TWI784718B - Method and system for processing alarm event in factory - Google Patents

Abstract

A method and a system for processing an alarm event in a factory are provided. The system provides a calculation platform and at least one sensing device within a factory. The system performs the method, in which the calculation platform receives sensing data from the at least one sensor device and clusters the sensing data. When outlier data is obtaind, the system records multiple solutions with respect to the outlier data and determines that the outlier data is about a normal event or an abnormal event. The related solutions are then directed to a probability allocation mode for obtaining a ranking of probabilities of the solutions corresponding to the outlier data. When a new sensing data is received, the ranking probabilities can be used to determine which solution is performed if the sensing data is determined to be the outlier data of an abnormal event. At last, a speaker is provided for outputting a voice relating to a description of a corresponding scenario and processing steps.

Description

Plant area alarm event processing method and system

The specification proposes a method for handling abnormal events in the factory area, especially a method and system for processing alarm events in the factory area to predict abnormal events through data learning.

According to the current factory safety management measures, most of them rely on personnel to monitor from the monitor or go to the site to patrol and check, and can cooperate with various sensing devices and monitoring equipment installed in the factory area, such as smoke alarms, temperature and humidity sensors, and machine monitoring. Equipment, etc., and then monitor through the electronic kanban. When an abnormal event occurs in the factory area, such as fire and smoke, it can be judged by the experience of the personnel, and the alarm signal generated by the monitoring equipment can be added, and the follow-up measures can be implemented after the judgment.

However, when encountering personnel negligence or differences in cognition, or abnormalities that were not known in the past, it is impossible to give immediate reminders. The main reason is that in the past, the information was based on known conditions, rules or gates The values are summarized and provided to managers in the form of a dashboard for monitoring and query before taking the next step.

What the present disclosure aims to solve is the problem that the prior art cannot achieve instant warning and provide an effective solution at the same time.

In order to solve the above problems, the present disclosure provides a plant alarm event processing method, which operates on a computing platform, including: receiving a plurality of first sensing data generated by at least one sensing device; clustering the first sensing data to obtain An outlier sensing data; recording a plurality of solutions for the outlier sensing data, and judging the outlier sensing data as a normal event or an abnormal event based on the solutions; importing these solutions into a probability distribution mode , to obtain a probability sorting of the solutions corresponding to the outlier sensing data; after receiving the second sensing data and determining that the second sensing data is classified as the outlier sensing data of an abnormal event, the probability sorting is performed based on the probability sorting at least one of these solutions.

In order to solve the above problems, the present disclosure provides another plant alarm event processing system, including a computing platform and at least one sensing device, and the computing platform runs a plant alarm event processing method. In the method, the computing platform receives information generated by at least one sensing device. A plurality of first sensing data, analyze to extract the characteristics of the sensing data, and record multiple solutions for the outlier sensing data when the outlier sensing data is obtained by clustering the first sensing data number , and based on the solution, it is determined that the outlier sensing data is a normal event or an abnormal event, and then the solution is introduced into a probability allocation mode to obtain a probability ranking of the solutions corresponding to the outlier sensing data, thus, After receiving the second sensing data and determining that the second sensing data is classified as the outlier sensing data of the abnormal event, performing at least one of the solutions based on probability sorting.

Based on the above, with the factory alarm event processing method and system disclosed in this disclosure, multiple solutions are recorded for the outlier sensing data of abnormal events, so new sensing data (ie, second sensing data) is received and new After the sensing data is judged as the outlier sensing data of an abnormal event, at least one of the solutions can be immediately provided to solve the problem of ignoring the event due to personnel fatigue or differences in experience and cognition and need to take immediate countermeasures mistakes.

In order to further understand the features and technical content of the present invention, please refer to the following detailed description and drawings related to the present invention. However, the provided drawings are only for reference and description, and are not intended to limit the present invention.

The implementation of the present invention is described below through specific specific examples, and those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various modifications and changes can be made to the details in this specification based on different viewpoints and applications without departing from the concept of the present invention. In addition, the drawings of the present invention are only for simple illustration, and are not drawn according to the actual size, which is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the protection scope of the present invention.

It should be understood that although terms such as "first", "second", and "third" may be used herein to describe various elements or signals, these elements or signals should not be limited by these terms. These terms are mainly used to distinguish one element from another element, or one signal from another signal. In addition, the term "or" used herein may include any one or a combination of more of the associated listed items depending on the actual situation.

