CN112052794A - Internet of things intelligent training room safety management method and device - Google Patents
Internet of things intelligent training room safety management method and device Download PDFInfo
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- CN112052794A CN112052794A CN202010925131.6A CN202010925131A CN112052794A CN 112052794 A CN112052794 A CN 112052794A CN 202010925131 A CN202010925131 A CN 202010925131A CN 112052794 A CN112052794 A CN 112052794A
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Abstract
The invention discloses a safety management method and a safety management device for an intelligent training room of the Internet of things, wherein the method comprises the following steps: acquiring room attribute information and practical training equipment information of the first practical training room; inputting the room attribute information and the training equipment information into a training model; obtaining danger level information of the first training room; determining whether to obtain the information of the lessees in the first training room or not according to the danger level information and a first preset condition; and determining whether the intelligent management platform sends safety training information to the lessees or not according to the lessee information and the second preset information. The technical problems that safety precaution consciousness of students is not enough, emergency measures taken by managers for dealing with emergency accidents are not in place, and personnel are untimely to evacuate are solved, safety education of the students is strengthened, emergency measures can be taken for emergency events and hidden dangers, evacuation of the students is organized, early warning is achieved, and loss is reduced.
Description
Technical Field
The invention relates to the technical field of safety management of a training room, in particular to a safety management method and device of an intelligent training room of the Internet of things.
Background
The development of modern professional education changes the situation that the traditional theory and the practical training are given separately. The replaced integrated training room integrates theory, training, teaching and demonstration, operation and assessment and the like, and better embodies the vivid environment and production process, thereby endowing the training room with a new function of teaching integration and extending the connotation of the training. Each large education institution highly attaches importance to the safe work of the laboratory and enhances the awareness of safety and precaution. And safety inspection is carried out regularly, problems are found and solved in time, and hidden dangers are eliminated.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the security awareness of students is not enough, and the management personnel take inadequate emergency measures against accidents, personnel evacuation is not timely and the like.
Disclosure of Invention
The embodiment of the application provides a safety management method and device for an intelligent training room of the Internet of things, and solves the technical problems that the safety precaution consciousness of students is insufficient, emergency measures taken by managers in response to an emergency accident are not in place and personnel are not evacuated timely, safety education of the students is strengthened, the emergency measures can be taken for emergency events and hidden dangers, students are organized to evacuate, early warning is carried out in advance, and loss is reduced.
The embodiment of the application provides a safety management method for an intelligent training room of the Internet of things, which is applied to an intelligent management platform, and the method comprises the following steps: acquiring room attribute information of the first training room; obtaining training equipment information of the first training room; inputting the room attribute information and the training equipment information into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the room attribute information, the training equipment information and the preset danger level identification information; obtaining output information of the training model, wherein the output information comprises danger level information of the first training room; judging whether the first practical training room meets a first preset condition or not according to the danger level information; when the training information does not meet the requirement, acquiring the information of the lessees in the first training room; judging whether the lessee meets a second preset condition or not; when the video information is not satisfied, the intelligent management platform sends first video information to the lessees, wherein the first video information comprises safety training information.
In a second aspect, the application further provides a real room safety control device of instructing of thing networking wisdom, wherein, the device includes: a first obtaining unit, configured to obtain room attribute information of the first training room; the second obtaining unit is used for obtaining the practical training equipment information of the first practical training room; a first input unit, configured to input the room attribute information and the training device information into a training model, where the training model is obtained through training of multiple sets of training data, and each set of training data in the multiple sets includes: the room attribute information, the training equipment information and the preset danger level identification information; a third obtaining unit, configured to obtain output information of the training model, where the output information includes danger level information of the first training room; the first judgment unit is used for judging whether the first practical training room meets a first preset condition or not according to the danger level information; a fourth obtaining unit, configured to obtain, when the first training room is not satisfied, the information of the lesson staff of the first training room; the second judging unit is used for judging whether the lessee meets a second preset condition or not; the intelligent management platform comprises a first sending unit, wherein the first sending unit is used for sending first video information to the lessee when the first video information does not meet the requirement, and the first video information comprises safety training information.
