CN116991102A - Laboratory intelligent control method, system and storage medium - Google Patents
Laboratory intelligent control method, system and storage medium Download PDFInfo
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Abstract
The application discloses a laboratory intelligent control method, a laboratory intelligent control system and a storage medium. Wherein the method comprises the following steps: acquiring laboratory environment data and weather data of a current region; inputting the laboratory environment data and the weather data into a trained laboratory environment prediction model to obtain the change trend of the laboratory environment data; judging whether the laboratory environment data can reach a set environment data threshold value in a preset time period based on the change trend; if yes, generating an early warning signal. The application can remind the user before the laboratory possibly generates abnormal environment data, and can effectively reduce the loss to the user caused by the abnormal laboratory environment.
Description
Technical Field
The application relates to the technical field of system control, in particular to an intelligent laboratory control method, an intelligent laboratory control system and a storage medium.
Background
With the development of technology, the laboratory industry is gradually developed to intelligent informatization, and a laboratory intelligent control system is generated.
In the use process of a laboratory, the stability of various laboratory environments needs to be ensured, but an intelligent laboratory control system may fail, so that loss is easy to cause, an alarm is often generated after the failure occurs in the prior art, but the alarm can only partially reduce the loss caused by the failure after the failure occurs, but the loss is not minimized in advance, so that a technical scheme capable of reminding a user of overhauling before the failure occurs is needed.
Disclosure of Invention
The application provides a laboratory intelligent control method, a laboratory intelligent control system and a laboratory intelligent control storage medium, which are used for informing a user before a laboratory fault occurs, so that the safety of a laboratory can be effectively improved, and the probability of laboratory occurrence risk is reduced.
In a first aspect, the present application provides a laboratory intelligent control method, comprising the steps of:
acquiring laboratory environment data and weather data of a current region, wherein the laboratory environment data comprises temperature data, air pressure data, air quality data and air speed data;
inputting the laboratory environment data and the weather data into a trained laboratory environment prediction model to obtain the change trend of the laboratory environment data;
judging whether the laboratory environment data can reach a set environment data threshold value in a preset time period based on the change trend;
if yes, generating an early warning signal.
By adopting the technical scheme, the laboratory environment data and the weather data of the current region can be input into the laboratory environment prediction model to acquire the change trend of each laboratory environment data, whether the laboratory environment data possibly breaks through a set environment data threshold value at a certain time in the future or not is predicted based on the change trend, if the laboratory environment data possibly breaks through the set environment data threshold value, an early warning signal is sent to a user, so that the user can predict the abnormality of the laboratory environment in advance, and accordingly response is made in advance, and the probability of risk of occurrence in the laboratory can be effectively reduced.
In combination with the embodiments of the first aspect of the present invention, in some embodiments, the preset time period includes a first preset time period and a second preset time period, and the step of determining whether the laboratory environment data reaches the set environment data threshold value within a future preset time period includes:
determining a level threshold of the laboratory environment data, the level threshold comprising a first preset threshold and a second preset threshold;
judging whether the laboratory environment data reaches the first preset threshold value in the first preset time period in the future;
and judging whether the laboratory environment data reaches the second preset threshold value in the second preset time period in the future.
By adopting the technical scheme, the laboratory environment data can be classified, and the severity of the abnormality of the laboratory environment data can be effectively informed to a user.
With reference to the embodiment of the first aspect of the present invention, in some embodiments, the step of generating the early warning signal includes:
generating a first early warning signal if the laboratory environment data reach the first preset threshold value;
generating a second early warning signal if the laboratory environment data reach the second preset threshold value; wherein the second early warning signal is triggered after the first early warning signal.
Through adopting above-mentioned technical scheme, can realize the early warning of different modes to the laboratory environmental data abnormality of different degrees and remind, can let the user know the laboratory environmental data abnormal conditions very first time.
With reference to the embodiment of the first aspect of the present invention, in some embodiments, the step of determining, based on the trend of variation, whether the laboratory environment data reaches a set environment data threshold value within a preset time period in the future includes:
and inputting the change trend into a trained trend judging model so that the trend judging model judges whether the laboratory environment data reaches the environment data threshold value in a future preset time period according to the change trend and the set environment data threshold value.
Through adopting above-mentioned technical scheme, can be with the trend judgement model after the change trend input training to whether obtain this laboratory environmental data possibly break through the environmental data threshold value of predetermineeing in the time quantum in the future, thereby early warning whether there is laboratory environmental data unusual in advance.
