CN115345527B - Chemical experiment abnormal operation detection method, device, equipment and readable storage medium - Google Patents
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Abstract
The invention provides a method, a device, equipment and a readable storage medium for detecting abnormal operation of a chemical experiment, which relate to the technical field of information and comprise the following steps: acquiring real-time experiment video data and historical experiment video data, wherein the historical experiment video data information comprises normal operation video data and abnormal operation video data; preprocessing historical experimental video data and then performing feature extraction to obtain a first feature set and a weight set; clustering operation is carried out on the historical experimental video based on the first feature set and the weight set to obtain classified video data; performing feature conversion on the classified video data to obtain a second feature set; and establishing an experiment abnormal operation detection mathematical model according to the second feature set and the classified video data, taking the real-time experiment video data as an input value, and solving the experiment abnormal operation detection mathematical model to obtain a detection result. According to the method, the characteristic matrix of the classified video data is linearly divisible by establishing the low-dimensional matrix, and the accuracy of abnormal operation in the real-time video is improved.
Description
Technical Field
The invention relates to the technical field of information, in particular to a method, a device and equipment for detecting abnormal operation of a chemical experiment and a readable storage medium.
Background
When a chemical experiment operation examination is carried out, examination clerks are often required to carry out invigilation on site, the operation steps of students are observed to supervise, and meanwhile, abnormal operations are recorded and corrected. What is needed is a method and apparatus for detecting abnormal operation of real-time experiment video based on wavelet feature extraction and feature transformation, so as to detect chemical experiment in real time, thereby reducing the dependence on manual invigilation and ensuring the accuracy of detecting abnormal operation of chemical experiment.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a readable storage medium for detecting abnormal operation of a chemical experiment, so as to improve the problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present application provides a method for detecting abnormal operation in a chemical experiment, comprising:
acquiring real-time experiment video data and historical experiment video data, wherein the historical experiment video data information comprises normal operation video data and abnormal operation video data;
preprocessing the historical experimental video data and then extracting features to obtain a first feature set and a weight set;
clustering the historical experimental videos based on the first feature set and the weight set to obtain classified video data;
performing feature conversion on the classified video data to obtain a second feature set;
and establishing an experiment abnormal operation detection mathematical model according to the second feature set and the classified video data, taking the real-time experiment video data as an input value, and solving the experiment abnormal operation detection mathematical model to obtain a detection result.
In a second aspect, the present application also provides a chemical experiment abnormal operation detection apparatus, including:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring real-time experiment video data and historical experiment video data, and the historical experiment video data information comprises normal operation video data and abnormal operation video data;
the extraction module is used for preprocessing the historical experimental video data and then extracting features to obtain a first feature set and a weight set;
the analysis module is used for carrying out clustering operation on the historical experimental video based on the first feature set and the weight set to obtain classified video data;
the conversion module is used for performing feature conversion on the classified video data to obtain a second feature set;
and the detection module is used for establishing an experiment abnormal operation detection mathematical model according to the second feature set and the classified video data, taking the real-time experiment video data as an input value, and solving the experiment abnormal operation detection mathematical model to obtain a detection result.
In a third aspect, the present application further provides a chemical experiment abnormal operation detection apparatus, including:
a memory for storing a computer program;
a processor for implementing the steps of the chemical experiment abnormal operation detection method when executing the computer program.
In a fourth aspect, the present application further provides a readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the above-mentioned method for detecting abnormal operation based on chemical experiment.
The invention has the beneficial effects that:
according to the invention, the historical experiment video and the real-time experiment video are subjected to wavelet transformation and the characteristics are extracted, and the advantages of wavelet transformation rapid decomposition and local characteristic extraction are utilized, so that the detection speed of abnormal operation in the real-time video is increased.
According to the method, clustering is carried out after feature extraction is carried out on the historical experimental video, then feature conversion is carried out again in the classified video data, a low-dimensionality matrix is established to enable the feature matrix of the classified video data to be linearly separable, and the accuracy rate of abnormal operation in the real-time video is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required 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 those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a schematic flow chart of a method for detecting abnormal operation in a chemical experiment according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an abnormal operation detection apparatus for a chemical experiment according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of the abnormal operation detection device for the chemical experiment according to the embodiment of the present invention.