In a third aspect, the invention provides an internet of things intelligent training room security management device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the room attribute information and the practical training equipment information are input into a training model, so that the danger level information of the first practical training room is output; the training model is a machine learning model, and the machine learning model can continuously learn through a large amount of data so as to continuously modify the model and finally obtain satisfactory experience to process other data; each set of training data in the plurality of sets of training data comprises: the room attribute information, the training equipment information and the preset danger level identification information; through training many times, thereby obtain the accurate danger level information of first real room of instructing, and through to output information judges to the personnel of giving lessons send the mode of safety training information, realized strengthening student's safety education, can take emergency measures to emergency and hidden danger, organize student's evacuation, early warning in advance, reduce the technological effect of loss.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flow chart illustrating a security management method for an intelligent training room of the internet of things according to an embodiment of the present application;
fig. 2 is a schematic flowchart illustrating a process of obtaining room attribute information of a first training room in an internet of things intelligent training room security management method according to an embodiment of the present application;
fig. 3 is a schematic flow chart illustrating evacuation of people according to danger prediction information in a safety management method for an intelligent training room of the internet of things according to the embodiment of the application;
fig. 4 is a schematic flowchart illustrating a process of sending a first alarm signal in a security management method for an intelligent training room of the internet of things according to an embodiment of the present application;
fig. 5 is a schematic flowchart illustrating a process of obtaining a more accurate training model in a security management method for an intelligent training room of the internet of things according to an embodiment of the present application;
fig. 6 is a schematic flowchart illustrating a process of sending a second alarm signal in the method for managing security of the intelligent training room of the internet of things according to the embodiment of the present application;
fig. 7 is a schematic flowchart illustrating a process of sending a third alarm signal in the method for managing security of the intelligent training room of the internet of things according to the embodiment of the application;
fig. 8 is a schematic structural diagram of a security management device of an intelligent training room of the internet of things according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third input unit 13, a fourth obtaining unit 14, a first judging unit 15, a fifth obtaining unit 16, a second judging unit 17, a first transmitting unit 18, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The embodiment of the application provides a safety management method and device for an intelligent training room of the Internet of things, and solves the technical problems that the safety precaution consciousness of students is insufficient, emergency measures taken by managers in response to an emergency accident are not in place and personnel are not evacuated timely, safety education of the students is strengthened, the emergency measures can be taken for emergency events and hidden dangers, students are organized to evacuate, early warning is carried out in advance, and loss is reduced. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
The development of modern professional education changes the situation that the traditional theory and the practical training are given separately. The replaced integrated training room integrates theory, training, teaching and demonstration, operation and assessment and the like, and better embodies the vivid environment and production process, thereby endowing the training room with a new function of teaching integration and extending the connotation of the training. Each large education institution highly attaches importance to the safe work of the laboratory and enhances the awareness of safety and precaution. And safety inspection is carried out regularly, problems are found and solved in time, and hidden dangers are eliminated. However, the technical problems that the safety precaution consciousness of students is not enough, the emergency measures taken by managers for dealing with the emergency accidents are not in place, the personnel are not evacuated in time and the like exist in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a safety management method for an intelligent training room of the Internet of things, which is applied to an intelligent management platform, wherein the method comprises the following steps: acquiring room attribute information of the first training room; obtaining training equipment information of the first training room; inputting the room attribute information and the training equipment information into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the room attribute information, the training equipment information and the preset danger level identification information; obtaining output information of the training model, wherein the output information comprises danger level information of the first training room; judging whether the first practical training room meets a first preset condition or not according to the danger level information; when the training information does not meet the requirement, acquiring the information of the lessees in the first training room; judging whether the lessee meets a second preset condition or not; when the video information is not satisfied, the intelligent management platform sends first video information to the lessees, wherein the first video information comprises safety training information.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the application provides a method and a device for managing safety of an intelligent training room of the internet of things, wherein the method includes:
step S100: acquiring room attribute information of the first training room;
specifically, the room attribute information of the first practical training room includes size information of the practical training room and infrastructure information of the practical training room; the size information is embodied in how many people can be accommodated in the practical training room, and the infrastructure information of the practical training room shows whether the practical training room has basic configuration, such as whether the facilities such as a projector, a printer, an air conditioner, a console, a monitoring system, an alarm system and the like are complete; by obtaining the room attribute information of the first training room, a foundation is laid for subsequently obtaining the danger level information of the first training room.
