A biological laboratory intelligent control method, system, terminal and storage medium

Abstract

The present invention belongs to the technical field of experimental research sites, and specifically relates to a biological laboratory intelligent control method, system, terminal and storage medium, including: constructing a situational awareness control platform; the situational awareness control platform includes an environmental perception device, a data processing gateway, a background management device and a user device; the environmental perception device obtains the real-time environmental perception data and real-time experimental data information of the biological laboratory to generate real-time summary data information; the data processing gateway obtains and judges the real-time summary data information to generate a real-time judgment result and establish a preliminary data model; the background management device verifies the real-time judgment result to generate a prediction analysis result; according to the prediction analysis result, the background management device generates early warning adjustment data information and feeds it back to the user device. The present invention can quickly and accurately obtain the environmental data and experimental data of the biological laboratory, and can improve the control management efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of experimental research sites, and in particular relates to an intelligent control method, system, terminal and storage medium for a biological laboratory. Background Art

[0002] Situational awareness technology plays an important role in the experimental data and site environment process of experimental research sites; it is the basis of safe experimental data and site environment, and is of great significance for improving the decision-making and analysis capabilities of experimental research sites and ensuring the safety and traceability of experimental data and site environment; the situational awareness of experimental research sites must not only perceive dynamic and static data such as the environmental safety of the experimental research site itself, personnel entry and exit, sample handover, confidentiality process, etc., but also perceive temperature, humidity, voltage and current, voice conversations, experimental processes, experimental data and site environment information; safe and reliable perception systems include sensors, perception algorithms and perception models; with the development of science and technology, experimental data and site environment technologies are gradually being applied in experimental research sites. With the continuous development of science and technology and the continuous deepening of biological research, new technologies such as artificial intelligence and big data have been widely used in the research field of experimental research sites, improving the intelligent perception capabilities of experimental data and site environment.

[0003] However, existing traditional experimental research sites usually rely on manual monitoring and management, which is not only inefficient but also prone to human errors; the perception accuracy is not enough and is greatly affected by the environment. Therefore, simple perception can no longer meet the needs of experimental data and site environment and traceability in existing experimental research sites. Summary of the invention

[0004] The purpose of the present invention is to provide a biological laboratory intelligent control method to solve any of the above technical problems in view of the deficiencies of the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A biological laboratory intelligent control method, comprising:

[0007] S1. Build a situation awareness control platform; the situation awareness control platform includes environment awareness equipment, data processing gateway and background management equipment and user equipment;

[0008] S2, the environmental sensing device obtains real-time environmental sensing data and real-time experimental data information of the biological laboratory to generate real-time summary data information;

[0009] S3, the data processing gateway obtains and determines the real-time summary data information to generate a real-time determination result and establish a preliminary data model;

[0010] S4, the background management device verifies the real-time judgment result to generate a prediction analysis result;

[0011] S5. According to the prediction and analysis results, the background management device generates early warning adjustment data information and feeds it back to the user device.

[0012] Preferably, the step of constructing a situation awareness control platform includes the following:

[0013] Add at least two environmental sensing devices according to the layout of the biological laboratory;

[0014] All of the environment sensing devices are controlled by at least two data processing gateways;

[0015] All the data processing gateways are controlled by the background management device;

[0016] The background management device is communicatively connected with the user device to form a four-layer Internet of Things structure of increasing levels of the environment perception device, the data processing gateway and the background management device.

[0017] Preferably, the step of the environment sensing device acquiring the real-time environment sensing data and real-time experimental data information of the biological laboratory includes the following:

[0018] The environmental sensing device obtains different types and at least two groups of parameter data in the real-time environmental sensing data of the biological laboratory; wherein the parameter data include magnetic field sensing parameters, temperature and humidity parameters, balance parameters of experimental instruments, laboratory air pressure parameters, laboratory smoke condition parameters, laboratory lighting parameters, laboratory user condition parameters and real-time operation experiment data parameters and laboratory real scene data;

[0019] All parameter data is filtered and categorized to generate real-time summary data information.

