Ice maker production safety monitoring system

Abstract

The invention belongs to the technical field of ice makers. The invention provides an ice maker production safety monitoring system. The system comprises a matching analysis module, a first safety risk analysis module and a second safety risk analysis module. The ice maker production safety monitoring system can autonomously analyze whether the built-in safety risk judgment module is adapted to the latest condition of the corresponding production area, and if not, the adapted safety risk judgment module can be obtained from the background server in time; therefore, various potential safety hazards in the production process of the ice maker can be identified, the production safety is comprehensively guaranteed, and the accident rate is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of ice makers, and more particularly, to a production safety monitoring system for ice makers. Background Art

[0002] In the daily operation and management of an ice maker production factory, safety in production is of utmost importance. To ensure the safety of the production process, it has become a common practice to use cameras to monitor various production scenarios. By capturing the production site images with cameras, extracting the production features contained therein, and then deeply analyzing these features to check for potential safety hazards, and promptly alarming once a problem is detected, the accident risk can be greatly reduced, personnel and equipment safety can be ensured, and the overall production efficiency can be improved.

[0003] However, the currently adopted technical solutions have significant drawbacks. Existing monitoring systems all require manual configuration of rules or models for determining safety hazards for each camera that shoots various production scenarios. However, the production layout of the factory is often dynamically adjusted, with the addition of new production equipment and the optimization of production processes being common occurrences. Whenever such changes occur, the corresponding cameras need to be reconfigured with the determination rules or models, which undoubtedly increases the management cost and time cost. Under the intertwining of various factors, some cameras often fail to be timely configured with the determination rules or models for safety hazards, resulting in blind spots in the monitoring system and an inability to accurately identify potential safety hazards, posing a huge threat to production safety. Summary of the Invention

[0004] In response to this, the present invention provides a production safety monitoring system for ice makers, an electronic device, a computer storage medium, and a computer program product to improve the safety monitoring efficiency during ice maker production.

[0005] The present invention discloses a production safety monitoring system for an ice maker. The system includes a matching and analysis module, a first safety risk analysis module, and a second safety risk analysis module. The matching and analysis module is configured to control a monitoring camera to capture a set of first video images of the production area of the ice maker, extract first production features from the first video images, and perform matching and analysis on the first production features with a first safety risk determination module correspondingly configured for the monitoring camera. Among them, the first production features include production equipment features, production material features, and production personnel operation features. The first safety risk analysis module is configured to, if the matching and analysis result is a pass, control the monitoring camera to capture a set of second video images of the production area of the ice maker, extract second production features from the video images, use the first safety risk determination module to perform safety risk identification and analysis on the second production features, and decide whether to output a first safety risk alarm signal. The second safety risk analysis module is configured to, if the matching and analysis result is a fail, feedback the first production features to the background server and receive a second safety risk determination module fed back by the background server, where the second safety risk determination module is matched by the background server according to the first production features. Use the second safety risk determination module to perform safety risk identification and analysis on the second production features, and decide whether to output a second safety risk alarm signal.

[0006] In some embodiments, the first safety risk determination module and / or the second safety risk determination module includes a safety risk determination strategy or a safety risk determination model.

[0007] In some embodiments, the second safety risk determination module is obtained through the following method: The background server performs matching and analysis on the first production features with each safety risk determination module stored in the database. If there is a matching safety risk determination module, it is used as the second safety risk determination module and fed back to the corresponding monitoring camera. If there is no matching safety risk determination module, production accident cases of similar equipment are matched according to the production equipment features in the first production features in a specified production accident case library, semantic analysis of the accident causes of each production accident case is performed to obtain a number of training accident feature data. Among them, each production accident case at least includes an accident video and accident cause analysis information. A new safety risk determination module is constructed, and the new safety risk determination module is trained with each training accident feature data, and the trained new safety risk determination module is fed back to the corresponding monitoring camera.