This disclosure proposes a method and system for processing alarm events in a factory area, and the method is suitable for the needs of abnormal event alarms in factories or related fields. In addition to each hardware device, the plant area alarm event processing system also uses software means to run a machine learning method. According to the defined conditions, rules, thresholds and other parameters, learn the data generated by various sensing devices and monitoring devices in the factory area. A cyclic and automatically optimized machine learning algorithm is used to establish a plant area alarm model based on artificial intelligence technology, so that in the plant area alarm event processing method running in the system, it is only necessary to integrate various sensing devices in the plant area (such as images and various (environmental monitoring equipment) real-time data access to establish the factory alarm model based on historical data, that is, to automatically distinguish normal phenomena or abnormal information, thereby predicting abnormal events and generating alarm information.

Further, according to the embodiment of the factory area alarm event processing method, the received data generated by each device in the factory area is classified through the machine learning method. Even if the related event is marked as a normal event by human intervention in the end, it can also be added to the training cycle as the basis for the next judgment . One of the purposes of the method is to enable a specific device of the system to automatically generate alarm information before an abnormal event occurs through the established plant alarm model, and provide the situation and information faced by the relevant personnel in the form of text, voice, light signal or image. Corresponding processing steps. The personnel can immediately know what action is to be taken next, and to solve the problem of ignoring the event due to fatigue or differences in experience and cognition of the personnel needs to take immediate measures corresponding to the error. In particular, the alarm information generated by the system is to form an audio data 112 by combining voice files for the upcoming abnormal event, and then broadcast the corresponding situation description voice in voice mode through the speaker (actual implementation does not exclude other forms of alarm) And situational resolution steps voice, these follow-up actions derived through machine learning may be the measures to solve the same abnormal event in the past.

The factory area alarm event processing system can refer to the schematic diagram of the architecture embodiment shown in FIG. 1. The factory area alarm event processing system proposes a computing platform 10. The computing platform 10 uses computer equipment and software to realize various functional modules to operate the factory area alarm event processing method. When the sensing device 111 in the factory senses the event 110 and generates sensing data, such as real-time images, sounds, signals or environmental data (such as temperature and humidity, smoke, entry and exit events, etc.), the sensing data is transmitted to the computing platform 10. For the method for processing alarm events in the factory area, reference may be made to the flow chart of the embodiment shown in FIG. 2 and the implementation example of the method shown in FIG. 5 .

In one embodiment, the number of sensing devices 111 depends on the needs of the factory area. It can be one or a plurality of sensing devices 111 located at different locations to obtain the sensing of one or more specific monitoring areas in the factory area. measurement data, so that the computing platform 10 can obtain a large amount of streaming-loaded sensing data in real time, which is represented as a plurality of first sensing data in this embodiment (step S201 ). The sensing device 111 can be a video camera, an audio receiver, or a specific sensor, etc., and these sensing data can be real-time images, sounds, signals or environmental data corresponding to any event 110 that occurs in a certain monitoring area, and can be used as a machine Learning samples.

Next, the first sensing data is grouped by a clustering method, thereby obtaining outlier sensing data (step S203 ). According to the embodiment, the sensing data is, for example, the real-time images produced by the workers operating the machines in the corresponding monitoring area captured by the cameras in the factory area, the sound is captured by the audio receiver, or the information of the machine or the environment is obtained by specific sensors. . When the computing platform 10 receives the sensing data, the software program in the computing platform 10 can run an unsupervised learning (unsupervised learning) to obtain outlier sensing data (outlier) by clustering. According to one of the embodiments, outlier data obtained by using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can be used, and reference can be made to the implementation example shown in FIG. 8 .

Further, after the system obtains the outlier data from the first sensing data, in step S205, the plant alarm event processing system can use the management personnel or machines to isolate the outlier data from the sensing data generated within a period of time Marking and setting solutions to the outlier data, wherein the same outlier sensing data may have one or more solutions (step S205 ), and the outlier data is also determined to be a normal event or an abnormal event (step S207 ). The computing platform 10 is to record the solutions set by management personnel or machines for outlier data, and store them in the database 105 . In this way, the marked abnormal data is still fed back to the program of machine learning sample creation and learning in the computing platform 10 for automatic cyclic learning.

Afterwards, the outlier sensing data and solutions related to the determined abnormal events are imported into the probability allocation mode, and in this embodiment, the solutions of each outlier sensing data are sorted by probability through the computing platform 10 (step S209 ).