The labels in the figure are: 1. an acquisition module; 2. an extraction module; 21. a first processing unit; 22. a second processing unit; 23. a first calculation unit; 3. an analysis module; 31. a second calculation unit; 32. a third processing unit; 33. a fourth processing unit; 34. a fifth processing unit; 4. a conversion module; 41. a sixth processing unit; 42. a third calculation unit; 43. a fourth calculation unit; 44. a seventh processing unit; 5. a detection module; 51. an eighth processing unit; 52. a ninth processing unit; 53. a tenth processing unit; 54. an eleventh processing unit; 541. a twelfth processing unit; 542. a first analysis unit; 543. a first judgment unit; 544. a second judgment unit; 800. abnormal operation detection equipment for chemical experiments; 801. a processor; 802. a memory; 803. a multimedia component; 804. an I/O interface; 805. and a communication component.
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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope 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. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not construed as indicating or implying relative importance.
Example 1:
the embodiment provides a method for detecting abnormal operation of a chemical experiment.
Referring to fig. 1, it is shown that the method includes step S100, step S200, step S300, step S400, step S500.
Step S100, real-time experiment video data and historical experiment video data are obtained, wherein the historical experiment video data information comprises normal operation video data and abnormal operation video data.
It can be understood that, in this step, historical experiment video data is uploaded and stored, real-time experiment video data is detected on the basis of the uploaded and stored video data, whether current operators perform abnormal operations is judged, normal operations in a chemical experiment examination include experiment standard actions and human body common actions, abnormal operations include a medicine error taking mode, an error use of a rubber head dropper and the like, and in this embodiment, historical experiment video data does not need to be subdivided, and only the historical experiment video data needs to be divided into "normal" or "abnormal" and corresponding labels are marked.
And S200, preprocessing the historical experimental video data and then extracting features to obtain a first feature set and a weight set.
It can be understood that in this step, the historical experimental video data is subjected to the unification processing and then the feature extraction is performed, so as to extract the action feature parameters when the operator performs normal operation or abnormal operation in the continuous frame images. It should be noted that step S200 includes step S210, step S220, and step S230.
And step S210, performing time-averaged sampling and uniform-size cutting on the historical experimental video data to obtain frame image data.
It can be understood that in this step, image pyramid downsampling processing is performed on historical experimental video data, each historical experimental video is subjected to average sampling of image frames, the fixed frame number of each obtained historical experimental video image set is 30, the frame rate is 10 frames per second, and the sizes of the images are uniformly cut into the size of 240 × 143 pixel points.
S220, pre-extracting the features of the frame image data based on a preset Gaussian mixture model to obtain a first feature set.
It can be understood that in this step, a plurality of gaussian distributions are established for each pixel point in the frame image data, background modeling is performed by combining the plurality of gaussian distributions, a foreground picture is segmented, and then median filtering and gray level processing are performed on the foreground picture to obtain a feature set. When the examinee is subjected to experimental operation, a machine position arranged in front of the examinee for overlooking is usually adopted for real-time recording, the background is basically unchanged, the image data is processed by adopting a Gaussian mixture model so as to accurately segment the image, the characteristic image is extracted and then filtered to obtain a characteristic image, and the processing speed of the subsequent steps is improved.
S230, calculating the first feature set based on a preset activation function to obtain a weight value corresponding to each feature, and combining all the weight values to obtain a weight set.
It can be understood that in this step, the first feature set is processed through the activation function to obtain the weight of each feature, so as to prepare for subsequent clustering operation.
And S300, performing clustering operation on the historical experimental video based on the first feature set and the weight set to obtain classified video data.
It can be understood that in the step, the historical videos are divided into a plurality of types by clustering the characteristics of all the videos in combination with the corresponding weights, wherein the types belonging to normal operation can be defined and explained according to experimental operation regulations, and abnormal operation does not need to be particularly defined. It should be noted that step S300 includes steps S310, S320, S330, and S340.