[0008] In some embodiments, semantic analysis is performed on the accident causes of each of the production accident cases to obtain a number of accident feature data for training, including: performing semantic analysis on the accident causes of each of the production accident cases to obtain a number of accident feature data for training, and using each of the accident feature data for training as positive data; obtaining a number of normal production videos of the same type of equipment, obtaining a number of normal feature data for training based on each of the normal production videos, and using each of the normal feature data for training as negative data; constructing the positive data and the negative data into training data.

[0009] In some embodiments, the second safety risk analysis module is further configured to: after receiving the second safety risk determination module fed back by the background server, determine the remaining duration for clearing the first safety risk determination module according to the obtaining method of the second safety risk determination module; wherein, if the second safety risk determination module is obtained by training a new safety risk determination module, set the remaining duration to a first duration, otherwise set the remaining duration to a second duration; wherein, the first duration is greater than the second duration.

[0010] In some embodiments, the second safety risk analysis module is further configured to: after reaching the remaining duration, output a prompt message for clearing the first safety risk determination module to relevant personnel, and if a confirmation message for the prompt message fed back by relevant personnel is received, clear the first safety risk determination module from the corresponding monitoring camera; otherwise, do not clear the first safety risk determination module from the corresponding monitoring camera.

[0011] In some embodiments, clearing the first safety risk determination module from the corresponding monitoring camera includes: clearing the first safety risk determination module from the corresponding monitoring camera and feeding back the first safety risk determination module to the background server for storage.

[0012] The present invention also discloses an electronic device applied to the ice maker production safety monitoring system described in any one of the foregoing; the electronic device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

[0013] The present invention also discloses a computer storage medium applied to the ice maker production safety monitoring system described in any one of the foregoing; the computer storage medium stores a computer program.

[0014] The present invention also discloses a computer program product applied to the ice maker production safety monitoring system described in any one of the foregoing; the computer program product is pre-packaged with computer program code.

[0015] The beneficial effects of the present invention are as follows: The ice machine production safety monitoring system of the present invention can autonomously analyze whether the built-in safety risk determination module is adapted to the latest situation of the corresponding production area. If it is no longer adapted, it can obtain an adapted safety risk determination module from the background server in a timely manner, so as to ensure that various safety hazards in the ice machine production process can be identified, comprehensively guarantee production safety, and reduce the accident rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 is a schematic structural diagram of an ice machine production safety monitoring system disclosed in an embodiment of the present invention.

[0018] Figure 2 is a schematic diagram of the internal structure of a safety risk determination module disclosed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following specific embodiments illustrate the implementation manners of the present application. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present application.

[0020] In addition, the technical features involved in different implementation manners of the present application described below can be combined with each other as long as they do not conflict with each other.

[0021] As Figure 1 shown, an embodiment of the present invention discloses an ice machine production safety monitoring system, and the system includes a matching analysis module, a first safety risk analysis module, and a second safety risk analysis module; the matching analysis module is used to control a monitoring camera to capture a set of first video images of the ice machine production area, extract first production features from the first video images, and perform matching analysis on the first production features with a first safety risk determination module corresponding to the monitoring camera; wherein, the first production features include production equipment features, production material features, and production personnel operation features.

[0022] Among them, a plurality of key production areas in the ice maker production workshop are pre-laid with monitoring cameras to obtain a set of first video images. The key production areas are, for example, raw material stacking areas, around core production equipment, personnel operation workstations, etc., to ensure full coverage of all links of the production process. The frame rate of the captured first video images is stable and the picture is clear, so as to accurately capture the production dynamics.

[0023] Using image recognition technology and deep learning algorithms, the collected first video images are analyzed to obtain production equipment characteristics, production material characteristics, and production personnel operation characteristics respectively. Among them, the production equipment characteristics include the model of the production equipment and the operating status (such as whether it is running normally, whether there are loose parts). The production material characteristics include the material type, stacking height and quantity. The production personnel operation characteristics include the standard degree of the monitoring personnel's limb movements and whether the operation actions conform to the preset standard process. Through feature extraction, structured first production feature data is formed.