Please refer to FIG. 3 . FIG. 3 is an embodiment diagram of a probability matrix of a solution to an outlier event. FIG. 3 shows a probability ranking 301 formed according to the probability values of solutions for each outlier sensing data. The probability distribution mode assigns a weight to each solution to obtain a probability value, and then sorts the obtained probability values to obtain the probability ranking. For example, the probability moment sorting 301 is to set the weight 303 according to each event occurrence scenario, and then sort according to the probability value from high to low to obtain the probability sorting 301 . The setting of the weight 303 is based on the actual application. For example, when an abnormal event occurs in a specific situation, there will be multiple solutions, and when the system sets the weight 303, a higher weight can be set for the more commonly used solutions, and less used ones The solution is to set a lower weight. As such, the weight 303 will become an important parameter for decision-making when ordering solutions according to probability values. Meanwhile, the computing platform 10 can determine the outlier sensing data as a normal event 305 or an abnormal event 307 based on the solution.

Regarding an implementation example of the decision context of the weight 303, different managers may have the same approach to the same outlier sensing data, assuming that for the same outlier sensing data, the first, second and third managers are all Provide the same solution A, the computing platform will record the weight of solution A as: 1+1+1, and another fourth staff member provides solution B, so this outlier sensing data will have the probability of solution A value of 3, while solution B has a probability value of 1. Accordingly, the probabilities of solutions for the outlier sensing data are obtained.

In an example embodiment, the solution may include an image stream processed for the outlier sensing data, a situation description voice corresponding to the image stream, and a situation solution step voice. The voice of the situation description refers to what situation may occur next after the event occurs, and the voice of the situation resolution step indicates the processing steps after the event occurs. In addition, the speaker can be used to play the situation description voice and the situation solution step voice.

Next, the factory area alarm event processing system continues to operate, and continues to receive new sensing data generated by the sensing device 111, which is referred to as the second sensing data in this embodiment (step S211), and determines that the second sensing data belongs to After the outlier sensing data of the abnormal event, at least one of multiple solutions may be executed based on the probability ranking calculated according to the above method (step S213 ). In detail, after the computing platform 10 receives the new second sensing data, it is also determined to be outlier sensing data through clustering. The next step is to determine the outlier sensing data of which abnormal event the second sensing data is classified into. For example, the computing platform 10 can calculate the distance between the feature value of the new sensing data (ie, the second sensing data) and the feature value of each outlier sensing data. Based on the distance being less than a threshold, it is determined that the new sensing data is classified as outlier sensing data, wherein the distance calculation method can be used to represent the Euclidean distance between two points in a multi-dimensional space (Euclidean distance) calculation method .

When the new sensory data is classified, at least one of a plurality of solutions corresponding to the classified outlier sensory data can be implemented, for example, through the speaker 115 of the mobile device 113 or various devices fixed in the factory area. The speaker 116 of the non-mobile device 114 emits the solution. The execution solution can also be called an alarm message, that is, the solution is played in a voice mode. For example, the speakers 115 and 116 are used to inform the on-site personnel 117 in the monitoring area of the factory that an abnormal event has occurred or is about to occur, and the corresponding situation description and corresponding processing steps can be further broadcasted, so that the on-site personnel 117 can immediately know what to do next what kind of action. What should be mentioned here is that the above-mentioned embodiment of the plant alarm event processing method forms alarm information in the form of voice, and it is still not ruled out that the on-site personnel 117 can be notified through the non-mobile device 114 that the incident has occurred or is about to occur. Abnormal events also include broadcasting various information to the mobile device 113 held by the on-site personnel 117 .

In one embodiment, data related to the outlier sensing data, such as image data, on-site sound, environmental data and/or machine operation data, etc., can also be input into the machine learning algorithm for learning, and the solution is also provided. Plant alarm model. For example, after a certain event is triggered, the computing platform 10 stores the relevant sensing data in the database. For outlier events, after the on-site personnel complete the processing or eliminate the trigger event, the copy data can be stored. That is to say, when there are multiple outlier sensing data sets obtained in the above clustering step (such as step S203 ), a query index can be established for each outlier sensing data set and the corresponding solution. What is mentioned here is that the system can provide relevant personnel to view the copy data retrieval, and provide an editing interface for correction or supplementary information.