Step S200: obtaining training equipment information of the first training room;
specifically, the practical training equipment information of the first practical training room is equipment information used for practical training in the first practical training room, and includes information of models, functions and the like of related equipment; the training equipment in the first training room is related to the function of the first training room; if the first practical training room is a mechanical processing practical training room, the practical training equipment comprises equipment such as a machining lathe, a grinding machine and the like, and if the first practical training room is a vehicle maintenance practical training room, the equipment information of the practical training room comprises equipment such as an automobile chassis test bed, a gearbox rack and the like; and by obtaining the practical training equipment information of the first practical training room, a foundation is laid for the subsequent evaluation of the danger level information of the first practical training room.
Step S300: inputting the room attribute information and the training equipment information into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the room attribute information, the training equipment information and the preset danger level identification information;
specifically, the identification information for identifying the preset danger level is accurate information of the danger level of the first training room. The training model is a machine learning model, and the machine learning model can continuously learn through a large amount of data, further continuously correct the model, and finally obtain satisfactory experience to process other data.
Further, the machine model is obtained by training a plurality of sets of training data, and the process of training the neural network model by the training data is essentially a process of supervised learning. Each set of training data in the plurality of sets of training data comprises: the room attribute information, the training equipment information and the preset danger level identification information; under the condition of obtaining room attribute information and practical training equipment information, matching the attribute information of the first practical training room with the practical training equipment information, outputting preset danger level information of the first practical training room by a machine learning model, verifying the preset danger level information of the first practical training room output by the machine learning model through the identified preset danger level information of the first practical training room, and if the output preset danger level information of the first practical training room is consistent with the identified preset danger level information of the first practical training room, finishing the data supervision learning and carrying out next group of data learning supervision; and if the output preset danger level information of the first practical training room is inconsistent with the identified preset danger level information of the first practical training room, adjusting the machine learning model by the machine learning model, and performing supervised learning on the next group of data until the machine learning model reaches the expected accuracy. The machine learning model is continuously corrected and optimized through training data, the accuracy of the machine learning model for processing the data is improved through the process of supervised learning, the preset danger level information of the first practical training room is accurate, and the accuracy of obtaining the preset danger level information is improved through accurately evaluating the preset danger level information of the first practical training room. The technical effects of strengthening the management of a safe training room and avoiding risks in advance are achieved.
Step S400: obtaining output information of the training model, wherein the output information comprises danger level information of the first training room;
specifically, the training model continuously corrects and optimizes data through supervised learning, so as to obtain more accurate output information, namely the danger level information of the first training room; the danger level information of the first practical training room is obtained by continuously learning a training model by integrating the attribute information of the practical training room and the practical training equipment information, and is information for evaluating the danger level of the first practical training room; by obtaining the danger level information of the first practical training room, the technical aims of improving the safety management of the practical training room and early warning the occurrence of danger can be achieved.
Step S500: judging whether the first practical training room meets a first preset condition or not according to the danger level information;
specifically, the first preset condition is a preset danger level threshold of a first practical training room, and is used for judging whether the first practical training room danger level information is within a safety range, and if the first practical training room danger level information exceeds the preset condition, it indicates that the first practical training room danger coefficient is high, and safety precaution consciousness and safety management need to be strengthened. Whether the danger level information of the first practical training room meets a first preset condition or not is judged, and the technical purposes of improving the safety precaution consciousness and timely and accurately taking emergency measures for emergencies and hidden dangers are achieved.
Step S600: when the training information does not meet the requirement, acquiring the information of the lessees in the first training room;
specifically, when the danger level information of the first practical training room does not meet the first preset condition, that is, when the danger level of the first practical training room is too high, safety precaution awareness and management need to be further strengthened; the information of the lesson attendants in the first training room is represented by information such as whether the lesson attendants have been trained, the level of safety basic knowledge, physical conditions and the like. And a foundation is laid for further strengthening the safety management.