Step S310, performing clustering operation based on distance on the first feature set to obtain at least two clustering clusters, wherein each clustering cluster comprises at least one normal operation clustering cluster and at least one abnormal operation clustering cluster.
It is understood that the present step is based on a clustering algorithm of distance class, such as K-nearest neighbor algorithm, to cluster the features of all historical experimental videos, and at least two segment types are separated under the classification of normal operation and abnormal operation, which can facilitate the recording and analysis in the subsequent use.
And S320, performing feature extraction on each clustering cluster to obtain a third feature set.
It is understood that this step is to calculate a new feature again in each cluster, and the similarity between the feature and the second feature is greater than the set threshold.
Step S330, performing feature fusion on the first feature set and the third feature set to obtain a fourth feature set;
it can be understood that in this step, the new feature is obtained by performing a weighted calculation on the first feature set used for the initial clustering and the second-extracted third feature set.
And step S340, classifying the historical experiment videos according to a fourth feature set to obtain classified video data.
It can be understood that in this step, the historical videos are classified through the fused fourth feature set, so that the similarity of the video set of each class in the classified video set is guaranteed to be the highest.
And S400, performing feature conversion on the classified video data to obtain a second feature set.
It is understood that this step is to perform feature transformation on the classified video data, so that the video data between the classes can be more easily distinguished. It should be noted that step S400 includes steps S410, S420, S430, and S440.
And S410, performing wavelet transformation on the classified video data to obtain characteristic matrix data.
It will be appreciated that this step is to decompose the frame image using a wavelet transform, preserving more of the useful information of the image.
And step S420, calculating to obtain matrix parameters according to the characteristic matrix data, wherein the matrix parameters comprise a total mean value, a category mean value, an inter-class divergence matrix and an intra-class divergence matrix.
It can be understood that in this step, the frame image data after wavelet transformation is recombined into a matrix, and the overall mean, the class mean, the inter-class divergence matrix and the intra-class divergence matrix in the classified video are calculated according to the matrix parameters.
And step S430, calculating the matrix parameters based on a linear discrimination algorithm to obtain an optimal projection matrix.
It can be understood that in the step, a low-dimensional feature space is established according to matrix parameters by adopting a linear discrimination algorithm, so that the dimension can be compressed, and the speed of subsequent classification operation is improved.
Step S440, projecting the feature matrix data to the optimal projection matrix to obtain a second feature set.
It can be understood that in this step, the feature matrix data with high dimensionality is projected to the optimal projection matrix with low dimensionality, so that the similarity of the projected video data in each category is highest, the similarity between each category is lowest, and a clear interface is obtained.
Step S500, establishing an experiment abnormal operation detection mathematical model according to the second feature set and the classified video data, taking the real-time experiment video data as an input value, and solving the experiment abnormal operation detection mathematical model to obtain a detection result.
It can be understood that in this step, an abnormal operation detection mathematical model is established by using the second feature set obtained through the secondary conversion, the operation situations in the real-time experimental video data are classified, the currently performed experimental operation is determined to be a normal operation or an abnormal operation, and corresponding measures are taken. It should be noted that step S500 includes steps S510, S520, S530, and S540.
Step S510, training an experiment operation classifier according to the classified video data and the second feature set, and establishing an experiment abnormal operation detection mathematical model based on the experiment operation classifier.
It can be understood that this step is to train an experimentally operated classifier, wherein the weights of the respective layers of the classifier are adjusted by the second feature set, so as to better fit the boundaries between the classified video data of the respective categories. Preferably, in other embodiments, a BP neural network may be used as the classifier of the method, and the use of the BP neural network is common knowledge of those skilled in the art and will not be described in detail in this embodiment.
And step S520, performing wavelet transformation on the real-time experimental video data to obtain a transformed image.
It can be understood that in this step, the real-time experimental video data is decomposed by wavelet transformation to obtain data in a matrix form, so as to improve the subsequent processing speed and the detection accuracy.
And step S530, performing feature extraction on the transformed image to obtain local image data.