[0020] Preferably, the step of the data processing gateway acquiring and determining the real-time summary data information includes the following:

[0021] The data processing gateway receives the real-time summary data information of all the corresponding environment sensing devices;

[0022] The data processing gateway generates an active consultation instruction and sends the active consultation instruction to the corresponding environmental perception device to determine whether the content of the real-time aggregated data information is consistent and whether it is sent by the environmental perception device, thereby generating an active judgment result.

[0023] Preferably, the step of generating an active judgment result includes the following:

[0024] When the active judgment result is that the content of the real-time aggregated data information is consistent and the real-time aggregated data information is sent by the environment sensing device; a preliminary data model can be established according to the real-time aggregated data information;

[0025] When the active judgment result is that the content of the real-time aggregated data information is inconsistent, and / or the real-time aggregated data information is not sent by the environmental perception device; the data processing network generates a re-collection instruction; and at the same time stops establishing the preliminary data model.

[0026] Preferably, the step of the background management device verifying the real-time judgment result includes the following:

[0027] The backend management device directly sends a direct verification instruction to the data processing gateway that establishes the preliminary data model to obtain direct verification feedback; the direct verification instruction includes verification of the content of the real-time summary data information and verification of the source of the real-time summary data information;

[0028] The background management device sends an indirect verification instruction to the standby gateway, so that the standby gateway establishes an indirect verification path with all the environment perception devices corresponding to the data processing gateway;

[0029] The standby gateway executes the contents of step S2 and step S3 to obtain indirect verification feedback;

[0030] The direct verification feedback and the indirect verification feedback are compared to generate the predictive analysis result.

[0031] Preferably, the step of the background management device generating early warning adjustment data information and feeding it back to the user device includes the following:

[0032] The background management device generates early warning adjustment data information, and sends the early warning adjustment data information as feedback to the user device;

[0033] The user device generates an adjustment control instruction to a corresponding execution device according to the early warning adjustment data information.

[0034] The present invention also discloses a biological laboratory intelligent control system, which is used to execute the biological laboratory intelligent control method; wherein the biological laboratory intelligent control system comprises:

[0035] An information creation module, the information creation module is used to build a situation awareness control platform and establish a preliminary data model;

[0036] A data acquisition module, which is used to obtain real-time environmental perception data and real-time experimental data information of the biological laboratory to generate real-time summary data information;

[0037] An analysis and judgment module, the analysis and judgment module is used to obtain and judge the real-time summary data information to generate a real-time judgment result and to verify the real-time judgment result;

[0038] An analysis and warning module is used to generate warning adjustment data information and feed it back to the user equipment.

[0039] The present invention also discloses a biological laboratory intelligent control terminal, which includes: a memory, a processor, and a biological laboratory intelligent control program stored in the memory and executable on the processor. When the biological laboratory intelligent control program is executed by the processor, the steps of the biological laboratory intelligent control method are implemented.

[0040] The present invention also discloses a storage medium, on which a biological laboratory intelligent control program is stored. When the biological laboratory intelligent control program is executed by a processor, the steps of the biological laboratory intelligent control method are implemented.

[0041] The beneficial effect of the present invention is that the present technical scheme first constructs a multi-level situational awareness control platform to realize a distributed architecture platform, which is beneficial to the accuracy and efficiency of the control of the laboratory's environmental perception data; then the real-time environmental perception data and real-time experimental data information of the biological laboratory are obtained through environmental perception equipment to generate real-time summary data information, so that the real-time environmental perception data and real-time experimental data information can be obtained quickly and comprehensively, which is beneficial to the accuracy of subsequent data processing and the making of correct early warning feedback; then the real-time summary data information is obtained and judged through the data processing gateway to generate a real-time judgment result and establish a preliminary data model, so as to realize preliminary judgment and verify the authenticity of the real-time data. And generate a more accurate preliminary data model, which is conducive to the early warning feedback of subsequent data; then the real-time judgment results are verified and processed through the background management equipment to generate predictive analysis results, so as to realize the authenticity of the real-time data and the rationality of the preliminary data model again; finally, according to the predictive analysis results, the background management equipment generates early warning adjustment data information and feeds it back to the user equipment to generate corresponding early warning adjustment data information with safety rectification suggestions; thus, the environmental data and experimental data of the biological laboratory can be quickly and accurately obtained to ensure its safe and stable operation, and improve the control management efficiency; and it can meet the needs of experimental data and site environment of existing experimental research sites and the needs of traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The following will refer to the attached Figures 1 to 4 To describe the features, advantages and technical effects of exemplary embodiments of the present invention.

