by different
Functional unit, module are completed, i.e., the internal structure of described device is divided into different functional unit or module, more than completing
The all or part of function of description.Each functional unit in embodiment, module can integrate in one processing unit, can also
To be that each unit physically exists alone, can also be integrated in one unit with two or more units, it is above-mentioned integrated
Unit both can take the form of hardware realization, can also realize in the form of software functional units.In addition, each function list
Member, the specific name of module are also only for convenience of distinguishing each other, the protection scope being not intended to limit this application.Above system
The specific work process of middle unit, module, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in detail or remembers in some embodiment
The part of load may refer to the associated description of other embodiments.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician
Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed
The scope of the present invention.
In embodiment provided by the present invention, it should be understood that disclosed device/terminal device and method, it can be with
It realizes by another way.For example, device described above/terminal device embodiment is only schematical, for example, institute
The division of module or unit is stated, only a kind of logical function partition, there may be another division manner in actual implementation, such as
Multiple units or components can be combined or can be integrated into another system, or some features can be ignored or not executed.Separately
A bit, shown or discussed mutual coupling or direct-coupling or communication connection can be through some interfaces, device
Or the INDIRECT COUPLING or communication connection of unit, it can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated module/unit be realized in the form of SFU software functional unit and as independent product sale or
In use, can store in a computer readable storage medium.Based on this understanding, the present invention realizes above-mentioned implementation
All or part of the process in example method, can also instruct relevant hardware to complete, the meter by computer program
Calculation machine program can be stored in a computer readable storage medium, the computer program when being executed by processor, it can be achieved that on
The step of stating each embodiment of the method.Wherein, the computer program includes computer program code, the computer program generation
Code can be source code form, object identification code form, executable file or certain intermediate forms etc..The computer-readable medium
It may include: any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic that can carry the computer program code
Dish, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory (RAM,
Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It should be noted that described
The content that computer-readable medium includes can carry out increasing appropriate according to the requirement made laws in jurisdiction with patent practice
Subtract, such as does not include electric carrier signal and electricity according to legislation and patent practice, computer-readable medium in certain jurisdictions
Believe signal.
Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations；Although referring to aforementioned reality
Applying example, invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each
Technical solution documented by embodiment is modified or equivalent replacement of some of the technical features；And these are modified
Or replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution should all
It is included within protection scope of the present invention.
Claims (10)
1. a kind of method for safety monitoring, which is characterized in that be applied to server, the server and sensing node communicate to connect；
The described method includes:
Obtain the environmental data that sensing node is sent；
The environmental data is analyzed, and judges risk class based on the analysis results；
Corresponding work order is generated according to the risk class, and the work order is sent to the sensing node, with
It controls the sensing node and corresponding operation is executed according to the work order.
2. method for safety monitoring as described in claim 1, which is characterized in that the environmental data is analyzed, and according to
Analysis result judges risk class, comprising:
Pre-establish the standard deviation and wind of the slope of the sequence of environmental data and the corresponding relationship of risk class or environmental data
The corresponding relationship of dangerous grade；
Calculate the slope of the sequence of environmental data or the standard deviation of environmental data；
Corresponding risk class is searched according to the slope or the standard deviation.
3. method for safety monitoring as described in claim 1, which is characterized in that the environmental data is analyzed, and according to
Analysis result judges risk class, further includes:
Establish machine learning model；
The machine learning model is inputted using environmental data as input data, obtains the risk class of output.
4. method for safety monitoring as claimed in claim 3, which is characterized in that the step of establishing machine learning model packet
It includes:
Obtain history fire sample data；
Machine learning model is trained according to the history fire sample data, obtains machine learning mould after training
Type.
5. method for safety monitoring as described in claim 1, which is characterized in that generate corresponding work according to the risk class
Instruction, and the work order is sent to the sensing node, it is held with controlling the sensing node according to the work order
The corresponding operation of row, comprising:
If the risk class is low-risk, the first work order is generated；Wherein, first work order is for controlling institute
Sensing node is stated into low sample frequency and low power loss communication mode；
If the risk class is medium risk, the second work order is generated；Wherein, second work order is for controlling
The sensing node enters moderate sampling frequency and medium power consumption communication pattern；
If the risk class is high risk, third work order is generated；Wherein, the third work order is for controlling institute
It states sensing node and enters high sample frequency and high power consumption communication pattern.
