CN117406628B - Laboratory ventilation control system based on sensing monitoring - Google Patents
Laboratory ventilation control system based on sensing monitoring Download PDFInfo
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- CN117406628B CN117406628B CN202311547316.8A CN202311547316A CN117406628B CN 117406628 B CN117406628 B CN 117406628B CN 202311547316 A CN202311547316 A CN 202311547316A CN 117406628 B CN117406628 B CN 117406628B
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- 238000009423 ventilation Methods 0.000 title claims abstract description 132
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
Abstract
The invention discloses a laboratory ventilation control system based on sensing monitoring, which comprises a ventilation control module, a control module and a control module, wherein the ventilation control module is used for acquiring ventilation sensing data of a fume hood device and controlling a variable air volume butterfly valve of the fume hood device; the access control module is used for acquiring the personnel access sensing data of the target laboratory area and controlling the switch of the access control equipment; the equipment control module is used for acquiring equipment data of the working equipment and controlling the work of the working equipment; and the total control module is used for determining the ventilation control strategy of the target laboratory area. Therefore, the invention can realize comprehensive monitoring of laboratory environment and effectively improve the efficiency and effect of ventilation control.
Description
Technical Field
The invention relates to the technical field of laboratory safety monitoring, in particular to a laboratory ventilation control system based on sensing monitoring.
Background
With the increase in demand for residential water and the popularity of water health concepts, water plants are also facing increasing challenges in management, especially as intelligent management concepts are pursued, more and more water plants are beginning to seek to manage in a more intuitive and efficient manner. However, the existing water plant management technology generally adopts a mode of sensor and manual audit control to manage, and is not considered to adopt a more visual three-dimensional technology to manage, and is not considered to reduce management errors by utilizing the advantages of a neural network algorithm, so that defects exist, and improvement is needed.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a laboratory ventilation control system based on sensing monitoring, which can realize comprehensive monitoring of laboratory environment and effectively improve the efficiency and effect of ventilation control.
To solve the above technical problem, a first aspect of the present invention discloses a laboratory ventilation control system based on sensing monitoring, the system comprising:
the ventilation control module is arranged in the ventilation cabinet equipment of the target laboratory area and used for acquiring ventilation sensing data of the ventilation cabinet equipment and controlling a variable air volume butterfly valve of the ventilation cabinet equipment;
the access control module is arranged in the access control equipment of the target laboratory area and used for acquiring the personnel access sensing data of the target laboratory area and controlling the opening and closing of the access control equipment;
the equipment control module is arranged in the working equipment of the target laboratory area and used for acquiring equipment data of the working equipment and controlling the work of the working equipment;
and the total control module is connected to the ventilation control module, the access control module and the equipment control module and is used for determining a ventilation control strategy of the target laboratory area according to the ventilation sensing data, the personnel access sensing data and the equipment data.
In an alternative embodiment, the ventilation sensing data includes air volume sensing data, air pressure sensing data, and air quality sensing data.
In an alternative embodiment, the ventilation control module includes:
the air quantity sensor is arranged in a ventilation pipeline of the ventilation cabinet equipment and used for acquiring the air quantity sensing data;
the air pressure sensor is arranged on the fume hood equipment and used for acquiring the air pressure sensing data of the interior of the fume hood equipment and the target laboratory area;
the air quality sensor is arranged in the ventilating duct and used for acquiring the air quality sensing data;
and the butterfly valve controller is connected to the variable air volume butterfly valves of the fume hood equipment and is used for controlling the variable air volume butterfly valves according to the received control instruction.
In an alternative embodiment, the personnel access sensing data includes access image data, access punch data, and access voice data.
In an alternative embodiment, the access control module includes:
the access control camera is arranged in an access area of the target laboratory area and used for acquiring the access image data;
the card punching machine communication module is used for being connected to the card punching machine in the in-out area and used for acquiring the in-out card punching data;
the sound monitor is arranged in the access area and used for acquiring the access sound data;
and the access controller is connected to the access switch of the access area and is used for controlling the access switch according to the acquired control command.