Step S700: judging whether the lessee meets a second preset condition or not;
specifically, the second preset condition is an index for evaluating the degree of safety training received by the lessee; if the number of times of receiving safety training by the lessee is small and the safety basic knowledge is deficient, further strengthening training is needed; if the lessees meet the conditions, the safety education level is high, and a foundation is laid for realizing safety management, strengthening safety education of students and carrying out timely emergency and early warning on emergencies through judgment.
Step S800: when the video information is not satisfied, the intelligent management platform sends first video information to the lessees, wherein the first video information comprises safety training information.
Specifically, when the lessee does not meet the second preset condition, the lessee is specifically shown to have few times of receiving safety training and lack of safety basic knowledge, and then safety training needs to be further strengthened; the intelligent management platform sends first video information to the personnel of giving lessons, first video information is the information of safety training, through judge with to the personnel of giving lessons send first video information, realized having strengthened student's safety education's technological effect.
As shown in fig. 2, in order to obtain room attribute information of a first practical training room, step S100 in this embodiment of the present application further includes:
step S101: obtaining room dimension information of the first training room;
step S102: obtaining infrastructure information of the first training room;
step S103: and acquiring the infrastructure information of the first training room according to the room size information and the infrastructure information.
Specifically, the dimension information of the first practical training room is embodied in how many people can be accommodated in the practical training room, and the infrastructure information of the first practical training room represents whether the practical training room has infrastructure configuration, such as whether the facilities such as a projector, a printer, an air conditioner, a console, a monitoring system, an alarm system and the like are complete; by obtaining the room attribute information of the first training room, a foundation is laid for better subsequent analysis and obtaining of the danger level information of the first training room.
As shown in fig. 3, in order to evacuate people according to the danger prediction information, step S600 in the embodiment of the present application further includes:
step S601: obtaining first class information of the first training room in a first time;
step S602: obtaining second lesson information of the first training room in a second time;
step S603: according to the first class information and the second class information, danger prediction information of the first practical training room in a third time is obtained;
step S604: judging whether the danger prediction information of the first practical training room meets the first preset condition or not;
step S605: if not, obtaining second practical training room information, wherein the second practical training room and the first practical training room have a first association degree;
step S606: obtaining ambient environment information;
step S607: obtaining a first route according to the second training room information and the ambient environment information;
step S608: and issuing the first route before the third time comes.
Specifically, the first class information and the second class information are conditions of class attendance of the class attendants in the first training room in a first time period and a second time period respectively, including information such as use conditions of training equipment and indoor voltage, the first and second class information is used for obtaining danger prediction information of the first training room in a third time period, the first preset condition is a condition for measuring a danger level of the first training room in the third time period, and if the danger prediction information of the first training room does not meet the first preset condition, that is, the danger level of the first training room is not in a safe range, the second training room information needs to be obtained before the third time comes to transfer the learners. The second practical training room has similar functions to the first practical training room and can carry out practical training of the same technology. The ambient environment information is the ambient environment information around the second practical training room, a first route is obtained according to the second practical training room information and the ambient environment information, the first route is the optimal route from the first practical training room to the second practical training room, the optimal route is issued to a learner before the third time, and the transfer is carried out according to the first route. The technical effects of taking emergency measures to emergencies and hidden dangers, organizing student evacuation, early warning and reducing loss are achieved.
As shown in fig. 4, in order to send the first alarm signal, step S800 in this embodiment of the present application further includes:
step S801: obtaining safety protection information of the lessee;
step S802: judging whether the safety protection information of the lessees meets a third preset condition or not;
step S803: if not, the intelligent management platform obtains a dangerous area and sends a first alarm signal.
Specifically, the safety protection information of the lessee is personal protection measures of the lessee, such as whether to wear a protective garment or whether to wear a safety helmet, gloves, goggles and the like. The third preset condition is a wearing standard of safety protection measures which the personnel in class should reach, if the safety protection information of the personnel in class does not meet the third preset condition, namely the safety protection measures of the personnel in class are not in place, the intelligent management platform obtains a dangerous area, namely an area where the personnel in class lack safety protection, and sends the first alarm signal for prompting the personnel that the safety protection is not in place. The technical purpose of early warning the dangerous condition is achieved.