In this way, the computing platform 10 stores the outlier sensing data of each normal and abnormal event, the established query index and related sensing data in the database, and can automatically learn in a loop after marking. Scenes do not require additional development resources to be invested.

In the process of the above-mentioned embodiment, the system learns the sensing data obtained immediately through machine learning algorithms, that is, the first sensing data and the second sensing data, as shown in step S205 in Figure 2, the marking of outlier data in the plant alarm event processing system After the solution is set, a plant alarm model for judging and predicting abnormal events is established, and at the same time, the sensor data generated in real time is continuously learned to update the plant alarm model.

According to the process embodiment described above, the plant alarm event processing system automatically learns the marking results through software programs, and extracts the scene features recorded with time stamps. If the same features appear in the future, the purpose of early warning notification before the event occurs can be achieved. .

Based on the method flow described in the above embodiments, the following describes an embodiment of a method for processing an alarm event in a plant using image data as an embodiment.

The flow of the method embodiment shown in FIG. 4 uses image features to establish a plant alarm model in the plant alarm event processing system in the plant area. The sensing device can be a surveillance camera installed in various places in the plant area. Surveillance cameras are used to capture real-time images of workers operating machines in the set surveillance area to form image data. Without human intervention, the machine can automatically classify the incoming streaming images into scenes, and automatically identify new scenes over time, so that the system can predict abnormalities based on the features in the sensing data The event is about to occur, and the corresponding situation description and the processing steps corresponding to the situation are provided. According to FIG. 5 , the flow chart of an embodiment of the method for processing an alarm event in the factory area is shown, which can cooperate with the schematic diagram of the system embodiment shown in FIG. 1 and the embodiment diagrams shown in FIGS. 5 to 8 .

Taking the image data generated by image monitoring as an example, the sensing device is used to take images of a specific monitoring area in the factory area to form the first sensing data and the second sensing data in the above embodiment, which can be at least A sensing device obtains the real-time images, sounds or signals of the workers operating the machines in the corresponding monitoring area, and the sensing data is loaded into the computing platform in a streaming manner. In particular, these image, sound or signal data can be used as samples for machine learning, preferably high-quality files can be obtained. According to an embodiment, before running the factory area alarm event processing method, a control interface is started through the software program in the computing platform for setting monitoring areas, for example, the plant area can be divided into multiple areas (step S401 ). Refer to the schematic diagram of the graphic user interface provided to the management personnel to monitor the factory area in the system shown in Figure 5. This figure shows an interface 52 for setting the monitoring area realized by software means. The management personnel selects the option 501 of the factory layout diagram, and the The setting screen 503 of the display shows a screen that divides a factory area into multiple areas, marking the location of each area, related information and equipment in it, such as a monitoring area 505 represented in the figure, and each monitoring area can be configured according to its field Features are provided with one or more various sensing devices.

According to the selected monitoring area 505 , a sensing device therein is set (step S403 ). Reference can be made to the embodiment shown in FIG. 6 , which shows a table for managers to set the sensing devices in each monitoring area. This example is an interface 60 for setting sensing devices, which lists the information of each sensing device, such as the area to which it belongs. , machine address, communication port, etc., for management personnel to query and modify settings.

In this embodiment, a sensing device such as a video camera is used to photograph a specific surveillance area of the factory. After the above settings are completed, it will start to operate, and generate a large amount of high-quality image data in real time, and the computing platform in the system can obtain a large amount of stream-loaded image data in real time (step S405 ). Next is to perform data analysis and obtain image feature values (step S407 ). Among them, software methods can be used to obtain the image features in the image. For example, in the image processing method, the image is divided into multiple regions, and the maximum value, minimum value and average value of each region are obtained from the pixel value of the image to detect the image in the image. Edge and shape can be compared with image feature data in the database to perform image matching; in another way, a convolutional neural network (CNN) can be used to establish image features.

A schematic diagram of an embodiment of constructing an image feature using a convolutional neural network may refer to FIGS. 7A to 7C . Figure 7A shows the value of each pixel in an image frame, Figure 7B shows the maximum pixel value obtained after the image is partitioned, and can also be processed for the minimum or average pixel value, and then Figure 7C shows the convolution operation ( In the convolutional operation), the feature map after the important information is retained after the pooling operation (pooling), can obtain the image features of the specific area in the selected factory area.