With reference to the embodiment of the first aspect of the present invention, in some embodiments, after the step of generating the early warning signal for the laboratory environment data about to reach the preset threshold based on the variation trend, the method further includes:
Determining laboratory environment data reaching a set environment data threshold value within a preset time period in the future as abnormal data;
generating early warning information according to the abnormal data;
the early warning information is sent to a user mobile terminal;
and responding to instruction information returned by the mobile terminal of the user, and controlling the laboratory equipment to process the abnormal data to obtain processed laboratory environment data.
Through adopting above-mentioned technical scheme, can use mobile terminal to control laboratory equipment and handle unusual data after the user receives early warning information, can reduce the loss because of laboratory environment unusual data brings.
With reference to the embodiments of the first aspect of the present invention, in some embodiments, current working information returned by the laboratory device is received, where the current working information includes laboratory environment data processed by the laboratory device;
obtaining experimental environment prediction data based on the trained trend judgment model and the processed laboratory environment data;
judging whether the processed laboratory environment data can reach a set environment data threshold value in a preset time period in the future;
if not, generating exception processing completion information;
And sending the exception handling completion information to the user mobile terminal.
By adopting the technical scheme, when the user responds to the abnormal data of the experimental environment, a more accurate operation instruction is obtained, and the operation instruction sent to the laboratory by the user can be more accurately improved in the laboratory environment.
In combination with the embodiments of the first aspect of the present application, in some embodiments, after the step of determining whether the processed laboratory environment data can reach the set environment data threshold value within the future preset time period, the method further includes:
if yes, generating optimal laboratory environment data;
and adjusting the working state of the laboratory equipment according to the optimal laboratory environment data.
By adopting the technical scheme, after the user inputs an operation instruction which is not ideal enough, the laboratory intelligent control system outputs the best laboratory environment data to control the working state of the laboratory equipment, so that the laboratory environment can be safer.
In a second aspect, embodiments of the present application provide a laboratory intelligent control system, comprising: one or more processors and memory; the memory is coupled to one or more processors, the memory for storing computer program code comprising computer instructions that the one or more processors call to cause the laboratory intelligent control system to perform the method as described in the first aspect and any possible implementation of the first aspect.
In a third aspect, embodiments of the present application provide a computer readable storage medium comprising instructions that, when run on the above-described instruction laboratory intelligent control system, cause the above-described laboratory intelligent control system to perform a method as described in the first aspect and any one of the possible implementations of the first aspect.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. according to the application, the laboratory environment data and the weather data of the current region can be input into the laboratory environment prediction model to acquire the change trend of each laboratory environment data, whether the laboratory environment data possibly breaks through a set environment data threshold value at a certain time in the future or not is predicted based on the change trend, and if the laboratory environment data possibly breaks through the set environment data threshold value, an early warning signal is sent to a user, so that the user can predict the abnormality of the laboratory environment in advance, and thus response is made in advance, and the probability of laboratory occurrence risk can be effectively reduced.
2. The application can provide early warning information for the user when the laboratory environment data is abnormal, and can control the corresponding equipment of the laboratory to adjust the laboratory environment through the mobile terminal after the user receives the early warning information, thereby effectively reducing the loss caused by the laboratory environment abnormality.
3. The application can provide optimal allocation parameters when a user regulates laboratory equipment by using the mobile terminal, so that the regulated laboratory environment data is kept stable, and the problem of abnormal secondary laboratory environment data caused by inaccurate parameter regulation data of the user is avoided.
Drawings
FIG. 1 is a schematic diagram of an exemplary scenario of a laboratory intelligent control method according to an embodiment of the present application;
FIG. 2 is a schematic flow diagram of an exemplary laboratory intelligent control method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an exemplary scenario of a laboratory intelligent control method according to an embodiment of the present application;
FIG. 4 is an exemplary flow chart of a laboratory intelligent control method according to an embodiment of the present application;
FIG. 5 is an exemplary block diagram of a laboratory intelligent control system according to an embodiment of the present application;
fig. 6 is a schematic diagram of an electronic device in the present application.
Detailed Description
The terminology used in the following embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates to the contrary. It should also be understood that the term "and/or" as used in this disclosure refers to and encompasses any or all possible combinations of one or more of the listed items.
The terms "first," "second," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature, and in the description of embodiments of the application, unless otherwise indicated, the meaning of "a plurality" is two or more.
As shown in fig. 1, an interactive scenario of information transmission according to the present application is shown, a laboratory intelligent control system receives laboratory environment data and local weather data, where the laboratory environment data includes data such as temperature data, air pressure data, air quality data, and wind speed data in a laboratory, the local weather data includes data such as local temperature, humidity, precipitation, air quality, and the like, and the laboratory environment data and the local weather data are imported into an environment prediction model, so that a change trend of the laboratory environment data is predicted by the environment prediction model, and a part of the change trend of various environment data, which may break through a threshold, may generate an early warning signal by the laboratory intelligent control system and transmit the early warning signal to an application program of a user mobile terminal.