As shown in fig. 5, in order to obtain a more accurate training model, step S300 in this embodiment of the present application further includes:
step S301: obtaining second video information in the first training room;
step S302: according to the second video information, dangerous source information in the second video information is obtained;
step S303: setting danger level identification information according to the danger source information;
step S304: and inputting the danger level identification information serving as supervision data into each group of training data, performing supervision learning on the infrastructure information and the course arrangement information, and determining that the output information of the training model reaches a convergence state.
Specifically, the second video information in the first training room is provided for monitoring in the first training room, and the video information of dangerous accidents can be monitored in real time. And acquiring danger source information in the second video from the second video information, wherein the set danger level identification information is used for identifying the danger level of the first practical training room, the danger level identification information is used as supervision data and is input into each group of training data, the infrastructure information and the course arrangement information are supervised and learned, and the output information of the training model is determined to reach a convergence state. The machine learning model is continuously corrected and optimized through training data, the accuracy of the machine learning model for processing the data is improved through the process of supervised learning, the output danger level information is accurate, and the early warning accuracy before an accident occurs is improved through accurately evaluating the danger level of the first training room.
As shown in fig. 6, in order to send the second alarm signal, step S200 in this embodiment further includes:
step S201 a: acquiring the information of the electrified device in the first training room;
step S202 a: obtaining the working voltage of the charged device;
step S203 a: attaching a first electronic tag to the electrified device according to the working voltage of the electrified device;
step S204 a: acquiring sensor information of the charged device, wherein the sensors are connected with the charged device in a one-to-one correspondence manner;
step S205 a: judging whether the working state of the charged device is normal or not according to the sensor information;
step S206 a: and when the working state of the electrified device is abnormal, the first electronic tag sends a second alarm signal.
Specifically, basic information of the first practical training indoor electric device is obtained by obtaining information of the first practical training indoor electric device and working voltage of the first practical training indoor electric device, and the first electronic tag is used for marking and reminding the working voltage of the electric device. The sensor information of the electrified device is used for detecting information such as whether the electrified device leaks electricity or not, whether the working state of the electrified device is normal or not is judged according to the sensor information, when the working state of the electrified device is abnormal, the first electronic tag sends a second alarm signal, and the second alarm signal is used for early warning the electrified device of the first practical training room, so that the technical effects of strengthening safety management and realizing early warning on dangerous conditions are achieved.
As shown in fig. 7, in order to send the third alarm signal, step S200 in this embodiment further includes:
step S201 b: setting use permission information according to the practical training equipment information;
step S202 b: obtaining unlocking information input by a target user;
step S203 b: judging whether the unlocking information is consistent with the use permission information or not;
step S204 b: if not, acquiring the input times of the target user;
step S205 b: judging whether the input times meet a preset threshold value or not;
step S206 b: if the real-time training equipment does not meet the requirements, the intelligent management platform obtains the position information of the real-time training equipment and sends a third alarm signal.
Specifically, the usage right information is the usage right of the practical training equipment, the usage right of the practical training equipment can be limited by adding password information, the unlocking information input by the user is the right password of the practical training equipment, whether the unlocking information is consistent with the usage right information or not is judged, that is, whether the set password information is consistent with the input password information or not is judged, if not, the input frequency of the target user is obtained, the preset threshold value is the input frequency of the preset password information, and if the input frequency exceeds the threshold value, that is, the input frequency reaches the upper limit, the intelligent management platform obtains the position information of the practical training equipment and sends a third alarm signal. The third alarm signal is used for carrying out early warning on the practical training equipment. Through the early warning, the technical effects that emergency measures can be taken for emergencies and hidden dangers, early warning is carried out in advance, and loss is reduced are achieved.