[0024] The extracted first production features are matched with the first safety risk determination module pre-configured for the monitoring camera. This determination module stores a variety of preset standard production features and corresponding risk thresholds. When the coincidence degree between the actually extracted first production features and the standard production features reaches a certain proportion, it is determined that the match is passed, that is, it is recognized that the first safety risk determination module stored in the monitoring camera is adapted to this production area and can be used to identify safety risk events in this production area; when the coincidence degree between the actually extracted first production features and the standard production features does not reach a certain proportion, it is determined that the match fails, that is, it is recognized that the first safety risk determination module stored in the monitoring camera is not adapted to this production area and cannot be used to identify safety risk events in this production area. For example, the production function of this production area has been adjusted recently, other production equipment has been replaced or added, but the staff has not updated the corresponding safety risk determination module of this monitoring camera in time.

[0025] The first safety risk analysis module is used to, if the matching analysis result is passed, control the monitoring camera to capture a set of second video images of the ice maker production area, extract second production features from the video images, use the first safety risk determination module to perform safety risk identification and analysis on the second production features, and decide whether to output a first safety risk alarm signal.

[0026] Among them, when the first security risk determination module stored in the monitoring camera is adapted to the production area, it indicates that the production function of the production area, the production equipment it contains, etc. have not changed. At this time, it can be determined that the first security risk determination module stored in the monitoring camera can still be used to identify security risk events in the production area. The monitoring camera continues to capture the second video image of the production area, and uses the first security risk determination module to perform risk analysis on the second production features extracted from the second video image. When the analyzed risk value exceeds the preset risk value, a first security risk alarm signal is output, that is, relevant personnel are reminded that there are security risks in the production area.

[0027] It should be noted that the feature content included in the first production features and the second production features is the same, that is, both include the above-mentioned production equipment features, production material features, and production personnel operation features.

[0028] The second security risk analysis module is used to, if the matching analysis result fails to pass, feedback the first production features to the background server and receive the second security risk determination module feedback by the background server, where the second security risk determination module is obtained by the background server matching according to the first production features; use the second security risk determination module to perform security risk identification analysis on the second production features and decide whether to output a second security risk alarm signal.

[0029] Among them, when the first security risk determination module stored in the monitoring camera is not adapted to the production area, it indicates that the production function of the production area, the production equipment it contains, etc. have changed, but the staff has not updated the corresponding security risk determination module of the monitoring camera in time. It is determined that the first security risk determination module stored in the monitoring camera is no longer applicable to identify security risk events in the production area. At this time, the monitoring camera sends the extracted first production features to the background server, and the background server obtains a suitable second security risk determination module according to the first production features. The monitoring camera performs security risk identification analysis on the second production features extracted above according to the received second security risk determination module and decides whether to output a second security risk alarm signal. This process is the same as the foregoing content and will not be elaborated here.

[0030] The above ice maker production safety monitoring system can autonomously analyze whether the built-in security risk determination module is adapted to the latest situation of the corresponding production area. If it is no longer adapted, it can obtain a suitable security risk determination module from the background server in time, so as to ensure that various security hazards in the ice maker production process can be identified, comprehensively guarantee production safety, and reduce the accident rate.

[0031] In some embodiments, the first safety risk determination module and / or the second safety risk determination module includes a safety risk determination strategy or a safety risk determination model.

[0032] In the embodiments of the present invention, as Figure 2 shown, the first safety risk determination module may package a set of safety risk determination strategies adapted to the corresponding production area. The strategies include risk determination items for various safety risk types and their corresponding risk thresholds. When the risk value of any risk determination item exceeds the corresponding risk threshold, a safety risk alarm signal is output. Additionally, the first safety risk determination module may also be a corresponding safety risk determination model, which may be constructed using deep learning architectures such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), etc.