In an alternative embodiment, the device data includes device operational status, device operational instructions, and device sensory data; the device sensing data includes device sound data, device temperature data, and device image data.
In an alternative embodiment, the device control module includes:
the equipment communicator is used for being in communication connection with the working equipment and acquiring the equipment working state and the equipment working instruction of the working equipment; the middleware is arranged in an application program of the working equipment and used for capturing a plurality of equipment working instructions sent or ready to be received by the working equipment in real time and sending the equipment working instructions to the equipment communicator;
the equipment sensor group is arranged on the working equipment and used for acquiring the equipment sensing data;
and the equipment controller is connected to the working equipment and is used for receiving the control instruction and converting the control instruction into a specific type of parameter instruction to be sent to the working equipment for execution.
In an alternative embodiment, the overall control module is specifically configured to perform the following steps:
respectively inputting the ventilation sensing data into a trained ventilation smoothness prediction neural network and a ventilation effect prediction neural network to obtain an output corresponding ventilation smoothness index and ventilation effect index; the ventilation smooth prediction neural network is obtained through training a training data set which comprises a plurality of training ventilation sensing data and a corresponding whether ventilation blockage marks exist or not; the ventilation effect prediction neural network is obtained through training a training data set comprising a plurality of training ventilation sensing data and corresponding manual evaluation ventilation effect labels;
inputting the personnel access sensing data into a trained personnel panic prediction neural network and a trained personnel quantity prediction neural network to obtain an output corresponding personnel panic index and the quantity of personnel in an area; the personnel panic prediction neural network is obtained through training a training data set which comprises a plurality of training personnel access sensing data and corresponding personnel panic phenomenon marks; the personnel quantity prediction neural network is obtained through training of a training data set comprising a plurality of training personnel access sensing data and corresponding personnel quantity labels in the region;
inputting the equipment data of any one of the working equipment into a trained equipment runaway prediction neural network and an equipment preparation runaway prediction neural network to obtain the equipment runaway probability and equipment preparation runaway probability corresponding to any one of the working equipment; the equipment out-of-control prediction neural network is obtained by training a training data set which comprises a plurality of training equipment data and corresponding whether equipment out-of-control labeling occurs or not; the equipment preparation uncontrolled neural network is obtained through training of a plurality of training equipment data and a corresponding training data set whether equipment is out of control or not;
and determining a ventilation control strategy of the target laboratory area according to the ventilation smoothness index, the ventilation effect index, the personnel confusion index, the personnel number in the area, the equipment out-of-control probability corresponding to the working equipment and the equipment preparation out-of-control probability, and judging a correction rule based on preset parameters.
In an alternative embodiment, the ventilation control strategy includes a ventilation control command, an access control command, and a device control command.
In an optional implementation manner, the overall control module determines a specific manner of the ventilation control strategy of the target laboratory area according to the ventilation smoothness index, the ventilation effect index, the personnel confusion index, the number of personnel in the area, the equipment out-of-control probability corresponding to the working equipment and the equipment preparation out-of-control probability, and determines a correction rule based on preset parameters, wherein the specific manner includes:
calculating a first weight inversely proportional to the ventilation smoothness index;
calculating a second weight inversely proportional to the ventilation effect index;
calculating a first difference value between the ventilation smoothness index and a preset first index threshold;
calculating a second difference between the personal confusion index and a preset second index threshold;
calculating a third weight proportional to the number of people in the area;
calculating a third difference value between the equipment runaway probability corresponding to any one of the working equipment and a preset first probability threshold;
calculating a fourth difference value between the equipment preparation run-away probability corresponding to any one of the working equipment and a preset second probability threshold;
judging whether the average value of the third difference value and the fourth difference value of any working equipment is larger than a preset first difference value threshold value, if so, determining that the equipment control instruction corresponding to the working equipment is stop working;
calculating the average value of the sum of the third difference values and the fourth difference values of all the working devices to obtain a device difference value average value;
when the first difference value is smaller than a second difference value threshold value, the second difference value is larger than a third difference value threshold value, and the equipment difference value average value is larger than a fourth difference value threshold value, determining an entrance guard control instruction of an entrance guard switch of the target laboratory area as an opening entrance guard, and determining control instructions of all the variable air volume butterfly valves of the target laboratory area as opening the variable air volume butterfly valves to an air volume parameter; the value of the air quantity parameter corresponding to each variable air quantity butterfly valve is equal to the sum of the current air quantity parameter and the air quantity increasing value of the variable air quantity butterfly valve; and the value of the air volume increasing value is equal to the product of a preset air volume increasing reference value and the first weight, the second weight and the third weight.