[0043] Figure 1 A flow chart of a biological laboratory intelligent control method according to an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of the structure of an environment sensing device according to an embodiment of the present invention;

[0045] Figure 3 A structural block diagram of a biological laboratory intelligent control system according to an embodiment of the present invention;

[0046] Figure 4 For an embodiment of the present invention Figure 1 The structure block diagram of the biological laboratory intelligent control terminal according to one embodiment of the present invention.

[0047] In the figure: 1001 - processor; 1002 - communication bus; 1003 - user interface; 1004 - network interface; 1005 - memory; 1 - housing; 11 - display screen; 2 - control component; 3 - sensing detection component. DETAILED DESCRIPTION

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0049] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0050] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0051] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and multiple situations exist alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0052] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0053] The present invention provides a biological laboratory intelligent control method, system, terminal and storage medium.

[0054] like Figure 4 As shown, Figure 4 It is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiment of the present invention.

[0055] The terminal in the embodiment of the present invention can be a PC, or it can be a mobile terminal device with display function such as a smart phone, a tablet computer, an e-book reader, an MP3 (Moving Picture Experts Group Audio Layer III) player, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a portable computer, etc.

[0056] like Figure 4As shown, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Dis l ay), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volat ile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0057] Optionally, the terminal may also include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. Among them, sensors include light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display screen according to the brightness of the ambient light, and the proximity sensor may turn off the display screen and / or backlight when the mobile terminal is moved to the ear. As a type of motion sensor, the gravity acceleration sensor can monitor the magnitude of acceleration in all directions (generally three axes), and can monitor the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometers, tapping), etc.; of course, the mobile terminal may also be configured with other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which will not be repeated here.

[0058] Those skilled in the art will appreciate that the terminal structure shown in the figure does not constitute a limitation on the terminal, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0059] Reference Figure 1 One embodiment of the biological laboratory intelligent control method, system, terminal and storage medium of the present invention provides a biological laboratory intelligent control method, such as Figure 1 As shown, the biological laboratory intelligent control method comprises the following steps:

[0060] S1. Build a situation awareness control platform; the situation awareness control platform includes environment awareness equipment, data processing gateway and background management equipment and user equipment;

[0061] S2, the environmental sensing device obtains real-time environmental sensing data and real-time experimental data information of the biological laboratory to generate real-time summary data information;

[0062] S3, the data processing gateway obtains and determines the real-time summary data information to generate a real-time determination result and establish a preliminary data model;

[0063] S4, the background management device verifies the real-time judgment result to generate a prediction analysis result;

[0064] S5. According to the prediction and analysis results, the background management device generates early warning adjustment data information and feeds it back to the user device.

