CN114998985A - Early warning control method of intelligent experiment table and intelligent experiment table - Google Patents
Early warning control method of intelligent experiment table and intelligent experiment table Download PDFInfo
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Abstract
The invention provides an early warning control method of an intelligent experiment table and the intelligent experiment table. Wherein the method comprises the following steps: acquiring a first image of an intelligent experiment table area, and extracting first operation data according to the first image; and performing matching calculation on the first operation data and the standard operation data, and if the matching calculation result does not meet a preset condition, executing an early warning scheme. The scheme of the invention can identify the non-standard and even wrong experiment operation behaviors of an experimenter in time, and effectively reduce the experiment risk through the execution of the early warning scheme.
Description
Technical Field
The invention relates to the technical field of intelligent experiment equipment, in particular to an early warning control method of an intelligent experiment table, the intelligent experiment table, electronic equipment and a storage medium.
Background
The experiment table is used for the experimental detection and instrument storage of enterprises and institutions such as hospitals, schools, chemical plants, scientific research institutions and the like. The existing experiment table can only provide installation or line interfaces and the like of relevant experimental equipment, can not identify and analyze operation behaviors of experimenters, and especially can not early warn irregular experiment operation of students who are not totally aware of experiment specifications, so that experiment risks are high.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides an early warning control method of an intelligent experiment table, the intelligent experiment table, electronic equipment and a storage medium.
The invention provides an early warning control method of an intelligent experiment table, which comprises the following steps:
acquiring a first image of an intelligent experiment table area, and extracting first operation data according to the first image;
and performing matching calculation on the first operation data and the standard operation data, and if the matching calculation result does not meet a first condition, executing an early warning scheme.
Optionally, the extracting first operation data according to the first image includes:
identifying a hand region and a laboratory equipment region in the first image and continuously tracking to obtain palm operation data, palm position sequence data, laboratory equipment operation data and laboratory equipment position sequence data;
and performing position matching calculation on the palm position sequence data and the experimental equipment position sequence data to obtain a plurality of operation data pairs, and taking the plurality of operation data pairs as the first operation data.
Optionally, the standard operation data is obtained by:
receiving experiment attribute data issued by a main control experiment table, and determining the standard operation data according to the experiment attribute data;
alternatively, the first and second electrodes may be,
extracting the attribute of experimental equipment according to the first image, and determining candidate experimental attribute data according to the attribute of the experimental equipment; and outputting the candidate experiment attribute data to an experimenter, determining target experiment attribute data based on feedback operation of the experimenter, and determining the standard operation data according to the target experiment attribute data.
Optionally, the standard operation data includes a plurality of operation data, and the operation data includes an operation serial number;
the matching calculation of the first operation data and the standard operation data comprises:
calculating a first similarity between each first operation data and the corresponding operation data in the standard operation data according to the operation sequence number, and if the first similarity is greater than or equal to a first threshold value, marking and recording, otherwise, marking as null;
and establishing a mark sequence according to the operation serial number and the mark record, judging whether the mark sequence meets a second condition, if so, setting the first operation data to meet a first condition, and otherwise, setting the first operation data not to meet the first condition.
Optionally, the operation data further comprises an operation attribute;
the determining whether the marker sequence satisfies a second condition includes:
determining each breakpoint in the mark sequence, and determining the operation attribute according to the operation sequence number corresponding to each breakpoint; and if the operation attribute is the key operation, judging that the marking sequence does not meet a second condition, otherwise, judging that the marking sequence meets the second condition.
Optionally, the operation data pair comprises action data and position data, the action data comprises palm operation data and experimental equipment operation data, and the position data comprises palm position sequence data and experimental equipment position sequence data;
the calculating a first similarity between each of the first operation data and the corresponding operation data in the standard operation data includes:
in the formula, sim represents a first similarity of first operation data and corresponding operation data in the standard operation data; y is i I-th data, Y, representing motion data in the operation data j J-th data representing position data in the operation data;
represents an average value of data in the motion data in the first operation data,
representing an average of each of the position data in the first operation data; m is the amount of data in the motion data, and n is the amount of data in the position data.