[0033] The process of the above-mentioned safety risk determination module for performing safety risk analysis is generally as follows: First, various types of equipment in the production of ice machines in the production area need to be identified, such as steel plate cutting equipment, stamping and shaping equipment, welding equipment, assembly equipment, etc. Whether there are safety risks in the operating states of these equipment is judged according to the above-mentioned safety risk determination strategy or safety risk determination model. For example, whether there are situations such as waste material splashing, abnormal trajectories of handling robotic arms, etc.

[0034] And the states of metal materials, plastic fittings, refrigerants, etc. used in the production of ice machines are identified. Whether there are safety risks in the states of these production materials is judged according to the above-mentioned safety risk determination strategy or safety risk determination model. For example, whether there are situations such as the stacking height of production materials being too high, or materials that should not be mixed being mixed together.

[0035] At the same time, the operator is identified and the operator's operation actions are extracted, and the standard degree of the operation actions is analyzed. For example, when assembling a key component, there are strict regulations on the order and torque size of tightening the screws. Illegal operations are regarded as risk events; there are also restrictions on the operation duration in dangerous areas (such as near the electrical control cabinet), and staying overtime also triggers risk determination.

[0036] When the safety risk value corresponding to any of the above situations reaches the above-mentioned risk threshold, a safety risk alarm signal can be output.

[0037] In some embodiments, the second security risk determination module is obtained through the following method: The background server matches and analyzes the first production feature with each security risk determination module stored in the database. If there is a matching security risk determination module, it is used as the second security risk determination module and fed back to the corresponding monitoring camera; if there is no matching security risk determination module, production accident cases of similar equipment are obtained by matching the production equipment features in the first production feature in a specified production accident case library, and semantic analysis of the accident causes of each production accident case is performed to obtain several accident feature data for training; where each production accident case includes at least accident videos and accident cause analysis information; a new security risk determination module is constructed, and each training accident feature data is used to train the new security risk determination module, and the trained new security risk determination module is fed back to the corresponding monitoring camera.

[0038] In an embodiment of the present invention, when the background server receives the first production feature fed back by the monitoring camera, it knows that the monitoring camera has the above situation of "the production function of this production area has been adjusted recently, other production equipment has been replaced or added, but the staff has not updated the corresponding security risk determination module of this monitoring camera", and a new security risk determination module needs to be configured for it.

[0039] At this time, the background server first matches and analyzes the received first production feature with various preset standard production features in each security risk determination module stored in the database. If any standard production feature matches successfully, it is determined that this security risk determination module is suitable for analyzing the security risk type corresponding to this first production feature. At this time, this security risk determination module is used as the second security risk determination module and fed back to the corresponding monitoring camera. This security risk determination module may be the model in the monitoring camera originally located in other production areas in the ice maker production workshop. Correspondingly, all security risk determination modules adapted to each production area in this production workshop are stored in the database.

[0040] In addition, if all security risk determination modules in the database do not match the first production feature, the background server needs to construct a new security risk determination module and train it. Specifically, search for production accident cases of similar equipment in a specified production accident case library. Each production accident case includes at least accident videos and accident cause analysis information (written by the staff). By using a semantic analysis component to analyze the accident causes of each production accident case, several accident feature data for training can be obtained. These accident feature data for training include production equipment features, production material features, production personnel operation features before the accident, and corresponding accident labels (uniformly "there is an accident").

[0041] Use the above accident feature data for training to train a new safety risk determination module, and send the trained new safety risk determination module to the monitoring camera. In this way, the monitoring camera can identify risks for new production functions and production equipment.

[0042] In some embodiments, the semantic analysis of the accident causes of each production accident case is performed to obtain a number of accident feature data for training, including: performing semantic analysis of the accident causes of each production accident case to obtain a number of accident feature data for training, and using each of the accident feature data for training as positive data; obtaining a number of normal production videos of the same type of equipment, obtaining a number of normal feature data for training based on each of the normal production videos, and using each of the normal feature data for training as negative data; constructing the positive data and the negative data into training data.