It is understood that this step is to extract useful data in the transformed image to obtain local image data, and this step can increase the speed of subsequent processing.
And S540, inputting the local image data into the experiment abnormal operation detection mathematical model for classification and judgment to obtain a detection result.
It can be understood that this step is to automatically detect the category of the current operation by inputting the local image data processed into matrix data and extracting useful data into the mathematical model for detecting the experimental abnormal operation, and to make corresponding response measures according to the category. Note that step S540 includes steps S541, S542, S543, and S544.
And S541, classifying the characteristic images based on a nearest neighbor algorithm to obtain class information.
It is understood that this step is to identify the partial image data and output the category information.
And S542, obtaining a detection result according to the category information and a preset detection rule.
It can be understood that the detection rule preset in this step is formulated according to the rule and regulation of the experimental operation examination, and the corresponding treatment measure is associated according to the obtained current operation type to obtain the detection result.
Step S543, if it is detected that the category information belongs to the normal experiment operation, the detection result is that no processing is performed.
It can be understood that the obtained detection result shows that the current operation is the normal experiment operation without interfering with the examination process.
And S544, if the type information is detected to belong to abnormal experimental operation, warning information is sent to the examination staff.
It can be understood that, if the obtained detection result shows that the current operation is an abnormal experimental operation, a correct processing method is made for the information request sent by the examiner, and the video of the abnormal experimental operation is backed up and marked.
Example 2:
as shown in fig. 2, the present embodiment provides a chemical experiment abnormal operation detecting apparatus, comprising:
acquiring marks in the block diagram: the system comprises a data processing device and a data processing method, wherein the data processing device is used for acquiring real-time experiment video data and historical experiment video data, and the historical experiment video data information comprises normal operation video data and abnormal operation video data;
the extraction module 2 is used for preprocessing the historical experimental video data and then extracting features to obtain a first feature set and a weight set;
the analysis module 3 is used for carrying out clustering operation on the historical experimental video based on the first feature set and the weight set to obtain classified video data;
the conversion module 4 is used for performing feature conversion on the classified video data to obtain a second feature set;
and the detection module 5 is used for establishing an experiment abnormal operation detection mathematical model according to the second feature set and the classified video data, taking the real-time experiment video data as an input value, and solving the experiment abnormal operation detection mathematical model to obtain a detection result.
In a specific embodiment of the present disclosure, the extraction module 2 includes:
the first processing unit 21 is configured to perform time-averaged sampling and uniform-size clipping on the historical experimental video data to obtain frame image data;
the second processing unit 22 performs feature pre-extraction on the frame image data based on a preset gaussian mixture model to obtain a first feature set;
the first calculating unit 23 calculates the first feature set based on a preset activation function to obtain a weight value corresponding to each feature, and combines all the weight values to obtain a weight set.
In a specific embodiment of the present disclosure, the analysis module 3 includes:
the second calculating unit 31 is configured to perform distance-based clustering operation on the first feature set to obtain at least two clustering clusters, where each clustering cluster includes at least one normal operation clustering cluster and at least one abnormal operation clustering cluster;
the third processing unit 32 is configured to perform feature extraction on each cluster to obtain a third feature set;
a fourth processing unit 33, configured to perform feature fusion on the first feature set and the third feature set to obtain a fourth feature set;
and the fifth processing unit 34 is configured to classify the historical experimental video according to a fourth feature set to obtain classified video data.
In a specific embodiment of the present disclosure, the conversion module 4 includes:
a sixth processing unit 41, configured to perform wavelet transformation on the classified video data to obtain feature matrix data;
a third calculating unit 42, configured to calculate matrix parameters according to the feature matrix data, where the matrix parameters include a total mean value, a category mean value, an inter-class divergence matrix, and an intra-class divergence matrix;
a fourth calculating unit 43, which calculates the matrix parameters based on a linear discrimination algorithm to obtain an optimal projection matrix;
a seventh processing unit 44, configured to project the feature matrix data to the optimal projection matrix to obtain a second feature set.