Compared with the prior art, the invention has the following beneficial effects:
the invention can monitor the ventilation condition, the personnel access condition and the equipment condition in the laboratory in real time, and comprehensively determine the ventilation control strategy according to the monitoring results, thereby realizing the comprehensive monitoring of the laboratory environment and effectively improving the efficiency and effect of ventilation control.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a laboratory ventilation control system based on sensing monitoring according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or article that comprises a list of steps or modules is not limited to the list of steps or modules but may, in the alternative, include steps or modules not listed or inherent to such process, method, article, or article.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Specifically, referring to fig. 1, fig. 1 is a schematic structural diagram of a laboratory ventilation control system based on sensing monitoring according to an embodiment of the present invention. As shown in fig. 1, the sensing monitoring-based laboratory ventilation control system includes:
the ventilation control module 101 is arranged in the ventilation cabinet equipment in the target laboratory area and is used for acquiring ventilation sensing data of the ventilation cabinet equipment and controlling a variable air volume butterfly valve of the ventilation cabinet equipment;
the access control module 102 is arranged in access equipment of the target laboratory area and is used for acquiring personnel access sensing data of the target laboratory area and controlling the opening and closing of the access equipment;
an equipment control module 103, which is arranged in the working equipment of the target laboratory area, and is used for acquiring equipment data of the working equipment and controlling the work of the working equipment;
the overall control module 104 is connected to the ventilation control module 101, the access control module 102 and the equipment control module 103, and is configured to determine a ventilation control policy of the target laboratory area according to ventilation sensing data, personnel access sensing data and equipment data.
In an alternative embodiment, the ventilation sensing data includes air volume sensing data, air pressure sensing data, and air quality sensing data.
In an alternative embodiment, the ventilation control module 101 includes:
the air quantity sensor is arranged in a ventilation pipeline of the ventilation cabinet equipment and used for acquiring air quantity sensing data;
the air pressure sensor is arranged on the fume hood equipment and used for acquiring air pressure sensing data of the interior of the fume hood equipment and a target laboratory area;
the air quality sensor is arranged in the ventilating duct and used for acquiring air quality sensing data;
and the butterfly valve controller is connected to a plurality of variable air volume butterfly valves of the fume hood equipment and is used for controlling the variable air volume butterfly valves according to the received control instruction.
In an alternative embodiment, the personnel access sensing data includes access image data, access punch data, and access voice data.
In an alternative embodiment, the access control module 102 includes:
the access control camera is arranged in an access area of the target laboratory area and used for acquiring access image data;
the card punching machine communication module is used for being connected to a card punching machine in the in-out area and used for acquiring in-out card punching data;
the sound monitor is arranged in the access area and used for acquiring access sound data;
and the access controller is connected to the access switch of the access area and is used for controlling the access switch according to the acquired control command.
In an alternative embodiment, the device data includes device operational status, device operational instructions, and device sensory data; the device sensing data includes device sound data, device temperature data, and device image data.
In an alternative embodiment, the device control module 103 includes:
the device communicator is used for being connected to the working device in a communication way and used for acquiring the device working state and the device working instruction of the working device; the method comprises the steps that a middleware is realized in the working equipment through a calling technology, and the middleware is arranged in an application program of the working equipment and is used for capturing a plurality of equipment working instructions sent or ready to be received by the working equipment in real time and sending the equipment working instructions to an equipment communicator;
the equipment sensor group is arranged on the working equipment and used for acquiring equipment sensing data;
and the equipment controller is connected to the working equipment and used for receiving the control instruction and converting the control instruction into a specific type of parameter instruction to be sent to the working equipment for execution.