[0065] The technical solution of the present invention first constructs a multi-level situational awareness control platform to realize a distributed architecture platform, which is beneficial to the accuracy and efficiency of the control of the laboratory's environmental perception data; then the real-time environmental perception data and real-time experimental data information of the biological laboratory are obtained through environmental perception equipment to generate real-time summary data information, so that the real-time environmental perception data and real-time experimental data information can be obtained quickly and comprehensively, which is beneficial to the accuracy of subsequent data processing and making correct early warning feedback; then the real-time summary data information is obtained and judged through the data processing gateway to generate real-time judgment results and establish a preliminary data model, so as to realize preliminary judgment to verify the authenticity of real-time data and generate more Adding an accurate preliminary data model is conducive to the early warning feedback of subsequent data; then the real-time judgment results are verified and processed through the background management equipment to generate predictive analysis results, so as to realize the authenticity of the real-time data and the rationality of the preliminary data model again; finally, according to the predictive analysis results, the background management equipment generates early warning adjustment data information and feeds it back to the user equipment to generate corresponding early warning adjustment data information with safety rectification suggestions; thus, the environmental data and experimental data of the biological laboratory can be quickly and accurately obtained to ensure its safe and stable operation, and improve the control management efficiency; and it can meet the needs of experimental data and site environment of existing experimental research sites and the needs of traceability.

[0066] Specifically, in some implementations, in S1, the step of building a situation awareness control platform includes the following:

[0067] Add at least two environmental sensing devices according to the layout of the biological laboratory;

[0068] All of the environment sensing devices are controlled by at least two data processing gateways;

[0069] All the data processing gateways are controlled by the background management device;

[0070] The background management device is communicatively connected with the user device to form a four-layer Internet of Things structure of increasing levels of the environmental perception device, the data processing gateway and the background management device, thereby forming a perception layer, an understanding layer, a verification and analysis layer and an early warning adjustment layer to identify information; thereby realizing multi-level and systematic information visualization of the experimental data and the environmental situation of the experimental research site.

[0071] In other words, through a multi-level distributed architecture, the accuracy and security of data transmission can be greatly improved, and control efficiency can be guaranteed.

[0072] Wherein, in some embodiments, Figure 2 As shown, the environmental sensing device includes a box 1, a control component 2 and a sensing detection component 3; a display screen 11 is provided on the side end surface of the box 1; the control component 2 includes a processor, a storage, an Ethernet switch and a visual processing chip; the sensing detection component 3 includes a magnetic field sensing module, a temperature and humidity sensor, a gyroscope, a pressure sensor, a smoke sensor, a light sensor and a visual sensor; the processor, the storage, the visual processing chip, the backup power supply, the magnetic field sensing module (Hall switch), the temperature and humidity sensor, the gyroscope, the pressure sensor, the smoke sensor, the light sensor, the visual sensor and the Ethernet switch are respectively connected to the storage and the visual processing chip for mutual communication; the processor, the storage, the visual processing chip, the backup power supply, the magnetic field sensing module (Hall switch), the temperature and humidity sensor, the gyroscope, the pressure sensor, the smoke sensor, the light sensor, the visual sensor and the Ethernet switch are also respectively connected to the backup power supply for discharging when the power supply is in an abnormal state. Power the visual processing chip. The display screen 11 can be a touch screen display screen, etc.

[0073] Specifically, in some embodiments, in S2, the environment perception device is provided with a perception detection component 3; wherein the perception detection component 3 includes a magnetic field sensing module, a temperature and humidity sensor, a gyroscope, a pressure sensor, a smoke sensor, a light sensing sensor and a visual sensor; the step of the environment perception device acquiring the real-time environment perception data and real-time experimental data information of the biological laboratory includes the following:

[0074] The environmental sensing device obtains different types and at least two groups of parameter data in the real-time environmental sensing data of the biological laboratory; wherein the parameter data include magnetic field sensing parameters, temperature and humidity parameters, balance parameters of experimental instruments, laboratory air pressure parameters, laboratory smoke condition parameters, laboratory lighting parameters, laboratory user condition parameters (user condition includes user identity and operation process data) and real-time operation experiment data parameters;