The user mobile terminal is a mobile phone in fig. 1, the application program is an APP named as a laboratory intelligent management platform in fig. 1, after the mobile phone receives an early warning signal, red bubbles can be popped up on the APP of the laboratory intelligent management platform to remind a user of receiving a new message, the user can enter an operation interface of the APP after clicking the APP, and the laboratory environment options are clicked to obtain the latest laboratory environment conditions, so that the laboratory environment conditions can be predicted in advance before abnormality occurs, abnormal data can be processed in advance, and harm to a laboratory or staff in the laboratory environment due to the abnormality can be effectively reduced.
For example, when the temperature in the laboratory is rising at an unusual speed, the change trend of the temperature monitored by the laboratory intelligent control system is very likely to break through the preset upper temperature limit, at this time, the laboratory intelligent control system pushes early warning information to the mobile phone of the user, the right upper corner of the icon of the app laboratory intelligent control platform on the mobile phone of the user is lightened to light red bubbles to prompt the user that new information is pushed in the laboratory, the user clicks the app to enter the interface of the laboratory intelligent control platform, meanwhile, a new message is found in the laboratory environment, the laboratory temperature change abnormality can be checked by clicking the laboratory environment, a processing instruction can be sent to the laboratory intelligent control system by the mobile phone in advance, and some devices in the laboratory are controlled by the laboratory intelligent control system, for example, an air conditioner in the laboratory is started to perform cooling processing, and then the user goes to the laboratory to check the reason of the abnormal rise of the temperature, so that the influence of the temperature rise on the laboratory is reduced or even eliminated.
It should be noted that, the present invention is not only aimed at abnormality of laboratory environment data, but also can be applied to other parts in the laboratory, such as laboratory stored data, laboratory equipment in the laboratory, etc., and after abnormality is detected by the laboratory intelligent control system, remote reminding or control can be performed by a mobile phone connected to the laboratory intelligent control system, so as to reduce various losses in the laboratory.
The invention provides an intelligent laboratory control method, which is characterized in that laboratory environment data are input into an environment prediction model through a laboratory management system to monitor the laboratory environment data, so that the change trend of the laboratory environment data is monitored at any time, meanwhile, whether certain laboratory environment data possibly exceed a preset threshold value is analyzed according to the change trend of the laboratory environment data, so that the laboratory environment is abnormal, and the change trend of the laboratory environment data which possibly exceed the preset threshold value, such as the conditions of too low air pressure, too high temperature and the like, is timely sent to a mobile terminal of a user and early warning information is pushed, so that the user can think about processing countermeasures before an environment abnormal event occurs to avoid loss or reduce loss.
The following describes a laboratory intelligent control method in the embodiment of the application:
referring to fig. 2, a schematic flow chart of a laboratory intelligent control method according to an embodiment of the application is shown;
step 201, acquiring laboratory environment data and weather data of a current region;
monitoring laboratory environment data and weather data for a current region may be set by a user to a monitoring range, including monitoring which portion of laboratory environment data and weather data for the current region.
It will be appreciated that the laboratory environment data is affected by the weather in the current region to some extent, so that in order to effectively monitor the change of the laboratory environment data, it is necessary to take the weather data in the current region into consideration, and the weather data may be set to a source range, for example, the weather data within 1km around the laboratory is taken into the monitoring range to increase the accuracy of the influence of the weather data on the laboratory environment data as much as possible, and if the monitoring range is too large, the data which may be acquired is too complicated and the influence on the laboratory environment is not accurate, so that a suitable weather monitoring range needs to be selected as much as possible according to the location of the laboratory.
Step 202, inputting laboratory environment data and weather data into a trained laboratory environment prediction model to obtain a change trend of the laboratory environment data;
In some embodiments, step 202 is shown in FIG. 1, after the laboratory intelligent control system has acquired laboratory environmental data and local weather data, it enters the environmental prediction model to determine its future trend.
It will be appreciated that the input of the acquired laboratory environmental data and local weather data to the environmental prediction model is not a process that needs to be actively triggered, but is a process that is performed at one time, and the environmental prediction model may be various, and may be a statistical-based model, such as time series analysis, regression analysis, etc., to model and analyze the historical environmental data. Predicting a change in a future environment by analyzing trends, periodicity, and correlation of the data; or a machine learning model, and training and learning environment data by using a machine learning algorithm, such as a decision tree, a support vector machine, a neural network and the like, so as to establish a prediction model. Predicting the change of future environment by learning the mode and rule of the data; the method can also be a deep learning model, the deep learning model is a special form of machine learning, the environment data is modeled and predicted through a multi-layer neural network, the deep learning model can process large-scale data and complex modes, and the change of environment variables can be predicted more accurately; and the method can also be a comprehensive model, combines a plurality of models and algorithms, comprehensively considers the influence of different factors on the environment, and improves the accuracy and the comprehensiveness of prediction. The comprehensive model can obtain more comprehensive and reliable environment prediction results by integrating various data sources, various prediction methods and various algorithms.