To sum up, the early warning method for safety construction provided by the embodiment of the application has the following technical effects:
1. the room attribute information and the practical training equipment information are input into a training model, so that the danger level information of the first practical training room is output; the training model is a machine learning model, and the machine learning model can continuously learn through a large amount of data so as to continuously modify the model and finally obtain satisfactory experience to process other data; each set of training data in the plurality of sets of training data comprises: the room attribute information, the training equipment information and the preset danger level identification information; through training many times, thereby obtain the accurate danger level information of first real room of instructing, and through to output information judges to the personnel of giving lessons send the mode of safety training information, realized strengthening student's safety education, can take emergency measures to emergency and hidden danger, organize student's evacuation, early warning in advance, reduce the technological effect of loss.
2. Due to the fact that the first alarm signal, the second alarm signal and the third alarm signal are obtained, the safety protection hidden danger of the class attendants, the hidden danger existing in the electrified device and the use hidden danger of the practical training equipment are subjected to accurate and timely early warning, emergency measures can be taken for the emergency and the hidden danger, students are organized to evacuate, early warning is achieved, and the technical effect of reducing loss is achieved.
Example two
Based on the same inventive concept as the method for managing the safety of the intelligent training room of the internet of things in the foregoing embodiment, the invention further provides a device for managing the safety of the intelligent training room of the internet of things, as shown in fig. 8, the device includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain room attribute information of the first training room;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain training device information of the first training room;
a first input unit 13, where the first input unit 13 is configured to input the room attribute information and the training device information into a training model, where the training model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the room attribute information, the training equipment information and the preset danger level identification information;
a third obtaining unit 14, where the third obtaining unit 14 is configured to obtain output information of the training model, where the output information includes danger level information of the first training room;
the first judging unit 15 is configured to judge whether the first training room meets a first preset condition according to the danger level information;
a fourth obtaining unit 16, wherein the fourth obtaining unit 16 is configured to obtain the information of the lessee in the first training room when the information is not satisfied;
a second judging unit 17, where the second judging unit 17 is configured to judge whether the lessee meets a second preset condition;
a first sending unit 18, where the first sending unit 18 is configured to, when the first video information is not satisfied, send, by the intelligent management platform, first video information to the lessee, where the first video information includes safety training information.
Further, the apparatus further comprises:
a fifth obtaining unit, configured to obtain room size information of the first training room;
a sixth obtaining unit, configured to obtain infrastructure information of the first training room;
a seventh obtaining unit, configured to obtain infrastructure information of the first training room according to the room size information and the infrastructure information.
Further, the apparatus further comprises:
an eighth obtaining unit, configured to obtain first class information of the first training room in a first time;
a ninth obtaining unit, configured to obtain second lesson information of the first training room in a second time;
a tenth obtaining unit, configured to obtain risk prediction information of the first training room in a third time according to the first and second lesson information;
the third judgment unit is used for judging whether the danger prediction information of the first practical training room meets the first preset condition or not;
an eleventh obtaining unit, configured to obtain second practical training room information if the first practical training room information is not satisfied, where the second practical training room and the first practical training room have a first association degree;
a twelfth obtaining unit configured to obtain ambient environment information;
a thirteenth obtaining unit, configured to obtain a first route according to the second training room information and the ambient environment information;
a second sending unit, configured to publish the first route before the third time comes.
Further, the apparatus further comprises:
a fourteenth obtaining unit, configured to obtain safety protection information of the lessee;
the fourth judging unit is used for judging whether the safety protection information of the lessees meets a third preset condition or not;
and the fifteenth obtaining unit is used for obtaining the dangerous area and sending a first alarm signal by the intelligent management platform if the situation is not met.
Further, the apparatus further comprises:
a sixteenth obtaining unit, configured to obtain second video information in the first training room;
a seventeenth obtaining unit, configured to obtain, according to the second video information, hazard source information in the second video information;
the first setting unit is used for setting danger level identification information according to the danger source information;
and the second input unit is used for inputting the danger level identification information serving as supervision data into each group of training data, performing supervision learning on the infrastructure information and the course arrangement information, and determining that the output information of the training model reaches a convergence state.