[0075] All parameter data are screened and classified to generate real-time summary data information. That is, the magnetic field sensing parameters of the laboratory environment are detected by the magnetic field sensing module; the temperature and humidity parameters of the laboratory environment are detected by the temperature and humidity sensor; the balance parameters of the experimental instruments in the laboratory environment are detected by the gyroscope; the laboratory air pressure parameters of the laboratory environment are detected by the pressure sensor; the smoke condition parameters in the laboratory are detected by the smoke sensor; the light sensor is used to detect the lighting parameters in the laboratory; and the visual sensor is used to detect the parameters of the personnel situation in the laboratory (including the personnel situation, including the identity of the personnel and the operation process data) and the real scene data in the laboratory. Among them, the visual sensor is used to collect the overall view of the laboratory environment, and the collected data (i.e., the real scene data in the laboratory) is transmitted to the NVR device for storage; so that the supervised video stream can be stored through the NVR.

[0076] The step of screening and classifying all parameter data includes the following:

[0077] Compare all parameter data of the same type;

[0078] If the difference between all the parameter data of the same type is within the preset error range, the parameter data of this type is weighted averaged, and the real-time summary data information of all the data is generated in combination with the parameter data of this type;

[0079] If the difference between at least one individual data and at least two other comparison data in all the same type of parameter data exceeds or is less than the preset error range, the individual data is screened out and marked as abnormal data; at the same time, the remaining parameter data is weighted averaged, and the real-time summary data information of all data is generated in combination with the remaining parameter data. That is, when too many abnormal data are screened out, it means that there is a problem with this type of parameter in the laboratory environment, which is conducive to the subsequent maintenance personnel to carry out targeted location and maintenance target inspection and maintenance, and improve the safety of use.

[0080] Specifically, in some implementations, in S3, the step of the data processing gateway acquiring and determining the real-time summary data information includes the following:

[0081] The data processing gateway receives the real-time summary data information of all the corresponding environment sensing devices;

[0082] The data processing gateway generates an active consultation instruction, and sends the active consultation instruction to the corresponding environment perception device to determine whether the content of the real-time summary data information is consistent and whether it is sent by the environment perception device, thereby generating an active judgment result;

[0083] When the active judgment result is that the content of the real-time aggregated data information is consistent and the real-time aggregated data information is sent by the environment sensing device; a preliminary data model can be established according to the real-time aggregated data information;

[0084] When the active judgment result is that the content of the real-time summary data information is inconsistent and / or the real-time summary data information is not sent by the environment sensing device, the data processing network generates a re-collection instruction and stops establishing the preliminary data model, wherein the preliminary data model is an artificial neural network model.

[0085] That is to say, due to the high error rate of the first-level equipment during data transmission, it is necessary to perform preliminary verification of the real-time aggregated data information through the data processing gateway to generate a preliminary data model, thereby ensuring the accuracy of data processing of the first-level equipment (environmental perception equipment).

[0086] Among them, artificial neural network is a mathematical model that uses a structure similar to the synaptic connection of the brain to process information. A neural network is an operation model composed of a large number of nodes (or neurons) and interconnections between them. Each node represents a specific output function, called an activation function. The connection between each two nodes represents a weighted value for the signal passing through the connection, called a weight, which is equivalent to the memory of the artificial neural network. The output of the network varies depending on the connection method of the network, the weight value and the activation function. The network itself is usually an approximation of a certain algorithm or function in nature, or it may be an expression of a logical strategy. In other words, the neural network operation model is composed of a large number of nodes (environmental perception devices) and data processing gateways that are interconnected.

[0087] Specifically, in some implementations, in S4, the step of the background management device verifying the real-time judgment result includes the following:

[0088] The backend management device directly sends a direct verification instruction to the data processing gateway that establishes the preliminary data model to obtain direct verification feedback; the direct verification instruction includes verification of the content of the real-time summary data information and verification of the source of the real-time summary data information;

[0089] The background management device sends an indirect verification instruction to the standby gateway, so that the standby gateway establishes an indirect verification path with all the environment perception devices corresponding to the data processing gateway;

[0090] The standby gateway executes the contents of step S2 and step S3 to obtain indirect verification feedback;

[0091] The direct verification feedback and the indirect verification feedback are compared to generate the prediction analysis result. The backup gateway belongs to another data processing gateway and is in a peer relationship with the data processing gateway for establishing the preliminary data model, but the two are not in communication connection with each other.