Step 203, judging whether the laboratory environment data can reach the set environment data threshold value in the future time period based on the change trend;
if yes, go to step 204, if not, go to step 201.
After analysis of future trends in laboratory environmental data by the environmental prediction model of step 202, a determination is made by the laboratory intelligent control system as to whether the laboratory environmental data is likely to exceed a predetermined threshold, wherein the predetermined threshold may be a default conventional threshold, such as ISO 14644-1, which is the first part of the international organization for standardization (ISO) established "classification of clean rooms and related controlled environments" standard, wherein a classification system for particulate matter concentration in clean rooms is specified. A total of 9 classes are defined, from ISO Class 1 to ISO Class 9, lower classes indicating a higher cleanliness requirement. Particulate matter concentration limit: an upper limit value of the concentration of particulate matter is defined for each class of clean room. These limits are determined based on actual application requirements and risk assessment. Meanwhile, the user can customize the environmental data threshold according to the actual situation so as to adapt to the actual situation of a laboratory, if the user finds that certain environmental data in the laboratory possibly exceeds a preset threshold, an early warning signal is sent to a mobile phone of the user as shown in fig. 1, the user can click an app on the mobile phone to acquire early warning information, if the risk that the environmental data in the laboratory environment exceeds a preset value is not monitored, the method returns to the step 201 to continuously monitor the environment, and the whole process monitoring of the safety of the laboratory environment is realized.
And 204, generating early warning information.
As shown in FIG. 1, after the early warning information is generated, the early warning information is transmitted to the mobile phone of the user, the user can receive the latest information in real time, the user can not only receive the early warning information, but also check which data are possibly abnormal in the application program of the mobile phone, so that the situation of a laboratory can be known in time.
In the embodiment, the laboratory environment data can be monitored, the change trend of the laboratory environment data is predicted by utilizing the environment prediction model in combination with the local weather conditions within the set place range, an early warning signal is generated to the mobile phone application program of the user after the abnormal data is detected, reminding information is generated on the application program icon, the user is reminded to view the latest information in time, and precautionary measures are timely made. The method avoids the loss caused by processing the abnormal laboratory environment data, and can early warn in advance before the abnormal laboratory environment data occurs, so that the abnormal laboratory environment data is processed in time, and the potential dangerous situation is reduced.
In some embodiments of the present invention, in step 203, multiple preset thresholds may be set for actual situations in the laboratory, so as to set a first preset threshold and a second preset threshold for different levels of data anomalies, for example, to set a first preset threshold and a second preset threshold for distinguishing severity of an environmental anomaly trend, where the first preset threshold corresponds to a situation with a lower severity, and the second preset threshold is used for determining whether the environmental data in the laboratory can reach the first preset threshold in a first preset time period and then send the early warning information to the user mobile phone in a second preset time period.
Aiming at the abnormal information reaching the first preset threshold in the embodiment, a first early warning signal is sent to the user mobile terminal, the abnormal information reaching the second preset threshold is sent to the user mobile terminal, different early warning signals are different in feedback mode, the first early warning signal only needs to generate sound to remind a user when being sent to the user mobile terminal, and the second early warning signal can strengthen the prompting effect in modes such as vibration.
It can be appreciated that, as in the laboratory intelligent control system of fig. 1, the received laboratory environment data and local weather data may be input to the trend determination model after being sent to the environment prediction model, so that the trend determination model may be used to determine whether the laboratory environment data may exceed the set environment data threshold in combination with the set environment data threshold, and the trend determination model may be used to determine whether the laboratory environment data may exceed the set environment data threshold more accurately.
The laboratory intelligent control method of the present invention is described in more detail below with reference to fig. 3 and 1.
In fig. 3, the connection relationship between the transmission condition of each data in the flow and each layout in the invention is shown, firstly, laboratory intelligent control system transmits laboratory environment data and weather data of the current region to laboratory environment prediction model, thereby obtaining the change trend of laboratory environment data, after obtaining the change trend of laboratory environment data, inputting the change trend into trend judgment model, thereby judging whether laboratory environment data exceeding the preset threshold is likely to exist in laboratory environment data, after detecting the situation, sending early warning signal to user mobile terminal, after receiving early warning signal, user can send operation command to abnormal data to control laboratory intelligent control system to process abnormal data in laboratory, detect the processed laboratory environment data, and send abnormal processing completion information to user terminal, the set of flow can clearly show the process from monitoring laboratory environment data and weather data of the current region by laboratory intelligent control system until processing completion information is sent, at the same time, user can control laboratory intelligent control system to control laboratory environment data in laboratory environment after receiving early warning signal, thereby being convenient for remote processing of user in laboratory environment, and can not process the situation in laboratory mobile phone when user's remote control system is not used.