Optionally, after the matching calculation result does not satisfy the first condition, further comprising:
acquiring a second image of the main control experiment table area, and extracting second operation data according to the second image;
and calculating a second similarity of the second operation data and the first operation data, if the second similarity is greater than or equal to a second threshold, not executing the early warning scheme, otherwise executing the early warning scheme.
The invention provides an intelligent experiment table, which comprises a processing module, a storage module and an acquisition module, wherein the processing module is respectively connected with the storage module and the acquisition module; wherein the content of the first and second substances,
the storage module is stored with a computer program;
the acquisition module is used for acquiring image data of the experiment table area and transmitting the image data to the processing module;
the processing module is configured to invoke the computer program to implement the method as described in any one of the above.
A third aspect of the invention provides a computer storage medium having stored thereon a computer program which, when executed by a processor, performs the method as set out in any one of the preceding claims.
A fourth aspect of the invention provides an electronic device comprising a processor and a memory, said memory having stored thereon a computer program which, when executed by the processor, performs the method of any of the above.
The invention has the beneficial effects that: the method comprises the steps of obtaining an image of a laboratory table area, extracting first operation data of an experimenter through an image recognition technology, then carrying out matching calculation on the first operation data and standard operation data corresponding to a current experiment, and if a matching calculation result is unqualified, executing an early warning scheme in time. Therefore, the scheme of the invention can timely identify the non-standard and even wrong experiment operation behaviors of an experimenter, and effectively reduce the experiment risk through the execution of the early warning scheme.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of an early warning control method for an intelligent laboratory bench disclosed in the embodiments of the present invention;
FIG. 2 is a schematic structural diagram of an intelligent laboratory bench disclosed in the embodiments of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. indicate an orientation or a positional relationship based on that shown in the drawings or that the product of the present invention is used as it is, this is only for convenience of description and simplification of the description, and it does not indicate or imply that the device or the element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of an early warning control method for an intelligent laboratory bench according to an embodiment of the present disclosure. As shown in fig. 1, a method for controlling an early warning of an intelligent laboratory bench according to an embodiment of the present invention includes:
acquiring a first image of an intelligent experiment table area, and extracting first operation data according to the first image;
and performing matching calculation on the first operation data and the standard operation data, and executing an early warning scheme if the matching calculation result does not meet the first condition.
In the embodiment of the present invention, as described in the background art, the experiment table in the prior art can only provide the relevant experimental equipment and line interfaces, and cannot identify and analyze the experimental operation behavior of the beginner, so that the risk caused by the non-standardized experimental operation behavior cannot be effectively reduced. In view of this, the invention acquires the image of the experiment table area, extracts the first operation data of the experimenter through the image recognition technology, then performs matching calculation on the first operation data and the standard operation data corresponding to the current experiment, and if the matching calculation result is not qualified, executes the early warning scheme in time. Therefore, the scheme of the invention can timely identify the non-standard and even wrong experiment operation behaviors of an experimenter, and effectively reduce the experiment risk through the execution of the early warning scheme. The early warning scheme can be freely set, for example, alarm output is carried out in a sound and/or light mode.
It should be noted that the first image in the present invention may be obtained by shooting with a camera disposed at each intelligent laboratory, or may be obtained by shooting with at least one camera disposed in a laboratory, where the camera may be a rifle bolt, a ball machine, and the like, and is not limited specifically.
In addition, aspects of the present invention may be implemented in a variety of processing entities, such as field processing devices and servers. The field Processing device may be a Processing device disposed in a single laboratory, such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a discrete gate or transistor logic device, a discrete hardware component, and the like; the server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a web service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), and a big data and artificial intelligence platform, and the server may monitor multiple laboratories.
Optionally, the extracting first operation data from the first image includes:
identifying a hand region and a laboratory equipment region in the first image and continuously tracking to obtain palm operation data, palm position sequence data, laboratory equipment operation data and laboratory equipment position sequence data;
and performing position matching calculation on the palm position sequence data and the experimental equipment position sequence data to obtain a plurality of operation data pairs, and taking the plurality of operation data pairs as the first operation data.