[0092] That is to say, through double verification of direct verification of the data processing gateway that establishes the preliminary data model and hidden indirect verification of the backup gateway, it is possible to avoid collusion between the operator of the data processing gateway that establishes the preliminary data model and the operator of the backup gateway that affects the authenticity of the data, thereby improving the security and efficiency of control.

[0093] Specifically, in some implementations, the step of the background management device sending an indirect verification instruction to the standby gateway includes:

[0094] The backend management device creates an indirect verification path of the backend management device with authority-backup gateway-corresponding environment perception device;

[0095] The standby gateway executes the contents of step S2 and step S3 to obtain indirect verification feedback;

[0096] Encrypting the indirect verification feedback so that not all users who can operate the standby gateway have the authority to process the indirect verification feedback;

[0097] The indirect verification feedback is sent to the background management device.

[0098] That is to say, in order to avoid data leakage, the backup gateway only has the function of data collection, storage and transmission. However, the data collected, stored and sent cannot be directly obtained by the backup gateway by clicking. The background management device needs to perform corresponding authorization according to the needs, so as to achieve data preservation integrity and ensure data security.

[0099] Specifically, in some implementations, in S5, the step of generating, according to the prediction analysis result, early warning adjustment data information fed back by the background management device to the user device includes the following:

[0100] The background management device generates early warning adjustment data information, and sends the early warning adjustment data information as feedback to the user device;

[0101] The user device produces adjustment control instructions to the corresponding execution device according to the early warning adjustment data information. In other words, the user device controls the safety status of the biological laboratory according to the prediction results of the intelligent perception model; specifically, it includes the control of temperature and humidity, or the automatic isolation of hazardous substances, etc.; thereby further improving the safety and reliability of the system. Among them, the user device can be a mobile device, a computer or other electronic device. The user can remotely view the experimental data through the terminal device, understand the experimental status, and receive warnings and notifications issued by the system.

[0102] The invention also provides a biological laboratory intelligent control system.

[0103] Specifically, Figure 3 As shown, the biological laboratory intelligent control system includes:

[0104] An information creation module 610, which is used to build a situational awareness control platform and establish a preliminary data model;

[0105] A data acquisition module 620, which is used to obtain real-time environmental perception data and real-time experimental data information of the biological laboratory to generate real-time summary data information;

[0106] An analysis and judgment module 630, the analysis and judgment module 630 is used to obtain and judge the real-time summary data information to generate a real-time judgment result and to verify the real-time judgment result;

[0107] The analysis and warning module 640 is used to generate warning adjustment data information and feed it back to the user equipment.

[0108] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a biological laboratory intelligent control program is stored. When the biological laboratory intelligent control program is executed by a processor, the following operations are implemented:

[0109] Constructing a situational awareness control platform; the situational awareness control platform includes environmental awareness equipment, data processing gateways and background management equipment and user equipment;

[0110] The environmental sensing device acquires real-time environmental sensing data and real-time experimental data information of the biological laboratory to generate real-time summary data information;

[0111] The data processing gateway acquires and determines the real-time summary data information to generate a real-time determination result and establish a preliminary data model;

[0112] The background management device verifies the real-time judgment result to generate a prediction analysis result;

[0113] According to the prediction analysis result, the background management device generates early warning adjustment data information and feeds it back to the user device.

[0114] Furthermore, the steps of constructing the situational awareness control platform include the following:

[0115] Add at least two environmental sensing devices according to the layout of the biological laboratory;

[0116] All of the environment sensing devices are controlled by at least two data processing gateways;

[0117] All the data processing gateways are controlled by the background management device;

[0118] The background management device is communicatively connected with the user device to form a four-layer Internet of Things structure of increasing levels of the environment perception device, the data processing gateway and the background management device.