According to one embodiment, when the monitoring area is selected, the system uses a software program to create a temporary storage space in the memory or database, stores the received image data in chronological order, and can be loaded into the system memory during operation , the image can be based on key frames. In one embodiment, the temporary memory space can store images with a minimum number of frames (for example, 32 frames) as a reference, and then start to extract features therein. In the neural network-like machine learning method, feature extraction uses different numbers of input layers, convolutional layers, pooling layers and different initial parameters to stack and combine to obtain image features, and then store the feature results in in memory. The above steps are continuously run based on the image data received in real time, and the new input image can replace the oldest image in the original temporary storage space. Next, the feature distance can be calculated based on the image features temporarily stored in the memory, and the previous and subsequent residual calculations can be performed as the basis for classification (step S409). feature differences between images for scene classification.

In particular, when the sensing data is image data, the calculation platform can obtain continuous frames of images over time, and extract the image features of each frame. If the monitored scene does not change much, the 8 shows a method based on density spatial clustering (DBSCAN), which shows that the number of groups formed by classification will tend to be stable, as shown in the legends (a) to (c) in Figure 8, and evolve to (d) to (f) Changes in the graph indicate changes in density characteristics in the monitoring area, and the software program in the calculation platform will automatically recognize this new group (outlier), and then send a message accordingly. Here, clustering algorithms such as pattern match, k-means clustering, or specific algorithms are performed. The main concept is to apply algorithms to obtain image features, and to learn the clustering phenomenon, and based on This performs classification to obtain different groups (image classification).

When it is judged that there is an outlier event through the change of image features over a period of time, the management personnel at the system end will mark the outlier event (step S411 ). The outlier events can be marked as belonging to the normal group or the abnormal group, and the program of machine learning sample creation and learning is also added, and the above-mentioned marked normal group or abnormal group can be fed back to the computing platform for machine learning sample creation and learning The program learns in an automatic cycle, and finally forms a plant alarm model, which can be sorted according to the probability of abnormal events so that the system can warn relevant plant personnel (step S413).

In an implementation example, the factory area alarm event processing system uses cameras to shoot machines and staff on the production line in the factory area. The camera continues to shoot the actions of the staff operating the machines, and can simultaneously capture the sound of the scene and the sound generated by the operation of the machines. information. The computing platform in the system can obtain the data generated by various sensing devices in real time, through the factory area alarm event processing method described in the above embodiments, and through the plant area alarm model to judge whether an abnormal event will occur, and execute relevant alarm measures, for example, the upcoming The event situation that occurs is converted into audio data 112, and then the situation description and the corresponding processing steps are automatically spoken out through the speaker, so that the on-site staff can immediately know what action to take next.

One of the purposes of the proposed method is to send out warning information before abnormal events occur in the field of a factory, that is, the description of the situation and the corresponding processing steps. If the staff in the factory area performs a certain action, the system captures the relevant sensing data, captures the characteristics in real time and analyzes it, and judges a treatment measure. The system will inform the staff of the follow-up by voice before any abnormal event occurs. Handling measures.

To sum up, according to the above-mentioned embodiments of the factory area alarm event processing method and system, the plant area alarm model obtained by the machine learning method is used to predict and tell the upcoming abnormal events, and execute the corresponding processing steps. The alarm information is issued, so that relevant personnel can immediately know what action to take next, and feedback the data generated by the subsequent execution, realize the machine learning process of instant self-circulation, and solve the problem of prior training due to the slow transmission speed in the past.

The content disclosed above is only a preferred feasible embodiment of the present invention, and does not therefore limit the scope of the patent application of the present invention. Therefore, all equivalent technical changes made by using the description and drawings of the present invention are included in the application of the present invention. within the scope of the patent.

110: Event 111: Sensing device 10:Computing platform 105: Database 112: audio data 113:Mobile equipment 114:Non-mobile device 115, 116: speaker 117: On-site personnel 301: Probability sorting 303: weight 305: normal event 307: Abnormal event 52: Interface for setting monitoring area 501: Plant layout 503: Setting screen 505: Surveillance area 60: Setting the interface of the sensing device Steps S201-S213: one of the embodiment flow of the plant area alarm event processing method Steps S401-S413: The second embodiment flow of the plant area alarm event processing method

Fig. 1 shows the schematic diagram of the framework embodiment of factory area alarm event processing system;

Fig. 2 shows one flow chart of the embodiment of plant area alarm event processing method;

Figure 3 shows an example diagram of a probability matrix for a solution to outlier sensing data;

Fig. 4 shows the second flow chart of the embodiment of the plant area alarm event processing method;

Figure 5 shows a schematic diagram of an embodiment of a graphical user interface for setting a monitoring area;

6 shows a schematic diagram of an embodiment of a graphical user interface for setting a sensing device;

7A to 7C are diagrams showing an embodiment of the image processing process; and

FIG. 8 shows a schematic diagram of an embodiment of the group distribution of feature values over time.