In the embodiment of the invention, the experimenter may have many actions irrelevant to the experiment during the experiment process, such as talking with others, organizing the equipment of the previous experiment, and the like, and the irrelevant actions may cause misjudgment. Therefore, the invention simultaneously tracks and identifies the palm operation data and the palm position sequence data, the experimental equipment operation data and the experimental equipment position sequence data from the first image, and performs matching calculation on the two data to extract the real experimental operation, thereby reducing the probability of misjudgment. The matching calculation can be to calculate the degree of coincidence of the positions of the two types of data, that is, when the palm and the experimental device coincide, it can be determined that the experimenter is operating the experimental device.
The palm operation data may include various gesture data, such as grabbing, pressing, pinching and the like, and the experimental device operation data may include various posture data, such as tilting angle, horizontal traversing, vertical traversing and the like. The palm operation data and the experimental device operation data may describe respective operation actions of the experimenter, and the palm position sequence data and the experimental device position sequence data are operation positions corresponding to the respective operation actions.
Optionally, the standard operation data is obtained by:
receiving experiment attribute data issued by a main control experiment table, and determining the standard operation data according to the experiment attribute data;
alternatively, the first and second electrodes may be,
extracting the attribute of experimental equipment according to the first image, and determining candidate experimental attribute data according to the attribute of the experimental equipment; and outputting the candidate experiment attribute data to an experimenter, determining target experiment attribute data based on feedback operation of the experimenter, and determining the standard operation data according to the target experiment attribute data.
In the embodiment of the invention, the standard operation data has the two determination modes, namely, an experiment leader (such as a teacher) can set the experiment type of each experiment table on the main control experiment table, and at the moment, the standard operation data can be determined and obtained based on the experiment attribute data sent by the main control experiment table; or the attributes of the experimental equipment can be extracted through an image recognition technology, and then a plurality of candidate experimental attribute data related to the experimental equipment can be screened out and output to an experimenter, and standard operation data can be determined based on the selection operation of the experimenter.
Optionally, the standard operation data includes a plurality of operation data, and the operation data includes an operation serial number;
the matching calculation of the first operation data and the standard operation data comprises the following steps:
calculating a first similarity between each first operation data and the corresponding operation data in the standard operation data according to the operation sequence number, and if the first similarity is greater than or equal to a first threshold value, marking and recording, otherwise, marking as null;
and establishing a mark sequence according to the operation serial number and the mark record, judging whether the mark sequence meets a second condition, if so, setting the first operation data to meet a first condition, and otherwise, setting the first operation data not to meet the first condition.
In the embodiment of the invention, the standard operation data comprises a plurality of operation data which are sequentially arranged, after each first operation data is identified and obtained, the first similarity calculation is carried out on the first operation data and the corresponding operation data in the standard operation data, if the similarity is greater than or equal to a first threshold value, the first operation data is matched, and the matching condition is marked and recorded; with the calculation of the first similarity, the alignment sequence data can be obtained according to the operation serial number and the mark record, and when the alignment sequence data meet the second condition, a series of prior experiment operations of an experimenter can be proved to be in accordance with the specification.
Optionally, the operation data further comprises an operation attribute;
the determining whether the marker sequence satisfies a second condition includes:
determining each breakpoint in the marking sequence, and determining the operation attribute according to the operation sequence number corresponding to each breakpoint; and if the operation attribute is the key operation, judging that the marking sequence does not meet a second condition, otherwise, judging that the marking sequence meets the second condition.
In the embodiment of the present invention, in the actual experimental situation, not all experimental steps are critical and dangerous, and the proper adjustment of the sequence of some experimental steps will not have much influence. For the actual situation, the invention further performs key operation marking (i.e. operation attribute) on each operation data in the standard operation data, and correspondingly, for each breakpoint in the marking sequence, if it is a key operation, it indicates that the operation of the critical and dangerous experiment step is not normal, and at this time, an early warning should be performed; otherwise, it indicates that the irregular operation only involves unimportant experimental steps and does not need to be warned.
Optionally, the operation data pair comprises action data and position data, the action data comprises palm operation data and experimental device operation data, and the position data comprises palm position sequence data and experimental device position sequence data;
then, the calculating a first similarity between each of the first operation data and the corresponding operation data in the standard operation data includes:
in the formula, sim represents a first similarity of first operation data and corresponding operation data in the standard operation data; y is i Ith data, Y, representing action data in the operation data j J-th data representing position data in the operation data;
represents an average value of data in the motion data in the first operation data,
representing the mean of the data in the position data in the first operation data; m isThe amount of data in the motion data, and n is the amount of data in the position data.