[0043] In the embodiments of the present invention, to ensure the training effect of the new safety risk determination module, the training data used in the present invention includes positive data and negative data. The positive data is data with safety accidents, and the negative data is normal production data without safety accidents.

[0044] Among them, the normal production videos of the same type of equipment can be obtained by crawling through network channels, or can be collected by staff and fed back to the background server after prompting the staff. The present invention does not make specific limitations on this.

[0045] In some embodiments, the second safety risk analysis module is further configured to: after receiving the second safety risk determination module fed back by the background server, determine the remaining duration for clearing the first safety risk determination module according to the obtaining method of the second safety risk determination module; wherein, if the second safety risk determination module is obtained by training a new safety risk determination module, set the remaining duration to a first duration, otherwise set the remaining duration to a second duration; wherein, the first duration is greater than the second duration.

[0046] In an embodiment of the present invention, after the monitoring camera obtains a new second security risk determination module, it is necessary to decide whether to clear the original first security risk determination module that is no longer adapted. In this regard, the present invention first analyzes the acquisition method of the second security risk determination module. If it is obtained by training a new security risk determination module, it indicates that the security risk type corresponding to the first risk feature in this production area is not present in this production workshop. Since it is not clear whether the newly added production function is temporary or long-term, a longer waiting time is set before clearing the first security risk determination module to avoid mistakenly deleting the first security risk determination module that may still be used later; otherwise, the second security risk determination module is a model in the monitoring camera of the original other production areas in the ice maker production workshop, indicating that the newly added production function in this production area is transferred from other production areas, and the probability of it being a long-term transfer is greater. At this time, a shorter waiting time is set before clearing the first security risk determination module.

[0047] In some embodiments, the second security risk analysis module is further configured to: after the remaining time is reached, output a prompt message for clearing the first security risk determination module to relevant personnel. If a confirmation message for the prompt message is received from the relevant personnel, the first security risk determination module is cleared from the corresponding monitoring camera; otherwise, the first security risk determination module is not cleared from the corresponding monitoring camera.

[0048] In an embodiment of the present invention, to avoid the clearing of the first security risk determination module not meeting the actual requirements of the ice maker production workshop, after the remaining time is reached, a prompt for clearing can be given to relevant personnel. If they confirm, it indicates that the adjustment of the production function in this ice maker production workshop is long-term. At this time, the first security risk determination module stored in the monitoring camera and adapted to the old production function division can be cleared.

[0049] In some embodiments, clearing the first security risk determination module from the corresponding monitoring camera includes: clearing the first security risk determination module from the corresponding monitoring camera and feeding back the first security risk determination module to the background server for storage.

[0050] In an embodiment of the present invention, during the process of performing security risk analysis, the first security risk determination module in the surveillance camera also performs periodic self-training based on the actually extracted production features. Therefore, the performance of the first security risk determination module for security risk analysis has been greatly improved. Therefore, in this embodiment of the present invention, it is not directly deleted from the surveillance camera, but is sent back to the background server, and the background server makes a decision on its subsequent use. For example, the background server transmits the first security risk determination module to the surveillance camera in the corresponding production area after the production function is adjusted.

[0051] An embodiment of the present invention also discloses an electronic device, which is applied to the ice maker production safety monitoring system described in any one of the foregoing; the electronic device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

[0052] An embodiment of the present invention also discloses a computer storage medium, which is applied to the ice maker production safety monitoring system described in any one of the foregoing; the computer storage medium stores a computer program.

[0053] An embodiment of the present invention also discloses a computer program product, which is applied to the ice maker production safety monitoring system described in any one of the foregoing; the computer program product is pre-packaged with computer program code.

[0054] It should be noted that in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more.

[0055] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of the present invention belong.

[0056] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following technologies well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0057] Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0058] In addition, in each embodiment of the present invention, each functional unit can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0059] The above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disc, etc.