In an embodiment of the invention, the laboratory intelligent control system can also verify the processed laboratory environment data and judge whether the processed laboratory environment data possibly breaks through a preset environment data threshold again until the laboratory environment data is stable, so that the processed laboratory environment data is verified, because the operation instruction sent to the laboratory intelligent control system by a user aiming at the early warning information possibly cannot perfectly solve the problem, the laboratory environment data is influenced by various factors, and the laboratory environment data is singly modified to only temporarily ensure the stability of the environment, so that after the processed laboratory environment data is obtained, whether the processed laboratory environment data possibly becomes abnormal data can still be judged according to the trend of the processed laboratory environment data.
It can be understood that if the instruction information sent by the user is judged by the trend judging model in the above scheme to possibly cause that the laboratory environment data exceeds the preset threshold in the future time, the laboratory intelligent control system can push the optimal operation instruction information to the user mobile terminal, so that the user can timely give a complete parameter adjustment scheme without knowing how to adjust each environment parameter, the occurrence of secondary abnormal conditions of the environment data is reduced as much as possible, and the experimental environment stability after adjustment can be effectively increased.
FIG. 4 is a flow chart of an embodiment of the present invention;
step 401, acquiring laboratory environment data and weather data of a current region;
step 401 is used to obtain data to be monitored.
Step 402, inputting laboratory environment data and weather data of a current region into a trained environment prediction model to acquire a change trend of the laboratory environment data;
and combining the laboratory environment data with weather data of the current region, and combining with a trained environment prediction model to obtain the change trend of the laboratory environment data, wherein the change trend of the laboratory environment data such as the change trend of data such as the rising or falling trend of temperature.
Step 403, inputting the change trend of the laboratory environment data into a trend judging model;
the trend of the laboratory environment data obtained in step 402 is input to a trend determination model, and the trend determination model is used to determine whether the current trend may exceed a predetermined environment data threshold.
Step 404, determining the level threshold of the laboratory environment data;
and judging the change trend of the laboratory environment data according to the trend judging model in the step 403, classifying the laboratory environment data which is possibly abnormal according to the trend change speed, and classifying the laboratory environment data into an environment abnormality grade I and an environment abnormality grade II, wherein the grade I is a milder environment abnormality grade, and the grade II is a more serious environment abnormality grade.
Step 405, determining whether a first preset threshold can be reached within a first preset time in the future, if yes, jumping to step 406, if not, jumping to step 407;
by determining the level threshold of the laboratory environment in step 404, determining whether there is laboratory environment data that may reach the level one environmental anomaly, and also determining that there is a time setting when determining the laboratory environment data, the first preset time is one day for the laboratory environment data of the level one environmental anomaly, so determining whether it reaches the first preset threshold in the future day, if it reaches the first preset threshold in the future day, jumping to step 406, and if it does not reach the first preset threshold in the future day, jumping to step 407.
Step 406, generating a first early warning signal, and jumping to step 409;
according to the environmental anomaly information that can reach the first preset threshold in the future day determined in step 405, a first early warning signal is generated, and step 409 is directly skipped.
Step 407, judging whether a second preset threshold can be reached within a second preset time in the future, if so, jumping to step 408, otherwise jumping to step 401;
By determining the level threshold of the laboratory environment in step 404, determining whether there is laboratory environment data that may reach the level two environmental abnormality in the laboratory environment data, and setting the laboratory environment data in time at the same time, wherein the second preset time is two days for the laboratory environment data of the level two environmental abnormality, so as to determine whether it reaches the second preset threshold in two days in the future, if it reaches the second preset threshold in two days in the future, the process goes to step 408, and if it does not reach the second preset threshold in two days in the future, the process goes to step 401.
Step 408, generating a second pre-warning signal;
and generating a second early warning signal according to the environmental abnormality information which can reach a second preset threshold value in two days in the future, which is judged in the step 407.
Step 409, sending the early warning information to the user mobile terminal;
the generated first early warning signal or second early warning signal is sent to the mobile terminal of the user, the user is timely reminded that the situation that the environment is abnormal possibly occurs, different reminding modes can be adopted when different levels of early warning information are sent to the user, for example, the first early warning information can only be sent to remind the user, and the second early warning information can remind the user that urgent information needs to be checked in a more obvious mode by adopting modes such as mobile phone vibration.