In the embodiment of the invention, the palm operation data and the experimental equipment operation data are integrated into the action data, the palm position sequence data and the experimental equipment position sequence data are integrated into the position data, so that the operation data pair forms the first operation data, and whether the current first operation is matched with the corresponding standard operation in the standard operation data can be analyzed by calculating the first similarity of the corresponding operation data in the first operation data and the standard operation data.
Note that the motion data and the position data related to the similarity calculation formula should be normalized data.
Optionally, after the matching calculation result does not satisfy the first condition, further comprising:
acquiring a second image of the main control experiment table area, and extracting second operation data according to the second image;
and calculating a second similarity of the second operation data and the first operation data, if the second similarity is greater than or equal to a second threshold, not executing the early warning scheme, otherwise executing the early warning scheme.
In the embodiment of the present invention, the standard operation data is generally instructive but not mandatory, so that it may be reasonable when the actual first operation data is different from the standard operation data, and no warning is needed. Aiming at the actual situation, the invention further acquires a second image of the main control experiment table area, extracts second operation data of a teacher from the second image, and if the second similarity between the first operation data of the experimenter and the second operation data of the teacher is greater than or equal to a second threshold value, the experimenter performs an experiment according to the requirement of the teacher, although the first operation data and the second operation data of the teacher do not completely accord with the standard operation data, the experimenter also belongs to the scope of standard operation, and at the moment, early warning operation is not performed. The second similarity may be calculated in the same manner as the first similarity, and of course, other similarity calculation manners may also be adopted, and the present invention is not limited herein.
In addition, for this example, the present invention further provides the following modifications:
the operational attributes include a key rank;
the acquiring a second image of the main control experiment table area includes:
and acquiring the second image in a preset time period of the main control experiment table area, wherein the length of the preset time period is in negative correlation with the key level.
In the improved embodiment, the key operation is internally provided with a certain grade, the higher the key grade is, the higher the risk of the key operation is, and accordingly, the teacher can delay the experiment step to be performed so as to give more explanation and understanding time to the experimenter. In view of the situation, the length of the preset time period is further determined according to the key level, and the second operation data for guiding the teacher is extracted from the second image corresponding to the preset time period, so that the higher the key level of the key operation is, the longer the second operation data in the preset time period is obtained to obtain more comprehensive and accurate second operation data, and otherwise, the shorter the second operation data in the preset time period is obtained to avoid obtaining the second operation data in the adjacent step, thereby improving the accuracy of the second operation data.
The preset time interval may be a time interval obtained by tracing before taking a time/time interval corresponding to the first operation data as a starting point.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of an intelligent experiment table according to an embodiment of the present invention. As shown in fig. 2, an intelligent laboratory bench according to an embodiment of the present invention includes a processing module (101), a storage module (102), and an obtaining module (103), where the processing module (101) is connected to the storage module (102) and the obtaining module (103), respectively; wherein the content of the first and second substances,
the storage module (102) having a computer program stored thereon;
the acquisition module (103) is used for acquiring image data of a laboratory table area and transmitting the image data to the processing module (101);
the processing module (101) is used for calling the computer program to realize the method according to the first embodiment.
For specific functions of the intelligent experiment table in this embodiment, reference is made to the first embodiment, and since the intelligent experiment table in this embodiment adopts all the technical solutions of the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and details are not repeated here.
EXAMPLE III
Referring to fig. 3, fig. 3 is an electronic device according to an embodiment of the present invention, the electronic device includes: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program code stored in the memory to execute the method according to the first embodiment.
Example four
The embodiment of the invention also discloses a computer storage medium, wherein a computer program is stored on the storage medium, and the computer program executes the method in the first embodiment when being executed by a processor.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An early warning control method for an intelligent experiment table is characterized by comprising the following steps:
acquiring a first image of an intelligent experiment table area, and extracting first operation data according to the first image;
and performing matching calculation on the first operation data and the standard operation data, and executing an early warning scheme if the matching calculation result does not meet the first condition.