In an alternative embodiment, the overall control module 104 is specifically configured to perform the following steps:
respectively inputting ventilation sensing data into a trained ventilation smoothness prediction neural network and a ventilation effect prediction neural network to obtain output corresponding ventilation smoothness indexes and ventilation effect indexes; the ventilation smooth prediction neural network is obtained by training a training data set which comprises a plurality of training ventilation sensing data and a corresponding whether ventilation blockage marks exist or not; the ventilation effect prediction neural network is obtained through training a training data set comprising a plurality of training ventilation sensing data and corresponding manual evaluation ventilation effect labels;
inputting the personnel access sensing data into a trained personnel panic prediction neural network and a trained personnel quantity prediction neural network to obtain corresponding output personnel panic indexes and the quantity of personnel in the area; the personnel panic prediction neural network is obtained through training a training data set which comprises a plurality of training personnel access sensing data and corresponding personnel panic phenomenon marks; the personnel quantity prediction neural network is obtained by training a training data set comprising a plurality of training personnel access sensing data and corresponding personnel quantity labels in the region;
inputting the equipment data of any one working equipment into a trained equipment runaway prediction neural network and an equipment preparation runaway prediction neural network to obtain the equipment runaway probability and equipment preparation runaway probability corresponding to the output any working equipment; the equipment runaway prediction neural network is obtained by training a training data set which comprises a plurality of training equipment data and corresponding whether equipment runaway labeling occurs or not; the equipment preparation uncontrolled neural network is obtained through training of a plurality of training equipment data and a corresponding training data set whether equipment is out of control or not;
and determining a ventilation control strategy of the target laboratory area according to the ventilation smoothness index, the ventilation effect index, the personnel confusion index, the number of personnel in the area, the equipment out-of-control probability corresponding to the working equipment and the equipment preparation out-of-control probability, and judging a correction rule based on preset parameters.
In an alternative embodiment, the ventilation control strategy includes a ventilation control command, a door access control command, and a device control command.
In an alternative embodiment, the overall control module 104 determines the specific mode of the ventilation control strategy of the target laboratory area according to the ventilation smoothness index, the ventilation effect index, the personnel confusion index, the personnel number in the area, the equipment out-of-control probability corresponding to the working equipment and the equipment preparation out-of-control probability, and based on the preset parameter, the correction rule is determined, and the specific mode includes:
calculating a first weight inversely proportional to the ventilation smoothness index;
calculating a second weight inversely proportional to the ventilation effect index;
calculating a first difference value between the ventilation smoothness index and a preset first index threshold;
calculating a second difference between the personal confusion index and a preset second index threshold;
calculating a third weight proportional to the number of people in the area;
calculating a third difference value between the equipment runaway probability corresponding to any one of the working equipment and a preset first probability threshold;
calculating a fourth difference value between the equipment preparation run-away probability corresponding to any one of the working equipment and a preset second probability threshold;
judging whether the average value of the third difference value and the fourth difference value of any working equipment is larger than a preset first difference value threshold value, if so, determining that the equipment control instruction corresponding to the working equipment is stop working;
calculating the average value of the sum of the third difference value and the fourth difference value of all the working devices to obtain a device difference value average value;
when the first difference value is smaller than the second difference value threshold value, the second difference value is larger than the third difference value threshold value, and the equipment difference value average value is larger than the fourth difference value threshold value, determining an entrance guard control instruction of an entrance guard switch of a target laboratory area as an opening entrance guard, and determining control instructions of all variable-air-volume butterfly valves of the target laboratory area as opening the variable-air-volume butterfly valves to air volume parameters; the value of the air quantity parameter corresponding to each variable air quantity butterfly valve is equal to the sum of the current air quantity parameter and the air quantity increasing value of the variable air quantity butterfly valve; the value of the air volume increasing value is equal to the product of a preset air volume increasing reference value and the first weight, the second weight and the third weight.