Step 410, in response to the instruction of the user mobile terminal, controlling the laboratory equipment to process the abnormal data;
after the user receives the early warning signal, the mobile terminal can be connected with the intelligent laboratory control system and control equipment in the laboratory to process abnormal data, for example, when the laboratory wind speed or air pressure data is abnormal, the ventilation system can be controlled to solve the problem of data abnormality.
Step 411, receiving the current working information returned by the laboratory device, judging whether the processed laboratory environment data exceeds the set environment data threshold value, if yes, jumping to step 413, and if not, jumping to step 412;
the current working information returned by the laboratory equipment after step 410 is received, and it is determined whether the processed laboratory environment data still has the possibility of exceeding the set environment data threshold, if the processed laboratory environment data is still not stable enough, the risk of exceeding the predetermined threshold is still present, the process goes to step 413, and if it is determined that the processed laboratory environment data is processed, the risk of exceeding the predetermined threshold is not present, the process goes to step 412.
Step 412, sending the exception handling completion information to the user mobile terminal;
After the judgment in step 411, after the laboratory environment data is adjusted, the data exception processing completion information is sent to the user mobile terminal.
In step 413, optimal laboratory environment data is generated and the operating state of the laboratory equipment is adjusted.
According to step 411, the laboratory environment data still cannot reach a long-term stable state after regulation, and the user is pushed with the best laboratory environment data parameters, and the working state of the laboratory equipment is adjusted by using the best laboratory environment data parameters after confirmation, so that the laboratory environment can be kept stable for a long time after treatment.
According to the method, whether the generation of the abnormal data is possible in the laboratory can be monitored in real time, the user can be informed in advance before the generation of the abnormal data, the abnormal data can be classified according to the severity of the abnormal data, and the user can be reminded in different modes according to different grades, so that the user can know the severity of the abnormal data in the first time when the user obtains the early warning information, the user can remotely control the laboratory by using the mobile phone and the laboratory networking after receiving the early warning information, the processing of the abnormal data in the laboratory can be achieved when the user is not near the laboratory, the laboratory environment data after the analysis processing is continued on the abnormal data in the laboratory is also possible to be abnormal secondarily, the optimal regulation parameters are recommended for the user according to the possible abnormal secondarily data, the laboratory environment data can be kept stable for a long time, the loss caused by the abnormal environment data in the laboratory can be effectively reduced, the laboratory can be remotely processed when the environment data is possible to be abnormal, and the processing space of the user is greatly facilitated.
A specific exemplary scenario is constructed below to better illustrate the inventive arrangements;
a user applies the scheme to a biological laboratory, and a laboratory intelligent control system monitors laboratory environment data and weather data of a current region and combines the laboratory environment data and the weather data into an environment prediction model. When the variation trend of the temperature data monitored at a certain moment is abnormal, the variation trend is input into a trend judging model, and the trend judging model predicts that the temperature of the temperature data possibly reaches 25 degrees in one day and possibly reaches 30 degrees in two days. The first preset time is one day, the first preset threshold value is 25 degrees, the second preset time is two days, and the second preset threshold value is 30 degrees.
Under the condition, the laboratory intelligent control system sends a first early warning signal and a second early warning signal to the user mobile phone, if the condition that the temperature of the user mobile phone is 30 ℃ in two days is not met, the first early warning signal is only sent to the user mobile phone, and after the user receives the early warning signal, the user can send a control instruction to the laboratory intelligent control system to laboratory equipment by using the mobile phone to control the opening of the laboratory air conditioner so as to achieve the purpose of cooling.
The trend judging model also carries out secondary analysis according to the processed temperature change trend to judge whether the trend is 25 degrees in a future day or 30 degrees in two future days, if the trend is stable and the trend is not 25 degrees again, the laboratory intelligent control system sends an abnormal information processing completion message to the mobile phone of the user; if the detected temperature change trend still reaches 25 degrees in one day or 30 degrees in two days, a better solution is pushed to the user, for example, a mode of using cooling equipment and controlling a heat source is provided for the user on the intelligent laboratory management system app, for example, an air conditioner is turned on, meanwhile, a heat source such as an illuminating lamp and a computer in a laboratory is turned off so as to achieve a better cooling effect, and after the user confirms that the cooling effect is achieved on the app, the intelligent laboratory control system controls the corresponding laboratory equipment to be turned on or off.
Fig. 5 is a schematic block diagram of a laboratory intelligent control system according to an embodiment of the present application, including:
an acquisition module 501, configured to receive laboratory environment data and weather data of a current region;
the change analysis module 502 is configured to analyze the data in the acquisition module 501, and input the data into the environmental prediction model 503 to obtain a change trend of the laboratory environmental data;
a trend judging module 504, configured to receive the trend of the laboratory environment data in the environment prediction model 503, and judge whether the laboratory environment data has laboratory environment data exceeding a predetermined environment threshold according to the trend;
the alarm generation module 505 is configured to generate early warning information and send the early warning information to the user when detecting that the trend determination module 504 has abnormal environmental data.