In a specific embodiment of the present disclosure, the detection module 5 includes:
an eighth processing unit 51, configured to train an experiment operation classifier according to the classified video data and the second feature set, and establish an experiment abnormal operation detection mathematical model based on the experiment operation classifier;
a ninth processing unit 52, configured to perform wavelet transformation on the real-time experimental video data to obtain a transformed image;
a tenth processing unit 53, configured to perform feature extraction on the transformed image to obtain local image data;
and an eleventh processing unit 54, configured to input the local image data into the mathematical model for performing the detection of the experimental abnormal operation, and perform classification and judgment to obtain a detection result.
In a specific embodiment of the present disclosure, the eleventh processing unit 54 includes:
a twelfth processing unit 541, classifying the feature images based on a nearest neighbor algorithm to obtain category information;
the first analysis unit 542 obtains a detection result according to the category information and a preset detection rule;
a first judging unit 543, configured to, if it is detected that the category information belongs to a normal experimental operation, determine that no processing is performed on a detection result;
a second determining unit 544, configured to send warning information to the test staff if it is detected that the category information belongs to the abnormal experimental operation.
It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3:
corresponding to the above method embodiment, the present embodiment further provides a chemical experiment abnormal operation detection apparatus, and a chemical experiment abnormal operation detection apparatus described below and a chemical experiment abnormal operation detection method described above may be referred to in correspondence with each other.
Fig. 3 is a block diagram illustrating a chemical experiment abnormal operation detection apparatus 800 according to an exemplary embodiment. As shown in fig. 3, the chemical experiment abnormal operation detecting apparatus 800 may include: a processor 801, a memory 802. The chemical experiment abnormal operation detection apparatus 800 may further include one or more of a multimedia component 803, an I/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the chemical experiment abnormal operation detection apparatus 800, so as to complete all or part of the steps in the chemical experiment abnormal operation detection method. The memory 802 is used to store various types of data to support the operation of the chemical experiment abnormal operation detection apparatus 800, and the data may include, for example, instructions for any application or method operating on the chemical experiment abnormal operation detection apparatus 800, and application related data such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 802 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically Erasable Programmable Read-Only Memory (EEPROM), erasable Programmable Read-Only Memory (EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 803 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 802 or transmitted through the communication component 805. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the chemical experiment abnormal operation detection apparatus 800 and other apparatuses. Wireless communication, such as Wi-Fi, bluetooth, near field communication (NFC for short), 2G, 3G, or 4G, or a combination of one or more of them, so the corresponding communication component 805 may include: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the chemical experiment abnormal operation detecting apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for performing the chemical experiment abnormal operation detecting method.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the above-described chemical experiment abnormal operation detection method is also provided. For example, the computer readable storage medium may be the above-mentioned memory 802 comprising program instructions executable by the processor 801 of the chemical experiment abnormal operation detection apparatus 800 to perform the above-mentioned chemical experiment abnormal operation detection method.
Example 4:
corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment, and a readable storage medium described below and a chemical experiment abnormal operation detection method described above may be referred to in correspondence.
A readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for detecting abnormal operation of a chemical experiment of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A method for detecting abnormal operation of a chemical experiment is characterized by comprising the following steps:
acquiring real-time experiment video data and historical experiment video data, wherein the historical experiment video data information comprises normal operation video data and abnormal operation video data;
preprocessing the historical experimental video data and then extracting features to obtain a first feature set and a weight set;
clustering the historical experimental videos based on the first feature set and the weight set to obtain classified video data;
performing feature conversion on the classified video data to obtain a second feature set;
establishing an experiment abnormal operation detection mathematical model according to the second feature set and the classified video data, taking the real-time experiment video data as an input value, and solving the experiment abnormal operation detection mathematical model to obtain a detection result;
performing feature conversion on the classified video data to obtain a second feature set, wherein the second feature set comprises;
performing wavelet transformation on the classified video data to obtain characteristic matrix data;
calculating to obtain matrix parameters according to the characteristic matrix data, wherein the matrix parameters comprise a total mean value, a category mean value, an inter-category divergence matrix and an intra-category divergence matrix;
calculating the matrix parameters based on a linear discrimination algorithm to obtain an optimal projection matrix;
and projecting the feature matrix data to the optimal projection matrix to obtain a second feature set.