6. a kind of safety monitoring device, which is characterized in that be applied to server, the server and sensing node communicate to connect；
Described device includes:
Module is obtained, for obtaining the environmental data of sensing node transmission；
Analysis module for analyzing the environmental data, and judges risk class based on the analysis results；
The work order for generating corresponding work order according to the risk class, and is sent to institute by generation module
Sensing node is stated, executes corresponding operation to control the sensing node according to the work order.
7. safety monitoring device as claimed in claim 6, which is characterized in that the analysis module, comprising:
First establishing unit, for pre-establishing the slope of the sequence of environmental data and the corresponding relationship of risk class, Huo Zhehuan
The standard deviation of border data and the corresponding relationship of risk class；
Computing unit, for calculating the slope of the sequence of environmental data or the standard deviation of environmental data；
Searching unit, for searching corresponding risk class according to the slope or the standard deviation.
8. safety monitoring device as claimed in claim 6, which is characterized in that the analysis module, further includes:
Second establishes unit, for establishing machine learning model；
Input unit obtains the risk etc. of output for inputting the machine learning model for environmental data as input data
Grade.
9. a kind of terminal device, including memory, processor and storage are in the memory and can be on the processor
The computer program of operation, which is characterized in that the processor realizes such as claim 1 to 5 when executing the computer program
The step of any one the method.
10. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists
In when the computer program is executed by processor the step of any one of such as claim 1 to 5 of realization the method.
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| CN111930081A (en) * | 2020-07-15 | 2020-11-13 | 西门子工厂自动化工程有限公司 | Method and device for monitoring AGV state, edge device and storage medium |
| CN112649101A (en) * | 2020-12-15 | 2021-04-13 | 中国电力科学研究院有限公司 | Battery module early warning method and system and fire detection device |
| CN113223263A (en) * | 2021-04-25 | 2021-08-06 | 深圳市芯中芯科技有限公司 | Electrical fire monitoring system based on Internet of things |
| CN113398521A (en) * | 2021-03-22 | 2021-09-17 | 东莞市三航军民融合创新研究院 | Fire fighting monitoring system and method based on Internet of things |
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| CN111047815A (en) * | 2019-12-27 | 2020-04-21 | 深圳知路科技有限公司 | Method and system for identifying false alarm of fire-fighting detector based on self-learning model |
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| CN113223263A (en) * | 2021-04-25 | 2021-08-06 | 深圳市芯中芯科技有限公司 | Electrical fire monitoring system based on Internet of things |
| CN113740775A (en) * | 2021-08-17 | 2021-12-03 | 广州番禺电缆集团有限公司 | Cable sheath on-line detection method, device, equipment and storage medium |
| CN113740775B (en) * | 2021-08-17 | 2023-10-24 | 广州番禺电缆集团有限公司 | Online detection method, device and equipment for cable sheath and storage medium |
| CN115171319A (en) * | 2022-05-18 | 2022-10-11 | 清华大学 | Electrical fire early warning system, method, electronic device and storage medium |
| WO2024082504A1 (en) * | 2022-10-19 | 2024-04-25 | 中国华能集团清洁能源技术研究院有限公司 | Fire alarm control system, fire monitoring method and apparatus thereof, and controller |
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| CN116403381A (en) * | 2023-06-08 | 2023-07-07 | 光交澳(上海)智能科技有限公司 | Smoke monitoring method and device, and smoke alarm method and system |
| CN116403381B (en) * | 2023-06-08 | 2023-09-01 | 光交澳(上海)智能科技有限公司 | Smoke monitoring method and device, and smoke alarm method and system |
| CN117130016A (en) * | 2023-10-26 | 2023-11-28 | 深圳市麦微智能电子有限公司 | Personal safety monitoring system, method, device and medium based on Beidou satellite |
| CN117130016B (en) * | 2023-10-26 | 2024-02-06 | 深圳市麦微智能电子有限公司 | Personal safety monitoring system, method, device and medium based on Beidou satellite |
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Application publication date: 20191119 |