[0119] Furthermore, the step of the environmental sensing device acquiring the real-time environmental sensing data and real-time experimental data information of the biological laboratory includes the following:

[0120] The environmental sensing device obtains different types and at least two groups of parameter data in the real-time environmental sensing data of the biological laboratory; wherein the parameter data include magnetic field sensing parameters, temperature and humidity parameters, balance parameters of experimental instruments, laboratory air pressure parameters, laboratory smoke condition parameters, laboratory lighting parameters, laboratory user condition parameters and real-time operation experimental data parameters;

[0121] All parameter data is filtered and categorized to generate real-time summary data information.

[0122] Furthermore, the step of the data processing gateway acquiring and determining the real-time summary data information includes the following:

[0123] The data processing gateway receives the real-time summary data information of all the corresponding environment sensing devices;

[0124] The data processing gateway generates an active consultation instruction and sends the active consultation instruction to the corresponding environmental perception device to determine whether the content of the real-time aggregated data information is consistent and whether it is sent by the environmental perception device, thereby generating an active judgment result.

[0125] Furthermore, the step of generating the active judgment result includes the following:

[0126] When the active judgment result is that the content of the real-time aggregated data information is consistent and the real-time aggregated data information is sent by the environment sensing device; a preliminary data model can be established according to the real-time aggregated data information;

[0127] When the active judgment result is that the content of the real-time aggregated data information is inconsistent, and / or the real-time aggregated data information is not sent by the environmental perception device; the data processing network generates a re-collection instruction; and at the same time stops establishing the preliminary data model.

[0128] Furthermore, the step of the background management device verifying the real-time judgment result includes the following:

[0129] The backend management device directly sends a direct verification instruction to the data processing gateway that establishes the preliminary data model to obtain direct verification feedback; the direct verification instruction includes verification of the content of the real-time summary data information and verification of the source of the real-time summary data information;

[0130] The background management device sends an indirect verification instruction to the standby gateway, so that the standby gateway establishes an indirect verification path with all the environment perception devices corresponding to the data processing gateway;

[0131] The standby gateway executes the contents of step S2 and step S3 to obtain indirect verification feedback;

[0132] The direct verification feedback and the indirect verification feedback are compared to generate the predictive analysis result.

[0133] Furthermore, the step of generating early warning adjustment data information and feeding it back to the user device by the background management device includes the following:

[0134] The background management device generates early warning adjustment data information, and sends the early warning adjustment data information as feedback to the user device;

[0135] The user device generates an adjustment control instruction to a corresponding execution device according to the early warning adjustment data information.

[0136] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode includes only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

[0137] According to the disclosure and teaching of the above description, those skilled in the art to which the present invention belongs can also change and modify the above embodiment. Therefore, the present invention is not limited to the above specific embodiment, and any obvious improvement, replacement or modification made by those skilled in the art on the basis of the present invention belongs to the protection scope of the present invention. In addition, although some specific terms are used in this specification, these terms are only for the convenience of description and do not constitute any limitation to the present invention.

Claims

1. A biological laboratory intelligent control method, characterized in that: include: S1. Build a situation awareness control platform; the situation awareness control platform includes environment awareness equipment, data processing gateway and background management equipment and user equipment; S2, the environmental sensing device acquires real-time environmental sensing data and real-time experimental data information of the biological laboratory to generate real-time summary data information; S3, the data processing gateway obtains and judges the real-time summary data information to generate a real-time judgment result and establish a preliminary data model; S4, the background management device verifies the real-time judgment result to generate a prediction analysis result; S5. According to the prediction and analysis results, the background management device generates early warning adjustment data information and feeds it back to the user device; The step of the background management device verifying the real-time judgment result includes the following: The backend management device directly sends a direct verification instruction to the data processing gateway that establishes the preliminary data model to obtain direct verification feedback; the direct verification instruction includes verification of the content of the real-time summary data information and verification of the source of the real-time summary data information; The background management device sends an indirect verification instruction to the standby gateway, so that the standby gateway establishes an indirect verification path with all the environment perception devices corresponding to the data processing gateway; The standby gateway executes the contents of step S2 and step S3 to obtain indirect verification feedback; The direct verification feedback and the indirect verification feedback are compared to generate the predictive analysis result.