[0060] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0061] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An ice making machine production safety monitoring system, characterized in that: The system includes a matching analysis module, a first security risk analysis module, and a second security risk analysis module; The matching analysis module is used to control the monitoring camera to shoot a group of first video images of the ice-making machine production area, extract the first production feature from the first video image, and match and analyze the first production feature with the first safety risk determination module corresponding to the monitoring camera; wherein the first production feature includes production equipment features, production material features, and production personnel operation features; the first safety risk analysis module is used to control the monitoring camera to shoot a group of second video images of the ice-making machine production area if the matching analysis result is a pass, extract the second production feature from the video image, use the first safety risk determination module to perform safety risk identification analysis on the second production feature, and decide whether to output a first safety risk alarm signal; the second safety risk analysis module is used to feed back the first production feature to the backend server if the matching analysis result is a fail to match, and receive the second safety risk determination module fed back by the backend server; wherein the second safety risk determination module is obtained by the backend server according to the matching of the first production feature; the second safety risk determination module is used to perform safety risk identification analysis on the second production feature, and decide whether to output a second safety risk alarm signal.

2. The ice making machine production safety monitoring system according to claim 1, characterized in that: The first security risk determination module and / or the second security risk determination module includes a security risk determination strategy or a security risk determination model.

3. The ice making machine production safety monitoring system according to claim 1, characterized in that: The second safety risk determination module is obtained by matching in the following manner: the background server performs matching analysis based on the first production feature and the safety risk determination modules stored in the database. If there is a matching safety risk determination module, it is used as the second safety risk determination module and fed back to the corresponding monitoring camera; if there is no matching safety risk determination module, the production equipment feature in the first production feature is matched in the specified production accident case library to obtain production accident cases of similar equipment, and a semantic analysis of the cause of the accident is performed on each of the production accident cases to obtain a number of training accident feature data; wherein each of the production accident cases includes at least an accident video and accident cause analysis information; a new safety risk determination module is constructed, and the new safety risk determination module is trained using each of the training accident feature data, and the trained new safety risk determination module is fed back to the corresponding monitoring camera.

4. The ice making machine production safety monitoring system according to claim 3, characterized in that: Performing semantic analysis on the cause of each of the production accident cases to obtain a number of accident feature data for training, including: performing semantic analysis on the cause of each of the production accident cases to obtain a number of accident feature data for training, and using each of the accident feature data for training as positive data; acquiring a number of normal production videos of the same type of equipment, obtaining a number of normal feature data for training based on each of the normal production videos, and using each of the normal feature data for training as negative data; and constructing the positive data and the negative data into training data.

5. The ice making machine production safety monitoring system according to claim 4, characterized in that: The second security risk analysis module is also used to: after receiving the second security risk determination module feedback from the background server, determine the remaining time for clearing the first security risk determination module according to the method for obtaining the second security risk determination module; wherein, if the second security risk determination module is obtained by training a new security risk determination module, then set the remaining time to the first time, otherwise set the remaining time to the second time; wherein, the first time is greater than the second time.

6. The ice making machine production safety monitoring system according to claim 5, characterized in that: The second security risk analysis module is further configured to: after the remaining time is reached, output a prompt message to a relevant person to clear the first security risk determination module, and if confirmation information for the prompt message is received from the relevant person, clear the first security risk determination module from the corresponding surveillance camera; Otherwise, the first security risk determination module is not cleared from the corresponding surveillance camera.

7. The ice making machine production safety monitoring system according to claim 6, characterized in that: The clearing of the first security risk determination module from the corresponding surveillance camera includes: clearing the first security risk determination module from the corresponding surveillance camera, and feeding back the first security risk determination module to a background server for storage.

8. An electronic device, characterized in that: Applicable to an ice making machine production safety monitoring system as described in any one of claims 1-7; the electronic device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

9. A computer storage medium, characterized in that: Applicable to an ice making machine production safety monitoring system as described in any one of claims 1 to 7; the computer storage medium stores a computer program.

10. A computer program product, characterized in that: Applicable to an ice-making machine production safety monitoring system as described in any one of claims 1 to 7; the computer program product is pre-packaged with computer program code.

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