To facilitate understanding of a laboratory intelligent control method according to an embodiment of the present application, an exemplary electronic device 600 provided by an embodiment of the present application is first described below.
In some embodiments, the electronic device 600 is a computer device, which may be a terminal device or a server. The computer device includes a processor, a memory, and a network interface connected by a system bus.
Wherein the processor of the computer device is configured to provide computing and control capabilities. The processor may include one or more processing units, such as: the processor may include one or more of an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a memory, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and/or a neural network processor (neural-network processing unit, NPU), etc. Wherein the different processing units may be separate devices or may be integrated in one or more processors.
If the processing unit included in the processor includes an NPU, the efficiency of performing the deep learning process can be improved. The NPU is a neural-network (NN) computing processor, and can rapidly process input information by referencing a biological neural network structure, for example, referencing a transmission mode between human brain neurons, and can also continuously perform self-learning. Applications such as intelligent awareness of the electronic device 600 may be implemented through the NPU, for example: image recognition, face recognition, speech recognition, text understanding, etc.
The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing data.
The network interface of the computer device is used for communicating with other terminals or servers outside through network connection. In some embodiments, the network interface may be a wired network interface, and in some embodiments, the network interface may also be a wireless network interface.
The processor 603 may include one or more processing units, such as: the processor 603 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a memory, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and/or a neural network processor (neural-network processing unit, NPU), etc. Wherein the different processing units may be separate devices or may be integrated in one or more processors.
The controller may be a neural hub and a command center of the electronic device 600, among others. The controller can generate operation control signals according to the instruction operation codes and the time sequence signals to finish the control of instruction fetching and instruction execution.
A memory may also be provided in the processor 603 for storing instructions and data. In some embodiments, the memory in the processor 603 is a cache memory. The memory may hold instructions or data that the processor 603 has just used or recycled. If the processor 603 needs to reuse the instruction or data, it can be called directly from the memory. Repeated accesses are avoided and the latency of the processor 603 is reduced, thus improving the efficiency of the system.
In some embodiments, the processor 603 may include one or more interfaces. The interfaces may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, a universal asynchronous receiver transmitter (universal asynchronous receiver/transmitter, UART) interface, a mobile industry processor interface (mobile industry processor interface, MIPI), a general-purpose input/output (GPIO) interface, a subscriber identity module (subscriber identity module, SIM) interface, and/or a universal serial bus (universal serial bus, USB) interface, among others.
The I2C interface is a bi-directional synchronous serial bus comprising a serial data line (SDA) and a serial clock line (derail clock line, SCL). In some embodiments, the processor 110 may contain multiple sets of I2C buses.
The GPIO interface may be configured by software. The GPIO interface may be configured as a control signal or as a data signal. In some embodiments, a GPIO interface may be used to connect the processor 603 with the input device 601, the output device 602, and so on. The GPIO interface may also be configured as an I2C interface, an I2S interface, a UART interface, an MIPI interface, etc.
The USB interface is an interface conforming to the USB standard specification, and can be specifically a Mini USB interface, a Micro USB interface, a USB Type C interface and the like. The USB interface may be used to connect a charger to charge the electronic device 600, or may be used to transfer data between the electronic device 600 and a peripheral device. And can also be used for connecting with a headset, and playing audio through the headset. The interface may also be used to connect other electronic devices, such as AR devices, etc.
It should be understood that the interfacing relationship between the modules illustrated in the embodiments of the present application is only illustrative, and is not meant to limit the structure of the electronic device 600. In other embodiments of the present application, the electronic device 600 may also use different interfacing manners, or a combination of multiple interfacing manners, as in the above embodiments.
The computer program, when executed by a processor, implements a laboratory intelligent control method in an embodiment of the application.
It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In some embodiments, the electronic device 600 is a computer device, which may be a server. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a laboratory intelligent control method in an embodiment of the application.
It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In some embodiments of the present application, a computer-readable storage medium is also provided, comprising instructions that, when executed on the electronic device 600, cause the electronic device 600 to perform a laboratory intelligent control method in embodiments of the present application.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
As used in the above embodiments, the term "when …" may be interpreted to mean "if …" or "after …" or "in response to determination …" or "in response to detection …" depending on the context. Similarly, the phrase "at the time of determination …" or "if detected (a stated condition or event)" may be interpreted to mean "if determined …" or "in response to determination …" or "at the time of detection (a stated condition or event)" or "in response to detection (a stated condition or event)" depending on the context.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk), etc.