2. The biological laboratory intelligent control method according to claim 1, characterized in that: The steps of constructing the situation awareness control platform include the following: Add at least two environmental sensing devices according to the layout of the biological laboratory; All of the environment sensing devices are controlled by at least two data processing gateways; All the data processing gateways are controlled by the background management device; The background management device is communicatively connected with the user device to form a four-layer Internet of Things structure of increasing levels of the environment perception device, the data processing gateway and the background management device.

3. The biological laboratory intelligent control method according to claim 1 is characterized in that: The step of the environment sensing device acquiring the real-time environment sensing data and real-time experimental data information of the biological laboratory includes the following: The environmental sensing device obtains different types and at least two groups of parameter data in the real-time environmental sensing data of the biological laboratory; wherein the parameter data include magnetic field sensing parameters, temperature and humidity parameters, balance parameters of experimental instruments, laboratory air pressure parameters, laboratory smoke condition parameters, laboratory lighting parameters, laboratory user condition parameters and real-time operation experiment data parameters and laboratory real scene data; All parameter data is filtered and categorized to generate real-time summary data information.

4. The biological laboratory intelligent control method according to claim 1 is characterized in that: The step of the data processing gateway acquiring and determining the real-time summary data information includes the following: The data processing gateway receives the real-time summary data information of all the corresponding environment sensing devices; The data processing gateway generates an active consultation instruction and sends the active consultation instruction to the corresponding environmental perception device to determine whether the content of the real-time aggregated data information is consistent and whether it is sent by the environmental perception device, thereby generating an active judgment result.

5. The biological laboratory intelligent control method according to claim 4 is characterized in that: The step of generating the active judgment result includes the following: When the active determination result is that the content of the real-time aggregated data information is consistent and the real-time aggregated data information is sent by the environment sensing device; A preliminary data model can be established based on the real-time aggregated data information; When the active judgment result is that the content of the real-time aggregated data information is inconsistent, and / or the real-time aggregated data information is not sent by the environmental perception device; the data processing network generates a re-collection instruction; and at the same time stops establishing the preliminary data model.

6. The biological laboratory intelligent control method according to claim 1 is characterized in that: The step of the background management device generating early warning adjustment data information and feeding it back to the user device includes the following: The background management device generates early warning adjustment data information, and sends the early warning adjustment data information as feedback to the user device; The user device generates an adjustment control instruction to a corresponding execution device according to the early warning adjustment data information.

7. A biological laboratory intelligent control system, characterized in that: Used to execute the biological laboratory intelligent control method according to any one of claims 1 to 6 above; wherein the biological laboratory intelligent control system comprises: An information creation module, the information creation module is used to build a situation awareness control platform and establish a preliminary data model; A data acquisition module, which is used to obtain real-time environmental perception data and real-time experimental data information of the biological laboratory to generate real-time summary data information; An analysis and judgment module, the analysis and judgment module is used to obtain and judge the real-time summary data information to generate a real-time judgment result and to verify the real-time judgment result; An analysis and warning module is used to generate warning adjustment data information and feed it back to the user equipment.

8. A biological laboratory intelligent control terminal, characterized by: The biological laboratory intelligent control terminal includes: a memory, a processor, and a biological laboratory intelligent control program stored in the memory and executable on the processor. When the biological laboratory intelligent control program is executed by the processor, the steps of the biological laboratory intelligent control method according to any one of claims 1 to 6 are implemented.

9. A storage medium, characterized in that: The storage medium stores a biological laboratory intelligent control program, and when the biological laboratory intelligent control program is executed by the processor, the steps of the biological laboratory intelligent control method according to any one of claims 1 to 6 are implemented.

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