2. The early warning control method of the intelligent experiment table according to claim 1, characterized in that: extracting first operation data according to the first image, wherein the extracting comprises the following steps:
identifying a hand region and a laboratory equipment region in the first image and continuously tracking to obtain palm operation data, palm position sequence data, laboratory equipment operation data and laboratory equipment position sequence data;
and performing position matching calculation on the palm position sequence data and the experimental equipment position sequence data to obtain a plurality of operation data pairs, and taking the plurality of operation data pairs as the first operation data.
3. The early warning control method of the intelligent experiment table according to claim 1 or 2, which is characterized in that: the standard operation data is obtained by the following method:
receiving experiment attribute data issued by a main control experiment table, and determining the standard operation data according to the experiment attribute data;
alternatively, the first and second electrodes may be,
extracting experimental equipment attributes according to the first image, and determining candidate experimental attribute data according to the experimental equipment attributes; and outputting the candidate experiment attribute data to an experimenter, determining target experiment attribute data based on feedback operation of the experimenter, and determining the standard operation data according to the target experiment attribute data.
4. The early warning control method of the intelligent experiment table according to claim 1, characterized in that: the standard operation data comprises a plurality of operation data, and the operation data comprises an operation serial number;
performing matching calculation on the first operation data and standard operation data, including:
calculating a first similarity between each first operation data and the corresponding operation data in the standard operation data according to the operation sequence number, and if the first similarity is greater than or equal to a first threshold value, marking and recording, otherwise, marking as null;
and establishing a mark sequence according to the operation serial number and the mark record, judging whether the mark sequence meets a second condition, if so, setting the first operation data to meet a first condition, and otherwise, setting the first operation data not to meet the first condition.
5. The early warning control method of the intelligent experiment table according to claim 4, characterized in that: the operational data further comprises operational attributes;
the determining whether the marker sequence satisfies a second condition includes:
determining each breakpoint in the marking sequence, and determining the operation attribute according to the operation sequence number corresponding to each breakpoint; and if the operation attribute is the key operation, judging that the marking sequence does not meet a second condition, otherwise, judging that the marking sequence meets the second condition.
6. The early warning control method of the intelligent experiment table according to claim 4 or 5, characterized in that: the operation data pair comprises action data and position data, the action data comprises palm operation data and experimental equipment operation data, and the position data comprises palm position sequence data and experimental equipment position sequence data;
then, the calculating a first similarity between each of the first operation data and the corresponding operation data in the standard operation data includes:
in the formula, sim represents a first similarity of first operation data and corresponding operation data in the standard operation data; y is i I-th data, Y, representing motion data in the operation data j J-th data representing position data in the operation data;
represents an average value of data in the motion data in the first operation data,
representing the mean of the data in the position data in the first operation data; m is the amount of data in the motion data, and n is the amount of data in the position data.
7. The early warning control method of the intelligent experiment table according to claim 6, characterized in that: after the matching calculation result does not satisfy the first condition, the method further comprises:
acquiring a second image of the main control experiment table area, and extracting second operation data according to the second image;
and calculating a second similarity of the second operation data and the first operation data, if the second similarity is greater than or equal to a second threshold, not executing the early warning scheme, otherwise executing the early warning scheme.
8. An intelligent experiment table comprises a processing module, a storage module and an acquisition module, wherein the processing module is respectively connected with the storage module and the acquisition module; wherein the content of the first and second substances,
the storage module is stored with a computer program;
the acquisition module is used for acquiring image data of the experiment table area and transmitting the image data to the processing module;
the method is characterized in that: the processing module is for invoking the computer program to implement the method of any one of claims 1-7.
9. A computer storage medium having a computer program stored thereon, characterized in that: the computer program, when executed by a processor, performs the method of any one of claims 1-7.
10. An electronic device comprising a processor and a memory, the memory having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, performs the method of any one of claims 1-7.
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| CN117373134A (en) * | 2023-12-07 | 2024-01-09 | 广东莱博仕教育设备有限公司 | Data management method and device for practical training room and practical training system |
| CN117373134B (en) * | 2023-12-07 | 2024-03-26 | 广东莱博仕教育设备有限公司 | Training room data management method, device and training system |
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