2. The method for detecting abnormal operation in chemical experiment according to claim 1, wherein the preprocessing the historical experimental video data and then performing feature extraction to obtain a first feature set and a weight set, comprises:
carrying out time average sampling and uniform size cutting on the historical experimental video data to obtain frame image data;
performing feature pre-extraction on the frame image data based on a preset Gaussian mixture model to obtain a first feature set;
and calculating the first feature set based on a preset activation function to obtain a weight value corresponding to each feature, and combining all the weight values to obtain a weight set.
3. The method for detecting abnormal operation in chemical experiment according to claim 1, wherein the establishing a mathematical model for detecting abnormal operation in experiment according to the second feature set and the classified video data, using the real-time experimental video data as the input value, and solving the mathematical model for detecting abnormal operation in experiment to obtain a detection result includes:
training an experimental operation classifier according to the classified video data and the second feature set, and establishing an experimental abnormal operation detection mathematical model based on the experimental operation classifier;
performing wavelet transformation on the real-time experimental video data to obtain a transformation image;
performing feature extraction on the transformed image to obtain local image data;
and inputting the local image data into the experiment abnormal operation detection mathematical model for classification and judgment to obtain a detection result.
4. A chemical experiment abnormal operation detecting device, comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring real-time experiment video data and historical experiment video data, and the historical experiment video data information comprises normal operation video data and abnormal operation video data;
the extraction module is used for preprocessing the historical experimental video data and then extracting features to obtain a first feature set and a weight set;
the analysis module is used for carrying out clustering operation on the historical experimental video based on the first feature set and the weight set to obtain classified video data;
the conversion module is used for performing feature conversion on the classified video data to obtain a second feature set;
the detection module is used for establishing an experiment abnormal operation detection mathematical model according to the second feature set and the classified video data, taking the real-time experiment video data as an input value, and solving the experiment abnormal operation detection mathematical model to obtain a detection result;
wherein the conversion module comprises:
the sixth processing unit is used for performing wavelet transformation on the classified video data to obtain characteristic matrix data;
the third calculation unit is used for calculating matrix parameters according to the characteristic matrix data, wherein the matrix parameters comprise a total mean value, a category mean value, an inter-class divergence matrix and an intra-class divergence matrix;
the fourth calculation unit calculates the matrix parameters based on a linear discrimination algorithm to obtain an optimal projection matrix;
and the seventh processing unit is used for projecting the feature matrix data to the optimal projection matrix to obtain a second feature set.
5. The chemical experiment abnormal operation detection apparatus of claim 4, wherein the extraction module comprises:
the first processing unit is used for performing time average sampling and uniform size cutting on the historical experimental data to obtain frame image data;
the second processing unit is used for carrying out feature pre-extraction on the frame image data based on a preset Gaussian mixture model to obtain a first feature set;
the first calculation unit is used for calculating the first feature set based on a preset activation function to obtain a weight value corresponding to each feature, and combining all the weight values to obtain a weight set.
6. The chemical experiment abnormal operation detection apparatus of claim 4, wherein the detection module comprises:
the eighth processing unit is used for training an experimental operation classifier according to the classified video data and the second feature set and establishing an experimental abnormal operation detection mathematical model based on the experimental operation classifier;
the ninth processing unit is used for performing wavelet transformation on the real-time experimental video data to obtain a transformed image;
a tenth processing unit, configured to perform feature extraction on the transformed image to obtain local image data;
and the eleventh processing unit is used for inputting the local image data into the experiment abnormal operation detection mathematical model for classification and judgment to obtain a detection result.
7. A chemical experiment abnormal operation detection apparatus, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method for detecting abnormal operation in a chemical experiment according to any one of claims 1 to 3 when executing the computer program.
8. A readable storage medium, characterized by: the readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method for detecting abnormal operation in a chemical experiment according to any one of claims 1 to 3.
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