Further, the apparatus further comprises:
an eighteenth obtaining unit, configured to obtain information of the charged device in the first training room;
a nineteenth obtaining unit configured to obtain an operating voltage of the charging device;
a twentieth obtaining unit, configured to attach a first electronic tag to the charging device according to the working voltage of the charging device;
a twenty-first obtaining unit, configured to obtain sensor information of the charging device, where the sensors are connected to the charging device in a one-to-one correspondence manner;
a fifth judging unit, configured to judge whether the working state of the charging device is normal according to the sensor information;
and the third sending unit is used for sending a second alarm signal by the first electronic tag when the working state of the electrified device is abnormal.
Further, the apparatus further comprises:
the second setting unit is used for setting the use permission information according to the practical training equipment information;
a twenty-second obtaining unit, configured to obtain unlocking information input by a target user;
a sixth judging unit, configured to judge whether the unlocking information is consistent with the usage right information;
a twenty-third obtaining unit, configured to obtain the input times of the target user if the input times are inconsistent;
a seventh judging unit, configured to judge whether the input frequency satisfies a preset threshold;
and the twenty-fourth obtaining unit is used for obtaining the position information of the practical training equipment and sending a third alarm signal by the intelligent management platform if the position information is not met.
Various changes and specific examples of the method for managing the safety of the intelligent training room of the internet of things in the first embodiment of fig. 1 are also applicable to the device for managing the safety of the intelligent training room of the internet of things in the present embodiment.
Exemplary electronic device
The electronic apparatus of the embodiment of the present application is described below with reference to fig. 9.
Fig. 9 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the method for managing the safety of the intelligent training room of the internet of things in the foregoing embodiments, the invention further provides a device for managing the safety of the intelligent training room of the internet of things, wherein a computer program is stored on the device, and when the program is executed by a processor, the steps of any one of the methods for managing the safety of the intelligent training room of the internet of things are realized.
Where in fig. 9 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. An Internet of things intelligent training room safety management method is applied to an intelligent management platform and is characterized by comprising the following steps:
acquiring room attribute information of the first training room;
obtaining training equipment information of the first training room;
inputting the room attribute information and the training equipment information into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the room attribute information, the training equipment information and the preset danger level identification information;
obtaining output information of the training model, wherein the output information comprises danger level information of the first training room;
judging whether the first practical training room meets a first preset condition or not according to the danger level information;
when the training information does not meet the requirement, acquiring the information of the lessees in the first training room;
judging whether the lessee meets a second preset condition or not;
when the video information is not satisfied, the intelligent management platform sends first video information to the lessees, wherein the first video information comprises safety training information.
2. The method of claim 1, wherein the obtaining room attribute information for a first training room comprises:
obtaining room dimension information of the first training room;
obtaining infrastructure information of the first training room;
and acquiring the infrastructure information of the first training room according to the room size information and the infrastructure information.
3. The method of claim 1, wherein the method further comprises:
obtaining first class information of the first training room in a first time;
obtaining second lesson information of the first training room in a second time;
according to the first class information and the second class information, danger prediction information of the first practical training room in a third time is obtained;
judging whether the danger prediction information of the first practical training room meets the first preset condition or not;
if not, obtaining second practical training room information, wherein the second practical training room and the first practical training room have a first association degree;
obtaining ambient environment information;
obtaining a first route according to the second training room information and the ambient environment information;
and issuing the first route before the third time comes.
4. The method of claim 1, wherein after the intelligent management platform sends the first video information to the class attendees, the method comprises:
obtaining safety protection information of the lessee;
judging whether the safety protection information of the lessees meets a third preset condition or not;
if not, the intelligent management platform obtains a dangerous area and sends a first alarm signal.
5. The method of claim 1, wherein the inputting the infrastructure information and the course arrangement information into a training model, wherein the training model is obtained by training a plurality of sets of training data, each set of training data in the plurality of sets comprising: the infrastructure information, the course arrangement information and the preset danger level identification information comprise:
obtaining second video information in the first training room;
according to the second video information, dangerous source information in the second video information is obtained;
setting danger level identification information according to the danger source information;
and inputting the danger level identification information serving as supervision data into each group of training data, performing supervision learning on the infrastructure information and the course arrangement information, and determining that the output information of the training model reaches a convergence state.