Those of ordinary skill in the art will appreciate that implementing all or part of the above-described method embodiments may be accomplished by a computer program to instruct related hardware, the program may be stored in a computer readable storage medium, and the program may include the above-described method embodiments when executed. And the aforementioned storage medium includes: ROM or random access memory RAM, magnetic or optical disk, etc.
Claims (10)
1. The intelligent laboratory control method is characterized by comprising the following steps of:
acquiring laboratory environment data and weather data of a current region, wherein the laboratory environment data comprises temperature data, air pressure data, air quality data and air speed data;
inputting the laboratory environment data and the weather data into a trained laboratory environment prediction model to obtain the change trend of the laboratory environment data;
judging whether the laboratory environment data can reach a set environment data threshold value in a future preset time period based on the change trend;
if yes, generating an early warning signal.
2. The laboratory intelligent control method according to claim 1, wherein the preset time period includes a first preset time period and a second preset time period, and the step of determining whether the laboratory environment data reaches a set environment data threshold value within a future preset time period includes:
Determining the level threshold of the laboratory environment data, the level threshold comprising a first preset threshold and a second preset threshold;
judging whether the laboratory environment data reach the first preset threshold value in the first preset time period in the future;
and judging whether the laboratory environment data reaches the second preset threshold value within the second preset time period in the future.
3. The laboratory intelligent control method according to claim 2, wherein: the step of generating the early warning signal comprises the following steps:
generating a first early warning signal if the laboratory environment data reach the first preset threshold value;
generating a second early warning signal if the laboratory environment data reach the second preset threshold value; wherein the second early warning signal is triggered after the first early warning signal.
4. A laboratory intelligent control method according to any one of claims 1 to 3, characterized in that: the step of judging whether the laboratory environment data reaches a set environment data threshold value in a preset time period based on the change trend comprises the following steps:
and inputting the change trend into a trained trend judging model, so that the trend judging model judges whether the laboratory environment data reaches the set environment data threshold value in a future preset time period according to the change trend and the set environment data threshold value.
5. The laboratory intelligent control method according to claim 4, wherein: after the step of generating the early warning signal for the laboratory environment data about to reach a preset threshold based on the variation trend, the method further comprises:
determining laboratory environment data reaching the set environment data threshold value within a preset time period in the future as abnormal data;
generating early warning information according to the abnormal data;
the early warning information is sent to a user mobile terminal;
and responding to instruction information returned by the mobile terminal of the user, and controlling laboratory equipment to process the abnormal data to obtain processed laboratory environment data.
6. The laboratory intelligent control method according to claim 5, further comprising, after the step of transmitting the pre-warning information to the user mobile terminal:
receiving current working information returned by the laboratory equipment, wherein the current working information comprises laboratory environment data processed by the laboratory equipment;
obtaining experimental environment prediction data based on the trained trend judgment model and the processed laboratory environment data;
judging whether the processed laboratory environment data can reach the set environment data threshold value in a future preset time period;
If not, generating exception processing completion information;
and sending the exception handling completion information to the user mobile terminal.
7. The laboratory intelligent control method according to claim 6, wherein: after the step of determining whether the processed laboratory environment data can reach the set environment data threshold value within a preset time period, the method further includes:
if yes, generating optimal laboratory environment data;
and adjusting the working state of the laboratory equipment according to the optimal laboratory environment data.
8. A laboratory intelligent control system, comprising:
the acquisition module is used for acquiring laboratory environment data and weather data of a current region, wherein the laboratory environment data comprises temperature data, air pressure data, air quality data and wind speed data;
the change analysis module is used for inputting the laboratory environment data and the weather data into a trained laboratory environment prediction model to obtain a change trend of the laboratory environment data;
the trend judging module is used for judging whether the laboratory environment data reach a set environment data threshold value in a preset time period in the future based on the change trend;
And the alarm generation module is used for generating an early warning signal.
9. A laboratory intelligent control system, comprising: one or more processors and memory;
the memory is coupled to the one or more processors, the memory for storing computer program code comprising computer instructions that the one or more processors invoke to cause the laboratory intelligent control system to perform the method of any of claims 1-7.
10. A computer readable storage medium comprising instructions which, when run on an electronic device, cause the electronic device to perform the method of any of claims 1-7.
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| CN117631708A (en) * | 2024-01-25 | 2024-03-01 | 南京诺丹工程技术有限公司 | Pressure information control system and method for clean laboratory |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117631708A (en) * | 2024-01-25 | 2024-03-01 | 南京诺丹工程技术有限公司 | Pressure information control system and method for clean laboratory |
| CN117631708B (en) * | 2024-01-25 | 2024-03-29 | 南京诺丹工程技术有限公司 | Pressure information control system and method for clean laboratory |
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