110: Event

111: Sensing device

10:Computing platform

105: Database

112: audio data

113:Mobile equipment

114:Non-mobile device

115, 116: speaker

117: On-site personnel

Claims (14)

A plant alarm event processing method, operating on a computing platform, comprising: receiving a plurality of first sensing data generated by at least one sensing device; clustering the first sensing data to obtain an outlier sensing data; recording A plurality of solutions for the outlier sensing data, and based on these solutions, it is determined that the outlier sensing data is a normal event or an abnormal event; these solutions are introduced into a probability distribution mode, and the outlier is obtained A probability ranking of the solutions corresponding to the sensing data; and after receiving a second sensing data and determining that the second sensing data is classified as the outlier sensing data of the abnormal event, sorting based on the probability Perform at least one of these solutions. The factory area alarm event processing method as described in claim 1, wherein the computing platform receives the first sensing data and the second sensing device generated by the at least one sensing device installed in one or more monitoring areas in a factory area 2. Sensing data. The factory area alarm event processing method as described in claim 2, wherein the first sensing data and the second sensing data respectively include the real-time information of the staff operating the machine in the corresponding monitoring area obtained by the at least one sensing device image, sound or signal. The factory area alarm event processing method as described in claim item 1, wherein the solutions for the outlier sensing data include providing an image stream and playing a situation description voice corresponding to the image stream through a loudspeaker and a Situational resolution steps voice. The method for processing plant alarm events as described in Claim 1, wherein the probability distribution mode is to assign a weight to each solution to obtain a probability value, and then sort the probability values to obtain the probability ranking. The factory area alarm event processing method as described in claim item 5, wherein when there are multiple outlier sensing data groups obtained in the clustering step, for each outlier A query index is established for the sensing data set and the corresponding solution. The factory area alarm event processing method as described in Claim 1 further includes calculating a distance between the feature of the second sensing data and the feature of the outlier sensing data, and judging the first based on the distance being smaller than a threshold Two sensing data are classified as the outlier sensing data. A plant area alarm event processing system, comprising: a computing platform; and at least one sensing device; wherein the computing platform runs a plant area alarm event processing method, the method includes: receiving a plurality of first generated by the at least one sensing device sensing data; clustering the first sensing data to obtain an outlier sensing data; recording a plurality of solutions for the outlier sensing data, and judging the outlier sensing data as an outlier based on the solutions a normal event or an abnormal event; introducing the solutions into a probability allocation mode to obtain a probability ranking of the solutions corresponding to the outlier sensing data; and receiving a second sensing data and determining the second After the sensing data is classified into the outlier sensing data of the abnormal event, at least one of the solutions is executed based on the probability ranking. The factory area alarm event processing system as described in claim 8, wherein the computing platform receives the first sensing data and the first sensing data generated by the at least one sensing device installed in one or more monitoring areas in a factory area 2. Sensing data. The plant area alarm event processing system as described in claim item 9, wherein the first sensing data and the second sensing data respectively include the real-time information of the staff operating the machine in the corresponding monitoring area obtained by the at least one sensing device image, sound or signal. The factory area alarm event processing system as described in claim 8, wherein the solutions for the outlier sensing data include providing an image stream and playing a situation description voice corresponding to the image stream through a loudspeaker and a Situational resolution steps voice. The factory area alarm event processing system as described in Claim 8, wherein the probability allocation mode is to assign a weight to each solution to obtain a probability value, and then sort the probability values to obtain the probability ranking. The plant alarm event processing system as described in claim item 12, wherein when there are multiple outlier sensing data groups obtained in the clustering step, for each outlier sensing data group and the corresponding solution Create a query index. The factory area alarm event processing system as described in Claim 8 further includes calculating a distance between the feature of the second sensing data and the feature of the outlier sensing data, and judging the first based on the distance being smaller than a threshold Two sensing data are classified as the outlier sensing data.

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