6. The method of claim 1, wherein the method further comprises:
acquiring the information of the electrified device in the first training room;
obtaining the working voltage of the charged device;
attaching a first electronic tag to the electrified device according to the working voltage of the electrified device;
acquiring sensor information of the charged device, wherein the sensors are connected with the charged device in a one-to-one correspondence manner;
judging whether the working state of the charged device is normal or not according to the sensor information;
and when the working state of the electrified device is abnormal, the first electronic tag sends a second alarm signal.
7. The method of claim 1, wherein the method further comprises:
setting use permission information according to the practical training equipment information;
obtaining unlocking information input by a target user;
judging whether the unlocking information is consistent with the use permission information or not;
if not, acquiring the input times of the target user;
judging whether the input times meet a preset threshold value or not;
if the real-time training equipment does not meet the requirements, the intelligent management platform obtains the position information of the real-time training equipment and sends a third alarm signal.
8. An Internet of things intelligent training room safety management method and device are provided, wherein the device comprises:
a first obtaining unit, configured to obtain room attribute information of the first training room;
the second obtaining unit is used for obtaining the practical training equipment information of the first practical training room;
a first input unit, configured to input the room attribute information and the training device information into a training model, where the training model is obtained through training of multiple sets of training data, and each set of training data in the multiple sets includes: the room attribute information, the training equipment information and the preset danger level identification information;
a third obtaining unit, configured to obtain output information of the training model, where the output information includes danger level information of the first training room;
the first judgment unit is used for judging whether the first practical training room meets a first preset condition or not according to the danger level information;
a fourth obtaining unit, configured to obtain, when the first training room is not satisfied, the information of the lesson staff of the first training room;
the second judging unit is used for judging whether the lessee meets a second preset condition or not;
the intelligent management platform comprises a first sending unit, wherein the first sending unit is used for sending first video information to the lessee when the first video information does not meet the requirement, and the first video information comprises safety training information.
9. An internet of things intelligent training room security management device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1-7 when executing the program.
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| CN202010925131.6A CN112052794A (en) | 2020-09-06 | 2020-09-06 | Internet of things intelligent training room safety management method and device |
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| CN202010925131.6A CN112052794A (en) | 2020-09-06 | 2020-09-06 | Internet of things intelligent training room safety management method and device |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112668429A (en) * | 2020-12-21 | 2021-04-16 | 宿松县远景矿业有限公司 | Intelligent preparation method and device of white marble powder |
| CN112750272A (en) * | 2020-12-30 | 2021-05-04 | 江苏河马自动化设备有限公司 | Intelligent control method and system of fire alarm |
| CN113156475A (en) * | 2021-04-30 | 2021-07-23 | 中国人民解放军66072部队 | Dynamic command monitoring method and device |
| CN113408857A (en) * | 2021-05-24 | 2021-09-17 | 柳州东风容泰化工股份有限公司 | Management method and system for thioacetic acid leakage emergency treatment |
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2020
- 2020-09-06 CN CN202010925131.6A patent/CN112052794A/en not_active Withdrawn
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112668429A (en) * | 2020-12-21 | 2021-04-16 | 宿松县远景矿业有限公司 | Intelligent preparation method and device of white marble powder |
| CN112668429B (en) * | 2020-12-21 | 2023-06-20 | 湛江申翰科技实业有限公司 | Intelligent preparation method and device of white marble powder |
| CN112750272A (en) * | 2020-12-30 | 2021-05-04 | 江苏河马自动化设备有限公司 | Intelligent control method and system of fire alarm |
| CN112750272B (en) * | 2020-12-30 | 2022-07-26 | 江苏河马自动化设备有限公司 | Intelligent control method and system of fire alarm |
| CN113156475A (en) * | 2021-04-30 | 2021-07-23 | 中国人民解放军66072部队 | Dynamic command monitoring method and device |
| CN113408857A (en) * | 2021-05-24 | 2021-09-17 | 柳州东风容泰化工股份有限公司 | Management method and system for thioacetic acid leakage emergency treatment |
| CN113408857B (en) * | 2021-05-24 | 2023-03-24 | 柳州东风容泰化工股份有限公司 | Management method and system for thioacetic acid leakage emergency treatment |
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