The ventilation control system realized in the embodiment of the invention can monitor the ventilation condition, the personnel access condition and the equipment condition in a laboratory in real time, and comprehensively determine the ventilation control strategy according to the monitoring results, thereby realizing the comprehensive monitoring of the laboratory environment and effectively improving the efficiency and effect of ventilation control.
The foregoing describes certain embodiments of the present disclosure, other embodiments being within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings do not necessarily have to be in the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-transitory computer readable storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to portions of the description of method embodiments being relevant.
The apparatus, the device, the nonvolatile computer readable storage medium and the method provided in the embodiments of the present disclosure correspond to each other, and therefore, the apparatus, the device, and the nonvolatile computer storage medium also have similar advantageous technical effects as those of the corresponding method, and since the advantageous technical effects of the method have been described in detail above, the advantageous technical effects of the corresponding apparatus, device, and nonvolatile computer storage medium are not described herein again.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., a field programmable gate array (Field Programmable gate array, FPGA)) is an integrated circuit whose logic function is determined by the user programming the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware DescriptionLanguage), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (RubyHardware Description Language), etc., VHDL (Very-High-SpeedIntegrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
Finally, it should be noted that: the embodiment of the invention discloses a laboratory ventilation control system based on sensing monitoring, which is only a preferred embodiment of the invention, and is only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (7)
1. A laboratory ventilation control system based on sensory monitoring, the system comprising:
the ventilation control module is arranged in the ventilation cabinet equipment of the target laboratory area and used for acquiring ventilation sensing data of the ventilation cabinet equipment and controlling a variable air volume butterfly valve of the ventilation cabinet equipment;
the access control module is arranged in the access control equipment of the target laboratory area and used for acquiring the personnel access sensing data of the target laboratory area and controlling the opening and closing of the access control equipment;
the equipment control module is arranged in the working equipment of the target laboratory area and used for acquiring equipment data of the working equipment and controlling the work of the working equipment;
the total control module is connected to the ventilation control module, the access control module and the equipment control module and is used for determining a ventilation control strategy of the target laboratory area according to the ventilation sensing data, the personnel access sensing data and the equipment data; the general control module is specifically configured to execute the following steps:
respectively inputting the ventilation sensing data into a trained ventilation smoothness prediction neural network and a ventilation effect prediction neural network to obtain an output corresponding ventilation smoothness index and ventilation effect index; the ventilation smooth prediction neural network is obtained through training a training data set which comprises a plurality of training ventilation sensing data and a corresponding whether ventilation blockage marks exist or not; the ventilation effect prediction neural network is obtained through training a training data set comprising a plurality of training ventilation sensing data and corresponding manual evaluation ventilation effect labels;
inputting the personnel access sensing data into a trained personnel panic prediction neural network and a trained personnel quantity prediction neural network to obtain an output corresponding personnel panic index and the quantity of personnel in an area; the personnel panic prediction neural network is obtained through training a training data set which comprises a plurality of training personnel access sensing data and corresponding personnel panic phenomenon marks; the personnel quantity prediction neural network is obtained through training of a training data set comprising a plurality of training personnel access sensing data and corresponding personnel quantity labels in the region;
inputting the equipment data of any one of the working equipment into a trained equipment runaway prediction neural network and an equipment preparation runaway prediction neural network to obtain the equipment runaway probability and equipment preparation runaway probability corresponding to any one of the working equipment; the equipment out-of-control prediction neural network is obtained by training a training data set which comprises a plurality of training equipment data and corresponding whether equipment out-of-control labeling occurs or not; the equipment preparation uncontrolled neural network is obtained through training of a plurality of training equipment data and a corresponding training data set whether equipment is out of control or not;
determining a ventilation control strategy of the target laboratory area according to the ventilation smoothness index, the ventilation effect index, the personnel confusion index, the personnel number in the area, the equipment out-of-control probability corresponding to the working equipment and the equipment preparation out-of-control probability, and judging a correction rule based on preset parameters; the ventilation control strategy comprises a ventilation control instruction, an access control instruction and an equipment control instruction; the total control module determines a specific mode of the ventilation control strategy of the target laboratory area according to the ventilation smoothness index, the ventilation effect index, the personnel confusion index, the personnel number in the area, the equipment out-of-control probability corresponding to the working equipment and the equipment preparation out-of-control probability, and based on a preset parameter judgment correction rule, the method comprises the following steps:
calculating a first weight inversely proportional to the ventilation smoothness index;
calculating a second weight inversely proportional to the ventilation effect index;
calculating a first difference value between the ventilation smoothness index and a preset first index threshold;
calculating a second difference between the personal confusion index and a preset second index threshold;
calculating a third weight proportional to the number of people in the area;
calculating a third difference value between the equipment runaway probability corresponding to any one of the working equipment and a preset first probability threshold;
calculating a fourth difference value between the equipment preparation run-away probability corresponding to any one of the working equipment and a preset second probability threshold;
judging whether the average value of the third difference value and the fourth difference value of any working equipment is larger than a preset first difference value threshold value, if so, determining that the equipment control instruction corresponding to the working equipment is stop working;
calculating the average value of the sum of the third difference values and the fourth difference values of all the working devices to obtain a device difference value average value;
when the first difference value is smaller than a second difference value threshold value, the second difference value is larger than a third difference value threshold value, and the equipment difference value average value is larger than a fourth difference value threshold value, determining an entrance guard control instruction of an entrance guard switch of the target laboratory area as an opening entrance guard, and determining control instructions of all the variable air volume butterfly valves of the target laboratory area as opening the variable air volume butterfly valves to an air volume parameter; the value of the air quantity parameter corresponding to each variable air quantity butterfly valve is equal to the sum of the current air quantity parameter and the air quantity increasing value of the variable air quantity butterfly valve; and the value of the air volume increasing value is equal to the product of a preset air volume increasing reference value and the first weight, the second weight and the third weight.
2. The sensory monitoring-based laboratory ventilation control system of claim 1, wherein the ventilation sensory data comprises air volume sensory data, air pressure sensory data, and air quality sensory data.
3. The sensor monitoring-based laboratory ventilation control system of claim 2, wherein the ventilation control module comprises:
the air quantity sensor is arranged in a ventilation pipeline of the ventilation cabinet equipment and used for acquiring the air quantity sensing data;
the air pressure sensor is arranged on the fume hood equipment and used for acquiring the air pressure sensing data of the interior of the fume hood equipment and the target laboratory area;
the air quality sensor is arranged in the ventilating duct and used for acquiring the air quality sensing data;
and the butterfly valve controller is connected to the variable air volume butterfly valves of the fume hood equipment and is used for controlling the variable air volume butterfly valves according to the received control instruction.
4. The sensory monitoring based laboratory ventilation control system of claim 2, wherein the personnel access sensory data comprises access image data, access punch card data, and access sound data.
5. The sensor monitoring-based laboratory ventilation control system of claim 4, wherein the access control module comprises:
the access control camera is arranged in an access area of the target laboratory area and used for acquiring the access image data;
the card punching machine communication module is used for being connected to the card punching machine in the in-out area and used for acquiring the in-out card punching data;
the sound monitor is arranged in the access area and used for acquiring the access sound data;
and the access controller is connected to the access switch of the access area and is used for controlling the access switch according to the acquired control command.
6. The sensory monitoring-based laboratory ventilation control system of claim 4, wherein said equipment data comprises equipment operational status, equipment operational instructions, and equipment sensory data; the device sensing data includes device sound data, device temperature data, and device image data.
7. The sensor monitoring-based laboratory ventilation control system of claim 6, wherein the equipment control module comprises:
the equipment communicator is used for being in communication connection with the working equipment and acquiring the equipment working state and the equipment working instruction of the working equipment; the middleware is arranged in an application program of the working equipment and used for capturing a plurality of equipment working instructions sent or ready to be received by the working equipment in real time and sending the equipment working instructions to the equipment communicator;
the equipment sensor group is arranged on the working equipment and used for acquiring the equipment sensing data;
and the equipment controller is connected to the working equipment and is used for receiving the control instruction and converting the control instruction into a specific type of parameter instruction to be sent to the working equipment for execution.
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